modelling air pollution dispersion at different urban scales

DOTTORATO DI RICERCA IN
ENERGETICA E TECNOLOGIE INDUSTRIALI INNOVATIVE
UNIVERSITÀ DEGLI STUDI DI FIRENZE
Sede Amministrativa : DIPARTIMENTO DI ENERGETICA – S. STECCO
TESI DI DOTTORATO
MODELLING AIR POLLUTION DISPERSION AT DIFFERENT
URBAN SCALES
Tutor Univesitario: Il Coordinatore
Prof. Ing. Ennio A. Carnevale Prof. Ing. Francesco Martelli
Tutor Esterno:
Prof. Alan G. Robins
DOTTORANDO: Paolo Giambini
Anno Accademico 2007-2008 (XXI ciclo)

DIPARTIMENTO ENERGETICA “SERGIO STECCO”
(University of Florence)
MODELLING AIR POLLUTION DISPERSION
AT DIFFERENT URBAN SCALES
By
Paolo Giambini
A thesis submitted in fulfilment of the requirements
for the degree of Doctor of Philosophy of the University of Florence
January 2009
1

Abstract
Urban air quality modelling was investigated in this thesis by studying the topic at
different scales and for different purposes. At the street and neighbourhood scales,
pollutant dispersion from vehicle traffic sources within an urban quartier were
experimentally analysed, both in terms of mean and peak concentrations. At the urban
and regional scales, advanced operational mathematical dispersion models where
applied for studying the environmental impacts deriving from different emission
scenarios. A model evaluation and validation work was carried out using monitoring
data in order to develop an integrated meteorological and dispersion modelling system
for supporting an Air Quality Action Plan.
The work was aimed to enhance the understanding of the involved phenomena and
analysing the current practice in dispersion modelling in order to put the basis for the
development of reliable tools for air quality management and assessment.
Wind tunnel experiments were carried out in the Environmental Flow Research Centre
(EnFlo) laboratory, located in Guildford (UK). An advanced experimental technique
such as fast flame ionization detectors was used for measuring pollutant dispersion in
reduced scale models. A novel method for evaluate pollutant dispersion from vehicles
taking into account the effects of non uniform distribution of pollutant emissions due to
traffic queuing was also investigated.
Mathematical modeling was performed for scenario analysis purposes and involved
several models, using different approaches: second generation Gaussian plume models,
Gaussian puff models, Eulerian grid models, chemical and multiscale approaches were
applied. Mathematical models were also evaluated by means of statistical validation
techniques and uncertainty analysis methods.
The experimental results allowed to improve our knowledge of the dispersion
phenomena occurring in real urban canopies, and provided reliable data sets for model
validation, improvement and development.
2

The application of mathematical models to several emission scenarios and the related
evaluation work, highlighted advantages and limitation of the adopted approaches, and
permitted to develop an effective tool for air quality management and assessment in
urban area.
3

Acknowledgements
I would like to thank my supervisors, Prof. Ennio Carnevale and Prof. Alan Robins for
their support. I would like to thank Prof. Carnevale for allowing me to pursue this PhD
and Prof. Robins for hosting me at the EnFlo, in Guildford, and for giving me the
opportunity to collaborate at DAPPLE-HO project. My most heartfelt thank goes to
Prof. Andrea Corti for his excellent advice and discussion on all aspects of my research,
and his constant support, encouragement and personal involvement.
Many thanks to Mr. Matteo Carpentieri for the indispensable collaboration and for the
insightful advice during these years. I would also like to express my grateful to my other
colleagues at the Dipartimento di Energetica, especially Mrs. Lidia Lombardi, Ms.
Isabella Pecorini, Mr. Giacomo Cenni and Mr. Lorenzo Burberi, for their kindness and
cheerfulness that kept me going when the going was tough.
I also wish to acknowledge the very much appreciated help of the staff and researchers
at EnFlo, namely, that of Dr. Paul Hayden, Ms. Frauke Pascheke, Mr. Tom Lawton and
Mr. Allan Wells.
The work related to this research carried out by James Hamilton during their graduation
thesis was also very appreciated.
I gratefully acknowledge the support and the collaboration of the staff at the ‘Settore
Qualit`a dell’Aria, Rischi Industriali, Prevenzione e Riduzione Integrata
dell’Inquinamento’ of Tuscan Regional Administration (Mr. Mario Romanelli and Mr.
Furio Forni in particular) and of the Air Quality groups of LaMMA (namely, Dr.
Francesca Calastrini, Mr. Giovanni Gualtieri and Mrs. Caterina Busillo) and ARPAT
(namely, Dr. Silvia Maltagliati and Dr. Franco Giovannini) during the MoDiVaSET-2
project.
Many, many thanks is due to my wonderful family and my special girlfriend, Lucrezia;
I’m grateful for all the care, love and infinite patience demonstrated during these last
months of PhD.
4

Publications by the author during thesis research
Modelling traffic pollutant concentration fluctuations and dosages in urban area
through wind tunnel experiments Giambini P, Robins AG, Hayden P, Corti A, 7th
International conference on Air Quality – Science and Application, Istanbul, Turkey,
24-27 March, 2009. To be presented
Intercomparison, sensitivity and uncertainty analysis between different urban
dispersion models applied to an Air Quality Action Plan in Tuscany, Italy Giambini
P, Carpentieri M, Corti A, Poster presentation and paper, 12th International Conference
on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes,
Cavtat, Croatia, 6-9 Ottobre, 2008
Modelling tracer dispersion from landfills. Carpentieri M, Giambini P, Corti A,
Environmental Modeling and Assessment 13 (2008), pp. 415-429
Accumulation chamber method and landfill gas diffuse emissions monitoring Corti
A, Lombardi L, Pecorini I, Carpentieri M, Giambini P, Spoken presentation and paper,
SIDISA 2008 – Simposio internazionale di Ingegneria Sanitaria Ambientale, Firenze,
24-27 Giugno, 2008
Landfill gas emission monitoring: direct and indirect methodologies Corti A,
Carpentieri M, Giambini P, Lombardi L, Pecorini I. In: Velini AA (ed.), Landfill
Research Trends, Chapter 1, Nova Science Publishers, 2007 (ISBN 978-1-60021-776-0)
Uncertainty and validation of urban scale modelling systems applied to scenario
analysis in Tuscany, Italy. Carpentieri M, Giambini P, Corti A, Spoken presentation
and paper, 11th International Conference on Harmonisation within Atmospheric
Dispersion Modelling for Regulatory Purposes, Cambridge, UK, 2-5 July, 2007
5

Wind tunnel experiments of flow and dispersion in idealised urban areas
Carpentieri M, Giambini P, Corti A, Poster presentation at 28th NATO/CCMS
International Technical Meeting on Air Pollution Modelling and Its Application,
Leipzig, Germany, 15-19 May, 2006 and paper in ‘Air Pollution Modeling and Its
Application XVIII’, Editors Borrego C and Renner E, Elsevier, 2007 (ISBN 978-0-444-
52987-9)
Modelling emission scenarios in Tuscany: the MoDiVaSET project Corti A, Busillo
C, Calastrini F, Carpentieri M, Giambini P and Gualtieri G, Spoken presentation and
paper, 15th IUAPPA Regional Conference, Lille, France, 6-8 September, 2006.
6

Table of Contents
Abstract 2
Acknowledgements 4
Publications by the author during thesis research 5
Table of Contents 7
List of Figures 10
List of Tables 15
List of Tables 15
List of Symbols 16
1. Introduction 20
1.1 Background 20
1.2 Motivation and aims of the research 24
1.3 Scope of research and simplifying assumptions 27
1.4 Outline of the Thesis 29
2. Review of urban air pollution dispersion modeling and evaluation methods 30
2.1 Introduction 30
2.2 The air pollution problem 30
2.2.1 Definition 30
2.2.2 Components of an air pollution problem 31
2.2.3 Source of air pollution 32
2.2.4 The atmosphere structure and dynamics 34
2.2.5 Effects of air pollution 37
2.2.6 Solutions 39
2.2.7 The role of modeling 42
2.3 Scales of pollutant transport in the atmosphere 43
2.3.1 Space and time scales in the urban context 47
2.4 Modelling methods applied to urban dispersion 52
2.4.1 The range and types of urban dispersion models 52
2.4.2 Field experiments 55
2.4.3 Physical models 56
2.4.4 Statistical models 58
2.4.5 Parametric models 58
2.4.6 Computational models 60
2.4.7 Multiscale approach 66
7

2.5 City scale and regional scale modelling 67
2.5.1 Flow and dispersion phenomena 67
2.5.2 Experimental modelling 74
2.5.3 Mathematical modelling 75
2.6 Neighbourhood scale modelling 79
2.6.1 Flow and dispersion phenomena 79
2.6.2 Experimental modelling 82
2.6.3 Mathematical modelling 83
2.7 Street scale modelling 86
2.7.1 Flow and dispersion phenomena 87
2.7.2 Experimental modelling 92
2.7.3 Mathematical modelling 95
2.8 Multi-scale modelling 98
2.9 Model evaluation of urban dispersion modelling 102
2.9.1 Model evaluation and model quality assurance 103
2.9.2 Model validation 106
2.9.3 Uncertainty analysis 109
3. Neighbourhood/street scale modelling by means of wind tunnel methods 115
3.1 Introduction 115
3.2 Background on wind tunnel modelling 116
3.2.1 The simulation of the PBL flow 116
3.2.2 The physical model 120
3.2.3 Emission and dispersion simulation 122
3.3 The EnFlo Laboratory wind tunnel 124
3.4 Experimental strategy 126
3.5 Tracer dispersion experiments 132
3.5.1 Moving source experiments 136
3.5.2 Correlation experiments 139
3.5.3 Quality assurance of the wind tunnel data 144
4. City scale modelling by means of mathematical methods 149
4.1 Introduction 149
4.2 Background on mathematical dispersion modelling 149
4.2.1 The theoretical basis of mathematical dispersion modelling 149
4.2.2 Gaussian model 152
4.2.3 Eulerian grid models 165
4.3 Mathematical modelling strategy 171
4.4 Mathematical modelling applications 173
4.4.1 Introduction 173
4.4.2 Data pre- and post-processing 175
4.5 Model evaluation 186
4.5.1 Introduction 186
4.5.2 Validation and sensitivity analysis 187
8

4.5.3 Uncertainty analysis 193
4.6 Scenario analysis 195
5. Neighbourood/Street scale results 197
5.1 Introduction 197
5.2 Moving source experiments 199
5.2.1 Concentration vs. receptor location 200
5.2.2 Concentration vs. source location 210
5.2.3 Dose estimation 218
5.3 Correlation experiment results 226
5.3.1 Undisturbed boundary layer 226
5.3.2 Dapple model 231
5.4 Discussion of the relevance and application of the results 239
6. City scale results 242
6.1 Introduction 242
6.2 Modelling applications 242
6.3 Model evaluation 247
6.3.1 Sensitivity study of ADMS-Urban 247
6.3.2 Validation exercise 250
6.3.3 Uncertainty analysis 255
6.4 Scenario analysis 258
6.5 Discussion of the relevance and application of the results 264
7. Conclusions 267
7.1 Summary of main findings and conclusions 267
7.2 Limitations and recommendations for future research 269
References 271
9

List of Figures
Figure 1-1 The air quality system and related monitoring and modelling studies
(adapted from Scaperdas, 2000)..................................................................22
Figure 1-2 Optimal model application (Zannetti, 1990) ...............................................25
Figure 2-1 Schematic of the components of the urban air pollution problem (Seinfeld
1986)............................................................................................................32
Figure 2-2 Schematic presentation of a typical development of urban air pollution
levels. Depending upon the time of initiation of emission control the
stabilisation and subsequent improvement of the air quality may occur
sooner or later in the development. (Based on WHO/UNEP, 1992; Mage et
al., 1996)......................................................................................................34
Figure 2-3 The vertical structure of the atmosphere: average temperature variation with
altitude (from Arya, 1999) ..........................................................................36
Figure 2-4 A schematic representation of the Tropospheric sub-layers .......................36
Figure 2-5 Spatial and temporal scales of atmospheric phenomena (Oke, 1987).........46
Figure 2-6 Urban dispersion scales defined according to Hall et al. (1996).................50
Figure 2-7 Eulerian (a) and Lagrangian (b) reference system for the atmospheric
motion (Zannetti, 1990)...............................................................................61
Figure 2-8 Urban heat island with light regional wind (left) and urban plume with
moderate regional wind (right) [Zannetti 1990]..........................................68
Figure 2-9 Schematic of the flow through and over an urban area (Britter and Hanna
2003)............................................................................................................69
Figure 2-10 Regimes of flow over obstacle arrays .........................................................72
Figure 2-11 Components of a comprehensive air quality model. From Seinfeld (1986)76
Figure 2-12 An illustration of the model SAMPU (Theurer et al., 1996).......................84
Figure 2-13 The different components of SIRANE. (a) Modelling a district by a network
of streets. (b) Box model for each street, with corresponding flux balance.
(c) Fluxes at a street intersection. (d) Modified Gaussian plume for rooflevel
transport..............................................................................................85
Figure 2-14 Characteristics of street canyon (Ahmad et al. 2005) .................................87
Figure 2-15 Pollutant dispersion in a regular street canyon............................................88
Figure 2-16 Flow field at a street intersection (Robins and Macdonald, 2001)..............90
Figure 2-17 Schematic of the modelling system developed by Soulhac et al.(2003).....99
Figure 2-18 The THOR integrated model system (NERI, 2008)..................................100
Figure 2-19 Components of Model Quality Assurance ................................................104
Figure 2-20 Types of uncertainty present in transport and transformations model
applications and their interrelationships (based on Georgopoulos, 1995) 110
Figure 2-21 Optimal model choice that minimizes the total uncertainties (Hanna, 1989)
111
Figure 2-22 Schematic diagram showing flow of data into and out of the atmospheric
dispersion model, and three categories of uncertainty that can be introduced
(Colvile et al., 2002)..................................................................................113
Figure 3-1 Schematic of a boundary layer simulation system in an atmospheric wind
tunnel.........................................................................................................117
Figure 3-2 Factors which determine the length of wind tunnel working section needed
to undertake a plume dispersion simulation..............................................120
Figure 3-3 Schematic of the Enflo meteorological wind tunnel .................................125
10

Figure 3-4 Aerial view (left) and 3D rendering (right) of the Dapple test site ...........127
Figure 3-5 Several views of the Dapple model in the wind tunnel.............................127
Figure 3-6 Irwin spires and surface roughness (left) and mean flow vertical profiles
(right) in the EnFlo wind tunnel................................................................128
Figure 3-7 Plan view of the Dapple model, showing the model coordinates system and
wind directions analyzed in the experiments ............................................129
Figure 3-8 Instrumentation set-up for dispersion experiment at EnFlo laboratory.....132
Figure 3-9 CAMBUSTION FFID for hydrocarbon concentration measurements;
support equipment (left) and head with its sampling tube mounted on the 3-
D traverse in the wind tunnel (right) .........................................................133
Figure 3-10 System used for the tracer release: pipes mounted on the 1-D traverse
mechanism (left) and aerodynamic stack (right).......................................135
Figure 3-11 Flow control systems: HITEC electronic flow meters (left) and rotameters
(right).........................................................................................................135
Figure 3-12 Source and receptor locations for the GLC investigation: -90° (top) and
+90° model orientation (bottom)...............................................................137
Figure 3-13 Map of the Dapple model showing the wind direction, the source and
receptor locations studied in the investigation of concentrations at different
heights .......................................................................................................138
Figure 3-14 Combination of receptor and reference source locations in the correlation
experiment: main (top) and secondary intersection along Dorset Square -
Melcombe Road ........................................................................................143
Figure 3-15 Repeated mean concentration (top) and variance (bottom) using different
sampling time (arbitrary ±5% and ±15% error bars are shown for reference)
144
Figure 3-16 Error test: correlation coefficient and correlation error for increasing
sampling time ............................................................................................145
Figure 3-17 Repeated correlation measurements (left) and relative errors (right) using
different sampling time: inline with source (top) and offset (bottom)
receptor locations ......................................................................................146
Figure 3-18 Repeated correlation measurements and relative errors using different
measuring procedure: sequential measures (procedure 1) and separated
experiments (procedure 2) for c1 2 , c2 2 and cB 2 ...........................................148
Figure 4-1 Coordinate system for the Gaussian plume model....................................153
Figure 4-2 Gaussian plume with reflexion and virtual source: single (left) and multiple
reflexions (right)........................................................................................154
Figure 4-3 The segmented plume approach (Zannetti, 1990).....................................155
Figure 4-4 Segmented plume or puff representation in SAFE AIR............................159
Figure 4-5 Element series represented by series of equivalent finite line sources (left)
and mixing zone (right) of CALINE4 .......................................................162
Figure 4-6 Schematic of ADMS-Urban input and output...........................................163
Figure 4-7 Area studied in the MoDiVaSET-2 project...............................................171
Figure 4-8 Meteorological stations for the MoDiVaSET-2 project............................176
Figure 4-9 Point sources included in the IRSE-RT, 2001...........................................177
Figure 4-10 Line sources included in the IRSE-RT, 2001............................................177
Figure 4-11 Annual NOx emission of line sources (Mg/km year) included in the IRSE-
RT 2001.....................................................................................................178
Figure 4-12 Annual PM10 emission of line sources (Mg/km year) included in the IRSE-
RT 2001.....................................................................................................178
11

Figure 4-13 Annual SOx emission of line sources (Mg/km year) included in the IRSE-
RT 2001.....................................................................................................179
Figure 4-14 Annual NOx emission of grid area sources (kg/km 2 year) included in the
IRSE-RT 2001...........................................................................................179
Figure 4-15 Annual PM10 emission of grid area sources (kg/km 2 year) included in the
IRSE-RT 2001...........................................................................................180
Figure 4-16 Annual SOx emission of grid area sources (kg/km 2 year) included in the
IRSE-RT 2001...........................................................................................180
Figure 4-17 Virtual meteorological stations used for the CALINE4 simulations ........182
Figure 4-18 Pre- and post-processing for the CALINE4 simulations in the MoDiVaSET
project........................................................................................................183
Figure 4-19 Virtual meteorological stations used for the SAFE AIR simulations .......185
Figure 4-20 Pre- and post-processing of SAFE AIR in the MoDiVaSET project........185
Figure 4-21 Monitoring stations in the MoDiVaSET domain ......................................186
Figure 5-1 Plots of the (non-dimensional) mean concentration against receptor location
along Dorset square/Melcombe Street for different source locations along
Marylebone road .......................................................................................201
Figure 5-2 Plots of the (non-dimensional) concentration variances against receptor
location along Dorset square/Melcombe Street for different source locations
along Marylebone road..............................................................................202
Figure 5-3 Plots of the (non-dimensional) mean concentrations against receptor
location along the three street considered (Bickenhall St, top graphs, York
St, center graphs, and Crawford St, bottom graphs) for different source
locations along Marylebone road ..............................................................207
Figure 5-4 Plots of the (non-dimensional) concentration variances against receptor
location along the three street considered (Bickenhall St, top graphs, York
St, center graphs, and Crawford St, bottom graphs) for different source
locations along Marylebone road ..............................................................208
Figure 5-5 Comparison of the (non-dimensional) mean concentrations (left) and
variances (right) against receptor location along Bickenhall St, York St and
Crawford St with source located at the Marylebone road-Gloucester Place
intersection ................................................................................................209
Figure 5-6 Non-dimensional mean concentration against source location along
Marylebone road for different receptor locations and heights along Dorset
square/Melcombe Street............................................................................212
Figure 5-7 Non-dimensional mean concentration due to a steady line source in
Marylebone Road for different receptor locations and heights along Dorset
square/Melcombe Street............................................................................213
Figure 5-8 Non-dimensional mean concentration against source location along
Marylebone Rd for ground level receptor locations inside the street canyon
of Melcombe St: leeward side, X=515mm, windward side, X=580mm and
center of the street canyon, X=580mm .....................................................214
Figure 5-9 Non-dimensional mean concentration against source location along
Marylebone Rd for different receptor locations and height in the north side
of Dorset Square (Y=-845)........................................................................215
Figure 5-10 Non-dimensional mean concentration against source location along
Marylebone road for ground level (Z=10mm) receptor locations along
Bickenhall Street (top-left), York Street (bottom) and Crawford Street (topright)
..........................................................................................................216
12

Figure 5-11 Non-dimensional mean concentration due to a steady line source in
Marylebone Road for receptor locations in Bickenhall St, York St and
Crawford St ...............................................................................................216
Figure 5-12 Comparison of the (non-dimensional) mean concentrations (left) and
variances (right) against source location at Marylebone Rd for receptor
locations in Bickenhall St (Y=-340mm), York St (Y=-719mm) and
Crawford St (Y=-1080mm).......................................................................217
Figure 5-13 Non-dimensional mean dose at ground level receptors along Melcombe
St/Dorset Sq for different driving scenarios..............................................220
Figure 5-14 Non-dimensional mean dose at ground level receptors along Bickenhall St
for different driving scenarios ...................................................................220
Figure 5-15 Non-dimensional mean dose at ground level receptors along York St for
different driving scenarios.........................................................................221
Figure 5-16 Non-dimensional mean dose at ground level receptors along Crawford St
for different driving scenarios ...................................................................221
Figure 5-17 Schematic of the experimental field campaign carried out on November ‘04
in the framework of the DAPPLE-HO project (Shallcross et al., 2005)...223
Figure 5-18 Car speed and position during experiment 1 (top) and 2 (bottom) carried out
on November ‘04 in the framework of the DAPPLE-HO project (Shallcross
et al., 2005)................................................................................................224
2 2
Figure 5-19 Non dimensional c 1 , c 2 and ( ) 2
c 1 + c2
against source separation (left
graphs) and inferred correlation (right) for receptor locations inline with the
reference source (X=0mm) at different distances (Y=270, 540 and 1080
mm) ...........................................................................................................228
2 2
Figure 5-20 Non dimensional c 1 , c 2 and ( ) 2
c 1 + c2
against source separation (left
graphs) and inferred correlation (right) for receptor locations inline with the
reference source (X=0mm) at different downstream distances (Y= 540 and
1080 mm) ..................................................................................................229
Figure 5-21 Correlation coefficient - Downstream distances of receptors inline with the
ref. source (X=0 mm) profiles for fixed source separation (± 50 and ± 100
mm) ...........................................................................................................230
2 2
Figure 5-22 Comparison of non-dimensional c 1 , c 2 and ( ) 2
c 1 + c2
against source
separation (top) and correlation (bottom) obtained with Dapple Model and
UBL for receptor at (X=0 mm, Y=540 mm) and reference source at
(X=0mm, Y=0mm)....................................................................................233
2 2
Figure 5-23 Comparison of non-dimensional c 1 , c 2 and ( ) 2
c 1 + c2
against source
separation (top) and correlation (bottom) obtained with Dapple Model and
UBL for receptor at (X=100 mm, Y=540 mm) and reference source at
(X=0mm, Y=0mm)....................................................................................234
Figure 5-24 Correlation against source separation for different reference sources (X=-
100, -50, 0 and 50 mm, Y=0 mm) at receptor (X=0 mm, Y=540mm).....234
Figure 5-25 Correlation against source separation for different reference sources (X=-
100, -50, 0 and 50 mm, Y=0 mm) at receptor (X=-150 mm, Y=540 mm)
235
Figure 5-26 Correlation against source separation for different reference sources (X=-
100, and 0 mm, Y=0 mm) at receptor (X=-75 mm, Y=540 mm) .............235
Figure 5-27 Correlation against source separation for different reference sources
(X=300, and 400 mm, Y=0 mm) at receptor (X=390 mm, Y=540 mm) ..235
13

Figure 5-28 Correlation against source separation for different reference sources
(X=300, and 400 mm, Y=0 mm) at receptor (X=240 mm, Y=540 mm) ..236
Figure 5-29 Correlation against source separation for different reference sources
(X=300, and 400 mm, Y=0 mm) at receptor (X=75 mm, Y=540 mm) ....236
Figure 6-1 NOx annual mean concentration maps [µg/m3]: ADMS-Urban (top-left
map), CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right) 243
Figure 6-2 NO2 annual mean concentration maps [µg/m3]: ADMS-Urban (top-left
map), CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right) 243
Figure 6-3 PM10 annual mean concentration maps [µg/m3]: ADMS-Urban (top-left
map), CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right) 244
Figure 6-4 SO2 annual mean concentration maps [µg/m3]: ADMS-Urban (top-left
map), CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right) 244
Figure 6-5 Scatter (left) and quantile-quantile (right) plots........................................249
Figure 6-6 Scatter (top) and quantile-quantile (bottom) of the observed and predicted
NO2 concentrations for background sites (left) and roadside sites (right) 251
Figure 6-7 Scatter (top) and quantile-quantile (bottom) of the observed and predicted
PM10 concentrations for background sites (left) and roadside sites (right)
252
Figure 6-8 Maps of RME of the NO2 annual mean concentration: ADMS (top-left),
CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right)..........257
Figure 6-9 Maps of RME of the NO2 annual mean concentration: ADMS (top-left),
CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right)..........257
Figure 6-10 NO2 annual mean concentrations [µg/m3] of actual (top-left) and future
scenario (top-right) and relative percentage difference.............................259
Figure 6-11 NO2 maximum hourly mean concentrations [µg/m3] of actual (top-left) and
future scenario (top-right) and relative percentage difference (bottom) ...259
Figure 6-12 PM10 annual mean concentrations [µg/m3] of actual (top-left) and future
scenario (top-right) and relative percentage difference (bottom)..............260
Figure 6-13 PM10 maximum daily mean concentrations [µg/m3] of actual (top-left) and
future scenario (top-right) and relative percentage difference (bottom) ...260
Figure 6-14 NO2 annual mean concentrations [µg/m3]. Comparison between actual (left
maps) and future scenario(right) concentrations due to the different sources:
POINT (first row), IND (second row), LINE (third row) and ROAD (fourth
row) ...........................................................................................................262
Figure 6-15 NO2 annual mean concentrations [µg/m3]. Comparison between actual (left
maps) and future scenario (right maps) concentrations due to different types
of source: HEAT (first row) and OTHER (second row)...........................263
Figure 6-16 Maximum, mean and minimum ratios, Ri-all (percentage), for the actual (top
graphs) and future scenarios (bottom graphs) of NO2 and PM10 ..............263
14

List of Tables
Table 2-1 Atmospheric dispersion scales based on horizontal distance from the
source, according to Oke (1987) .................................................................44
Table 2-2 Horizontal and vertical transport scales in the atmosphere (adapted from
Boeker et al., 1995) .....................................................................................45
Table 2-3 Urban dispersion scales (as defined by Britter and Hanna, 2003) ..............48
Table 2-4 Urban dispersion modelling methods: experimental, parametric and
computational ..............................................................................................54
Table 2-5 Urban dispersion modelling approaches, meso- to micro-scale (Scaperdas,
2000)............................................................................................................55
Table 2-6 Classification of commonly used dispersion models at the street scale
(Vardoulakis et al., 2003)............................................................................95
Table 2-7 Examples of the sources of uncertainty in the formulation and application
of transport-transformation models (Isukapalli, 1999) .............................112
Table 4-1 Estimation of the standard deviation of the wind direction based on the
stability class .............................................................................................183
Table 4-2 Modelling quality objectives established by European Directives ...........192
Table 5-1 Comparison of doses (in non-dimensional form) measured in the field
campaign and estimated from wind tunnel experimentation.....................225
Table 6-1 NOx annual concentration at the monitoring stations: comparison between
observations and calculated data by ADMS, CGPL, CGSA and CAMx..245
Table 6-2 NO2 annual concentration at the monitoring stations: comparison between
observations and calculated data by ADMS, CGPL, CGSA and CAMx..246
Table 6-3 PM10 annual concentration at the monitoring stations: comparison between
observations and calculated data by ADMS, CGPL, CGSA and CAMx..246
Table 6-4 SO2 annual concentration at the monitoring stations: comparison between
observations and calculated data by ADMS, CGPL, CGSA and CAMx..247
Table 6-5 Base scenario parameters ..........................................................................248
Table 6-6 Sensitivity scenarios..................................................................................248
Table 6-7 Statistical indices based on annual mean concentrations of NO2. Model
performances are defined acceptable if FA2>0.5, -0.3

Latin symbols
Ad mean lot area
Af mean frontal area.
List of Symbols
Ap mean plan area
c (pollutant) concentration fluctuation
c 2
(pollutant) concentration variance
C mean (pollutant) concentration
C* non-dimensional mean concentration
Cdb Drag coefficient for the buildings
Co Observed concentration
Cp specific heat of air at constant pressure
Cs Simulated concentration
COR correlation coefficient
d zero displacement height
ds stack diameter
Ec Eckert number
f general function
FA2 Fraction within a factor of 2
Fr Froude number
FB buoyancy flux
FM momentum flux
FB Fractional bias
FS Fractional sigma
g Gravitational acceleration
H (Mean) building(s) height or canyon height
h Mixing height
hs height of source above ground or stack height
Hs Surface heat flux
Ki pollutant concentration eddy diffusivity
16

L length of street canyons or reference length
LMO Monin-Obukhov length
NMSE Normalized mean square error
NNR NMSE of the distribution of normalised ratios
Pe Peclet Number
Pr Prandtl number
PBL Planet boundary layer
Q volumetric pollutant source strength
QM (pollutant) mass flux
Re Reynolds number
Rib Bulk Richardson Number
Ro Rossby number
RME Relative maximum error
S Length of a line source
Sc Schmidt number
SD Standard deviation
t time
T air temperature
u* friction velocity
ui flow velocity vector component
ui′ turbulence velocity component
U longitudinal mean velocity component
UH approach free stream velocity at mean building height
Uref free stream velocity at reference height
UBL undisturbed boundary layer
Vs settling velocity
Vd deposition velocity
W building width
Ws initial emission velocity of the source
WNNR Weighted NMSE of the normalised ratios
X, Y, Z Dapple model coordinates
x longitudinal (along flow) direction (or component)
y lateral direction (or component)
17

z vertical direction (or component)
zo surface roughness length
Greek symbols
α molecular diffusivity
δ boundary layer height
θ potential temperature
κ Von Karman’s constant
λf array frontal density
λp array plan density
µ molecular viscosity
ν kinematic diffusivity
ρ air density
ρs density of the emission
σx, σy, σz Standard deviation in direction x, y, z of a Gaussian distribution
Ω Generic angular velocity vector
Ω0 Earth’s angular velocity
Operators and notation
G Scalar quantity
Gi Component of a vector
Gij Component of a 2D matrix
dG
dF
Total derivative of G with respect to F
∂G
∂F
Partial derivative of G with respect to F
generic average
x longitudinal (along flow) direction (or component)
y lateral direction (or component)
z vertical direction (or component)
i cartesian tensor notation suffix
18

* non-dimensional quantity
ref reference value suffix
19

1.1 Background
Chapter 1
1.Introduction
The air pollution constitutes today one of the main environmental concerns of the
population and of the political authorities. Even if this interest is particularly increased
during last decades, the air pollution constitutes nevertheless an old problem; human
activities have caused air pollution ever since our ancestors began building fires (Arya
1999). During Antiquity, several authors like Hippocrates or Seneque mentioned
atmospheric harmful effect. With the Middle Ages, the first rules were taken in England
to limit the air pollution due to the coal use (Brimblecombe, 1987). The industrial
revolution of XIX century and the rural migration contributed to gather more and more
inhabitants in increasingly polluted urban centers. This evolution led to extreme
situations, as the catastrophe due to the coal smokes which occurred in London in 1952
and caused the death of more than 4000 people. During XX century, the development of
the chemical and oil industries led to a diversification of the pollutants, while the
demographic explosion involved a considerable rise of the energy needs and
consequently an increase of the pollution caused by the production of this energy.
Moreover, the appearance of the car generated new emissions which were going to
become, one century later, the main cause of urban pollution; the increase in traffic
volume within the densely built and poorly ventilated urban areas is a worrying trend,
especially since increasingly larger populations live and work in cities.
This aggravation of the situation caused an awakening of the policy makers and of the
population. Increasingly severe rules were adopted to limit the emissions of pollutants,
in particular those of industrial origin, but removing pollutant emissions is neither
practically possible nor always desirable (indeed nowadays industrial activities and
vehicles are necessary and important for a high quality of life); thinking about a
‘reduction’ is more realistic and this can allow us to reach an equilibrium between
industrial development and the natural environment (environmental sustainability).
20

Chapter 1 Introduction
Assessing and managing urban air pollution is a very complex problem, implying many
scientific specialties, like meteorology, fluid mechanics, chemistry, medicine, urban
planning, etc. Each one of these disciplines approaches only one small part of the
problems and it can be interesting in order to have a more general sight of the air
pollution problem. The research presented in this thesis is part of the wider effort
towards improving the existing understanding of the urban air quality system, which
forms the basis of air quality management practices and policy.
As illustrated in Figure 1-1, the urban air quality system involves four main elements
acting in sequence, from cause to effect. Pollutant ‘emissions’, the fundamental cause of
the air pollution problem, form the left end of the system. The next stage is the process
of ‘dispersion’, whereby pollutants emitted by vehicles are transported, diluted and
chemically transformed in the atmosphere. This creates a distribution of pollutant
concentrations, to which receptors of air pollution (e.g. people, vegetation, and
buildings) become ‘exposed’. At the right end of the system, the ‘impact’ of air
pollution is the adverse effect resulting from exposure to air pollution (Scaperdas 2000).
The issue that attracts most of the attention of the media and the policy makers is the
right end of the air quality system, since the impact of air pollution on human health is
the primary concern. However, one of the main interests of the authorities is to be able
to know the consequences induced by a modification of the causes of the problem. This
is the reason for which the modeling of the pollutant dispersion is the base of the air
pollution study; the understanding of these processes provides the causal link between
emissions and impact, a crucial part of both urban air quality assessment and
management.
The first response to the growth of concern about air quality in cities was an increase of
the efforts in air pollution monitoring. In other words, the first obvious step was to
extend the monitoring networks in order to evaluate the extension and the entity of the
problem. However, these networks control a limited number of receptors, and there are
no guarantees that they are truly representative of the air quality, especially in situations
such as large urban areas or in presence of complex terrain (see, e.g., Scaperdas and
Colvile, 1999).
21

Chapter 1 Introduction
Figure 1-1 The air quality system and related monitoring and modelling studies
(adapted from Scaperdas, 2000)
Air pollution models can be used in a complementary manner to air quality
measurements, with due regard for the strengths and the weaknesses of both analysis
techniques. However, as stated by Seinfeld (1975), air quality models have become a
primary tool for analysis in most air quality assessments mainly for the following
reasons:
1. Establishing emission control legislation; i.e. determining the maximum
allowable emission rates that will meet fixed air quality standards
2. Evaluating proposed emission control techniques and strategies; i.e., evaluating
the impacts of future control
3. Selecting locations of future sources of pollutants, in order to minimize their
environmental impacts
4. Planning the control of air pollution episodes; i.e., defining immediate
intervention strategies (i.e. warning systems and real-time short term emission
reduction strategies) to avoid severe air pollution episodes in a certain region
5. Assessing responsibility for existing air pollution levels; i.e., evaluating present
source-receptor relationship
22

Chapter 1 Introduction
Air quality modelling is an indispensable tool for all the above analyses. It is, however,
only a tool. Modeling, like monitoring, is not the solution of the air pollution problem.
Monitoring and modeling studies constitute only an activity whose results, if properly
validated, can provide useful information for possible future implementations of
emission and control strategies. The important role of modelling in air quality
assessment and management is also recognized by the European legislation (Council
Directive 96/92/EC on Air Quality Assessment and Management and following
directives), which recommends to use information from three main assessment methods
(measurements, emission inventories and modelling).
The research presented in this thesis falls within the field of urban dispersion modelling,
i.e. linking emissions to concentrations. The issue has been addressed by considering
the problem at different scales and resolutions, with particular attention to the smallest
scales, where the effects on human health are most significant because of the proximity
between the emission sources (vehicles) and the potential receptors (population).
A major pollutant source within a city is vehicle emissions and this leads to interactions
between mobility, air quality, and the possible regulation of vehicles in cities.
Dispersion of pollution from vehicles within urban areas is a particular difficult task and
is less understood than dispersion from industrial sources in rural areas, despite its
obvious importance (Louka et al., 2000). The added difficulty in modelling dispersion
in the urban environment is mainly due to the complexity of the boundary conditions
associated with the three-dimensional shape (topography) of the urban canopy. The
interaction of the atmosphere with the urban canopy leads to complex localized mean
flow patterns and enhanced turbulence around the buildings, where the majority of both
sources and receptors of pollutants are situated. Modelling such phenomenons is
currently one of the most challenging problems in the field of the environmental fluid
dynamics; urban areas are complex networks of interconnected streets and buildings,
and localised flows in the spaces between buildings and streets can affect overall
dilution and create significant small-scale spatial variations of pollutant concentrations.
Consequently, it is important to extend the study about the dispersion from traffic at the
smallest scales, not only for assessing personal exposure to pollutants in the vicinity of
roads, but also because air quality in a city as a whole is assessed on the basis of data
from a few urban monitoring sites.
23

Chapter 1 Introduction
In this thesis, state-of-the-art wind tunnel experimental techniques have been used to
understand the dispersion from traffic sources at the smallest scales, while advanced
parametric models and multi-scales approaches have been used to model air quality in
large urban areas. The model evaluation process and quality assurance have received
particular attention in this work. Practical applications of the results presented in this
thesis include the developement of techniques for modelling pollutant dispersion from
vehicles at neighbourhood scale and the application of state-of-the-art modelling chains
for the management and assessment of air quality basing on emission scenarios.
1.2 Motivation and aims of the research
The research presented in this thesis was motivated by the need to improve, and extend
the scope of existing urban dispersion modelling approaches. Urban emissions occur
mainly within or nearly above the canopy layer, i.e. within a zone where the atmosphere
flow is heavily disturbed by buildings and other obstacles; it is well known that, in
comparison to unobstructured terrain, buildings effects can change local concentrations
by more than one order of magnitude (Schatzmann and Leitl, 2002). As a consequence,
understanding the flow of the wind through and above the urban area and/or the
dispersion of material in that flow is necessary in order to obtain reliable tools for the
management and assessment of urban air quality (Britter and Hanna, 2003).
In this thesis these issues have been addressed by considering the problem at different
scales. At each of the scales there are observations from the field and the laboratory that
are interpreted in terms of various physical (and often chemical) processes. These
processes, once recognized, are often combined and reformed into mathematical models
that can form a hierarchy of complexity or sophistication: each model has its own
regime of applicability and accuracy. A detailed interpretation at one scale is commonly
parameterized to assist interpretation at the next larger scale (Britter and Hanna, 2003).
In the scientific community a big variety of modelling techniques exist for urban
dispersion modelling, but because of the complexity of the problem many processes at
all the involved scales still need to be further investigated, beginning from the most
relevant, i.e. air pollution from car exhaust gas. Nevertheless, urban models are widely
24

Chapter 1 Introduction
used for impact assessment and air quality management. The optimal application of a
dispersion model for control strategies analysis, due to the big quantities of uncertainties
involved (model physics, natural or stochastic and input data uncertainties), should
incorporate calibration and evaluation procedure with local air quality monitoring and
experimental data (field or laboratory) in order to determine its applicability and
minimize forecasting errors to the specific study case (see Figure 1-2 , Zannetti, 1990).
Figure 1-2 Optimal model application (Zannetti, 1990)
Only models that have been verified should be reliably used for future forecasting.
Calibration and evaluation are, however, difficult in many cases, when sufficient
monitoring and experimental data are not available. As a consequence, one of the most
important limitations for the development of urban dispersion models is the lack of
experimental data. In order to overcome this problem it is necessary to extend
experimental data sets especially for the most relevant process implicated in the urban
air quality problem, i.e. pollution from traffic at the local/neighbourhood scale. With
25

Chapter 1 Introduction
this purpose in these thesis wind tunnel experiments of pollutant dispersion from
vehicles traffic were performed with a model of a real urban area and considering both
mean and fluctuation concentrations; particular attention was given to the effect on
localised emissions of traffic flow and queuing associated with street intersections. All
these aspects have not been considered in the scientific literature up to now; most of the
works used a line source in order to simulate vehicle pollution emission (uniform
emission that doesn’t allow to consider the effects of different traffic conditions, i.e.
congestion, stops and waits at the intersections), and focuse on simple geometries, i.e.
urban street canyon (for example, Meroney et al. 1996, Kastner-Klein and Plate 1999,
Pavageau 1999).
By modelling the pollutant dispersion from traffic at urban canyon intersections, it was
envisaged that current understanding on idealised, isolated urban canyons could be
extended to include the effect of interactions between neighboring canyons, helping to
bridge the gap between street canyon and building array modelling approaches. This
knowledge could be used in connection to models operating at a wider scale (urban
scale), with the aim of taking into account traffic impacts at the neighbourhood scale.
For this purpose an assessment of the existing operational models results is essential and
the application of multi-scale modelling techniques in perspective seems to be very
interesting and powerful.
Besides experimental work, the focus of the thesis has been on operational models. In
particular, the interest is on models suitable for Integrated Assessment Modelling
(IAM). Because of the large economic, public health and environmental impacts often
associated with the use of air quality model results, it is important that these models be
properly evaluated (Chang and Hanna, 2004). For this reason, during the thesis,
particular attention has been given to the model evaluation process. Because there is not
a single best performance measure or best evaluation methodology, a suite of different
performance measures and procedures, including various model evaluation (Chang and
Hanna, 2004) and uncertainty methods (Colvile et al. 2002, Stern and Fleming 2007,
Denby et al. 2007), has been applied and discussed. Often broad estimates rather than
exact calculations are used as basis for the decision processes in urban air quality
management; assessing the uncertainties deriving from the modelling process is
therefore a very important issue.
26

Chapter 1 Introduction
The main objectives of the thesis research were therefore defined as follows:
1. To study the pollutant dispersion from motor vehicles in urban street canyons
and intersections (microscale approach) using advanced experimental modelling
techniques (wind tunnel).
2. To set an original modelling technique for evaluating the effects of different
traffic conditions (free flow, congestion, queuing at the intersection, etc,…) in
terms of pollutant dispersion
3. To evaluate current operational urban dispersion models suitable for
management and assessment of urban air pollution, with particular attention to
the performances, limitations and uncertainties of different approaches (multisource,
multi-scale and full-chemistry).
4. To develop an effective tool for air quality management and assessment in urban
area.
5. To use both experimental and mathematical modelling techniques (wind tunnel
and operational models) to study the pollutant dispersion in urban area.
6. To suggest ways in which existing urban dispersion modelling could be
improved on the basis of the research on all the issues mentioned above.
1.3 Scope of research and simplifying assumptions
Urban air quality modelling was studied using different approaches. At the smallest
scale (micro and neighbourhood scale, see section 2.2) the research regarded, in
particular, dispersion of pollutant from vehicles, that is the cause of much concern about
the effects of urban air quality on human health. The performed research was mainly
experimental (wind tunnel) and regarded a model of a real (simplified) urban area
located in central London and analysis in terms of mean concentration and fluctuation,
that is a very significative parameter for the evaluation of the maximum human
27

Chapter 1 Introduction
exposure specially in urban area. This was studied in the framework of the DAPPLE-
HO project (see section 3.4).
At larger scales an application of state-of-the-art dispersion models in urban areas was
realized; different approaches (multi source, full chemistry, multi-scale) were applied
and compared. The modelling application was performed for the MoDiVaSET-2 project
(see section 4.3), funded by the Regional Authority of Tuscany for the development of
an integrated meteorological and dispersion modelling system which can be reliably
used for simulating and evaluating different future emission scenarios in order to
understand the weight of the different emission sources (road traffic, industrial sites and
domestic heating) and establishing the efficiency of the environmental actions that
could be adopted to ensure compliance with the air quality limits in an Air Quality
Action plan.
The different approaches were evaluated and discussed focusing particularly on issues
related to the development of integrated assessment models and multi-scale urban
dispersion models for air quality management. The performed research covers a wide
range of research topics, and therefore cannot be exhaustive. Several issues, such as
traffic generated turbulence, differential heating effects and long-range pollutant
transport, have been neglected. However the results of the work carried out during the
thesis research can constitute a useful basis for further research and for the development
of reliable tools for air quality management in urban areas.
The wind tunnel experiments were performed in the large atmospheric boundary layer
wind tunnel of the Environmental Flow Laboratory, University of Surrey (EnFlo). Some
of the most widespread urban and regional scale models were used for model
intercomparison purposes: CALINE4, CALPUFF, CALGRID, ADMS-Urban, SAFE
AIR and CAMx.
The scope of research defined above is original, and involves a variety of disciplines
and areas of expertise: urban air quality assessment and modelling, wind tunnel
modelling, wind engineering, urban meteorology.
28

Chapter 1 Introduction
1.4 Outline of the Thesis
The thesis is structured into eight chapters. Chapter 2 is a review of urban dispersion
modelling, with an emphasis on the evaluation processes and on operational models
used at different spatial scales. The research strategy and methods used in this work are
described and discussed in two separate chapters: neighbourhood scale study through
wind tunnel experimental methods are presented in chapter 3, and urban/regional scale
study by means of mathematical modelling and evaluation methods in chapter 4.
The results of the wind tunnel experiments carried out within the DAPPLE project are
presented in chapter 5, while chapter 6 reports results from the MoDiVaSET project,
consisting of mathematical models simulations, model evaluation exercises and scenario
analyses. Also practical applications of these results and their relevance within the
wider context of urban air quality modelling and integrated assessment modelling are
discussed in these chapters.
The main findings and conclusions of this thesis are drawn together in chapter 8,
together with a discussion of the limitations of this work and recommendations for
future research.
29

Chapter 2
2.Review of urban air pollution dispersion modeling and
2.1 Introduction
evaluation methods
In this chapter, the wider context of the thesis is presented through a review of urban
dispersion modeling and model evaluation methods, with a focus on the aspects
correlated with the analysis of different space and time scales. In the section 2.2, a short
outline of the complex problem of the urban air pollution, through an inventory of the
causes, consequences and solutions is discussed. Classification of urban dispersion
models according to space and time scale is discussed in section 2.3. Existing urban
dispersion modelling methods and practices are described in 2.4. Sequent sections (2.5-
2.8) presents the main findings and model applications of past research in the field of
urban dispersion modelling; the topic is discussed separately for each relevant scale
(city and regional scales, section 2.5, neighbourhood scale, section 2.6, street scale,
section 2.7, and multiscale approach, section 2.8), which are related to or relevant to the
issues and arguments discussed in this thesis. Each subsection starts with a general
description of the flow and dispersion phenomena at the considered scale, followed by a
review of the most relevant experimental (full scale and in the laboratory) and
mathematical works. In 2.9 the current practices in dispersion model evaluation are
presented.
2.2 The air pollution problem
2.2.1 Definition
The air pollution is a complex phenomenon, which was the subject of multiple
definitions. Progressively, with advanced knowledge, the term pollution covered
increasingly various realities. In a general way, the air pollution is defined by the
consequences which it involves, from the simple olfactive embarrassment to
harmfulness mortal. According to some authorities, the concept of pollution corresponds
30

Chapter 2 Review of urban air pollution modeling and evaluation methods
only to the consequences of the human activity, whereas others include the pollution of
natural origin (volcanic eruptions, forests fires...). The definition of the Council of
Europe (March 1968) summarizes these various nuances:
There is air pollution when the presence of a foreign substance or a significant
variation in the proportion of its components is likely to cause a harmful effect,
taking into account scientific knowledge of the moment, or to create a harmful
effect or an embarrassment.
2.2.2 Components of an air pollution problem
Air pollution problem has three main components:
1. emission sources that produce air pollutants,
2. the atmosphere in which transport, diffusion, chemical transformations and removal
processes occur,
3. receptors near the ground that respond to trace amounts of air pollutants reaching
them.
Thus, air pollution is an interdisciplinary problem whose study and solution require
interdisciplinary efforts by scientists, engineers, environmental protection agencies,
legislators, and the public at large.
Figure 2-1 represent a schematic of the various components of the air pollution problem
on a local/urban scale. Control is represented at three points. Of these, control at the
emission source (source control) is the most efficient, feasible and practical. Legislative
action has become commonplace, particularly in developed country. Automatic control
of emissions based on detector response is rarely used. As a result of the various control
measures adopted, air quality has improved appreciably over the past 25 years (Arya
1999).
31

Chapter 2 Review of urban air pollution modeling and evaluation methods
Figure 2-1 Schematic of the components of the urban air pollution problem
(Seinfeld 1986)
2.2.3 Source of air pollution
The atmospheric sources of pollutants are very varied, by their nature (accidental,
chronic) or by their origin (natural, anthropic). Many natural mechanisms emit harmful
compounds for the man and disturb some fragile environmental balances. The
contribution of the human emissions strongly comes to worsen the consequences of this
pollution, in particular in urban environment where the density of population is high.
According to their nature, the anthropic emissions have very different effects. The
accidental releases of toxic products can be dramatic and very often mortals (Seveso,
Bhopal, Tchernobyl...). Chronic pollution has less spectacular immediate consequences
but its cost for the society is certainly very significant especially in urban areas,
although it is still difficult to evaluate.
The main sources of air pollution are as follows:
a) Natural emissions: The main source of natural origin is volcanic eruptions. Several
million tonnes of pollutant gas and particles are rejected each year in the atmosphere.
The violence of the eruptions can project these pollutants into the stratosphere. In some
cases (Indonesia, 1815; MT St Helens, 1980), these pollutants form a cloud which
covers the Earth during several months, reflecting the solar radiation and causing a fall
of the average temperature. Forest or bush fires, frequent in certain areas (in Africa in
particular), can also have consequences on the climate of these areas. The life cycle of
32

Chapter 2 Review of urban air pollution modeling and evaluation methods
plants produces many toxic compounds (H2S, CH4, VOC) which contribute to the total
assessment of emissions. Lastly, one can also quote other natural mechanisms like the
lightning, the wind erosion or the maritime spray.
b) Emissions of agricultural origin: The development of the intensive agriculture and
the massive use of manure and pesticides cause pollution not only of water and grounds
but also of the atmosphere, in the form of gas or aerosols. Furthermore methane is
rejected by the livestock, which constitute a considerable share of the whole of the
methane rejected into the atmosphere.
c) Industrial emissions: Industry produces various pollutants (SO2, fluorine, heavy
metals, NOX, PM10...). The sectors which reject the most significant quantities are the
energy production (power stations), the chemical, oil and metallurgical industries, the
incinerators of household refuse. Industry is also at the origin of the major part of the
accidental releases.
d) Domestic emissions: In urban environment, the contribution of the domestic heating
is significant, in particular for the winter period. The pollutants emitted by the heating
(CO2, CO, SO2, PM10, NOX) contribute not only to external pollution but also to the air
pollution inside the buildings.
e) Transport emissions: The emissions of automobile origin constitute the main source
of the urban air pollution. The improvement of the performances of the engines and the
fuels compensate with difficulty the continual increase in the automobile traffic. The
main pollutants discharged by the vehicles are: CO2, CO, NOX, VOC, particles.
Moreover, NOX is precursor of a secondary pollutant, ozone (O3). The other means of
transport (air, railway, maritime, river) remain overall minority but can cause local
effects, i.e. in the vicinity of the airports or harbors.
The atmospheric pollutant emissions that need to be taken into consideration in
assessment and management of ambient air are specified by Council Directives
96/62/EC and 99/30/EC are the carbon oxides (CO and CO2), the nitrogen oxides (NO
and NO2), the sulphur oxides (SO2), the Volatile Organic Compounds (VOC), the
33

Chapter 2 Review of urban air pollution modeling and evaluation methods
particles (PM10, PM2.5), the heavy metals (lead, cadmium, mercury) and the Polycyclic
Aromatic hydrocarbons (PAH).
The evolution of the urban air pollution concentrations is illustrated by figure 2-2. After
a long period of growth, the pollutant emissions tended to decrease during last years,
particularly because of the technological improvements, the legislation or the socioeconomic
changes (decline of heavy industry to the profit of the services sector). Instead
of it, a vast majority of the urban and suburban population in the world is still exposed
to conditions that exceed air quality standards set by World Health Organization
(WHO). In order to respect WHO guideline it is necessary to improve existing
understanding of the urban air quality system, which forms the basis of air quality
management practices and policy.
Figure 2-2 Schematic presentation of a typical development of urban air pollution
levels. Depending upon the time of initiation of emission control the stabilisation and
subsequent improvement of the air quality may occur sooner or later in the
development. (Based on WHO/UNEP, 1992; Mage et al., 1996).
2.2.4 The atmosphere structure and dynamics
An understanding of the atmospheric processes is central in the urban air quality
problem. Air pollution is not only an emission problem; it is also a weather-related
condition or phenomenon and, such as, should be considered one of the weather hazards
(Pielke, 1979). Pollutants are advected by the atmosphere’s wind flow patterns, ranging
from a local to a global scale. Diffusion is carried out by atmospheric turbulence, which
34

Chapter 2 Review of urban air pollution modeling and evaluation methods
is generated mechanically (due to velocity gradients), or thermally (due to temperature
gradients), and is subsequently dissipated or enhanced depending on atmospheric
conditions.
The ‘scale’ of pollutant transport, at a particular distance from the source, is also a
fundamental consideration in urban air quality modelling. The dominant mechanisms of
advection and turbulent diffusion in the atmosphere differ spatially over a variety of
vertical and longitudinal scales, depending on the vertical structure of the atmosphere
and its meteorological systems. Identifying the scale of influence of a particular source
or ensemble of sources of pollution is another important issue, since modelling pollutant
concentration at any given point usually depends on the relative contribution of a
multitude of sources, over the vast range of possible scales of transport in the
atmosphere.
The atmosphere has a vertical structure of layers, each with a distinctive temperature
profile. Figure 2-3 shows the average temperature variation with altitude in the
atmosphere on the basis of which the structure of the atmosphere is defined.
Nearly all air pollutants emitted from the surface of the Earth are dispersed and retained
within the ‘troposphere’, the layer extending between the ground and an altitude of 9 to
16km. There is little mixing between the troposphere and the stratosphere above, and
thus the only pollutants that can reach the stratosphere are relatively inert pollutants that
remain in the troposphere over long times (e.g. CFCs). The troposphere is further
divided into the ‘mixing layer’ and a ‘free’ layer aloft. The mixing layer or ‘planetary
boundary layer’ (PBL) is the shear layer that couples the atmosphere to the rough
surface of the Earth. As any boundary layer over a rough surface, it can be further
subdivided into the ‘outer’, ‘surface’ and ‘roughness’ sub-layers, on the basis of the
different velocity and shear profiles in each sub-layer (Figure 2-4).
35

Chapter 2 Review of urban air pollution modeling and evaluation methods
Figure 2-3 The vertical structure of the atmosphere: average temperature
variation with altitude (from Arya, 1999)
Figure 2-4 A schematic representation of the Tropospheric sub-layers
36

Chapter 2 Review of urban air pollution modeling and evaluation methods
The planetary boundary layer (PBL) is characterized (on average) by intense turbulent
mixing, and is a ‘buffer zone’ in which heat, moisture and pollution from sources at the
surface are stored for a few days, before being released into the rest of the troposphere.
Turbulence in the mixing layer is generated mechanically, due to vertical shear
(variation of wind speed with height), and thermally, due to temperature differences
between the ground and the air above. Depending on the variable balance of thermal
energy into and out of the PBL and corresponding temperature (and density) profiles,
turbulence generated in the layer may either be suppressed or enhanced.
Atmospheric conditions are typically classified as ‘unstable’ when sources of energy
outweigh sinks, ‘stable’ when the opposite is true, and ‘neutral’ in the case of
equilibrium (Smith and Hunt, 1977). There is a particular vertical temperature (and
density) gradient associated with each regime, and the depth of the PBL also differs
correspondingly.
In an unstable PBL, temperature increases with height at a rate that causes buoyant
mixing. Turbulent mixing is enhanced and large scales of turbulence due to convection
can span the depth of the PBL, between 400 and 2000m. In a stable PBL, turbulence is
suppressed, and the depth of the layer may be only few tens of meters to about 400m. In
a neutral PBL (associated with windy, cloudy atmospheric conditions) mechanically
generated turbulence is neither enhanced nor suppressed.
2.2.5 Effects of air pollution
Our concern about air pollution is essentially a reflection of the accumulating evidence
that air pollutants adversely affect the health and the welfare of human beings.
Extensive effects research has established that air pollutants affect the health of humans
and animals, damage vegetation and materials, reduce visibility and solar radiation, and
affect weather and climate. Although some of the effects are direct, specific and
measurable, such as damages to vegetation and materials and visibility reduction, many
other effects are indirect and more difficult to measure, such as health effects on human
beings and animals (Arya, 1999). Comprehensive reviews effects of air pollution have
37

Chapter 2 Review of urban air pollution modeling and evaluation methods
been given elsewhere (see e.g., Stern 1986; Godish 1991). Here only a brief summary of
these effects is given.
Effects on human health
If the man is affected psychologically by the effects of the air pollution on his
environment, his health is also directly sensitive to this pollution, knowing already that
an adult inhales on average 11 m 3 of air per day. The pollutants present in the air assign
health to short and long-term. At short term, they often act like factors starting or
worsening for fragile people. By causing an irritation of the lungs and a reduction in the
respiratory function, short-term pollution touches especially risk people like the
children, the old people and the asthmatic ones. The long-term effects of pollution are
much more difficult to evaluate. It would seem that the particles worsen the risk of lung
cancer. The carbon monoxide causes psychic diseases and worsens the cardiovascular
diseases. The air pollution would also tend to decrease the life expectancy (Dockery et
al., 1993). The impact of pollution on health thus depends on the nature of the
pollutants, of the type of implied people but also of the amount received by the
organism. The factors acting on this amount are the concentration of pollutants, the
exposure time and the physical activity. The effects of pollution on health represent a
significant cost for the society (Arya 1999).
Effects on vegetation and animals
The presence in the air of toxic substances disturbs fauna and the flora; a large number
of food, forage, crops, as well as trees have been identified to be damages by air
pollutants. The effects are in the form of leaf damage, stunting of growth, decrease size
and yield of fruits, and wilting and destruction of flowers. The scavenging of the acid
compounds by precipitations allows a transfer of the atmospheric pollution towards
water or grounds pollution. Thus, many forests and lakes of the north of Europe were
seriously affected by these acid rains. Moreover, these pollutants are likely to be found
in the drink water and the food chains (i.e. dioxins). The effects of pollution on certain
plants, like the tobacco, are also used as a biological indicator of the air pollution.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Effects on materials and structures
Air pollutants affect materials by soiling or through chemical reactions. Damage of
structural metals, building stones, surface coatings, fabrics, rubber, leather, paper and
other materials occurs extensively. The acid rains degrade many materials, in particular
the rocks limestones used in the construction of the buildings or the monuments, which
are dissolved by the acid rains. To this irreparable erosion come to be added the stains
caused by the deposit of the suspended particles in the air, which generate costs of very
significant rough-casting and restauration. The total annual loss from these and related
cleaning and protective activities in the United States have been estimated at several
billion dollars (Arya 1999). The material damage is mainly attributed to acid mist,
oxidants of various kind, hydrogen sulfide and particulate matter. Material can be
affected by both physical and chemical processes. Physical damage may result from
soiling due to dust deposition and also from the abrasive effect of wind-blow particulate
matter. Chemical damage to materials is a more serious and pervasive problem, because
certain pollutants readily react with materials after coming in direct contact with them.
Effects on atmosphere, weather and climate
The air pollution acts directly on the composition of the atmosphere, locally or overall.
Ai r pollution frequently cause widespread haze and fog, reducing visibility and solar
radiation near the ground; alter near surface energy balance; and possibly change local
weather and climate. In addition, the important problems of stratospheric ozone
depletion, acid deposition and climate change are attributed to air pollution. As matter
of facts, weak variations of the atmospheric composition involve an unbalance of the
climatic system, whose most known examples are the greenhouse effect or the
destruction of the ozone layer. It is however often difficult, on our scale of time, to
distinguish the natural climatic fluctuations from those caused by the man.
2.2.6 Solutions
Too often the people tend to forget that the only solution to decrease the air pollution is
to reduce the pollutant emissions. Unfortunately, for socio-economic, cultural or
political reasons, this reduction is difficult to implement and it requires time to find
alternative solutions. This is why it is essential to determine the consequences of
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Chapter 2 Review of urban air pollution modeling and evaluation methods
pollution and to fight against these consequences by setting up efficient policies. In this
paragraph these two aspects of the atmospheric pollution control (reduction of the
emissions and pollution managment) are described.
Reduction of the emissions
The reduction of the pollutant emissions generally is realized by means of the
implementation of alternative technological solutions. The improvements of a process
can relate to:
• Used products: replacement of a toxic material with another less polluting. For
example, CFC, used as propellant gases in the aerosols bombs, were replaced by
products based on hydrocarbons. Lead present in the gasoline was replaced by
compounds (MTBD) which has the same function.
• Consumption of energy: the energy consumption and the pollution generated by
the energy production decrease, optimizing the processes.
• Effluents processing: sometimes it is possible to reprocess the effluents and
reduce the pollutant releases in the atmosphere using filtering techniques (i.e.
fume de-dusting devices, catalyst for the vehicles, etc.).
The technological efforts must be accompanied by a limitation of the use of polluting
technologies. Many information campaigns encourage the population to limit the
consumption of energy. Referring to automobile traffic, the authorities try to better
organize the urban traffic and to promote alternative means of transport (public
transport, electric vehicles, etc.).
In order to favor the reduction of the emissions, increasingly severe legislative
measurements were adopted; they define the thresholds that have not to be exceeded,
impose a control of the polluting systems and limit or prohibit the use of certain toxic
substances. The effectiveness of legislative measurements necessarily needs a uniform
world politics; unfortunately, it is not already true, because the laws applied in the
developed countries are not a priority for the countries in development.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
From an economical point of view, the reduction of the emissions is difficult to
implement. Existing technologies are generally the result of an evolution aiming at
obtaining at lower cost an optimal output in a short period. The alternative solutions are
more expensive and not easily feasible; the organization of our society developed too
often without taking into account the environmental aspects. It is possible to see, for
example, that whole sides of our economy is based on the car and oil industries. The
duty on the petroleum products constitutes for the state a financial gain which is
difficult to replace. In certain cases, the overcost generated by environmental action can
be reflected on the price paid by the consumers, but when it is about prohibition to use a
specific product, the loss for the productive activities is inescapable (e.g. of asbestos or
CFC). In this case, the economic interests enter directly in competition with the
environmental interests. This dilemma constitutes a true problem for society, whose
solution largely exceeds the technological or legislative framework. For all these
reasons, it is not realistic to imagine a disappearance of the air pollution of anthropic
origin before many years. This is why it is necessary, parallel to the reduction of the
pollutant releases, “to manage” the emissions and their consequences on the air
pollution.
Pollution management
Trying to limit the effects of the air pollution, practices of pollution management were
adopted. They are usually based on the prevention of the risks for the populations and
include the monitoring, the public information and the emergency rules:
• Monitoring: The monitoring of air quality is a fundamental element of the air
pollution management. Measurement networks have been developed in all the
great agglomerations for more than twenty years and the laws provide for their
extension to obtain a national cover.
• Information: As recognized by the European legislation (Council Directive
96/92/EC on Air Quality Assessment and Management) the air quality data
collected in the inspection networks has to be propagated by the medias in order
to inform the people about the situation of air pollution and to allow them to
adapt their activity to the level of pollution.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
• Emergency rules: In case of strong pollution episodes, thresholds of alarm
suggest to intervene using emergency actions. These actions can be related to the
reduction of the emissions (i.e. suspension of industrial activities, traffic block)
and to the protection of the population (i.e. evacuation of the population).
The management of pollution aims to prevent that the episodes of strong pollution do
not have consequences on the human health. The purpose is to identify the most
polluted areas, where an effort in terms of emission reduction must be done. To satisfy
these objectives, dispersion modeling tools can be used to complement air quality
measurements.
2.2.7 The role of modeling
Although its role seems often secondary compared to reduction of the emissions,
modeling is a significant component of the air pollution management. It allows to better
understand the physical phenomena that are responsible of the problem and constitutes
an essential auxiliary tool. Modeling is a tool useful to several levels in the management
of the air pollution.
• Analysis and exploitation of monitoring measurements
• Decision-making aid – study of scenarios
• Forecast
Modeling is useful first of all in the verification and realization of the monitoring
networks. Taking into account the spatial variability of pollution, it is necessary to
evaluate the representativeness of the concentration data measured by the existing fixed
sensors and to choose the optimal location of the future monitoring site. In the event of
exceeding of a threshold, it is interesting to be able to provide the percentage of the
population actually affected by the measured level of pollution. Consequently the
modeling tools have an essential role in the exploitation of the data. They permit in
particular to study episodes of strong pollution, to interpolate and extrapolate the
measured concentrations, to provide cartography of the polluted areas, to evaluate the
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Chapter 2 Review of urban air pollution modeling and evaluation methods
exposure of the population (Ryan et al., 1986), to constitute data bases for the
epidemiologic investigations.
One of the main interests of modeling, especially for deterministic modeling, is to be
able to study the modifications generated by a change of the conditions of a problem. It
is possible to simulate various scenarios and to determine the consequences of longterm
policies like regional, town and transport planning. In particular case
(establishment of a factory, reorganization of the traffic in a quartier), environmental
impact assessment are frequently carried out through modeling approach in order to
evaluate the future consequences on the environment and air quality. Modeling also
constitutes a tool of diagnosis of current pollution. It allows the evaluation of the
contribution of different sources of pollutants (traffic, domestic heating, industry), of the
use of various fuels and the effectiveness of the policies implemented.
The forecast of the air pollution is a long-term aim. Some forecasting models are
gradually set up at the level of the agglomerations. They provide primarily qualitative
information on the presence or not of a peak of pollution in the hours which follow. The
generalized development of the forecast of pollution is unfortunately limited by the
capacities of the weather forecasting, of which the operational resolution is still too
coarse to be able to simulate urban pollution correctly (Soulhac 2000).
2.3 Scales of pollutant transport in the atmosphere
Pollution present at a given place comes from the sources located in the vicinity but also
from those located at distances much more significant, being able to reach several
thousands of kilometers (i.e. Chernobyl accident). It is possible to break up the
concentration measured in a street into a sum of contributions of the sources
corresponding to various scales:
C = Cmacro + Cmeso + Clocal + Cmicro
The main difficulty of modeling the air pollution is that none of the terms of the
previous equation is really dominating in comparison with the others. The relative
contribution of these terms depends on the site considered (located in the vicinity or not
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Chapter 2 Review of urban air pollution modeling and evaluation methods
of the great sources of emissions), on the type of pollutant (i.e. ozone can be transferred
at very long distances and tends to disappear in the core of the agglomerations) and on
the weather conditions. No scale can be neglected and it is necessary to model the entire
atmospheric spectrum. Unfortunately, it is not possible to simulate all these scales with
only one model; the limitation of the computer power does not enable us to treat the
atmosphere with such a variety of scales.
These difficulties led to the development of categories of models that work at different
scales and exchange information relating to their boundary conditions. For each model,
the dimension of the study field and its resolution respectively constitute the upper limit
and the lower limit of the range of solved scales.
Traditionally, the scale of atmospheric motions and related phenomena have been
classified according to their horizontal dimensions into three (or four) broad categories:
macroscale, mesoscale, (local scale), and microscale. There are differences in the
literature on the definition of these atmospheric flow scales. Table 2-1 summarizes the
definition by Oke (1987). Atmospheric phenomena as associated with each scale are
illustrated in Figure 2-5.
Micro-scale 10 -2 to 10 3 m
Local scale 10 2 to 5x10 4 m
Meso-scale 10 4 to 2 x 10 5 m
Macro-scale 10 5 to 10 8 m
Table 2-1 Atmospheric dispersion scales based on horizontal distance from the
source, according to Oke (1987)
Pollutants in the atmosphere are advected and diffused over the entire range of
atmospheric scales defined above. The length scales of horizontal and vertical transport
of any pollutant range from a scale of a few meters near the source, up to a global scale.
The time scales involved for pollutants to spread and mix over these length scales, range
from minutes to years (Table 2-2).
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Horizontal distance Dispersion scale Time scale
1 to 1000 m Microscale Seconds to minutes
3 to 30 km Local scale Hours
100 to 1000 km Mesoscale Days
Hemisphere Macroscale Months
Globe Global scale Years
Vertical distance Atmospheric layer involved Time scale
Ground to 100m-3km Surface layer Minutes to hours
100m –3km to 10km-15km Mixing layer to Troposphere Days to weeks
10km-15km to 50km Troposphere to Stratosphere Years
Table 2-2 Horizontal and vertical transport scales in the atmosphere (adapted
from Boeker et al., 1995)
Pollutants released into the atmosphere at ground level are initially advected by
localized flow and turbulent mixing in the PBL near the ground. Pollutant transport is
then taken over by local scale winds and mesoscale circulations (e.g. urban heat islands,
land and sea breezes, mountain and valley winds, thunderstorms). Up to several hours
and a distance of tens of kilometers later, pollutants are advected along two or three
dimensional mean flow patterns (limited in the vertical by the depth of the PBL),
turbulent diffusion varying according to the conditions in the PBL. Within a travel time
of about a day to a few days, synoptic scale systems (such as cyclones and anticyclones)
transport pollutants along generally complex horizontal pathways (‘trajectories’).
Horizontal and vertical spread about the mean trajectories is determined by the
embedded mesoscale and microscale motions, and pollutant exchange between the PBL
and the rest of the troposphere occurs as a result of synoptic scale vertical motions (e.g.
at fronts, hydraulic jumps over mountains and large scale convective motions in the
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Chapter 2 Review of urban air pollution modeling and evaluation methods
PBL). Over a time scale of weeks, pollutants have become mixed vertically throughout
the depth of the troposphere and horizontally over synoptic scale systems. During this
time, pollutants are not only transported but also transformed chemically and removed
due to gravitational settling and wet deposition. Only relatively inert gases remain in the
atmosphere by the time macroscale systems take over. These are global circulation
patterns (such as the trade winds and jet streams) which cause pollutants to disperse
over the entire hemisphere (northern or southern), and eventually over the entire globe,
with some vertical mixing into the stratosphere.
Figure 2-5 Spatial and temporal scales of atmospheric phenomena (Oke, 1987)
As a result, appropriate assumptions and simplifications for modelling the physics of
dispersion differ markedly according to the scale of meteorological phenomena
involved. The scope of dispersion studies is therefore usually defined in terms of the
atmospheric scales defined above. Based on the distance from the source, dispersion
studies are thus classified into micro-, local-, meso- and macro- scales.
However, dispersion at any given scale cannot be studied in isolation, since phenomena
at each scale influence and co-exist with each other. Coupling models of different scales
is thus a desirable goal, though in practice usually it is very difficult to achieve.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Linkages between dispersion at different scales are often implicit and complex,
especially since flow patterns interact non-linearly over a vast range of scales.
2.3.1 Space and time scales in the urban context
Urban air pollution involves physical and chemical processes over a wide range of
space and time scales. Cities have typical scales up to 10-20 km, or possibly even larger
when several adjacent urban areas coalesce to form a conurbation. Pollutant dispersion
from near-ground level sources would be present through much of the atmospheric
boundary layer over these distances. Processes at the city scale influence the larger
regional scale up to 100 or 200 km by providing a momentum sink and a thermal and
pollutant source. At the same time the regional, or larger, scale physical processes
provide the background state for the city scale processes.
The city contains its own inhomogeneities, frequently with a city centre having larger
buildings and an outlying area of lower industrial or residential areas. Some researchers
find it useful to consider the city as consisting of many semi-homogeneous
neighbourhoods of typical scale 1 to 2 km. Over these distances the dispersion processes
may be influenced by the buildings themselves and the roughness sublayer that extends
up to 2 or 3 times the average building height.
Peak pollutant concentrations will obviously occur when the pollutant source and
receptor are nearby, for example when the receptor (an individual or a pollutant
monitoring station) is at roadside and the pollutant source (vehicles, at high traffic
density) is also within the street. Consequently processes at the street (or street canyon)
scale, that may be up to 100 or 200m, are of particular interest. At this scale the
geometric arrangement of the buildings, street canyons and intersections directly affect
the dispersion processes (Britter and Hanna 2003).
All dispersion modelling scales are involved in determining concentrations at any
particular point in the urban environment, especially since vehicle traffic is spread over
a dense and extensive road network, throughout the city. The concentration at any given
location within a city is the added effect of dispersion from all surrounding sources,
some nearby and more further away.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
In Italy, a distinction is drawn between two types of urban air pollution monitoring
locations, in an attempt to quantify the relative contributions of near- and far-field
sources. ‘Roadside’ monitors are located near busy roads, and ‘background’ locations
are situated in open spaces to measure concentrations due to contributions from sources
further away.
On the base of the preceding consideration Britter and Hanna (2003), with some
modifications with respect to the original work (Munn 1981), proposed a broad
classification of urban dispersion scales (Table 2-3). For each spatial scale there is a
corresponding time scale that is the spatial scale divided by a representative advective
velocity, typically the wind speed.
Urban scale definitions Horizontal length scale Time scale
Microscale Less than100-200 m Seconds
Neighbourhood scale Up to 1-2 km Minutes
Urban scale Up to 10-20 km Hours
Regional scale Up to 100-200 km Days
Table 2-3 Urban dispersion scales (as defined by Britter and Hanna, 2003)
The regional scale is affected by the urban area. For example, the urban heat island
circulations, any enhanced precipitation, and the urban pollutant plume can extend to
these distances. At this scale the mean synoptic meteorological patterns are given and
urban area represents a perturbation, causing deceleration and deflection of the flow, as
well as changes to the surface-energy budget and the thermal structure.
The city scale, or urban scale, represents the diameter of the average urban area. At
these scales the variations in flow and dispersion around individual buildings or groups
of similar buildings have been mostly averaged out. Wind flow models developed for
this range pay little attention to the details of the flow within the urban canopy layer.
Most of the mass of any pollutant cloud travelling over this distance will be above the
height of the buildings.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
On the neighbourhood scale buildings may still be treated in a statistical way; however,
the approach may be different to that on the city scale. At the neighbourhood scale we
want to know more about the flow within the urban canopy. The wind flow, particularly
within the canopy, may also be changing as it moves from one neighbourhood to the
next. Much of the mass of a pollutant cloud travelling over this distance may remain
within the urban canopy.
The street (canyon) scale addresses the flow and dispersion within and near one or two
individual streets, buildings or intersections. This would be of interest when considering
turbulence affecting pedestrian comfort and the direct exposure of pedestrian and nearroad
residences to vehicular emissions. It can be of particular interest when regulatory
pollutant stations are placed within street canyons.
Hall et al. (1996) proposed another way of defining dispersion scales applicable to the
urban environment, by considering the evolution of a plume within the urban canopy,
and comparing the width of the plume to the dominant length scale of the turbulence
around the buildings. Turbulence within the canopy is predominantly generated due to
velocity shear around the surface of the buildings and flow separation, and its largest
scale L, is of the order of the building dimensions. Three dispersion scales were defined
as follows (illustrated in Figure 2-6):
1) Plume width

Chapter 2 Review of urban air pollution modeling and evaluation methods
the growing plume and contributes to plume meandering. However the dispersion
patterns are less variable than those in the first case, and only the overall shape of the
buildings affects the rate of dispersion.
3) Plume width >> L : Far-field dispersion
A longer distance away from the source the plume is very broad, and concentrations
follow a near-Gaussian distribution. The turbulence scales of the PBL and surface
roughness, rather than turbulence generated by individual buildings, now dominate
plume growth.
Figure 2-6 Urban dispersion scales defined according to Hall et al. (1996)
These dispersion scales span the microscale and neighbourhood scales defined by Munn
(1981) and slightly modified by Britter and Hanna (2003). The ‘far-field’ dispersion
scale corresponds to the upper limit of Munn’s ‘neighbourhood’ scale. In this thesis
both the above described classification schemes will be used.
The different spatial scales show themselves more clearly when one considers how to
develop models for flow and dispersion. It is natural that the larger the spatial scale
under study the less the importance and hence necessity for any detailed information.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Alternatively the limitations on analytical or computational resources will not allow the
detail to be studied over a large spatial scale. A further way of addressing this issue is to
consider the concentration at a point receptor within a city. The concentration or
concentration-time history will have a contribution from the background concentration
arising on a regional scale and this will vary slowly and relatively smoothly with time.
There will also be a contribution from the pollutant sources upwind of the receptor. The
variations of the source positions and strengths will be smoothed out to a greater extent
when well upwind and to a lesser extent for closer sources. (Moving) sources within the
same street and near the receptor will produce rapidly varying and high magnitude
concentrations. Thus the degree of detail required to address each scale reduces as the
scale itself increases.
Dispersion models are usually applicable over only one, or a limited range of dispersion
scales, since the dominant physical mechanisms and assumptions at each scale are
different. However, dispersion at any one scale is closely linked with all others as part
of a continuum, since phenomena at each scale co-exist with, and influence, each other.
Modelling dispersion at any given scale in isolation is a significant approximation, and
therefore, coupling models of different scales is a desirable goal, though in practice it is
usually very difficult to achieve. For example, microscale models require the
‘background’ concentration calculated by urban and regional scale models. Urban and
regional scale models also depend on an accurate characterization of the macroscopic
flow and dispersion features of a city that are influenced by microscale effects (e.g. the
aerodynamic roughness of the urban surface). As discussed in Moussiopoulos (1999), in
order to be able to ‘nest’ urban models at different scales effectively, the understanding
of the linkages between scales needs to be improved.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
2.4 Modelling methods applied to urban dispersion
2.4.1 The range and types of urban dispersion models
Following the classification used by Zannetti (1990), dispersion models can be divided
in two main categories:
a) experimental models: field experiments (around full-scale or scaled buildings), or
scaled simulations in the laboratory (using a wind tunnel or water tank).
b) mathematical models: a set of analytical/numerical algorithms that describe the
physical and chemical aspects of the problem. Mathematical models are usually
divided in two sub-categories
• statistical models: models based upon semiempirical statistical relations among
available data and measurements
• deterministic models: models based on fundamental mathematical description of
atmospheric processes, in which effects (air pollution) are generated by causes
(emissions). This models are the most important mathematical ones for practical
application, since, if properly validated, they provide an unambiguous sourcereceptor
relationship, thus allowing the definition of appropriate emission control
strategies. This sub-category can be further divided in two:
o parametric models: deterministic models that express concentration
values as a function of a set of variables (parameters) and conditions.
o computational models: models that solve the flow and dispersion
conservation (or transport) equations numerically for any given boundary
conditions, using either Eulerian or Lagrangian approaches.
Table 2-4 summarizes the main urban modelling approaches divided according to the
categories defined above.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Different modelling approaches are applicable to each of the urban dispersion scales.
Models can therefore be usefully categorized according to the scale of their applicability
(Table 2-5, Scaperdas 2000).
By definition, any modelling method is a simulation of reality based on particular
assumptions, simplifications and approximations, and these determine the degree of
accuracy and uncertainty inherent in any type of modelling. This is a fundamental
consideration in deciding whether a particular method is appropriate for a given
problem and therefore, whether a particular model is ‘fit-for-purpose’, i.e. likely to
predict concentrations reliably over a required confidence level and under given
conditions. The choice of an appropriate methodology for urban environments is a
difficult task. For example the U.S.EPA (USEPA, 2005a) has designated certain
‘preferred’ models to be used for regulatory air quality assessment from stationary
source emissions, but there is not a similar approach for urban dispersion models. The
situation is even more uncertain in Europe, where only general guidelines are given
(Van Aalst et al., 1998) and a number of different models are used in the European
countries (see e.g. EEA, 2005).
Field experiments
Physical models
Experimental models
Measurements in the field under selected
conditions:
at real urban sites
around full-scale or scaled obstacles of idealized
shape or arrangement
Wind tunnel experiments
Water tank experiments
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Mathematical modelling
Statistical models Derived from statistical analyses of air quality
monitoring data:
Deterministic models
Parametric
Computational
Empirical
Semi –
Empirical
Lagrangian
Eulerian
Regression statistical analysis
Neural networks analysis
Based on the analysis of field or laboratory based
controlled experiments
Based on a combination of theoretical analysis
and empirical data:
Simple ‘box’ models
Urban canyon models
Urban array models
Gaussian ‘plume’ models
Mesoscale trajectory models
Stochastic Lagrangian dispersion models
Large scale grid models
RANS and LES Computational Fluid Dynamics
Table 2-4 Urban dispersion modelling methods: experimental, parametric and
computational
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Meso-scale
Urban and local
scale
Micro-scale
Lagrangian trajectory models
Eulerian (meteorological and dispersion) models
Physical modelling (e.g. urban roughness, urban topography)
Statistical modelling (e.g. correlation models, neural networks)
City-wide box models
Gaussian plume models
Full-scale modelling (e.g. within urban canyons)
Physical modelling (e.g. array and canyon studies)
Canyon models
Array models (e.g. modified Gaussian plume models)
Computational Fluid Dynamics (CFD) modelling
Table 2-5 Urban dispersion modelling approaches, meso- to micro-scale
(Scaperdas, 2000)
2.4.2 Field experiments
Field experiments involve measuring the flow and concentration either directly in the
urban environment, or around idealized (and often scaled) obstacle shapes and
arrangements in the open country. The difference between full-scale urban dispersion
experiments and urban air quality monitoring is that the former are conducted under
selected or controlled conditions in order to isolate particular aspects of dispersion
behaviour amidst the complex effects of many interrelated factors. For example,
measurements may be taken only for selected wind directions and atmospheric
conditions, during periods when traffic counts (and thus emissions) are nearly uniform,
or by releasing a tracer at a controlled emission rate for quantitative measurements or
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Chapter 2 Review of urban air pollution modeling and evaluation methods
visualization purposes. Such carefully controlled field measurements can then form
suitable datasets for parametric and computational model development and evaluation.
Field measurements provide the most direct and realistic way of simulating dispersion
in actual urban environment and atmospheric conditions. However, the natural
variability found in the field also significantly complicates the process of understanding
and isolating particular effects of interest. In the field there is no control over the wind
direction and speed at any given time, so it is usually necessary to wait for the
appropriate winds. Quality field experiments are typically the most time-consuming and
expensive of available modelling methods. Long measurement periods are required,
including waiting for suitable meteorological and other conditions. There are also
considerable practical problems associated with the organization and setting up of the
experiments, such as instrumentation siting and calibration, measurement capture and
automation, and risk of damage to instrumentation due to vandalism or environmental
hazards. In addition, experimental conditions can never be exactly replicated and
ensembles of experiments in nominally similar condition have to be analyzed to derive
satisfactory statistical properties of the dispersion process. Without such ensemble
averaging, field experiments effectively become a collection of individual realizations
and their use in model development or evaluation is greatly weakened by the inherent
uncertainty attached to each. These factors greatly reduce productivity relative to wind
tunnel work. Nevertheless, field data are an essential component of model testing and
development because they encompass all the features of dispersion in the atmosphere.
(Robins and Macdonald, 2001).
2.4.3 Physical models
Physical modeling constitutes an essential tool in many fields of the fluid mechanics. It
allows to study the real physical phenomena on models with a different scale from the
reality. The interesting aspect is the opportunity to reproduce a complex problem on the
scale and under the conditions of a laboratory, and consequently to be able to have
powerful means of investigation and measurement. It also permits to take into account
very detailed geometrical configurations, which it is still difficult to simulate
numerically.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Scaled modelling in the laboratory is carried out either in a wind tunnel or water tank.
Wind tunnels are and have been most commonly used for the physical modelling
dispersion in arrays of obstacles, although water channels were used by civil engineers
in some earlier studies of flow and drag characteristics of arrays (see, for example,
Morris 1955, Sayre and Albertson, 1963). Water channels do offer some advantages for
the visualization of dispersion since simple dyes can be used and the much lower flow
speeds produce longer time scales which are more suitable for video recording;
however, wind tunnel work predominates (Robins and Macdonald, 2001). The
principles and considerations are generally equally valid for both types. Scale modelling
is based on theoretical assumption that flow and dispersion around scaled obstacles is
dynamically similar to that at full scale (as discussed in more detail in Chapter 3). The
more the mean incident wind velocity and turbulence conditions in the atmosphere can
be faithfully reproduced in the laboratory, the more the flow and dispersion behaviour
measured around the scaled obstacles will be similar to those measured in the field.
However, a faithful representation of the turbulence variability in the atmosphere is
difficult to achieve, and this is the main limitation of wind tunnel experiments.
The compelling advantage of wind tunnel modelling is that conditions can be controlled
accurately and reproducibly. It is therefore much easier to control and assure the quality
of wind tunnel experimental data, in contrast to field experiments. The time requirement
for wind tunnel experiments is also significantly less than that for field experiments.
Furthermore, as highlighted by Schatzmann et al. (1999), wind tunnel results are
averaged over sufficiently long periods of time (to give ‘ensemble averages’) in marked
contrast to typical field data, which are obtained during relatively limited sampling
times due to the variability in wind direction and other meteorological conditions.
Despite the considerable cost of operating and maintaining a wind tunnel facility, the
overall cost of wind tunnel experiments compares very favourably to that of field
experiments. For all these reason, despite the limitations in representing the full range of
atmospheric phenomena in the wind tunnel, they are generally considered the most
reliable of available modelling methods and they are a fundamental instrument in the
development of the majority of parametric models, especially if combined with field
experiments.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
2.4.4 Statistical models
Statistical (also sometimes referred to as ‘stochastic’) models are based on a purely
statistical treatment of air quality monitoring data, alongside limited information on
meteorological and traffic conditions, with virtually no theoretical input. There are
many different statistical treatments used, such as traditional regression statistical
methods (as described in Berlyand, 1991), or non-linear ‘neural network’ analyses
which are now gaining popularity (e.g. Gardner et al., 1999). Correlations based on a
selected set of variables can be ‘tuned’ to be very good, e.g. for predicting
concentrations in an urban canyon at a particular site, based on traffic count and simple
meteorological data.
Although statistical models can be useful and appropriate for particular applications,
they are nevertheless extremely case-specific. Even if the statistical analysis were to be
perfect (of a high statistical confidence level), there are far more variables involved in
practice than are usually accounted for. Typical examples of neglected parameters
include near-field effects due to details of the urban topography, and variations in
emission factors, e.g. due to introduction of a new clean technology, such as catalytic
converters. As a result statistical models should be expected to break down when the
conditions on which the model was based change, e.g. when applied to a different city
or indeed in different locations within the same city, or to predict future scenarios
2.4.5 Parametric models
Parametric models express concentration as a mathematical function of a set of
governing, input variables. The functions defining a parametric model may be either
based on experimental data (empirical), analytical solutions or a combination of the two
(semi-empirical). For example, Urban Gaussian plume models are semi-empirical
parametric expressions. The mean concentration downstream of a continuous passive
source over a flat rough surface (a city) is determined by functions of the form:
C = f1 ( x, y, z, hs,σ y ,σ z ,Q,U )
σ y = f2 ( x, zo , stability class)
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Chapter 2 Review of urban air pollution modeling and evaluation methods
σ z = f3 ( x, zo , stability class)
where f1 is derived from an analytical solution of the advection-diffusion equation (see
section 4.2.1), f2 and f3 are functions derived from considerations on the physics of the
problem (e.g. dimensional analysis) and empirical constants are derived based on field
experimental data. There are many other parametric models used for urban near-field
dispersion, mainly urban canyon models.
Parametric models are the simplest and most straightforward method of all dispersion
modelling methods, even though they some involve a large number of input data (e.g.
when calculating time series from variable input data) and may require considerable
computational resources.
The design and development of any parametric dispersion model is based on identifying
the important parameters in a given dispersion problem, and deriving appropriate
mathematical expressions for calculating concentrations. Ideally, this is based on a
fundamental theoretical understanding, but due to the complexity of flow and dispersion
in the urban environment, especially in the near-field, parametric models often have to
be based on empirical correlations or case-specific simplifying assumptions that limit
the applicability and confidence range of the model.
Gaussian models
As described above, Gaussian plume models are a sub-set of parametrical models. They
are discussed separately because they are particularly popular and a ‘recommended’
method by organizations such as the USEPA. There are numerous Gaussian plume
models available, e.g. ADMS (Carruthers et al., 1994), ISC3 (USEPA 1995), CALINE4
(Benson, 1992) and AERMOD (Cimorelli et al.., 1998).
Gaussian plume models are particularly popular and are used in a variety of urban
dispersion models because of the relative simplicity and the easiness with which
additional effects due to source buoyancy, atmospheric conditions, deposition, and
surface roughness can be included by modifying expressions for pollutant spreads σy
and σz. Also, although the basic Gaussian plume models calculate time-averaged
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Chapter 2 Review of urban air pollution modeling and evaluation methods
concentrations over time scales from about 10 min to an hour, time-averages for longer
time-scales can be derived. For example, statistical or meteorological time series data
can be used, to predict annual mean concentrations and percentiles, or worst case
scenario concentrations.
Gaussian models are not applicable to near-field dispersion in the urban canopy, due to
the complex boundary of the urban topography and the localized flow and turbulence,
which invalidate the fundamental theoretical assumptions on which Gaussian models
are based (unbounded uniform flow field of velocity). Gaussian plume models should
therefore only be used to calculate ‘background’ concentrations, above the urban
canyon and at scales larger than the neighbourhood scale. They are also not directly
applicable to larger scale modelling (macroscale) due to the spatial non-uniformity of
the velocity field over larger distances, and the different assumptions applicable to
calculating turbulent mixing and thus pollutant spread at that scale.
The performance of a Gaussian model depends on an appropriate selection of plume
spread functions σy and σz and the quality of the input emission and meteorological
data. Expressions for σy and σz typically depend on meteorological conditions, the
urban surface roughness and distance from each source.
2.4.6 Computational models
Computational models are based on the numerical solution of the conservation/transport
equations of flow and dispersion, expressed either in Lagrangian or Eulerian form. The
basic difference between the Eulerian and Lagrangian approach is illustrated in figure
2-7, where the Eulerian reference system is fixed while the Lagrangian reference system
follow the average atmospheric motion.
The Eulerian formulation is aimed to link the main statistical parameters of the
concentration field to the statistical properties of the Eulerian fluid velocities, directly
measurable in the PBL. This approach is straightforward and has the great advantage to
simply implement calculations involving chemical reactions. The main limitation lies in
the solution of the fundamental equations used in this approach: there are not
sufficiently general solutions and the presence of turbulence introduces the closure
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Chapter 2 Review of urban air pollution modeling and evaluation methods
problem, as described earlier. Moreover, the accuracy of the results (and the time
necessary to achieve them) is strongly influenced by the numerical algorithm used to
solve the equations. This is the usual method for describing physical phenomena such as
heat and mass transfer, and it is commonly used in order to calculate
micrometeorological parameters.
The main difference of the Lagrangian approach, with respect to the Eulerian
description, is not only the different spatial reference system. While the Eulerian
approach tries to describe the dispersion phenomena in a deterministic way, the
Lagrangian method uses a statistical description. This mathematical approach is more
effective than that used in the Eulerian description, but has the disadvantage of a
difficult description of the chemical processes.
Each of the two approaches can be a valid description of turbulent dispersion in the
PBL. The choice is driven by many different factors, and it should be based on a caseby-case
approach.
Figure 2-7 Eulerian (a) and Lagrangian (b) reference system for the atmospheric
motion (Zannetti, 1990)
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Lagrangian models
Two main types of Lagrangian computational models exist: Lagrangian trajectory
models (also known as Lagrangian box models) and Lagrangian particle models (also
known as stochastic Lagrangian models).
The first type is applied to dispersion modelling by following a box or a column of
boxes (allows explicit computation of vertical diffusion) of polluted air as it is
transported along mean flow paths or ‘trajectories’, according to the local time-varying
average wind speed and direction. Pollutants are introduced into the column as it passes
over various sources. Once within the column, pollutants disperse and may become
chemically transformed, and finally leave the column by deposition along the length of
the column’s path. This is an approach that is particularly suitable for long range
dispersion where the dominant dispersion mechanism is horizontal advection, and
vertical mixing over the height of the PBL. Flow trajectories are calculated either using
meteorological measurements or weather prediction meteorological models. No lateral
diffusion across the sides of the column is included, and concentrations are assumed
uniform (fully mixed) over the height of the column, taken equal to the height of the
PBL.
This technique is particularly useful for photochemical simulations and provides
average time-varying concentration estimates along the trajectory of the boxes. The
major shortcoming of this technique is the forced assumption of a constant wind speed
and direction throughout the PBL, while, in reality, wind shear plays an important role.
Another problem is the difficulty in comparing their outputs (time-varying
concentrations along a trajectory) with fixed (Eulerian) air quality monitoring data.
The compelling advantage of using Lagrangian models is that it they require far less
computational resources than Eulerian models. Several Lagrangian box models have
been developed for simulating photochemical reactions inside a moving air mass. This
development was triggered by the high computational cost of Eulerian photochemical
models, in which chemical and photochemical reactions need to be computed in each
fixed grid cell of the three dimensional computational domain. Lagrangian box models,
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Chapter 2 Review of urban air pollution modeling and evaluation methods
instead, perform these calculations on a smaller number of moving cells (Zannetti
1990).
Examples of Lagrangian dispersion models applied to urban air pollution problems
include the urban scale London Plume Model (Hough and Derwent, 1987), and the
EMEP model (Tsyro, 1998) which includes a representation of ozone and PAN
chemistry due to urban emissions over a Europe-wide scale.
The second type of Lagrangian models, the stochastic models, are mainly used in shotrange
dispersion modelling. Emitted gaseous material is characterized by a set of
computational particles and each particle is moved at each time step by deterministic
velocities which take into account the three basic dispersion components: the transport
due to the mean fluid velocity; the turbulent fluctuations of wind components (both
vertical and horizontal); the molecular diffusion. In the Lagrangian particle model, the
turbulent dispersion is modeled by tracking the release of a large number of particles as
they are advected with the flow, which is typically generated by a meteorological model
and represented by mean flow and turbulent fluctuations. They have some advantages
compared to the Eulerian models; the turbulent diffusion is actually a Lagrangian
phenomenon. The Lagrangian particle models do not have problems with sharp
gradients in the emissions, and may easily be applied for point and line sources.
Eulerian models may in some cases have problems with negative concentrations due to
numerical errors, which cannot appear in Lagrangian models. Finally, Lagrangian
models are generally flexible, computationally inexpensive and easy to apply compared
to Eulerian models. It is still difficult to incorporate chemical reactions in Lagrangian
particle models, but some encouraging results have been obtained (see e.g. Alessandrini
et al., 2007), though they are still highly time-consuming.
Eulerian grid models
Eulerian grid models solve the flow and advection-diffusion equation in a fixed frame
of reference. The flow domain is divided into boxes (or ‘cells’) and the transport terms
in the equations are represented by fluxes through the boundary of each box. Such
models are also called ‘gradient transport’ models, since the transport terms are
expressed in terms of spatial (and temporal) gradients of velocity and concentration.
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They are used particularly at the urban scale or mesoscale, when complex chemical
processes are involved (e.g. photochemical models). One of the first developed models
of this type for applications in urban areas was the Urban Airshed Model (UAM). Ohter
Eulerian photochemical models are listed in the EPA website (USEPA, 2005b): CAMx,
CALGRID, CMAQ.
Computational Fluid Dynamics modelling
Computational Fluid Dynamics (CFD) modelling is a term used to describe the analysis
of fluid flow, mass and heat transfer, and associated phenomena (such as chemical
reactions), using sophisticated numerical methods for solving the (Eulerian) equations
of flow, mass and heat transfer, and other associated equations. Also coupled Eulerian-
Lagrangian CFD models exist. They are often called hybrid dispersion models and they
have been developed in order to surpass some of the disadvantages in Eulerian and
Lagrangian models. They usually solve the flow using an Eulerian approach and then
calculate concentrations using a Lagrangian particle model.
The fundamental principle of CFD is the same as the other Eulerian models described
above, but CFD codes were mainly developed for engineering applications that typically
involve very complex shaped wall and other boundary conditions (e.g. aircraft and
automobile aerodynamics, turbomachinery design, chemical process engineering). The
capability of CFD codes to deal with complex boundary conditions with the use of
flexible and fine-scale grids is what distinguishes them from larger scale, simple grid
Eulerian models, and what makes CFD the only computational model applicable to
microscale, near-field urban dispersion applications.
CFD modelling is typically divided into the following categories:
a) Direct Numerical Simulation (DNS): consists in solving the equations of Navier-
Stokes in their most general form, by considering all the scales of turbulence. This
method is certainly nearest to an exact resolution but it requires a very fine space
resolution, which generates excessively long computing times and limit its
application to relatively low Reynolds numbers. Its use is restricted to the
fundamental studies on the mechanisms of turbulence.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
b) Large Eddy Simulation (LES): is based on an explicit resolution of the great
structures of the turbulence, coupled to a statistical parameterization of the effect of
the small structures. The space resolution of the mesh constitutes a filter which
allows to simulate only the eddies whose diameter is higher than the size of the
mesh. The contribution of the small eddies on the diffusion of the momentum is
taken into account by a sub-grid scale model (Smagorinsky, 1963). The LES models
are particularly adapted to the study of the atmospheric turbulence, for which they
were initially developed (for example Deardorff, 1970). It is possible to mention
some examples of application to the study of the urban canopy and of the flow
inside a street (Ca et al., 1995 ; Chabni et al., 1998). However, the geometrical
complexity of these configurations and the difficulty in imposing realistic boundary
conditions still restrict this type of applications.
c) Reynolds Averaged Navier-Stokes simulation (RANS): based on statistical
modeling of the equations of Navier-Stokes, it consists in solving the Reynolds
Averaged Navier-Stokes flow equations with the use of ‘turbulence model’
approximations for turbulence closure (such as the standard k-ε model, ASM and
RSM models). It is an approach which does not allow to detail the characteristics of
turbulence, however, it often proves to be a good tool for the study of the average
fields, in particular when one is interested in the flows around complex geometries.
Many authors applied this type of modeling to study the atmospheric flow around
obstacles (i.e. Paterson and Apelt, 1986 ; Murakami and Mochida, 1988 ; Hunter et
al., 1992) and groups of obstacle (e.g. simulation developed in the framework of
COST 732 action, URL 2008).
The cost of the accuracy of the predictions in terms of computational resources is very
high for DNS and LES simulations, which typically require large amounts of
supercomputer time and, at present, are undertaken only for the purposes of academic
research. RANS solving CFD methods with turbulence models are less demanding, and
are therefore widely used for practical applications.
CFD modelling studies are providing increasingly realistic results, although there is still
much uncertainty about the range of its applicability, the accuracy of the results, and the
choice of appropriate modelling strategies. It is therefore necessary to validate CFD
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Chapter 2 Review of urban air pollution modeling and evaluation methods
results carefully against wind tunnel and field data; big efforts in this direction were
done in the framework of COST action 732 ‘Quality assurance of microscale
meteorological models’ (URL 2008).
2.4.7 Multiscale approach
There are two possible approaches for constructing modelling systems based on nested
models (Soulhac et al., 2003). The first is based on the use of a single three-dimensional
Eulerian model, nested at different scales; the main advantage of this type of model is
that it uses the same methodology for all the different scales, and this facilitates the
exchange of information between calculations at the different scales; the main
disadvantage is that the models are computationally intensive, requiring considerable
computing time and resources. Soulhac et al. (2003) raised doubts about the validity of
using the same modelling assumptions for all the scales involved. The second approach
is based on the use of different types of models for different scales, with each model
chosen to represent the dominant physical process at that particular scale. Such a
modelling system might therefore consist of different models for calculations at the
regional, urban, neighbourhood and street scales. The principal advantage of this
approach is that each model can be chosen to reproduce the dominant processes at the
relevant scale, and this usually means that they are much more efficient than a general
3-D model. The major disadvantage is that the models often use very different
approaches, so that, for example, a modelling system might consist of a combination of
Eulerian, box, Gaussian plume, canyon and Lagrangian stochastic models. It is
extremely difficult to couple such a disparate ensemble of models, particularly when
coupling needs to be both up-scale and down-scale. For this reason, most current
modelling systems which use a variety of different models do not implement any strong
coupling between the models. This simplification is acceptable when the phenomena of
interest are only weakly dependent on processes at other scales, but in many cases it is
necessary to model several different scales simultaneously, or quasi-simultaneously
(Soulhac et al., 2003).
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2.5 City scale and regional scale modelling
2.5.1 Flow and dispersion phenomena
The city scale is, essentially, the urban area: an area distinguished from its surroundings
by its relatively large obstacles (buildings and other structures) and, hence, by a large
drag force, by the infusion of heat from man’s activities and by the large heat-storage
capacity of concrete, other building materials, and parking lots. The city scale can
include variations in urban building types and spacing and primarily concerns the
boundary layer above the average building height, H. The regional scale is the larger
surrounding area that is influenced by or influences phenomena at the city scale (Britter
and Hanna 2003).
The regional scale acts as the “background” for the flow on the city scale while the city
scale acts to modify the regional scale flow. The city will divert the regional scale flow
vertically and laterally both kinetically due to direct flow blockage and dynamically due
to the increased drag force provided by the buildings. The city acts as a source of
turbulent kinetic energy that will enhance vertical mixing over the city and as a
momentum sink for the regional flow, producing a modification to the velocity profiles.
The regional scale flow may also be dynamically affected through a thermally generated
“urban heat island” (formation of a cap cloud over the urban area); the urban heat island
produces a convergence into the city and vertical motions over the city (see figure 2-8).
The regional scale provides the background chemical composition of the air within
which the pollutants released in the city will mix, react and dilute. The city provides the
pollutant source for an “urban plume” that can be easily observed 100 to 200 km
downwind (figure 2-8).
The transport and dispersion of pollutants over the urban area is altered as a result of
increased mechanical turbulence caused by the relatively large obstacles over which the
pollutants must travel. Furthermore, the urban heat island causes the boundary layer
over an urban area to become more unstable as thermal turbulence increases. Both of
these effects enhance dispersion. The wind velocities are altered in magnitude and
direction. Wind speeds over the urban area are slower owing to the increased roughness
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Chapter 2 Review of urban air pollution modeling and evaluation methods
of these areas, and wind directions could change as a result of the heat island
circulations or the bending of the flow around and over the urban area.
Figure 2-8 Urban heat island with light regional wind (left) and urban plume with
moderate regional wind (right) [Zannetti 1990]
Mathematical modeling of flows on the regional scale requires the parameterization of
the effects of the urban surface on the flow. For example, mesoscale meteorological
models with horizontal grid scales of 2–8 km are run for special purposes around urban
areas and can better resolve the types of land use (Brown 2000). The mesoscale models
use a typical vertical grid spacing of approximately 20 m near the surface and
approximately 200 m in the middle and top of the boundary layer. Such models often
parameterize the urban area by using a simple prescription of land use. Thus an “urban”
land-use grid may be assigned a single surface roughness length, soil moisture, albedo,
and Bowen ratio. At the city scale a similar need for the parameterization of the urban
surface exists.
Urban obstacles exert a relatively large drag force on the atmosphere. This can be
treated by standard atmospheric boundary-layer formulas (Stull 1988), as long as the
mean building height, H, is small compared to the surface boundary-layer depth, which
is usually approximately higher than 100 or 200 m, and the surface has some statistical
homogeneity.
Figure 2-9, from Britter and Hanna (2003), depicts a cross section of a typical urban
area, showing the three major sublayers: the urban canopy sublayer, the roughness
sublayer, and the inertial sublayer. The inertial sublayer is the area where the boundary
layer has adapted to the integrated effect of the underlying urban surface, and it is
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Chapter 2 Review of urban air pollution modeling and evaluation methods
treated by standard atmospheric boundary-layer formulas. In the urban canopy sublayer
the flow at a specific point is directly affected by the local obstacles, and in the
roughness sublayer the flow is still adjusting to the effects of many obstacles.
Figure 2-9 Schematic of the flow through and over an urban area (Britter and
Hanna 2003)
The surface shear stress (averaged over the urban surface) defines a friction velocity,
u*, that can be used to derive wind and turbulence profiles. It is assumed that, regardless
of the underlying surface, the wind speed at the top of the boundary layer is
approximately equal to the equilibrium wind speed defined by the geostrophic wind
speed, which is based on the synoptic pressure gradient. For larger surface roughness
the drag force (or u*) is larger, and the wind speed at any given level in the boundary
layer is smaller. The friction velocity, u*, is the key scaling velocity in Monin-Obukhov
similarity theory (Stull 1988). The other key scaling parameter is the Monin-Obukhov
length, L, which accounts for the effects of atmospheric stability and is proportional to
u* 3 divided by the surface turbulent (or sensible) heat flux from the ground surface Hs:
L =
3 ( u*
κ )
( gH C ρT
)
s
p
where g is the acceleration due to gravity, Cp is the specific heat of air at constant
pressure, ρ and T are the air density and temperature, respectively, and κ is von
Karman’s constant taken to be 0.40. Hs is positive in the day and is negative at night.
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The wind-speed profile conforms to Monin-Obukhov similarity theory, with the
addition of the two scaling lengths, z0 (the surface roughness length) and d (the surface
displacement length):
u * ⎛ z − d ⎞ ⎛ z ⎞
U ( z)
= ( ln
⎜
⎟ + ψ ⎜ ⎟ )
κ ⎝ z0
⎠ ⎝ L ⎠
where ψ(z/L) is a universal dimensionless function (Stull 1988) that equals zero in
neutral or adiabatic conditions (i.e., when L=∞ or z/L=0). For the purposes of the
following discussion of the urban boundary layer, neutral conditions are assumed for
which the preceding equation reduces to
u * ⎛ z − d ⎞
U ( z)
= ln
⎜
⎟
κ ⎝ z 0 ⎠
Thus, given a wind-speed observation at a height greater than approximately 2H (see
figure 2-9) and given an estimate of z0 and d, then u* can be estimated from the
equation.
Parameterizations for z0 and d are possible using several approaches. Land-use methods
are used in most applied dispersion models. These methods are based on the tables of z0
versus descriptive land-use types (e.g., Stull 1988). The problem for those interested in
urban sites is that the land-use categories are usually very broadly defined, as they have
to cover all land uses, ranging from ice flats and deserts to water surfaces and crops and
forests. For example, the categories suggested by Stull (1988) do not account for
commercial or industrial sites, even though there are four separate categories for towns
and cities, with z0 ranging from 0.3 m for “outskirts of towns” to 2 m for “centers of
cities with very tall buildings.” More detailed descriptions of urban land-use types have
been proposed by several authors (e.g., Davenport et al., 2000; Grimmond and Oke,
1999; Theurer, 1999), improving the reliability of this type of parameterisation in urban
areas. Results presented by Grimmond and Oke (1999), based on an extensive review of
wind-profile observations in many urban areas, show that z0/H ranges from
approximately 0.06 to 0.20, and d/H ranges from approximately 0.35 to 0.85.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
More precise estimates of z0 and d can be made using information about building sizes
and spacing. In particular parameterizations based on the lambda parameters may be
useful. The lambda parameters are defined as:
λ
≡ A
p
f
p
f
A
d
d
plan density
λ ≡ A A frontal density
where Ad is the mean lot area, Ap is the mean plan area, and Af is the mean frontal area.
The dimensionless frontal area λf is more important to drag because it represents the
surface facing the wind flow. Typical values are about 0.1 for areas with a moderate
density of buildings and 0.3 for downtown areas. Grimmond and Oke (1999) reviewed
and evaluated many competing techniques for determining z0 and d from λp and λf. They
also acknowledged three types of urban flow, classified according to the aerodynamic
interactions between the constituent buildings, as described by Hussain and Lee (1980)
and illustrated in figure 2-10:
a) isolated roughness: buildings are so distant that their wakes re-attach on the ground
and decay significantly in the intervening distance, and therefore cannot interact
strongly with each other. In this case the flow field around the group can be thought
as a simple superposition of the flow fields around each building as if in isolation.
b) wake interference: the spacing between buildings is not large enough for the wakes
of the buildings to decay in the spaces between the building, but wide enough for the
flow above the buildings to reach between the buildings down to the ground. The
interaction between the building wakes leads to the formation of complex flow
patterns between them that are strongly coupled with the flow above, leading to an
increase of aerodynamic roughness.
c) skimming flow: as the spacing between buildings becomes closer, the flow above
the buildings becomes relatively de-coupled from the flow between the buildings,
and appears to ‘skim’ over the top of the buildings, as if over a flat rough surface.
The flow between the buildings is characterized by stable re-circulation vortices,
channeled flow and relatively stagnant areas.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Figure 2-10 Regimes of flow over obstacle arrays
Typical values for z0 and d as function of λp and λf are given by Britter and Hanna
(2003).
One aspect omitted from most techniques used for describing urban areas is the great
variability in building heights. Ratti et al. (2002) show that the ratio of the standard
deviation of building heights to the mean building height, H, ranges up to 1.0 for some
urban areas. As a consequence the skimming-flow regime may not be well represented
by laboratory studies that use obstacles of constant height.
Because of the difficulties involved in processing geometrical data from multiple
individual buildings that are needed to apply some of the morphological methods, these
methods have not been widely used in the past for estimating the roughness length for
air-quality modeling applications. However, very recently there has been demand for
more detailed modeling, and this has led to the increased use of digital elevation models
of cities (Ratti et al. 2002) for morphological studies.
Currently, the dynamical effect of urban areas in mesoscale or larger scale models is
usually represented through the specification of a single roughness length. This
approach is too simplistic as it does not give any information on the mixing and
transport within the urban canopy. Urban areas might be better represented as regions
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Chapter 2 Review of urban air pollution modeling and evaluation methods
(or neighbourhoods) of gradually varying roughness, because the flow is continuously
readjusting to these changing surface conditions. Very few operational transport and
dispersion models applied to the city scale allow inputs of space-varying z0. Instead, it
may be more robust to simply estimate an average or representative z0 over an area
(Britter and Hanna, 2003).
Because the rate of dispersion of a pollutant cloud is proportional to the turbulent
velocity components, u’i, investigators have expressed much interest in observations of
these turbulent velocity components over urban, suburban, commercial, and industrial
surfaces (Britter and Hanna, 2003). Rotach (1995) and Roth (2000) have measured and
reviewed, respectively, the turbulence above urban areas. The turbulent velocity
components, scaled on a u* calculated from the local height-dependent Reynolds stress,
were approximately constant. Britter and Hanna (2003) give the following expressions:
u’x = 2.4u*, u’y = 1.9u*, and u’z = 1.3u*, where the x axis is parallel to the wind
direction, u* is based on the surface shear stress, usually assumed for the ABL at
heights above approximately 2H. At lower heights measured in the roughness sublayer
the turbulent velocity components decrease somewhat as the ground surface is
approached.
At the regional scale, the urban plume is observed to extend downwind of urban areas.
A complex mix of pollutants is present, and there are likely to be chemical reactions and
gas-to-particle conversions (Britter and Hanna, 2003). At the city scale, where it is
assumed that a pollutant plume extends vertically over a layer of depth at least 2H, there
is no need to account for effects around individual buildings. Consequently, dispersion
can be calculated with standard approaches that apply for general boundary layers.
Larger surface roughness produces larger z0, u* and turbulence levels, and these lead to
greater dilution of a plume and reduced concentrations downwind (Hanna et al., 1982;
Roberts et al., 1994).
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Chapter 2 Review of urban air pollution modeling and evaluation methods
2.5.2 Experimental modelling
When studying air quality at the urban scale, we are not interested in geometric details
of the considered area. Nevertheless, as described previously, the phenomena involved
are indeed very complex, and several processes need to be simulated and studied. A
very high number of different emission sources is involved, and ideal case-studies may
differ substantially from real situations. This limits our understanding of the dispersion
phenomena in urban areas, because comprehensive experimental data are very rare and
they are difficult to obtain and very expensive.
One of the first urban experiments, the St. Louis dispersion study (McElroy and Pooler,
1968), was carried out from 1963 to 1965. The data from this experiment led to the
‘urban category’ curves for σy and σz of Briggs (1973) for use in conventional Gaussian
plume models (see Hanna, et al., 1982). In 1978 and 1979, an experiment was
conducted in Copenhagen (Gryning and Lyck, 1984) to study dispersion of elevated
releases over urban areas. Because these experiments were limited by the lack of
instrumentation to measure vertical profiles of turbulent velocities, the associated data
sets cannot be readily used to understand the relationship between dispersion and urban
meteorology (Venkatram et al., 2004). In order to overcome the lack of comprehensive
experimental data for dispersion in urban areas, particularly from low-level sources
(apart from routine air quality monitoring), several urban dispersion experiment relevant
to a compact source were conducted in Europe and in the USA during the last years.
The main experiments are the tests performed in Birmingham (UK) in 1999 (Cooke et
al. 2000), the Urban 2000 study conducted in the Salt Lake City valley (Allwine et al.,
2002), the experiments performed in Los Angeles in 2001 (Rappolt, 2001) and in San
Diego in 2002 (Venkatram et al., 2004), the Joint Urban 2003 program realized at
Oklahoma City (Allwine et al., 2004) and the DAPPLE project carried out and planned
for London (Arnold et al., 2004). The main objective of these experiments was to make
available experimental data for the evaluation and development of mathematical
dispersion models. With the same aim, urban field campaigns, consisting of
contemporary use of advanced meteorological measures (SODAR, RASS and LIDAR)
and routine air quality monitoring, were realized in Europe, in Japan and in the USA;
one example are the campaigns carried out in Lisbon, Graz and Milan in the framework
of SATURN project (Mestayer et al. 2003).
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Physical modelling in wind tunnel at this scale is not simple. Large urban areas are too
large to be modeled in laboratory with the usual scaling factors. However, wind tunnel
experiments may be useful to characterize flows over urban areas simulated as large
arrays of obstacles, and have been used in order to find suitable parameterizations for
the wind profile, as explained in the previous section (see, for example, Davidson et al.
1996, MacDonald 1997a, MacDonald et al. 1998, Leitl et al. 2007). In order to avoid
some of the problems of scaling and resolution in a wind tunnel model (both in space
and time) these experiments were often combined with field experiments with artificial
structures (obstacle arrays erected in the PBL). The basic idea is to choose an
appropriate intermediate obstacle scale (about 1:10) using suitable obstacles which can
be laid out on flat terrain such as a mowed field. This methodology has been used
successfully in the experiments conducted at Cardington (UK) in 1993 (Davidson et al.
1995), in the experiments performed at the UMIST Environmental Technology Centre
Dispersion Test Site (Macdonald 1997b), in the KIT FOX field experiments (Hanna and
Chang 2001) and in the Mock Urban Setting Test experiment (MUST, Biltoft, 2001).
2.5.3 Mathematical modelling
There is a hierarchy in complexity for models for treating dispersion in urban areas at
the city scale. They range from the simplest parametric models to complex threedimensional
computational models. The various components of a comprehensive urban
airshed or regional air quality model are schematically shown in figure 2-11 (Arya,
1999). A simpler analytical urban air quality model for a particular pollutant species
may contain only a few of these, while a regional photochemical oxidant or acidic
precipitation model would have most, if not all, of the components depicted in figure 2-
11. The key parts of an urban scale dispersion model are: source and emission
inventories, wind fields, diffusion parameterizations, chemical transformations and
removal processes (Arya, 1999).
A variety of urban diffusion models are used to predict concentrations of primary
pollutants resulting from urban emissions. Most urban air quality models also predict
concentrations of secondary pollutants resulting from photochemical reactions in urban
air during transport and dispersion.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Figure 2-11 Components of a comprehensive air quality model. From Seinfeld
(1986)
Simpler urban diffusion models apply the Gaussian plume formulation (see section
4.2.2) to multiple point and area sources of pollution in an urban area (see, e.g., Turner,
1964; Gifford and Hanna, 1973). Due to the distributed nature of these sources,
however, a numerical model becomes necessary even when an analytical formulation is
used for each individual point source or each grid element of the area source emission.
Simple Gaussian models account for the urban environment by using modified
parameterizations of σy and σz. However, due to the lack of experimental data cited
earlier, these parameterizations are less reliable for urban areas than for rural areas.
Models such as ISC3 (USEPA, 1995), are capable of treating multiple sources (point,
line and area sources), both in short-term and in long-term applications. Complex
terrain, deposition and chemical transformation phenomena are treated in a very
simplified manner. Meteorological input usually consists of wind speed, wind direction,
stability class and mixing height (one value for the whole domain). Temperature may be
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Chapter 2 Review of urban air pollution modeling and evaluation methods
specified in order to simulate the plume rise for buoyant emissions. Despite their
simplicity, however, such models have been applied for air quality assessment in many
cities (see, e.g., Pratt et al., 2004; Metallo et al., 1995).
New generation Gaussian models have become available in the last decade. They are
capable of treating complex terrain and three-dimensional meteorological field using
modified Gaussian formulations (e.g., the segmented plume approach or the Lagrangian
puff approach, see section 4.2.2). Example of widely used models of this type are:
AERMOD (Cimorelli et al.,1998), CALPUFF (Scire et al., 2000b), ADMS-Urban
(CERC, 2003), UDM-FMI (Karppinen et al., 1998), and SAFE AIR (Canepa et al.,
2003). Meteorological fields are provided by advanced meteorological preprocessors,
capable of calculating the variables of interest for the dispersion phenomena in the
entire domain using scaling parameters such as the Monin-Obukhov length and the
friction velocity rather than the simpler stability class. Applications of such models for
air quality assessment in urban areas have been carried out, for example, by: Owen et al.
(1999); Karppinen et al. (2000b).
Another category of simple parametric urban air quality model is the so called box
model. Diffusion from individual sources is not considered, but all sources are
considered in estimating source emission into the box. With the simplified treatment of
meteorology in terms of the effective transport winds and mixing height, one can use a
sophisticated chemical and photochemical module (Arya, 1999). They have been used
for predicting concentrations of ozone and other photochemical pollutants in urban
areas (for example, Demerjian and Schere, 1979; Dodge, 1977).
Other non-Gaussian parametric models have also been used for air quality modelling in
urban areas. They have been often developed in order to provide background
concentrations to smaller scale (usually street scale) models (for example, Berkowicz,
2000b; Mazzeo and Venegas, 1991; Venegas and Mazzeo, 2002).
Many mesoscale flow models include a dispersion extension that may be attained
through an Eulerian modeling approach or a Lagrangian stochastic approach, and these
models or the modeling approach have been extended to the city scale (Schatzmann et
al. 1997). Especially in recent years, however, most applications of urban modelling at
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Chapter 2 Review of urban air pollution modeling and evaluation methods
the city scale have focused on photochemical pollutants and aerosols. For such
applications, comprehensive models that incorporate realistic descriptions of emissions,
meteorology, chemistry, and removal processes occurring in an air basin are needed.
Eulerian grid models are frequently used in such cases. These are based on the
ensemble-averaged advection-diffusion equations including chemical transformation
terms for all the chemical species of interest. One of the first regulatory photochemical
models applied to urban areas was the Urban Airshed Model, developed by the
U.S.EPA. Its current version UAM-V (SAI, 1999) is one of the recommended
photochemical models by the USEPA (2005b). Another recommended model is CAMx
(ENVIRON, 2005), with enhanced capabilities of treating chemical transformations.
Another commonly used Eulerian grid models is CALGRID (Scire et al., 1989). Many
applications in urban areas for air quality assessment can be found (for example,
Scheffe, 1990; Harley et al., 1993; Giovannoni and Russell, 1995; Polla Mattiot et al.,
2002)
Also the Lagrangian approach is often used in order to study dispersion at the urban
scale. Dispersion modelling using a Lagrangian particle model is routinely applied in a
broad range of permit and assessment domains in different parts of the world; for
example Austal 2000 has been applied since 2002 as regulatory model in Germany
(Janicke U. and Janicke L., 2007).
Another attempted approach for air quality assessment at the city scale is that of
HYPACT (Walko et al., 2001), which couples an Eulerian grid model for flow
simulation and a Lagrangian particle model for dispersion calculations.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
2.6 Neighbourhood scale modelling
The neighbourhood scale is a spatial scale of 1–2 km; it is also a scale over which some
statistical homogeneity may be expected; the city then is seen as being composed of
these neighbourhoods. At this scale two different approaches are possible: the first is a
parameterization of the urban form appropriate for flow and dispersion (MacDonald
2000), while the second is to attempt, although at large expense, computational studies
that are obstacle resolving (Schatzmann et al. 2002).
The first approach has become very popular recently; the popularity stemming mainly
through concern about the dispersion of accidental or deliberate releases of hazardous
materials in urban or industrial areas. The second approach can obviously be extended
down to encompass the street scale. Such computational studies then become a balance
between the region to be studied, the level of geometrical detail required and the
computing resources available. Dispersion studies on this scale will likely require a
more refined knowledge of the flow within and immediately above the urban canopy
(Brtitter and Hanna 2003).
2.6.1 Flow and dispersion phenomena
The flow over and through urban areas is characterized in Section 2.5.1 with the
descriptive terms isolated, wake interference, and skimming. Real urban areas often
have large variations in building heights. Hall et al. (1996) noted the importance of
height variability in inhibiting the skimming-flow regime. This suggests a fundamental
difference between flows in an urban canopy and those in a typical vegetative canopy
(Finnigan 2000).
Raupach et al. (1980) and Rotach (1995) have highlighted the existence of a roughness
sublayer within the atmospheric boundary layer below the inertial sublayer. The
roughness sublayer is thought of as a region in which the underlying buildings lead to a
spatial horizontal inhomogeneity of the flow. The roughness sublayer extends to
approximately 1.5-2 times the average building height H (Britter and Hanna 2003). The
mean velocity profile at neighbourood scale is, by implication, a horizontally spatially
averaged profile. The profile well above the urban canopy (1.5-2 H) is of a conventional
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Chapter 2 Review of urban air pollution modeling and evaluation methods
logarithmic form; instead, closer to the buildings, individual velocity profiles deviate as
they respond directly to the real surface that produces the spatial inhomogeneity
(Kastner-Klein et al. 2000a).
At positions away from a roughness change and for constant height obstacles the
maximum shear stress occurs at approximately the height of the obstacles (Macdonald
et al. 2000, Cheng & Castro 2002). Below this height the shear stress carried by the
fluid decreases to zero as the buildings take up part of the stress through the drag forces
on them. The shear stress approaches zero at the underlying surface for small λp or λf,
although it approaches zero at elevation for large λp or λf. Above the obstacle height the
shear stress decreases with height.
A change of roughness (from small to large roughness) viewed at the neighbourhood
scale appears as a high velocity flow impinging on an array of obstacles. This produces
a large drag force on the most upstream obstacles (producing a large surface shear
stress) and a divergence of the flow as it is turned vertically up and laterally out of the
array. Further downstream the velocity within the array decreases to a level in some
quasi-equilibrium with that above the urban canopy. This vertical deflection near the
upper edge of the roughness change produces an elevated maximum shear stress quite
distinct from that discussed above. A change of roughness from large to small produces
a decline in the elevation of the maximum shear stress.
For varied-height obstacles the maximum shear stress should occur at approximately the
height of the highest obstacle, decreasing to zero shear stress in much the same manner
as for constant-height obstacles. This statement relies on the dispersive stresses, those
due to the inhomogeneity of the mean flow, being negligible, as demonstrated by Cheng
& Castro (2002). The maximum shear stress should equate to the surface shear stress
and determine the surface friction velocity u*. As a consequence the maximum shear
stress occurs (well) above the mean height of the buildings.
Some disagreement exists in the literature over the form of the (spatially averaged)
mean-velocity profile over an urban canopy or very rough surface. There is agreement
that above the surface roughness layer the velocity profile has a conventional
logarithmic form based on u*, z0 and d (for the neutrally stratified boundary layer).
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Below this level Raupach et al. (1980) showed clearly that the velocity is increased
above that expected by extrapolation toward the surface of the logarithmic profile.
However, other velocity profiles (Macdonald 2000) show the mean velocity to be
decreased (below that given by the extrapolated logarithmic form) in the surface
roughness layer at positions above the mean building height and increased at positions
below the mean building height. This apparent inconsistency has arisen because of the
difficulty of specifying u*, z0, and d from experimentally determined velocity profiles.
There is general agreement that the wind speeds are more uniform with height below
rather than above the average building height, except for positions very close to the
underlying surface where the velocity must decrease to zero. Cionco (1965) developed a
model for the velocity profile within a vegetative canopy of constant height; the model
has been extended to the urban canopy:
U
U H
⎡ ⎛
= exp ⎢−
a⎜1
−
⎣ ⎝
z
H
⎞⎤
⎟⎥
⎠⎦
where UH is the velocity at the mean building height, H. The profile requires
specification of an empirical constant a. Macdonald (2000) found relationships between
λf and a by means of laboratory experiments. This approach is simple, but relies on
knowledge of the wind speed at the building height (the height at which the velocity
profile is changing most rapidly), which may not be easily defined (Britter and Hanna,
2003).
An alternative, even simpler approach is to define a spatially and temporally averaged
characteristic velocity, UC, within the urban canopy. Bentham and Britter (2003)
showed that this can be related to u*, λf, and the average drag coefficient for the
buildings Cdb with:
U
−1
2
C ⎛ Cdb
⎞ −1
2
= ⎜ ⎟ λ
f
u *
⎝
2
⎠
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Results from the literature reviewed by Britter and Hanna (2003) suggest that turbulence
levels may be assumed to be approximately uniform throughout the canopy, to scale on
u* and to be less than that above the canopy.
In the far-field regime, as shown in 2.5.1, an increase in surface roughness produces a
significant increase in turbulence levels, and these, in turns, cause a reduction in
ground-level concentrations. It is less clear whether the same conclusion can be drawn
for the intermediate and near-field regimes (Britter and Hanna, 2003). Laboratory
experiments (Davidson et al., 1995; Macdonald et al., 1998) showed that a conventional
Gaussian plume model provides an appropriate structure for the problem: the increased
turbulence levels within the urban canopy produce larger dispersion coefficients that
tend to reduce concentrations, while the accompanying reduction in the advection
velocity tends to increase them. The relative magnitudes of these opposing effects
determine whether the obstacles lead to increased or decreased concentrations as the
roughness is increased.
2.6.2 Experimental modelling
Observations from field and laboratory experiments have been used to develop an
understanding of flow characteristics. In wind tunnels and water flumes, laboratory
experiments allow for detailed flow measurement, the use of idealized urban areas
consisting of simple geometric shapes in ordered arrays, as well as the modeling of real
urban areas. Field measurements are far less common, difficult and expensive to
perform, and provide limited data. They are, however, essential in providing data to
ensure that the laboratory modeling has a sound basis.
Dispersion-field experiments from low-level sources in real cities are rare. The recent
and planned dispersion-field studies referred to in Section 2.5.2 (for example, Urban
2000 study, Urban Join 2003 and Dapple Project) provided data on the neighbourhood
scale in particular, very useful for a better knowledge of the flow within and
immediately above the urban canopy and for the evaluation and development of
mathematical dispersion models.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Most of the experimental studies at the neighbourhood scale in wind tunnels has
regarded flow and dispersion in regular arrays of building (for example, Davidson et al.,
1996; Macdonald et al., 1998, 2000; Cheng and Castro, 2002). The study of flow and
dispersion in building arrays is briefly reviewed in Plate (1999). Experiments in models
of real urban areas are less common; in the last years several experiments have
attempted to overcome the limitation of the past experiments; we can remember, for
example, the laboratory measurements with a model of a 400 m diameter section of
central Nantes, France, (Kastner-Klein and Rotach 2001) and the wind tunnel
experiments with an urban area with a diameter of about 1km of the central Basel,
Switzerland (Bubble Project).
2.6.3 Mathematical modelling
Model development at the neighbourhood scale has proceeded along several diverse
lines. Jerram et al. (1994) developed a linearized perturbation model with a force
distribution representing the drag on the buildings. Theurer et al. (1996) described a
semi-empirical or hybrid Gaussian Plume model, SAMPU (figure 2-12), which
combines the advantages of the basic plume model with wind tunnel modelling to
account for the details of the urban topology near a source; this work allowed for the
plume to be influenced by the urban layout in the near-field, where the dispersion
depends strongly on the flow channeling. Hall et al. (1996) used extensive laboratory
experiments in order to develop a simple urban dispersion model (Hall et al. 1997)
based principally on data correlations. Similarly the arguments in Hanna & Britter
(2002) have been recast into another simple urban dispersion model.
Since the way that the main street canyons deviate the wind direction leads to the
dominant process for dispersion in closely-packed city centres, in that case, as Soulhac
(2000) demonstrated, fully computational models can be used for dispersion from
sources in urban areas by computing the dispersion along the major streets and in the
open flow above the buildings. This would be similar to the widely-adopted method of
modelling traffic-generated pollution in urban areas by calculating the concentrations in
a few key street canyons with the rest of the urban area being regarded as a rough
surface (e.g. Owen et al., 1999). Using this approach Soulhac (2000) developed the
model SIRANE. The model incorporate a street canyon model for the street scale (see
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Chapter 2 Review of urban air pollution modeling and evaluation methods
section 2.7). In this model, the streets in a neighbourhood are modelled as a network of
connected street segments (see figure 2-13a). The flow within each street is driven by
the component of the external wind parallel to the street, and the pollutant is assumed to
be uniformly mixed within the street. The model contains two main mechanisms for
transport in and out of a street segment (see figure 2-13b): diffusion across the interface
between the air in the street and the overlying air and exchanges with other streets, at
street intersections, due to the advection along the street (see figure 2-13c). The
dispersion of pollutants advected or diffused into the overlying air is taken into account
using a Gaussian plume model (see figure 2-13d), with the standard deviations σy and
σz parameterised by similarity theory. The model has been applied, for example, to a
district of Lyon (Soulhac, 2000; Soulhac et al., 2001).
An interesting approach consists in combining Eulerian models (or simpler techniques)
for the flow field with a Lagrangian particle model for the dispersion simulation. For
example, Kaplan & Dinar (1996) derived a mass-consistent wind model accounting for
flow recirculation behind buildings and combined this with a Lagrangian particle model
for dispersion. Other models of this type are: the VADIS model (Borrego et al., 2003),
or the MSS suite (Moussafir et al., 2004)
Figure 2-12 An illustration of the model SAMPU (Theurer et al., 1996)
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Figure 2-13 The different components of SIRANE. (a) Modelling a district by a
network of streets. (b) Box model for each street, with corresponding flux balance. (c)
Fluxes at a street intersection. (d) Modified Gaussian plume for roof-level transport.
CFD codes, especially those applying the standard k-ε model, have been widely applied
for predicting neighbourhood scale flow and dispersion patterns. Lists of such models
can be found, for example, in the EEA Model Documentation System website (EEA,
2005) or in the SATURN page maintained by the University of Hamburg (Schlunzen,
2002; Moussiopoulos et al., 2003). Most of them are obstacle-resolving (for example,
MIMO, or MITRAS) but there are also obstacle-accomodating models, such as
SUBMESO, where the effect of obstacles may be included implicitly by using specific
influence functions. Also computational techniques of much greater complexity like
large eddy simulation have been used (Boris 2002, Pullen et al. 2005). It is only now
that data that allow formal scientific evaluation of such models are becoming available.
At present there has been only limited effort (e.g., Soulhac 2000) to use advanced
computational techniques to develop a general understanding of the flow and to give
guidance as to how best to view and parameterize the flow as distinct from a direct
application to specific scenarios.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
2.7 Street scale modelling
The street (canyon) scale is particularly studied in the context of urban air quality where
the dominant sources of pollution, vehicle emissions, are in close proximity to the
pollutant receptors of concern, people (and the regulatory pollutant-monitoring
stations). In cities both the source and the receptor are within a very confined geometry,
the street canyon, and this confined geometry exhibits a sheltering effect from the
diluting influence of the wind. Flow and dispersion near street intersections are also of
interest as it is the acceleration of vehicles away from traffic lights and/or pedestrian
crossings that gives rise to large pollutant emission rates and consequently poor urban
air quality. The location of building ventilation intakes is sensitive to the anticipated
distribution of pollutants on the spatial scale of the buildings or the street (Britter and
Hanna 2003).
Street canyon ideally refers to a relatively narrow street with buildings lined up
continuously along both sides (Nicholson 1975). The dimensions of a street canyon are
expressed by its ‘aspect ratio’, i.e., the ratio of the height of the building (H) to width of
the street (W). The canyon is uniform, if it has an aspect ratio of approximately equal to
1 with no major openings on the walls. A shallow canyon has an aspect ratio below 0.5;
and the aspect ratio of 2, represents a deep canyon. The length of canyon (L) expresses
the road distance between two major intersections subdividing the street canyon into
short (L=H/3), medium (L=H/5) and long (L=H/7). If buildings, flanking the canyon are
of equal heights, the canyon is ‘symmetric’ and vice-versa (Vardoulakis et al., 2003).
Asymmetric canyons with high-rise buildings in downwind direction are termed as step
up canyons and reversibly step down canyons. The upwind side of the canyon is called
leeward and downwind, is windward when the wind flow is perpendicular to the street
canyon (figure 2-14).
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Figure 2-14 Characteristics of street canyon (Ahmad et al. 2005)
Intersections are a three three-dimensional problem, as opposite to street canyons. Due
to the the complex situation and the infinite possible geometries, modelling studies at
street intersections are very difficult. Intersections in real cities are more common that
regular street canyons, thus studies focused on this problem can be very useful for
model development.
2.7.1 Flow and dispersion phenomena
The climate of street canyons is primarily controller by the micro-meteorological effects
of urban geometry rather than the mesoscale forces controlling the climate of the
boundary layer (Hunter et al., 1992). A clear distinction should be made between the
synoptic above roof-top wind conditions and the local wind flow within the cavity of the
canyon (figure 2-15). Depending on the synoptic wind (or free-stream velocity), three
main dispersion conditions can be identified: (1) low wind conditions, for synoptic
winds lower than 1.5 m/s; (2) perpendicular or near-perpendicular flow for synoptic
winds over 1.5 m/s blowing at an angle of more than 30° than to the canyon axes, and
(3) parallel or near-parallel flow for winds over 1.5 m/s blowing from all other
directions. In the case of perpendicular flow, the up-wind side of the canyon is usually
called leeward, and the downwind windward.
The emphasis has often been on the two-dimensional nature of the flow, studying
vertical cross-sections at mid-canyon level. When the above roof flow is perpendicular
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Chapter 2 Review of urban air pollution modeling and evaluation methods
to the canyon and the wind speed is over 1.5–2 m/s, flow may be described in terms of
the descriptive terms isolated, wake interference, and skimming, depending on the
dimensions of the street (see section 2.5.1). For wide canyons (H/W

Chapter 2 Review of urban air pollution modeling and evaluation methods
increasing street length (Theurer, 1999). The strength of the wind vortices inside the
canyon mainly depends on wind speed at roof-top level. However, the local wind flow
is also affected by the mechanical turbulence induced by moving vehicles (Eskridge and
Rao, 1986) or created near urban roughness elements within the street (e.g. trees, kiosks,
balconies, slanted building roofs, etc.) (Hoydysh and Dabberdt, 1994; Theurer, 1999).
Furthermore, the shape and strength of the wind vortices might also be affected by the
atmospheric stability and other thermal effects induced by the differential heating of the
walls and/or the bottom of the canyon (Sini et al., 1996; Kim and Baik, 2001). In
relatively deep canyons (H/W>1.3) the main wind vortex is usually displaced towards
the upper part of the cavity, with almost stagnant air below (DePaul and Sheih, 1986).
As the aspect ratio increases (H/W≈2) a weak counter-rotating secondary vortex maybe
observed at street level (Pavageau et al., 1996). For even higher aspect ratios (H/W≈3) a
third weak vortex might be also formed (Jeong and Andrews, 2002). In most cases,
small week vortices occupy the bottom side corners of the canyon.
The dispersion of gaseous pollutants in a street canyon depends generally on the rate at
which the street exchanges air vertically with the above roof-level atmosphere and
laterally with connecting streets (Riain et al., 1998). A significant exchange takes place
between the intersecting streets. The wind is somewhat normal to the cross street and
this modifies the basic street canyon vortex, changing it into a helical vortex (Figure 2-
16). Hoydysh and Dabberdt (1988) observe the formation of intermittent vortices at the
corners of the building. Skimming flow, a feature of regular canyons, provides minimal
ventilation of the canyon and is relatively ineffective in removing pollutants (Hunter et
al., 1992). Field measurements (DePaul and Sheih, 1985; Qin and Kot, 1993) show
increased concentrations of traffic-related pollutants on the leeward side of the canyon,
and decreasing concentrations along with height above the ground on both sides of the
street. The increased leeward concentrations are due to the accumulation of pollutants
locally advected by the large wind vortex that covers most of the canyon. Minor
pollution hotspots might be also created in small cavities where additional recirculation
phenomena can take place.
Street-level cross-road gradients observed in wind tunnel experiments (Hoydysh and
Dabberdt, 1988) for perpendicular wind conditions indicate that concentrations are
generally a factor of two greater for the leeward than for the windward side, except for
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Chapter 2 Review of urban air pollution modeling and evaluation methods
stepdown canyons where windward concentrations are slightly greater than leeward.
Concentrations are generally lower in the step-up canyons relative to the even and stepdown
notches.
Figure 2-16 Flow field at a street intersection (Robins and Macdonald, 2001)
Flow visualization experiments have shown that the strength of the canyon vortices
varies. As a result, pollutants are periodically flushed out of the canyon (Pavageau et al.,
1996). In relatively long canyons without connecting streets, field measurements have
shown that maximum street-level concentrations are more likely to occur when the
synoptic wind is parallel to the street axis (Vardoulakis et al., 2002). In that case, the
accumulation of emissions along the line source outweighs the ventilation induced by
the parallel winds (Soulhac, 2000; Dabberdt and Hoydysh, 1991).
Low synoptic winds create a well-known meteorological situation that favours air
pollution built-up in urban areas (Qin and Kot, 1993; Vignati et al., 1996; Jones et al.,
2000). There is evidence that when the synoptic wind speed is below about 1.5 m/s the
wind vortex within the canyon tends to disappear and the air stagnates in the street
(DePaul and Sheih, 1986). In that case, the mechanical turbulence induced by moving
vehicle as well as the atmospheric stability conditions might play a significant role in
the dispersion of traffic generated pollutants.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Fine and especially ultrafine particles are expected to disperse in the air like gases. The
larger-sized particles, instead, are greatly affected by gravity and thus have a shorter
residence time in the air (Chan and Kwok, 2000).
Due to the very short distances between sources and receptors, only very fast chemical
reactions have a significant influence on the measured concentrations within street
canyons (Berkowicz et al., 1997). For this reason, most traffic-related pollutants (e.g.
CO and hydrocarbons) can be considered as practically inert species within these
distances. This is not the case either for NO2; which dissociates extremely fast in the
presence of light, or for NO, which also reacts very fast with O3 (Palmgren et al., 1996).
The time scales of these chemical reactions are of the order of tens of seconds, thus
comparable with residence times of the pollutants in a street canyon.
From a population exposure point of view, air quality in street canyons is of a major
importance, since the highest pollution levels and the larger targets of impact are often
concentrated in this kind of streets (Hertel et al., 2001). The so-called canyon effect (i.e.
the reduced natural ventilation in urban streets) results in greater health impacts (e.g.
indicated by an increased number of respiratory hospital admissions) and damage costs
for the exposed population (Spadaro and Rabl, 2001). Personal exposure can be
calculated as the product of the pollutant concentration and time spent in a specific
microenvironment, which is defined as a confined space (e.g. bedroom, office, car,
parking, pavement, etc.) where pollutant concentrations are assumed to be uniform
(Colls and Micallef, 1997). The total personal exposure will be then the sum of all such
products. However, the assumption of spatial uniformity of air pollution might be
erroneous for certain microenvironments like street canyons or urban intersections,
where strong spatial concentration gradients are often observed. In these cases, exposure
calculations should be refined by subdividing microenvironments into submicroenvironments,
taking into account pollution hot spots and refined human breathing
zones (e.g. for residents, pedestrians, cyclists, drivers, etc.). In order to predict or/and
estimate the maximum expected dosage and the exposure time within which the dosage
exceeds certain health limits, the knowledge of the behaviour of concentration
fluctuations, not only the mean, is needed. For hazard associated with short-term
concentration levels the concentrations fluctuations can be very significant (Efthimiou
et al., 2008)
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Most commonly, street canyon studies have combined both mathematical modelling and
experimental work. Recently, the European research network TRAPOS (Optimisation
of Modelling Methods for Traffic Pollution in Streets) gave new insights in a number of
street canyon related issues: (a) the influence of moving vehicles on pollutant dispersion
and turbulence in urban streets (Kastner-Klein et al., 2000, 2001; Vachon et al., 2001);
(b) the thermal effects on flow and dispersion within street canyons especially under
low wind conditions (Kovar-Panskus et al., 2001a; Louka et al., 2001); (c) the
sensitivity of flow and turbulence characteristics to the geometry of the street and its
surroundings (Kovar-Panskus et al., 2001b; Kastner-Klein and Rotach, 2001; Leitl et al.,
2001; Chauvet et al., 2001); (d) the dispersion and transformation of traffic-related
particles (Le Bihan et al., 2001; Wahlin et al., 2001). TRAPOS included field and wind
tunnel measurements, as well as mathematical simulations carried out with advanced
numerical and a simpler parametric model. A significant part of the work within the
network was devoted to the inter-comparison and evaluation of these models (Louka et
al., 2000; Sahm et al., 2001; Ketzel et al., 2001). In the following sections,
representative studies covering several aspects of street canyon research are briefly
discussed.
2.7.2 Experimental modelling
Several experimental field studies aiming at establishing pollutant dispersion and
transformation patterns within street canyons have been carried out in the past.
Depending on their objectives, different modelling and monitoring techniques have
been adopted. Unfortunately, results from field studies are often not very conclusive
(Berkowicz et al.,1997; Louka et al., 2000). The main reason for this is that only few
measurement points are usually available and even those can be significantly influenced
by local structures.
DePaul and Sheih (1985, 1986) carried out a tracer gas (SF6) experiment in an urban
street canyon in Chicago (USA) in order to obtain measurements of pollutant retention
times and resulting concentrations within the canyon. The mean wind velocities were
determined by analysing trajectories of air balloons that were released in the street.
Nakamura and Oke (1988) studied the climate of urban canyons using field observations
of wind and temperature from a street canyon in Kyoto (Japan). These observations
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were used to derive simple algorithms relating the above roof-level to the within canyon
meteorological conditions. Vachon et al. (2000) reported results (i.e. concentration,
temperature and wind fields) from a full-scale experiment carried out in a street canyon
(Rue de Strasbourg) in Nantes, France. This was the first campaign of the URBCAP
project, which has the aim of assessing pollutant transformation processes within the
urban canopy and validating small-scale dispersion models. Within the frameworks of
the LIFE RESOLUTION project (Wright, 2001), benzene and NO2 measurements were
taken in four European cities (Dublin, Madrid, Paris and Rome) in order to assess
pollution levels with reference to established air quality standards, optimize the design
of monitoring networks, and provide experimental data to support the validation of
urban dispersion models. As described in the previous sections, recent field experiments
have been performed in order to provide new data sets of high quality (SATURN
project, and TRAPOS project)
The most extensive investigations of flow and dispersion regimes in street canyons are
performed in wind tunnels (Berkowicz et al., 1997). Many studies (see e.g. Kastner-
Klein et al. 1997, Schatzmann et al. 1997) showed that wind-tunnel data sets of flow
and dispersion in the near field of buildings are well suited for verification of numerical
model results. Major efforts of the scientific research focused on an extension of a
wind-tunnel database for evaluation purposes of numerical models and in designing the
wind-tunnel studies for the investigation of physical mechanisms that are only poorly
understood so far.
Based on available wind tunnel data, especially the works of Hussain and Lee (1980),
Hosker (1985), and Oke (1988) provided a systematic classification of flow regimes in
urban street canyons. One of the most systematic investigations of dispersion
characteristics in a wind tunnel model of urban streets was performed by Hoydysh and
Dabberdt (1988), and Dabberdt and Hoydysh (1991), using tracer gas and flow
visualization techniques. Extensive reviews of wind tunnel experiments in street
canyons can be found in Scaperdas (2000), Robins and Macdonald (2001), Vardoulakis
et al. (2003) and Ahmad et al. (2005).
In the past, less attention was given to: street intersections, source receptor relationships
and concentration fluctuations.
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The first systematic wind tunnel study on urban intersection was performed by Hoydysh
and Dabberdt (1994). In their study the concentration field for various wind directions
was measured using quantitative smoke visualization and tracer dispersion techniques
and concerning a regular urban network of streets, with uniform buildings. More
recently the topic has been investigated in considerable detail, as it turns out that the
transfer of pollutants from one street to another can be substantial and, at the same time,
very sensitive to details of the geometry (Scaperdas et al. 2000).
Many urban dispersion studies employed line sources to represent traffic emissions (for
example Meroney et al., 1996; Kastner-Klein and Plate 1999), but this is not an
effective aid to understanding because it hides so much of the detail. Robins et al.
(2002) show how point sources can be used to investigate source-receptor relationships,
which prove to be particularly complex at intersections and in terms of concentration
fluctuations.
Although its importance in the process of population exposure evaluation, the dispersion
phenomena at the street scale in terms of maximum expected concentrations are not
often analyzed; only few studies consider this problem. The most important study in this
field is the one of Pavageau and Schatzmann (1999), that investigated the concentration
fluctuations in a urban street canyon.
Physical modelling has been performed mostly within idealized shape models. As stated
by Robins and Macdonald (2001) there is a need for tests on less ‘regular’ geometries in
order to produce more reliable data sets. An attempt of studying actual conditions
occurring in street intersection has been performed in the DAPPLE project (Arnold et
al., 2004). The focus is on an urban intersection in central London, and both field
experiments and wind tunnel tests are planned. The wind tunnel experiments carried out
in this thesis are in the framework of this project and try to extend the knowledge of
dispersion phenomena at the street scale taking into account some of the main critic
aspects: street intersections, traffic emissions and concentration fluctuations.
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2.7.3 Mathematical modelling
There is a plethora of dispersion models specially developed for or simply used in street
canyon applications. They can be useful in air quality and traffic management, urban
planning, interpretation of monitoring data, pollution forecasting, population exposure
studies, etc. Although there are no clear-cut distinctions between different categories,
models might be classified into groups according to their physical or mathematical
principles (e.g. reduced-scale, box, Gaussian, CFD) and their level of complexity (e.g.
screening, semi-empirical, numerical). Some of these (often overlapping) categories and
corresponding models are presented in Table 2-6.
Table 2-6 Classification of commonly used dispersion models at the street scale
(Vardoulakis et al., 2003)
Gaussian models are not directly applicable to small-scale dispersion within the urban
canopy, since they treat buildings and other obstacles only via a surface roughness
parameterization (Scaperdas, 2000). Nevertheless, in some cases, they include
specialized modules for street canyons. This is the case of ADMS-Urban (Owen et al.,
1999), a second generation urban-scale dispersion model that includes a street canyon
module nested within the core Gaussian code. Another widely used Gaussian model is
CALINE4 (Benson, 1989), recommended by the U.S.EPA for assessing the impact of
vehicle traffic on roadside air quality, and mainly applied for highway development and
management (Jones et al., 2000). The model uses Gaussian plume theory to simulate the
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dispersion of pollutants emitted from a line source, but is also able to handle street
canyons. The canyon algorithm devised by Turner (1970) computes the effect of single
or multiple horizontal reflections of the plume on the walls of the canyon.
The simplest of the street canyon models is probably the box model, which is an
expression of conservation of pollutant flux. Johnson et al. (1973) used a single box
model, together with some simplified assumptions concerning initial dispersion and car
induced turbulence, to derive a street canyon sub-model usually called STREET or SRI
(i.e. Stanford Research Institute). It is based on the assumption that concentrations of
the pollutant occurring on the roadside consist of two components, the urban
background concentration and the concentration component due to vehicle emissions
generated within the specific street. Then, it calculates pollutant concentrations on both
sides of the street, taking into account the height and distance of the simulated receptor
from the kerb. An innovative approach was introduced by Yamartino and Wiegand
(1986) in their Canyon Plume-Box Model (CPBM). It combines a Gaussian plume
model for the direct impact of pollutants emitted in the street, with a box model that
accounts for the additional impact of pollutants trapped within the wind vortex formed
inside the canyon. A similar approach is used by the widely used street canyon models
OSPM (Berkowicz et al., 1997) and AEOLIUS (Manning et al., 2000), and the street
canyon module of ADMS-Urban.
None of the cited urban dispersion models are applicable to near-field dispersion
modelling at urban intersections, and the development of a specialised intersection
dispersion model has received only very limited attention; intersections of canyons are a
singularity that the urban canyon model cannot deal with. Yamartino and Wiegand
(1986) briefly considered a simple methodology for including the effect of street canyon
intersections, as part of the development of the CPBM model; the intersection was
considered in terms of a ‘well mixed reactor’ that is fed polluted air by one or more
street canyons, and drained of pollutants by other neighbouring street canyon.
Unfortunately they did not have experimental data to evaluate their approach. Recently,
with the increasing availability of experimental data, some attempts of including
‘intersection modules’ in street canyon models have been made. The already described
SIRANE (see section 2.7.3) is one of these.
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In recent years significant progress in computational fluid dynamics (CFD) and widely
available computer power stimulated the development of numerous dispersion models
that have delivered promising results for urban areas. The commercially available
general-purpose CFD codes PHOENICS, FLUENT, STAR-CD, CFX-TASC- flow and
Fluidyn-PANACHE have been used in a number of street canyon applications. Other
numerical models like MERCURE (Carissimo et al., 1995), CHENSI (Levi Alvares and
Sini, 1992) and MISKAM (Eichhorn, 1995) were specially designed to simulate
pollutant dispersion at local scale. MISKAM was used to create a database of numerical
three-dimensional simulations that was integrated in a screening model called STREET.
Furthermore, the street canyon module PROKAS-B, which forms part of the Gaussian
urban-scale model PROKAS-V, was also based on dimensionless concentrations
calculated using a version of MISKAM. The microscale models MIMO and MITRAS
were also specially designed for street canyon applications and nested within the
mesoscale MEMO and METRAS, respectively (Ehrhard et al., 2000).
In the last years CFD applications were also used as an auxiliary tool in understanding
the near-field dispersion effects of buildings in correspondence of urban intersections
and in terms of concentration fluctuations; some examples of urban intersections and
concentration fluctuations CFD applications are respectively the studies by Scaperdas
(2000) and the study by Andronopoulous et al. (2001).
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2.8 Multi-scale modelling
Urban air pollution phenomena encompass a wide range of spatial and temporal scales:
from a few meters (street canyon pollution) to hundreds of kilometers (secondary
pollutant formation in city plumes). Mesoscale wind circulations associated with
horizontal temperature gradients, e.g. mountain-valley wind systems and sea/land
breezes, particularly affect air quality in cities. In addition, atmospheric circulations
created by the city itself, notably the so-called urban heat island, directly influence the
dispersion of pollutants. The local (neighborood and street) concentration levels are also
in many cases influenced by regional scale processes such as the atmospheric transport
of pollutants emitted in surrounding cities and industrialized areas.
The combined local scale, city and regional scale effects on urban air quality can be
investigated with multi-scale model cascades. Several methods for coupling individual
models were proposed in the last year. They have often very simple coupling interfaces
between the various scales; typical implementations of such systems are based on a oneway
coupling (usually top-down), where the city (or regional) scale model provide
initial and boundary conditions for each off-line application of the microscale (or city
scale) models. Only few attempts of two-coupling models exist (for example Tsegas et
al. 2008)
As reported in section 2.4.7, Soulhac et al. (2003) found two possible approaches for
constructing multi-scale modelling systems based: the first one relies on the use of a
single three-dimensional Eulerian model, nested at different scales; the second one is
based on the use of different types of models for different scales, with each model
chosen to represent the dominant physical process at that particular scale.
Examples of multi-scale models have already been presented in the previous sections.
Models such as ADMS-Urban (city to street scales) and SIRANE (neighbourhood to
street scale) are, in fact, multiscale models, where different scales are treated by
different models (modules).
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An attempt of building a multiscale modelling system using the first approach cited by
Soulhac et al. (2003) is the Community Multiscale Air Quality (CMAQ) model,
developed by the U.S.EPA within the Models-3 system framework (Ching and Byun,
1999). Currently the modelling systems encompass the regional-to-urban scales, but the
framework allows the future development of single models for other scales. The model
is based on the ‘one-atmosphere’ concept, solving numerically the governing equations
by using different hypothesis and approximations for different scales and purposes
(meteorological modelling or dispersion modelling, for example).
Soulhac et al. (2003) contested this type of approach and developed a multiscale
modelling system using different modelling techniques for each scale involved. As
reported in figure 2-17, regional scale meteorological fields and concentration are
calculated by SAIMM and UAM-V. Results are fed to the urban scale Eulerian grid
model MERCURE, which calculates urban background concentrations. Then the system
performs the final calculation by means of SIRANE (in urban areas) or ADMS-3 (in
industrial areas). The application of this modelling system to a district of Lyon showed
encouraging results (Soulhac et al., 2003), despite the simple one-way coupling
technique adopted.
Figure 2-17 Schematic of the modelling system developed by Soulhac et al.(2003)
Similar approaches (regional to street coupling) were adopted by Kukkonen et al.
(2003), Brandt et al. (2001) and Mensink et al. (2003).
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The modelling system developed by Kukkonen et al. (2003) includes a meteorological
pre-processor (MPP-FMI) and two Gaussian models (UDM-FMI for stationary sources
and CAR-FMI for traffic emission sources) for the calculation of the urban background;
street canyon concentrations are then calculated by OSPM.
The system THOR, developed by Brandt et al. (2001), is used in Danmark for air
quality forecasting in cities and includes (see figure 2.18): a continental scale
meteorological forecasting model (ETA), initialized using data from the global scale
model of the U.S. National Center for Environmental Forecast; a long-range transport
model (DEHM, Danish European Hemispheric Model), that produces European scale
forecasts; a simple semi-empirical urban background model (BUM, Background Urban
Model); a street canyon model (OSPM); two models for emissions from point sources, a
short-range model (OML) and a long-range model for accidental releases (DREAM).
This modelling system has been applied in several urban areas and the results have been
compared with monitoring data, showing the high quality and accuracy of the results
(Brandt et al., 2003).
Figure 2-18 The THOR integrated model system (NERI, 2008)
AURORA is another multi-scale model, which has been implemented in the city of
Antwerp, Belgium (Mensink et al., 2003). It includes a meteorological model (ARPS)
and emission and terrain inputs are integrated in a GIS system. Background
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concentrations are calculated by the Lagrangian trajectory model OPS, while
concentrations in streets are computed by means of a street box model. The system also
includes specific dose-response functions in order to assess both health effects and
ecosystem
An example of regional to urban coupling is the OFIS model, developed for calculating
urban ozone levels in conjunction with a regional scale chemical transport model. The
model represents a combination of a box model and an Eulerian multilayer multi-box
model describing transport and photochemical transformation processes in an urban
plume (Moussiopoulos and Sahm, 2000).
One example of two-way coupling is the model proposed by Tsegas et al. (2008). In this
work, a novel scheme is developed for introducing effects of the urban canopy
calculated on the microscale to a mesoscale calculation, by means of two-way coupling
between the mesoscale MEMO model (Moussiopoulos et al., 1993) and the microscale
model MIMO (Ehrhard et al., 2000). One-way coupling of mesoscale-to-microscale
systems was initially devised as a way to provide microscale calculations with accurate
initial and boundary conditions aiming to improve the accuracy of flow computations
within and around elements of the urban canopy (Kunz et al., 2000). On the other hand,
a two-way coupling scheme would ideally be able to account for the effect of the urban
structures, to the extent represented by the microscale calculations, on the calculated
mesoscale flow (Martilli, 2007). The main obstacle for the implementation of such a
coupled system would be the large gap of spatial and temporal scales between the two
models, as well as a prohibitive gap in their execution speeds: the smallest practical
mesoscale grid could cover an area of the order of a few tens of kilometres with a grid
resolution of 500 m, using a timestep on the order of 2 to 10 s, while typical calculation
times for a 24-hour simulation is of the order of tens of minutes. A metamodelling
scheme, aiming to approximate the time-consuming microscale calculations with a
simple interpolating model during the course of the two-way coupled run, was proposed
in order to solve these difficulties. The performance of the coupled MEMO-MIMO
model was assessed using a springtime case for the greater area of Athens, Greece.
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2.9 Model evaluation of urban dispersion modelling
Air quality models are powerful tools to predict the fate of pollutant gases or aerosols
upon their release into the atmosphere; complex numerical models, widely used to assist
decision-makers in environmental studies, have not always been submitted to proper
analysis of their performance. Therefore, decisions with political and economical
consequences could be based on model predictions of unknown quality (Schatzmann
and Leitl 2002). It is imperative that these dispersion models be properly evaluated with
observational data before their predictions can be used with confidence, because the
model results often influence decisions that have large public-health and economic
consequences. Decision makers should be able to make use of available information on
air quality model performance. An example of a decision maker is a person in a
regulatory agency who uses the results of photochemical models to decide emission
control strategies which clearly will have large economic and social impacts.
Dispersion is primarily controlled by turbulence in the atmospheric boundary layer.
Turbulence is random by nature and thus cannot be precisely described or predicted,
other than by means of basic statistical properties such as the mean and variance. As a
result, there is spatial and temporal variability that naturally occurs in the observed
concentration field. On the other hand, uncertainty in the model results can also be due
to factors such as errors in the input data, model physics, and numerical representation.
Because of the effects of uncertainty and its inherent randomness, it is not possible for
an air quality model to ever be ‘‘perfect’’, and there is always a base amount of scatter
that cannot be removed. As Oreskes et al. (1994) pointed out, it is impossible to validate
environmental models thoroughly because of the infinite number of scenarios that can
occur, as well as the large amount of natural variables. Hence, it may not be appropriate
to talk of a valid model, but only of a model that has agreed upon regions of
applicability and quantified levels of performance (accuracy) when tested upon certain
specific and appropriate data sets (Hanna et al., 2003b). For all these reasons the
definition of model quality is not a simple task and there is no universal approach.
Although these difficulties, an integrated system of activities must be established in
order to guarantee and to improve the quality of modelling results.
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2.9.1 Model evaluation and model quality assurance
Model Quality Assurance (Model QA) is a relatively new approach. Nevertheless, QA
as a general concept is far from being a new term and it is broadly used in many
different fields, such as waste disposal and incineration, air and water quality
monitoring, laboratory measurements, software development, etc. The main objective of
QA is to provide quality assured data, and it requires the outlining of objectives,
appropriate tools and an implementation strategy.
In Borrego et al. (2003a), Model Quality Assurance is defined as follow:
Model Quality Assurance is an integrated system of management activities involving
planning, documentation, implementation and assessment established to ensure that the
model in use is of expected quality.
Implementation of QA for modelling introduces a new perspective for estimation of
model performance, being judged in terms of usefulness of modelling results or fitness
for intended use. To assess quality, Model Evaluation is usually carried out, and can be
defined as follows (Borrego et al., 2003a):
Model Evaluation is an overall system of procedures designed to measure the model
performance. The following procedures may be considered for Model Evaluation:
verification, validation, sensitivity analysis, uncertainty analysis and model
intercomparison.
Based on the above definitions, Model Evaluation is related to measuring model quality,
while Quality Assurance is a process to guarantee the expected quality. Model
Evaluation is therefore one of the inherent components of Model QA (figure 2-19).
Model QA is a management function, dealing with policy and designed to ensure that
appropriate methods and data are used, errors in calculations are minimised and the
documentation is adequate to meet user requirements.
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Figure 2-19 Components of Model Quality Assurance
Generally speaking, model evaluation should have the following components (Borrego
et al., 2003a; Schatzmann et al., 1997; Schatzmann and Leitl, 2002):
1. Verification: the process of showing that a technical model is a proper
representation of the conceptual model on which is based, and of checking that
mathematical equations have been correctly solved. It also includes quality
control of the computer code.
2. Validation: the process of showing that the conceptual model and the computer
code provide an adequate representation of the problem. It requires comparison
of model results with experimental data.
3. Sensitivity analysis: the process of identifying the magnitude, direction and
form (linear or nonlinear) of an individual parameter effect on model results.
4. Uncertainty analysis: the process of characterization of uncertainties associated
to model results. It can be: qualitative (define source and magnitude of error),
semi-quantitative (rating, relative indicators), and quantitative (statistical
parameters, probability method, error propagation).
5. Model intercomparison: the process to assess a model performance by
simultaneous comparison of modelling results provided by different models for
the chosen situation.
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Quality of models is understood as fitness-for-purpose (Britter et al., 1995). Selection of
the right model for the defined objectives, together with adequate evaluation
procedures, will provide good quality modelling results. Nevertheless, the same model
may produce unacceptable results if not used for appropriate conditions. There are no
‘good’ models or ‘poor’ models, the model is suitable or not for the specified objectives.
Due to the complexity of the phenomena simulated by air quality models, there are
always uncertainties associated with modelling results. Therefore, an important step is
the definition of the criteria to estimate the model performance. For this task, Quality
Indicators (QI) should be chosen. They can be quantitative statistical parameters as well
as qualitative measures such as ‘representativeness’ or ‘completeness’.
The definition of QI for modelling results is not a simple task. It should be taken into
consideration that no single quality indicator is good enough to assess model
performance, and that therefore a system of quantitative parameters has to be identified.
Application of such indicators helps to understand model limitations and provides a
support for model intercomparison. It is important to establish whether the uncertainties
make the modelling results not useful for answering specific questions. Estimation of
model acceptability (quality objectives, QO) is based on the definition of quality
indicators’ range within which the modelling results may be considered satisfactory.
These values should be realistic and determined primarily according to modelling goals,
and should reflect state-of-the-science, or present-day models capabilities. Whenever
simulation results do not lie within the recommended range they are considered
unacceptable for the specific application. Simple QO, for example, are established under
the European Directives on air quality (see Borrego et al., 2003a).
Most of the activity surrounding modelling uncertainties at the smallest scales (less than
regional scale) has concerned with assessment of the quality of a model output in order
to identify where model improvement is required or which models are somehow better
than others. These tasks have been performed by means of model verification,
validation, model intercomparison and sensitivity analysis (see, e.g., Olesen, 1997;
Hanna, 1988, 1989, 1993; Hanna et al., 1996, 2004; Hanna and Chang, 2001;
Schatzmann et al., 1997; Canepa and Builtjes, 2001). Less attention has been paid to the
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question of how accurate a model needs to be to become useful in respect to a given
policy, nor to the implications of model uncertainty for policy decisions, despite the fact
that this is one of the key recommendations of Britter et al. (1995). This is usually done
by means of uncertainty analysis, but also validation and sensitivity analysis can be used
for this task (Colvile et al., 2002; Borrego et al., 2003b; Dabberdt and Miller, 2000,
Denby et al. 2007). The components mainly used in the Model Evaluation procedure,
validation and uncertainty analyses are better explained in the next sections.
2.9.2 Model validation
Over almost 100 years, models of air flow and transport and dispersion in the
atmosphere have been developed and validated, leading to a great amount of experience
and several standardized methods for model evaluation. For example, the American
Society of Testing and Materials has published a standard for air quality model
validation (ASTM, 2000). Most of this experience has involved straightforward
Gaussian plume models, but the same principles apply to more complex models such as
time dependent three-dimensional grid models, CFD models and others (Hanna et al.,
2003b).
A great increase in the number of air quality model validation studies took place about
1980, since U.S.EPA procedures were standardized (USEPA, 1981) at that time and
many research studies were begun in order to further improve the system. Prior to that
time, air quality model evaluations were often conducted on an arbitrary, ad hoc basis
(Hanna, 1989).
An important milestone in model validation was the Woods Hole workshop (Fox,
1981). At this workshop, an extensive set of performance measures was formulated and
recommended for future studies. After a few year of experience with this set of
performance measures, it was the felt that a reduced set was desirable, because the
volume of statistics was overwhelming and difficult to digest (Smith, 1984).
The Electric Power Research Institute launched a programme for Plume Model
Development and Validation (PMD&V). It has resulted in a number of useful data sets
from field experiments and numerous reports (e.g. Bowne et al., 1983).
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The focus of the research in this field has been basically on the development of
statistical model evaluation methods for the comparison between observed and
measured values, dealing mainly with local scale plume models. Most of these methods
are based on variants of the BOOT model evaluation software developed by Hanna
(1989). The initial focus of the BOOT software was to use Bootstrap or Jackknife
resembling to estimate 95% confidence intervals on model performance measures.
Because of the interest of the U.S.EPA in the method and their emphasis on maximum
concentrations, the early applications (e.g., Hanna et al., 1993) used maximum observed
and predicted concentrations on specific downwind monitoring arcs. However, the
statistical methods are the same no matter what data (e.g., paired in time, paired in
space, or paired in time and space) are being evaluated (Hanna et al., 2003b).
In Europe, the first initiative taking into account model evaluation aspects was launched
in 1991 in a meeting held at the European Joint Research Centre in Italy. The initiative
led to the organization of a series of workshops and conferences on ‘Harmonization
within Atmospheric Dispersion Modelling for Regulatory Purposes’ (see, e.g., Olesen,
2001b). At these meetings, work has been conducted in order to start establishing a
toolbox of recommended methods for model evaluation. The basis of much of this work
has been a so-called Model Validation Kit (Olesen, 1995). An updated version of the kit
has recently been released (Olesen, 2005).
The Model Validation Kit (MVK) addresses the classic problem of a single stack
emitting a non-reactive gas. The package contains the following main elements:
• Field data sets from the Kincaid, Indianapolis, Copenhagen and Lillestrom
experiments;
• The BOOT statistical model evaluation software package;
• Tools for exploratory data analysis, useful for diagnostic model evaluation;
• A recommended procedure (protocol) for model performance evalution.
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The two main limitations of the MVK are that it does not explicitly account for the
stochastic nature of dispersion problems, and only four experimental data sets are
considered. An alternative approach has been proposed by John Irwin (see, e.g., Lee and
Irwin, 1997; Irwin et al., 2003), and has resulted in an ASTM Standard Guide (ASTM,
2000). The standard guide is based, again, on Steven Hanna’s work and the BOOT
package, and, more or less, applies the same statistical indices. The main difference
with the MVK is on the regime averaging method: the fundamental premise of the
ASTM procedure is that observations and model predictions should not be compared
directly, and that observations should be properly averaged before comparison; the
comparison takes place within regimes, which for example can be defined according to
atmospheric stability and distance to the source; the ASTM procedure then calculates
performance measures based on regime averages (i.e., averaging over all experiments
within a regime), rather than the values for individual experiments.
Despite the fact that the statistical methods developed in the MVK and the ASTM
standard can be also applied to complex urban dispersion models, developing a simple
protocol or a toolbox, such as in the case of a single stack emitting a non-reactive
pollutant, is not easy at all. The physical phenomena involved in urban dispersion
modelling are more complex, and the difficulties in model evaluation are due mainly to:
• The natural variability in the urban environment and the urban canopy is even
greater than in a rural PBL.
• Good experimental data for validation purposes are more difficult to collect. It is
difficult to have representative data sets in a complex urban area, and
experiments are very expensive compared to the case of a single industrial stack.
• The uncertainty associated to the emission data is very high. Characterizing
emission sources in a large urban area is not easy, due to the large number of
sources and the high variability (see, e.g., vehicle emissions).
• Meteorological data are seldom representative of the actual situation in the urban
canopy.
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• Complex chemical modules are often involved in urban dispersion modelling
(for example photochemical models). This introduces more sources of
uncertainty both in the modelling process and in the evaluation process.
• The models involved in urban dispersion modelling have more capabilities than
simple Gaussian single-stack models (for example, they do not just simulate the
concentration field, but can calculate a large number of micrometeorological
parameters). For a complete assessment all these capabilities should be
evaluated when judging a model of this type.
Despite these difficulties, model validation work on urban dispersion models (at all
scales) has been carried out in the last decades. Especially during the most recent years,
great interest has been put in the evaluation process of complex numerical models, with
the proposal of several research projects, such as the EMU project (Hall, 1997), the
SATURN project (Borrego et al., 2003a), the DAPPLE project (Arnold et al., 2004), or
the Joint Urban 2003 dispersion study (Allwine et al., 2004) and COST 732 action
(URL 2008), taking into account model quality and evaluation and the execution of
several field and laboratory experiments. Efforts have also been put into the adaptation
of the BOOT statistical methodology for complex urban models (for example, Hanna et
al., 2003b, 2004). Statistical model evaluation methods for air quality models are
reviewed by Chang and Hanna (2004).
2.9.3 Uncertainty analysis
A dispersion model has several sources of uncertainty. Following the framework given
by Isukapalli (1999), the sources of uncertainty in air pollution transport and
transformation models can be classified depending on their origins and how they can be
addressed:
1. Natural uncertainty: environmental systems are inherently stochastic due to
unavoidable unpredictability (randomness). In particular, the presence of
turbulence is the main source of natural uncertainty and variability in air quality
models.
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Chapter 2 Review of urban air pollution modeling and evaluation methods
2. Model uncertainty: mathematical models are necessarily simplified
representations of the phenomena being studied and a key aspect of the
modelling process is the judicious choice of model assumptions.
3. Data uncertainty: uncertainties in model parameter estimates arise from a
variety of sources: measurement errors, misclassification, estimation of
parameters through a small sample, and estimation of parameters through nonrepresentative
samples.
Figure 2-20 shows the types of uncertainty present in transport and transformation
model application and their interrelationships.
Figure 2-20 Types of uncertainty present in transport and transformations model
applications and their interrelationships (based on Georgopoulos, 1995)
Uncertainty associated with model formulation and application can be also classified as
intrinsic and predictive. The first of these refers to the models uncertainties in terms of
input data, model formulation and numerical description. The second type, predictive
model uncertainty, refers to the models ability to predict a measurement made at some
point in space and will include both the intrinsic uncertainty as well as the uncertainty
due to spatial and temporal representativeness. Representativeness can be larger when
the scale of spatial variation is smaller than the model resolution (Denby et al., 2007).
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Chapter 2 Review of urban air pollution modeling and evaluation methods
Air pollution models vary from simple methods, which possess only a few parameters,
to complex ones, characterized by a large number of parameters. As illustrated in the
figure 2-21, the larger the number of parameters, the lower should be the uncertainty
associated with the model, and the smaller the errors in the model’s representation of
the physical reality. Unfortunately, however, the larger the number of input parameters
to be specified, the larger the input data error. As indicated in figure 2-21, there is an
optimum model choice that minimizes the total uncertainty. This simple interpretation
explains why the performance of complex models is often equal or inferior to that of
simpler methodologies. Complex models work well only when their extensive data
input requirements are satisfied, which rarely occurs (Zannetti, 1990). For this reason is
very important to realize detailed study in order to quantify the uncertainties.
Figure 2-21 Optimal model choice that minimizes the total uncertainties (Hanna,
1989)
The ideal mathematical model would provide the highest degree of simplifications
while providing an adequately accurate representation of the processes affecting the
phenomena of interest. Hence, the structure of mathematical models is often a key
source of uncertainty. In table 2-7 some of the sources of model and parametric
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Chapter 2 Review of urban air pollution modeling and evaluation methods
uncertainty associated with the formulation and the application of transport and
transformation models are listed (Moussiopoulos et al., 2001).
Uncertainty in model formulation
(structural uncertainty)
Simplification in conceptual uncertainty
Simplification in mathematical formulation
Ergodig-type hypotheses
Independence hypotheses
Spatial averaging
Temporal averaging
Process decoupling
Lumping of parameters
Discretisation
Numerical algorithm/operator splitting
Approximations in computer coding
112
Uncertainty in model application
(data/parametric uncertainty)
Constitutive parameter selection
Design/structural parameter selection
Input data development/selection
Source information
Topography/Land cover
Meteorology
Initial and boundary conditions
Emission
Operational model evaluation
Uncertainty in model estimation
Uncertainty in observations
Nonexistence of observations
Response Interpretation
Different emission levels
Table 2-7 Examples of the sources of uncertainty in the formulation and
application of transport-transformation models (Isukapalli, 1999)
Another representation of the sources of uncertainty for atmospheric dispersion models
is given by Colvile et al. (2002) and reported in figure 2-22.

Chapter 2 Review of urban air pollution modeling and evaluation methods
Figure 2-22 Schematic diagram showing flow of data into and out of the
atmospheric dispersion model, and three categories of uncertainty that can be
introduced (Colvile et al., 2002)
Several methods are available for estimating uncertainty associated with mathematical
models. For example, they are briefly reviewed by Isukapalli (1999). Ideally, we would
estimate the size of each source of uncertainty, calculate statistically the likely total
impact on model output quality, and then verify that the difference between model
output and validation measurement (for the base case year) is less than the estimated
total uncertainty.
This might be described as a bottom-up method of assessing model output quality.
Conventional methods of this type involve sensitivity analysis and uncertainty
propagation and, according to Moussiopoulos et al. (2001), can be divided in sensitivity
testing and sampling methods. Sensitivity testing involves studying the model response
for a set of input variables. The application of this approach is straightforward and it has
been widely employed in air quality modelling. The primary advantage of this approach
is that it accommodates both qualitative and quantitative information regarding variation
of the input data. The main disadvantage using this approach, however, is that the
sensitivity information obtained depends to a great extent on the choice of the sampling
points, especially when only a small number of simulations can be performed. Sampling
based methods, such as the Monte Carlo method, involve running the model for a set of
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Chapter 2 Review of urban air pollution modeling and evaluation methods
input combinations (sampling points) and estimating the sensitivity/uncertainty using
the model outputs at those points.
In practice, however, many of the sources of uncertainty are of unknown magnitude and
the interaction between the very large number of factors is poorly understood (Colvile et
al., 2002). Furthermore, since the sampling methods require a large number of samples
(or model runs), their applicability is sometimes limited to simple models. It is,
therefore, generally believed that it is currently impossible to adopt the bottom-up
approach, especially for the sources of uncertainty concerned with the modelling itself
(level of uncertainty 2, as shown in figure 2-22) as opposed to the input data (level 1 in
figure 2-22).
An alternative method was described by Colvile et al. (2002), Stern and Fleming (2007)
and Borrego et al. (2008), and might be referred to as top-down, in that the individual
causes of error in the model output are not considered, but the total effect of some or all
of these is quantified by using a sufficiently large number of validation measurement
points that are sufficiently representative of the conditions under which we wish to
quantify model uncertainties. This method involves comparison between models and
observation or experimental data, that is the same conceptual base used in the model
validation procedure.
This top-down approach benefits from its simplicity, but has the weakness of
introducing empiricism into the assessment of model output quality, and so it is
necessary to ensure that validation conditions are similar to application conditions.
Specifically, the top-down method is incapable of including the effect of the third class
of error in figure 2-22, concerning the accuracy with which we are capable of predicting
changes that will occur in the future.
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Chapter 3
3.Neighbourhood/street scale modelling by means of wind tunnel
3.1 Introduction
methods
Neighbourhood and street scale dispersion in an urban environment is a very complex
matter, primarily reflecting interactions between the ‘external’ PBL and irregular arrays
of three-dimensional obstacles, but also affected by traffic movement, emission
condition and so on. Computer modelling of these processes is generally based on very
simplistic concepts which, though often acceptable, will in many cases prove to be
inadequate. Examples, where this may be so, include the dispersion of emission from
special sources (e.g. road traffic), the prediction of short term concentrations and the
interpretation of point measurements. To this list should be added the development and
evaluation of improved parametric and CFD models. The only method available to
solve questions of this kind is the experimental method and in particular wind tunnel
modelling (Obasaju and Robins, 1998). A reduced-scale physical model has several
advantages with respect to field experiments; wind tunnel modelling is cheaper than
field experiments and has a high degree of reproducibility, thanks to the possibility of
controlling meteorological parameters as well as dispersion and geometric variables.
For all these reasons in this thesis neighbourhood and street scale dispersion were
studied by means of wind tunnel methods.
In this chapter, we begin by discussing the aspects involved in executing a wind tunnel
modelling study (section 3.2); we then describe the laboratory (section 3.3), where the
experiments were performed, and finally we present the experimental strategy (3.4) and
techniques (tracer dispersion measurements, section 3.5) used in this study.
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
3.2 Background on wind tunnel modelling
Wind tunnel modelling is a type of physical scaled modelling. Scaled modelling is
based on the fundamental premise that by reducing the geometrical scale of a given flow
domain, and by adjusting reference parameters such as flow velocity, fluid density etc.,
the original scale flow can be reproduced correctly. The development of various scaling
techniques is based on similarity theories, i.e. on the assumption that, once chosen the
governing parameters, the conservation of those parameters is sufficient for the correct
reproduction of the flow and dispersion phenomena for a wide range of scales. Physical
modelling criteria for undertaking scale-model dispersion experiments and relating their
outcome to their atmospheric counterparts have been widely discussed (see, for
example, Snyder, 1981; Obasaju and Robins, 1998). Although these are generally
discussed in terms of an isolated stack or a single building, they apply equally to arrays
of obstacles (Robins and Macdonald, 2001).
The main aspects in an air quality physical modelling study are basically:
1. the simulation of an appropriate atmospheric (boundary layer) flow in a wind
tunnel
2. the construction of a scale model of the topography and buildings of interest
3. the simulation of the emission and dispersion phenomena of interest
3.2.1 The simulation of the PBL flow
The first process to be reproduced in wind tunnel is the atmospheric boundary layer
flow. Wind shear in the PBL and the presence of the ground have a strong influence on
the flow around buildings (Castro and Robins, 1977). It is now generally agreed that it
is essential to match the atmospheric boundary layer structure both in terms of velocity
profile and intensity, as well as spectral distribution (Snyder, 1981). The main aim of
the physical modelling is to model the non-dimensional profiles of mean flow speed,
turbulence normal and shear-stresses, and turbulence length scales throughout the depth
of the simulated flow, Href. This may be obtained by producing an artificial turbulent
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
boundary layer using turbulence promotors at the inlet section of the wind tunnel and
roughness elements distributed on the wind tunnel floor (Counihan, 1969). Devices such
as vortex generators (Counihan, 1969) aid the quick development of a boundary layer,
and roughness elements on the tunnel floor maintain the boundary layer structure along
the length of the tunnel (see figure 3.1). More details about the simulation, development
and structure of a neutral atmospheric boundary layer can be found in Robins (1979).
Figure 3-1 Schematic of a boundary layer simulation system in an atmospheric
wind tunnel
General similarity between reduced-scale and full-scale flow requires geometric
similarity, kinematic similarity, dynamic similarity, and similarity of the boundary
conditions. The required conditions can be derived from dimensional analysis of the
governing equations of fluid flow (conservation equations of mass, momentum and
energy). These equations can be written in nondimensional form using the following
scaling factors: Lref for the lengths, Uref for the velocities, θref for the temperatures, ρref
for the density, Ωref for the angular velocities, and gref for the gravitational acceleration.
The subscript ref indicates a reference value. Usually values at the boundary layer edge
are used as reference (in particular Lref is usually the boundary layer depth, Ωref = Ω0).
When the equations governing fluid motion are non-dimensionalized the dimensionless
parameters that need to be matched between experiment and field to obtain kinematic
and dynamic similarity of the flows are the following:
Reynolds number Re =
LrefU
ref
ν
, where ν is the kinematic viscosity
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
Rossby number Ro =
L
U
ref
ref Ω0
∆θ
ref 1
Bulk Richardson number Rib = , where the Froude number Fr =
2
θ Fr
Peclet number Pe = Re·Pr where the Prandtl number Pr =
2
U ref
Eckert number Ec =
c θ
p
ref
ref
, where cp is the specific heat of the flow
118
L
U ref
2
ref
g
ref
ν
and α the thermal diffusivity
α
The similarity does not depend strongly upon the Eckert number until the flow speeds
approach the speed of sound; therefore this requirement is relaxed (Cermak, 1971).
The Rossby number is a measure of the locale acceleration relative to Coriolis
acceleration in the atmosphere and it is impossible to match in conventional wind
tunnels, although some experiments have been attempted using annular wind tunnels
and rotating tanks (Alessio et al., 1983; Howroyd and Slawson, 1975). The main effect
of the Coriolis force is the generation of the Ekman spiral, that is a deviation of wind
direction with height. According to several studies (e.g. Snyder, 1981), the Rossby
number requirement can be relaxed when modelling dispersion phenomena at a
maximum distance from the source of about 5000 m, in case of neutral or stable
conditions in relatively flat terrain.
Matching the Reynolds number of the full scale and model flows (known as ‘dynamic
similarity’) imposes a strict limitation on the scale reduction possible, especially in the
wind tunnel where the values of ρ and µ are identical to values in the atmosphere (in the
water tank these values can be considerably higher). The only reference parameter that
can be changed is therefore the reference velocity Uref. For atmospheric flows, a typical
scale reduction by 10 2 -10 3 requires an increase of 10 2 -10 3 in the velocity, which is not
possible to achieve in the wind tunnel. Various arguments have been presented to justify
neglecting strict dynamic similarity, and using a smaller Re number in a model (Snyder,
1981). The Reynolds number independence criterion is based on the premise that
‘geometrically similar flows are similar at all sufficiently large Reynolds numbers’.
Above a certain Re threshold (Re>10 4 ) a flow becomes turbulent and then the gross

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
structure of the turbulence becomes similar over a very wide range of Re numbers; this
value of Re is easily reproduced in wind tunnel experiments, where typical values of Re
are about 10 4 -10 5 . There is a large amount of experimental data supporting this
hypothesis, and it is a standard concept used by wind engineering tunnel modellers
(Snyder, 1981).
The Pellet number is the product of Re and the Prandtl number. The latter is a fluid
property and not a flow property, and does not vary strongly with temperature. Thus,
arguments similar to those constructed for Reynolds number independence may be used
to justify the neglect of the Peclet number as modelling criteria (Snyder, 1981).
The bulk Richardson number is the ratio of buoyancy and inertial forces, and is
regarded as the most important modelling criterion that must be matched between fullscale
and experiments. However, for the simulation of neutral boundary layers, Rib = 0,
and thus there is not any restriction on flow speed and temperature in the model. In case
of stratified flows modelling, it is the fundamental criterion, and can prove to be a
difficult parameter to be replicated in a fluid model.
It is clear that for a steady flow with neutral stratification no dimensionless parameters
have to be exactly matched. Lengths scales determined from physical model can be
converted to full-scale by simple multiplication with the geometric scaling factor.
Geometric similarity follows from the use of the same length scale for both horizontal
and vertical dimensions.
The last similarity criterion to be satisfied in PBL flow scaling is the similarity of
boundary conditions of the flow. These boundary conditions include distribution of
temperature and roughness over the area of interest, the vertical temperature and
velocity distribution of the approach flow (Cermak, 1971). Beyond, the flow must be
fully developed before the model investigated and, in absence of topographical
modification, must respect the following prerequisite (Obasaju and Robins 1998):
∂
∂x
U ref

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
3.2.2 The physical model
The choice of scale and the extent of the region to be represented are key decision in
wind tunnel experimentation. They are obviously related as the model size is
constrained by the dimensions of the working section. The choice of the scale is also a
key factor in establishing the flow speed and emission conditions in the wind tunnel (see
section 3.2.3). Figure 3.2 illustrates how the model and working section sizes are
related. Upstream of the emission location a sufficient fetch is needed to establish the
required atmospheric flow. Then a downstream fetch is required which extends from the
source region as far as necessary to answer the air quality question of issue. The figure
shows the case of a tall stack where the downstream fetch is extensive. This is by no
means always the case; e.g. in street canyon study the other side of the street may be the
limit.
Figure 3-2 Factors which determine the length of wind tunnel working section
needed to undertake a plume dispersion simulation.
There are two other rather weak constraints affecting the choice of simulation scale.
These are that the ratios of the obstacle or building size relative to the roughness length
and the boundary layer depth must be maintained in the laboratory simulation. The first,
coupled with similarity u*/UH (ratio between the friction velocity and the velocity at the
building height H) in the ambient flow, ensures the required velocity profile over the
building height, and the second the appropriate large eddy scales relative to the building
dimensions. The latter is often relaxed, particularly when short-range building-affected
dispersion is being simulated, the justification being that building-generated processes
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
and scales dominate (Robins and Macdonald 2001). In this special case the vertical
scale may be distorted relative to the horizontal scale in the final model and only a
fraction of the atmospheric boundary layer is simulated. In most circumstances this is
not to be recommended; so, decisions have to be made about the degree of detail to be
included. As much detail as possible is a good general rule, but financial and planning
consideration may dictate otherwise. A degree of compromise is often necessary, but
this must be settled on a case by case basis. Clearly, the model must not be simplified to
a level which jeopardizes the objectives of the study (Obasaju and Robins 1998).
Whatever is decided about the scale and detail, the maintenance of fully rough surface
conditions in the ambient flow is required; the model’s surface must be aerodynamically
rough at the lowest operating speed. This requires the flow speed to be large enough for
the roughness Reynolds number, Re*, to be high enough (Snyder and Castro 1997):
u * z0
≥
ν
Re * = 1
,
where u* is the friction velocity, z0 the roughness length and ν is the kinematic
viscosity.
Another Reynolds number arise in order to ensure the appropriate flow regime around
the buildings and in their wakes; Reynolds number independence of the flow past sharp
edged obstacles (buildings, etc.) is satisfied when the obstacle Reynolds number, RH, is
sufficiently large (Snyder, 1981):
ReH =
U H H
ν
4
≥ 10
,
where UH = U(H). This is a realistic approach for sharp edged buildings but poses
problem for smoother shaped obstacles.
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
3.2.3 Emission and dispersion simulation
The aim of the emission simulation is to define model source and wind condition which
will lead to the correct simulation of the non-dimensional plume trajectory (Yp/Lref,
Zp/Lref) and spread (σy/Lref, σz/Lref) as function of downstream position, x/Lref. The non-
dimensional concentration field (with Qref the reference source strength),
C* =
CU
ref
Q
ref
L
2
ref
is then automatically reproduced.
By considering a single source, emission simulation can be illustrated defining the
following parameters: stack diameter (ds) and height (hs), emission velocity (Ws) and
density (ρs) and the reference ambient wind speed (Uref) and density (ρref).
The dimensional analysis of the physical process involved in the release of a gas from a
source show that the following nondimensional parameters need to be conserved:
Ws/Uref, ρs/ρref and (gHref)/U 2 ref; this means that volume flux Q, mass flux QM,
momentum flux FM, Richardson number Ri and buoyancy flux FB are also conserved.
The conservation of these nondimensional parameters is termed the complete scaling
condition. The most telling consequence of complete scaling is the relation between
model and full-scale wind speeds; i.e. the velocity scaling ratio (Uref)m/Uref=S 1/2 , where
S=(Lref)m/Lref is the geometric scale ratio and the subscript m denotes the model state.
This demonstrates four things: 1) low speeds are necessary at model scale, 2) extreme
geometric scale ratio S are to be avoided, 3) large facilities are required and 4) the
demands of complete scaling often may be impossible to meet. As a matter of facts, S is
usually of the order of 10 2 −10 3 , and this implies velocity scaling factors of the order of
10 −100, that means a very slow flow with respect to real wind velocities. This is very
difficult to achieve and control in wind tunnels, and it is in contrast with the Reynolds
number requirements explained in the previous sections (3.2.1-3.2.2).
Usually, therefore, we must abandon the complete scaling and make some
approximations. These approximated relationships are called enhanced scaling
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
relationships (see e.g. Obasaju and Robins, 1998). The objective of this relationship is
to relax the strict requirements of complete scaling in some way to obtain a wind speed
scaling ratio which more favourable; i.e. to have (Uref)m/Uref=βS 1/2 , where β>1. The
usual approach is to distort the density ratio ρs/ρref and replace three complete scaling
factors (Ws/Uref, ρs/ρref, (gHref)/U 2 ref) with two physically substitutes. There are several
possibilities, though only combination of one from Ws/Uref and FM, together with one
from Ri and FB have generally be used.
In case of a passive emission, defined as one for which the source momentum and
buoyancy parameters, FM and FB, are so small that they have no effect on the behaviour
of the release, we can still use a complete scaling. FM and FB can effectively be set to
zero and then there is no relation between model and full scale wind conditions and a
single experiment is sufficient to define concentrations at all wind speeds, since they
will collapse when scaled as C*. The only warning is that there will generally be a
lower limit to the wind speed at which the assumption of passive emission condition
fails, since both FM and FB are inversely related to the wind speed; i.e. the emission
velocity Ws must be lower or at maximum equal to the wind speed at the source height
hs (Obasaju and Robins 1998).
The similarity conditions for the diffusion process can be derived from considering the
non-dimensional form of the molecular diffusion equation, for the concentration C of a
non-reactive, passive tracer:
∂C
∂t
*
*
+ U
*
i
∂C
∂x
*
*
i
= −
1
Re Sc
∂
∂
2
2
C
x
*
* 2
i
where Sc = ν/α is the Schmidt number, and α is the molecular mass diffusivity. Thus,
the diffusion similarity criteria reduce to the conservation of the product ScRe. Since Sc
is a property of the fluid and not of the flow, the same considerations already seen for
the Peclet and Prandtl numbers in the PBL scaling can be made.
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
3.3 The EnFlo Laboratory wind tunnel
The wind tunnel experiments were carried out at the boundary layer wind tunnel of the
Environmental Flow Research Centre (EnFlo), University of Surrey, UK. It is a suckthrough
open circuit wind tunnel with an external overall length of 27 m (figure 3-3).
The aerodynamic circuit consists of an axial ventilator that allow to change wind speed
between 0.3 and 4 m/s, an inlet and an outlet convergent, that permit to stabilize the
flow and to balance the various components of turbulence, and a working section.
Dimensions of the working section are 20 m (length), 1.5 m (height), 3.5 m (width). At
the exit and at the entry of the working section, honeycomb screens make it possible to
create a homogeneous turbulence.
The first twelve meters of the working section are devoted to the establishment of the
ABL. The BL simulation system consists of vortex generators, placed at the begin of the
test section, and roughness elements, placed on the floor over a length of a few meters.
The vortex generators reproduce the large scales of turbulence, while the roughness
element define the lower part of the wind profile.
About seven meters remain for the experimental fetch where flow and concentration
measurements downstream of sources and models can be taken; at the begin of this
section there is an automatic turntable, that permit to rotate the model in the desired
orientation. A computer controlled, 3 axis traverse mechanism is mounted in the wind
tunnel; the geometrical reach of the traverse is 1.5 m and 0.5 m respectively in the
lateral (y) and vertical (z) direction and from 11 to 18 m starting from the inlet of the
tunnel in the downwind direction (x). A mounting mechanism allows an easily
mounting of the instruments. The traverse can be used for all types of measuring
instruments available in the laboratory and for mounting movable sources for dispersion
experiments.
A differential heating facility, comprehensive of cooling and heating panels allows to
simulate both stable and unstable atmospheric conditions, although this feature was not
used in this study. Reference flow conditions are measured by two ultrasonic
anemometers, one held at a fixed location and the other positioned as required; the
motor shaft speed is also measured. Temperature conditions are monitored by
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
thermocouple rakes in the flow and individual thermocouples in each tunnel wall panel.
The pressure drop across the inlet is also monitored, primarily to indicate the state of the
inlet screens. The wind tunnel and the associated instrumentation are fully automated
and controlled using ‘virtual instrument’ software created by EnFlo research staff using
LabVIEW.
Research instrumentation includes hot wire, pulsed hot wire and two component (fibre
optic) laser Doppler anemometry, cold wire thermometry, tracer concentration
measurement (standard and fast FID systems), high sensitivity pressure measurement,
(fibre optic) laser light sheet, video recording systems (analogue and digital) and
calibration equipment.
Figure 3-3 Schematic of the Enflo meteorological wind tunnel
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
3.4 Experimental strategy
Several wind tunnel experiments of pollutant dispersion from moving sources were
carried out in this thesis. These experiments were performed at the EnFlo wind tunnel in
the framework of the DAPPLE-HO project on a model replicating an urban area in
central London.
Dispersion of Air Pollution and its Penetration into the Local Environment – Home
Office CBRN Research Programme (DAPPLE-HO), a 3-year UK Home Office CBRN
funded project, is a continuation of the DAPPLE-EPRSC project, a 4-year U.K.
Engineering and Physical Sciences Research Council (EPSRC) funded project within
the Engineering for Health, Infrastructure and Environment Programme, (see also
sections 2.6.2 and 2.6.3). The aim of DAPPLE and DAPPLE-HO projects is to provide
a better understanding of pollutant dispersion processes in realistic urban environments
and make possible improvements in predictive ability enabling better planning and
management of air quality. The interdisciplinary approach of the two projects will
enhance understanding of the physical processes affecting the street and neighbourhood
scale flow of air, traffic and people, by means of field measurements (meteorology,
roadside pollution levels, traffic flow, personal exposure and inert tracer releases), wind
tunnel modelling and computer simulations.
The DAPPLE site (Figure 3-4 left) is located at the intersection between Marylebone
Road and Gloucester Place in Central London, UK, with a surrounding study area
approximately 250 m in radius. Wind tunnel modelling extends to a radius of about 500
m. Marylebone Road is a busy dual carriageway (A501) and forms the northern
boundary of the London Congestion-Charging Zone. Gloucester Place is 3 lanes, oneway
northbound (Baker Street is southbound one block to the East). The roads intersect
perpendicularly and Marylebone Road runs approximately WSW-ENE. The average
building height is approximately 22 m. As it is a real site, the heights and sizes of the
buildings and streets were all different and this strongly influences the pollutant
dispersion mechanism within the intersections (see figure 3-4 right).
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
Figure 3-4 Aerial view (left) and 3D rendering (right) of the Dapple test site
The DAPPLE-HO wind tunnel studies are carried out at the EnFlo laboratory. The
experimental work presented in this thesis includes three series of experiments carried
out at the University of Surrey in the framework of this project. The model installed in
the wind tunnel is shown in figure 3-5. This is the simplest DAPPLE site model, where
all buildings have been reduced to simple blocks with flat roofs. The geometrical
scaling factor was 1:200.
Figure 3-5 Several views of the Dapple model in the wind tunnel
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
The boundary layer was generated using 5 Irwin spires and surface roughness (80mm
wide x 20mm high elements with fixed 240 mm lateral and longitudinal spacing)
upwind of the model (see figure 3-6 ). The tests were carried out with a reference wind
speed (Uref) of about 2.5 m/s. Uref was measured with the ultrasonic anemometer
positioned outside the boundary layer. The BL characteristics were verified in absence
of the model by means of flow measures with Laser Doppler anemometry along the
centreline of the wind tunnel at different distances from the inlet of the tunnel
(x=12000mm, x=13770, x=14000, x=14400). Mean flow vertical profiles measured and
corresponding logarithmic-law fit are reported in figure 3-6. The wind tunnel set up
allowed to create a neutral BL, with a thickness Href of about 1 m, a roughness length
z0/Href equal to 0.003 and a value of u*/Uref approximately equal to 0.03.
Figure 3-6 Irwin spires and surface roughness (left) and mean flow vertical profiles
(right) in the EnFlo wind tunnel
The model was oriented using the turntable, and most of the experiments were
performed with a rotation of −90 o in model coordinates (0 o corresponds to a wind
direction along the X axis of the model – W-E direction; wind direction is positive
clockwise, hence the selected direction is respectively from south, see figure 3-7); the
wind direction was chosen based on field tracer release experiment carried out in the
framework of DAPPLE Project (Arnold et al., 2004).
The main aim of the experiments was to investigate pollutant release from vehicles
sources in terms of mean concentrations, fluctuations and dosages in realistic urban
environments. The experimentation includes mean concentration and concentration
fluctuations measurements, and involves several advanced experimental techniques
described below and in the next sections. Both the street and the neighbourhood scales
are involved.
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
Figure 3-7 Plan view of the Dapple model, showing the model coordinates system
and wind directions analyzed in the experiments
The experimental programme covers two different series of experiments. All the series
of experiments concentrate on the characterization of dispersion phenomena connected
with the pollutant emission of vehicles travelling along Marylebone Road. Tracer
dispersion measurements were carried out in the streets downwind of the emission
sources. In these investigations, point sources were used to simulate a line source
(traffic emissions can be comparable with linear source placed on the level of the
ground) via mathematical integration. Noting that the concentration, δCp, due to a point
source emission, Q, in an element, δs, of a line source of finite length, from s = 0 to s =
S, is the same as would be due to the element itself with line source strength, q
(emission per unit length), where qδs = Q, the nondimensional mean concentration at a
receptor point due to emissions from a line source, Cline*, can be derived using
nondimensional mean concentration at a receptor location due to a point emission, Cp*,
by means of the following equations.
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
δC
C
C
line
line
*
line
C pUH
=
Q
=
∫
0
S
C
=
dC =
line
UH
q
2
∫
q s
. 2
UH
δ
0
S p
=
C UH
Q
∫
0
C UH
ds =
Q
S p
2
qds
.
UH
2
=
∫
0
∫
0
S p
C
S p
Q
C UH
Q
qds
2
ds
H
where U is the reference wind velocity, H is the reference length scale (mean building
height) and S is the length of the line source. The last expression describes, in non
dimensional form, the simulation of mean concentrations from a line source in terms of
measurements from a point source; hence, in order to evaluate the mean concentration
from a line source is sufficient to measure the mean concentrations from the point
sources belonging to the line and integrate these values.
The equivalent expression for the variance (cline 2 ) and consequently for the fluctuations
(cline), can be derived from similar consideration, by means of the following equations:
c
c
=
⎧
∫ ⎨∫
= S S 2
p p
line
0 0
2 *
line
⎩
c ( s')
c ( s''
) ⎫
qds''⎬qds'
Q Q ⎭
130
=
∫
S H
2 2
2
2 2
c UH S H ⎪⎧
c s UH S H s ⎪⎫
line ( ) / p ( ')
( ) /
''
s'
= = ∫ ⎨ ∫ R(
s',
s"
) d ⎬d
2
q
0
2
⎪⎩ Q
0
H ⎪⎭ H
H ⎧
S /
2 *
⎨c
p ( s')
0 ⎩
s''
R(
s',
s"
) d
H
s'
d
H
S /
H
∫ ∫ ⎭ ⎬⎫
0
where R(
s , s'')
c p ( s')
c p ( s''
)
' = is the correlation coefficient between the fluctuations of
2
c ( s')
p
two point sources belonging to the line source. Thus, the concentration fluctuations on a
receptor location determined by a line source can be estimated measuring how change
the correlation for a point source in a reference position s’, when the other point source
moves along the line emission, and repeating this procedure for different positions of
the reference source.
0
C
*
p
d
s
H
=

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
As a consequence, the contemporary use of two point sources enabled to produce
statistics of the concentration (mean and fluctuating concentration) on a receptor
location due to a line source emission. This experimental method, not very used in the
scientific literature (usually most of the works uses a uniform line source in order to
simulate vehicle pollution emission, see for example Meroney et al. 1996, Kastner-
Klein and Plate 1999, Soulhac 2000), enabled to establish source-receptor relationships
in terms of mean and fluctuating concentration, to simulate non-uniform emissions and,
consequently, to evaluate different traffic conditions by giving weightings to various
locations on the emission line source and estimating the dose, D, that is the integral of
the concentration with time, to which someone might be exposed due to the passage of a
mobile source along a road.
All these aspects were analyzed in the two different series of wind tunnel experiments
carried out in this thesis. The first series (called ‘moving source experiment’) gives
attention, in particular, to the mean concentrations and related exposure dosages,
deriving from traffic emission in Marylebone Road, while the second series (called
‘correlation experiment’) focus on concentration fluctuations from vehicles source and,
in particular, was aimed at investigating the correlation between two point emission
sources; during this phase an original experimental techniques for the evaluation of the
correlation coefficient was developed.
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
3.5 Tracer dispersion experiments
Dispersion measurement at the EnFlo laboratory are based on the release of an inert
carrier gas with correct thermodynamic state properties required by experiment, mixed
with a known concentration of a hydrocarbon (‘tracer’), such as propane, and on the
measure of the tracer concentration using air sampling at selected point downstream of
the emission source.
A schematic of the typical set-up used for tracer concentration measurements is shown
in figure 3-8. Three main components can be recognized: the concentration measuring
system, the source control system and the computerized control and data analysis
system.
Figure 3-8 Instrumentation set-up for dispersion experiment at EnFlo laboratory
The instrument used for the concentration measurements is a Fast Flame Ionization
Detector (Fast FID). It is a fast response instrument manufactured by CAMBUSTION,
and is capable of measuring hydrocarbons mean and fluctuating concentration; when in
use at measurement location, the FFID can sample the mixture gas through a sampling
tube (figure 3-9). The 3-D traverse mechanism in the wind tunnel (see Figure3-9) was
used to carry the FFID receptor to different locations; the traverse is able to move in the
132

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
x (along tunnel direction), y (cross-tunnel direction) and z axes (vertical tunnel
direction), and is fully computer controlled.
The FFID operates on the principle of the ionization of carbon atoms during combustion
of a hydrocarbon. A fixed proportion hydrocarbon molecules will be temporarily ionize
as a carbon cation and an electron. By applying an electrostatic charge over the
combustion volume the electrons will be attracted by the positive pole and an electric
current due to the flux of electrons will be created and the corresponding voltage can be
measured. The voltage is proportional to the concentration of hydrocarbons in the gas
mixture. The FFID has to be regularly calibrated at the begin, during and at the end of
the experiment; for calibration purpose, certified calibration gases with a known
reference concentration are used. In this experimentation the FFID calibrations were
done via an additional calibration source placed downstream of the model every two
hours; three known concentrations of propane in air (0, 150, 750 ppm) were passed
through the FFID and the voltages recorded.
Figure 3-9 CAMBUSTION FFID for hydrocarbon concentration measurements;
support equipment (left) and head with its sampling tube mounted on the 3-D traverse
in the wind tunnel (right)
The principles of FFID are exactly the same as that of an industry-standard FID; the
main difference is that the measurements in the FFID are done in real time rather than
by means of a storage and retrieval methods used in conventional FID. The real time
operation of the FFID implies that the samples are drawn directly into the flame
chamber of the FID by means of a vacuum pump; FFID has a unique sampling system
designed to preserve high frequency features. Depending on the sampling tube length
133

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
and suction rate response time of up to 1000 Hz are possible. Sample tube length of 250
mm is used in this study; the corresponding frequency response of the FFID with such
long tube is in the order of 500 Hz, this was the sampling frequency used for all the
experiments. Two FFIDs head with their support equipment were used during this
experimentation to reduce the experimental execution time.
In this experimentation tracer gas concentration were measured using an effective
sampling time varying from 3 to 12 min depending on the accuracy needed, i.e. the
correlation factor measures needed sampling time longer than mean concentrations and
fluctuations. The time series of the concentrations measured are analyzed by means of
Labview measurement software that is able to calculate the desired quantities, mean
concentration and variance.
The background concentration of hydrocarbons was recorded every 15 minutes during
the runs by analyzing a sample from an extra sampling tube placed at the end of wind
tunnel. This regular checking was important as many things can affect the background,
including activities not directly related with the experimental work. Having these
background readings and the calibration curves, the Labview measurement software
was able to take them into account and adjusting results accordingly, during the post
processing analysis.
Speaking of the source control system, dispersion experiments in this thesis were
carried out by releasing a neutrally buoyant gas tracer into the flow. The tracer used to
obtain a neutrally buoyant release was a gas mixture of 2% (20000 ppm) of propane in
air; this concentration was selected for convenience of detection. The source gas was
piped into the wind tunnel and released near the wind tunnel floor via a stack (see figure
3-10). To minimize the interference between the stacks and the incoming flow and to
simulate a passive, ground level point source, the tracer gas was released out of the pipe
via an aerodynamic release arrangement (see figure 3-10), oriented in the direction of
the mean wind tunnel flow and positioned at a small distance over the ground (z=10
mm), and with very low initial momentum. To avoid jetting of the tracer flow out of the
stack, the emission flow rate selected for each sources was 2.4 l/min, equivalent to a
release velocity of 0.2 m/s, which can be considered as passive, because it was an order
of 10 lower than the mean flow speed at the height source (about 1 m/s with a wind
134

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
tunnel reference speed in the UBL of 2.5 m/s). The flow rate for the sources was
controlled by HITEC flow meters (see figure 3-11). Two HITEC flow meters were
used, one to provide air and the other propane; the gases were mixed in a calm chamber
and then delivered. Depending on the series of experiment one or two sources were
used; when 2 sources were used the flow was controlled by the HITEC flow meters and
then split using two rotameters to give equal flow into the two stacks (see figure 3-11).
Figure 3-10 System used for the tracer release: pipes mounted on the 1-D traverse
mechanism (left) and aerodynamic stack (right).
Figure 3-11 Flow control systems: HITEC electronic flow meters (left) and
rotameters (right)
The sources were mounted on a 1D traverse mechanism; this traverse only moved in the
X (model) direction (see figure 3-10). During the experiments there were one or two
stacks mounted on this traverse, depending on the situation being investigated. For the
135

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
correlation experiments, where two sources were in use, there was an ‘active’ stack and
a ‘passive’ stack with identical internal (di = 8.5mm) and external diameters (de =
10mm). The active stack was powered by the traverse and could move in the positive or
negative X direction; while the passive stack was not powered and therefore had to be
pushed into the desired position by the active stack.
A detailed description of the experimental programme used in the three series of
experiments carried out in this thesis was reported, separately, in the next paragraphs:
moving source experiment (3.5.1) and correlation experiment (3.5.2).
3.5.1 Moving source experiments
Moving source experiment was carried out to investigate, in terms of mean
concentration and dose, the effect of a moving source on a receptor at a particular
location. With the aim of simulating a traffic line source using point sources, a number
of ground level (Z=10 mm) source locations were chosen along Marylebone Road, the
main street in the Dapple model; these were selected so that an acceptable
representation of a full ‘line source’ between Balcombe Street (X=-531 mm) and Baker
Street (X=770mm) was obtained (see figure 3-12). Receptor locations were then chosen
along streets downwind of the source; only streets that cover the width of the model
were chosen for receptor locations, as it was preferred to build up full horizontal
profiles.
To obtain the moving source data, a stack (providing the source) was placed on a single
axis traverse, that permits to move the source along Marylebone Road. The stack would
move to a source location and remain there whilst the FFID would measure
concentrations on horizontal profiles or at specific points in the desired streets. Once the
measurements were completed for that source location the stack would move by means
of 3-D traverse mechanism to the next source position.
During these experiments, two different series of investigations were carried out; the
first focused on ground level concentrations, while the second concentrated on the
comparison between concentration measurements at different heights.
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Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
Figure 3-12 Source and receptor locations for the GLC investigation: -90° (top) and
+90° model orientation (bottom)
In the first series two model orientation were analyzed, -90° and +90° model
orientation. The +90° model orientation was added to permit the comparison with a
field experiment realized on 11 th November 2004 (Shallcross et al. 2005) and the
analysis of more than one street downwind of the source. This orientation allows to
137

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
study three different parallel streets downwind of the source: Bickenhall Street (Y=
-340mm), York Street (Y=-719mm) and Crawford Street (Y=-1060mm). Ground level
concentration measurements were made in the centre of Dorset Square/Melcombe Street
(-90° model rotation), Bickenhall Street (90°), York Street (90°) and Crawford Street
(90°). Source and receptor locations adopted for this investigation were reported in
figure 3.12.
In the second series only -90° model orientation and the most relevant receptor locations
along Dorset Square/Melcombe Street, determined from the previous investigation,
were considered; two more point were considered in Dorset Square, to analyze the
dispersion effect of the square. Four different heights were analyzed: ground level
(Z=10mm), mid canyon height (Z=50mm), roof height of building 1 (Z=90mm) and of
building 3 (Z=160mm). Source and receptor locations adopted for this investigation
were reported in figure 3.13.
The average sampling time has generally been set to about 3 minutes for all the series of
experiments; only ground level concentration of the second series were measured with a
longer sampling time (12 minutes), because this measure were also used in the
experimental procedure developed in the correlation experiments.
Figure 3-13 Map of the Dapple model showing the wind direction, the source and
receptor locations studied in the investigation of concentrations at different heights
138

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
3.5.2 Correlation experiments
In this study an advanced experimental methodology was developed in order to analyze
the correlation between concentration fluctuations from two point sources belonging to
a line emission. Remembering that concentration variance connected with a line source
can be expressed in term of correlation between the fluctuations produced by two point
sources (see section 3.4), the measure of these correlations permits to evaluate the effect
of different source locations in a line emission in terms of instantaneous concentration
(fluctuations), to estimate expected fluctuations for different driving pattern and,
consequently, to better understand the cause of maximum pollutant exposure levels
achievable in highly congestioned urban areas. Despite these facts, investigation of the
superposition of multiple plumes has not been used before in the scientific community
to study concentration fluctuations connected with traffic emission in urban area. The
only piece of scientific literature that uses the superposition of multiple sources to
determine the correlation between fluctuations produced by the sources is a study by
Warhaft (1984) on the interference of thermal fields from line sources in grid
turbulence. The methodology suggested in this study was applied in the present wind
tunnel work.
The math basis of the method for the determination of the correlation, used by Warhaft
(1984) and adopted in this work, derive from the following consideration. If we
consider two point sources located in a turbulent flow the resultant concentration
variance,
2
c B , will be:
2
2 2 2
c B = ( c 1 + c2
) = c1
+ c2
+ 2c1c2 where
2
c 1 e
2
c 2 are the concentration variances produced by each point source (source 1
usually indicates the reference source) and c1c2 is the correlation between the
fluctuations produced by each point source. Consequently, as shown by Warhaft (1984),
the correlation factor c1c2 may be inferred by operating each point source separately
(thus determining
2
c 1 and
c ) and then both together (determining ( ) 2
c
2
2
2 2 2
( c + c ) − c c )
1
c1c2 =
1 2 1 −
2
2
139
c + ), thus:
1
2

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
This method is called interference method; the term interference for the variance field is
used in the same sense that it is used in linear wave theory where destructive or
constructive interference may occur depending on the phase relation on the two
superposed waves (Warhaft 1984).
For practical derivation of the fluctuations from a line source via mathematical
integration, in this work, the correlation c1c2 was nondimensionalized dividing this
2
parameter by c 1 , the variance of the source 1, assumed to be the reference source, and
obtaining, thus, the formulation of the correlation coefficient requested by integration
procedure (see section 3.4):
COR=
1 2
2
c1
( c + c )
2
c c 1 2 − c1
− c
=
2 2c
2
2
1
2
2
Correlation coefficient, thus obtained, can be either positive or negative, thereby
enhancing (constructive interference) or diminishing (destructive interference) the total
concentration variance from the combined sources. Its value can be included between -1
(sources perfectly and negatively correlated) and 1 (sources perfectly and positively
correlated); when is equal to 0 the two sources are uncorrelated.
For the application of the interference method to the present case study, two identical
ground level (Z=10 mm) point sources, mounted to the single axis traverse, were used;
for each source the same emission strength Q, equal to 2.4 l/m with 2% of propane
concentration and internal stack diameter di equal to 8.5 mm, were adopted. The aim of
this choice was to have exactly the same release behaviour for both sources; this
condition was necessary to obtain reliable results. Preliminary tests conducted with two
slightly different stack diameters, as reported in Fackrell and Robins (1982), showed
that small differences between the initial source diameters can determine great
differences in the level of fluctuations that developed in the near field (about an order of
10 of magnitude) and heavily affected the results. For these reason great attention was
dedicated to the set up of the release system.
The procedure adopted in this series of experiments for the analysis of the correlation
coefficients on a receptor location can be summarized as follows. Firstly a reference
140

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
source location (source 1) was chosen and the corresponding concentration fluctuation
2
c 1 (obtained operating source 1 alone) was measured. Then, with one stack fixed in the
reference location, the other stack was moved incrementally outwards in order to
measure, separately, 2
2
c 1 + c2
(operating both
sources) and, consequently, evaluate the correlation for different distance between the
sources (separation) by means of the interference method; the separation to be
investigated was chosen case by case with the aim of covering all the range of
significant correlation values. Repeating this procedure for different positions of the
c (operating only source 2) and ( ) 2
reference source along the line emission, a data set of concentration fluctuations ( c ,
c and ( ) 2
c
2
2
c + ) and correlation values against source separations, essential for the
1
2
evaluation of the concentration fluctuations determined by a line source, was created. A
new code in Labview, that integrate the ‘virtual instrument’ software of the EnFlo
laboratory, was realized on purpose in order to automated the procedure for the
evaluation of the correlation coefficients, described previously, and to ensure the control
of the two sources involved in the experiments.
Preliminary tests were carried out to verify the method of measure (see also section
3.5.4). The tests showed that
2
c 1 ,
c and ( ) 2
c
2
2
c + sampling times equal to 12-minutes
were necessary to obtain reliable calculations of the correlation coefficients; this
measurement time was used during all the correlation experiments.
Two different investigations were carried out during this set of experiments; firstly a
preliminary work with the undisturbed boundary layer and then an extensive study with
the Dapple model. Only ground level receptor locations were analyzed in both
investigations.
The experiments with the undisturbed boundary layer (UBL) allowed to obtain a
reference case useful to better understand the results with the urban model; different
downstream distances (Y=270 mm, 540mm and 1080 mm) and ground level locations
(inline with the source, X=0mm or offset, X=100mm) of the receptors were analyzed.
For comparison purpose, the central downstream distance adopted in this phase (Y=540
mm) was the same of the street investigated in the following experiments with the
1
141
2
2
1

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
Dapple model. Only one reference source location (X=0mm, Y=0mm and Z=10 mm)
was used in this preliminary experiment.
The experiments with the Dapple model were carried out moving the two point sources
along Marylebone road by means of the 1D traverse mechanism. Only -90° model
orientation and receptor locations along Dorset Square/Melcombe Street (the unique
parallel street to Marylebone Road with this model orientation) were considered; in
particular, the receptor locations previously analyzed with UBL at the downstream
distance in model coordinate Y=540, plus the most relevant receptor sites along Dorset
Square/Melcombe Street determined from the moving source experiments (main and
secondary intersections, street canyon and square sites, see section 3.5.1), were studied.
Several reference source locations were examined in order to evaluate the influence of
the geometry of the local environment in the determination of the correlation between
concentration fluctuations from two point sources. Combinations of receptor and
reference source locations analyzed in this experiment were reported in figure 3-14.
Contrary to what we thought initially, the original objective (quantification of the
concentration fluctuations of a line source via mathematical integration of the
correlation coefficient determined by two point sources belonging to the line emission)
was impossible to achieve, because when the reference source moves to a position
where it has a weak signal in comparison with the other source the uncertainty in the
calculation of the correlation coefficient become too high (see section 3.5.4). Although
this difficulty, the methodology applied in this wind tunnel experiment allowed to
establish source-receptor relationships in terms of fluctuating concentrations for the
most critical emission point in a line source.
142

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
Figure 3-14 Combination of receptor and reference source locations in the
correlation experiment: main (top) and secondary intersection along Dorset Square -
Melcombe Road
143

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
3.5.3 Quality assurance of the wind tunnel data
Fixed and mobile source experiments
The repeatability of the mean concentration, C, and variance, c 2 , results was tested with
duplicate measurements from nominally identical but separately set up experiments.
Different sampling times (3 and 12 minutes) were also used to demonstrate that the
quality of the results with lower measurement time is satisfactory and longer sampling
time is not needed (figure 3-15). These measurements indicated that the repeatability of
the mean concentration and variance measurements are respectively within ±5% and
±15% for a sampling time of 3 minutes; so, this measurements time was considered
adequate for the purpose of the experimentation.
C*
c 2
*
0
-500 -400 -300 -200 -100 0 100
X(mm)
12 minutes 3 minutes
0
-500 -400 -300 -200 -100 0 100
X(mm)
12 minutes 3 minutes
Figure 3-15 Repeated mean concentration (top) and variance (bottom) using
different sampling time (arbitrary ±5% and ±15% error bars are shown for reference)
144
40
35
30
25
20
15
10
5
700
600
500
400
300
200
100

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
Correlation experiments
Several tests, aimed at defining the sampling time and the less time-consuming
procedure required to obtain reliable result, were carried out during this
experimentation.
Firstly, an error test was realized; the test was designed to investigate the repeatability
of correlation coefficient measurements and, in particular, the behaviour of the
correlation error to increasing sampling times. Remembering that the correlation in this
experiment was inferred using three independent variance measures, the correlation
error was evaluated as follows.
2 2 2
( c ( err)
) + c ( err)
( ) ( ( ) ) 2
2 2
+ c
cor err)
= 1
2
B
where
( err
2
c 1 (err),
experiments.
2
c 2 (err) and
2
c B (err) are the errors of
145
2
c 1 ,
2
c 2 and
2
c B , evaluated during the
Only one experimental configuration (one combination of receptor and reference source
location and a unique separation between the sources) was analyzed in this preliminary
test; receptor was placed at X=0mm, Y=540mm and Z=10mm, the location of the
reference source was X=0 mm, Y=0mm and Z=10mm and the two sources were kept at
a fixed separation of 25mm. The sampling times considered were 3, 6, 9, 12, 18, 24 and
30 minutes. The results of these tests are shown below.
Figure 3-16 Error test: correlation coefficient and correlation error for increasing
sampling time
The results clearly show a downward trend in the correlation error with increased flow
measurement time. The errors associated with 3 and 6 minutes measurement times were

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
too large and didn’t permit to obtain satisfactory repeatability of the correlation measure
(in both cases larger than ±30%). For example, for a measurements taken with three
minutes measurement time, the correlation result could lie anywhere between about 0.6
and 1.35 with an absolute error of about 0.3-0.4; the 6-minutes sampling time is
marginally better, but still not satisfactory. 12 minutes seems to be a good compromise
between accuracy and execution time; a longer sampling time doesn’t permit to improve
significantly the accuracy of the results. The need of a sampling time of 12 minutes was
confirmed by subsequent tests; in these tests a complete range of separation between the
sources and several combinations of receptor and reference source location were
analyzed using 3, 6 and 12 minutes measurement times. Some of the results are reported
in the figure 3-17.
Figure 3-17 Repeated correlation measurements (left) and relative errors (right)
using different sampling time: inline with source (top) and offset (bottom) receptor
locations
146

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
These tests showed that a 12 minutes sampling time gave significant improvements in
comparison with a three minutes measurement time, especially when the reference
source was not inline with the receptor and its signal was weaker than the moving
source. A fundamental aspect highlighted by these tests was that the correlation error
turn out to be strongly influenced by the position of reference source; when the
reference source signal on the receptor was at least an order of 10 smaller than the other
source, percentage error in the calculation of the correlation coefficient became
unacceptable (larger than 50 % percent). So a careful control of the correlation error
was planned during the whole experimentation in order to obtain results characterized
by a repeatability within ±30%; this level of uncertainty, although rather high, was
considered acceptable in what was an exploratory study using a new experimental
technique and did not affect the nature of the overall conclusions.
After the preliminary investigations done with the UBL, a test aimed at reducing the
execution time was carried out during the initial phase of the experiments with the
Dapple model. Initially, the measures of
2
c 1 ,
147
c and ( ) 2
c
2
2
c + were taken sequentially
for every separation between the stacks; this implied a correlation measurement time for
each point equal to 36 minutes, too long to obtain an extensive analysis of the urban
model. The aim of this procedure was to evaluate and, when needed, take into account
the possible interference between the two stacks on the measure of the variances. As
expected and required for the correlation calculation, variance measurements of the
reference source, c1 2 , carried out in this first phase, showed generally constant value
throughout each of the separation; an interference between the two stacks can be seen
only at extremely close separation (about 20 mm), where it is known that the correlation
coefficient is approximately one. So, the correlation calculation was not significantly
affected by the relative position of the sources and, consequently, a reduction of the
experimental time was possible measuring the values of
1
2
c 1 ,
2
c and ( ) 2
c
2
2
c + by means
of separated experiments and then inferred the correlation coefficients. This new modus
operandi permitted to halve, and even more, the execution times. The reliability of the
new approach was confirmed by comparison of correlation profiles measured with the
two different procedures (see figure 3-18).
1
2

Chapter 3 Neighbourood/street scale modelling by means of wind tunnel methods
Correlation
-450 -350 -250 -150 -50
-0.2
50 150
Separation (mm)
148
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Procedure 1 Procedure 2
Figure 3-18 Repeated correlation measurements and relative errors using different
measuring procedure: sequential measures (procedure 1) and separated experiments
(procedure 2) for c1 2 , c2 2 and cB 2

Chapter 4
4.City scale modelling by means of mathematical methods
4.1 Introduction
In the last years, modelling pollutant dispersion at the urban scale assumed an
increasing importance in the air quality management process; one of the main interests
of urban scale modeling is to be able to assess responsibility for existing air pollution
levels in urban areas and to evaluate the consequences of long-term policies like
regional, town and transport planning. Physical modelling in wind tunnel and field
experiments at the city scale is not a practicable method to analyze these problems;
urban areas are too large to be modeled entirely in laboratory with the usual scaling
factors or by means of field experiments. For these reasons mathematical models have
been applied in this thesis for the simulation of pollutant dispersion at the city/regional
scale.
In this chapter, we begin by discussing the theoretical basis of mathematical dispersion
modeling and describing in detail the mathematical techniques and codes involved in
this research study (section 4.2); we then present the strategy (4.3) and the phases of the
research study (modelling applications, section 4.4, model evaluation, section 4.5, and
scenario analysis, section 4.6) carried out in this thesis.
4.2 Background on mathematical dispersion modelling
4.2.1 The theoretical basis of mathematical dispersion modelling
The fundamental problem in dispersion modelling is to calculate the instantaneous
concentration field of a pollutant c(x, y, z, t), introduced into a domain at a mass per
unit time rate Q, given a particular set of boundary conditions for the domain.
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Chapter 4 City scale modelling by means of mathematical methods
Any dispersion modelling calculation is based on an appropriate expression of pollutant
mass conservation in the domain. Using an Eulerian approach (see section 2.4.6) and
considering pollutant mass conservation applied to a control volume, as follows.
∂c
+ U
∂t
i
2
∂c
∂ c
= −α
+ Q
∂x
∂x
∂x
i
i
i
where Ui is the wind velocity through the control volume, Q refers to sources of
pollutants with the control volume, and α is the molecular diffusivity of the pollutant.
This equation together with the Navier-Stokes equations of flow provides the
mathematical basis of dispersion modelling. A pollutant release is considered to be
‘passive’ if introducing the pollutant does not affect the density of the fluid in which it
disperses (i.e. for sufficiently low values of pollutant concentration) and provided that
the pollutant is introduced without initial excess momentum. If this is not the case, the
Navier-Stokes equations have to be modified to account for the effect of the pollutant
release on the flow field.
An important property of equation 4-1 is that is it linear in c, unlike the equations of
flow, which are non-linear with respect to velocity. As a result, the concentration field
due to multiple sources is equal to the linear superposition (addition) of the
concentration fields due to each source separately.
Applying Reynolds averaging (averaging of flow quantities done using decomposition
of a flow variable (e.g. velocity (u) into a mean (U) and turbulent component (u′)) on
equation 4-1, the expression for the time averaged value of concentration C is given by:
2
∂C
∂C
∂ C ∂
+ U i = −α
−
∂t
∂x
∂x
∂x
∂x
i
i
i
( u′
c′
i ) + Q
(I) (II) (III)
i
Dispersion involves pollutant transport, due to ‘advection’ and ‘diffusion’. Advection is
the transport of the pollutants by the mean wind velocity (represented by term I of
Equation 4-2). Diffusion allows pollutant spread in additional directions to that of the
mean wind velocity; it is due to turbulent mass transfer (term III), and to molecular
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4-1
4-2

Chapter 4 City scale modelling by means of mathematical methods
processes (term II), although the later becomes relatively insignificant in high Re
number flows.
Dispersion is also a process of pollutant chemical and physical transformation.
Pollutants may interact with each other chemically, or in the case of particulate
pollution, also physically (e.g. particle coagulation and ground deposition). These
effects can be introduced as additional terms in Equation 4-2.
Terms of the form u ′ i c′
can be approximated by a relation to the mean concentration
gradient using the eddy diffusivity concept proposed by Boussinesq:
2
∂(
u′
ic′
) ∂ C
= K i
∂x
∂x
∂x
i
i
i
where Ki is the ‘eddy diffusivity’ in the i-direction. This approximation is referred to as
the ‘K-theory’ of turbulent diffusion
Substituting from 4-3, and neglecting the effect of molecular diffusion, Equation 4-2
can thus be re-written as:
∂C
+ U
∂t
i
2
∂C
∂ C
= −K
i + Q
∂x
∂x
∂x
i
i
i
often referred to as the ‘advection-diffusion’ equation.
K-theory provides a simple method of turbulence closure for diffusion, and is thus very
popular and widely used in the dispersion field. However, it has several limitations.
Only the scales of turbulent motion (eddies) that are smaller than the diffusing puff or
plume can be considered. Also, unlike α, Ki is not a property of the fluid, but varies in
time and space, depending on the type of flow, and thus there is no generally applicable
method of specifying eddy diffusivities.
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4-3
4-4

Chapter 4 City scale modelling by means of mathematical methods
Equation 4-4 can be solved analytically for simple boundary conditions. For example,
an approximate solution for an isolated, continuous point source in an unbounded
uniform flow field of velocity U, ignoring along-wind diffusion, is:
C(
x,
y,
z)
⎡ 2 ⎤
2
Q y ⎡ z ⎤
exp⎢−
⎥ exp⎢−
2
⎥
2πUσ
yσ
z ⎢⎣
2σ
y ⎥⎦
⎣ 2σ
z ⎦
= 2
This solution indicates that the pollutant spreads in the y and z directions as a ‘normal’
or ‘Gaussian’ distribution, with a standard deviation σy and σz in each direction. Both σy
and σz are related to Ky and Kz of Equation 4-3 (by σi 2 = 2Kix/U), however σy and σz are
in practice calculated using empirical relations, as a function of the distance x from the
source, and atmospheric stability conditions (e.g. as reviewed in Arya, 1999). On the
basis of Equation 4-5, other solutions can be obtained.
4.2.2 Gaussian model
Gaussian models are based on equation 4-5. The basic stationary Gaussian plume model
is the simplest air quality model, and has been applied for several decades. However,
given its simplicity, some considerations about the applicability must be made. In
particular, such models are valid when:
• The terrain in the spatial domain is approximately flat and the land use does not
change within it.
• The considered point source is sufficiently elevated.
• Meteorological conditions do not vary too much, both in space and time.
• The boundary-layer conditions are far from the convective case.
These conditions are quite restrictive and the applicability of simple Gaussian model
results very limited. However, Gaussian models have been widely applied, despite these
limitations. In fact, the basic formulation can be improved and more complex cases can
be investigated by means of such models.
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4-5

Chapter 4 City scale modelling by means of mathematical methods
A first improvement can be made considering the interaction between plume and ground
surface. Starting from eq. 4-5 and changing coordinate system, as described in figure 4-
1, it can be written:
C(
x,
y,
z)
( z h )
⎡ 2
Q y ⎤ ⎡ − s
exp⎢−
⎥ exp⎢−
2
2πUσ
yσ
z ⎢⎣
2σ
y ⎥⎦
⎢⎣
2σ
z
= 2
where hs is the source height.
-
Figure 4-1 Coordinate system for the Gaussian plume model
As stated above, this description does not take into account for the interaction with the
terrain. Assuming a perfectly reflecting surface as the most conservative boundary
condition, addition of an ‘image’ source (see figure 4-2) yields the following reflected-
Gaussian formula:
C(
x,
y,
z)
⎡ 2
Q y ⎤⎪⎧
⎡ − s
exp⎢−
⎥⎨exp⎢−
2
2
2πUσ
yσ
z ⎢⎣
2σ
y ⎥⎦
⎪⎩ ⎢⎣
2σ
z
153
2
⎤
⎥
⎦
2 ( z h ) ⎤ ⎡ ( z + h )
2
s
= 2
2σ
z
⎥ + exp⎢−
⎥⎦
⎢⎣
Equation 4-7 can be further improved by considering multiple reflexions on the ground
and the PBL top (see figure 4.2).
⎤⎪⎫
⎥⎬
⎥⎦
⎪⎭
4-6
4-7

Chapter 4 City scale modelling by means of mathematical methods
Figure 4-2 Gaussian plume with reflexion and virtual source: single (left) and
multiple reflexions (right)
Other modifications to the classical Gaussian formulation can be done by considering
line, area or volume sources. For example, considering and infinite line source along the
y axis, with strength q (g/m/s), an integration can be made, and equation 4-5 can be
written as:
C(
x,
y,
z)
2
q ⎡ z ⎤
exp⎢−
⎥
2πUσ
⎣ 2σ
z
z ⎦
= 2
Expressions similar to 4-6 and 4-7 for elevated line sources and with reflexion can be
derived. Concentration fields resulting from more realistic finite line and area (obtained
by further integration) sources can be derived through numerical integration of the point
source formula along the line segment or the source area of interest, using the principle
of superposition (see, e.g., Arya, 1999).
A big limitation of these simple Gaussian models is the stationarity of the
meteorological field. Usually, Gaussian models calculate the concentration field
separately for each meteorological step (generally 1 hour), in which meteorological
conditions are assumed to be constant. This type of models is ‘without memory’, that is
meteorological fields are calculated independently and are not affected by conditions in
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4-8

Chapter 4 City scale modelling by means of mathematical methods
the previous hours (or different time steps). This is not very realistic, and several
modifications have been proposed for solving this problem.
The first solution is the Gaussian segmented-plume formulation. In this approach, the
plume is broken up into independent elements (plume segments or sections) whose
initial features and time dynamics are a function of time-varying emission conditions
and the local time-varying meteorological conditions encountered by the plume
elements along their motion. The segmented plume features are illustrated in figure 4.3,
which shows a plan view (solid lines) of a segmented plume encountering a progressive
change of wind direction along its trajectory. Segments are sections of a Gaussian
plume. Each segment generates a concentration field that is still basically computed by
equation (4.5) and that represents the contribution of the entire virtual plume passing
through that segment, as figure 4.3 illustrates.
Figure 4-3 The segmented plume approach (Zannetti, 1990)
Alternative to the Gaussian plume and segmented-plume formulations is the Gaussian
puff formulation. The technique is quite different, because the appoach is Lagrangian
rather than Eulerian. Emissions are seen as discrete puffs, released at fixed intervals
(time steps). They are fully independent and are advected according the local
meteorological field (which can vary in space and time). The dispersion process is
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Chapter 4 City scale modelling by means of mathematical methods
assumed to be Gaussian and is treated separately from the advection. Thus, for each
puff, assuming the origin in its centre, it can be written:
C(
x,
y,
z)
Q
⎛
( ) ⎟ exp⎜−
− −
3 2
⎜ 2 2
2π
σ σ σ 2σ
2σ
2σ
= 2
x y z
x y z
⎝
x
2
y
in which the time-dependent sigma functions σx, σy, and σz (or puff-diffusion
parameters) are, in general, different from the plume-dispersion parameters. The
concentration field is then calculated by superposition, considering all the puffs emitted
in the domain.
The accuracy of the Gaussian models, as any other parametric model, depends strongly
on the (semi-)empirical parameters involveld (sigma-functions). First generation
Gaussian models used parameterisations of σy, and σz as function of the downwind
distance (x) and the Pasquill stability classes. The most used parameterisations of this
type are: the Pasquill-Gifford (P-G) scheme (Pasquill, 1961; Gifford, 1961); the
Brookhaven National Laboratory (BNL) scheme (Islitzer and Slade, 1968); the Briggs’
interpolation formulas (Briggs, 1973), also accounting for the urban case. A description
of these schemes can be found in Arya (1999).
Experimental efforts directed towards estimation of puff-diffusion parameters from
instantaneous or quasi-instantaneous releases have been far fewer than those for
estimating plume-dispersion parameters. These have been reviewed by Islitzer and
Slade (1968), Pasquill and Smith (1983), and Draxler (1984).
More complex models are the so-called second generation Gaussian models. They have
been developed in the last two decades, and imply more complex parameterisations (and
indeed rely on more complex meteorological preprocessors capable of calculating the
necessary variables). The new parameterisations are based on micrometeorological
similarity variables such as the friction velocity and the Monin-Obukhov length, rather
than the Pasquill stability classes. The second generation models often abandoned the
Gaussian description of the dispersion in the vertical direction for more realistic
schemes.
2
156
z
2
⎞
⎠
4-9

Chapter 4 City scale modelling by means of mathematical methods
The basic Gaussian formulations described above can be further enhanced to account
for other phenomena, such as: plume rise (buoyant releases or jet releases), building
downwash (entrainment in the wake of an obstacle), settling, dry and wet deposition,
chemical transformations, and complex terrain.
Detailed descriptions of the Gaussian models used during this thesis will be presented in
the following paragraphs.
SAFE AIR
The SAFE AIR II model (Simulation of Air pollution From Emissions Above
Inhomogeneous Regions, Version II) has been implemented at the Department of
Physics of the University of Genova (Italy); it simulates the transport and diffusion of
airborne pollutants above complex terrain at local and regional scale (Canepa et al.,
2003). SAFE AIR II is included in the Model Database of the European Topic Centre on
Air Quality of the European Environment Agency (EEA, 2005) and a previous version
of the model has been selected by the Italian Agency for Environmental Protection and
Technical Services (APAT) to be inserted in their list of air pollution models to be used
in air quality evaluation.
SAFE AIR II consists mainly of three parts: two linked meteorological pre-processors -
WINDS (Wind-field Interpolation by Non Divergent Schemes, Release 4.2) and ABLE
(Acquisition of Boundary Layer parameters, Release 1.2) - and a model which simulates
the airborne pollutant transport and diffusion (P6, Program Plotting Paths of Pollutant
Puffs and Plumes, release 2.1).
WINDS (Georgieva et al., 2003a) is a mass-consistent model developed at the
Department of Physics of the University of Genova (Italy). WINDS builds a threedimensional
wind field by the following two steps: first, an initial wind field is
constructed, through an interpolation procedure, starting from available wind data at
given points; then, an adjustment step, based on the variational approach (Sasaki, 1970).
WINDS can use different initialisation schemes: ground station data and/or geostrophic
wind, observed vertical profiles (SODAR, etc), profiles coming from larger scale
meteorological models, etc. WINDS is written in conformal coordinates, which are
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Chapter 4 City scale modelling by means of mathematical methods
terrain-following just above the terrain and usually flat at the top of the simulation
domain. Conformal coordinates have several advantages with respect to Cartesian
coordinates: the terrain surface is more accurately represented and, consequently,
simpler boundary conditions can be used which allow a higher resolution near the
terrain surface. WINDS considers the following phenomena: effects due to complex
orography, roughness effect, roughness change, variations of the wind direction due to
the Coriolis force, effects due to the atmospheric stability, etc. In neutral and stable
atmosphere, WINDS uses the formula of the wind profile proposed by Zilitinkevich
(1989).
The ABLE model (Georgieva et al., 2003b) calculates the horizontal distribution of
relevant boundary layer parameters like mixing height, Monin-Obukhov length, friction
velocity, convective velocity scale, starting from routinely measured meteorological
variables. ABLE is based on the energy balance method to determine the sensible heat
flux. In diurnal conditions, the scheme by Holtslag and Van Ulden (1983) and Van
Ulden and Holtslag (1985) is used. To take into account for the topographic effects on
the amount of incoming solar radiation a slope correction factor is included in the
algorithm. In nocturnal conditions, a semi-empirical approach is adopted as in the recent
versions of the CALMET (Scire et al., 2000a) model. Using correction factors
depending on the surface roughness the sensible heat flux reference value is extended to
each grid point. The calculation of the sensible heat flux is strictly connected with the
calculation of the friction velocity and the Monin-Obukhov length, which, in turn, are
relevant as far as the mixing height determination is concerned. After calculating the
cited parameters in the entire simulation domain, the mixing height can be computed as
a 2D field using different formulae for stable (night-time) and convective (day-time)
conditions over land, while a different procedure is adopted over sea. Day-time is
defined by an upward (positive) sensible heat flux, night-time by a downward (negative)
one. The pre-processor uses a slab model for the growth of the mixing height during
day-time conditions as proposed by Batchvarova and Gryning (1991). For the
parameterization in night-time conditions the user can select between different
diagnostic formulations, among them the Nieuwstadt (1981) formula based on friction
velocity and Monin-Obukhov stability parameter, and the Venkatram (1980) expression
158

Chapter 4 City scale modelling by means of mathematical methods
based only on the friction velocity. Having already estimated the sensible heat flux and
the mixing height the convective velocity scale is calculated following Stull (1988).
P6 (Canepa and Ratto, 2003a) is a multi-source model mainly designed to simulate the
air quality impact at local and regional scales from point sources; however this model
can also be used for line, area, and volume sources. P6 is a Lagrangian model based on
the basic Gaussian formula. The emitted pollutant is divided into a sequence of
‘elements’, either plume segments or puffs, which are connected together, but whose
dynamics is a function of local meteorological conditions (see figure 4-4). Therefore P6,
while maintains the simplicity of the Gaussian formula, allows to perform numerical
simulation of both non-stationary and inhomogeneous situations (e.g. dispersion above
complex terrain). Plume segments provide a numerically fast simulation of the
dispersion of air pollutants near their source, during transport conditions. Puffs allow a
proper simulation of diffusion, both far from the source and during calm or low-wind
situations. Note that the type of element (plume segment or puff) does not affect its
dynamics, but only the computation of the concentration field. Thus, the dynamics of
elements can be described independently of the type and consists of: 1) Generation at
the source; 2) Plume rise; 3) Advection, i.e. transport by the advective wind; 4)
Diffusion by atmospheric turbulence; 5) Possible chemical transformation (creating
secondary pollutant from a fraction of the primary pollutant); 6) Possible ground
deposition (dry and wet); 7) Possible gravitational settling of coarse particles.
Figure 4-4 Segmented plume or puff representation in SAFE AIR
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Chapter 4 City scale modelling by means of mathematical methods
The Release 2.1 of P6, included in SAFE AIR II, incorporates advances concerning both
diffusion (item 4) and ground deposition (item 6). Furthermore both dynamical
allocation of variables and additional statistical output (calculation of average
concentration every 15 and 30 minutes, beside every 60 minutes) have been included.
CALPUFF
The CALPUFF dispersion model (Scire et al., 2000b) and related models (included the
meteorological pre-processor CALMET) were developed by Sigma Research
Corporation (now part of Earth Tech, Inc.); it is one of the recommended models by the
U.S.EPA for regulatory applications (USEPA, 2005a). Originally, the development of
the model was supported by the California Air Resources Board (CARB). The
following specifications were taken into account for the development of the modelling
system:
• Possibility of treating area and point sources with time-varying emissions.
• Applicability of the model within spatial domains ranging from tens of metres to
hundreds of kilometres from the source.
• Long term simulations (1 year averages).
• Applicability to either inert or chemically active pollutants.
• Applicability in either flat or complex terrain.
The modelling system includes three main components (CALMET, CALPUFF and
CALPOST) and several utilities and pre-processors for the treatment of standard terrain
and meteorological data. CALMET is a meteorological pre-processor capable of
estimating three-dimensional fields of wind and temperature as well as two-dimensional
parameters such as mixing height, Monin-Obukhov length, friction velocity and other
surface micrometeorological variables. CALPUFF is a second generation Gaussian puff
dispersion model, parameterising the turbulence characteristics using the parameters
estimated by CALMET as input. CALPOST is a postprocessing model for the
elaboration of the CALPUFF output.
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Chapter 4 City scale modelling by means of mathematical methods
The CALMET meteorological model consists of a diagnostic wind field module and
micrometeorological modules for overwater and overland boundary layers. When using
large domains, the user has the option to adjust input winds to a Lambert Conformal
Projection coordinate system to account for Earth's curvature. The diagnostic wind field
module uses a two step approach to the computation of the wind fields. In the first step,
an initial-guess wind field is adjusted for kinematic effects of terrain, slope flows, and
terrain blocking effects to produce a Step 1 wind field. The second step consists of an
objective analysis procedure to introduce observational data into the Step 1 wind field to
produce a final wind field. An option is provided to allow gridded prognostic wind
fields to be used by CALMET, which may better represent regional flows and certain
aspects of sea breeze circulations and slope/valley circulations.
The dispersion model (CALPUFF) is a multi-layer, multi-pollutant non-steady-state
puff dispersion model, capable of simulating time- and space-varying meteorological
conditions and emissions and different type of emission source (point, line, area and
volume source). CALPUFF contains algorithms for near-source effects such as
transitional plume rise, stack-tip downwash, building downwash, partial plume
penetration in the inversion layer, subgrid scale terrain interactions as well as pollutant
removal (dry deposition and wet scavenging), chemical transformations, vertical wind
shear, overwater transport and sea-shore interaction effects. The complex terrain module
implemented in CALPUFF is based on the same approach as the Complex Terrain
Dispersion Model, CTDMPLUS (Perry et al., 1989).
CALINE 4
CALINE4 is a model suitable for air pollution modelling of pollutant emissions from
line sources (Benson, 1989). The CALINE model has been developed by the California
Department of Transport (Caltrans) since the ’70s, and the latest version is CALINE4. It
is based on the first generation Gaussian model, and uses the ‘mixing zone’ concept for
the early stages of the diffusion process within streets. The purpose of the model is to
assess air quality impacts near transportation facilities; it is the recommended models by
the U.S.EPA for regulatory applications connected with line emission (USEPA, 2005a).
Four types of pollutants can be simulated: CO, NO2, inert gases and particulate matter.
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Chapter 4 City scale modelling by means of mathematical methods
CALINE4 is capable of estimating concentrations at up to 20 receptors per run. The
input parameters are very limited, and this keeps the model very simple and
straightforward for any user. The model simulates line sources by dividing them in
smaller line sources with axis perpendicular to the wind direction. The length of these
equivalent line sources is determined by the street orientation with respect to the wind
direction and the receptor location (see figure 4-5).
Concentration at a given receptor is calculated by superposition of all the contributions
from the equivalent line sources, according to the Gaussian formula for finite line
sources. The Gaussian parameter σz is calculated accounting for thermal produced,
traffic produced and wind produced turbulence. The first two factors are the most
significant in the mixing zone (figure 4-5), while the latter prevails further away
(Benson, 1982). The method developed by Draxler (1976) is used for the calculation of
σy based on the standard deviation of the wind direction, σθ.
Figure 4-5 Element series represented by series of equivalent finite line sources
(left) and mixing zone (right) of CALINE4
Up to 20 line sources (links) can be treated simultaneously. The input data to be
provided to the model include: meteorological data, source data (streets geometry and
emission factors), other parameters (terrain data, receptor location, pollutant, etc).
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Chapter 4 City scale modelling by means of mathematical methods
ADMS-URBAN
ADMS-Urban pollution model is a comprehensive tool for tackling air pollution
problems in cities and towns; it is a model of dispersion in the atmosphere of pollutants
released from industrial, domestic and road traffic in urban areas. ADMS-Urban models
these using point, line, area, volume and grid source. It is designed to allow
consideration of dispersion problems ranging from the simplest (e.g. a single isolated
point source or a single road) to the most complex urban problems; it can be used to
examine emissions from 6000 sources simultaneously, including: road traffic (over
70000 roads links - 1500 road sources each with up to 50 vertices); industrial sources
(up to 1500 point, line, area or volume sources); aggregated sources (grid source - up to
3000 grid cells can be used to model emissions from sources that are too small to define
explicitly, for example, emissions from domestic housing). ADMS is used by over 70
UK local authorities for air quality review and assessment. The diagram in figure 4-6
shows some of the possible inputs to and outputs from the model, and some of the
modelling options available.
Figure 4-6 Schematic of ADMS-Urban input and output
ADMS-Urban is based on the dispersion model ADMS. Both models use a
parameterisation of the boundary layer physics in terms of boundary layer depth and
Monin-Obukhov length and use a skewed-Gaussian concentration profile in convective
meteorological conditions.
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Chapter 4 City scale modelling by means of mathematical methods
A variety of meteorological data can be used for input and the format required is very
simple. Wind speed, wind direction and temperature are required along with cloud
cover, heat flux or solar radiation. The meteorological pre-processor Flowstar calculates
the necessary boundary layer parameters from the user’s input and allow to obtain
realistic calculation of flow and dispersion over complex terrain and around buildings.
ADMS-Urban has the following additional features, which make it suitable for
modelling an urban environment:
• Traffic sources are entered in terms of number, type (cars or lorries), and speed
of vehicles.
• An integrated street canyon model based on the OSPM model (Berkowicz et al.,
1997).
• Chemical reactions involving NOx, ozone and VOCs based on the GRS (Generic
Reaction Set) scheme.
• Link to an emissions inventory database.
The model is most often used in conjunction with a GIS (Geographical Information
System) which allows rapid entry of data and easy analysis of results.
Source parameters used by the model include: source location data; road widths and
canyon heights for road sources; stack heights, diameters, exit velocities etc for
industrial sources; and, grid dimensions for aggregated emissions data. Once source
data have been loaded into the model users can use the GIS link to view the sources as
the part of the input data validation process. The point and road sources are being
modelled explicitly with all other emissions aggregated onto grid sources.
Up to 20 diurnal profiles can be included in any modelling run to take into account the
diurnal variation in traffic flows. Seasonal variations can also be included with up to 20
monthly profiles. Variation of sources with wind direction particularly useful for airport
sources can also be modelled.
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Chapter 4 City scale modelling by means of mathematical methods
For road sources, the user can enter hourly speed and traffic flow data into the model
and use ADMS-Urban’s built-in emission factors or, alternatively, the user can enter
pre-calculated emissions data. Both the effect of street canyons, and traffic-induced
turbulence are included when roads are modeled in ADMS-Urban.
In urban areas, it is also important to include the aggregated emissions from sources that
may be too small to define explicitly, but whose aggregate emissions contribute to
overall pollution levels. For example, domestic emissions of NOx from an individual
household may not be known, but the aggregated emissions could be calculated using
area-wide figures for fuel consumption. In ADMS-Urban, a grid source with up to 3000
grid cells can be included in any run to represent these aggregated emissions.
The chemistry scheme is a semi-empirical photochemical model that takes NO, NO2,
rural ozone and one ‘representative’ species of hydrocarbon with one rate constant as an
input. In ADMS-Urban the scheme is used in two ways. Firstly, background ozone
levels are calculated from a box model and then that background level is used to
calculate receptor-specific concentrations using a back trajectory model based on the
average age of pollutants at each receptor.
4.2.3 Eulerian grid models
Eulerian grid models are based on the Eulerian description of turbulent dispersion. In
particular, the fundamental equation of such models is eq. 4-2, usually replaced by eq.
4-4 using the K-theory of turbulent diffusion. Concentrations are then calculated by
numerically solving the equation set. Meteorological fields are usually provided by
Eulerian meteorological preprocessors.
The numerical calculation is performed in a closed volume, or calculation domain,
giving appropriate boundary conditions. Generally this calculation is performed by
dividing the domain in sub-domains (or cells) applying a 3-dimensional grid. Equation
4-4 is then applied to each cell, and the boundary conditions are given by the
surrounding cells.
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Chapter 4 City scale modelling by means of mathematical methods
The lower bound of the domain is the ground surface, and generally is not flat. For this
reason, the Cartesian coordinate system is not the best in order to describe the spatial
domain. More frequently, terrain-following coordinates are used, and the threedimensional
grid is consequently adapted.
Integration techniques used to solve the governing equations include (Zannetti, 1990):
finite difference methods, finite elements methods, spectral methods, boundary element
methods, particle methods. Finite difference methods (Richtmyer and Morton, 1967) are
the oldest technique. Although they possess several disadvantages, they still represent
the major and most applied (and best understood) numerical tool for this type of
applications.
Detailed descriptions of the Eulerian grid models used during this thesis will be
presented here.
CALGRID
CALGRID (Scire et al., 1989) is an advanced Eulerian photochemical grid model
developed and distributed by Earth Tech, Inc. that includes modules for horizontal and
vertical advection/diffusion, dry deposition and a detailed photochemical mechanism.
This model can use the three dimensional meteorlogical fields developer by CALMET,
previously presented in the CALPUFF description.
CALGRID was designed to correct errors in and limitations of the UAM-IV (Urban
Airshed Model-IV). Specifically, it contains state-of-the-science improvements
including:
• A horizontal advection scheme based on spectrally-constrained cubics
(Yamartino, 1993) that conserves mass exactly, prohibits negative
concentrations, and exhibits a level of numerical diffusion that is intermediate
between class E and F (PGT class) dispersion.
• A vertical transport and diffusion scheme that incorporates recent boundary
layer formulations
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Chapter 4 City scale modelling by means of mathematical methods
• A choice of several vertical level spacing schemes including dynamic,
semilogarithmic and arbitrary level spacing. These schemes account for all
vertical flux components with moving or stationary levels without use of
spurious uz components.
• A full resistance based model for the computation of dry deposition rates as a
function of geophysical parameters, meteorological conditions and pollutant
characteristics.
• A chemical integration solver based on an adaptive time-step implementation of
the quasi-steady-state method of Hesstvedt et al. (1978) and Lamb (1983). This
solver can efficiently and accurately handle the stiffest of modern schemes.
• Incorporation of more modern photochemical reaction schemes, such as the
SAPRC mechanisms, that can more accurately address the roles of biogenic and
intermediate chemical products formed over multiday episodes. In the current
version of the model, the user can select between the SAPRC-90 and CB-IV
chemical mechanisms.
• New, structured ANSI 77 FORTRAN code that is highly modular, machine
independent and designed to facilitate a high degree of vectorisation.
CALGRID essentially utilises the same dynamical equations as UAM-IV with the
important exception that density variation with height is accounted for in order to ensure
exact mass conservation during all of the transport and diffusion operations. Other
improvements over UAM include use of operator reversal to ensure second-order
temporal accuracy; use of three-dimensional space and time varying values of
meteorological parameters such as photolysis coefficients, humidity, diffusivity,
pressure, and temperature; and on-the-fly computation of plume rise and entrainment for
more precise vertical distribution of plume material.
CALGRID (Version 1.8) has also been upgraded for regional and multi-day applications
(Yamartino et al., 1996) through: estimation of photolysis rates at each grid cell to
correct for variation in solar angle across the modelling domain; ability to include
photolysis attenuation due to clouds, columnar haze, and ozone via input of hourly 3D,
UV extinction field; inclusion of map factors to account for wind rotations from true
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Chapter 4 City scale modelling by means of mathematical methods
north (e.g.,, Lambert conformal projections); 1-way nesting to smaller sub-domains;
and, finer time resolution of meteorology.
CAMx
The Comprehensive Air quality Model with extensions (CAMx) is an Eulerian
photochemical dispersion model that allows for an integrated “one-atmosphere”
assessment of gaseous and particulate air pollution (ozone, PM2.5, PM10, air toxics,
mercury) over many scales ranging from sub-urban to continental. It is designed to
unify all of the technical features required of “state-of-the-science” air quality models
into a single system that is computationally efficient and publicly available. The model
code has a highly modular and well documented structure which eases the insertion of
new or alternate algorithms and features. The input/output file formats are based on the
Urban Airshed Model and are compatible with many existing pre- and post-processing
tools.
CAMx simulates the emission, dispersion, chemical reaction, and removal of pollutants
in the troposphere by solving the pollutant continuity equation for each chemical species
on a system of nested three-dimensional grids. The Eulerian continuity equation
describes the time dependency of the average species concentration within each grid cell
volume as a sum of all of the physical and chemical processes operating on that volume.
Chemistry is treated by simultaneously solving a set of reaction equations defined from
specific chemical mechanisms. Pollutant removal includes both dry surface uptake
(deposition) and wet scavenging by precipitation.
CAMx can perform simulations on three types of Cartesian map projections: Universal
Transverse Mercator, Rotated Polar Stereographic, and Lambert Conic Conformal.
CAMx also offers the option of operating on a curvi-linear geodetic latitude/longitude
grid system as well. Furthermore, the vertical grid structure is defined externally, so
layer interface heights may be specified as any arbitrary function of space and/or time.
This flexibility in defining the horizontal and vertical grid structures allows CAMx to be
configured to match the grid of any meteorological model that is used to provide
environmental input fields.
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Chapter 4 City scale modelling by means of mathematical methods
In addition to the attributes it shares with most photochemical grid models, some of the
most notable features of CAMx are:
• Two-Way Nested Grid Structure: This feature allows CAMx to be run with
coarse grid spacing over a wide regional domain in which high spatial resolution
is not particularly needed, while within the same run, applying fine grid nests in
specific areas where high resolution is needed.
• Multiple Photochemical and Gas Phase Chemistry Mechanism Options: Users
can select among three versions of Carbon Bond IV (CB4) chemistry, or the
1999 version of SAPRC chemistry (CAMx Mechanism 5). SAPRC99 chemistry
was added as an alternate mechanism because it is chemically up-to-date, has
been tested extensively against environmental chamber data, and uses a different
approach for VOC lumping than the CB4 mechanism.
• Treatment of Particulate Matter: CAMx features a “one-atmosphere” treatment
for ozone and particulate matter (PM) with detailed algorithms for the relevant
science processes, including aqueous chemistry (RADM-AQ), inorganic aerosol
thermodynamics/partitioning (ISORROPIA), and secondary organic aerosol
formation/partitioning (SOAP). The particulate chemistry mechanism utilizes
products from the gas-phase photochemistry for production of sulfate, nitrate,
condensible organic gases, and chloride. Currently, the one-atmosphere
ozone/PM treatment is linked to CB4 gas-phase chemistry via a mechanism,
which incorporates seventeen extra inorganic gas-phase reactions that are
appropriate for any condensed chemical mechanism being used for regional
ozone and regional/annual PM, visibility, mercury, and toxics modeling. CAMx
provides two options for the representation of the particle size distribution: a
static two-mode coarse/fine (CF) scheme, and the multisectional CMU scheme,
which models the size evolution of each aerosol constituent among a number of
fixed size sections.
• Plume-in-Grid (PiG) Module: CAMx features a PiG sub-model to treat the
chemistry and dispersion of point source emission plumes at sub-grid scales;
individual plume segments are tracked by the Lagrangian module while
undergoing dispersion and chemical evolution, until such time as their pollutant
mass should be represented within the grid model framework.
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Chapter 4 City scale modelling by means of mathematical methods
• Chemical Kinetics Solver Options: ENVIRON has developed a fast and highly
efficient chemistry solver (referred to as the CMC solver) that is based on an
“adaptive-hybrid” approach; relative to the standard chemistry solvers for the
CB4 mechanism, this approach results in about a ten-fold speedup in the
chemistry solution and an overall model speedup by a factor of 3 to 4.
Alternatively, users may select the Implicit-Explicit Hybrid (IEH) chemical
solver of Sun, Chock and Winkler (1994).
• Horizontal Advection Solver Options: The Area Preserving Flux-Form
advection solver of Bott (1989) and the Piecewise Parabolic Method (PPM) of
Colella and Woodward (1984) area available in CAMx. These schemes possess
high-order accuracy, little numerical diffusion, and are sufficiently quick for
applications on very large grids. Either of these solvers may be selected via the
CAMx run control file.
• Advanced Photolysis Model: The TUV radiative transfer and photolysis model,
developed at the National Center of Atmospheric Research (NCAR), is used as a
CAMx preprocessor to provide the air quality model with a multi-dimensional
lookup table of photolytic rates by surface albedo, total ozone column, haze
turbidity, altitude, and zenith angle.
• Ozone and Particulate Source Apportionment Technology (OSAT and PSAT):
The OSAT and PSAT allow CAMx to track source region and/or source
category contributions to predicted grid cell ozone and particulate concentration.
The input data to be provided to the model include: meteorology data supplied by a
meteorological model (3-Dimensional Gridded Fields of Horizontal Wind Components,
Temperature, Pressure, Water Vapor, Vertical Diffusivity, Clouds/Precipitation),
emission obtained from an emission inventory (NOx, SOx, CO, speciated VOC and PM
emissions of point Sources and Gridded Sources), geographic data developed from
maps (land use and terrain elevation) and photolysis data derived from satellite or
radiative transfer model (Gridded Haze Opacity Codes, Gridded Ozone Column Codes,
Photolysis Rates Lookup Table).
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Chapter 4 City scale modelling by means of mathematical methods
4.3 Mathematical modelling strategy
Mathematical modelling has been carried out within the MoDiVaSET-2 project
(MOdellistica DIffusionale per la VAlutazione di Scenari Emissivi in Toscana 2). The
project was funded by the Regional Administration of Tuscany and involved the
Dipartimento di Energetica, University of Florence, and the Institute of
Biometeorology, National Research Council (CNRIBIMET/ LaMMA). The final aim of
the project was to evaluate different emission scenarios of PM10 and NO2 in the
metropolitan area of Florence, Prato and Pistoia in support of the Air Quality Action
Plan of Tuscany. The project involved the simulation of complex scenarios at the
city/regional scale, with an elevated number of sources of different types (road traffic,
industrial site, domestic heating, etc.). The studied 49×40 km 2 area is shown in figure 4-
7.
Figure 4-7 Area studied in the MoDiVaSET-2 project
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Chapter 4 City scale modelling by means of mathematical methods
The work carried out in this thesis was composed of two phases:
1. A first phase aimed at developing an integrated meteorological and dispersion
modelling system for reliably simulating atmospheric dispersion of PM10, NOx,
NO2 and SO2 in the study area. With this purpose, this phase of the study
included several long term (1-year long) dispersion modelling applications and a
detailed model evaluation study, consisting of model intercomparison,
sensitivity, validation and uncertainty analysis.
2. A second phase aimed at evaluating different emission scenarios (2003-2012) in
order to supply useful information which can be reliably used by administrators
and policy makers in the implementation of the Air Quality Action Plan of the
study area. With this objective, a scenario analysis of the different types of
emission sources involved in urban area (road traffic, industrial sites and
domestic heating) was carried out applying the modelling system developed in
the first phase of the work.
In the following sections, a description of the techniques used in the two phases of the
projects are reported; firstly the modelling applications are treated (section 4.4), then the
model evaluation procedure (section 4.5) and, at the end, the scenario analysis (section
4.6)
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Chapter 4 City scale modelling by means of mathematical methods
4.4 Mathematical modelling applications
4.4.1 Introduction
Several models have been applied in the framework of the MoDiVaSET-2 project. The
project involved the simulations of a large number of sources of different type: point
(main industries), line (motorways) and grid area sources (other sources). Basing on the
capability of treating all the types of source considered in the project and on good
performances of the model in the simulation of urban air dispersion problem (see for
example Colvile et al. 2002), the initial choice was ADMS-Urban and the simulations
were mainly carried out using this model. For comparison purpose, simulations were
also performed by means of CALGRID (simulation of grid area sources), CALPUFF
(simulation of point sources), CALINE4 (simulation of line sources), and SAFE AIR
(simulation of point and line sources). Combinations of these models permitted to
obtain chains of models capable to simulate the pollutant dispersion in urban area. In
order to compare results from different models and approaches, the following modelling
system were carried out in this thesis:
1. ADMS-Urban (‘ADMS’)
2. CALGRID-CALPUFF-CALINE4 (‘CGPL’, superposition of the results deriving
from CALGRID for the grid area sources, CALPUFF for the point sources and
CALINE4 for the line sources)
3. CALGRID-SAFE AIR (‘CGSA’, superposition of the results deriving from
CALGRID for the grid area source and SAFE_AIR for the point and line
sources)
The full year 2002 time period was chosen, using a 1-hour time step, thereby all models
were applied in a long-term mode.
The regional background concentrations were included in the simulations using the
monitored concentrations of the peripheral background site of Livorno-Maurogordato; it
is acknowledged by the regional administration as the reference regional background
site (see PATOS project, Particolato Atmosferico in TOScana).
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Chapter 4 City scale modelling by means of mathematical methods
All the simulations were realized without considering chemistry mechanisms, except for
NO2, where the NOx-NO2 empirical correlation of Derwent. and Middleton (1996) was
applied; in this approach, derived from statistical analysis of the correlation between
NOx-NO2 in urban areas of the UK, the concentration of NO2 is calculated from NOx
emissions using the following function where concentrations are hourly-averaged
concentrations in ppb ([NO2] and [NOx]).
2
3
[ NO ] = 2. 166 − ( 1.
236 − 3.
348A
+ 1.
933A
− 0.
326A
)[ NO ]
2
Full chemistry option was investigated by another research group of the Dipartimento di
Energetica, University of Florence, using CAMx. The result were used in this thesis in
order to compare data from simulations with pollutants treated as inert substances and
models with complete chemical mechanisms; in the CAMx application the line sources
were aggregated with the grid area source.
For all the model applications the receptors were located on a 1×1 km 2 computational
grid at a height of 10 m (1960 points). For the Gaussian models (CALPUFF, SAFE AIR
and ADMS-URBAN), concentrations were also evaluated at additional receptors
located at 3 m from the ground in correspondence of the monitoring stations within the
studied area, while for the Eulerian grid models (CALGRID, CAMx) the concentration
in a monitoring station site was evaluated using the concentration calculated by the
models in the corresponding grid cell.
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x

Chapter 4 City scale modelling by means of mathematical methods
4.4.2 Data pre- and post-processing
Two fundamental phases in a model application procedure are:
1. the collection and the pre-processing of the input data
2. the post-processing of the model results.
Referring to the collection of the model input, the categories of necessary data for the
application of a mathematical dispersion model are essentially three:
a. geographic data (orography and roughness length),
b. emission data (localisation and quantification of specific sources, emission
inventory),
c. meteorology data (standard observations, micrometeorological parameters,
remote sensing data, meteorological variables derived from high resolution
meteorological models).
In these thesis, terrain elevation and roughness data were derived from terrain and land
use/landcover maps of the Tuscan Region, according to the computational grid used by
the dispersion models (1×1 Km 2 spaced cell grid).
Meteorological data were derived from measurements of six meteorological stations
within the study domain (PO-Baciacavallo, FI-Monte Morello, Empoli, Montale, FI-
Ximeniano and FI-Peretola Airport, see figure 4-8) and from vertical profiles of wind
and temperature retrieved from the RAMS forecasting system archive of CNR-
IBIMET/LaMMA (see also Corti, A. et al., 2006); this data are referred to the full year
2002. A suitable scaling to the 1×1 km 2 final working resolution was performed by
using the CALMET meteorological model. This model permitted to obtain all the
meteorological variables necessary for the modelling system involved in this work: 3-
Dimensional Gridded Fields of horizontal wind components, temperature and pressure,
and 2-D fields of micrometeorological variables (Monin Obukhov length, Heat sensible
flux, PGT stability class and PBL height). The results were directly used as input for the
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Chapter 4 City scale modelling by means of mathematical methods
CALGRID and CALPUFF simulations, while a further elaboration proved to be
necessary for the other models. As explained in the following subsections
meteorological data were treated in different ways, depending on the model to be used.
Figure 4-8 Meteorological stations for the MoDiVaSET-2 project
Emissions were retrieved from the Tuscan Regional Emission Source Inventory (IRSE-
RT, 2001) according to an hour-by-hour time disaggregation. PM10 (primary only), NOx
and SOx were the chosen pollutant species. Three source categories have been
considered: main point sources (stacks of the main industries), main line sources
(motorways) and other sources treated as grid area sources. Figure 4-9 reports a map of
the 15 industrial plants included in the study; many of them have several stacks, thus the
total number of point sources is increased to 87. The considered line sources are
reported in figure 4-10. As reported earlier, they are the motorways of the study area
(A1 and A11 motorways). Emissions were provided according to junction-to-junction
spatial disaggregation. Figures 4-11, 4-12 and 4-13 report the annual line source
emission rate for the three pollutants. The grid sources include all other emission
sources not considered in the point and line sources groups. Area emissions were
provided according to a 1×1 Km 2 spaced cell grid exactly matching the computational
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Chapter 4 City scale modelling by means of mathematical methods
one used by the meteorological and dispersion models. A total number of 1960 grid
cells were used. Figures 4-14, 4-15 and 4-16 report the annual emission rate for the 3
pollutants.
Figure 4-9 Point sources included in the IRSE-RT, 2001
Figure 4-10 Line sources included in the IRSE-RT, 2001
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Chapter 4 City scale modelling by means of mathematical methods
Figure 4-11 Annual NOx emission of line sources (Mg/km year) included in the
IRSE-RT 2001
Figure 4-12 Annual PM10 emission of line sources (Mg/km year) included in the
IRSE-RT 2001
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Chapter 4 City scale modelling by means of mathematical methods
Figure 4-13 Annual SOx emission of line sources (Mg/km year) included in the
IRSE-RT 2001
Figure 4-14 Annual NOx emission of grid area sources (kg/km 2 year) included in
the IRSE-RT 2001
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Chapter 4 City scale modelling by means of mathematical methods
Figure 4-15 Annual PM10 emission of grid area sources (kg/km 2 year) included in
the IRSE-RT 2001
Figure 4-16 Annual SOx emission of grid area sources (kg/km 2 year) included in
the IRSE-RT 2001
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Chapter 4 City scale modelling by means of mathematical methods
Referring to the post-processing of the model outputs, two different types of results
were usually carried out in a model application for each air quality indicator and
pollutant of interest:
• contour maps
• tables of results in significant receptor points within the study area (monitoring
station, highly populated area, etc.)
Considering the purpose of the first part of the project (development of a reliable
modelling system to be used for scenario analysis) and according to the actual European
Air Quality legislation, the work concentrated on the annual concentration values of
NOx, NO2, SO2 and PM10. For these indicators, contour maps and table of concentration
values in correspondence of the monitoring stations were carried out; the contour maps
were obtained using the Kriging interpolation algorithm of ArcGis.
For CALPUFF, CALGRID and ADMS-Urban the extrapolation of the desired
indicators was realized using the post-processors included in these modelling system,
while for the other models post-processing algorithms where created ad hoc, as
described in- depth in the following subsections.
ADMS-Urban application
ADMS-Urban has a meteorological pre-processor able to calculate wind fields and other
variables over the considered domain. However, the CALMET output cannot be directly
used as input to the model. The CALMET output consists of a series of meteorological
variables calculated on a regular grid of points. One of these points was chosen as a
‘virtual’ meteorological station. It is located approximately at the intersection between
the A1 and the A11 motorways (see, for example, figure 4-10). The following data have
been used: wind speed, wind direction, temperature, Monin-Obukhov length and mixing
height. For sensitivity purpose, during this work simulations were also carried out using
standard observations of one of the meteorological stations (PO-Baciacavallo) within
the study area (wind velocity and direction, temperature, global and net radiation); in
this case micrometeorological variables were evaluated directly from the meteorological
pre-processor of ADMS-Urban using the global radiation and clouds coverage measured
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Chapter 4 City scale modelling by means of mathematical methods
at the meteorological station. PO-Baciacavallo meteorological stations was selected
between all the monitoring site within the study domain because it has the most
complete hourly time series data and it is located in the central position of the study area
(see figure 4-8).
ADMS-Urban can treat a variable-emission grid source, but the variation must be the
same for all the grid cells. Thus the hourly profiles were recalculated using an ‘average’
profile and then normalised in order to have the same overall emission for each
pollutant. No adjustments were made for the point and line sources.
CALINE4 application
CALINE4 does not have a meteorological pre-processor. As in the case of ADMS-
Urban, it was then decided to use 24 CALMET points chosen along the motorways.
Since only one set of meteorological data can be fed to the model, the modelling domain
was divided in 24 sub-domains, each with its own meteorological input. The 24 ‘virtual’
meteorological stations are reported in figure 4-17.
Figure 4-17 Virtual meteorological stations used for the CALINE4 simulations
The meteorological input data set includes: wind speed, wind direction, temperature,
relative humidity, stability class, mixing height and standard deviation of the wind
direction (σθ). All these parameters were calculated by CALMET, except σθ, which was
estimated using the following table.
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Chapter 4 City scale modelling by means of mathematical methods
STABILITY CLASS σθ
A
B
C
D
E
F
Table 4-1 Estimation of the standard deviation of the wind direction based on the
stability class
183
25
20
15
10
5
2.5
Figure 4-18 Pre- and post-processing for the CALINE4 simulations in the
MoDiVaSET project

Chapter 4 City scale modelling by means of mathematical methods
CALINE4 is a simple Gaussian model and thus it is not applicable in case of wind calm
(the reference manual sets a limit of 0.5 m/s). A maximum of 6% of the available data
in the 24 CALMET point was wind calm. Wind calms were treated as suggested in the
reference manual, setting the wind speed to 0.5 m/s.
Another difficulty in the application of CALINE4 comes from the fact that 20 receptors
is the maximum number allowed. For some of the sub-domains, thus, more than one
simulation was necessary in order to have the same receptors described in the previous
subsection.
Given the large amount of data to be treated, pre- and post- processing procedures
became necessary. Figure 4-18 shows a schematic of the applied procedure (using MS-
DOS batch files and MATLAB algorithms).
SAFE AIR application
As explained earlier, SAFE AIR has its own meteorological pre-processors. However,
in order to have consistent results, it was decided to adopt the same methodology used
in the ADMS-Urban and CALINE4 simulations. Thus, the WINDS meteorological preprocessor
was fed by data from 8 of the 24 available CALMET points (see figure 4-19).
The following data have been used: wind speed, wind direction, stability class, mixing
height, temperature. Thus, simulations using ABLE were not necessary. Receptors were
located as in the previous simulations.
NOx and SOx were treated as inert gases, while settling velocity (Vs) and deposition
velocity (Vd) were set, respectively, to 0.01 cm/s and 0.2 cm/s (Maggi, 2002) for PM10.
The PGT σ-functions were used for the simulations.
Given the large amount of data to be treated, also in this case pre- and postprocessing
procedures became necessary. Figure 4-20 shows a schematic of the applied procedure
(using MS-DOS batch files and MATLAB algorithms).
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Chapter 4 City scale modelling by means of mathematical methods
Figure 4-19 Virtual meteorological stations used for the SAFE AIR simulations
Figure 4-20 Pre- and post-processing of SAFE AIR in the MoDiVaSET project
185

Chapter 4 City scale modelling by means of mathematical methods
4.5 Model evaluation
4.5.1 Introduction
Model evaluation was performed in order to evaluate simulation results and compare the
different approaches used within the project; the final purpose of this study was the
determination of the most suitable model for the scenario analysis. The results of the
simulations were compared to measured data of the year 2002, provided by the
monitoring networks of the three Provinces involved: Florence, Prato and Pistoia. 25
monitoring stations are present in the study area (see figure 4-21): 17 background and 9
roadside sites; 24 (15 background and 9 roadside) able to measure NO2 and NOx
concentrations, 15 (9 background and 6 roadside) able to measure PM10 and only 9 (7
background and 2 roadside) SO2 concentrations. Following the indication of the
scientist community described in the section 2.9, the evaluation work carried out in this
thesis included: sensitivity study, validation exercise and uncertainty analysis. Both
exploratory analysis of the data and advanced statistical techniques were used in this
work; statistical analysis focus, in particular, on NO2, NOx and PM10, because only for
these pollutant the reference data (monitoring data) were quantitatively significant from
a statistical point of view.
Figure 4-21 Monitoring stations in the MoDiVaSET domain
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Chapter 4 City scale modelling by means of mathematical methods
4.5.2 Validation and sensitivity analysis
As discussed in the section 2.9, validation is the process of showing that the
mathematical models provide an adequate representation of the problem, while
sensitivity analysis is the process of identifying the magnitude of an individual
parameter effect on model results.
In this thesis validation procedure were applied with the purpose of evaluate the
performance of several modelling systems and between them determine the most
suitable model to be adopted for the scenario analysis; while the sensitivity analysis was
aimed at calibrating the models by means of the identification of the most sensitive
parameters and their effects on model results. This second analysis was carried out only
for ADMS-Urban.
Although the aim of these two processes is quite different, in this work they were
carried out by means of the same procedure, that is the comparison of model results
with monitoring data. Both exploratory analysis of the data and advanced statistical
techniques were used in this work.
Traditionally, model predictions are directly compared to observations. As described by
ASTM (2000) model evaluation guidelines, this direct comparison method may cause
misleading results, because (1) air quality models almost always predict ensemble
means, but observations represent single realizations from an infinite ensemble of cases
under the same conditions; and (2) the uncertainties in observations and model
predictions arise from different sources. For example, the uncertainty in observations
may be due to random turbulence in the atmosphere and measurement errors, whereas
the uncertainty in model predictions may be due to input data errors and model physics
errors. Therefore, an alternative approach has been proposed by the ASTM to compare
observations and model predictions for dispersion models (ASTM, 2000). The approach
calls for properly averaging the observations before comparison; the ASTM procedure,
in particular, suggest calculation of the performance measures based on regime averages
(i.e., averaging over all experiments within a regime), rather than the values for
individual experiments.
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Chapter 4 City scale modelling by means of mathematical methods
Having this framework in mind and considering the goals of the project, in this thesis,
we decide to perform validation and sensitivity analysis based only on annual mean
concentration than the hourly time series; in this way, we were able to apply a regime
averages as suggested by the ASTM methodology.
Before beginning the calculation of various statistical performance measures, it is
extremely useful to perform exploratory data analysis by simply plotting the data in
different ways. The human eye can often glean more valuable insights from these plots
than pure statistics, especially since many of the statistical measures depend on linear
relations; these plots can also provide clues as to why a model performs in a certain way
(Chang and Hanna 2004). For these reason some of the most commonly-used plots were
carried out in this thesis:
1. Scatter plot: in a scatter plot, the paired observations and predictions are plotted
against each other. Visual inspection can reveal the magnitude of the model’s
over or underpredictions. Also, as implied by its name, the scatter of the points
can be quickly seen and estimated by eye. Because of the obvious impacts on the
public health due to high pollutant concentrations or dosages, the high end of the
plots can be studied. On the other hand, correct predictions of low
concentrations may sometimes also be important for highly toxic chemicals.
2. Quantile–quantile plot: The quantile–quantile plot begins with the same paired
data as the scatter plots, but removes the pairing and instead ranks each of the
observed and predicted data separately from lowest to highest. Thus the 3rd
lowest predicted concentration would be plotted versus the 3rd lowest observed
concentration. It is often of interest to find out whether a model can generate a
concentration distribution that is similar to the observed distribution. Biases at
low or high concentrations are quickly revealed in this plot.
The quantitative performance measure of the model was performed in this thesis by
advanced statistical methods. The statistical indices applied in this thesis are mostly
derived from the BOOT package (Hanna 1989) and the Model Validation Kit (Olesen
2005). Some standard statistical measures were also used, along with two more
advanced measures proposed by Poli and Cirillo (1993). The statistical set is similar to
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Chapter 4 City scale modelling by means of mathematical methods
that applied by Canepa and Builtjes (2001) for the validation of SAFE AIR in complex
terrain. The following indices have been used:
MEAN can be related to both observed and simulated concentrations, it is defined as:
o
i
s
MEAN observed = C = ∑ ; simulated = C = ∑
i
o
C
N
s
Ci
MEAN ;
i N
where N is the total number of the receptors and o
i
189
C ( C ) is the observed
(simulated) concentration at the i-th receptor; a perfect model would give
MEAN = MEAN .
observed
BIAS is defined as:
BIAS −
o s
= C C ;
simulated
a perfect model would give BIAS = 0, while if BIAS > 0 (< 0) the model on average
underestimates (overestimates) the observed concentrations.
FB (Fractional Bias) is defined as:
o s
C − C
FB = ;
o s
( C + C ) 2
it ranges between – 2 and + 2, a perfect model would give FB = 0, while if FB>0
(< 0) the model on average underestimates (overestimates) the observed
concentrations.
SD (standard deviation), can be related to both observed and simulated concentrations,
it is defined as:
SD
observed
=
2
o o
s s
o ( Ci
C )
s ( Ci
C )
σ =
; SD = =
∑ −
i N
simulated
a perfect model would give SD observed = SDsimulated
.
s
i
∑ −
σ ;
i N
2

Chapter 4 City scale modelling by means of mathematical methods
FS (Fractional Standard deviation) is defined as:
o s
σ −σ
FS = ;
o s
( σ + σ ) 2
it ranges between – 2 and + 2, a perfect model would give FS = 0, while if FS>0
(< 0) the spreading of the simulated concentration values is smaller (bigger) than
the spreading of the measured ones.
COR (linear CORrelation coefficient) is defined as:
o o s s
( C − C )( C − C )
COR = ;
o s
σ σ
a perfect model would give COR = + 1, it ranges between – 1 and + 1.
FA2 (fraction within a FActor of 2) is defined as:
s
Ci
fraction of data with 0. 5 ≤ ≤ 2 ; o
C
a perfect model would give FA2 = 1.
NMSE (Normalised Mean Square Error) is defined as:
o s ( C − C )
i
190
∑
( k )
2
NMSE = or, if C i
o s
C C
o
2
∑ s −
i i 1
i ≠ 0 ∀ , NMSE =
sik
where
k = C C and
i
s
i
o
i
value of this index is always positive.
o o
s i = Ci
C ; a perfect model would give NMSE = 0, the
WNNR(Weighted Normalized mean square error of the Normalized Ratios)is defined as
∑
( 1 kˆ
)
2
∑ s −
i i
WNNR =
s kˆ
i
i
i
i
2
;
where kˆ i = 1 ki
(if ki > 1) and k ˆ
i = ki
(if k i ≤1
); a perfect model would give
WNNR=0, the value of this index is always positive.
i
i
i
2
;

Chapter 4 City scale modelling by means of mathematical methods
NNR (Normalized mean square error of the distribution of Normalized Ratios) is
defined as:
( 1 kˆ
)
∑ −
i NNR =
kˆ
∑
i i
i
2
;
a perfect model would give NNR = 0, the value of this index is always positive.
Note that the word ‘perfect’ here is used just to indicate that all the modeled data match
the observed data; this does not necessarily mean that a model is perfect (without
errors); for example it is possible for a model to have predictions completely out of phase
of observations and still have FB = 0 because of canceling errors.
One can directly judge the on average model performance just looking at the
MEANobserved and MEANsimulated values simultaneously. Looking at the BIAS value one
can immediately have an idea about the on average model performance with respect to
overestimation (BIAS0) of the measured values; but the
BIAS value could vanish even in the case of evident disagreement between simulated
and measured concentrations. Furthermore, looking at the BIAS value solely, all
information about the ratio between MEANsimulated and MEANobserved is lost. To try to
overcome the latter issue, the FB index can be helpful. As a matter of facts, the FB is
the BIAS normalised to the average values of MEANsimulated and MEANobserved.
The SD and FS indices give information about the spreading of the concentrations. One
can directly judge the model performance looking at the SDobserved and SDsimulated values
simultaneously. As far as the standard deviations are concerned, the FS index is
analogous to the FB index.
COR gives information about the linear correlation of the data. As already said, COR
ranges between – 1 and + 1. A value of + 1, the so-called “complete positive
o s
correlation” corresponds to all pairs ( C , ) laying on a straight line with positive
i Ci
slope in the scatter diagram. The “complete negative correlation” corresponds to all the
pairs on a straight line with negative slope, and it has COR = – 1. A value of COR near
to zero indicates the absence of linear correlation between the variables. A model will
191

Chapter 4 City scale modelling by means of mathematical methods
have COR = + 1 if
C −
o o s s
i − C = Ci
C for any (
o s
C ≠ C the previous equality does not mean
192
o
i
C , ). Because it is possible that
o s
i Ci
o s
C = C for any ( C , ) as we should
s
i
i Ci
expect for a perfect model. Furthermore, it should also be pointed out that a high
correlation coefficient does not necessarily indicate a direct dependence of the variables
(“spurious correlation”).
The FA2, NMSE, WNNR and NNR indices give information about the ratios between
simulated and measured concentrations. Only the FA2 and NNR indices, out of all
indices considered, depend solely on the ratios between simulated and measured
concentrations, and not on the data set itself, so they are the only indices strictly usable
to compare simulations of different experiments. NMSE gives more relevance to errors
relative sometimes to the highest measured concentrations, sometimes to the lowest
ones; WNNR gives more relevance to errors relative to the highest measured
concentrations; NNR gives the same relevance to errors independently of the position of
the data within the concentration range (Poli and Cirillo, 1993).
Quality objectives and acceptance criteria are not easy to be defined. Generally, they
will depend on the purpose and goal of the model evaluation procedure. For example,
simple quality objectives are recommended by the European directives on air quality.
They are listed in table 4-2 and are based on the accuracy of the model for a given
pollutant concentration measure over the period considered by the limit value.
Pollutant Air Quality indicator Air Quality objective EU Directive
SO2, NO2, NOx
PM10, Pb
CO
Benzene
Ozone
Hourly mean
Daily mean
Annual mean
Annual mean
8-h mean
Annual mean
8-h daily maximum
Hourly mean
50-60%
50%
30%
50%
50%
50%
50%
50%
1999/30/EC
2000/69/EC
2002/3/EC
Table 4-2 Modelling quality objectives established by European Directives

Chapter 4 City scale modelling by means of mathematical methods
Acceptance criterion based on the BOOT statistical indicators, is given by Chang and
Hanna (2004). They are based on an extensive review of applications of the BOOT
package and they are adopted in the model evaluation protocol for urban scale flow and
dispersion model developed in the framework of the COST 732 action (Di Sabatino et
al., 2008). According to the authors, ‘good’ performing models appear to have the
following typical characteristics based on unpaired in space comparisons:
1. The fraction of predictions within a factor of two of observation is about 50%
(i.e., FA2>0.5).
2. The mean bias is within ±30% of the mean (i.e., -0.3

Chapter 4 City scale modelling by means of mathematical methods
calculated concentration levels, over the period for calculating the appropriate threshold,
without taking into account the timing of the events. In Stern and Fleming (2007) and
Borrego et al. (2008) EC directive related uncertainty assessment parameters such as the
relative maximum error (RME) and relative percentile error (RPE) were explicitly
formulated and widely tested.
These latter top-down techniques were used in this work; in particular the uncertainties
were quantified by means of the relative maximum error (RME) and the Colvile et al.
(2002) method.
RME is the most relevant uncertainty parameter for annual mean concentration when
modelling uncertainty is required for directive purpose (AIR4EU, 2007). RME for the
annual means is simply the relative error of the annual average, because for this
averaging period the question of timing is not relevant (Stern and Fleming 2007). So,
for annual mean concentration RME can be calculated for every single monitoring
station as follows:
RME =
C
o,
i −
C
o,
i
C
s,
i
where Co,i is the observed concentration at the monitoring site i and Cs,i is the simulated
concentration at the site i.
Colvile et al. (2002) method is a very interesting alternative method, that allows to take
into account systematic underestimation or overestimation effects. In this case, to
calculate uncertainty, modeled concentration values with bias (mean difference between
modeled and monitored concentrations at all available monitoring stations) removed
were considered; in particular, these concentrations were normalized by the relevant air
quality standard and logarithms taken because the distribution of concentrations both
across a map of model output was much closer to lognormal than normally distributed.
Model precision was then estimated as the root mean square difference between
modeled and measured log-concentrations. The use of log-concentration leads to a
precision value expressed as a fraction or percentage error.
194

Chapter 4 City scale modelling by means of mathematical methods
Since models are generally applied to produce spatially resolving maps, even at points
without observations, it is equally important to indicate model uncertainty as a spatial
map; uncertainty maps may be used to indicate the quality of the model or as
information for decision makers in air quality assessment (Denby et al., 2007). For these
reasons, uncertainty in this work was also presented in the form of maps. Although its
importance, uncertainty is rarely, if ever, presented in the form of maps, but some
examples do exist (for example Van de Kassteele and Velders 2006, Fuentes and
Raftery 2005, Horalek et al. 2007). The guidelines given by Denby et al. (2007), based
on experience gained in the framework of the EU FP6 project Air4EU, were followed in
this thesis; in particular the uncertainties were represented as points on the map because
in the study area there isn’t a sufficient density of stations that allow to interpolate the
uncertainty in a reliable way.
4.6 Scenario analysis
On the basis of the model evaluation, the most reliable modeling system, opportunely
calibrated and validated, was adopted for the scenarios analysis.
Two scenarios were investigated in order to review of the actual air quality in the
metropolitan area of Florence, Prato and Pistoia and to evaluate future condition of the
air quality in the study area:
1. Base scenario or actual scenario: referred to the Tuscan Regional Emission
Source Inventory 2003
2. Future scenario or “business as usual” scenario: referred to 2012 year on the
basis of the anticipatory statistical modification of the Tuscan Regional
Emission Source Inventory 2003.
A newer emission source inventory was used in the scenarios analysis (IRSE-RT,
2004); the main difference with the inventory used in the first phase of the project is that
the grid area sources were further divided in four subcategories: small industries, local
road traffic, domestic heating and other sources. These subcategories permitted to
195

Chapter 4 City scale modelling by means of mathematical methods
evaluate and compare the contribute of all the significant type of sources present in the
urban area now and at a future time.
NO2 and primary PM10 concentrations were analyzed, considering the necessity of the
Air Quality Action Plan and the results of the model evaluation. According to the air
quality legislation actually in force, annual (for NO2 and primary PM10), maximum
hourly (for NO2) and maximum daily (for primary PM10) average concentrations maps
of the two scenarios were calculated considering all the source together; in order to
evaluate the contribute of every different type of emission source annual mean
concentration maps were also carried out, separately, for: main point sources (POINT),
main line sources (LINE), small industries (IND), local road traffic (ROAD), domestic
heating (HEAT) and other sources (OTHER).
In addition to the contour maps of the air quality indicators other two types of analysis
were carried out:
• Maps of the annual mean concentration variation (CF-A) between actual (CA) and
future (CF) scenario normalized by the relevant air quality standard (AQS).
CF − C A
CF-A =
AQS
This investigation was aimed at evaluating the variation of the concentration
levels between the two scenarios;
• Evaluation of the ratios (Ri-all) between the annual mean concentrations due to a
specific type of source (Ci) and the annual concentration levels deriving from all
the sources (Call) for the actual and future scenarios:
Ri-all =
C
C
i
ALL
This investigation was carried out for each receptor of the computational grid
used and was aimed at defining the weights of the different type of sources
involved in the study area and their changing at a future time.
196

5.1 Introduction
Chapter 5
5.Neighbourood/Street scale results
The following sections present results from the experiments performed on the DAPPLE
site model at the EnFlo wind tunnel. Two main sets of experiments were carried out:
moving source and correlation experiments.
Both the series of experiments involved tracer concentration measurements and focused
on the characterization of dispersion phenomena connected with the pollutant emission
of vehicles and, in particular, on the effect of a moving source on receptors located in
the streets downwind of the emission sources.
Moving source experiments were carried to investigate the mean concentrations and
related exposure dosages, while correlation experiments focused on concentration
fluctuations and, in particular, was aimed at investigating the correlation between two
point sources belonging to a line emission.
Details about the adopted experimental strategy, technique and programme are
described in the section 3.4 and 3.5.
All tracer concentration results are reported in non-dimensional form, as follows:
CUH 2
Q
2
c ( UH
2
Q
2
ClineUH q
)
2
non-dimensional mean concentration of a point source
non-dimensional concentration fluctuation of a point source
non-dimensional mean concentration of a line source
where C is the measured mean volume concentration from a point source, c 2 is the
measured volume concentration variance from a point source, Cline is the mean volume
197

Chapter 5 Neighbourood/Street scale results
concentration from a line source derived from the integral calculation of point source
measurements, U is equal to wind speed at the boundary layer edge U(z=δ=1m), H is the
average building height in the model (H=110mm), Q is the tracer volume emission rate
of a point source and q is the tracer volume emission per unit length of a line source.
It recommended that the map of the DAPPLE model (previously reported in the figure
3-7) be used when reading these results.
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Chapter 5 Neighbourood/Street scale results
5.2 Moving source experiments
A wide range of source and receptor locations were analyzed in the moving source
experiments; the source locations were located in Marylebone Road and the receptor
locations in various downwind streets, depending on the model rotation. Two model
orientation (-90° and +90°) were considered in this series of experiments.
The concentration data obtained from these experiments were analysed by means of the
following methods.
1. Plots of the mean concentration (or variance) against receptor location for a
given source location (see section 5.2.1). This type of elaboration permitted to
understand the behaviour of the pollutant dispersion phenomena changing the
location of the mobile source along the Marylebone road.
2. Plots of the mean concentration against source location for a particular receptor
locations and related estimation of the mean concentration from a uniform traffic
line source (see section 5.2.2). This elaboration method allowed to identify in
terms of mean concentration the receptor more exposed to the pollutant emission
of a line source; the integral calculation (by numerical methods) of these mean
concentration profiles permits to quantify the non-dimensional mean
concentration due to a uniform line emission, Cline * , in a receptor location.
C
*
line
C
=
line
UH
q
=
∫
0
S
CUH
Q
2
ds
H
199
where S is the length of the line source
3. Estimation of mean exposure dosages from a car (see section 5.2.3). The second
series of plots was also used to evaluate the effect of non uniform emission line
source. Integral of the concentration of point source measurements with space
(length of the line source, S) and time (driving time, T) permitted to evaluate the
effect of different traffic conditions (i.e. stop at the intersection, different speed).
D*
=
DUH
q
∫∫ ⎟ S T 2 ⎛ CUH ds ⎞
= ⎜
dt
⎝ Q H
0 0 ⎠

Chapter 5 Neighbourood/Street scale results
This integral represent the non dimensional form of the mean dose, D, to which
someone might be exposed due to the passage of a mobile source and permit to
predict the dosages from the emission of a single car. These estimations are
representative of a vehicle that travels along a street many times in the same
way.
5.2.1 Concentration vs. receptor location
This data analysis was carried out only for ground level concentration (Z=10mm). The
results for the -90 degree model orientation will be presented first, then the +90° model
orientation results will follow.
-90° model orientation
Plots of the (non-dimensional) mean concentration against receptor location along
Dorset square/Melcombe Street for every different source locations considered along
Marylebone road are reported in the figure 5-1; (non-dimensional) variances are shown
in the figure 5-2.
Starting with the analysis of the results in correspondence of the source locations in the
far negative X model (west) region, figure 5-1 (top-left curves) shows that for the
source locations at X=-531mm and X=-483mm, most of the pollutant is channelled
down Balcombe street, as is to be expected for sources at or in proximity of the
intersection of Balcombe St with Marylebone Road (X=-531). Having been channelled
down Balcombe St, the plume disperses into side streets and is detected in Melcombe
Street between X=-800mm and X=-200mm. The mean concentrations along Dorset
square/Melcombe Street are almost equal for both the source locations, while the
variances from the source location at X=-531 (centre of the intersection between
Marylebone road and Balcombe St.,) are higher at the receptor location directly
downwind of the intersection (see top-right curves in figure 5-2).
200

Chapter 5 Neighbourood/Street scale results
Figure 5-1 Plots of the (non-dimensional) mean concentration against receptor
location along Dorset square/Melcombe Street for different source locations along
Marylebone road
201

Chapter 5 Neighbourood/Street scale results
Figure 5-2 Plots of the (non-dimensional) concentration variances against
receptor location along Dorset square/Melcombe Street for different source locations
along Marylebone road
202

Chapter 5 Neighbourood/Street scale results
Moving the source location in direction of the main intersection at X=-434mm (see
Figure 5-1, top-right curves), the plume is effectively split with peaks occurring in
Balcombe Street and Gloucester Place. Tracer gas is detected almost the entire length of
Melcombe place, from the Balcombe Street intersection to the Gloucester Place
intersection, due to the channelling of pollutant into side streets and possibly the
increased mixing around the Marathon House tower (located between Marylebone road
and Dorset Square about at X=-365) having some additional effect as highlighted in the
study of Carpentieri (2005).
For source locations between X=-337mm and X=-56mm similar mean concentration
and variances profiles are seen. Peaks in mean concentration and fluctuation are
detected only at the intersection with Gloucester Place; this indicates that the pollutant
is channelled along Marylebone Road towards the intersection with Gloucester Place,
where the mean flow then carries pollutant along Gloucester place to the receptor
locations of Dorset Square\Melcombe Street situated between X=0mm and X=-300mm.
It is worth noting that with this range of source locations very low pollutant
concentration is seen in Balcombe Street even when the tracer release is actually closer
to Balcombe Street than Gloucester Place. For example, results with a release at X=-
337mm shows pollutant detected in Gloucester Place but not in Balcombe Street. This
low concentration levels observed when receptors are shielded by buildings suggests
that small quantity of pollutant is transported over roof tops and into Dorset Square, and
the concentrations that are seen between X=0mm and X=-300mm are more likely due to
the horizontal spreading and channelling of the plume in the street network.
With the source situated at the main intersection (X=0mm) sharp peak in mean
concentration and variance is detected at the receptor in Dorset Square\Melcombe Place
where pollutant is carried directly from the source to the receptor. Interestingly, the
mean concentration see by a receptor directly downwind is lower at the main
intersection than for a source at X=-483mm source location, near but not directly
upwind of the Balcombe Street intersection, C* = 1.5 and 1.7 respectively; opposite
behaviour characterized the variances; as expected, the variances are higher in a
receptor directly downwind the intersection (c 2 * = 0.85 respect to 0.45).
203

Chapter 5 Neighbourood/Street scale results
As the source moves towards east side of the model (positive X) the plume grows in
width with higher values of mean concentration and variance than ones seen earlier with
source located in the west side (negative X). This increased spread of the plume may be
due to the local geometry of the buildings. In the negative X direction the buildings (1
and 2) are low (respectively 55mm and 88mm high) and wide (respectively 452mm and
442mm long in the X direction) with a tower on Marathon House and receptors located
in a large square (Dorset Sq), while in the positive X direction the building (3) has a
more uniform shape being 115 mm tall and 279 mm wide and receptor are inside a deep
street canyon (Melcombe St, H/W=1.8). For example, we can see that the plume from
the X=44mm source location is detected in Melcombe Place at the Gloucester Place
intersection (X=0mm), the Glentworth intersection (X=390mm) and everywhere in
between inside the canyon. This would indicate that the plume is channelled along both
Gloucester Place and Glentworth Street and then mixed inside the Melcombe street,
instead in the case of negative X source locations (for example between X=-337 mm
and X=-56 mm) the tracer gas was detected only at the Gloucester Place intersection but
not at Balcombe Street intersection. From the source location X=88mm to X=260mm,
channelling and mixing effects inside the canyon seen at the receptor X=44mm are also
present in these cases; the channeling along Glentworth Street prevail over the
channeling along Gloucester Place, even when the source is nearer to the Gloucester
Place intersection.
Moving the source in proximity of the Glentworth Street intersection and, in particular,
between X=324 mm and X=514 mm, similar mean concentration and variances profiles
are identified; quite uniform values of mean concentration are detected almost the entire
length of Melcombe place, from X=50mm to the Baker street intersection (X=770mm).
This indicates that the pollutant is channelled along Marylebone Road towards the
intersection with Glentworth Street and Baker Street, here the mean flow carries
pollutant along these streets to the receptor locations of Melcombe Street, where the
mixing effect of the canyon produces a uniform value of mean concentration. For source
location further away from the Glentworth Street intersection in the positive X direction
(between X=632mm and X=770mm) peaks in mean concentration and variances are
detected only at the intersection with Baker street, demonstrating that in these cases
pollutant is channeled only along one street.
204

Chapter 5 Neighbourood/Street scale results
In general, concentration variance results (see figure 5-2) show almost the same pattern
as the mean concentration, but sharper peaks can be detected at the intersections when
the source is directly downwind of the receptor; in this case high variance values are
due to the fact that the plume is still narrow and not very well mixed and the complex
flow situations at the intersection cause high level of intermittency.
+90° model orientation
This model orientation allows to study the ground level concentration of three different
parallel streets downwind of the source: Bickenhall Street (Y=-340mm), York Street
(Y=-719mm) and Crawford Street (Y=-1060mm). Plots of the (non-dimensional) mean
concentrations against receptor location along the three streets considered for every
different source locations considered along Marylebone road are reported in the figure
5-3; (non-dimensional) variances are shown in the figures 5-4.
The concentration levels (mean and variances) and plume behaviour in York St will be
discuss first, Crawford St and Bickenhall St results will then be compared to this street.
In general mean concentrations and variances were highest in Bickenhall Street, closest
street to the sources; concentration levels reduced with downstream distance through
York Street and Crawford Street.
York Street data for source locations at X=-531 mm, -337 mm and -224 mm show
similar profiles with peaks of mean concentration and variance in correspondence of the
intersection with Upper Montagu St (X=-531 mm); this behaviour corresponds to the
pollutant channelling down Balcombe St. As the source locations move in a more
positive direction towards X=-224 mm the plume widens with higher concentration
towards X=0mm. For a source at X= -112 mm the plume is effectively split with peaks
in the two streets, Upper Montagu St and Gloucester Pl. Relatively high concentrations
were detected in York Street between the two parallel streets mentioned above; this is to
be expected as the pollutant appears to be channelled into the street from both
intersections and then well mixed inside the street canyon. Interestingly there is no peak
or increase in concentration for receptors close to the mouth of the canyon between
buildings 8-9. One might expect pollutant to be channelled along this road, however this
is not the case and it may be due to the presence of a building offset of 60 mm in the
205

Chapter 5 Neighbourood/Street scale results
upwind intersection; the effect of this particular geometry is widely described in
Scaperdas (2000). Results for source location at X=-60 mm are similar, showing two
peaks at the same parallel street intersection with a slightly higher value for the X=0
mm receptor; this is expected as the source is now close to the main intersection.
Receptors in Bickenhall St show that, for source locations with X more negative than
-112 mm, high peaks of mean concentration and variances are seen at the Upper
Montagu Street intersection, the location of the peaks is the same as in York St.; the
mean concentration and variance levels are higher in this case due to the proximity to
the source. Results with a source location at X=-337mm, -224mm and -112mm show a
region of high mean concentration values from around X=-337 mm to -531 mm. This is
due to the channelling of pollutant through the street canyon between buildings 5 and 6
or along Upper Montagu Street and then into the Bickenhall St, and to the presence of
an intersection with a building offset of 60 mm. Oddly for a source location directly
upwind of the street canyon between the buildings 5 and 6 (X=- 337mm) there are no
related peaks of concentration for the receptor at X=-337mm; we might expect the
receptor to see a high tracer level at this point but this is not the case: the highest mean
concentration level in this street is seen at X =-337mm for a source location at X= -
112mm. For the source location at X= -60 mm, results in Bickenhall St show two
distinct peaks in concentration; one for the 5-6 street canyon intersection and another
for the Gloucester place intersection. The York St data also shows this same ‘twin peak’
trend, but one of the peak is located in correspondence of the Balcombe St intersection
instead of the 8-9 street canyon intersection.
The Crawford Street data shows different set of characteristics from Bickenhall Street,
but analogous to York street for these negative X source locations; similar channelling
effects of the streets coming into play once again also in this case. The levels of
concentration are lower than in York Street and Bickenhall street, reflecting the more
diluted plume at Y=1070 mm downstream. At this distance downwind the plume does
not appear to have significantly widened at all, this behaviour is the same mentioned by
Hoydish and Dabbert (1994); this study highlighted that for wind directions parallel to
the direction of the streets within the array, a reduction in the lateral spread of the plume
can occur at large distances due to the channelling of the flow along the streets.
206

Chapter 5 Neighbourood/Street scale results
Figure 5-3 Plots of the (non-dimensional) mean concentrations against receptor
location along the three street considered (Bickenhall St, top graphs, York St, center
graphs, and Crawford St, bottom graphs) for different source locations along
Marylebone road
207

Chapter 5 Neighbourood/Street scale results
Figure 5-4 Plots of the (non-dimensional) concentration variances against
receptor location along the three street considered (Bickenhall St, top graphs, York
St, center graphs, and Crawford St, bottom graphs) for different source locations
along Marylebone road
208

Chapter 5 Neighbourood/Street scale results
With the source at the main intersection (X=0mm) the results of the three different
streets show a similar behaviour: a sharp peak of mean concentration and variance is
detected at the receptor directly downwind; the pollutant seems to reach the receptor
with relatively small dilution. An important point to note is that the maximum mean
concentration value for Bickenhall Street (closest street to the source) is lower than that
seen in York street, however the variances are considerably higher in Bickenhall Street
(see figure 5-5). For example, this means that a receptor at the intersection in Bickenhall
Street could be potentially exposed to a dosage that is 4.25 times the mean (C*=0.8,
c*=1.3 and consequently C*+2c*=3.4), whereas the most likely to be seen in York
Street is 2 times the mean (C*=1.2, c*=0.6 and consequently C*+2c*=2.4).
The consequence is that a person could be exposed to a much higher instantaneous
concentration in Bickenhall street even though the mean value is lower.
Figure 5-5 Comparison of the (non-dimensional) mean concentrations (left) and
variances (right) against receptor location along Bickenhall St, York St and Crawford
St with source located at the Marylebone road-Gloucester Place intersection
For source locations in the east side of the model (X= 66mm, 132mm, 260mm and
388mm), the mean concentration profiles in Bickenhall Street, York Street and
Crawford Street have fairly similar behaviour, with mean concentration and variance
levels reduced with downstream distance. All sets of profiles are dominated by
channelling of pollutant down Gloucester Place and into each side street respectively.
The pollutant is channelled into Bickenhall Street, York Street and Crawford Street and
is detected at up to around X= 400mm; ground level concentrations are not seen after
209

Chapter 5 Neighbourood/Street scale results
this point due to the presence of high and wide buildings (7, 10 and 10a). In Bickenhall
Street the peaks of the profile’s back is broader compared to the other profiles. This is
another example of the variability of the results due to the building geometry.
Moving the source further away from the intersection (X=514mm) peaks of mean
concentration and variance appear on all the profiles at x = 400 mm; it is unclear why
this peak occurs as it is far from an intersection or parallel street. Probably there is a
contribution to the pollutant levels coming from tracer carried over roof tops and
exchanged into the street; the lack of concentration measurements at roof height doesn’t
allow to verify this affirmation.
As previously observed for the -90° model orientation, concentration variance results,
(see figure 5-4) show almost the same pattern as the mean concentration, but with
sharper peaks at the intersections.
5.2.2 Concentration vs. source location
This type of analysis of the experimental measures was carried out for several receptor
locations in order to evaluate the mean concentrations due to the emission of a uniform
line source located in Marylebone road between Balcombe St and Baker St and
determine the contribution of the different source locations belonging to the line
emission. Only receptor locations where meaningful amounts of tracer concentration
have been detected (complete profiles between Balcombe St and Baker St, see for
example figure 5-6) were examinated. Receptor at different heights (experiments with -
90° model orientation) and at different distance downwind the line source (experiments
with +90° model orientation) were investigated to study how the mean concentration
from a line source can change for variation of this two parameters (height and distance).
Firstly, the results for the -90 degree model orientation will be shown, and then the +90°
model orientation result will be presented.
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Chapter 5 Neighbourood/Street scale results
-90° model orientation
Plots of the (non-dimensional) mean concentration against source location along
Marylebone road for different receptor locations (X=-150mm, -75mm, 0mm, 75mm,
150mm, 240mm, 300mm and 390mm, see figure 3-13) and heights (ground level,
Z=10mm, mid canyon, Z=50mm, roof of building 1 located in Dorset Sq, Z=90mm, and
roof of building 3 located in Melcombe St, Z=160mm) along Dorset square/Melcombe
Street (Y=540mm) are reported in the figure 5-6. Related mean concentration obtained
from integral calculation and attributable to a uniform and steady line source emission
in Marylebone road are presented in the figure 5-7.
As highlighted in figures 5-6, three groups of receptors can be identified on the base of
the extension of the line source that can influence exposure levels at a specific receptor
location:
1. Receptors at X=0mm (intersection between Gloucester Place and Dorset
square/Melcombe Street), X=-75 and -150 mm (located at Dorset Square, west
side of the model) are under the influence of the source locations between X=
-500mm and 150 mm.
2. Receptors at X=150, 240, 300 (located at Melcombe St, east side of the model)
and 390 mm (intersection between Glentworth Street and Melcombe Street) are
influenced by source locations between -100 and 600 mm.
3. Receptor at X=75 mm (spatially between the two groups listed before) is under
the influence of all sources that are a contributing factor for the previous groups
of receptors (locations between -500 and 600 mm).
Similar trends of the mean concentration profiles can be identified for each of the
groups of receptors cited above at all the different heights analyzed; receptors located at
the intersections show higher values than the other receptors of a single group, as it is to
be expected for a receptors directly downwind the line source. In general, intersections
are the most exposed locations to a uniform line emission (see figure 5-7).
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Chapter 5 Neighbourood/Street scale results
Figure 5-6 Non-dimensional mean concentration against source location along
Marylebone road for different receptor locations and heights along Dorset
square/Melcombe Street
212

Chapter 5 Neighbourood/Street scale results
ClineUH/q
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-200 -100 0 100 200 300 400
X receptor location (mm)
Z=10 mm Z=50mm Z=90 mm Z=160 mm
Figure 5-7 Non-dimensional mean concentration due to a steady line source in
Marylebone Road for different receptor locations and heights along Dorset
square/Melcombe Street
Under the roof height (Z=90mm for receptor in Dorset Sq and Z=160mm for receptors
in Melcombe St), the mean concentrations from every source location at a specific
receptor remains approximately the same for every different heights considered; this
evidence demonstrate that inside the canopy (both in correspondence of intersections,
squares and street canyons) the mixing of the pollutant and his recirculation behind the
building due to the local flow has the effect of spreading more or less uniformly the
pollutant over the entire height of the local geometry of the canopy. This behaviour is
particularly evident in the street canyon of Melcombe St, where the mean
concentrations appear to be almost the same not only for different heights, but also for
the entire length (see receptors at X=150, 240 and 300mm in the figures 5-6 and 5-7)
and width (same concentrations in the leeward side, X=515mm, windward side,
X=580mm and at the center of the street canyon, X=540mm, see figure 5-8) of the
canyon. Below the roof level all the receptor locations appear to be exposed to high
values of mean concentrations due to a uniform line source (see figure 5-7); difference
between concentrations from a line source detected at the intersections and elsewhere
are not so significant.
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Chapter 5 Neighbourood/Street scale results
Figure 5-8 Non-dimensional mean concentration against source location along
Marylebone Rd for ground level receptor locations inside the street canyon of
Melcombe St: leeward side, X=515mm, windward side, X=580mm and center of the
street canyon, X=580mm
For all the receptor locations, decreases on mean concentrations are detected at roof
level and outside the canopy. These reduces are very significant in correspondence of a
street canyon (see for example receptor at X=150, 240 and 300mm), where the
concentrations fall rapidly to very low values (till an order of 10 lower than
concentration inside the canyon), while smoother decreases are seen in proximity of
(X=75 and -75mm) and, especially, at the intersections (see X=0mm and X=390mm).
The concentration reduction at the roof height is quite uniform for every source location
considered, with the exclusion of the source locations between X=-400mm and X=-
200mm, for which the decrease is less evident. These sources are located in proximity
of the Marathon House tower (located in building 2 between X=-404 and X=-327); the
presence of a tall buildings (Z=265mm) creates additional mixing effects that permits to
lift and transport the pollutant over the roof. Pollutant exchange with the mean flow at
roof level is confirmed by the fact that, for the receptor location nearest to the Marathon
House tower (X=-150mm), source position between X=-400 and -300mm produce the
same mean concentration at ground level as the ones due to a source located at the main
intersection (X=0mm); in this case pollutant levels are defined not only by channeling
effects inside the canopy but also by exchange mechanisms at roof levels.
The effect of Marathon House tower is visible also in the north side of Dorset Square
(Y=-845), where mean concentration values at roof height are equal or higher than ones
214

Chapter 5 Neighbourood/Street scale results
inside the canopy (see figure 5-9). This behaviour shows that the re-circulation present
in a street canyon does not occur to the same degree as in an open space with a large
garden as Dorset Square, where much of the pollutant is simply carried over the roof
height and not caught in a re-circulation, giving lower dosage levels at ground levels.
Figure 5-9 Non-dimensional mean concentration against source location along
Marylebone Rd for different receptor locations and height in the north side of Dorset
Square (Y=-845)
+90° model orientation
Plots of the (non-dimensional) mean concentration against source location along
Marylebone road for different ground level (Z=10mm) receptor locations along the
three parallel streets downwind of the source (Bickenhall Street, Y=-340mm, York
Street, Y=-719mm, and Crawford Street, Y=-1060mm) are reported in the figure 5-10.
Related mean concentration due to a uniform and continuous line source derived from
integral calculation are shown in the figure 5-11.
Similar trends of the mean concentration profiles for every receptors considered can be
seen in York Street and Crawford Street (see figure 5-11); Bickenhall Street, on the
contrary, show a different behaviour of the mean concentrations profiles, especially, for
receptors west of the Gloucester Place intersection (X=-277mm, X=-337mm and X=-
431mm). This most probably arises because of the similar street geometry of the first
two streets, that it is quite different from Bickenhall Street one (geometry characterized
by the presence of an intersection with a building offset in the west side of the model).
215

Chapter 5 Neighbourood/Street scale results
Figure 5-10 Non-dimensional mean concentration against source location along
Marylebone road for ground level (Z=10mm) receptor locations along Bickenhall
Street (top-left), York Street (bottom) and Crawford Street (top-right)
ClineUH/q
-450 -350 -250 -150
0.0
-50 50 150 250
X receptor location (mm)
Bickenhall St York St Crawford St
Figure 5-11 Non-dimensional mean concentration due to a steady line source in
Marylebone Road for receptor locations in Bickenhall St, York St and Crawford St
216
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5

Chapter 5 Neighbourood/Street scale results
In general, mean concentrations from a uniform line source in Marylebone road (see
figure 5-11) are highest in Bickenhall Street, closest street to the line source.
Concentration levels reduce with downstream distance through York Street and
Crawford Street; the lower concentrations are due to the dilution of the plume over the
increased distance it has to travel. There is an important exception to this behaviour, that
is the mean concentration value at the receptor site located in correspondence of the
main intersection between Gloucester Place and Bickenhall Street; in this case the mean
concentration is lower than at York Street and Crawford street intersections. This is
probably due to the fact that the plume at Bickenhall intersection is still narrow, not well
mixed and consequently characterized by high concentration variances (see for example
figure 5-12) and contemporary further downstream concentrations may remain
relatively high at street level due to the limited vertical lofting of the channelized plume.
Figure 5-12 Comparison of the (non-dimensional) mean concentrations (left) and
variances (right) against source location at Marylebone Rd for receptor locations in
Bickenhall St (Y=-340mm), York St (Y=-719mm) and Crawford St (Y=-1080mm)
Mean concentrations from a line source appear to be significantly lower far away from
receptors located at the intersections directly exposed to the line source (Gloucester Pl-
Bickenhall St, Gloucester Pl-York St, Gloucester Pl-Crawford St and canyon 5/6-
Bickenhall street intersections). This behaviour is substantially different from the case
with a -90° model orientation (see figure 5-7), where the concentration due to the
uniform line emission are quite similar everywhere along Dorset Square/Melcombe
Street (both at the intersection and inside the canyon). The concentration levels seen in
Dorset Square\Melcombe Place are higher than concentrations detected with +90°
model orientation; the street geometry is more open in this case allowing for easier
exchange and transport of pollutant.
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Chapter 5 Neighbourood/Street scale results
5.2.3 Dose estimation
The mean concentration profiles presented in the previous section 5.2.2 (concentration
against source location) were used to calculate the dosage levels seen at various receptor
locations along downwind streets for a source moving along Marylebone Road. In
particular, this data were used to simulate air pollution levels due to emissions from a
single car driving along Marylebone Road. Several driving scenarios were devised to
give some insight into various dosage levels that might be experienced from a car
travelling along Marylebone Road in different manners (e.g. free traffic flow with
constant speed or waiting at the sets of traffic lights). The dosage values carried out are
ensemble averages of a car driving down the street many times in an identical manner.
These estimations were carried out only for ground level concentration (Z=10mm), the
most significant height for estimation of traffic exposure levels of the population.
The following driving scenarios were analyzed for the -90° model orientation; for better
understanding, the scenarios were described in full scale terms.
• Case 1: constant speed of 15 km/h along the entire route in Marylebone
Road
• Case 2: constant speed (15 km/h) with a stop of one minute (0.3 s in model
scale) at the traffic light location in the west side of the intersection between
Marylebone Road and Gloucester Place (X=-50mm in model coordinates).
• Case 3: constant speed (15 km/h) with a stop of one minute at the traffic
light location in the east side of the intersection between Marylebone Road
and Gloucester Place (X=50mm in the model),
• Case 4: constant speed (15 km/h) with a stop of one minute at the
intersection between Marylebone Road and Glenworth Street (X = 400mm in
model coordinates)
• Case 5: Constant speed (15 km/h) with a stop of one minute at the centre of
the intersection between Marylebone Road and Gloucester Place (X=0 mm
in model coordinates).
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Chapter 5 Neighbourood/Street scale results
For +90° model orientation the scenarios have been changed to reflect the differing
street geometry downwind of Marylebone Road; the following driving patterns were
examinated:
• Case 1: constant speed of 15 km/h along the entire route in Marylebone
Road
• Case 2: constant speed (15 km/h) with a stop of one minute at the traffic
light location in the west side of the intersection between Marylebone Road
and Gloucester Place
• Case 3: constant speed (15 km/h) with a stop of one minute at the traffic
light location in the east side of the intersection between Marylebone Road
and Gloucester Place (X=66mm in model coordinates)
• Case 4: constant speed (15 km/h) with a stop of one minute at the Tintersection
between Marylebone Road and street canyon 5-6 (X =-337mm
in model coordinates).
• Case 5: Constant speed (15 km/h) with a stop of one minute at the centre of
the intersection between Marylebone Road and Gloucester Place (X=0 mm
in model coordinates).
Waits of one minute at each chosen location are fairly realistic; traffic light timings
measured in the field during the DAPPLE campaign (Arnold et al., 2004) show that
usually traffic light stays red for around 50 seconds. A speed of 15 km/h was chosen on
the base of the mean speed recorded in the field experiments (releases from a car)
carried out on the 11 th November 2004 in the framework of the DAPPLE-HO project
(Shallcross et al., 2005). However, higher or lower constant speeds of the car can be
evaluated considering that increase of the dose are proportionally linear to decrease of
the car speed. As matter of facts, in the integration method used in this work, the travel
time between two source location was calculated by dividing the distance between the
sources by the car speed plus; additional waiting time were considered in that cases
where a stop of the car in a particular location was analyzed.
Non-dimensional mean dose of the 5 driving scenarios for ground level (Z=10mm)
receptor locations along Melcombe Street/Dorset Square obtained with the -90° model
219

Chapter 5 Neighbourood/Street scale results
orientation are reported in the figure 5-13; while non-dimensional mean dose carried out
with the +90° model orientation for ground level (Z=10mm) receptor locations along
the three parallel streets downwind of the source (Bickenhall Street, Y=-340mm, York
Street, Y=-719mm, and Crawford Street, Y=-1060mm) are reported respectively in the
figure 5-14 , 5-15 and 5-16.
Figure 5-13 Non-dimensional mean dose at ground level receptors along Melcombe
St/Dorset Sq for different driving scenarios
Figure 5-14 Non-dimensional mean dose at ground level receptors along
Bickenhall St for different driving scenarios
220

Chapter 5 Neighbourood/Street scale results
Figure 5-15 Non-dimensional mean dose at ground level receptors along York St
for different driving scenarios
Figure 5-16 Non-dimensional mean dose at ground level receptors along Crawford
St for different driving scenarios
The case where the car moved at constant speed through the model (Case 1) consistently
has the lowest associated dosage for all receptors downwind of the source; this is
evident for all orientations and downwind streets considered. Cars not stopping clearly
don’t leave any time for pollution to accumulate. This case evidently shows how
221

Chapter 5 Neighbourood/Street scale results
keeping traffic moving free through city streets can reduce overall dosage levels. The
highest dosages were always seen at the main intersection.
For all the scenarios where the simulated car stops at or near the main intersection
(Cases 2, 3, and 5), impressive increases on dosage levels were seen; in all cases (both -
90° and +90° model orientations) high dosage levels are seen in the downwind streets,
even for the streets more distant from the source (i.e. Crawford street with +90°model
orientation). In the cases 2 and 3, where the waiting location is at the traffic light
(location just off centre of the intersection), different behaviour can be identified for: 1)
receptor locations at the intersection or in the opposite side of the waiting location and
2) receptor locations in the same side of the waiting location. For the first group of
receptors slightly lower dosages are seen than in case 5 (stops at the centre of the
intersection), while for the second ones higher value can be detected. These results
show the effect of cars waiting at traffic lights: in all cases, with only one exception
(case 3 for the -90° orientation), the highest dosages are seen by people in the
downwind street in correspondence of the main intersection. In most of the cases the
dosage levels can be 3-4 times higher than a car moving through at constant speed. In
addition to this, considering street at different distance downwind the source (+90°
model orientation) the overall dosage levels remain high for the receptors in the centre
of all the downwind streets and not falling significantly over the entire length of the
street.
The exception previously mentioned was case 3 for the -90° model orientation, with
receptors in Dorset Square/Melcombe Street; although in this case the car stops near to
the intersection, higher dosage levels was seen elsewhere along the street canyon of
Melcombe Place and in particular at the intersection with Glentworth Street. This is a
good example of how predicting the dispersion and hence location of the highest
dosages is not always an easy task.
For cases with offset waits from the main intersection (case 4), generally lower dosage
levels were seen. However where street geometry dictates (i.e. in small canyons parallel
to oncoming flow, such as the 5-6 canyon or Glentworth St) dosage levels can be high.
Once, however, a receptor is far enough away from the ‘waiting location’ the dosages
levels fall to being similar to the constant speed case.
222

Chapter 5 Neighbourood/Street scale results
The results for all cases show that waiting at or near intersections greatly increases the
downwind dosage for a person immediately downwind, i.e. X=0mm. Often high dosage
remain in adjacent side streets and further street downwind. This shows how pollutant
from a main street can affect several streets downwind.
Variations in local street geometry can cause large variations in the dosages seen by
people nearby, such as those seen in Bickenhall Street at X=-139, just behind the
building 6 (+90° model orientation) or in Melcombe Street at X=75, just behind the
corner of building 3 (-90° model orientation), where dosages were seen to be greatly
reduced.
The estimation of the dose carried out in this work has given insight into the effect of
different car movements and their impact on receptors or people downwind.
Figure 5-17 Schematic of the experimental field campaign carried out on
November ‘04 in the framework of the DAPPLE-HO project (Shallcross et al., 2005)
223

Chapter 5 Neighbourood/Street scale results
In order to evaluate the reliability of these results, a comparison with the data of the
field campaign carried out on the 11 th November 2004 in the framework of the
DAPPLE-HO project (Shallcross et al., 2005) was done. In this campaign two
experiments involving tracer releases from a car moving along Marylebone Road were
executed; the wind direction was the same used in the experiments with model
orientation equal to +90° and receptors in York Street were investigated in analogy with
the wind tunnel experiments (see schematic diagram of the experimental arrangement in
figure 5-17).
For comparison purpose, two further cases were analyzed considering a +90° model
orientation, receptors 9, 10, 11, 12 and 13 in York Street and the car speed recorded
during the two field experiments (see figure 5-18); the car speeds between Baker Street
and Upper Montagu/ Balcombe Street were used to estimate the travel time between the
source locations analyzed in the wind tunnel experiments, necessary to carry out the
integral calculation of the dose.
Figure 5-18 Car speed and position during experiment 1 (top) and 2 (bottom)
carried out on November ‘04 in the framework of the DAPPLE-HO project
(Shallcross et al., 2005)
224

Chapter 5 Neighbourood/Street scale results
Results obtained are reported and compared with data from the field campaign in table
5-1.
Receptor
Experiment 1 Experiment 2
Field Wind tunnel Field Wind tunnel
9 0.227 0.231 --- 0.346
10 0.237 0.169 0.256 0.254
11 0.171 0.155 0.318 0.232
12 0.138 0.221 0.395 0.331
Table 5-1 Comparison of doses (in non-dimensional form) measured in the field
campaign and estimated from wind tunnel experimentation
Considering the unsteadiness characteristic of the field experiments, very encouraging
agreement between laboratory and field results were found; this demonstrate the
capability of the method used in wind tunnel to estimate reliably the exposure dosages
due to traffic emission.
225

Chapter 5 Neighbourood/Street scale results
5.3 Correlation experiment results
This experimentation was aimed at giving detailed information on how two point
sources interact to give increased or diminished total concentration fluctuations and the
associated correlation; the correlation of two sources can be vitally important in
measuring instantaneous concentration levels as a result of emissions from traffic
source. Two different investigations were carried out during this set of experiments;
firstly a preliminary work with the undisturbed boundary layer (UBL, see section 5.3.1),
aimed at testing the method of measure and, then, an extensive study with the Dapple
model (see section 5.3.2). Only ground level receptor locations were analyzed in both
investigations.
5.3.1 Undisturbed boundary layer
Several measurements of the correlation between concentration fluctuations of two
point sources were carried out for a wide range of separations of the sources, various
downstream distance of the receptor from the sources (Y=270mm, 540mm and
1080mm) and receptor locations (inline with the reference source, X=0mm, or offset,
X=100mm). Only one reference source location (X=0mm, Y=0mm) was used in this
preliminary experiment. The data obtained with receptor location at (X=100mm,
Y=270) were discarded because the reference source signal on this receptor was too
weak (at least an order of 10 smaller than the other source location), causing percentage
error in the calculation of the correlation coefficient unacceptable (larger than 50%, see
section 3.5.4). Results are shown in figures 5-19 and 5-20.
Figures 5-19 and 5-20 (left curves) show profiles of the concentration variances c 2
against source separation. 2
c 1 and 2
c 2 are variances measurements for each source
operating separately and ( ) 2
c + c for both source operating together;
1
2
226
2
c 1 referred to the
source that remain fixed in the reference position, while 2
c 2 comes from the source
which moves at different separation along the perpendicular direction to the wind.
The c1 variance measurements (fixed source) were generally constant throughout each
of the different case studied, as required for the correlation estimation. The variability of

Chapter 5 Neighbourood/Street scale results
the measurements appeared to depend on the receptor locations. Receptors closer to the
source showed scatter over a larger range in values; this may have had the effect of
slightly increasing the error of the correlation factor. The only factor that affected the c1
measurements was the interference seen between the two stacks at close separation.
This generally occurred in all the measurements, but can be seen only at extremely close
separation (about 20 mm), where it is known that the correlation coefficient is
approximately one; so the correlation estimation was not seriously affected. This same
interference was seen in Warhaft (1984) and appears to be unavoidable in this type of
experiment.
The c2 variance measurements all followed similar profile shapes; the concentrations
seen at the receptor showed a Gaussian behaviour with the maximum located at the
point directly upwind of the receptor. The c1+c2 variance measurements curves showed
that the total variance was increased when the sources were close together. In both
measurements the plume appeared to grow in width with the downstream distance.
The overall levels of fluctuations fell off rapidly with downstream distance (for example
with an inline receptor non-dimensional c1 variance decreased from about 37, at
Y=270mm, to 2.9, at Y=540mm, and 0.11, at Y=1080, see figure 5-19); this decay in
variance was also seen in other dispersion experiments, such as Fackrell & Robins
(1982).
Variance measurements with an offset receptor show similar behaviour to the case of an
inline receptor. The variance levels dropped with downstream distance and the plumes
appeared to grow in width. The total variance is reduced in comparison to inline
receptor (for example at Y=540mm the maximum value of the non-dimensional c1+c2
variance decreased from 9.0 at Y=270mm to 2.0 at Y=540mm) caused by the fact that
receptor was not located in the centre of the reference source plume. For an offset
receptor location the c2 variance measurements can be higher than the c1 values; this did
not occur for the receptor inline with the source as the reference source was always in a
location directly downwind and closest to the receptor. Despite the offset receptor
seeing these higher values of c2 when the mobile source was inline with receptor, the
highest total variance was still seen when the sources were closest together.
227

Chapter 5 Neighbourood/Street scale results
From 2
c 1 ,
c and ( ) 2
c
2
2
c + measurements (left curves of figures 5-19 and 5-20), the
1
2
profiles of the correlation against source separation (right curves of figures 5-19 and 5-
20) were obtained by means of the following formula:
COR =
( + c )
1
2
2
c − c − c
2c
2
1
2
1
2
2
Figure 5-19 Non dimensional
c ,
2
1
2
c 2 and ( ) 2
1 c2
228
c + against source separation (left
graphs) and inferred correlation (right) for receptor locations inline with the
reference source (X=0mm) at different distances (Y=270, 540 and 1080 mm)

Chapter 5 Neighbourood/Street scale results
Figure 5-20 Non dimensional
c ,
2
1
2
c 2 and ( ) 2
1 c2
229
c + against source separation (left
graphs) and inferred correlation (right) for receptor locations inline with the
reference source (X=0mm) at different downstream distances (Y= 540 and 1080 mm)
Correlation results for receptor locations inline with the reference source will be
analyzed first.
The correlation coefficient was consistently shown to be a function of the source
separation. At large spacing the results tended to show correlations of zero; as the
sources moved closer together the correlation increased to a maximum value of one,
when the separation tended to be zero. Where the increase occurs depends on the
downstream distance of the receptor. The maximum correlation of one (receptor sees
perfectly correlated plumes from both sources leading to an increase in the total
variance) was seen for all receptors, regardless of location (inline or offset).
The correlation coefficient turns out to be also a function of the downstream distance of
the receptor. Looking at the results with a receptor location inline with the reference

Chapter 5 Neighbourood/Street scale results
source (see right curves of figure 5-19), at short distances from the source (Y=270mm),
the receptor sees positively correlated results only for very small separation (±
37.5mm); the plumes from each source have not had enough time to grow sufficiently
wide, at these short distances, and do not overlap at anything other than small
separations. For increasing distances the correlation profiles widen; at Y=540mm and
1080mm correlation different from zero are seen respectively for separation between
about ± 75mm and ± 150mm. This means that the receptors further downwind sees
positively correlated concentrations for a larger range of source separations; this
behaviour is caused by the plumes mixing as they grow laterally over the downstream
distance, and hence the increased chance of seeing pollutant from both sources at the
same time increasing the total variance. A more clear demonstration of the influence of
the downstream distance on correlation results can be obtained plotting the correlation
coefficient against downstream distance of the receptors for fixed source separation (see
figure 5-21).
Correlation
0.7
0.6
0.5
0.4
0.3
0.2
0.1
separation=+50
separation=-50
separation=+100
separation=-100
0
200
-0.1
400 600 800 1000
Downstream distance (mm)
Figure 5-21 Correlation coefficient - Downstream distances of receptors inline with
the ref. source (X=0 mm) profiles for fixed source separation (± 50 and ± 100 mm)
The results obtained with an offset receptor location (X=100mm, see figure 5-20)
confirmed that correlation is a function of the source separation and of the downstream
distance of the receptor from the sources. Correlation coefficient obtained with this
configuration shows a dependence on these two factors similar to what we see with an
inline receptor location. For all receptor locations considered, correlation profiles widen
when the downstream distance increases, correlation values tends to be zero for large
source separation and the highest positive correlations are still present for close source
230

Chapter 5 Neighbourood/Street scale results
separations. In comparison to the inline receptor case, correlation results with an offset
receptor showed a different behaviour when the mobile source (source 2) moves from
reference source location towards a position inline with the receptor and further (for
example source separation between +75 and +250 mm with a downstream distance
X=540mm). In this region the correlation coefficient becomes negative indicating that
the interaction between the plumes generates a destructive interference that has the
effect of diminishing the total concentration variance. The negative correlations region
for increasing downstream distance of the receptor occur at a slightly larger source
separations (see figure 5-20).
The results obtained here agree with Warhaft (1984); both study showed that correlation
between two sources are dependent on the separation of the sources, the downstream
distance of the receptor from the sources and the receptor location (inline or offset).
This agreement permitted to demonstrate the reliability of the method of measure
implemented also for pollutant dispersion studies.
5.3.2 Dapple model
The correlation experiments with the Dapple model were carried out moving the sources
along Marylebone road; only -90° model orientation and receptor locations along Dorset
Square/Melcombe Street were considered. Several combinations of receptor and
reference source locations were examined in order to evaluate the full range of effects of
the geometry of the local environment in the determination of the fluctuations levels
deriving from two point sources; in particular, we studied the two combinations
previously analyzed with UBL at the downstream distance Y=540mm, plus the most
relevant combinations (receptors shielded and/or reference sources shielded or not by
buildings) in correspondence or in proximity of the main and secondary intersections
along Dorset Square/Melcombe Street, locations, where, during the moving source
experiments, we identified the maximum fluctuation levels due to a single point source
(for detail see section 3.5.3). The first two combinations were analyzed for comparison
purpose, while the other permitted to evaluate the most critical location in the model.
Results relative to the comparison between undisturbed boundary layer and Dapple
model correlation are shown in figures 5-22 and 5-23; both correlation against source
231

Chapter 5 Neighbourood/Street scale results
separation (bottom graph) and individual measures of the variances 2
c 1 ,
( ) 2
c
1
2
232
2
c 2 and
c + (top graphs) that had to be made in order to estimate the value of the
correlation coefficient are reported. All the other cases analyzed with the Dapple model
are presented in figures 5-24/5-29; for brevity measures of the variances 2
c 1 ,
( ) 2
c
c + are not reported here.
1
2
2
c 2 and
First, the comparison between undisturbed boundary layer and Dapple Model results
will be discussed. Figures 5-22 and 5-23 shows that the overall levels of concentration
variances ( c ,
2
1
c and ( ) 2
c
2
2
c + ) are much lower in presence of the urban model than
1
2
for the UBL case and that pollutant can be seen inside the Dapple model for larger
separation than in open terrain; these behaviours are due to the increased mixing and
dilution of the pollutant inside the urban canopy at short distances. As highlighted in the
moving source experiments (see section 5.2.1 and 5.2.2), the variances obtained with
the urban model appear to be significantly influenced by the local geometry of the
canopy.
2
c 2 measurements, in particular, show profiles considerably different from the
Gaussian profiles obtained for the UBL; also the total variances ( ) 2
different characteristics in the two cases.
c + c have
As a consequence, correlation profiles with Dapple model show behaviours directly
connected to the geometry of the local environment; this factor can generate important
differences with the UBL cases. For example, the results obtained with receptor location
and reference source at X=0mm and Dapple Model (figure 5-22) show a greatly larger
region characterized by positive correlation for negative separations than the UBL case.
This is due to strong circulating vortices developed for the presence of the Marathon
House tower in the west side of Marylebone road, that permits the plume to travel
perpendicularly to the prevailing wind direction for large distance (400mm) from the
intersection and so increase the fluctuation level at the receptor location. This same
behaviour even more evident is present also for the case with receptor location offset (at
X=100mm, see figure 5-22); in this case the receptor is not directly downwind the
reference source and for negative separation the mobile source contribution to the total
variances is more similar to the one of the reference source, giving higher correlation
values. Looking at positive separation, recirculation vortices that carry the plume
1
2

Chapter 5 Neighbourood/Street scale results
towards the intersection are not present and the correlations obtained with Dapple model
appear to be similar to the UBL case; only a slightly larger separation area with positive
correlation due to increased channelling and mixing of the plumes along Gloucester
Place can be seen (0/+100mm with model instead of 0/+75mm with UBL).
Although these significant differences, correlation profiles with Dapple model keep
some common characteristics with the open terrain case. In both cases the correlation is
a function of the source separation and, in particular, tends to be zero and one
respectively for large separation and very small separation; beyond, in both
configurations, the correlation depends on the receptor location (inline with the source
or offset) and region characterized by negative correlation (destructive interference) can
be appear. However, the building geometry is probably the factor that strongly
influences the correlation in urban areas; dependence on local geometry is widely
confirmed by the correlation results obtained using Dapple model and analyzing several
receptor and source shielded by buildings (see figure 5-24/5-29).
c 2
*
DAPPLE Model
0
-450 -350 -250 -150 -50
-0.5
50 150
Separation (mm)
c1+c2^2 non-d c2^2 non-d c1^2 non-d
Correlation
3
2.5
2
1.5
1
0.5
-450 -350 -250 -150
0.0
-50
-0.2
50 150
Separation (mm)
DAPPLE UBL
Figure 5-22 Comparison of non-dimensional
233
c ,
2
1
1.2
1.0
0.8
0.6
0.4
0.2
2
c 2 and ( ) 2
1 c2
c + against source
separation (top) and correlation (bottom) obtained with Dapple Model and UBL for
receptor at (X=0 mm, Y=540 mm) and reference source at (X=0mm, Y=0mm)

Chapter 5 Neighbourood/Street scale results
c 2
*
DAPPLE Model
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-500 -300 -100 100 300 500
Separation(mm)
c1+c2^2 non-d c2^2 non-d c1^2 non-d
Correlation
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-500 -300 -100-0.2 100 300 500
-0.4
-0.6
Separation (mm)
DAPPLE UBL
Figure 5-23 Comparison of non-dimensional
234
c ,
2
1
2
c 2 and ( ) 2
1 c2
c + against source
separation (top) and correlation (bottom) obtained with Dapple Model and UBL for
receptor at (X=100 mm, Y=540 mm) and reference source at (X=0mm, Y=0mm)
Figure 5-24 Correlation against source separation for different reference sources
(X=-100, -50, 0 and 50 mm, Y=0 mm) at receptor (X=0 mm, Y=540mm)

Chapter 5 Neighbourood/Street scale results
Figure 5-25 Correlation against source separation for different reference sources
(X=-100, -50, 0 and 50 mm, Y=0 mm) at receptor (X=-150 mm, Y=540 mm)
Figure 5-26 Correlation against source separation for different reference sources
(X=-100, and 0 mm, Y=0 mm) at receptor (X=-75 mm, Y=540 mm)
Figure 5-27 Correlation against source separation for different reference sources
(X=300, and 400 mm, Y=0 mm) at receptor (X=390 mm, Y=540 mm)
235

Chapter 5 Neighbourood/Street scale results
Figure 5-28 Correlation against source separation for different reference sources
(X=300, and 400 mm, Y=0 mm) at receptor (X=240 mm, Y=540 mm)
Figure 5-29 Correlation against source separation for different reference sources
(X=300, and 400 mm, Y=0 mm) at receptor (X=75 mm, Y=540 mm)
The correlation between fluctuating concentrations appears to be influenced by local
geometry of the buildings in a similar way to the mean concentration. Similar trends of
the correlation profiles can be recognized for the following three groups of receptors:
1. Receptors at X=0 mm (intersection between Gloucester Place and Dorset
square/Melcombe Street, see figure 5-24), X=-75 mm and -150 mm (located at
236

Chapter 5 Neighbourood/Street scale results
Dorset Square, west side of the model, and shielded by building 1, see
respectively figures 5-25 and 5-26);
2. Receptors at X=240 mm (located at Melcombe St, east side of the model and
shielded by building 3, Figure 5-28) and 390 mm (intersection between
Glentworth Street and Melcombe Street, figure 5-27);
3. Receptor at X=75 mm (located spatially between the two groups listed before
and shielded by building 3, figure 5-29);
these groups have been just identified in section 5.2.2 for mean concentration.
For the first group of receptors, only from the west side of Marylebone Road, strong
circulating vortices present in the street canyon of Marylebone Road permit the plume
to travel towards the intersection, then to be channelized along Gloucester Place and
transported up the Dorset Square; for these reason positive correlation can be seen for a
wide range of separation with the mobile source located in the west side of the
Marylebone-Gloucester intersection. On the contrary, when the mobile source moves in
the east side of the model the correlation tends to be rapidly zero; re-circulation in the
east side of Marylebone road doesn’t permit the pollutant to be travelled towards the
intersection. Correlation have the tendency to have larger separation region with high
positive correlation when the reference source moves inside the canyon of the west part
of Marylebone Road (X=50 mm and 100 mm), that is the area where the re-circulation
vortices is well developed.
The second group of receptors show a behaviour similar to the first group (a large
separation region with positive correlation), but in this case circulating vortices in
Marylebone Road transport the plume towards the intersection from both the different
sides of the intersection and, so, positive correlation values can be seen both for large
negative and positive source separations.
The correlation results for receptor at X=75mm are an example of how variability of the
local building geometry can generate destructive interaction between the fluctuation of
two sources and consequently produce negative values of correlation even for large
separation of the sources (see case with reference source in X=100 mm in figure 5-29).
237

Chapter 5 Neighbourood/Street scale results
All the cases analyzed show that channeling and re-circulation inside the street canyon
seem to be the dominant effects that define the correlation coefficient between
fluctuating concentrations of two sources in urban area. In general, with some exception
in presence of local variability of the flow, these effects generates larger region with
positive correlation between the fluctuations of two sources than in open terrain; this
demonstrate how the combination of traffic sources in urban area can produce very
significant increasing of instantaneous pollutant concentrations.
238

Chapter 5 Neighbourood/Street scale results
5.4 Discussion of the relevance and application of the results
The wind tunnel experimental results offer useful information about the dispersion
phenomena associated with traffic emission in a real urban area. The use of point
sources for simulating a traffic line source via mathematical integration demonstrate to
be a reliable method to understand the main characteristics of the dispersion field, to
establish source-receptor relationships (both in terms of mean and fluctuating
concentrations) and to evaluate the exposure dosages for different traffic conditions.
This technique has never been used before this work for measuring mean and
fluctuating concentration from traffic emission in urban area; in previous wind tunnel
work vehicles emission were usually simulated by uniform and steady line source
emission.
The work carried out in this thesis offers the possibility of comparing ideal cases with
real ones. In contrast, the results are obviously case-specific and not easily
generalisable; experiments highlighted the strong influence of the local geometry: small
difference in the geometry of the building can cause large variations of the pollutant
concentration levels.
The main characteristics phenomena of the traffic pollutant dispersion highlighted in
these experiments can be summarized as follows. Buildings act like a channel and force
the plume to travel down the street; vertical lofting of the air contaminant usually does
not occur and the plume is mainly trapped below building height. Pollutant exchanges
with the mean flow at roof level are not frequent and appeared to be dominated by the
local effects; for example additional mixing effects due to the presence of a tall
building, like in correspondence of Marathon House tower. In addition to the channeling
effect, the plume is usually transported up the side streets due to the circulating vortices
that develop in the adjacent cross street urban canyons; hence, the plume may travel up
side streets several blocks perpendicular to the prevailing wind direction.
Concentrations remain relatively high at street level further downstream due to limited
vertical lofting of the channelized plume. Intersections showed most pollutant travelling
at close to ground level, with a smooth reduction in concentrations up to the mean flow,
while street canyons showed uniform pollutant concentration levels inside the canyon
239

Chapter 5 Neighbourood/Street scale results
and sharp reductions in pollutant above roof level. Sharp concentration peaks (both
mean and fluctuations) are seen at the intersections; when sources are directly upwind
of a street parallel to the wind direction, high concentrations are seen downwind in this
street, and for sources offset from the parallel streets high levels are often still seen for
the receptor at a nearby downwind intersection.
The adopted experimental strategy allowed an estimation to be made of the pollutant
dose to which someone might be exposed due to the passage of a vehicle along upwind
streets. The dosage values given are ensemble averages of a car driving down the street
many times in an identical manner. The obtained results proved encouraging and the
used methodology has given insight into the effect of different car movements and their
impact on receptors or people downwind; several driving scenarios were analyzed. The
results shows how keeping traffic moving through city streets can reduce overall dosage
levels; a car driving at constant speed gave the lowest overall dosage levels. Waiting at
or near intersections greatly increases the downwind dosage for a person immediately
downwind. Often high dosage remains in adjacent side streets and further street
downwind; this shows how pollutant from a main street can affect several streets
downwind. The highest dosage levels, which were several times higher than the
constant speed case, were seen when the car stopped at or near the intersection directly
downwind the receptor. There were some exceptions where local building geometry
caused unexpectedly high dosages at receptors not located in correspondence of or in
proximity of the intersection; this is a good example of how predicting the dispersion
and hence location of the highest dosages is not always an easy task. When the car
stopped far from the main intersection dosages seen in downwind streets tended to be
lower; at receptors far from the waiting locations, the only dosages that were seen came
from the car as it passed by after the wait at constant speed.
Future work in the DAPPLE site could apply the results taken here to predicting
dosages from real engine emissions, which are often most significant during
acceleration. This would be achieved by modifying the weighting rules in the integral
calculation of the dose. This type of analysis can only be carried out by using a
simulated line source from many point sources, as used in these experiments, and not
with a uniform and steady line source emission.
240

Chapter 5 Neighbourood/Street scale results
An advanced experimental methodology based on the interference method of Warhaft
(1984) was developed in order to analyze the correlation between concentration
fluctuations from two point sources belonging to a line emission. Remembering that
concentration variance connected with a line source can be expressed in term of
correlation between the fluctuations produced by two point sources the measure of these
correlations permitted to establish source-receptor relationships in terms of fluctuating
concentrations and to evaluate the contribution of the most critical emission point in a
line source. The results showed that the correlation depends on the separation of the
sources, the downstream distance of the receptor from the sources, the receptor location
(inline or offset) and on the geometry of the local environment. The building geometry
turn out to be the factor that strongly influences the correlation between fluctuating
concentration from two sources in urban areas; channeling and re-circulation inside the
street canyon are the dominant effects that define the correlation coefficient and can
produce very significant increasing of correlation and consequently on instantaneous
pollutant concentrations.
The full experimental data-set turn out to be very detailed and, although the experiments
were carried out only for two wind direction, can be used for comparisons with both full
scale field experiments and mathematical models (numerical CFD models) within the
framework of the DAPPLE project. This will provide further insight on the validation of
the involved modelling techniques (both mathematical and physical modelling methods)
and could be also used in order to evaluate different models developed using simpler
idealised situations. The techniques developed for the evaluation of the mean exposure
dosages and the correlation between concentration fluctuations, if further perfected, can
lead to an improvement of the existing mathematical modelling techniques that, at the
moment, also due to a lack of experimental data, tend to neglect the effects of non
uniform distribution of pollutant emissions due to traffic queuing patterns at
intersections.
241

6.1 Introduction
Chapter 6
6.City scale results
The following sections present results from the research study carried out within the
MoDiVaSET-2 project (MOdellistica DIffusionale per la VAlutazione di Scenari
Emissivi in Toscana 2). As explained in the section 4.3, before starting with the
evaluation of the emission scenarios, an extensive preliminary work, aimed at
developing a reliable modelling system for the scenario analysis, was carried out. This
preliminary phase included the following studies: the application of several
mathematical models and the model evaluation. The results from these preliminary
studies are presented in the sections 6.2 (modelling applications) and 6.3 (model
evaluation). The scenario analysis results carried out with the modelling system selected
from the model evaluation are treated in the section 6.4.
6.2 Modelling applications
Results of the simulations carried out within the preliminary phase of the MoDiVaSET-
2 project are presented in this section. Contour maps of annual mean concentration for
NO2, NOx, PM10 and SO2 are reported respectively in figures 6-1, 6-2, 6-3 and 6-4. As
described in the section 4.4, simulations were performed by means of:
1. ADMS-Urban (‘ADMS’)
2. CALGRID-CALPUFF-CALINE4 (‘CGPL’, superposition of the results deriving
from CALGRID for the grid area sources, CALPUFF for the point sources and
CALINE4 for the line sources)
3. CALGRID-SAFE AIR (‘CGSA’, superposition of the results deriving from
CALGRID for the grid area source and SAFE_AIR for the point and line sources)
They were then compared with the CAMx full chemistry simulations carried out by
another research group of the Dipartimento di Energetica, University of Florence.
242

Chapter 6 City scale results
< 20
20 - 30
30 - 40
40 - 50
50 - 100
> 100
< 20
20 - 30
30 - 40
40 - 50
50 - 100
> 100
Figure 6-1 NOx annual mean concentration maps [µg/m3]: ADMS-Urban (topleft
map), CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right)
< 15
15 - 20
20 - 30
30 - 40
40 - 50
> 50
< 15
15 - 20
20 - 30
30 - 40
40 - 50
> 50
Figure 6-2 NO2 annual mean concentration maps [µg/m3]: ADMS-Urban (top-left
map), CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right)
243

Chapter 6 City scale results
Figure 6-3 PM10 annual mean concentration maps [µg/m3]: ADMS-Urban (topleft
map), CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right)
Figure 6-4 SO2 annual mean concentration maps [µg/m3]: ADMS-Urban (top-left
map), CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right)
< 14
244
14 - 16
16 - 18
18 - 20
20 - 25
> 25
< 14
14 - 16
16 - 18
18 - 20
20 - 25
> 25
< 2
2 - 3
3 - 4
4 - 5
5 - 6
> 6
< 2
2 - 3
3 - 4
4 - 5
5 - 6
> 6

Chapter 6 City scale results
Looking at the figures, it can be observed that for all the modelling system the pollution
hotspots are clearly located in correspondence of the most densely populated areas, (the
cities of Florence, Prato, Pistoia and Empoli can easily be spotted on the map); in
particular, as we expected, the city of Florence appear to be the most exposed zone for
all the modelling applications.
Already at a first sight, it can be noted how CAMx (the only model that use complete
chemical mechanism) tend to predict higher concentrations than the modelling systems
that use inert approaches (ADMS-Urban, CGPL and CGSA); the results of these last
modelling systems seems to be quite comparable. The simple comparison between
annual average concentrations calculated at the monitoring sites (both background and
roadside sites) by the four modelling systems involved (see tables 6-1/6-4) confirms
these first impressions. Comparison with observed concentrations (see again table 6-
1/6-4) shows, especially for the background sites, a quite good agreement between
ADMS-Urban, CGPL and CGSA and measures for NO2, NOx and SO2, while CAMx
leads to overestimated concentration for these pollutant; all the models seem to
underestimate PM10 concentration values, even if for CAMx is less evident. A more
detailed comparison of the results is reported in 6.3.2.
NOx Measures ADMS CGPL CGSA CAMx
BACKGROUND SITES
FI – Bassi 73.8 49.7 59.4 48.5 376.4
FI – Boboli 57.7 56.2 55.8 43.1 352.5
FI – Novoli 99.8 80.4 71.4 54.6 460.0
FI – Settignano 26.9 31.9 54.8 43.9 273.3
FI - Via di Scandicci 108.9 53.0 59.2 46.7 376.4
FI - Calenzano Giovanni XIII 87.5 35.6 74.2 50.3 165.8
FI - Montelupo Don Milani 59.2 16.0 35.6 32.6 68.7
PO – Fontanelle 76.2 29.3 59.8 42.2 164.3
PO – Papa Giovanni XIII 62.6 33.3 69.5 43.9 330.1
PO – Roma 64.8 47.8 64.7 41.3 247.9
PO - S.Paolo 83.8 36.3 57.2 39.9 159.8
ROADSIDE SITES
FI – Gramsci 185.8 70.2 57.2 45.3 483.9
FI – Mosse 182.4 67.2 63.8 51.5 439.1
FI – Rosselli 300.4 107.7 59.6 48.5 253.9
FI - Empoli Ridolfi 117.0 58.3 29.6 29.0 186.7
PO – Ferrucci 113.7 50.9 65.7 41.7 446.6
PO – Montalese 179.2 23.1 50.3 38.2 103.1
PO – Strozzi 99.8 41.9 60.9 41.6 194.2
Table 6-1 NOx annual concentration at the monitoring stations: comparison
between observations and calculated data by ADMS, CGPL, CGSA and CAMx
245

Chapter 6 City scale results
NO2 Measures ADMS CGPL CGSA CAMx
BACKGROUND SITES
FI – Bassi 37.0 31.4 36.5 31.1 82.2
FI – Boboli 29.8 35.1 34.8 28.1 81.1
FI – Novoli 51.6 43.4 41.7 34.2 85.8
FI – Settignano 20.0 23.0 34.3 28.6 76.4
FI - Via di Scandicci 54.3 34.1 36.4 30.1 82.2
FI - Calenzano Giovanni XIII 29.0 25.6 42.8 32.0 64.7
FI - Montelupo Don Milani 31.3 15.1 23.7 21.9 40.0
FI - Scandicci Buozzi 47.1 31.7 39.4 29.7 76.8
PO – Fontanelle 37.0 22.7 36.6 27.6 64.4
PO – Papa Giovanni XIII 35.6 22.0 40.9 28.6 80.0
PO – Roma 34.0 32.2 38.8 27.1 74.4
PO - S.Paolo 41.5 26.1 35.4 26.3 63.7
PT – Montale 31.5 18.4 32.1 26.2 44.9
PT – Signorelli 33.8 22.2 26.6 20.0 71.1
ROADSIDE SITES
FI – Gramsci 69.0 39.4 35.4 29.4 86.8
FI – Mosse 66.4 38.5 38.4 32.6 84.9
FI – Rosselli 85.8 50.6 36.6 31.1 74.9
FI - Empoli Ridolfi 57.8 36.3 19.9 19.5 67.8
PO – Ferrucci 48.2 29.3 39.3 27.3 85.2
PO – Montalese 60.6 19.0 32.0 25.3 51.3
PO – Strozzi 49.2 28.9 37.2 27.3 68.8
PO – XX Sett. 69.8 20.7 27.8 21.4 46.8
PT – Zamenhof 37.9 29.7 31.9 26.3 69.0
Table 6-2 NO2 annual concentration at the monitoring stations: comparison
between observations and calculated data by ADMS, CGPL, CGSA and CAMx
PM10 Measures ADMS CGPL CGSA CAMx
BACKGROUND SITES
FI – Bassi 42.5 16.6 17.3 16.9 39.8
FI – Boboli 36.8 17.7 17.0 16.5 27.3
FI - Calenzano Boccaccio 38.2 16.1 17.9 17.1 20.4
FI - Montelupo Don Milani 31.3 15.2 16.4 16.1 18.0
FI - Montelupo Pratelle 46.7 14.5 15.8 15.6 17.0
FI - Scandicci Buozzi 41.6 18.2 17.4 16.6 21.9
PO – Fontanelle 51.1 15.4 17.3 16.5 17.2
PO – Roma 38.4 18.0 17.2 16.4 22.9
PT – Montale 51.9 16.2 16.9 16.4 17.1
ROADSIDE SITES
FI – Gramsci 52.1 18.6 17.1 16.7 37.4
FI – Mosse 38.4 18.0 17.7 17.2 35.9
FI – Rosselli 47.2 17.9 17.4 17.0 34.1
FI - Empoli Ridolfi 25.6 22.9 15.8 15.5 23.8
PO – Ferrucci 30.1 18.0 17.3 16.5 26.5
PO – Strozzi 49.9 18.1 17.0 16.4 22.7
Table 6-3 PM10 annual concentration at the monitoring stations: comparison
between observations and calculated data by ADMS, CGPL, CGSA and CAMx
246

Chapter 6 City scale results
SO2 Measures ADMS CGPL CGSA CAMx
BACKGROUND SITES
FI – Bassi 3.8 2.9 4.2 3.6 7.7
FI – Boboli 2.9 3.1 3.6 2.9 5.5
FI - Via di Scandicci 1.7 3.1 3.9 3.3 6.2
FI - Scandicci Buozzi 2.7 3.0 3.9 3.2 4.7
PO – Roma 4.7 2.4 2.5 2.0 3.5
PO - S.Paolo 5.4 2.2 2.4 1.9 2.6
PT – Montale 2.7 2.0 2.1 1.7 1.3
ROADSIDE SITES
FI – Mosse 2.7 3.8 5.0 4.1 9.7
FI - Empoli Ridolfi 3.0 2.8 1.9 1.6 3.1
Table 6-4 SO2 annual concentration at the monitoring stations: comparison
between observations and calculated data by ADMS, CGPL, CGSA and CAMx
6.3 Model evaluation
Model evaluation was performed in order to evaluate simulation results, compare the
different approaches and, between them, determine the most reliable model for the
scenario analysis. The results of the simulations were compared to measured data from
the monitoring networks of the study area; the techniques used for the evaluation work
have been presented in 4.5. In 6.3.1 a sensitivity study of ADMS-Urban is reported.
Section 6.3.2 presents the results of the validation exercises, while the uncertainty
analysis has been performed in 6.3.3.
6.3.1 Sensitivity study of ADMS-Urban
A sensitivity analysis of ADMS-Urban modeling system have been carried out
calculating annual mean concentrations of NO2 at each background monitoring site in
the study area for four scenarios plus the base scenario. Only background monitoring
station were considered in this analysis, because, as also demonstrated in the validation
exercise (see section 6.3.2), the simulations carried out in this thesis are representative
of this type of site.
The base scenario parameters used in the modelling were shown in table 6-1, while
table 6-2 shows the scenarios considered in the sensitivity modelling. These include
changes in global parameters of the model, as minimum Monin-Obukhov length to limit
247

Chapter 6 City scale results
urban stability, the impact of using an hourly disaggregated emission inventory instead
of constant average emissions, the impact of using data from standard meteorological
observation from monitoring station instead of numerical prediction from a diagnostic
meteorological model and finally the impact of regional background.
PARAMETER VALUE
Minimum Monin-Obukhov
length
248
30 m
Emission inventory Hourly disaggregated IRSE-RT 2001 emissions
Meteorological data
Prediction from the diagnostic meteorological model
CALMET
Regional Background LI-Maurogordato monitoring station
SCENARIO
D1
Table 6-5 Base scenario parameters
PARAMETER
CHANGED
Minimum Monin-
Obukhov length
NEW PARAMETER VALUE
50 m
D2 Emission inventory Constant average IRSE-RT 2001 emissions
D3 Meteorological data
Standard meteorological observations of
PO-Baciacavallo monitoring station
D4 Regional Background Set to zero
Table 6-6 Sensitivity scenarios
NO2 annual mean concentrations calculated for the different scenarios have been
compared one to another and to the monitored values using the exploratory analysis of
the data and the advanced statistical techniques described in section 4.5.2. Scatter and
quantile-quantile plots are reported on figure 6-5, while statistics are shown in Table 6-
3.

Chapter 6 City scale results
NO 2 predicted conc. [ug/m 3 ]
80
70
60
50
40
30
20
10
0
0 10 20 30 40 50 60 70 80
NO 2 observed conc. [ug/m 3 ]
Base D1 D2 D3 D4
Scenario
NO 2 predicted conc. [ug/m 3 ]
249
80
70
60
50
40
30
20
10
0
0 10 20 30 40 50 60 70 80
NO 2 observed conc. [ug/m 3 ]
Base D1 D2 D3 D4
Figure 6-5 Scatter (left) and quantile-quantile (right) plots
MEAN
[µg/m 3 ]
FB SIGMA FS COR FA2 NMSE WNNR NNR
Measures 36.7 0.00 8.95 0.00 1.00 1.00 0.00 0.00 0.00
Base 19.4 0.62 5.10 0.58 0.64 0.46 0.50 0.50 0.42
D1 18.8 0.65 5.06 0.59 0.64 0.46 0.54 0.54 0.46
D2 26.6 0.32 6.96 0.29 0.66 1.00 0.16 0.16 0.13
D3 27.4 0.29 7.33 0.20 0.60 0.93 0.14 0.15 0.13
D4 16.4 0.77 5.10 0.58 0.64 0.46 0.77 0.77 0.69
Table 6-7 Statistical indices based on annual mean concentrations of NO2. Model
performances are defined acceptable if FA2>0.5, -0.3

Chapter 6 City scale results
The use of constant average emissions (scenario D2) instead of the hourly disaggregated
emissions of the IRSE-RT 2001 inventory also leads to considerable increased
calculated concentrations. The reason of this result is imputable to the fact that the
constant average emissions give higher emission values than hourly disaggregate ones
during the night; this is the most critical period of the day for pollutant dispersion due to
the presence of stable atmosphere that reduces the pollutant mixing rate. In this case
performance improvements are only a casualty due to the underestimation of the base
scenario.
The regional background turns out be another important factor that should be take into
account. Neglecting the background (scenario D4) leads to a significant reduction of the
calculated concentrations that decreases the performance of the model.
Contrary to the other parameters, increasing the minimum Monin-Obhukov length to 50
m (scenario D1) doesn’t affect significantly the simulations, although it slightly
increases the mixing of pollutants; the calculated concentrations are very similar
compared to the base scenario.
On the base of the critical analysis of the sensitivity results, the set of parameter used in
the modelling of scenario D3 (hourly disaggregated IRSE-RT 2001 emissions, PO-
Baciacavallo meteorological observation, minimum Monin-Obukhov length equal to
30m and regional background from LI-Maurogordato monitoring station) turn out to be
the best calibration for ADMS-Urban in the present application; for this reason ADMS-
Urban results used in the following validation exercise were carried out adopting the
parameters of the scenario D3.
6.3.2 Validation exercise
Before beginning the calculation of the various statistical indices, a first comparison
between the results calculated by the four modelling systems involved (ADMS, CGPL,
CGSA and CAMx) and the data measured by the monitoring stations was done using
scatter and quantile-quantile plots of the annual average concentrations. Statistics
described in the section 4.5.2 have then been calculated based on annual mean
concentrations. Both exploratory analysis and statistical techniques were carried out
250

Chapter 6 City scale results
separately for each type of monitoring site, first for the background site, then for the
roadside. Scatter and quantile-quantile plots are reported on figures 6-6 and 6-7, while
statistics are shown in Table 6-3. As NOx results are very similar to NO2 ones and the
SO2 monitoring data were not quantitatively significant from a statistical point of view,
only results about concentrations of NO2 and PM10 are presented here.
NO 2 predicted conc. [ug/m 3 ]
NO 2 predicted conc. [ug/m 3 ]
90
80
70
60
50
40
30
20
10
0
90
80
70
60
50
40
30
20
10
0
BACKGROUND SITES
0 10 20 30 40 50 60 70 80 90
NO 2 observed conc. [ug/m 3 ]
ADMS CGPL CGSA CAMx
BACKGROUND SITES
0 10 20 30 40 50 60 70 80 90
NO 2 observed conc. [ug/m 3 ]
ADMS CGPL CGSA CAMx
NO 2 predicted conc. [ug/m 3 ]
NO 2 predicted conc. [ug/m 3 ]
251
100
80
60
40
20
100
80
60
40
20
0
0
ROADSIDE SITES
0 20 40 60 80 100
NO 2 observed conc. [ug/m 3 ]
ADMS CGPL CGSA CAMx
ROADSIDE SITES
0 20 40 60 80 100
NO 2 observed conc. [ug/m 3 ]
ADMS CGPL CGSA CAMx
Figure 6-6 Scatter (top) and quantile-quantile (bottom) of the observed and
predicted NO2 concentrations for background sites (left) and roadside sites (right)

Chapter 6 City scale results
PM 10 pred. conc. [ug/m 3 ]
PM 10 pred. conc. [ug/m 3 ]
60
50
40
30
20
10
0
60
50
40
30
20
10
0
BACKGROUND SITES
0 10 20 30 40 50 60
PM 10 observed conc. [ug/m 3 ]
ADMS CGPL CGSA CAMx
BACKGROUND SITES
0 10 20 30 40 50 60
PM 10 observed conc. [ug/m 3 ]
ADMS CGPL CGSA CAMx
PM 10 pred. conc. [ug/m 3 ]
PM 10 pred. conc. [ug/m 3 ]
252
60
50
40
30
20
10
0
60
50
40
30
20
10
0
ROADSIDE SITES
0 10 20 30 40 50 60
PM 10 observed conc. [ug/m 3 ]
ADMS CGPL CGSA CAMx
ROADSIDE SITES
0 10 20 30 40 50 60
PM 10 observed conc. [ug/m 3 ]
ADMS CGPL CGSA CAMx
Figure 6-7 Scatter (top) and quantile-quantile (bottom) of the observed and
predicted PM10 concentrations for background sites (left) and roadside sites (right)
Both exploratory plots (scatter and quantile-quantile) and statistical analysis confirm the
impressions stated in the section 6.2 for the simple comparison between the observed
and the predicted annual mean concentrations at the monitoring sites; good
performances are obtained with inert modelling systems (ADMS, CGPL and CGSA) for
nitrogen oxides at the background sites, while performance values for PM10 and for the
roadside sites are rather low and provided underestimated values. The acceptability
criteria proposed by Chang and Hanna (2004, model performances are defined

Chapter 6 City scale results
acceptable if FA2>0.5, -0.3

Chapter 6 City scale results
As expected, the background sites results provided better performances than the
roadside ones; this is due to the fact that the modelling systems used did not consider
the small scale effects. These effects are fundamental as far as roadside sites are
concerned, often located in complex environments and characterized by high local
traffic emissions. This implies that the statistical analysis done for the background sites
are the most representative of the model performances. A possible way to investigate
the concentrations at the roadside sites, that are the most critical in urban area, would be
to include smaller scale nested model, for example street canyon models. The feasibility
of this approach is demonstrated by several studies carried out with encouraging results
using multiscale modelling (see section 2.8); for example the application of ADMS-
Urban and his street canyon module to the city of London (Colvile et al. 2002). At the
moment, the major obstacle to the use of the street canyon module of ADMS-Urban in
this study was the lack of reliable traffic volume data in the entire study area; when this
data will be available, it will be interesting verify this approach for the study of impacts
in roadside site of the metropolitan area of Florence, Prato and Pistoia.
The PM10 results showed a systematic underestimation of the concentrations for all inert
modelling systems (ADMS, CGPL and CGSA); this is due to the fact that primary PM10
levels are only a small part of the total PM10 concentrations; much of the urban PM10 is
actually produced by chemical transformations. In order to overcome this problem, full
chemistry applications were investigated using the CAMx model. Both exploratory
plots (see figures 6-6 and 6-7) and the statistical analysis of CAMx results (see Table 6-
8) provided very poor results for every pollutants (see also uncertainty analysis, section
6.3.3); the acceptability criteria proposed by Chang, and Hanna (2004) are never
verified for any pollutant, especially for NO2 and NOx. The PM10 estimations, although
better than in the inert approach, are not satisfactory. The unsatisfactory results are
probable due to the lack of reliable input data (speciation of VOC emissions, turbidity,
ozone column density, water vapor concentrations) needed for the application of the
CAMx’s chemistry module and to difficulties in the estimation of the vertical diffusivity
Kz from predicted meteorological data given by the meteorological model CALMET. In
this case the impossibility of reliably satisfying the extensive data input requirements of
CAMx is the cause of very large total uncertainty that explains why the performance of
a complex model with full chemistry mechanism as CAMx, even if characterized by
254

Chapter 6 City scale results
smaller errors in the model’s representation of the physical reality, is so inferior to that
of a simpler methodologies, as the inert approach.
6.3.3 Uncertainty analysis
On the base of the validation results, the uncertainty analysis was performed using only
annual mean concentrations at the background monitoring stations, because, as
demonstrated before in section 6.3.2, the simulations carried out in this work are
representative of this type of site. This choice is similar to that one done by Stern and
Fleming (2007) and Borrego et al. (2008).
It was not easy to perform an uncertainty analysis, especially for PM10, given the
systematic underestimation resulting from the models applications. This is confirmed by
the calculation of the “accuracy” as recommended by the EU Directive 1999/30/EC
(relative maximum error, RME), which both, for NO2 and PM10, doesn’t give acceptable
results for all the monitoring stations considered (maximum values of RME within the
studied area doesn’t respect EC quality objectives, see table 6-9). In the present case
referring to the average value and to the maps of the relative maximum error (see figure
6-8 and 6-9) is more useful. Both the maps and the average value of RME confirm the
results of validation exercise; good performance of ADMS, CGPL and CGSA for NO2
(the EC quality objective are meanly respected for almost the monitoring stations) and
poorer performance for estimations of PM10. CAMx leads to high and unsatisfactory
uncertainty values especially for NO2 (see table 6-9); PM10 results of CAMx appear to
be better, but looking at the uncertainty maps they seems not good enough. The
uncertainty maps presented here, besides indicating the quality of the models, are very
useful as information for decision makers in air quality assessment.
For PM10, the methodology proposed by Colvile et al. (2002) is more helpful, because it
allows the removal of the systematic underestimation effect caused by secondary
pollution. The calculated “precision” values are reported in Table 6-10 and show that,
taking into account the underestimation due to the neglect of the secondary component
of PM10, modelling systems that use inert approach gives encouraging results also for
primary PM10 (Colvile et al. 2002 precision gives uncertainty values lower than EC
quality objective).
255

Chapter 6 City scale results
NO2
256
PM10
MIN MEAN MAX MIN MEAN MAX
ADMS 1% 24% 42% 10% 56% 70%
CGPL 1% 21% 72% 38% 57% 68%
CGSA 6% 28% 45% 39% 59% 68%
CAMx 28% 102% 283% 6% 36% 67%
EC objective 30% 50%
Table 6-9 Minimum, average and maximum relative maximum error (RME) of
annual mean concentration of NO2 and PM10 and relative modelling quality
objectives established by EU directives
NO2 PM10
ADMS 23% 23%
CGPL 25% 21%
CGSA 24% 21%
CAMx 59% 27%
Table 6-10 Model precision calculated following Colvile et al. (2002) methodology

Chapter 6 City scale results
0 - 10 %
10 - 20 %
20 - 30 %
30 - 40 %
40 - 50 %
50 - 75 %
75 - 100 %
> 100 %
0 - 10 %
10 - 20 %
20 - 30 %
30 - 40 %
40 - 50 %
50 - 75 %
75 - 100 %
> 100 %
Figure 6-8 Maps of RME of the NO2 annual mean concentration: ADMS (topleft),
CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right)
0 - 10 %
10 - 20 %
20 - 30 %
30 - 40 %
40 - 50 %
50 - 75 %
75 - 100 %
> 100 %
0 - 10 %
10 - 20 %
20 - 30 %
30 - 40 %
40 - 50 %
50 - 75 %
75 - 100 %
> 100 %
Figure 6-9 Maps of RME of the NO2 annual mean concentration: ADMS (topleft),
CGPL (top-right), CGSA (bottom-left) and CAMx (bottom-right)
257

Chapter 6 City scale results
6.4 Scenario analysis
On the base of the model evaluation, ADMS-Urban turn out to be the most reliable
modelling system for the present scenarios analysis. In addition to good performances,
ADMS-Urban assured easy use, short computational, pre-processing and postprocessing
time, and the opportunity of analyzing small scale effects by means his street
canyon module, if reliable traffic volume data for the entire area will be available. For
all these reasons AMDS-Urban was selected in order to realize the scenario analysis.
NO2 and primary PM10 concentrations of the two scenarios considered (actual scenario,
2003, and future scenario, 2012) were analyzed, considering the necessity of the Air
Quality Action Plan and the results of the model evaluation.
First, according to the air quality legislation actually in force, annual (both NO2 and
PM10), maximum hourly (only NO2) and maximum daily (only PM10) average
concentrations maps were calculated considering all the source together and the regional
background for the actual and future scenario (see figures 6-10/6-13, top maps). Maps
of the relative percentage difference of the concentrations calculated in the two
scenarios normalized by the air quality standard (CF-A, see section 4-6 for details) were
also presented in figures 6-10/6-13 (bottom maps) in order to better evaluate the
evolution of the global air quality impact in the metropolitan area of Florence, Prato and
Pistoia.
Concentration maps of the base scenario shows, as expected (the need of an Air Quality
Action Plan derived from noncompliance with the air quality limits), that actually in the
metropolitan area of Florence, Prato and Pistoia there is a critical situations both for
NO2 and primary PM10, considering every different time average of the pollutant
concentration. In the most populated areas (Firenze, Prato, Pistoia and Empoli)
concentrations of both pollutants are higher than air quality standards or however very
close to the threshold limits (40 µg/m 3 for NO2 annual mean, 200 µg/m 3 for NO2
maximum hourly mean, 20 µg/m 3 for PM10 annual mean and 50 µg/m 3 for PM10
maximum daily mean). In particular PM10 situation appear to be particularly grave,
considering that only primary component of PM10 is considered in this study.
258

Chapter 6 City scale results
U
< 15
259
15 - 20
20 - 30
30 - 40
40 - 50
50 - 71
-46 - -30 %
-30 - -20 %
-20 - -15 %
-15 - -10 %
-10 - -5 %
-5 - -1 %
Figure 6-10 NO2 annual mean concentrations [µg/m3] of actual (top-left) and
future scenario (top-right) and relative percentage difference
< 60
60 -70
70 - 80
80 - 100
100 - 150
150 - 168
-28 - -20 %
-20 - -15 %
-15 - -10 %
-10 - -7.5 %
-7.5 - -5 %
-5 - -2 %
Figure 6-11 NO2 maximum hourly mean concentrations [µg/m3] of actual (top-left)
and future scenario (top-right) and relative percentage difference (bottom)

Chapter 6 City scale results
< 14
260
14 - 15
15 - 16
16 - 18
18 - 20
20 - 28
-65 - -20 %
-20 - -10 %
-10 - -5 %
-5 - -2 %
-2 - 0 %
0 - 2 %
Figure 6-12 PM10 annual mean concentrations [µg/m3] of actual (top-left) and
future scenario (top-right) and relative percentage difference (bottom)

Chapter 6 City scale results
Comparison between actual and future concentrations and relative percentage difference
maps (see figure 6-10/6-13) show a global reduction of the concentrations between the
actual and the future scenario in the entire investigated area (negative value of CF-A).
The concentration reductions are evident, especially, in correspondence of the most
populated areas, in particular in Florence and Empoli, where the highest relative
percentage difference between the two scenarios normalized by the air quality standard,
CF-A, can be seen (-46% for NO2 annual mean concentration, -28% for NO2 maximum
hourly mean concentration, -65% for PM10 annual mean concentration, -92% for PM10
maximum daily mean concentration). Areas with high concentration decrease seem to
be more extended for NO2 than for PM10, even if, locally, higher concentration
reduction can be observed for this last pollutant. This is confirmed by the analysis of the
CF-A value averaged over the entire area; a mean reduction of -0.9% and -1.3% are seen
respectively for PM10 annual and maximum daily average concentration, while mean
reduction of -8.2% and -6.6% for NO2 annual and maximum hourly average
concentration. Considering the uncertainty of the model (see section 6.3.3) this decrease
of the NO2 and primary PM10 concentrations doesn’t seem to be enough to ensure
compliance with the air quality limits at a future time.
To evaluate the importance of every different type of emission source and consequently
supply important information to establish the efficiency of the environmental actions
that could be adopted to reduce the pollutant levels in the investigated urban area,
annual mean concentration maps were then carried out, separately, for: main point
sources (POINT), main line sources (LINE), small industries (IND), local road traffic
(ROAD), domestic heating (HEAT) and other sources (OTHER). Results of these
simulations are reported on figures 6-14 and 6-15. For brevity only maps for NO2 are
reported here; PM10 maps are not shown in figures 6-14 and 6-15, but similar
consideration to that for NO2 can be obtained also for PM10.
Comparison between maps of the actual and future scenario for the different type of
sources show almost unchanged annual mean concentration values due to the industrial
sources (both main point sources, POINT, and small industries, IND) and to the other
sources; while significant concentration reduction can be observed for traffic sources
(both main line source, LINE, and local traffic, ROAD) and domestic heating.
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Chapter 6 City scale results
< 0.5
262
0.5 - 1
1 - 2
2 - 3
3 - 5
5 - 7.4

Chapter 6 City scale results
< 1
1 - 2.5
2.5 - 5
5 - 10
10 - 15
15 - 17.3
< 0.1
0.1 - 0.25
0.25 - 0.5
0.5 - 1
1 - 1.5
1.5 - 1.6
Figure 6-15 NO2 annual mean concentrations [µg/m3]. Comparison between actual
(left maps) and future scenario (right maps) concentrations due to different types of
source: HEAT (first row) and OTHER (second row)
Figure 6-16 Maximum, mean and minimum ratios, Ri-all (percentage), for the
actual (top graphs) and future scenarios (bottom graphs) of NO2 and PM10
263

Chapter 6 City scale results
To define the weights of the different emission sources in terms of pollutant
concentrations, evaluations of the ratios (Ri-all) between the annual mean concentrations
due to a specific type of source (Ci) and the concentration levels deriving from all the
sources (Call) for the actual and future scenarios were carried out for each receptor of the
used computational grid. The maximum (representative of the local effect), the mean
(representative of the global effects) and the minimum ratios, Ri-all (percentage), were
reported in figure 6-16 both for NO2 and PM10.
The percentage contributions of every different type of source are nearly unchanged
between the two scenarios (base and future) both for NO2 and PM10. For NO2
concentrations, traffic emission, especially local road traffic (ROAD), appear to be the
most significant type of source both considering the global and the local effects; the
contribution of industrial sources (both POINT and IND) and domestic heating (HEAT)
is less important. Differently, for PM10 concentrations the contribute of the different
type of source is rather homogeneous; very similar mean ratios can be observed for
traffic, industrial and domestic heating.
6.5 Discussion of the relevance and application of the results
Several advanced and widespread mathematical models were used for scenario analysis
purposes within the MoDiVaSET-2 project; different models for different sources were
chosen and multiscale modelling applied. The objective was to develop and assessing a
reliable modelling techniques for the study of future scenarios and, then, execute this
analysis with the most reliable modelling system
The results highlighted some issues related to urban dispersion modelling. In particular,
they put in evidence the importance of including the following critical factors:
1. Regional background concentrations: looking at the sensitivity study results (see
section 6.3.2), it comes clear that do not consider the regional background
produce a systematic underestimation of pollution levels. In this thesis the
background was considered simply taking into account the concentration
observed in a peripheral monitoring near the area of interest; it could be better to
264

Chapter 6 City scale results
use the data deriving from a regional models. However, this doesn’t not affect
the analysis of future scenarios (starting from the hypothesis that background
concentrations do not change).
2. Smaller scale effects: monitoring stations are often located in complex
environments; as we observed in section 6.3.3, this implies a decrease in the
effectiveness of validation studies if we want to consider not only background
sites but also roadside ones. A possible solution to evaluate the air quality
impact also in the hotspot located in complex environment would be to include
small scale effects (e.g. street canyon modelling) in order to increase the
resolution of the models. It is necessary to have reliable traffic volume data of
the entire study area, which very often are not available, as in the present case.
3. Secondary pollution: primary PM10 levels are only a small part of the total PM10
concentrations; besides high regional background levels, much of the urban
PM10 is actually produced by chemical transformations and other physical
mechanisms (for example, resuspension).
The limitations highlighted by the MoDiVaSET-2 results could be reduced by applying
a real multiscale model, integrating both regional scale modelling (background
concentrations), street (or neighbourhood) scale modelling and chemical mechanisms
modelling. But this it is not an easy task, because many input data are needed, but
unfortunately very often are not available or reliable, as demonstrated for the
application of CAMx’s full chemistry simulation.
All the issues listed above affected the evaluation work carried out in this thesis;
however, this does not alter the validity of the scenario analysis, because it is based on
the differences between calculated primary pollutants concentrations deriving from the
considered emissions. Despite the critical factors listed above, modeling results can be
trusted on the basis of the evaluation work and provide indispensable information to the
choice of efficient environmental actions that must be adopted in the Air Quality Action
Plan of Tuscany.
265

Chapter 6 City scale results
The approach proved very powerful and useful, and further development can lead to a
real application of integrated assessment models. The successful applications of
scenario analysis modelling techniques are only the first step for the development of
urban scale integrated assessment models (IAM). As a matter of facts, a detailed
description of the effects and impacts, can lead to the development of IAM by simply
adding modules for calculating economic effects as well as health effects, based on the
calculated pollution increase (or decrease). This further step, however, is not trivial.
266

Chapter 7
7.Conclusions
7.1 Summary of main findings and conclusions
Dispersion modelling is an important part of assessing and managing air quality in
cities. Urban air quality modelling is usually performed making simplifying
assumptions relevant at a particular scale. The scales involved in air quality modelling
in cities are basically four: the regional scale, the urban (city) scale, the neighbourhood
scale and the local (street) scale.
However, urban dispersion modelling is a complex task, which involves all the scales
cited above and limited by many uncertainties. As highlighted in chapter 2, one of the
main difficulties in urban air quality modelling and evaluation is the lack of reliable
experimental data, and this is a common feature of all the scales involved.
The aim of the research presented in this thesis was to extend existing urban dispersion
research using both experimental and mathematical approaches. The focus was on
operational urban air quality models, suitable for integrated assessment modelling, and
on model evaluation approaches.
The work has been carried out within the framework of two major projects: the
DAPPLE-HO project and the MoDiVaSET-2 project. Air quality modelling was applied
for different purposes and at different scales.
Wind tunnel experiments in a small scale physical model of a real urban area in central
London were performed; in particular, these focused on the dispersion phenomena
associated with traffic emissions. The experiments involved a state-of-the-art technique
such as tracer concentration measurements and a new methodology based on the use of
point sources for simulating a traffic line source via mathematical integration. In
previous wind tunnel works vehicles emission were usually simulated by uniform and
steady line source emission without taking into account the effects of non uniform
distribution of pollutant emissions due to traffic queuing.
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Chapter 8 Conclusions
The adopted technique demonstrated to be a reliable method to understand the main
characteristics of the dispersion field due to traffic sources in actual urban
environments, to establish source-receptor relationships and to evaluate the exposure
dosages for different traffic conditions.
An advanced experimental methodology based on the interference method of Warhaft
(1984) was also developed in order to analyze the correlation between concentration
fluctuations from two point sources belonging to a line emission. Remembering that
concentration variance connected with a line source can be expressed in term of
correlation between the fluctuations produced by two point sources, the measure of
these correlations permitted to establish source-receptor relationships also in terms of
fluctuating concentrations. The local topography of the building turn out to be a factor
that strongly influences the correlation between fluctuating concentration from two
sources in urban areas; channeling and re-circulation inside the canopy can produce
very significant increasing of correlation and consequently on instantaneous pollutant
concentrations in urban area, that, consequently, has to be carefully taken into account
in order to estimate the maximum expected exposure levels in the cities.
The obtained results proved encouraging and the used methodologies has given insight
into the effect of different car movements and their impact on receptors or people both
in terms of mean and fluctuating concentrations.
The data sets resulting from the performed experiments are not exhaustive or
systematic. However they can be useful validation data which can be used for model
improvement and development. The techniques developed for the evaluation of the
mean exposure dosages and the correlation between concentration fluctuations, if
further perfected, can lead to an improvement of the existing mathematical modelling
techniques that, at the moment, also due to a lack of experimental data, tend to neglect
the effects of non uniform distribution of pollutant emissions due to traffic queuing.
Scenario analysis was the focus of the MoDIVaSET-2 project. The complex situation of
a metropolitan area was studied by means of several models and modelling approaches.
Several advanced operational models were involved: CALPUFF, CALGRID,
CALINE4, CAMx, SAFE AIR and ADMS-Urban. A detailed model evaluation process
268

Chapter 8 Conclusions
was applied in order to assess the reliability of the modelling systems involved and to
find the main limitations. The results highlighted the necessity of an even higher
integration between the different modelling scales, in particular with the regional scale
and the local (street) scale. Another issue was the necessity of including chemical
transformations in a reliable way in order to estimate secondary pollutant
concentrations, which can be very important, for example, for assessing PM10 levels in
urban areas. The evaluation of CAMx’s full chemistry simulations show the importance
of reliably satisfying the extensive data input requirements of a model; in this case, the
lack of reliable input data of the CAMx’s chemistry module (speciation of VOC
emissions, turbidity, ozone column density, water vapor concentrations) caused larger
total uncertainty than models that use the inert approach.
However, the mathematical modelling techniques applied in this research proved very
useful for scenario analysis and they are suitable for integrated assessment models. IAM
can be a powerful tool for air quality management in urban areas.
7.2 Limitations and recommendations for future research
Simplifying assumptions have been made throughout the thesis, both for wind tunnel
experiments and mathematical modelling.
A first limitation comes from the fact that only two wind direction was used during the
tests carried out in the DAPPLE model. However the purpose of the experiments was
not to create extensive data sets such as in the case of many studies on idealised
configurations, but to investigate flow and dispersion field in real situations.
Other simplifying assumptions for the wind tunnel experiments included the fact that
only neutral atmospheric conditions were simulated, and thermal effects were ignored.
Another potentially important factor such as vehicle induced turbulence was also not
included in order to simplify the scope of the research.
Simplifying assumptions taken during the modelling applications within the
MoDiVaSET-2 project included: inclusion of regional background simply considering
269

Chapter 8 Conclusions
the concentration observed in a peripheral monitoring near the area of interest,
neglecting of chemical transformations and smaller scale effects. Nevertheless,
especially for the PM10 simulations, neglecting chemical reactions was a big limitation
for the validity of the results. However these simplifications did not affect the
effectiveness of the results for the analysis of different scenarios.
Many recommendations can be made for further study based on the methods and results
presented in this thesis. Some of the limitations of the wind tunnel experiments
explained above could be simply addressed by considering a more extensive
experimental set up, studying different wind direction configurations or including traffic
induced turbulence as well. Another interesting improvement could come from
considering the effects of wind calms, heating effects and stability conditions on the
flow and dispersion phenomena.
A big challenge for the improvement of the modelling systems proposed and applied for
the evaluation of emission scenarios would be the integration between different
dispersion scales. The multiscale approach proved very powerful for integrated
assessment modelling at larger scales, and the inclusion of the regional scale and the
local scale in urban studies could sensibly improve the modelling performance and
permit not only the analysis of the concentrations of background site but also of
roadside site, that are usually the hotspots of an urban area.
270

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