References

ACPD

Atmospheric Chemistry and Physics Discussions

ACPD

1680-7375

Copernicus GmbH

Göttingen, Germany

10.5194/acpd-12-5939-2012

Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle-component-based factor analysis

Stroud

C. A.

¹ Moran

M. D.

¹ Makar

P. A.

¹ Gong

¹ Zhang

¹ Slowik

J. G.

⁴ Abbatt

J. P. D.

⁴ Lu

¹ Brook

J. R.

¹ Mihele

¹ Li

² Sills

³ Strawbridge

K. B.

¹ McGuire

M. L.

⁴ Evans

G. J.

⁴

Air Quality Research, Science and Technology Branch, Environment Canada, 4905 Dufferin Street, Toronto, M3H 5T4, ON, Canada

Ontario Region Operational Forecasting, Environment Canada, 4905 Dufferin Street, Toronto, M3H 5T4, ON, Canada

Cloud Physics and Severe Weather Research, Science and Technology Branch, Environment Canada, 4905 Dufferin Street, Toronto, M3H 5T4, ON, Canada

Department of Chemistry University of Toronto 80 St. George St. Toronto, M5S 3H6, ON, Canada

24 02 2012

12 2 5939 6018

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Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007) in southern Ontario (ON), Canada, were used to evaluate Environment Canada's regional chemical transport model predictions of primary organic aerosol (POA). Environment Canada's operational numerical weather prediction model and the 2006 Canadian and 2005 US national emissions inventories were used as input to the chemical transport model (named AURAMS). 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. Co-located carbon monoxide (CO), PM2.5 black carbon (BC), and PM1 SO4 measurements were also used for evaluation and interpretation, permitting a detailed diagnostic model evaluation. At the urban site, good agreement was observed for the comparison of daytime campaign PM1 POA and HOA mean values: 1.1 μg m−3 vs. 1.2 μg m−3, respectively. However, a POA overprediction was evident on calm nights due to an overly-stable model surface layer. Biases in model POA predictions trended from positive to negative with increasing HOA values. This trend has several possible explanations, including (1) underweighting of urban locations in particulate matter (PM) spatial surrogate fields, (2) overly-coarse model grid spacing for resolving urban-scale sources, and (3) lack of a model particle POA evaporation process during dilution of vehicular POA tail-pipe emissions to urban scales. Furthermore, a trend in POA bias was observed at the urban site as a function of the BC/HOA ratio, suggesting a possible association of POA underprediction for diesel combustion sources. For several time periods, POA overprediction was also observed for sulphate-rich plumes, suggesting that our model POA fractions for the PM2.5 chemical speciation profiles may be too high for these point sources. At the rural Harrow site, significant underpredictions in PM1 POA concentration were found compared to observed HOA concentration and were associated, based on back-trajectory analysis, with (1) transport from the Detroit/Windsor urban complex, (2) longer-range transport from the US Midwest, and (3) biomass burning. Daytime CO concentrations were significantly overpredicted at Windsor but were unbiased at Harrow. Collectively, these biases provide support for a hypothesis that combines a current underweighting of PM spatial surrogate fields for urban locations with insufficient model vertical mixing for sources close to the urban measurement sites. The magnitude of the area POA emissions sources in the US and Canadian inventories (e.g., food cooking, road and soil dust, waste disposal burning) suggests that more effort should be placed at reducing uncertainties in these sectors, especially spatial and temporal surrogates.

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