Predicting the Nonlinear Response of PM<sub>2.5</sub> and Ozone to Precursor Emission Changes with a Response Surface Model
Reducing PM<sub>2.5</sub> 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 computation...
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doaj-355e0528ee40447e814d51173c0aaad02021-08-26T13:31:42ZengMDPI AGAtmosphere2073-44332021-08-01121044104410.3390/atmos12081044Predicting the Nonlinear Response of PM<sub>2.5</sub> and Ozone to Precursor Emission Changes with a Response Surface ModelJames T. Kelly0Carey Jang1Yun Zhu2Shicheng Long3Jia Xing4Shuxiao Wang5Benjamin N. Murphy6Havala O. T. Pye7Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USAOffice of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USASchool of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, ChinaSchool of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, ChinaState Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, ChinaState Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, ChinaCenter for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USACenter for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USAReducing PM<sub>2.5</sub> 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%. NO<sub>x</sub> emission reductions were more effective for reducing PM<sub>2.5</sub> and ozone concentrations than SO<sub>2</sub>, NH<sub>3</sub>, or traditional VOC emission reductions. NH<sub>3</sub> 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 NH<sub>3</sub> emissions to verify the responses of SOA to NH<sub>3</sub> 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.https://www.mdpi.com/2073-4433/12/8/1044response modelozonePM<sub>2.5</sub>air quality management |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
James T. Kelly Carey Jang Yun Zhu Shicheng Long Jia Xing Shuxiao Wang Benjamin N. Murphy Havala O. T. Pye |
spellingShingle |
James T. Kelly Carey Jang Yun Zhu Shicheng Long Jia Xing Shuxiao Wang Benjamin N. Murphy Havala O. T. Pye Predicting the Nonlinear Response of PM<sub>2.5</sub> and Ozone to Precursor Emission Changes with a Response Surface Model Atmosphere response model ozone PM<sub>2.5</sub> air quality management |
author_facet |
James T. Kelly Carey Jang Yun Zhu Shicheng Long Jia Xing Shuxiao Wang Benjamin N. Murphy Havala O. T. Pye |
author_sort |
James T. Kelly |
title |
Predicting the Nonlinear Response of PM<sub>2.5</sub> and Ozone to Precursor Emission Changes with a Response Surface Model |
title_short |
Predicting the Nonlinear Response of PM<sub>2.5</sub> and Ozone to Precursor Emission Changes with a Response Surface Model |
title_full |
Predicting the Nonlinear Response of PM<sub>2.5</sub> and Ozone to Precursor Emission Changes with a Response Surface Model |
title_fullStr |
Predicting the Nonlinear Response of PM<sub>2.5</sub> and Ozone to Precursor Emission Changes with a Response Surface Model |
title_full_unstemmed |
Predicting the Nonlinear Response of PM<sub>2.5</sub> and Ozone to Precursor Emission Changes with a Response Surface Model |
title_sort |
predicting the nonlinear response of pm<sub>2.5</sub> and ozone to precursor emission changes with a response surface model |
publisher |
MDPI AG |
series |
Atmosphere |
issn |
2073-4433 |
publishDate |
2021-08-01 |
description |
Reducing PM<sub>2.5</sub> 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%. NO<sub>x</sub> emission reductions were more effective for reducing PM<sub>2.5</sub> and ozone concentrations than SO<sub>2</sub>, NH<sub>3</sub>, or traditional VOC emission reductions. NH<sub>3</sub> 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 NH<sub>3</sub> emissions to verify the responses of SOA to NH<sub>3</sub> 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. |
topic |
response model ozone PM<sub>2.5</sub> air quality management |
url |
https://www.mdpi.com/2073-4433/12/8/1044 |
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