典型文献
A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions:A case study in the Pearl River Delta,China
文献摘要：
The conventional Ensemble Kalman filter(EnKF),which is now widely used to calibrate emission inventories and to improve air quality simulations,is susceptible to simulation errors of meteorological inputs,making accurate updates of high temporal-resolution emis-sion inventories challenging.In this study,we developed a novel meteorologically adjusted inversion method(MAEInv)based on the EnKF to improve daily emission estimations.The new method combines sensitivity analysis and bias correction to alleviate the inversion bi-ases caused by errors of meteorological inputs.For demonstration,we used the MAEInv to inverse daily carbon monoxide(CO)emissions in the Pearl River Delta(PRD)region,China.In the case study,60％of the total CO simulation biases were associated with sensitive me-teorological inputs,which would lead to the overestimation of daily variations of poste-rior emissions.Using the new inversion method,daily variations of emissions shrank dra-matically,with the percentage change decreased by 30％.Also,the total amount of poste-rior CO emissions estimated by the MAEInv decreased by 14％,indicating that posterior CO emissions might be overestimated using the conventional EnKF.Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better re-produce the magnitude and temporal patterns of ambient CO concentration,with a higher correlation coefficient(R,+37.0％)and lower normalized mean bias(NMB,-17.9％).Since er-rors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants,the MAEInv is also applicable to improve the daily emission inver-sions of reactive pollutants.
文献关键词：
中图分类号：
作者姓名：
Guanglin Jia;Zhijiong Huang;Xiao Tang;Jiamin Ou;Menghua Lu;Yuanqian Xu;Zhuangmin Zhong;Qing'e Sha;Huangjian Wu;Chuanzeng Zheng;Tao Deng;Duohong Chen;Min He;Junyu Zheng
作者机构：
School of Environment and Energy,South China University of Technology,University Town Campus,Guangzhou 510006,China;Institute for Environmental and Climate Research,Jinan University,Guangzhou 511486,China;Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;Faculty of Social and Behavioural Sciences,Utrecht University,Utrecht,CH 3584,the Netherlands;Guangzhou Institute of Tropical and Marine Meteorology,China Meteorological Administration,Guangzhou 510640,China;State Environmental Protection Key Laboratory of Regional Air Quality Monitoring,Guangdong Environmental Monitoring Center,Guangzhou 510308,China;Department of Environmental Science and Engineering,College of Architecture and Environment,Sichuan University,Chengdu 610065,China
文献出处：
引用格式：
[1]Guanglin Jia;Zhijiong Huang;Xiao Tang;Jiamin Ou;Menghua Lu;Yuanqian Xu;Zhuangmin Zhong;Qing'e Sha;Huangjian Wu;Chuanzeng Zheng;Tao Deng;Duohong Chen;Min He;Junyu Zheng-.A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions:A case study in the Pearl River Delta,China)[J].环境科学学报（英文版）,2022(04):233-248
A类：
meteorologically,inversing,MAEInv,teorological,poste,rors,inver
B类：
adjusted,ensemble,Kalman,filter,approach,daily,emissions,case,study,Pearl,River,Delta,China,conventional,Ensemble,EnKF,which,now,widely,calibrate,inventories,improve,air,quality,simulations,susceptible,errors,inputs,making,accurate,updates,temporal,resolution,challenging,this,developed,novel,inversion,method,estimations,new,combines,sensitivity,analysis,correction,alleviate,caused,by,For,demonstration,inverse,carbon,monoxide,PRD,region,total,biases,were,associated,sensitive,would,lead,overestimation,variations,Using,shrank,dra,matically,percentage,change,decreased,Also,amount,indicating,that,posterior,might,overestimated,using,Model,evaluations,independent,observations,revealed,better,produce,magnitude,patterns,ambient,concentration,higher,correlation,coefficient,+37,lower,normalized,mean,NMB,Since,are,major,sources,both,reactive,pollutants,also,applicable
AB值：
0.437356
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