Probing into regional ozone and particulate matter pollution in the United States: 1. A 1 year CMAQ simulation and evaluation using surface and satellite data

Author: Y. Zhang, K. Vijayaraghavan, X.‐Y. Wen, Hilary E. "Ned" Snell and M.Z. Jacobson
Date: 
December 31, 2009
Type: 
Journal Article
Venue: 
Journal of Geophysical Research
Citation: 

Zhang, Y., K. Vijayaraghavan, X.‐Y. Wen, H. E. Snell, and M. Z. Jacobson (2009), Probing into regional ozone and particulate matter pollution in the United States: 1. A 1 year CMAQ simulation and evaluation using surface and satellite data, J. Geophys. Res., 114, D22304, doi:10.1029/2009JD011898.

As part 1 in a series of papers describing long‐term simulations using the Community Multiscale Air Quality (CMAQ) modeling system and subsequent process analyses and sensitivity simulations, this paper presents a comprehensive model evaluation for the full year of 2001 over the continental U.S. using both ground‐based and satellite measurements. CMAQ is assessed for its ability to reproduce concentrations and long‐term trends of major criteria pollutants such as surface ozone (O3) and fine particulate matter (PM2.5) and related variables such as indicator species, wet deposition fluxes, and column mass abundances of carbon monoxide (CO), nitrogen oxides (NO2), tropospheric ozone residuals (TORs), and aerosol optical depths (AODs). The domain‐wide and site‐specific evaluation of surface predictions shows an overall satisfactory performance in terms of normalized mean biases for annual mean maximum 1 h and 8 h average O3 mixing ratios (−11.6 to 0.1% and −4.6 to 3.0%, respectively), 24 h average concentrations of PM2.5 (4.2–35.3%), sulfate (−13.0 to 43.5%), and organic carbon (OC) (−37.6 to 24.8%), and wet deposition fluxes (−13.3 to 31.6%). Larger biases, however, occur in the concentrations and wet deposition fluxes of ammonium and nitrate domain‐wide and in the concentrations of PM2.5, sulfate, black carbon, and OC at some urban/suburban sites. The reasons for such model biases may be errors in emissions, chemistry, aerosol processes, or meteorology. The evaluation of column mass predictions shows a good model performance in capturing the seasonal variations and magnitudes of column CO and NO2, but relatively poor performance in reproducing observed spatial distributions and magnitudes of TORs for winter and spring and those of AODs in all seasons. Possible reasons for the poor column predictions include the underestimates of emissions, inaccurate upper layer boundary conditions, lack of model treatments of sea salt and dust, and limitations and uncertainties in satellite data.