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

Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-relat...

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Bibliographic Details
Published in:	Environmental science & technology 2020-07, Vol.54 (14), p.8589-8600
Main Authors:	Xing, Jia, Zheng, Shuxin, Ding, Dian, Kelly, James T, Wang, Shuxiao, Li, Siwei, Qin, Tao, Ma, Mingyuan, Dong, Zhaoxin, Jang, Carey, Zhu, Yun, Zheng, Haotian, Ren, Lu, Liu, Tie-Yan, Hao, Jiming
Format:	Article
Language:	eng
Subjects:	Accuracy Air Pollutants - analysis Air Pollution - analysis Air quality Air quality assessments Air quality control Anthropogenic Impacts on the Atmosphere Atmospheric chemistry Chemical indicators Chemical pollution Chemical transport Computer applications Computer simulation Decision making Deep Learning Emission analysis Emissions Emissions control Environmental Monitoring Machine learning Mathematical models Models, Statistical Nonlinear response Nonlinear systems Nonlinearity Pollutants Quality Response functions Statistical models System effectiveness
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Summary:	Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-related decision making. Here, we demonstrate a novel method that combines deep learning approaches with chemical indicators of pollutant formation to quickly estimate the coefficients of air quality response functions using ambient concentrations of 18 chemical indicators simulated with a comprehensive atmospheric chemical transport model (CTM). By requiring only two CTM simulations for model application, the new method significantly enhances the computational efficiency compared to existing methods that achieve lower accuracy despite requiring 20+ CTM simulations (the benchmark statistical model). Our results demonstrate the utility of deep learning approaches for capturing the nonlinearity of atmospheric chemistry and physics and the prospects of the new method to support effective policymaking in other environment systems.
ISSN:	0013-936X 1520-5851