MEGAPOLI Megacities: Emissions, urban ... - MEGAPOLI - DMI

MEGAPOLI Scientific Report 11-17 

Evaluation of State-of-the-Art CTMs Using New 

Experimental Datasets 

MEGAPOLI Deliverable D3.6 

M. Beekmann, C. Fountoukis, Q.J. Zhang, S. N. Pandis 

and the MEGAPOLI Campaign Team 

Simulated CHIMERE urban OA plume on 16 July 2009 at 07 and 16 UTC 

/OA mass concentrations are given in µg m-3/ 

Paris - Patras, 2011

FP7 EC MEGAPOLI Project 

Colophon 

Serial title: 

MEGAPOLI Scientific Report 11-17 

Title: 

Evaluation of state-of-the-art CTMs using new experimental datasets 

Subtitle: 

MEGAPOLI Deliverable D3.6 

Editor(s): 

- 

Main Author(s): 

M. Beekmann (1), C. Fountoukis (2), Q.J. Zhang (1), S. N. Pandis (2) 

(1) Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA), Créteil, France 

(2) Institute of Chemical Engineering and High Temperature Chemical Processes, Foundation for Research 

Technology Hellas (FORTH), Patra, Greece 

Contributing Author(s): 

- 

Responsible institution(s): 

Centre National de Recherche, Laboratoire Interuniversitaire des Systèmes Atmosphériques 

(CNRS-LISA) 

Language: 

English 

Keywords: 

Chemistry-transport model, model evaluation, aerosol, megacity 

Url: 

http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-17.pdf 

Digital ISBN: 

978-87-92731-21-0 

MEGAPOLI: 

MEGAPOLI-43-REP-2011-09 

Website: 

www.megapoli.info 

Copyright: 



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Content: 

Abstract ................................................................................................................................................4 

1. Introduction....................................................................................................................................5 

2. Model Description and Application................................................................................................6 

2.1 PMCAMx-2008 .........................................................................................................................6 

2.1.1 Model description ...............................................................................................................6 

2.1.2 Model application ...............................................................................................................7 

2.2 CHIMERE model description and set-up ..................................................................................8 

3. Evaluation Results..........................................................................................................................11 

3.1 PMCAMx-2008 .......................................................................................................................11 

3.2 CHIMERE................................................................................................................................16 

3.2.1 Comparison for ground based sites...................................................................................16 

3.2.2 Simulations of the Paris urban plume ...............................................................................17 

3.2.3 Comparison between airborne observations and simulations...........................................17 

Acknowledgements............................................................................................................................21 

References..........................................................................................................................................22 

Previous MEGAPOLI reports............................................................................................................24 


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Abstract 

Detailed observations obtained during the summer 2009 and winter 2010 MEGAPOLI campaign in 

and around the Paris agglomeration are used for the evaluation of two state-of-the-art chemical 

transport models (CHIMERE and PMCAMx). Aerosol and gas phase measurements obtained at 

three sub(urban) grand based sites and from research flights in the pollution plume during the 

MEGAPOLI campaigns are compared against the model predicted concentrations for the same periods. 

The simulation of organic aerosol is a major focus of this evaluation, given the inability of 

past modeling efforts to reproduce observations in polluted areas. Model predictions are compared 

against high time resolution (AMS) measurements of fine particulate matter from the three sites and 

from the aircraft. 

PMCAMx-2008, a detailed three dimensional chemical transport model (CTM), is applied for the 

first time in the European domain to simulate the mass concentration and chemical composition of 

particulate matter (PM). The CHIMERE CTM has been largely used for pollution simulation and 

air quality forecast over Europe or European sub-domains. Both models have not yet been compared 

with detailed aerosol and gas phase measurement such as those obtained within the MEGA- 

POLI project. Both models include a state-of-the-art organic aerosol module which is based on the 

volatility basis set framework treating both primary and secondary organic components to be semivolatile 

and photochemically reactive. 

The comparison of the model predictions with the ground and aircraft measurements is encouraging. 

Models reproduce successfully the secondary organic aerosol (SOA) transport to the agglomeration, 

and its build-up within the Paris plume. This is clearly a positive result, given the difficulty 

for chemistry-transport models to correctly simulate SOA. 


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1. Introduction 

Sulfate and organics are the major components of sub-micrometer particles (PM1) in most locations 

throughout the world. Organic particulate matter, originating from many different natural and anthropogenic 

sources and processes, is the least understood component of atmospheric aerosols. 

More than 50% of the atmospheric fine aerosol mass can be comprised of organic compounds at 

continental mid-latitudes and as high as 90% in tropical forested areas (Andreae and Crutzen, 1997; 

Roberts et al., 2001; Kanakidou et al., 2005). Organic aerosol (OA) is generally categorized into 

two types; primary organic aerosol (POA) which is injected into the atmosphere in the particulate 

phase and secondary organic aerosol (SOA) which is emitted as volatile organic compounds 

(VOCs) in the gas phase and then reacts and condenses in the particulate phase. In order to gain 

more insight into sources and processes of organic aerosol, quantification, characterization and 

speciation of organic aerosol is needed which was until recently hindered by analytical difficulties 

(Kanakidou et al., 2005). For instance, conventional techniques (e.g. GC-MS) can only be used for 

the speciation of a small fraction of the OA mass. Within the last decade, several new methods have 

emerged that can analyze and quantify the different types of OA present in ambient aerosol. Among 

several measurement techniques, the Aerosol Mass Spectrometer (AMS) is the most commonly 

used to measure the size-resolved mass concentration and corresponding total mass spectrum of organic 

aerosols with a time resolution of minutes (Jayne et al., 2000; Zhang et al., 2005a; Takegawa 

et al., 2005). Information about processes or sources contributing to the OA levels can be provided 

from the Positive Matrix Factorization (PMF) method (Paatero and Tapper, 1994; Paatero, 1997; 

Lanz et al., 2007, 2009; Ng et al., 2009) or the custom principal component analysis (Zhang et al., 

2005b) of the AMS measurements. These methods allow a classification of the OA into two different 

types based on their different temporal and mass spectral signatures; the hydrocarbon-like organic 

aerosol (HOA) and the oxygenated organic aerosol (OOA) which together usually account for 

all the OA mass measured by the AMS (Zhang et al., 2005b). HOA correlates with fossil fuel fresh 

POA in urban areas while OOA correlates with secondary OA. 

During the MEGAPOLI summer and winter campaigns, detailed aerosol and gaseous measurements 

have been obtained at three ground based sites located within the Paris greater area: LHVP within 

Paris (13 Arrondissement), SIRTA at the south-western edge of the agglomeration, and GOLF at its 

north-eastern edge (see MEGAPOLI Deliverable 3.1). In addition, aircraft measurements were 

taken in the Paris pollution plume during more than ten flights (see MEGAPOLI Deliverable 3.4). 

These data are used for the evaluation of two state-of-the-art chemical transport models (CTMs), 

PMCAMx-2008 and CHIMERE. These models and their specific settings for this application are 

described in Chapter 2. Evaluation results are given in Section 3. PMCAMx-2008 is compared both 

to summer and winter campaign observations at ground based sites. CHIMERE is compared to 

summer campaign observations at the ground-based sites and aloft. 


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2. Model Description and Application 

2.1 PMCAMx-2008 

2.1.1 Model description 

PMCAMx-2008 (Murphy and Pandis, 2009; Tsimpidi et al., 2010; Karydis et al., 2010) uses the 

framework of the CAMx air quality model (Environ, 2003) describing the processes of horizontal 

and vertical advection, horizontal and vertical dispersion, wet and dry deposition, and gas-phase 

chemistry. For the aerosol processes, three detailed aerosol modules are used. The approach of 

Fahey and Pandis (2001) is used for the simulation of aqueous-phase chemistry. The inorganic 

aerosol growth is described in Gaydos et al., (2003) and Koo et al., (2003), and the secondary organic 

aerosol (SOA) formation and growth in Koo et al. (2003). These aerosol modules use a sectional 

approach to dynamically track the size evolution of the aerosol mass across 10 size sections 

spanning from 40 nm to 40 μm. The aerosol species modeled include sulfate, nitrate, ammonium, 

sodium, chloride, potassium, calcium, magnesium, elemental carbon, primary and secondary organics. 

The chemical mechanism used in the gas-phase chemistry is based on the SAPRC99 mechanism 

(Carter, 2000; Environ, 2003). The mechanism includes 211 reactions of 56 gases and 18 

radicals. 

Three options are available in PMCAMx-2008 for the simulation of inorganic aerosol growth. The 

most computationally efficient approach is the bulk equilibrium approach, which assumes equilibrium 

between the bulk inorganic aerosol and gas phase. At a given time step the amount of each 

species transferred between the gas and aerosol phases is determined by applying the aerosol thermodynamic 

equilibrium model ISORROPIA (Nenes et al., 1998) and is then distributed over the 

aerosol size sections by using weighting factors for each size section based on their surface area 

(Pandis et al., 1993). The second approach (hybrid approach) assumes equilibrium for the fine particles 

(


Chemical aging through gas-phase oxidation of OA vapors is modeled using a gas-phase OH reaction 

with a rate constant of k= 1 × 10 -11 cm 3 molec -1 s -1 for anthropogenic SOA and k = 4 × 10 -11 

cm 3 molec -1 s -1 for the primary OA (Atkinson and Arey, 2003). The base-case simulation does not 

age biogenic SOA (Ng et al., 2006; Presto et al., 2006; Lane et al., 2008b). Each reaction is assumed 

to decrease the volatility of the vapor material by a factor of 10. 

2.1.2 Model application 

The PMCAMx-2008 coarse grid modeling domain covers a 5400 × 5832 km 2 region in Europe with 

36 × 36 km grid resolution and 14 vertical layers covering approximately 6 km. PMCAMx-2008 

was set to perform simulations on a rotated polar stereographic map projection. The first three days 

of each simulation were excluded from the analysis to limit the effect of the initial conditions on the 

results. Concentrations of the major PM1 species at the boundaries of the domain are shown in Table 

1 (for the period of July 2009), representing background concentrations with each domain side 

having different boundary conditions. The boundary condition organic aerosol (BC-OA) is expected 

to consist of both SOA and oxidized POA. Here we assume that the BC-OA is all oxidized and half 

of it is biogenic OA and the other half oxidized primary OA (Kanakidou et al., 2005; Farina et al., 

2010). All concentrations reported here are under ambient temperature and pressure conditions. 

The necessary inputs to the model include horizontal wind components, vertical diffusivity, temperature, 

pressure, water vapor, clouds and rainfall. The meteorological model WRF (Weather Research 

and Forecasting; Skamarock et al., 2005) was used to create the above inputs. WRF was 

driven by static geographical data and dynamic meteorological data (near real-time and historical 

data generated by the Global Forecast System (1 x 1 deg)). 27 sigma-p layers up to 0.1 bars were 

used in the vertical dimension. Each layer of PMCAMx-2008 is aligned with the layers used in 

WRF. The WRF run for both periods was periodically re-initialized (every 3 days) to ensure accuracy 

in the corresponding fields that are used as inputs in PMCAMx-2008. 


Table 1: Aerosol concentrations (in μg m -3 ) at the boundaries of the domain. 

Species Boundaries 

North South West East 

OA 

1 0.5 0.5 1 

Sulfate 

1 1 1 1 

Ammonium 0.37 0.37 0.37 0.37 

Nitrate 0.1 0.02 0.01 0.1 

Sodium 0.001 0.005 0.09 0.03 

Chloride 0.002 0.01 0.1 0.05 

Anthropogenic and biogenic hourly emission gridded fields were developed for the European domain 

for gases and primary particulate matter. Volatile organic compounds are split based on the 

SAPRC 99 chemical mechanism. 

The model was run with coarse grid spacing over the wide regional (European) domain in which a 

spatial resolution of 36×36 km 2 was used, while within the same run, a fine grid nest was applied 

over the Megacity of Paris where high resolution (4×4 km 2 ) was used (Fig. 1). High resolution 

emissions were used for the fine grid nest simulation while for the meteorological input of the fine 

grid PMCAMx-2008 was set to interpolate the meteorological fields from the parent grid. The fine 

grid contains the same vertical resolution with the parent grid (14 vertical layers). The PMCAMx- 

2008 fine grid modeling domain covers a 216 × 180 km 2 region in Paris with the city center cen- 

7


trally located in the domain. 

Figure 1: Modeling domain of PMCAMx-2008 for Europe (36×36 km 2 grid resolution) and for the Paris 

greater area (4×4 km 2 grid resolution). 

2.2 CHIMERE model description and set-up 

The model used in this study is the Eulerian regional chemistry-transport model CHIMERE in its 

version V2008b (see http://euler.lmd.polytechnique.fr/chimere). The initial gas-phase only version 

of the model has been described in Schmidt et al. (2001) and Vautard et al. (2001), the aerosol part 

in Bessagnet et al. (2004 and 2008). The model has been largely applied for continental scale air 

quality forecast (Honoré et al., 2008; http://www.prevair.org), and simulations, including sensitivity 

studies, with respect to anthropogenic (Beekmann and Vautard, 2010) and biogenic emissions 

(Curci et al., 2009) and inverse emission modeling (Konovalov et al., 2006). The model has been 

extensively applied for simulating gas phase pollution levels over the Paris region (e.g. Deguillaume 

et al., 2008), and on several occasions particulate matter (e.g. Bessagnet, 2005; Hodzic et al., 

2006, Sciare et al., 2010). 

In this work, we used CHIMERE with nested domains, a continental domain covering Europe 

(CONT3) with a resolution of 0.5° ([35–57.5°N; 10.5°W–22.5°E]) and a regional domain over 

Northern France (MEG3) covering all the flight patterns during this campaign (see MEGAPOLI 

Deliverable 3.4) with a resolution of 3 km (see Figure 2). 8 hybrid-sigma vertical layers are used, 

with the first layer at about 40 m, extending to 500 hPa. 

Troposphere photochemistry is represented using the reduced MELCHIOR chemical mechanism 

(Lattuati, 1997; Derognat, 2003), including 120 reactions and 44 prognostic gaseous species. For 

simulation of the particulate phase, 8 bins of particulate sizes are used in the model from 0.04 to 10 

µm. Condensation of semivolatile organic and inorganic gases is simulated with a coupled dynamic-thermodynamic 

scheme. The thermodynamic mechanism of inorganic species (sulfate, ni- 


8


trate and ammonium) is interpolated from the tabulation calculated with the ISORROPIA model 

(Nenes et al., 1998). 

Figure 2: The two nested simulation domains, a continental domain CONT3 (in red) with a resolution of 

0.5° and a Northern France domain MEG3 (in blue) with a resolution of 3 km. 

For the MEGAPOLI project, the VBS approach for SOA formation has been implemented in CHI- 

MERE similar to work described in Hodzic et al. (2010). This scheme has already been described in 

Section 2.1 for the PMCAMx-2008 model. Here only specific settings in our application are described. 

The oxidation and aging of SVOC from evaporated POA is simulated with a kinetic reaction 

rate of OH of 4×10 -11 cm 3 molec -1 s -1 (Robinson et al., 2007). The oxidation of anthropogenic 

and biogenic VOCs and the aging of their semivolatile products are simulated according to Murphy 

and Pandis (2009) with yields corresponding to “low” NOx parameterization. Aging is included 

both for anthropogenic and biogenic VOC with a kinetic reaction rate of OH of 1 × 10 -11 cm 3 

molec -1 s -1 . Among others, numerical experiments with an explicit chemical mechanism coupled to 

a condensation scheme indicate that gas phase aging is important for SOA formation from α-pinene 

(Valorso et al., 2010). Sensitivity tests with different settings of the SOA formation scheme are described 

in the MEGAPOLI Deliverable 3.7. 

Meteorological parameters are input for CTM with results from simulations with PSU/NCAR MM5 

model (Dudhia, 1993) for the two nested domains with 45 km (European domain) and 15 km 

(North-West Europe) resolution. In the vertical, 23 vertical sigma layers extend up to 100hPa MM5 

is forced by the analyses from the Global Forecast System (GFS/FNL) operated daily by the American 

National Centers for Environmental Prediction (NCEP), using the grid nudging (grid FDDA) 

option implemented within MM5. 

Anthropogenic gas phase emissions are calculated from EMEP annual totals (http://www.ceip.at/ 

emission-data-webdab), black carbon (BC) and primary organic aerosol (POA) are prescribed from 

the Laboratoire d’Aerologie data-base (Junkle and Louisse, 2008). These emissions are scaled to 

hourly emissions applying temporal profiles provided by IER (Friedrich, 1997). They are downscaled 

to the high resolution grid over Northern France using an urban landuse fraction. Biogenic 

emissions are calculated using the MEGAN model data and parameterizations (Guenther et al., 

2006). 

CHIMERE simulations use boundary conditions from a monthly climatology simulated with the 

LMDz-INCA2 and LMDz-AERO general circulation model. The boundary condition for organic 

aerosol concentrations at the western model boundary (Atlantic) is set to 0.7 µg m -3 according to 


9


long-period measurements and climatologic analysis (Seinfeld and Pandis, 2006). 


10


3. Evaluation Results 

3.1 PMCAMx-2008 

Figures 3 and 4 show the PMCAMx-2008 average ground-level concentrations for PM1 total mass, 

organic aerosol, elemental carbon, sulfate, ammonium and nitrate over the two periods of simulation, 

respectively. Overall, during the summer period, OA is predicted to account for 25% of total 

PM1 at ground level averaged over the entire (nested) domain, followed by nitrate (21%), sulfate 

(16%), ammonium (12%), and EC (6%). The remaining is crustal material, sea-salt and metal oxides. 

During the winter period the contribution of EC to total PM1 increases to 15% while OA contributes 

almost the same as during the summer period, although its absolute concentrations increase 

by ~30% compared to the July 2009 period. Concentrations of ammonium, nitrated and sulfate are 

predicted to be lower during the January 2010 period, contributing 10, 11 and 8 %, respectively, to 

total PM1 over the subdomain of the Paris area. 

Figures 5 and 6 show time series of PM1 OA concentrations (predicted vs. observed) at the LHVP, 

SIRTA and GOLF sites during the summer 2009 and winter 2010 campaigns in Paris. Model results 

include predictions from both the coarse and fine grid resolution. During summertime, in LHVP the 

average predicted concentration for total OA is 2.1 μg m -3 with the fine grid and 2.0 μg m -3 with the 

coarse grid compared to an AMS measured value of 3.5 μg m -3 . In SIRTA the model captures the 

magnitude of the measured OA, predicting an average concentration of 1.8 and 1.9 μg m -3 with the 

fine and coarse grid, respectively, compared to an observed average OA concentration of 1.8 μg 

m -3 . In GOLF, where concentrations are locally influenced by the nearby road traffic, the model under-predicts 

peak concentrations during some days. On average, the fine grid simulation predicts a 

concentration of 2.1 μg m -3 while the measured OA value was 2.5 μg m -3 . During the winter period, 

concentrations of OA in Paris were higher than the summertime due to increased primary emissions 

from combustion processes. In the city center (LHVP site) the fine grid simulation predicts an average 

monthly value for OA of 4.0 μg m -3 , considerably higher than the coarse grid simulation prediction 

(2.9 μg m -3 ), and closer to the observed values. Contrary to the LHVP site, in the suburban site 

of SIRTA, the two simulations predict quite similar OA concentrations throughout the simulation 

period. At the GOLF site the 4x4 km simulation gives higher concentrations compared to the 36x36 

km run, and closer to the measurements, although an under-prediction is seen as with the summertime 

period. The use of the fine grid resolution in the model run seems to be much more necessary 

during the winter rather than the summer period in reproducing OA levels in the city center. The 2 

different horizontal grid resolutions produce almost identical sulfate concentrations at all sites during 

either the July or the January period. Figure 7 is an example of this behavior of the model, 

showing average diurnal profile of particulate sulfate at two sites during the summer period. Figure 

8 shows time series of elemental carbon concentrations (predicted vs. observed) at LHVP during 

both periods. In July the model captures the magnitude of observed values reproducing the majority 

(90%) of the hourly average data points within a factor of 2. In winter, however, the model predicted 

levels of EC show an overestimation of the peak values of elemental carbon during the rush 

hours, which is more pronounced in the 4x4 km grid resolution, indicating possible errors in the 

emission rates. Overall the model performance against high time resolution measurements of OA, 

EC and sulfate from a highly polluted Megacity seems encouraging. 


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Figure 3: Ground-level concentration predictions of PMCAMx-2008 averaged over the entire simulation 

period (1-30 July 2009) for PM1 (a) total mass, (b) total organic aerosol, (c) elemental carbon, (d) sulfate, 

(e) ammonium and (f) nitrate (in μg m -3 ). Different scales are used. 

Figure 4: Ground-level concentration predictions of PMCAMx-2008 averaged over the entire simulation 

period (January 10 – February 10, 2010) for PM1 (a) total mass, (b) total organic aerosol, (c) elemental 

carbon, (d) sulfate, (e) ammonium and (f) nitrate (in μg m -3 ). Different scales are used. 


12


Figure 5: Comparison of PMCAMx-2008 model predictions with AMS measurements of PM1 OA (in μg m -3 ) 

taken at LHVP, SIRTA and GOLF during the summer 2009 campaign in Paris. 


13


Figure 6: Comparison of PMCAMx-2008 model predictions with AMS measurements of PM1 OA (in μg m -3 ) 

taken at LHVP, SIRTA and GOLF during the winter 2010 campaign in Paris. 


14


Figure 7: Average PMCAMx-2007 predicted diurnal profiles of PM1 sulfate (in μg m -3 ) in SIRTA and GOLF 

during the summer 2009 campaign in Paris. Model predictions are shown for high and low model resolution 

in the Paris area. 

Figure 8: Comparison of PMCAMx-2008 model predictions with measurements of elemental carbon (in μg 

m -3 ) taken at LHVP during the summer 2009 and winter 2010 campaign in Paris. Model predictions are 

shown for high (blue) and low (dashed black) model resolution in the Paris area. 


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3.2 CHIMERE 

In the section, gas phase pollutants (NOx and O3) and particulate pollutants (black carbon (BC), sulfate 

(SO4 2- ), nitrate (NO3 - ), ammonium (NH4 + ) and organic aerosols (OA) simulated with the CHI- 

MERE model for the summer campaign are evaluated by comparisons with the measurements at 3 

ground measurement sites GOLF, LHVP and SIRTA. Simulated OA is also compared to airborne 

gas phase and aerosol measurements. Common statistical measures as bias (relative bias to the 

mean concentration from both the measurements and the model (RB)), root mean square error 

(RMSE), relative RMSE) and correlation coefficient for particulates are presented in Table 2 for the 

summer campaign. 

n mod mes 

100 ( V −V 

) 

RB = ∑ (1) 

mod mes 

n i= 

1 ⎛ V + V ⎞ 

⎜ 

⎟ 

⎜ 

⎟ 

⎝ 

2 

⎠ 

n 1 mod mes 2 

RMSE = ∑ ( V −V 

) 

(2) 

n i= 

1 

where n is the number of observations and, V mod and V mes are the modeled and measured values, respectively. 

Table 2: Statics of model results comparing to measurements for BC, inorganic aerosols and organic aerosol 

in both PM1 and PM10 sections at GOLF, LHVP and SIRTA in summer. 

Statistic in 

PM1 

PM10 

summer 2009 

(µg m -3 ) 

Bias (RB) 

RMSE (relative 

RMSE) 

R Bias (RB) 

RMSE (relative 

RMSE) 

R 

BC 1.96 (92%) 2.90 (136%) 0.48 2.07 (94.8%) 3.00 (137%) 0.51 

NO3 0.22 (50%) 0.95 (220.5%) 0.45 0.44 (80.9%) 1.11 (206.5%) 0.78 

GOLF 

SO4 

NH4 

0.40 (29.6%) 

0.14 (22%) 

1.06 (82.4%) 

0.54 (86.5%) 

0.44 

0.40 

0.97 (59.2%) 

0.40 (53.2) 

1.40 (85.8%) 

0.62 (82.2%) 

0.74 

0.81 

OA -1.61 (-58.8%) 

2.65 (96.8%) 0.70 -0.47 (-14.2%) 2.04 (61.7%) 0.79 

LHVP 

SIRTA 


BC 1.98 (88.5%) 2.81 (125.5%) 0.51 2.08 (91%) 2.95 (128.3%) 0.52 

NO3 0.02 (5.9%) 0.96 (231%) 0.54 0.23 (44.5) 0.82 (158%) 0.85 

SO4 0.26 (18.8%) 0.99 (70.9%) 0.44 0.83 (49.4%) 1.36 (81.4%) 0.70 

NH4 0.19 (34%) 0.56 (101.6%) 0.38 0.45 (65.4%) 0.62 (91.1%) 0.82 

OA 

-1.36 (-52.2%) 2.20 (84.0%) 0.74 -0.27 (-8.6%) 2.14 (67.6%) 0.76 

BC -0.29 (58.3%) 0.67 (136.1%) 0.34 -0.24 (-47.4%) 0.66 (128%) 0.36 

NO3 0.24 (79.4%) 0.64 (212%) 0.54 0.43 (108%) 1.04 (260%) 0.81 

SO4 0.60 (51.7%) 1.07 (92.6%) 0.26 1.16 (80.4%) 1.81 (126%) 0.37 

NH4 0.33 (70.5%) 0.48 (103.5%) 0.31 0.58 (98.2%) 0.85 (142.9%) 0.56 

OA 

-0.79 (-58.4%) 1.25 (92.1%) 0.81 -0.10 (-5.8%) 1.49 (82.7) 0.84 

3.2.1 Comparison for ground based sites 

Figure 9 shows the daily variation of NOx, BC and O3 at GOLF, LHVP and SIRTA, respectively, 

during summer. The simulated and measured peaks of NOx occur at about 8 a.m., while ozone 

peaks in late afternoon due to important photochemical production in the afternoon in summer. 

NOx values are overestimated with a bias (RB) of 6.1 (44.7%), 2.7 (14.6%) and 1.7 ppb (20.7%) at 

GOLF, LHVP and SIRTA, respectively. The O3 concentrations vary between 15 to 40 ppb. It anticorrelates 

with NOx and the RB at these three sites are all within 10%. 

16


The BC peaks are correlated with those of NOx, both pollutants being due to traffic emissions. The 

correlation coefficients between simulated and observed time series of BC at GOLF and LHVP are 

around 0.5 in summer. It is lower in SIRTA (~0.35). However, the daily variation of BC from simulation 

agrees well correlated with the measurements at SIRTA, but it is overestimated by a factor of 

two to three at GOLF and LHVP. This points to an overestimation in BC emissions in the urban 

area. Inorganic aerosols are often overestimated up to a factor of two for the nitrate concentrations 

in PM10 at LHVP. However, the correlation coefficients are often around 0.7-0.8 at these three sites, 

except for sulfate and ammonium at SIRTA where they are only 0.37 and 0.56, respectively. Simulated 

OA is slightly underestimated in the model by no more than 15% in PM10 in the summer (Fig. 

10). Simulations captured almost all peaks for the summer campaign and the correlation coefficients 

are around 0.8. In particular the large OA peaks on July 16 and 21, due to long range transport of 

biogenic SOA (see MEGAPOLI Deliverable 3.5), are well captured, which is encouraging. 

Figure 9: Daily variation of NOx (red, in ppb), BC (black, shadow is the range of PM1 to PM10, in µgm -3 ) 

and O3 (blue, in ppb) from model (lines) and measurement (starred) at GOLF (left), LHVP (middle) and 

SIRTA (right) in July 2009. The x axis is in local time. 

3.2.2 Simulations of the Paris urban plume 

The pollutants emitted in the Paris agglomeration have a local impact, such as POA, BC and NOx, 

etc. In addition, they may also have regional and continental impact when they form secondary pollutants 

such as inorganic aerosols, O3 and SOA during their transport away from the agglomeration. 

Fig. 11 gives a classical picture about ground surface OA concentration in the plume from Paris. On 

July 16th, a morning peak of OA was formed due to POA emissions and low boundary layer height 

and transported towards north-east. It disappeared at midday due to decrease of POA emissions and 

increase of PBL height. An OA plume is formed due to SOA production at 13h, in the north of Paris 

agglomeration and further transported towards north. The plume concentration reaches its highest 

values farther from Paris due to condensation of semivolatile VOC products formed by photochemical 

reactions in the afternoon. During evening and night northward transport and SOA formation 

continue. Therefore, although OA has a high value of about 13 µg m -3 in the morning in Paris 

related to local POA emissions, its highest value about (17 µg m -3 ) occurs during the early night at 

about 150 km north of the agglomeration, due to SOA build-up from anthropogenic VOC emissions 

in the Paris agglomeration and additional biogenic VOC showing enhanced emissions under the 

high temperatures prevailing in the afternoon of this day. 

3.2.3 Comparison between airborne observations and simulations 

AMS measurements from 8 flights are of good quality and are used for evaluation of the simulations. 

Here, as an example the flight N°30 on July 16 is analyzed. This day was cloudless with high 

temperature, and southerly winds. Large primary pollution peaks were observed in the urban area in 


17


the morning, due to low winds. The Paris urban pollution plume was found from measurements and 

in simulations as heading towards north of France, with slight differences (about 15° in the plume 

direction) (Fig. 12). Enhanced BC and nitrate concentrations were observed within the plume both 

in the models and from measurements, with somewhat larger values in the simulations. Scaling nitrate 

with BC as a primary pollution tracer to take into account plume dilution (not shown), indicates 

a NO3 build-up when the plume is moving away from Paris. Background sulfate concentrations 

are slightly overestimated in simulations (2.5 instead of 2 µg m -3 ). The production of sulfate 

was underestimated within the plume in simulations (about 0.5 µg m -3 compared to 1 µg m -3 observed). 

µg/m3 

µg/m3 

µg/m3 

Figure 10: Comparisons of modeled OA (PM1-PM10) from simulation (green) at GOLF (top), LHVP (middle) 

and SIRTA (bottom) and measurement (x in black) in summer. The green area shows the range of values 

for PM1 to PM10 aerosol which best corresponds to the PM1 observations, due uncertainty in the simulated 

particle size. All values are given in µg m -3 . 

Simulated background OA concentrations are about 1 µg m -3 lower than the observations (2–3 instead 

of 3 – 4 µg m -3 ). Highest concentrations were measured when the aircraft returned to the airport 

on the northwest of Paris agglomeration. Enhanced plume values increments for OA are larger 

in simulations than observed (about 2–3 instead of 1–2 µg m -3 ). OA build-up in the plume was more 

closely analysed. OA (POA+SOA) is plotted against Ox (NO2+O3) (Fig. 13). Ox is used here as a 

tracer of photochemical activity. The idea is that under polluted conditions both SOA and Ox are 

related to the oxidation of VOC. The slope of OA vs. Ox thus should indicate the accuracy of the 

representation of SOA formation in the model. From PMF analysis of the AMS measurements (Deliverable 

3.5), it appears that SOA related fractions are dominant with respect to POA. Thus in this 

analysis the more robust OA value is preferred to SOA. First, a good correlation of OA with Ox is 

observed both in simulations and observations (Fig. 13). The slope is 0.27 in the simulation compared 

to 0.13 in the observations. Thus the level of normalized SOA production in the Paris plume 

is thought to be correct within a factor of two. This is an encouraging result when considering the 


18



07h 

13h 

19h 

Figure 11: Simulated CHIMERE urban OA plume on July 16 at different times of the day. Surface OA mass 

concentrations are given in µg m -3 . 

µg/m3 

y = 0.13 x + 3.0 

y = 0.27 x + 12.2 

Figure 13: OA/Ox ratio from measurements (black) and simulations CHIMERE (green) for the flight on July 

16. The x-axis denotes Ox (O3+NO2) in ppb, the y-axis denotes OA in µg m -3 . 

10h 

16h 

22h 

ppb 

19


uncertainties of the SOA scheme of VBS approach, and when keeping in mind the traditional difficulty 

of models to simulate SOA formation. 

Figure 12: Comparisons of OA, nitrate, sulfate, ammonium, BC from measurement (left, with suffix m) and 

from the simulation with CHIMERE (right, with suffix v) for the flight on July 16. All values are given in µg 

m -3 . 


20


Acknowledgements 

The MEGAPOLI project is very thankful and acknowleges the Laboratoire d’Hygiène de Paris (LHVP), The 

SIRTA/IPSL, le Golf de la Poudrérie à Livry-Gargan for hosting campaign sites. Without this help, the campaign 

would not have been possible. Also University Paris-Est, Créteil, INRA/Gignon, Météo-France, Roissy 

and ENPC, Marne la Vallée are thanked to host lidar instruments. It is also grateful to all voluntary participants 

who made this campaign a great success. 

The research leading to these results has received funding from the European Union’s Seventh Framework 

Programme FP/2007-2011 within the project MEGAPOLI, grant agreement n°212520. In additions the project 

has been supported through the French ANR MEGAPARIS and LEFE- CHAT MEGAPOLI-FRANCE 

projects, and through an Ile de France SEPPE project. 

All contributions from the MEGAPOLI campaign team are acknowledged: 

• M. Beekmann 1 , U. Baltensperger 2 , A. Borbon 1 , J. Sciare 3 , V. Gros 3 , A. Baklanov 4 , M. Lawrence 5 , S. 

Pandis 6 , V.Kostenidou 6 , M.Psichoudaki 6 , L. Gomes 7 , P. Tulet 7 , A. Wiedensohler 8 , A. Held * , L. Poulain 

8 , K.Kamilli 8 , W. Birmli 8 , A. Schwarzenboeck 9 , K. Sellegri 9 , A. Colomb 9 , J.M. Pichon 9 , E.Fernay 9 , 

J.L. Jaffrezo 10 , P. Laj 10 , C. Afif 1 , V. Ait-Helal 1 *, B. Aumont 1 , S. Chevailler 1 , P. Chelin 1 , I. Coll 1 , J.F. 

Doussin 1 , R. Durand-Jolibois 1 , H. Mac Leod 1 , V. Michoud 1 , K. Miet 1 , N. Grand 1 , S. Perrier 1 , H. 

Petetin 1 , T. Raventos 1 , C. Schmechtig 1 , G. Siour 1 , C. Viatte 1 , Q. Zhang 1 **, P. Chazette 3 , M. Bressi 3 , 

M. Lopez 5 , P. Royer 3 , R. Sarda-Esteve 3 , F. Drewnick 5 , J. Schneider 5 , M. Brands 5 , S. Bormann 5 , K. 

Dzepina 5 , F. Freutel 5 , S. Gallavardin 5 , T. Klimach 5 , T. Marbach 5 , R. Shaiganfar 5 , S.L. Von der Weiden 

5 , T. Wagner 5 , S.Zorn 5 , P. De Carlo 2 , A. Prevot 2 , M. Crippa 2 , C. Mohr 2 , Marie Laborde 2 , M. 

Gysel 2 , Roberto Chirico 2 , Maarten Heringa 2 , A. Butet 11 , A. Bourdon 11 , E. Mathieu 11 , T. Perrin 11 , 

SAFIRE team, J.Wenger 12 , R. Healy 12 , I.O. Connor 12 , E. Mc Gillicuddy 12 , P. Alto 13 , J.P.Jalkanen 13 , 

M. Kulmala 13 , P Lameloise 14 , V. Ghersi 14 , O. Sanchez 14 , A. Kauffman 14 , H. Marfaing 14 , C. Honoré 14 , 

L. Chiappini 15 , O. Favez 15 , F. Melleux 15 , G. Aymoz 15 , B. Bessagnet 15 , L. Rouil 15 , S. Rossignol 15 , M. 

Haeffelin 16 , C. Pietras 16 , J. C. Dupont 16 , and the SIRTA team, S. Kukui 17 , E. Dieudonné 17 , F. Ravetta 

17 , J.C.Raut 17 ,G. Ancellet 17, F. Goutail 17 , J.L Besombes 18 , N. Marchand 19 , Y. Le Moullec 20 , J. 

Cuesta 21 , Y. Té 22 , N. Laccoge 23 , S. Lolli 24 , L. Sauvage 24 , S.Loannec 24 , D. Ptak 25 , A. Schmidt 25 , S. 

Conil 26 , M. Boquet 27 , 

1 Laboratoire InterUniversitaire des Systèmes Atmosphériques (LISA), Université Paris Est et 7, CNRS, Créteil, 

France, 2 Paul Scherrer Institut, Villigen, Switzerland, 3 Laboratoire des Sciences du Climat et de 

l’Environnement (LSCE), Gif sur Yvette, France, 4 Danish Meteorological Institute, Copenhagen, Denmark, 

5 Max-Planck-Institute for Chemistry, Mainz, Germany, 6 Foundation for Research and Technology, Hellas, 

University of Patras, Greece, 7 Game,Centre National de Recherche Météorologique, Toulouse , France , 

8 Institut für Troposphärenforschung, Leipzig, Germany, 9 Laboratoire de Météorologie Physique, Clermont- 

Ferrand, France, 10 Laboratoire de Glaciologie et Géophysique de l’Environnement, Grenoble, France , 

11 SAFIRE, Toulouse, France, 12 University College Cork, Ireland, 13 University of Helsinki, Finland , 

14 AIRPARIF, Paris, France, 15 INERIS, France, , 16 SIRTA/IPSL, Palaiseau, France, 17 Laboratoire Atmosphères, 

Milieux, Observations Spatiales, Paris, France, , 18 Laboratoire de Chimie Moléculaire et Environnement, 

Chambery, France, 19 Laboratoire de Chimie Provence, Marseille, France, 20 Laboratoire de l’Hygiène de la 

Ville de Paris, France,, 21 Laboratorie de Météorologie Dynamique, Palaiseau, France , 22 Laboratorie de Physique 

Moléculaire pour l'Atmosphère et l'Astrophysique, 23 Département Environnement et Chimie, Ecole de 

Mines de Douais, France, , 24 LEOSPHERE, France, 25 Universität Duisburg-Essen), Germany, 26 ANDRA, 

Châtenay-Malabry, France, 27 CEREA, Marne La Vallée, France ,**also ARIA-Technologie. France 


21


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23


Previous MEGAPOLI reports 

Previous reports from the FP7 EC MEGAPOLI Project can be found at: 

http://www.megapoli.info/ 

Collins W.J. (2009): Global radiative forcing from megacity emissions of long-lived greenhouse 

gases. Deliverable 6.1, MEGAPOLI Scientific Report 09-01, 17p, MEGAPOLI-01-REP-2009- 

10, ISBN: 978-87-992924-1-7 


Denier van der Gon, HAC, AJH Visschedijk, H. van der Brugh, R. Dröge, J. Kuenen (2009): A base 

year (2005) MEGAPOLI European gridded emission inventory (1st version). Deliverable 1.2, 

MEGAPOLI Scientific Report 09-02, 17p, MEGAPOLI-02-REP-2009-10, ISBN: 978-87- 

992924-2-4 


Baklanov A., Mahura A. (Eds) (2009): First Year MEGAPOLI Dissemination Report. Deliverable 

9.4.1, MEGAPOLI Scientific Report 09-03, 57p, MEGAPOLI-03-REP-2009-12, ISBN: 978- 

87-992924-3-1 


Allen L., S Beevers, F Lindberg, Mario Iamarino, N Kitiwiroon, CSB Grimmond (2010): Global to 

City Scale Urban Anthropogenic Heat Flux: Model and Variability. Deliverable 1.4, MEGA- 

POLI Scientific Report 10-01, MEGAPOLI-04-REP-2010-03, 87p, ISBN: 978-87-992924-4-8 


Pauli Sievinen, Antti Hellsten, Jaan Praks, Jarkko Koskinen, Jaakko Kukkonen (2010): Urban Morphological 

Database for Paris, France. Deliverable D2.1, MEGAPOLI Scientific Report 10-02, 

MEGAPOLI-05-REP-2010-03, 13p, ISBN: 978-87-992924-5-5 


Moussiopoulos N., Douros J., Tsegas G. (Eds.) (2010): Evaluation of Zooming Approaches Describing 

Multiscale Physical Processes. Deliverable D4.1, MEGAPOLI Scientific Report 10- 

03, MEGAPOLI-06-REP-2010-01, 41p, ISBN: 978-87-992924-6-2 


Mahura A., Baklanov A. (Eds.) (2010): Hierarchy of Urban Canopy Parameterisations for Different 

Scale Models. Deliverable D2.2, MEGAPOLI Scientific Report 10-04, MEGAPOLI-07-REP- 

2010-03, 50p, ISBN: 978-87-992924-7-9 


Dhurata Koraj, Spyros N. Pandis (2010): Evaluation of Zooming Approaches Describing Multiscale 

Chemical Transformations. Deliverable D4.2, MEGAPOLI Scientific Report 10-05, 



Igor Esau (2010): Urbanized Turbulence-Resolving Model and Evaluation for Paris. Deliverable 

D2.4.1, MEGAPOLI Scientific Report 10-06, MEGAPOLI-09-REP-2010-03, 20p, ISBN: 978- 

87-992924-9-3 


Grimmond CSB., M. Blackett, M.J. Best, et al. (2010): Urban Energy Balance Models Comparison. 

Deliverable D2.3, MEGAPOLI Scientific Report 10-07, MEGAPOLI-10-REP-2010-03, 72p, 

ISBN: 978-87-993898-0-3 


Gerd A. Folberth, Steve Rumbold, William J. Collins, Tim Butler (2010): Determination of Radia- 


24


tive Forcing from Megacity Emissions on the Global Scale. Deliverable D6.2, MEGAPOLI 

Scientific Report 10-08, MEGAPOLI-11-REP-2010-03, 19p, ISBN: 978-87-993898-1-0 


Thomas Wagner, Steffen Beirle, Reza Shaiganfar (2010): Characterization of Megacity Impact on 

Regional and Global Scales Using Satellite Data. Deliverable D5.1, MEGAPOLI Scientific 

Report 10-09, MEGAPOLI-12-REP-2010-03, 25p, ISBN: 978-87-993898-2-7 


Baklanov A., Mahura A. (Eds.) (2010): Interactions between Air Quality and Meteorology, Deliverable 

D4.3, MEGAPOLI Scientific Report 10-10, MEGAPOLI-13-REP-2010-03, 48p, ISBN: 

978-87-993898-3-4 


Baklanov A. (Ed.) (2010): Framework for Integrating Tools. Deliverable D7.1, MEGAPOLI Scientific 



Sofiev M., Prank M., Vira J., and MEGAPOLI Modelling Teams (2010): Provision of global and 

regional concentrations fields from initial baseline runs. Deliverable D5.2, MEGAPOLI Technical 

Note 10-12, MEGAPOLI-15-REP-2010-03, 10p. 


H.A.C. Denier van der Gon, J. Kuenen, T. Butler (2010): A Base Year (2005) MEGAPOLI Global 

Gridded Emission Inventory (1st Version). Deliverable D1.1, MEGAPOLI Scientific Report 

10-13, MEGAPOLI-16-REP-2010-06, 20p, ISBN: 978-87-993898-5-8 


Lawrence M. G., Butler T. M., Collins W., Folberth G., Zakey A., Giorgi F. (2010): Meteorological 

Fields for Present and Future Climate Conditions. Deliverable D6.5, MEGAPOLI Technical 

Note 10-14, MEGAPOLI-17-REP-2010-09, 9p. 


Beekmann M., Baltensperger U., and the MEGAPOLI campaign team (2010): Database of Chemical 

Composition, Size Distribution and Optical Parameters of Urban and Suburban PM and its 

Temporal Variability (Hourly to Seasonal). Deliverable D3.1, MEGAPOLI Scientific Report 



Beekmann M., Baltensperger U., and the MEGAPOLI campaign team (2010): Database of the Impact 

of Megacity Emissions on Regional Scale PM Levels. Deliverable D3.4, MEGAPOLI 

Scientific Report 10-16, MEGAPOLI-19-REP-2010-10, 29p, ISBN: 978-87-993898-7-2 


Kuenen J., H. Denier van der Gon, A. Visschedijk, H. van der Brugh, S. Finardi, P. Radice, A. 

d’Allura, S. Beevers, J. Theloke, M. Uz-basich, C. Honoré, O. Perrussel (2010): A Base Year 

(2005) MEGAPOLI European Gridded Emission Inventory (Final Version). Deliverable D1.6, 

MEGAPOLI Scientific Report 10-17, MEGAPOLI-20-REP-2010-10, 37p, ISBN: 978-87- 

993898-8-9 


Karppinen A., Kangas L., Riikonen K., Kukkonen J., Soares J., Denby B., Cassiani M., Finardi S., 

Radice P., (2010): Evaluation of Methodologies for Exposure Analysis in Urban Areas and 

Application to Selected Megacities. Deliverable D4.4, MEGAPOLI Scientific Report 10-18, 



Soares J., A. Karppinen, B. Denby, S. Finardi, J. Kukkonen, M. Cassiani, P. Radice, M.Williams 


25


(2010): Exposure Maps for Selected Megacities. Deliverable D4.5, MEGAPOLI Scientific Report 



Rumbold S.T., W.J. Collins, G.A. Folberth (2010): Comparison of Coupled and Uncoupled Models. 


ISBN: 978-87-92731-01-2 


Baklanov A., Mahura A. (Eds) (2010): Second Year MEGAPOLI Dissemination Report. Deliverable 

D9.4.2, MEGAPOLI Scientific Report 10-21, MEGAPOLI-24-REP-2010-12, 89p, ISBN: 

978-87-92731-02-9 


Moussiopoulos N., Douros J., Tsegas G. (Eds) (2010): Evaluation of Source Apportionment Methods. 

Deliverable D4.6, MEGAPOLI Scientific Report 10-22, MEGAPOLI-25-REP-2010-12, 

54p, ISBN: 978-87-92731-03-6 


Theloke J., M.Blesl, D. Bruchhof, T.Kampffmeyer, U. Kugler, M. Uzbasich, K. Schenk, H. Denier 

van der Gon, S. Finardi, P. Radice, R. S. Sokhi, K. Ravindra, S. Beevers, S. Grimmond, I. 

Coll, R. Frie-drich, D. van den Hout (2010): European and megacity baseline scenarios for 

2020, 2030 and 2050. Deliverable D1.3, MEGAPOLI Scientific Report 10-23, MEGAPOLI- 

26-REP-2010-12, 57p, ISBN: 978-87-92731-04-3 


Galmarini S., Vinuesa J.F., Cassiani M., Denby B., Martilli A., (2011): Evaluation of Sub-Grid 

Models with Interactions between Turbulence and Urban Chemistry. Recommendations for 

Emission Inventories Improvement. Deliverable D2.6, MEGAPOLI Scientific Report 11-01, 



Butler T., H.A.C. Denier van der Gon, J. Kuenen (2011): The Base Year (2005) Global Gridded 

Emission Inventory used in the EU FP7 Project MEGAPOLI (Final Version). Deliverable 

D1.5, MEGAPOLI Scientific Report 11-02, MEGAPOLI-28-REP-2011-01, 27p, 978-87- 

92731-06-7 


Schlünzen K.H., M. Haller (Eds) (2011): Evaluation of Integrated Tools. Deliverable D7.2 MEGA- 

POLI Scientific Report 11-03, MEGAPOLI-29-REP-2011-03, 50p, 978-87-92731-07-4 


Sofiev M., M. Prank, J. Kukkonen (Eds) (2011): Evaluation and Improvement of Regional Model 

Simulations for Megacity Plumes. Deliverable D5.3, MEGAPOLI Scientific Report 11-04, 

MEGAPOLI-30-REP-2011-03, 88p, 978-87-92731-08-1 


Baltensperger U., Beekmann M., and the MEGAPOLI campaign team (2011): Source Apportionment 

of Major Urban Aerosol Components Including Primary and Secondary PM Sources. 


ISBN: 978-87-92731-09-8 


Kampffmeyer T., U. Kugler, M. Uzbasich, J. Theloke, R. Friedrich, D. van den Hout (2011): Short, 

Medium and Long Term Abatement and Mitigation Strategies for Megacities. Deliverable 

D8.1, MEGAPOLI Scientific Report 11-06, MEGAPOLI-32-REP-2011-05, 40p, ISBN: 978- 

87-92731-10-4 



26


Folberth G.A., S. Rumbold, W.J. Collins, T. Butler (2011): Regional and Global Climate Changes 

due to Megacities using Coupled and Uncoupled Models. Deliverable D6.6, MEGAPOLI Scientific 



Petetin H., M. Beekmann, V. Michoud, A. Borbon, J.-F. Doussin, A. Colomb, A. Schwarzenboeck, 

H. Denier van der Gon, C. Honore, A. Wiedensohler, U. Baltensperger, and the MEGAPOLI 

Campaign Team (2011): Effective Emission Factors for OC and BC for Urban Type Emissions. 

Deliverable D3.3, MEGAPOLI Scientific Report 11-08, MEGAPOLI-34-REP-2011-06, 

30p, ISBN: 978-87-92731-12-8 


Folberth G.A., S. Rumbold, W.J. Collins, T. Butler (2011): Estimate of Megacity Impacts in a Future 

Climate. Deliverable D5.7, MEGAPOLI Scientific Report 11-09, MEGAPOLI-35-REP- 

2011-06, 21p, ISBN: 978-87-92731-13-5 


Eckhardt S., M. Cassiani, A. Stohl (2011): Influence of North American Megacities on European 

Atmospheric Composition. Deliverable D5.6, MEGAPOLI Scientific Report 11-10, MEGA- 

POLI-36-REP-2011-06, 28p, ISBN: 978-87-92731-14-2 


Cassiani M., Stohl S., Eckhardt S., Sovief M., Prank M., Butler T., Lawrence M., Collins W.J., Folberth 

G.A., Rumbold S., Pyle J.A., Russo M.R., Stock Z., Siour G., Coll I., D’Allura A., Finardi 

S., Radice P., Silibello C. (2011): Prediction of Megacities Impact on Regional and 

Global Atmospheric Composition. Deliverable D5.4, MEGAPOLI Scientific Report 11-11, 



Sofiev M., M. Prank, A. Baklanov (Eds) (2011): Influence of Regional Scale Emissions on 

Megacity Air Quality. Deliverable D5.5, MEGAPOLI Scientific Report 11-12, MEGAPOLI- 

38-REP-2011-06, 60p, ISBN: 978-87-92731-16-6 


Hannukainen M., de Leeuw G., Collins W.J. (2011): Comparison of Measured and Modeled Radiative 

Effects. Deliverable D6.3, MEGAPOLI Scientific Report 11-13, MEGAPOLI-39-REP- 

2011-08, 22p, ISBN: 978-87-92731-17-3 


Esau I., Baklanov A., Zilitinkevich S. (2011): Improved Urban Parameterizations Based on Prognostic 

Equations, Utilizing LES Results. Deliverable D2.5, MEGAPOLI Scientific Report 11- 

14, MEGAPOLI-40-REP-2011-09, 61p, ISBN: 978-87-92731-18-0 


Esau I. (2011): Improved Urban Parameterizations Based on Prognostic Equations, Utilizing LES 

Results. Deliverable D2.4.2. MEGAPOLI Scientific Report 11-15, MEGAPOLI-41-REP-2011- 

09, 47p, ISBN: 978-87-92731-19-7 


Borbon A., E. Freney, N. Marchand, M. Beekmann, E. Abidi, W. Ait-Helal, A. Colomb, J. Cozic, 

N. Locoge, S. Sauvage, K. Sellegri, B. Temine-Roussel, J. Sciare, V. Gros, J.L. Jaffrezo, U. 

Baltensperger, and the MEGAPOLI campaign team (2011): Evaluation of Links between Secondary 

VOCs and Secondary Organic Aerosols of Anthropogenic and Biogenic Origin. Deliverable 

D3.5, MEGAPOLI Scientific Report 11-16, MEGAPOLI-42-REP-2011-09, 42p, ISBN: 

978-87-92731-20-3 


Beekmann M., C. Fountoukis, Q.J. Zhang, S. N. Pandis, and the MEGAPOLI campaign team 

(2011): Evaluation of State-of-the-Art CTMs Using New Experimental Datasets. Deliverable 

D3.6, MEGAPOLI Scientific Report 11-17, MEGAPOLI-43-REP-2011-09, 28p, ISBN: 978- 


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87-92731-21-0 



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MEGAPOLI 

Megacities: Emissions, urban, 

regional and Global Atmospheric 

POLlution and climate 

effects, and Integrated tools for 

assessment and mitigation 

EC FP7 Collaborative Project 


2008-2011 

Theme 6: Environment (including climate change) 

Sub-Area: ENV-2007.1.1.2.1: 

Megacities and regional hot-spots air quality and climate 

MEGAPOLI Project web-site 

http://www.megapoli.info 

MEGAPOLI Project Office 

Danish Meteorological Institute (DMI) 

Lyngbyvej 100, DK-2100 

Copenhagen, Denmark 

E-mail: alb@dmi.dk 

Phone: +45-3915-7441 

Fax: +45-3915-7400 

MEGAPOLI Project Partners 

• DMI - Danish Meteorological Institute (Denmark) 

- Contact Persons: Prof. Alexander Baklanov (coordinator), 

Dr. Alexander Mahura (manager) 

• FORTH - Foundation for Research and Technology, 

Hellas and University of Patras (Greece) - 

Prof. Spyros Pandis (vice-coordinator) 

• MPIC - Max Planck Institute for Chemistry (Germany) 

- Dr. Mark Lawrence (vice-coordinator) 

• ARIANET Consulting (Italy) – Dr. Sandro Finardi 

• AUTH - Aristotle University Thessaloniki (Greece) 

- Prof. Nicolas Moussiopoulos 

• CNRS - Centre National de Recherche Scientifique 

(incl. LISA, LaMP, LSCE, GAME, LGGE) (France) – 

Dr. Matthias Beekmann 

• FMI - Finnish Meteorological Institute (Finland) – 

Prof. Jaakko Kukkonen 

• JRC - Joint Research Center (Italy) – Dr. Stefano 

Galmarini 

• ICTP - International Centre for Theoretical Physics 

(Italy) - Prof. Filippo Giorgi 

• KCL - King's College London (UK) – Prof. Sue 

Grimmond 

• NERSC - Nansen Environmental and Remote 

Sensing Center (Norway) – Dr. Igor Esau 

• NILU - Norwegian Institute for Air Research 

(Norway) – Dr. Andreas Stohl 

• PSI - Paul Scherrer Institute (Switzerland) – Prof. 

Urs Baltensperger 

• TNO-Built Environment and Geosciences (The 

Netherlands) – Prof. Peter Builtjes 

• MetO - UK MetOffice (UK) – Dr. Bill Collins 

• UHam - University of Hamburg (Germany) – Prof. 

Heinke Schluenzen 

• UHel - University of Helsinki (Finland) – Prof. 

Markku Kulmala 

• UH-CAIR - University of Hertfordshire, Centre for 

Atmospheric and Instrumentation Research (UK) 

– Prof. Ranjeet Sokhi 

• USTUTT - University of Stuttgart (Germany) – 

Prof. Rainer Friedrich 

• WMO - World Meteorological Organization (Switzerland) 

– Dr. Liisa Jalkanen 

• CUNI - Charles University Prague (Czech Republic) 

– Dr. Tomas Halenka 

• IfT - Institute of Tropospheric Research (Germany) 

– Prof. Alfred Wiedensohler 

• UCam - Centre for Atmospheric Science, University 

of Cambridge (UK) – Prof. John Pyle 

Work Packages 

WP1: Emissions 

(H. Denier van der Gon, P. Builtjes) 

WP2: Megacity features 

(S. Grimmond, I. Esau) 

WP3: Megacity plume case study 

(M. Beekmann, U. Baltensperger) 

WP4: Megacity air quality 

(N. Moussiopoulos) 

WP5: Regional and global atmospheric composition 

(J. Kukkonen, A. Stohl) 

WP6: Regional and global climate impacts 

(W. Collins, F. Giorgii) 

WP7: Integrated tools and implementation 

(R. Sokhi, H. Schlünzen) 

WP8: Mitigation, policy options and impact assessment 

(R. Friedrich, D. van den Hout) 

WP9: Dissemination and Coordination 

(A. Baklanov, M. Lawrence, S. Pandis) 

29

MEGAPOLI Megacities: Emissions, urban ... - MEGAPOLI - DMI

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