Yang Zhang 1, Shiang-Yuh Wu2

  • 1 Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA
  • 2 Department of Air Quality and Environmental Management, Las Vegas, NV 89155, USA

Received: December 7, 2012
Revised: March 31, 2013
Accepted: March 31, 2013
Download Citation: ||https://doi.org/10.4209/aaqr.2012.12.0347  

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Cite this article:
Zhang, Y. and Wu, S.Y. (2013). Fine Scale Modeling of Agricultural Air Quality over the Southeastern United States Using Two Air Quality Models. Part II. Sensitivity Studies and Policy Implications. Aerosol Air Qual. Res. 13: 1475-1491. https://doi.org/10.4209/aaqr.2012.12.0347



Sensitivity simulations using CMAQ at various grid resolutions are evaluated. Compared with the simulations at 12- and 4-km, the 1.33-km simulation shows large improvement in most meteorological predictions in July and some chemical predictions in January and July 2002. Limited improvements at 1.33-km and 4-km are attributed to current limitations in meteorological parameterizations and lack of accurate data for land use and emissions at a fine scale. NH3 plays an important role in PM2.5 formation, but the emission control strategies focus only on SO2 and NOx in the southeastern U.S. To understand the impact of NH3, NH3 to NH4+ conversion and the chemical regimes of PM2.5 formation are examined. The conversion rates of NH3 to NH4+ from CMAQ and CAMx simulations are 10–60% in January and 10–50% in July at and near major sources. The eastern North Carolina and northeastern Georgia are NH3-rich and the remaining areas are NH3-neutral in both months. To further assess the impact of NH3 emission reductions, the sensitivity of CMAQ to emission reductions is evaluated for four emission scenarios: reducing emissions of SO2, NOx, agricultural livestock-NH3 (AL-NH3) by 50%, respectively and collectively. The largest reductions of PM2.5 are by up to 19.2% in January and 18.3% in July when all these emissions are reduced by 50%. AL-NH3 reductions result in the largest decrease in January by up to 16%, dominated by a reduction in NH4NO3, while SO2 reductions result in the largest decrease in July (up to 11%) due to decreases in NH4+ and SO42–. This indicates that reducing AL-NH3 emissions together with SO2 and NOx emissions can reduce PM2.5 concentrations more than reducing emissions of SO2 and NOx alone, particularly in winter. Future emission control strategies for PM2.5 controlling should consider the reduction of NH3 emissions, in addition to the emissions of SO2 and NOx.

Keywords: CMAQ; CAMx; Fine scale modeling; Sensitivity simulation; Emission reduction

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