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A meteorologically adjusted ensemble Kalman filter approach for inversing Daily emissions:A case study in the Pearl River Delta,China

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Journal of Environmental Sciences 2022年第4期34卷 233-248页

作者： 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 School of Environment and Energy South China University of TechnologyUniversity Town CampusGuangzhou 510006China Institute for Environmental and Climate Research Jinan UniversityGuangzhou 511486China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Faculty of Social and Behavioural Sciences Utrecht UniversityUtrechtCH 3584the Netherlands Guangzhou Institute of Tropical and Marine Meteorology China Meteorological AdministrationGuangzhou 510640China State Environmental Protection Key Laboratory of Regional Air Quality Monitoring Guangdong Environmental Monitoring CenterGuangzhou 510308China Department of Environmental Science and Engineering College of Architecture and EnvironmentSichuan UniversityChengdu 610065China

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 emission 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 biases 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 meteorological inputs,which would lead to the overestimation of daily variations of posterior emissions.Using the new inversion method,daily variations of emissions shrank dramatically,with the percentage change decreased by 30%.Also,the total amount of posterior 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 reproduce 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 errors 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 inversions of reactive pollutants.

关键词： Emission inversion Daily emissions Meteorological adjustment Ensemble Kalman filter

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