Deep Learning for Prediction of the Air Quality Response to Emission Changes
- 1 July 2020
- journal article
- research article
- Published by American Chemical Society (ACS) in Environmental Science & Technology
- Vol. 54 (14), 8589-8600
- https://doi.org/10.1021/acs.est.0c02923
Abstract
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.Keywords
Funding Information
- Ministry of Science and Technology of the People's Republic of China (2018YFC0213805)
- National Natural Science Foundation of China (21625701, 41907190, 51861135102, 71690244, 71722003, 71974108)
- MSRA
This publication has 43 references indexed in Scilit:
- Modeling of Atmospheric ChemistryPublished by Cambridge University Press (CUP) ,2017
- Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015The Lancet, 2017
- Current and future ozone risks to global terrestrial biodiversity and ecosystem processesEcology and Evolution, 2016
- Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013The Lancet, 2015
- Response of fine particulate matter concentrations to changes of emissions and temperature in EuropeAtmospheric Chemistry and Physics, 2013
- Response of Atmospheric Particulate Matter to Changes in Precursor Emissions: A Comparison of Three Air Quality ModelsEnvironmental Science & Technology, 2007
- Nonlinear Response of Ozone to Emissions: Source Apportionment and Sensitivity AnalysisEnvironmental Science & Technology, 2005
- Nonlinearity in atmospheric response: A direct sensitivity analysis approachJournal of Geophysical Research: Atmospheres, 2004
- Marginal PM25: Nonlinear Aerosol Mass Response to Sulfate Reductions in the Eastern United StatesJournal of the Air & Waste Management Association, 1999
- Smoke, Dust and Haze: Fundamentals of Aerosol BehaviorPhysics Today, 1977