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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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Investigating the Changes in Air Pollutant emissions over the Beijing-Tianjin-Hebei Region in February from 2014 to 2019 through an Inverse emission Method

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Advances in Atmospheric Sciences 2023年第4期40卷 601-618页

作者： Xuechun LUO Xiao TANG Haoyue WANG Lei KONG Huangjian WU Weiguo WANG Yating SONG Hongyan LUO Yao WANG Jiang ZHU Zifa WANG School of Earth Sciences Yunnan UniversityKunming 650500China LAPC&ICCES Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Center for Excellence in Regional Atmospheric Environment Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China

In recent years,China has implemented several measures to improve air quality.The Beijing-Tianjin-Hebei(BTH)region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years.How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future.To evaluate the changes in major air pollutant emissions over this region,this paper employs ensemble Kalman filtering(EnKF)for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System(NAQPMS)simulation data to inversely estimate the emission rates of SO_(2),NOX,CO,and primary PM_(2.5)over BTH region in February from 2014 to 2019.The results show that SO_(2),NOX,CO,and primary PM_(2.5)emissions in the BTH region decreased in February from 2014 to 2019 by 83%,37%,41%,and 42%,while decreases in Beijing during this period were 86%,67%,59%,and 65%,respectively.Compared with the prior emission inventory,the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region,with simulated root mean square errors of the monthly average concentrations of SO_(2),NOX,PM_(2.5),and CO reduced by 41%,30%,31%,and 22%,respectively.The average uncertainties of SO_(2),NOX,PM_(2.5),and CO inversion emissions in2014-19 are±14.03%yr^(-1),±28.91%yr^(-1),±126.15%yr^(-1),and±43.58%yr^(-1).Compared with the uncertainty of MEIC emission,the uncertainties of all species changed by+2%yr^(-1),-2%yr^(-1),-26%yr^(-1),and-4%yr^(-1),respectively.The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions.The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014.The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up e

关键词： Emission inversion emission trend air pollutants Beijing-Tianjin-Hebei

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