14 Comprehensive Modelling and Visualization of Particulate Matter …
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14.2 Methods
The Calpuff modelling system was used to simulate the dispersion and transport of
particulate matter (PM 10 and PM 2.5 ), as well as SO 2 and NO x , with a goal of accurately simulating both primary and secondary PM 2.5 in the Prince George airshed. In
total, the transport and dispersion of about 1500 individual sources were simulated
at hourly resolution from 2003–2005. Of these, there were 33 industrial sources with
a total of about 350 emitting units. Calmet was used to specify the meteorological
conditions, with surface observations from six stations and vertical sounding data
from one station used as input. The computational domain was 24 grid points (west–
east) by 36 grid points (south–north) set at 1 km resolution with a 500 m resolution
receptor grid plus ten discrete receptors (at monitoring locations) nested within it,
for a total of 1883 receptors.
A major challenge in conducting a community-wide dispersion modelling study
of PM is accurately specifying all sources in the airshed. While the major industrial
sources are relatively well quantified based on permit levels and stack monitoring,
most sources are unmonitored and must be estimated using emission factors and
activity levels. To evaluate the modelling system, including the emission inventory,
we compare the model outputs with meteorological observations, ambient air pollution levels at several monitoring stations, and with data and information from a
speciation study and receptor modelling that was conducted in 2005. Because of
uncertainty in PM emissions from many sources, an iterative approach was used
in refining the emission inventory. First the “best guess” emissions were modelled
and the resulting ambient concentrations compared with speciation data and ambient concentrations. In addition, scaling factors for each source were calculated by
optimizing the fit between modelled concentration and ambient concentrations at
monitoring locations.
Dispersion model output quantifies the link between ambient concentrations at all
receptors to the specific emission sources that contribute to those ambient concentrations. Consequently model output can be used to identify the most important sources
to target for reduction: an analysis of dispersion model output is the ideal tool to
effectively manage air quality. Because modelled ambient concentrations scale linearly with the emission rate, changes in emissions can be assessed by linearly scaling
the modelled ambient levels by the same amount, without the need to re-run the dispersion model. This was exploited by developing the web-based AirQuest decision
support tool for air quality mangers to use to run source reduction scenarios, without
having to re-run Calpuff.
14.3 Results and Discussion
The evaluation of model output from the 2010 iteration of the model suggested that
emissions from road dust, locomotives, and restaurants were over-estimated, while
fugitive dust emissions from commercial operations, gravel pits and industrial storage
85
14.2 Methods
The Calpuff modelling system was used to simulate the dispersion and transport of
particulate matter (PM 10 and PM 2.5 ), as well as SO 2 and NO x , with a goal of accurately simulating both primary and secondary PM 2.5 in the Prince George airshed. In
total, the transport and dispersion of about 1500 individual sources were simulated
at hourly resolution from 2003–2005. Of these, there were 33 industrial sources with
a total of about 350 emitting units. Calmet was used to specify the meteorological
conditions, with surface observations from six stations and vertical sounding data
from one station used as input. The computational domain was 24 grid points (west–
east) by 36 grid points (south–north) set at 1 km resolution with a 500 m resolution
receptor grid plus ten discrete receptors (at monitoring locations) nested within it,
for a total of 1883 receptors.
A major challenge in conducting a community-wide dispersion modelling study
of PM is accurately specifying all sources in the airshed. While the major industrial
sources are relatively well quantified based on permit levels and stack monitoring,
most sources are unmonitored and must be estimated using emission factors and
activity levels. To evaluate the modelling system, including the emission inventory,
we compare the model outputs with meteorological observations, ambient air pollution levels at several monitoring stations, and with data and information from a
speciation study and receptor modelling that was conducted in 2005. Because of
uncertainty in PM emissions from many sources, an iterative approach was used
in refining the emission inventory. First the “best guess” emissions were modelled
and the resulting ambient concentrations compared with speciation data and ambient concentrations. In addition, scaling factors for each source were calculated by
optimizing the fit between modelled concentration and ambient concentrations at
monitoring locations.
Dispersion model output quantifies the link between ambient concentrations at all
receptors to the specific emission sources that contribute to those ambient concentrations. Consequently model output can be used to identify the most important sources
to target for reduction: an analysis of dispersion model output is the ideal tool to
effectively manage air quality. Because modelled ambient concentrations scale linearly with the emission rate, changes in emissions can be assessed by linearly scaling
the modelled ambient levels by the same amount, without the need to re-run the dispersion model. This was exploited by developing the web-based AirQuest decision
support tool for air quality mangers to use to run source reduction scenarios, without
having to re-run Calpuff.
14.3 Results and Discussion
The evaluation of model output from the 2010 iteration of the model suggested that
emissions from road dust, locomotives, and restaurants were over-estimated, while
fugitive dust emissions from commercial operations, gravel pits and industrial storage