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He C, Liu J, Zhou Y, Zhou J, Zhang L, Wang Y, Liu L, Peng S. Synergistic PM 2.5 and O 3 control to address the emerging global PM 2.5-O 3 compound pollution challenges. ECO-ENVIRONMENT & HEALTH 2024; 3:325-337. [PMID: 39281068 PMCID: PMC11400616 DOI: 10.1016/j.eehl.2024.04.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/05/2024] [Accepted: 04/02/2024] [Indexed: 09/18/2024]
Abstract
In recent years, the issue of PM2.5-O3 compound pollution has become a significant global environmental concern. This study examines the spatial and temporal patterns of global PM2.5-O3 compound pollution and exposure risks, firstly at the global and urban scale, using spatial statistical regression, exposure risk assessment, and trend analyses based on the datasets of daily PM2.5 and surface O3 concentrations monitored in 120 cities around the world from 2019 to 2022. Additionally, on the basis of the common emission sources, spatial heterogeneity, interacting chemical mechanisms, and synergistic exposure risk levels between PM2.5 and O3 pollution, we proposed a synergistic PM2.5-O3 control framework for the joint control of PM2.5 and O3. The results indicated that: (1) Nearly 50% of cities worldwide were affected by PM2.5-O3 compound pollution, with China, South Korea, Japan, and India being the global hotspots for PM2.5-O3 compound pollution; (2) Cities with PM2.5-O3 compound pollution have exposure risk levels dominated by ST + ST (Stabilization) and ST + HR (High Risk). Exposure risk levels of compound pollution in developing countries are significantly higher than those in developed countries, with unequal exposure characteristics; (3) The selected cities showed significant positive spatial correlations between PM2.5 and O3 concentrations, which were consistent with the spatial distribution of the precursors NOx and VOCs; (4) During the study period, 52.5% of cities worldwide achieved synergistic reductions in annual average PM2.5 and O3 concentrations. The average PM2.5 concentration in these cities decreased by 13.97%, while the average O3 concentration decreased by 19.18%. This new solution offers the opportunity to construct intelligent and healthy cities in the upcoming low-carbon transition.
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Affiliation(s)
- Chao He
- College of Resources and Environment, Yangtze University, Wuhan 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
| | - Jianhua Liu
- College of Resources and Environment, Yangtze University, Wuhan 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
| | - Yiqi Zhou
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
| | - Jingwei Zhou
- Hydrology and Environmental Hydraulics Group, Wageningen University and Research, Wageningen 6700 HB, the Netherlands
| | - Lu Zhang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Yifei Wang
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, School of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Lu Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Sha Peng
- Collaborative Innovation Center for Emissions Trading System Co-constructed by the Province and Ministry, Hubei University of Economics, Wuhan 430205, China
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Qu K, Yan Y, Wang X, Jin X, Vrekoussis M, Kanakidou M, Brasseur GP, Lin T, Xiao T, Cai X, Zeng L, Zhang Y. The effect of cross-regional transport on ozone and particulate matter pollution in China: A review of methodology and current knowledge. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174196. [PMID: 38942314 DOI: 10.1016/j.scitotenv.2024.174196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/29/2024] [Accepted: 06/20/2024] [Indexed: 06/30/2024]
Abstract
China is currently one of the countries impacted by severe atmospheric ozone (O3) and particulate matter (PM) pollution. Due to their moderately long lifetimes, O3 and PM can be transported over long distances, cross the boundaries of source regions and contribute to air pollution in other regions. The reported contributions of cross-regional transport (CRT) to O3 and fine PM (PM2.5) concentrations often exceed those of local emissions in the major regions of China, highlighting the important role of CRT in regional air pollution. Therefore, further improvement of air quality in China requires more joint efforts among regions to ensure a proper reduction in emissions while accounting for the influence of CRT. This review summarizes the methodologies employed to assess the influence of CRT on O3 and PM pollution as well as current knowledge of CRT influence in China. Quantifying CRT contributions in proportion to O3 and PM levels and studying detailed CRT processes of O3, PM and precursors can be both based on targeted observations and/or model simulations. Reported publications indicate that CRT contributes by 40-80 % to O3 and by 10-70 % to PM2.5 in various regions of China. These contributions exhibit notable spatiotemporal variations, with differences in meteorological conditions and/or emissions often serving as main drivers of such variations. Based on trajectory-based methods, transport pathways contributing to O3 and PM pollution in major regions of China have been revealed. Recent studies also highlighted the important role of horizontal transport in the middle/high atmospheric boundary layer or low free troposphere, of vertical exchange and mixing as well as of interactions between CRT, local meteorology and chemistry in the detailed CRT processes. Drawing on the current knowledge on the influence of CRT, this paper provides recommendations for future studies that aim at supporting ongoing air pollution mitigation strategies in China.
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Affiliation(s)
- Kun Qu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Laboratory for Modeling and Observation of the Earth System (LAMOS), Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
| | - Yu Yan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Sichuan Academy of Environmental Policy and Planning, Chengdu 610041, China
| | - Xuesong Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China.
| | - Xipeng Jin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Mihalis Vrekoussis
- Laboratory for Modeling and Observation of the Earth System (LAMOS), Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany; Center of Marine Environmental Sciences (MARUM), University of Bremen, Bremen, Germany; Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Nicosia, Cyprus
| | - Maria Kanakidou
- Laboratory for Modeling and Observation of the Earth System (LAMOS), Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany; Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, Greece; Center of Studies of Air quality and Climate Change, Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, Greece
| | - Guy P Brasseur
- Max Planck Institute for Meteorology, Hamburg, Germany; National Center for Atmospheric Research, Boulder, CO, USA
| | - Tingkun Lin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Teng Xiao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Xuhui Cai
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Limin Zeng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing 100871, China; CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen 361021, China.
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3
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Li Y, Yao L, Yang J, Wu J, Tang X, Liang S, Zhang Y, Feng Y. Characterizing the emission trends and pollution evolution patterns during the transition period following COVID-19 at an industrial megacity of central China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 278:116354. [PMID: 38691882 DOI: 10.1016/j.ecoenv.2024.116354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/11/2024] [Accepted: 04/18/2024] [Indexed: 05/03/2024]
Abstract
After the resumption of work and production following the COVID-19 pandemic, many cities entered a "transition phase", characterized by the gradual recovery of emission levels from various sources. Although the overall PM2.5 emission trends have recovered, the specific changes in different sources of PM2.5 remain unclear. Here, we investigated the changes in source contributions and the evolution pattern of pollution episodes (PE) in Wuhan during the "transition period" and compared them with the same period during the COVID-19 lockdown. We found that vehicle emissions, industrial processes, and road dust exhibited significant recoveries during the transition period, increasing by 5.4%, 4.8%, and 3.9%, respectively, during the PE. As primary emissions increased, secondary formation slightly declined, but it still played a predominant role (accounting for 39.1∼ 43.0% of secondary nitrate). The reduction in industrial activities was partially offset by residential burning. The evolution characteristics of PE exhibited significant differences between the two periods, with PM2.5 concentration persisting at a high level during the transition period. The differences in the evolution patterns of the two periods were also reflected in their change rates at each stage, which mostly depend on the pre-PE concentration level. The transition period shows a significantly higher value (8.4 μg m-3 h-1) compared with the lockdown period, almost double the amount. In addition to local emissions, regional transport should be a key consideration in pollution mitigation strategies, especially in areas adjacent to Wuhan. Our study quantifies the variations in sources between the two periods, providing valuable insights for optimizing environmental planning to achieve established goals.
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Affiliation(s)
- Yafei Li
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China
| | - Lu Yao
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China
| | - Jingyi Yang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China
| | - Jianhui Wu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China.
| | - Xiao Tang
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Shengwen Liang
- Wuhan Biological Environment Monitoring Center, Wuhan 430022, China
| | - Yufen Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China
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Sun X, Zhao T, Tang G, Bai Y, Kong S, Zhou Y, Hu J, Tan C, Shu Z, Xu J, Ma X. Vertical changes of PM 2.5 driven by meteorology in the atmospheric boundary layer during a heavy air pollution event in central China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:159830. [PMID: 36343804 DOI: 10.1016/j.scitotenv.2022.159830] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/28/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Regional PM2.5 transport is a crucial factor affecting air quality, and the meteorological mechanism in the atmospheric boundary layer (ABL) has not been fully understood over the receptor region in the regional transport of air pollutants. Based on the intensive vertical measurements of air pollutants and meteorology in the ABL during a transport-induced heavy air pollution event in Xiangyang, an urban site over a receptor region in central China, we investigated the meteorological mechanism in vertical PM2.5 changes in the ABL for heavy air pollution over the receptor region. Driven by northerly winds, regional PM2.5 transport was built from upstream northern China to downstream central China, where the observed ABL structures were unstable throughout the air pollution event. We assessed the ABL structures with meteorological and PM2.5 profiles at growth, maintenance, and dissipation stages, and elucidated the mechanism of regional PM2.5 transport inducing air pollution over the receptor region with the contribution of thermal and mechanical factors. The regional PM2.5 transport was concentrated in the upper ABL over the downwind receptor region with high PM2.5 concentrations at altitudes of 600-800 m, where the transported PM2.5 peaks were downwards mixed by vertical wind shear, forming the vertical PM2.5 transport from the upper ABL to near-surface in the growth stage; the weakened winds and less unstable structures in the ABL favored the sustained pollution with slight vertical PM2.5 changes in the maintenance stage, which was dominated by thermal factors with 87 % contribution; the removal of PM2.5 was triggered by increasing winds from the upper ABL, activating the dissipation of heavy PM2.5 pollution with the mechanical effect accounting for 60 % in the dissipation stage. These findings could improve our understanding of ABL's influence on air pollution over the receptor region with implications for the regional transport of air pollutants in environmental changes.
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Affiliation(s)
- Xiaoyun Sun
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, PREMIC, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Tianliang Zhao
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, PREMIC, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Guiqian Tang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yongqing Bai
- Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
| | - Shaofei Kong
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences (Wuhan), Wuhan 430074, China
| | - Yue Zhou
- Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
| | - Jun Hu
- Fujian Academy of Environmental Sciences, Fuzhou 350011, China
| | - Chenghao Tan
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhuozhi Shu
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, PREMIC, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jiaping Xu
- Jiangsu Climate Center, Nanjing 210009, China
| | - Xiaodan Ma
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, PREMIC, Nanjing University of Information Science and Technology, Nanjing 210044, China
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5
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Chen J, Song X, Zang L, Mao F, Yin J, Zhang Y. Spatio-temporal association mining of intercity PM 2.5 pollution: Hubei Province in China as an example. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:7256-7269. [PMID: 36031675 DOI: 10.1007/s11356-022-22574-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
The complex interaction between emissions, meteorology, and atmospheric chemistry makes accurate predictions of particulate pollution difficult. Advanced data mining techniques can reveal potential laws, providing new possibilities for understanding the evolution and causes of air pollution. Based on the Granger method and block modeling analysis, this paper explored the intercity spillover effects of hourly PM2.5 in Hubei Province, China, to determine the specific role (i.e., overflow, limited overflow, bilateral, inflow, and limited inflow) of each city on regional pollution formation. Furthermore, a dynamic Apriori algorithm considering time-lag effects was used to mine the spatio-temporal associations of extreme PM2.5 pollution events among different cities. Results suggest that the northern and central cities with high-level PM2.5 concentration in Hubei have a significant spillover effect, whereas the eastern and southern cities generally play a role as the sink of pollutants. Based on the association rules of extreme PM2.5 pollution, four main pollutant transport channels were excavated and well matched with the trajectories extracted by the atmospheric model. This paper provides new insights for exploring the interaction of intercity particulate pollution, which is a supplement and cross-validation of the model results.
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Affiliation(s)
- Jiangping Chen
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
| | - Xiaofeng Song
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
| | - Lin Zang
- Electronic Information School, Wuhan University, Wuhan, 430079, China.
| | - Feiyue Mao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
| | - Jianhua Yin
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
| | - Yi Zhang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
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Xiao Y, Wang Y, Yuan Q, He J, Zhang L. Generating a long-term (2003-2020) hourly 0.25° global PM 2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS). THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 848:157747. [PMID: 35921929 DOI: 10.1016/j.scitotenv.2022.157747] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/07/2022] [Accepted: 07/28/2022] [Indexed: 06/15/2023]
Abstract
Generating a long-term high-spatiotemporal resolution global PM2.5 dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003-2020) global reanalysis dataset Copernicus Atmosphere Monitoring Service (CAMS) reanalysis has drawbacks in fine-scale research due to its coarse spatiotemporal resolution (0.75°, 3-h). Hence, this paper developed a deep learning-based framework (DeepCAMS) to downscale CAMS PM2.5 product on the spatiotemporal dimension for resolution enhancement. The nonlinear statistical downscaling from low-resolution (LR) to high-resolution (HR) data can be learned from the high quality (0.25°, hourly) but short-term (2018-2020) Goddard Earth Observing System composition forecast (GEOS-CF) system PM2.5 product. Compared to the conventional spatiotemporal interpolation methods, simulation validations on GEOS-CF demonstrate that DeepCAMS is capable of producing accurate temporal variations with an improvement of Root-Mean-Squared Error (RMSE) of 0.84 (4.46 to 5.30) ug/m3 and spatial details with an improvement of Mean Absolute Error (MAE) of 0.16 (0.34 to 0.50) ug/m3. The real validations on CAMS reflect convincing spatial consistency and temporal continuity at both regional and global scales. Furthermore, the proposed dataset is validated with OpenAQ air quality data from 2017 to 2019, and the in-situ validations illustrate that the DeepCAMS maintains the consistent precision (R: 0.597) as the original CAMS (R: 0.593) while tripling the spatiotemporal resolution. The proposed dataset will be available at https://doi.org/10.5281/zenodo.6381600.
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Affiliation(s)
- Yi Xiao
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei 430079, China.
| | - Yuan Wang
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei 430079, China
| | - Qiangqiang Yuan
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei 430079, China.
| | - Jiang He
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei 430079, China.
| | - Liangpei Zhang
- The Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, Hubei 430079, China.
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7
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Effect of Vertical Wind Shear on PM2.5 Changes over a Receptor Region in Central China. REMOTE SENSING 2022. [DOI: 10.3390/rs14143333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Vertical wind shear (VWS) significantly impacts the vertical mixing of air pollutants and leads to changes in near-surface air pollutants. We focused on Changsha (CS) and Jingmen (JM), the upstream and downstream urban sites of a receptor region in central China, to explore the impact of VWS on surface PM2.5 changes using 5-year wintertime observations and simulations from 2016–2020. The surface PM2.5 concentration was lower in CS with higher anthropogenic PM2.5 emissions than in JM, and the correlation between wind speed and PM2.5 was negative for clean conditions and positive for polluted conditions in both two sites. The difference in the correlation pattern of surface PM2.5 and VWS between CS and JM might be due to the different influences of regional PM2.5 transport and boundary layer dynamics. In downstream CS, the weak wind and VWS in the height of 1–2 km stabilized the ABL under polluted conditions, and strong northerly wind accompanied by enhanced VWS above 2 km favored the long-range transport of air pollutants. In upstream JM, local circulation and long-range PM2.5 transport co-determined the positive correlation between VWS and PM2.5 concentrations. Prevailed northerly wind disrupted the local circulation and enhanced the surface PM2.5 concentrations under polluted conditions, which tend to be an indicator of regional transport of air pollutants. The potential contribution source maps calculated from WRF-FLEXPART simulations also confirmed the more significant contribution of regional PM2.5 transport to the PM2.5 pollution in upstream region JM. By comparing the vertical profiles of meteorological parameters for typical transport- and local-type pollution days, the northerly wind prevailed throughout the ABL with stronger wind speed and VWS in transport-type pollution days, favoring the vertical mixing of transported air pollutants, in sharp contrast to the weak wind conditions in local-type pollution days. This study provided the evidence that PM2.5 pollution in the Twain-Hu Basin was affected by long-distance transport with different features at upstream and downstream sites, improving the understanding of the air pollutant source–receptor relationship in air quality changes with regional transport of air pollutants.
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