Accurate prediction of air quality response to emissions for effective control policy design

作     者：Min Cao Jia Xing Shovan Kumar Sahu Lei Duan Junhua Li Min Cao;Jia Xing;Shovan Kumar Sahu;Lei Duan;Junhua Li

作者机构：State Key Joint Laboratory of Environmental Simulation and Pollution ControlSchool of EnvironmentTsinghua UniversityBeijing 100084China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution ComplexBeijing 100084China 

出 版 物：《Journal of Environmental Sciences》 (环境科学学报（英文版）)

年 卷 期：2023年第123卷第1期

页      面：116-126页

核心收录：

学科分类：07[理学] 070602[理学-大气物理学与大气环境] 0706[理学-大气科学] 

基　　金：supported by the National Key R&D program of China(Nos.2019YFC0214800 and 2018YFC0213805) the National Natural Science Foundation of China(No.41907190) Shanghai Science and Technology Commission Scientific Research Project(No.19DZ1205006)。 

主　　题：Air pollution Nonlinear response Air quality management Reduced form models(RFMs) Precursor emissions 

摘      要：Designing effective control policy requires accurate quantification of the relationship between the ambient concentrations of O3and PM2.5and the emissions of their precursors.However,the challenge is that precursor reduction does not necessarily lead to decreases in the concentrations of O3and PM2.5,which are formed by multiple precursors under complex physical and chemical processes;this calls for the development of advanced model technologies to provide accurate predictions of the nonlinear responses of air quality to emissions.Different from the traditional sensitivity analysis and source apportionment methods,the reduced form models(RFMs)based on chemical transport models(CTMs)are able to quantify air quality responses to emissions more accurately and efficiently with lower computational cost.Here we review recent approaches used in RFMs and compare their structures,advantages and disadvantages,performance and applications.In general,RFMs are classified into three types including(1)sensitivity-based models,(2)models with simplified chemistry and physical processes,and(3)statistical models,with considerable differences in principles,characteristics and application ranges.The prediction of nonlinear responses by RFMs enables more in-depth analysis,not only in terms of real-time prediction of concentrations and quantification of human exposure,health impacts and economic damage,but also in optimizing control policies.Notably,data assimilation and emission inventory inversion based on the nonlinear response of concentrations to emissions can also be greatly beneficial to air pollution control management.In future studies,improvement in the performance of CTMs is exceedingly crucial to obtain a more reliable baseline for the prediction of air quality responses.Development of models to determine the air quality response to emissions under varying meteorological conditions is also necessary in the context of future climate changes,which pose great challenges to the quantification of response relationships.Additionally,with rising requirements for fine-scale air quality management,improving the performance of urban-scale simulations is worth considering.In short,accurate predictions of the response of air quality to emissions,though challenging,holds great promise for the present as well as for future scenarios.