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

PM2.5 emissions and receptor modelling
4.6.2 Comparison of receptor modelling results with output from
the PCM model
90. Both receptor modelling methods and the PCM model inevitably have
weaknesses and uncertainties associated with their outputs. The chemical
mass balance approach to source apportionment is limited to quantifying
those sources for which source chemical profiles are available as inputs and
consequently this may miss many minor sources. Additionally, the source
chemical profile information is derived in the main from US studies which
may not be wholly applicable to the UK situation. One example is that of road
vehicle emissions, for which Yin et al. (2010) highlight differences in the vehicle
parc between the fleet at the time of sampling and the vehicles used in the
key North American study which characterised the source emissions profiles.
This inevitably adds uncertainties to the assignments. In the case of PCM,
those components estimated by dispersion modelling are only as good as the
source emissions inventories, which for some sources are subject to very large
uncertainties arising from the difficulties in collecting suitable data.
91. Comparison of the two approaches to source apportionment is made especially
difficult by the fact that the source categories in the two models do not map
directly onto one another. However, by making certain assumptions, it is possible
to compare generic categories. The other key reservation in comparing the two
modelling approaches is that data are not available for the same time periods.
The Yin et al. (2010) study involved aerosol sampling over a 12-month period
from May 2007 to April 2008. Daily PM2.5 samples were collected for five
days (Monday to Friday) at the beginning of each month using two co-located
samplers at each site. Consequently, the results, although covering a 12-month
period, represent the analysis of only 60 weekday samples. On the other hand,
results from the PCM represent the analysis of annual means for the year 2009.
A comparison of the outputs of the two approaches appears in Table 4.6 and
Figure 4.11. In order to make this comparison, source categories disaggregated
by the PCM have had to be combined in order to map onto the sources identified
by the chemical mass balance receptor model, and in some cases categories
identified by the receptor model have had to be combined in order to match the
PCM outputs. The assumptions made are listed in the notes to Table 4.6.
92. Viewing Table 4.6 and Figure 4.11, the most obvious difference is in the
secondary inorganic fraction and this can be explained by the use of different
sampling periods. The traffic and off-road/smoking engine categories are
broadly similar for the two models, especially when viewing the sum of the two
categories, given that the CMB model probably does not adequately distinguish
off-road emissions from malfunctioning on-road vehicles. By far the largest
divergence between the models is in the category of industry/commercial/
domestic emissions (14% of total emissions in the PCM and 2% in the CMB).
This category (see note to Table 4.6) in the case of the PCM comprises the
sum of industry, commercial and domestic categories and half of the longrange
transported primary particulate matter, while for the CMB model it is
the sum of natural gas, coal and wood combustion aerosol. Since a substantial
proportion of the industrial, commercial and domestic categories in the PCM
model comprises particles from the combustion of natural gas and coal, there is
a very real divergence. It appears that the NAEI uses a very high emission factor
for emissions from natural gas combustion. However, the PCM also includes
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