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

Modelling PM2.5 and the future
wide range of applications related to predicting and understanding PM2.5.
These applications include work to better quantify the non-linearities of PM2.5
formation in response to precursor emission reductions, the prediction of the
individual components that contribute to PM2.5 mass, short-term forecasting
and the longer term prediction of trends, and how predictions compare with
measured values.
11. The CMAQ model outlined in A2.1 is used to provide two-day forecast
predictions of PM10 and PM2.5 and predict how concentrations respond to
reductions in precursor emissions of sulphur dioxide (SO2), nitrogen oxides
(NOx) and ammonia (NH3). The CMAQ model described in A2.2 is used to
predict hourly concentrations of PM2.5 at several sites, including the rural
(Harwell) and urban background (London North Kensington) sites. Whilst much
of the temporal variation is captured by the model, predictions of PM2.5 are
generally 30-40% lower than observed values. Model underpredictions are
also observed in other CMAQ modelling discussed in A2.3, which considers
the major components that constitute PM2.5 and shows that model predictions
underestimate some of the major components of PM2.5 in terms of absolute
mass. The estimated relative contributions of each component to total PM2.5
are, however, similar to observed values.
12. The Lagrangian NAME model described in A2.4 shows how an understanding
of the emission sensitivity of PM2.5 concentrations can be developed. Emission
sensitivity is expressed as a coefficient that provides a measure of how the
concentration of PM changes as a result of a known change in emission of
a precursor gas. Such calculations provide useful information on how PM2.5
concentrations are likely to respond to controls in precursor emissions and can
also reveal important non-linear behavior between pollutants in secondary
particle formation. Annex 2.6 shows the PTM model which has been used to
model PM2.5 and other species using detailed chemical schemes. Predictions of
PM2.5 have been compared with observations and the response of reductions
to precursor emissions on PM2.5 considered. The PTM model again captures
important non-linearities in the chemical system, which are essential to
understand if policies are to be developed to reduce concentrations of PM2.5.
The receptor–oriented Lagrangian model, FRAME, is described in A2.7; this
model illustrates the effect of a finer grid resolution, for example with respect
to ammonia emissions and ammonium nitrate formation, and is used to provide
source–receptor relationships for UKIAM.
13. In A2.5 the Eulerian EMEP4UK model is used to predict fine particulate nitrate
over seven years at a site in Scotland. Longer-term predictions such as these
provide useful information on trends and also capture important episodes, such
as occurred in the spring of 2003. Similarly, EMEP4UK and the other regionalscale
models can provide surface concentration maps that help to better
understand the spatial distribution of PM2.5 in the UK and Europe.
14. It is noteworthy that the regional-scale models do not predict PM2.5 mass
directly, but estimate each component that contributes to its mass such as
particulate sulphate and nitrate (see Chapter 3). The mass of PM2.5 is then
calculated from the size distribution of the different components.
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