Atmos. Chem. Phys., 12, 8297-8321, 2012
www.atmos-chem-phys.net/12/8297/2012/
doi:10.5194/acp-12-8297-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle component-based factor analysis
C. A. Stroud1, M. D. Moran1, P. A. Makar1, S. Gong1, W. Gong1, J. Zhang1, J. G. Slowik4, J. P. D. Abbatt4, G. Lu1, J. R. Brook1, C. Mihele1, Q. Li2, D. Sills3, K. B. Strawbridge1, M. L. McGuire4, and G. J. Evans4
1Air Quality Research, Environment Canada, 4905 Dufferin Street, Toronto, ON, M3H 5T4, Canada
2Meteorological Service of Canada – Ontario Operations, Environment Canada, 4905 Dufferin Street, Toronto, ON, M3H 5T4, Canada
3Cloud Physics and Severe Weather Research Environment Canada 4905 Dufferin Street Toronto, ON, Canada, M3H 5T4, Canada
4Department of Chemistry University of Toronto 80 St. George St. Toronto, ON, Canada, M5S 3H6, Canada

Abstract. 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.


Citation: Stroud, C. A., Moran, M. D., Makar, P. A., Gong, S., Gong, W., Zhang, J., Slowik, J. G., Abbatt, J. P. D., Lu, G., Brook, J. R., Mihele, C., Li, Q., Sills, D., Strawbridge, K. B., McGuire, M. L., and Evans, G. J.: Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle component-based factor analysis, Atmos. Chem. Phys., 12, 8297-8321, doi:10.5194/acp-12-8297-2012, 2012.
 
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