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High-resolution Simulation Dataset of Hourly PM_(2.5)Chemical Composition in China(CAQRA-aerosol)from 2013 to 2020

Advances in Atmospheric Sciences 2025年第4期42卷 697-712页

作者： Lei KONG Xiao TANG Jiang ZHU Zifa WANG Bing LIU Yuanyuan ZHU Lili ZHU Duohong CHEN Ke HU huangjian wu Qian wu Jin SHEN Yele SUN Zirui LIU Jinyuan XIN Dongsheng JI Mei ZHENG Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China China National Environmental Monitoring Centre Beijing 100012China Guangdong Ecological Environment Monitoring Centre Guangzhou 510308China wuhan Environmental Monitoring Centre Hubei 430015China Hubei Province Academy of Eco-environmental Sciences Hubei 430072China Peking University Beijing 100871China

Scientific knowledge on the chemical compositions of fine particulate matter(PM_(2.5)) is essential for properly assessing its health and climate effects,and for decisionmakers to develop efficient mitigation strategies.A high-resolution PM_(2.5) chemical composition dataset(CAQRA-aerosol)is developed in this study,which provides hourly maps of organic carbon,black carbon,ammonium,nitrate,and sulfate in China from 2013 to 2020 with a horizontal resolution of 15 *** paper describes the method,access,and validation results of this *** shows that CAQRA-aerosol has good consistency with observations and achieves higher or comparable accuracy with previous PM_(2.5) composition *** on CAQRA-aerosol,spatiotemporal changes of different PM_(2.5) compositions were investigated from a national viewpoint,which emphasizes different changes of nitrate from other *** estimated annual rate of population-weighted concentrations of nitrate is 0.23μg m^(−3)yr^(−1) from 2015 to 2020,compared with−0.19 to−1.1μg m^(−3)yr^(−1) for other *** whole dataset is freely available from the China Air Pollution Data Center(https://***/10.12423/capdb_***).

关键词： PM_(2.5)composition dataset black carbon organic carbon ammonium nitrate sulfate

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High-resolution Simulation Dataset of Hourly PM2.5 Chemical Composition in China（CAQRA-aerosol） from 2013 to 2020

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Advances in Atmospheric Sciences 2025年第4期 697-712页

作者： Lei KONG Xiao TANG Jiang ZHU Zifa WANG Bing LIU Yuanyuan ZHU Lili ZHU Duohong CHEN Ke HU huangjian wu Qian wu Jin SHEN Yele SUN Zirui LIU Jinyuan XIN Dongsheng JI Mei ZHENG Institute of Atmospheric Physics Chinese Academy of Sciences University of Chinese Academy of Sciences China National Environmental Monitoring Centre Guangdong Ecological Environment Monitoring Centre wuhan Environmental Monitoring Centre Hubei Province Academy of Eco-environmental Sciences Peking University

Scientific knowledge on the chemical compositions of fine particulate matter(PM2.5) is essential for properly assessing its health and climate effects, and for decisionmakers to develop efficient mitigation strategies. A high-resolution PM2.5chemical composition dataset（CAQRA-aerosol） is developed in this study, which provides hourly maps of organic carbon,black carbon, ammonium, nitrate, and sulfate in China from 2013 to 2020 with a horizontal resolution of 15 km. This paper describes the method, access, and validation results of this dataset. It shows that CAQRA-aerosol has good consistency with observations and achieves higher or comparable accuracy with previous PM2.5composition datasets. Based on CAQRA-aerosol, spatiotemporal changes of different PM2.5compositions were investigated from a national viewpoint,which emphasizes different changes of nitrate from other compositions. The estimated annual rate of population-weighted concentrations of nitrate is 0.23 μg m-3yr-1from 2015 to 2020, compared with-0.19 to -1.1 μg m-3yr-1for other compositions. The whole dataset is freely available from the China Air Pollution Data Center（https://***/10.12423/capdb＿***）.

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Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network

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Advances in Atmospheric Sciences 2018年第12期35卷 1522-1532页

作者： huangjian wu Xiao TANG Zifa WANG Lin wu Miaomiao LU Lianfang WEI Jiang ZHU State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Science Beijing 100049China

Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM_(10) than PM_(2.5) in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants(PM_(2.5), PM_(10),SO_2,NO_2,CO and O_3) from 1436 stations of the China National Environmental Monitoring Network during 2014-16. Among the measurements, 0.65%-5.68% are marked as outliers. with PM_(10) and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016,which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM_(2.5),with differences exceeding 10 μg m^(-3) at 66 sites.

关键词： probabilistic automatic outlier detection air quality observation low pass filter spatial regression bivariate normal distribution

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The Impact of the Numbers of Monitoring Stations on the National and Regional Air Quality Assessment in China During 2013-18

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Advances in Atmospheric Sciences 2022年第10期39卷 1709-1720页

作者： Hongyan LUO Xiao TANG huangjian wu Lei KONG Qian wu Kai CAO Yating SONG Xuechun LUO Yao WANG Jiang ZHU Zifa WANG 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

China national air quality monitoring network has become the core data source for air quality assessment and management in ***,during network construction,the significant change in numbers of monitoring sites with time is easily ignored,which brings uncertainty to air quality *** study aims to analyze the impact of change in numbers of stations on national and regional air quality assessments in China during *** results indicate that the change in numbers of stations has different impacts on fine particulate matter(PM_(2.5))and ozone concentration *** increasing number of sites makes the estimated national and regional PM_(2.5) concentration slightly lower by 0.6−2.2μg m^(−3) and 1.4−6.0μg m^(−3) respectively from 2013 to *** main reason is that over time,the monitoring network expands from the urban centers to the suburban areas with low population densities and pollutant *** ozone,the increasing number of stations affects the long-term trends of the estimated concentration,especially the national trends,which changed from a slight upward trend to a downward trend in 2014−***,the impact of the increasing number of sites on ozone assessment exhibits a seasonal difference at the 0.05 significance level in that the added sites make the estimated concentration higher in winter and lower in *** results suggest that the change in numbers of monitoring sites is an important uncertainty factor in national and regional air quality assessments,that needs to be considered in long-term concentration assessment,trend analysis,and trend driving force analysis.

关键词： monitoring network newly added sites PM_(2.5) ozone

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Investigating the Transport Mechanism of PM2.5 Pollution during January 2014 in wuhan, Central China

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Advances in Atmospheric Sciences 2019年第11期36卷 1217-1234,I0012-I0017页

作者： Miaomiao LU Xiao TANG Zifa WANG Lin wu Xueshun CHEN Shengwen LIANG Hui ZHOU huangjian wu Ke HU Longjiao SHEN Jia YU Jiang ZHU State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and Engineering Nankai University Tianjin 300350 China Center for Excellence in Regional Atmospheric Environment Institute of Urban Environment Chinese Academy of Sciences Xiamen 361021 China wuhan Environmental Monitoring Center Wuhan 430015 China Hunan Meteorological Observatory Changsha 410118 China University of Chinese Academy of Sciences Beijing 100049 China

Severe haze pollution that occurred in January 2014 in wuhan was investigated. The factors leading to wuhan’s PM2.5 pollution and the characteristics and formation mechanism were found to be significantly different from other megacities, like Beijing. Both the growth rates and decline rates of PM2.5 concentrations in wuhan were lower than those in Beijing, but the monthly PM2.5 value was approximately twice that in Beijing. Furthermore, the sharp increases of PM2.5 concentrations were often accompanied by strong winds. A high-precision modeling system with an online source-tagged method was established to explore the formation mechanism of five haze episodes. The long-range transport of the polluted air masses from the North China Plain (NCP) was the main factor leading to the sharp increases of PM2.5 concentrations in wuhan, which contributed 53.4% of the monthly PM2.5 concentrations and 38.5% of polluted days. Furthermore, the change in meteorological conditions such as weakened winds and stable weather conditions led to the accumulation of air pollutants in wuhan after the long-range transport. The contribution from wuhan and surrounding cities to the PM2.5 concentrations was determined to be 67.4% during this period. Under the complex regional transport of pollutants from surrounding cities, the NCP, East China, and South China, the five episodes resulted in 30 haze days in wuhan. The findings reveal important roles played by transregional and intercity transport in haze formation in wuhan.

关键词： haze pollution regional transport wuhan North China Plain source-tagged method

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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 *** 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 *** to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the *** 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 *** 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%,*** 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%,*** 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),*** spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH *** spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to *** results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH reg

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

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Severe Global Environmental Issues Caused by Canada’s Record-Breaking Wildfires in 2023

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Advances in Atmospheric Sciences 2024年第4期41卷 565-571页

作者： Zhe WANG Zifa WANG Zhiyin ZOU Xueshun CHEN huangjian wu Wending WANG Hang SU Fang LI Wenru XU Zhihua LIU Jiaojun ZHU State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Institute of Applied Ecology Chinese Academy of SciencesShenyang 110016China

Due to the record-breaking wildfires that occurred in Canada in 2023,unprecedented quantities of air pollutants and greenhouse gases were released into the *** wildfires had emitted more than 1.3 Pg CO_(2)and 0.14 Pg CO_(2)equivalent of other greenhouse gases(GHG)including CH4 and N_(2)O as of 31 *** wildfire-related GHG emissions constituted more than doubled Canada’s planned cumulative anthropogenic emissions reductions in 10 years,which represents a significant challenge to climate mitigation *** model simulations showed that the Canadian wildfires impacted not only the local air quality but also that of most areas in the northern hemisphere due to long-range transport,causing severe PM_(2.5)pollution in the northeastern United States and increasing daily mean PM_(2.5)concentration in northwestern China by up to 2μg *** observed maximum daily mean PM_(2.5)concentration in New York City reached 148.3μg m-3,which was their worst air quality in more than 50 years,nearly 10 times that of the air quality guideline(i.e.,15μg m-3)issued by the World Health Organization(WHO).Aside from the direct emissions from forest fires,the peat fires beneath the surface might smolder for several months or even longer and release substantial amounts of CO_(2).The substantial amounts of greenhouse gases from forest and peat fires might contribute to the positive feedback to the climate,potentially accelerating global *** better understand the comprehensive environmental effects of wildfires and their interactions with the climate system,more detailed research based on advanced observations and Earth System Models is essential.

关键词： Canada forest fire greenhouse gases PM_(2.5) transboundary air pollution

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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 *** this study,we developed a novel meteorologically adjusted inversion method(MAEInv)based on the EnKF to improve daily emission *** new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological *** demonstration,we used the MAEInv to inverse daily carbon monoxide(CO)emissions in the Pearl River Delta(PRD)region,*** 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 *** 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 *** 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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一种基于集合最优插值的排放源快速反演方法

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气候与环境研究 2021年第2期26卷 191-201页

作者：吴煌坚林伟孔磊唐晓王威王自发陈松蹊北京大学光华管理学院北京100871 北京大学数学科学学院北京100871 北京大学统计科学中心北京100871 中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室北京100029 中国科学院大学北京100049 中国环境监测总站北京100012 中国科学院区域大气环境研究卓越创新中心福建厦门361021

基于集合卡尔曼滤波的源反演方法是估计排放源、提高空气质量模拟和预报精度的有效方法。为构建排放源与污染物浓度之间的误差协方差矩阵,该方法通常需要运行几十次大气化学传输模式。庞大的计算量限制了该方法的应用,使其无法为实时预... 详细信息

基于集合卡尔曼滤波的源反演方法是估计排放源、提高空气质量模拟和预报精度的有效方法。为构建排放源与污染物浓度之间的误差协方差矩阵,该方法通常需要运行几十次大气化学传输模式。庞大的计算量限制了该方法的应用,使其无法为实时预报系统快速更新排放源。本研究发展了一种基于集合最优插值的排放源反演方法。该方法使用历史集合数据构建误差协方差矩阵,仅需一次常规的空气质量模拟便可根据观测模拟差异反演排放源,从而显著降低计算量。本文使用该方法同化2015年1月全国1107个地面站点观测的CO小时浓度数据,结合2014年1月的历史集合数据集,估计2015年1月全国15 km分辨率的CO排放源。该方案反演的全国CO排放总量仅比使用2015年1月集合数据集的反演量高1%,表明历史时段与反演时段的气象条件差异对月均CO排放的影响有限。使用历史集合数据集更新的排放源再次模拟可将全国349个独立验证站点的平均低估从0.74 mg m^(−3)降至0.01 mg m^(−3),均方根误差降低18%,表明该方法可快速更新排放源并降低其不确定性。

关键词：排放源反演集合最优插值 CO排放源

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山东省2015年PM_(2.5)和O_3污染时空分布特征

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中国环境监测 2019年第2期35卷 59-69页

作者：隋玟萱王颢樾唐晓卢苗苗吴煌坚朱莉莉云南大学大气科学系云南昆明650500 复旦大学环境科学与工程系上海市大气颗粒物污染防治重点实验室(LAP3) 上海200000 中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室(LAPC) 北京100089 中国环境监测总站国家环境保护环境监测质量控制重点实验室北京100012

利用中国环境监测总站的城市空气质量自动监测数据,分析了2015年山东省细颗粒物(PM_(2.5))和臭氧(O_3)污染的时空分布特征,并初步探讨了其与气象要素的相互关系。研究发现:山东省PM_(2.5)年均质量浓度和年超标天数的空间分布均呈现由东... 详细信息

利用中国环境监测总站的城市空气质量自动监测数据,分析了2015年山东省细颗粒物(PM_(2.5))和臭氧(O_3)污染的时空分布特征,并初步探讨了其与气象要素的相互关系。研究发现:山东省PM_(2.5)年均质量浓度和年超标天数的空间分布均呈现由东部向西部递增的趋势,半岛地区的浓度最低,其他地区浓度均较高,年均质量浓度最大值出现在德州(101μg/m^3)。各城市PM_(2.5)的月均质量浓度均呈现出季节性变化,冬季最高,夏季最低。O_3-8h年均值和O_3年超标天数的空间分布与PM_(2.5)不同,半岛地区污染天数最少,其次为南部地区,中部地区和西北部地区污染最为严重并且各区域的城市之间O_3污染情况存在较大差异,具有明显的局地性特征。O_3质量浓度在春末夏初最高,超标现象主要出现在5—8月。分析各城市PM_(2.5)污染和O_3污染的协同性与差异性发现,虽然不同城市之间两者污染情况存在一定差异,但整体上看,山东省大气复合污染特征明显,全年有10个城市的PM_(2.5)和O_3同时超标天数都在20 d以上,并且该现象主要发生在夏季。夏季高温低湿的大陆气团控制更有利于O_3和PM_(2.5)叠加共存形成复合型污染。温度≥26℃时,O_3-8 h与PM_(2.5)日均质量浓度的相关系数为0. 63,相对湿度≤60%时,两者相关系数为0. 69。此外,当在大陆气团的控制下发生O_3污染时,相对湿度的提高更有利于PM_(2.5)浓度的增加。

关键词： PM2.5 O3 山东省时空分布特征气象要素

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