@article{wu_hu_zhang_aneja_2008, title={Modeling atmospheric transport and fate of ammonia in North Carolina - Part II: Effect of ammonia emissions on fine particulate matter formation}, volume={42}, ISSN={["1873-2844"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-41449110003&partnerID=MN8TOARS}, DOI={10.1016/j.atmosenv.2007.04.022}, abstractNote={Accurate estimates of ammonia (NH3) emissions are needed for reliable predictions of fine particulate matter (PM2.5) by air quality models (AQMs), but the current estimates contain large uncertainties in the temporal and spatial distributions of NH3 emissions. In this study, the US EPA Community Multiscale Air Quality (CMAQ) modeling system is applied to study the contributions of the agriculture–livestock NH3 (AL-NH3) emissions to the concentration of PM2.5 and the uncertainties in the total amount and the temporal variations of NH3 emissions and their impact on the formation of PM2.5 for August and December 2002. The sensitivity simulation results show that AL-NH3 emissions contribute significantly to the concentration of PM2.5, NH4+, and NO3−; their contributions to the concentrations of SO42− are relatively small. The impact of NH3 emissions on PM2.5 formation shows strong spatial and seasonal variations associated with the meteorological conditions and the ambient chemical conditions. Increases in NH3 emissions in August 2002 resulted in >10% increases in the concentrations of NH4+ and NO3−; reductions in NH3 emissions in December 2002 resulted in >20% decreases in their concentrations. The large changes in species concentrations occur downwind of the high NH3 emissions where the ambient environment is NH3-poor or neutral. The adjustments in NH3 emissions improve appreciably the model predictions of NH4+ and NO3− both in August and December, but resulted in negligible improvements in PM2.5 in August and a small improvement in December, indicating that other factors (e.g., inaccuracies in meteorological predictions, emissions of other primary species, aerosol treatments) might be responsible for model biases in PM2.5.}, number={14}, journal={ATMOSPHERIC ENVIRONMENT}, author={Wu, Shiang-Yuh and Hu, Jian-Lin and Zhang, Yang and Aneja, Viney P.}, year={2008}, month={Apr}, pages={3437–3451} }