Open AccessDeep Learning for Prediction of the Air Quality Response to Emission Changes
Author(s) -
Jia Xing,
Shuxin Zheng,
Dian Ding,
James T. Kelly,
Yafei Wang,
Siwei Li,
Tao Qin,
Mingyuan Ma,
Zhaoxin Dong,
Carey Jang,
Yun Zhu,
Haotian Zheng,
Lu Ren,
Tie-Yan Liu,
Jiming Hao
Publication year - 2020
Publication title -
environmental science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.851
H-Index - 397
eISSN - 1520-5851
pISSN - 0013-936X
DOI - 10.1021/acs.est.0c02923
Subject(s) - air quality index , benchmark (surveying) , nonlinear system , quality (philosophy) , computer science , environmental science , biochemical engineering , meteorology , engineering , physics , quantum mechanics , geography , philosophy , geodesy , epistemology
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.