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| Titel |
Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle component-based factor analysis |
| VerfasserIn |
C. A. Stroud, M. D. Moran, P. A. Makar, S. Gong, W. Gong, J. Zhang, J. G. Slowik, J. P. D. Abbatt, G. Lu, J. R. Brook, C. Mihele, Q. Li, D. Sills, K. B. Strawbridge, M. L. McGuire, G. J. Evans |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 12, no. 18 ; Nr. 12, no. 18 (2012-09-17), S.8297-8321 |
| Datensatznummer |
250011448
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| Publikation (Nr.) |
 copernicus.org/acp-12-8297-2012.pdf |
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| Zusammenfassung |
Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met
2007) in Southern Ontario, Canada, were used to evaluate predictions of
primary organic aerosol (POA) and two other carbonaceous species, black
carbon (BC) and carbon monoxide (CO), made for this summertime period by
Environment Canada's AURAMS regional chemical transport model. Particle
component-based factor analysis was applied to aerosol mass spectrometer
measurements made at one urban site (Windsor, ON) and two rural sites (Harrow
and Bear Creek, ON) to derive hydrocarbon-like organic aerosol (HOA) factors.
A novel diagnostic model evaluation was performed by investigating model POA
bias as a function of HOA mass concentration and indicator ratios (e.g.
BC/HOA). Eight case studies were selected based on factor analysis and back
trajectories to help classify model bias for certain POA source types. By
considering model POA bias in relation to co-located BC and CO biases, a
plausible story is developed that explains the model biases for all three
species.
At the rural sites, daytime mean PM1 POA mass concentrations were
under-predicted compared to observed HOA concentrations. POA
under-predictions were accentuated when the transport arriving at the rural
sites was from the Detroit/Windsor urban complex and for short-term periods
of biomass burning influence. Interestingly, the daytime CO concentrations
were only slightly under-predicted at both rural sites, whereas CO was
over-predicted at the urban Windsor site with a normalized mean bias of
134%, while good agreement was observed at Windsor for the comparison
of daytime PM1 POA and HOA mean values, 1.1 μg m−3 and
1.2 μg m−3, respectively. Biases in model POA predictions
also trended from positive to negative with increasing HOA values. Periods of
POA over-prediction were most evident at the urban site on calm nights due to
an overly-stable model surface layer. This model behaviour can be explained
by a combination of model under-estimation of vertical mixing at the urban
location, under-representation of PM emissions for on-road traffic exhaust
along major urban roads and highways, and a more structured allocation of
area POA sources such as food cooking and dust emissions to urban locations.
A downward trend in POA bias was also observed at the urban site as a
function of the BC/HOA indicator ratio, suggesting a possible association of
POA under-prediction with under-representation of diesel combustion sources.
An investigation of the emission inventories for the province of Ontario and
the nearby US state of Indiana also suggested that the top POA area emission
sources (food cooking, organic-bound to dust, waste disposal burning)
dominated over mobile and point sources, again consistent with a mobile
under-estimation.
We conclude that more effort should be placed at reducing uncertainties in
the treatment of several large POA emission sources, in particular food
cooking, fugitive dust, waste disposal burning, and on-road traffic sources,
and especially their spatial surrogates and temporal profiles. This includes
using higher spatial resolution model grids to better resolve the urban road
network and urban food cooking locations. We also recommend that additional
sources of urban-scale vertical mixing in the model, such as a stronger
urban heat island effect and vehicle-induced turbulence, would help model
predictions at urban locations, especially at night time. |
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