Literature DB >> 18323109

Response of atmospheric particulate matter to changes in precursor emissions: a comparison of three air quality models.

Betty K Pun1, Christian Seigneur, Elizabeth M Bailey, Larry L Gautney, Sharon G Douglas, Jay L Haney, Naresh Kumar.   

Abstract

Three mathematical models of air quality (CMAQ, CMAQ-MADRID, and REMSAD) are applied to simulate the response of atmospheric fine particulate matter (PM2.5) concentrations to reductions in the emissions of gaseous precursors for a 10 day period of the July 1999 Southern Oxidants Study (SOS) in Nashville. The models are shown to predict similar directions of the changes in PM2.5 mass and component (sulfate, nitrate, ammonium, and organic compounds) concentrations in response to changes in emissions of sulfur dioxide (SO2), nitrogen oxides (NO(x)), and volatile organic compounds (VOC), except for the effect of SO2 reduction on nitrate and the effect of VOC reduction on PM2.5 mass. Furthermore, in many cases where the directional changes are consistent, the magnitude of the changes are significantly different among models. Examples are the effects of SO2 and NO(x) reductions on nitrate and PM2.5 mass and the effects of VOC reduction on organic compounds, sulfate and nitrate. The spatial resolution significantly influences the results in some cases. Operational model performance for a PM2.5 component appears to provide some useful indication on the reliability of the relative response factors (RRFs) for a change in emissions of a direct precursor, as well as for a change in emissions of a compound that affects this component in an indirect manner, such as via oxidant formation. However, these results need to be confirmed for other conditions and caution is still needed when applying air quality models for the design of emission control strategies. It is advisable to use more than one air quality model (or more than one configuration of a single air quality model) to span the full range of plausible scientific representations of atmospheric processes when investigating future air quality scenarios.

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Year:  2008        PMID: 18323109     DOI: 10.1021/es702333d

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  2 in total

1.  Deep Learning for Prediction of the Air Quality Response to Emission Changes.

Authors:  Jia Xing; Shuxin Zheng; Dian Ding; James T Kelly; Shuxiao Wang; Siwei Li; Tao Qin; Mingyuan Ma; Zhaoxin Dong; Carey Jang; Yun Zhu; Haotian Zheng; Lu Ren; Tie-Yan Liu; Jiming Hao
Journal:  Environ Sci Technol       Date:  2020-07-01       Impact factor: 9.028

2.  Development and application of observable response indicators for design of an effective ozone and fine particle pollution control strategy in China.

Authors:  Jia Xing; Dian Ding; Shuxiao Wang; Zhaoxin Dong; James T Kelly; Carey Jang; Yun Zhu; Jiming Hao
Journal:  Atmos Chem Phys       Date:  2019       Impact factor: 6.133

  2 in total

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