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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Veröffentlicht in:	Environmental science & technology 2020-07, Vol.54 (14), p.8589-8600
Hauptverfasser:	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:	Artikel
Sprache:	eng
Schlagworte:	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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