Yang Zhang , Kristen M. Olsen, Kai Wang

  • Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA

Received: December 7, 2012
Revised: March 18, 2013
Accepted: March 18, 2013
Download Citation: ||https://doi.org/10.4209/aaqr.2012.12.0346 

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Cite this article:
Zhang, Y., Olsen, K.M. and Wang, K. (2013). Fine Scale Modeling of Agricultural Air Quality over the Southeastern United States Using Two Air Quality Models. Part I. Application and Evaluation. Aerosol Air Qual. Res. 13: 1231-1252. https://doi.org/10.4209/aaqr.2012.12.0346


 

ABSTRACT


Two air quality models, the U.S. EPA Community Multiscale Air Quality (CMAQ) model and ENVIRON’s Comprehensive Air Quality Model with extensions (CAMx), are evaluated for their applications in simulating ambient air quality, in particular, the fate and transport of agriculturally-emitted NH3 over an area in the southeastern U.S. in January and July 2002 using a fine-scale horizontal grid resolution of 4-km. Both models moderately overpredict maximum 1-hr and 8-hr ozone (O3) and fine particulate matter (PM2.5) in January, due likely to a weaker vertical mixing and insufficient dry and wet removal of PM2.5 species simulated by the models. They either slightly underpredict or overpredict O3 but significantly underpredict PM2.5 in July. The large underprediction in PM2.5 is due to an excess wet deposition removal of sulfate, an excess dry deposition removal of precursors, and an underestimation of emissions of primary PM and precursors of secondary PM and secondary organic aerosol concentrations. Both models show large biases in the simulated concentrations of several gases (e.g., CO in CAMx, NO in CMAQ, NO2 in both models in both months and NH3 by both models in July) and PM species (in particular, nitrate in both months and carbonaceous PM in July), visibility indices, and dry and wet deposition fluxes. They also show some inaccuracies in reproducing temporal variations of NH3, PM2.5, dry and wet deposition fluxes. Differences in model performance between the two models are attributed to different model treatments such as vertical mixing, wet and dry deposition, SOA formation, and PM size representations. These results indicate a need to improve accuracies of the emissions and measurements of NH3, the emissions of primary PM and precursors of secondary PM, as well as model treatments of vertical mixing and dry and wet removal processes.


Keywords: CMAQ; CAMx; Air quality; Agricultural emissions; Fine scale modeling


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