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

of some secondary organic aerosol formation pathways from the model.
This underprediction can arise for a number of reasons including: incomplete
knowledge of precursor VOCs; inadequacies in the description of VOC oxidation
processes; and poor treatment of vapour partition processes. Most probably, all
three factors play a role.
43. Of the models considered in this report, there are several important points to
note with respect to model evaluation. For models which aim to model chemical
and physical processes explicitly, for example CMAQ, NAME and EMEP, there is a
tendency to underestimate total PM2.5 mass, sometimes by substantial amounts.
The CMAQ model described in A2.2, for example, underpredicts PM2.5 mass at
a London background location by 30-40%, consistent with the underprediction
noted in A2.3. However, a consideration of specific components shown in
A2.3 reveals mixed model performance. For example, fine particulate nitrate
is underpredicted by about a factor of two, whereas the performance for
coarse particulate nitrate was considerably worse. The general underprediction
compared with measurements seems not to have a single, dominant cause but
is the result of underestimates in many key PM2.5 components.
44. It is worth stressing that while there remain many challenges involved in
evaluating models that predict PM2.5, there is active ongoing research in
this area. Model evaluation initiatives such as AQMEII, which bring together
many models (from the USA and Europe) and large datasets with which to
evaluate them, should help lead to an improved understanding of PM2.5 model
evaluation (Galmarini and Rao, 2011).
5.6 Prediction of future trends
Modelling PM2.5 and the future
45. Of particular importance is the change in PM2.5 concentrations over the next
decade. This section brings together preliminary modelling projections already
undertaken, and indicates some of the main uncertainties and needs for further
work.
46. Figures 5.1 and 5.2 compare future concentrations calculated for 2020 derived
from the PCM and UKIAM models, together with corresponding maps for
recent years (2009 and 2010 respectively). The maps from the two models
look broadly similar, with both still showing higher concentrations in 2020
in the south-east and around London where higher SIA concentrations are
superimposed on higher primary emissions. But there is a bigger decrease in
concentration estimates in the UKIAM model over the time span illustrated than
in the estimated concentrations in the PCM model.
47. Tables 5.4 and 5.5 provide a breakdown of population-weighted means for
different source components for each model and indicate that overall there is
a greater predicted percentage change in PM2.5 (21%) according to the UKIAM
scenario analysis compared to the PCM estimates (12% change). A large part of
this difference is in the SIA concentrations. Whereas the UKIAM scenarios used
emissions from other countries outside the UK in 2020 (from a recent study
with the GAINS model based on energy projections from the PRIMES model
(PRIMES, 2010) and assumed implementation of currently agreed legislation up
to 2020 to limit emissions), PCM used earlier estimates with higher emissions
reported to EMEP. In addition, the UKIAM scenario allowed for the MARPOL
Convention leading to reductions of the order of 85% in SO2 emissions from
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