Fine Scale Modeling of Agricultural Air Quality over the Southeastern United States Using Two Air Quality Models. Part II. Sensitivity Studies and Policy Implications

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

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

ABSTRACT

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 PM_2.5 formation, but the emission control strategies focus only on SO₂ and NO_x in the southeastern U.S. To understand the impact of NH₃, NH₃ to NH₄⁺ conversion and the chemical regimes of PM_2.5 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, NO_x, agricultural livestock-NH₃ (AL-NH₃) by 50%, respectively and collectively. The largest reductions of PM_2.5 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 SO₄^2–. This indicates that reducing AL-NH₃ emissions together with SO₂ and NO_x emissions can reduce PM_2.5 concentrations more than reducing emissions of SO₂ and NO_x alone, particularly in winter. Future emission control strategies for PM_2.5 controlling should consider the reduction of NH₃ emissions, in addition to the emissions of SO₂ and NO_x.

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