Predicting the Nonlinear Response of PM_2.5 and Ozone to Precursor Emission Changes with a Response Surface Model.

ABSTRACT: Reducing PM_2.5 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%.

SUBMITTER: Kelly JT 

PROVIDER: S-EPMC8459679 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature