Air quality expert group - Fine particulate matter (PM2.5) in ... - Defra

concentration (µg m -3 )
10.0
7.5
5.0
2.5
0.0
POC at N Kensington
Aug-08 Sep-08 Oct-08 Nov-08 Dec-08
observed POC modelled POC
Figure A2.3.7: Comparison between observed and CMAQ POC between
August and December 2008 at London North Kensington.
29. The CMAQ model was able to predict average POC concentrations reasonably
well (Figure A2.3.7), although it failed to predict a number of concentration
peaks. On average the model underpredicts measurements by 0.7 µg m -3 or
37%. However, the CMAQ model fails to predict SOC by a large margin, with
the model and measured averages being 0.04 and 1.3 µg m -3 respectively (not
shown). Poor results of this kind are reported elsewhere (Zhang and Ying, 2011)
and are the focus of changes to the chemical scheme in version 5 of the CMAQ
model.
A2.4: Numerical Atmospheric-dispersion Modelling
Environment (NAME)
Annex 2: PM modelling in the UK
30. NAME is a three-dimensional Lagrangian dispersion model that simulates
dispersion and deposition processes occurring in the atmosphere and
parameterises the key chemical processes involved in formation of secondary
inorganic and organic aerosols. Pollutant emissions are represented by releasing
hundreds of thousands of air parcels, each able to represent the released mass
of many different species, driven by three-dimensional meteorological data from
the Met Office’s Unified Model (UM).
31. Recent modelling work (Redington et al., 2009) has studied the sensitivity of
modelled sulphate and nitrate aerosol to cloud, pH and ammonia emissions.
Sulphate aerosol production in the aqueous phase was found to be very
sensitive to modelled cloud pH. As the cloud becomes acidic, sulphate
production is greatly limited, conversely if the cloud is basic, large amounts of
sulphate aerosol are produced. Aqueous phase sulphate aerosol production
was also found to be sensitive to ±30% ammonia emissions due to the effect
of ammonia on the modelled pH. The work highlighted the importance
of ammonia in understanding complex links between the precursors and
subsequent production of secondary inorganic aerosol (SIA).
32. The NAME model has been run for 2006 to produce maps of emission
sensitivity coefficients (ESCs) for SIA components over the UK output as a 20
km national grid. The emission sensitivity coefficient is defined as the change in
concentration of the PM component divided by the base case PM component
concentration, all divided by the change in precursor emission divided by
the base case precursor emission. These emission sensitivities have been
incorporated into the PCM model (A2.8) and used to calculate projections of
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