Abstract
Reducing PM2.5 and ozone concentrations is important to protect human health and the environment. Chemical transport models, such as the Community Multiscale Air Quality (CMAQ) model, are valuable tools for exploring policy options for improving air quality but are computationally expensive. Here, we statistically fit an efficient polynomial function in a response surface model (pf-RSM) to CMAQ simulations over the eastern U.S. for January and July 2016. The pf-RSM predictions were evaluated using out-of-sample CMAQ simulations and used to examine the nonlinear response of air quality to emission changes. Predictions of the pf-RSM are in good agreement with the out-of-sample CMAQ simulations, with some exceptions for cases with anthropogenic emission reductions approaching 100%. NOX emission reductions were more effective for reducing PM2.5 and ozone concentrations than SO2, NH3, or traditional VOC emission reductions. NH3 emission reductions effectively reduced nitrate concentrations in January but increased secondary organic aerosol (SOA) concentrations in July. More work is needed on SOA formation under conditions of low NH3 emissions to verify the responses of SOA to NH3 emission changes predicted here. Overall, the pf-RSM performs well in the eastern U.S., but next-generation RSMs based on deep learning may be needed to meet the computational requirements of typical regulatory applications.
Highlights
PM2.5 and ozone air pollution lead to harmful effects on human health and the environment [1,2]
Information is limited on the seasonal variation in the simultaneous response in ozone and PM2.5 and its components for multiple precursors under recent conditions in the U.S Consideration of relatively recent conditions is important because NOx emissions declined by 57% and SO2 emissions by 85% between 2000 and 2017
Predictions of the polynomial function in a response surface model (pf-response surface models (RSMs)) are compared with Community Multiscale Air Quality (CMAQ) results for the 30 OOS runs
Summary
PM2.5 and ozone air pollution lead to harmful effects on human health and the environment [1,2]. Air quality modeling is important for effective air quality management because the response of PM2.5 and ozone to precursor emission changes is nonlinear and depends on hundreds of chemical reactions. Ozone concentrations decrease in response to NOx emission reductions when NOx is the limiting precursor for oxidant formation but increase under NOx -saturated conditions, where NOx inhibits oxidant production [7]. Previous studies have individually reported seasonal variations in the nonlinear response of ozone and PM2.5 to NOx emission reductions in the U.S for retrospective periods [13,14]. Information is limited on the seasonal variation in the simultaneous response in ozone and PM2.5 and its components for multiple precursors under recent conditions in the U.S Consideration of relatively recent conditions is important because NOx emissions declined by 57% and SO2 emissions by 85% between 2000 and 2017
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