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Mo H, Wang S. Assessing the spatiotemporal evolution and socioeconomic determinants of PM 2.5-related premature deaths in China from 2000 to 2021. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174323. [PMID: 38955281 DOI: 10.1016/j.scitotenv.2024.174323] [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: 03/25/2024] [Revised: 06/12/2024] [Accepted: 06/25/2024] [Indexed: 07/04/2024]
Abstract
China's swift socioeconomic development has led to extremely severe ambient PM2.5 levels, the associated negative health outcomes of which include premature death. However, a comprehensive explanation of the socioeconomic mechanism contributing to PM2.5-related premature deaths has not yet to be fully elucidated through long-term spatial panel data. Here, we employed a global exposure mortality model (GEMM) and the system generalized method of moments (Sys-GMM) to examine the primary determinants contributing to premature deaths in Chinese provinces from 2000 to 2021. We found that in the research period, premature deaths in China increased by 46 %, reaching 1.87 million, a figure that decreased somewhat after the COVID-19 outbreak. 62 thousand premature deaths were avoided in 2020 and 2021 compared to 2019, primarily due to the decline in PM2.5 concentrations. Premature deaths have increased across all provinces, particularly in North China, and a discernible spatial agglomeration effect was observed, highlighting effects on nearby provinces. The findings also underscored the significance of determinants such as urbanization, import and export trade, and energy consumption in exacerbating premature deaths, while energy intensity exerted a mitigating influence. Importantly, a U-shaped relationship between premature deaths and economic development was unveiled for the first time, implying the need for vigilance regarding potential health impact deterioration and the implementation of countermeasures as the per capita GDP increases in China. Our findings deserve attention from policymakers as they shed fresh insights into atmospheric control and Health China action.
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Affiliation(s)
- Huibin Mo
- Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Shaojian Wang
- School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China.
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2
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Li H, Zheng B, Lei Y, Hauglustaine D, Chen C, Lin X, Zhang Y, Zhang Q, He K. Trends and drivers of anthropogenic NO x emissions in China since 2020. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 21:100425. [PMID: 38765893 PMCID: PMC11099326 DOI: 10.1016/j.ese.2024.100425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/19/2024] [Accepted: 04/20/2024] [Indexed: 05/22/2024]
Abstract
Nitrogen oxides (NOx), significant contributors to air pollution and climate change, form aerosols and ozone in the atmosphere. Accurate, timely, and transparent information on NOx emissions is essential for decision-making to mitigate both haze and ozone pollution. However, a comprehensive understanding of the trends and drivers behind anthropogenic NOx emissions from China-the world's largest emitter-has been lacking since 2020 due to delays in emissions reporting. Here we show a consistent decline in China's NOx emissions from 2020 to 2022, despite increased fossil fuel consumption, utilizing satellite observations as constraints for NOx emission estimates through atmospheric inversion. This reduction is corroborated by data from two independent spaceborne instruments: the TROPOspheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI). Notably, a reduction in transport emissions, largely due to the COVID-19 lockdowns, slightly decreased China's NOx emissions in 2020. In subsequent years, 2021 and 2022, reductions in NOx emissions were driven by the industry and transport sectors, influenced by stringent air pollution controls. The satellite-based inversion system developed in this study represents a significant advancement in the real-time monitoring of regional air pollution emissions from space.
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Affiliation(s)
- Hui Li
- Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bo Zheng
- Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yu Lei
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation and Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Didier Hauglustaine
- Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Cuihong Chen
- Center for Satellite Application on Ecology and Environment, Ministry of Ecology and Environment of China, Beijing 100094, China
| | - Xin Lin
- Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Yi Zhang
- Institute of Future Human Habitats, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
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3
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Cai Y, Irie H, Damiani A, Itahashi S, Takemura T, Khatri P. Detectability of the potential climate change effect on transboundary air pollution pathways in the downwind area of China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 939:173490. [PMID: 38796018 DOI: 10.1016/j.scitotenv.2024.173490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/03/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
Long-term aerosol optical depth (AOD) datasets focused on the Pacific Ocean in the downwind area of China over a 19-year period from 2003 to 2021 were derived from satellite observations, reanalysis datasets, and numerical simulations. Considering the significant year-to-year changes in the amounts of aerosols transported from China to the Pacific Ocean during this period, we proposed a metric named RAOD. This is defined as the AOD over the ocean relative to that near the eastern coast of China within the same latitude band (25-30°N). RAOD was identified as a valuable metric for quantifying the long-term changes in transboundary air pollution pathways. Our analysis revealed a clear exponential decrease in RAOD values from China toward the Pacific Ocean; this was consistent with the prevailing meteorological conditions observed over the 19-year period. However, the possible long-term changes in RAOD due to climate change were found to be insignificant and were overshadowed by much larger year-to-year variations in the meteorological field. Additionally, significant seasonal variations in the absolute slope of the linear regression between RAOD and longitude were observed, and there correlated with wind patterns in the lower troposphere. Elevated slope values in the spring and winter suggested a west-to-east aerosol transport facilitated by strong winds, whereas the lower slope values in summer and autumn indicated a northward aerosol movement under weaker winds. In recent years, aerosols have become less likely to be transported far eastward from the coast of China. Based on these findings, to enhance the detectability of the climate change impacts on meteorological field affecting transboundary air pollution pathways, the RAOD metric derived using a continued long-term satellite observation of aerosols is proposed.
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Affiliation(s)
- Ying Cai
- Center for Environmental Remote Sensing (CEReS), Chiba University, Chiba, Chiba 263-8522, Japan.
| | - Hitoshi Irie
- Center for Environmental Remote Sensing (CEReS), Chiba University, Chiba, Chiba 263-8522, Japan
| | - Alessandro Damiani
- Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan
| | - Syuichi Itahashi
- Research Institute for Applied Mechanics, Kyushu University, Kasuga, Fukuoka 816-8580, Japan
| | - Toshihiko Takemura
- Research Institute for Applied Mechanics, Kyushu University, Kasuga, Fukuoka 816-8580, Japan
| | - Pradeep Khatri
- Department of Science and Engineering for Sustainable Innovation, Faculty of Science and Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan
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4
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Diao L, Xu Z, Song D, Zhu C, Li X, Zhou X, Jing X, Yu L, Liu B. Dry deposition fluxes and inhalation risks of toxic elements in total suspended particles in the Bohai Rim region: Long-term trends and potential sources. JOURNAL OF HAZARDOUS MATERIALS 2024; 474:134692. [PMID: 38810575 DOI: 10.1016/j.jhazmat.2024.134692] [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: 02/22/2024] [Revised: 04/23/2024] [Accepted: 05/20/2024] [Indexed: 05/31/2024]
Abstract
Long-term changes in dry deposition fluxes (DDF) and health risks for toxic elements (TE) in total suspended particles (TSP) in the Bohai Rim region are important for assessing control effects of pollution sources. Thus, we investigated the trends in DDF and concentrations for TSP and TE and health risks of TE in eight cities in the region from 2011-2020. TSP concentration and DDF showed general downward trends. Compared to the before Clear Air Action Plan (BCAAP, 2011-2012) period, concentration and DDF of TE over the Clear Air Action Plan (CAAP, 2013-2017) period substantially decreased, with the highest decrease rates in Zn, Cd, and Cr. During the study period, non-carcinogenic (HI) and total carcinogenic (TCR) risks for children and adults were 0.09 and 0.04, and 1.54 × 10-5 and 2.65 × 10-5, respectively, with Cr6+ and As being dominant contributors. Compared to the BCAAP period, HI and TCR over the CAAP period decreased by 36.8 % and 32.4 %, respectively. However, their risks increased over the Blue Sky Protection Campaign (BSPC, 2018-2020) period. Potential source contribution function suggested substantial changes in potential risk areas over different control periods, with the BSPC primarily being on land and the Yellow Sea.
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Affiliation(s)
- Liuli Diao
- National Marine Environmental Monitoring Center, Dalian 116023, China
| | - Zizhou Xu
- National Marine Environmental Monitoring Center, Dalian 116023, China
| | - Derui Song
- National Marine Environmental Monitoring Center, Dalian 116023, China.
| | - Cheng Zhu
- National Marine Environmental Monitoring Center, Dalian 116023, China
| | - Xuchun Li
- National Marine Environmental Monitoring Center, Dalian 116023, China
| | - Xiaoyu Zhou
- National Marine Environmental Monitoring Center, Dalian 116023, China
| | - Xindi Jing
- National Marine Environmental Monitoring Center, Dalian 116023, China
| | - Limin Yu
- National Marine Environmental Monitoring Center, Dalian 116023, China
| | - Baoshuang Liu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of 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 300350, China.
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5
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Hua C, Ma W, Zheng F, Zhang Y, Xie J, Ma L, Song B, Yan C, Li H, Liu Z, Liu Q, Kulmala M, Liu Y. Health risks and sources of trace elements and black carbon in PM 2.5 from 2019 to 2021 in Beijing. J Environ Sci (China) 2024; 142:69-82. [PMID: 38527897 DOI: 10.1016/j.jes.2023.05.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 05/12/2023] [Accepted: 05/14/2023] [Indexed: 03/27/2024]
Abstract
A comprehensive health risk assessment of PM2.5 is meaningful to understand the current status and directions regarding further improving air quality from the perspective of human health. In this study, we evaluated the health risks of PM2.5 as well as highly toxic inorganic components, including heavy metals (HMs) and black carbon (BC) based on long-term observations in Beijing from 2019 to 2021. Our results showed that the relative risks of chronic obstructive pulmonary disease, lung cancer, acute lower respiratory tract infection, ischemic heart disease, and stroke decreased by 4.07%-9.30% in 2020 and 2.12%-6.70% in 2021 compared with 2019. However, they were still at high levels ranging from 1.26 to 1.77, in particular, stroke showed the highest value in 2021. Mn had the highest hazard quotient (HQ, from 2.18 to 2.56) for adults from 2019 to 2021, while Ni, Cr, Pb, As, and BC showed high carcinogenic risks (CR > 1.0×10-6) for adults. The HQ values of Mn and As and the CR values of Pb and As showed constant or slight upwards trends during our observations, which is in contrast to the downward trends of other HMs and PM2.5. Mn, Cr, and BC are crucial toxicants in PM2.5. A significant shrink of southern region sourcesof HMs and BCshrank suggests the increased importance of local sources. Industry, dust, and biomass burning are the major contributors to the non-carcinogenic risks, while traffic emissions and industry are the dominant contributors to the carcinogenic risks in Beijing.
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Affiliation(s)
- Chenjie Hua
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Wei Ma
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Feixue Zheng
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yusheng Zhang
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jiali Xie
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Li Ma
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Boying Song
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Chao Yan
- Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Hongyan Li
- School of Environment and Safety, Taiyuan University of Science and Technology, Taiyuan 030024, China
| | - Zhen Liu
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Qian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Markku Kulmala
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China; Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Yongchun Liu
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
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6
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Huang W, Xu H, Wu J, Ren M, Ke Y, Qiao J. Toward cleaner air and better health: Current state, challenges, and priorities. Science 2024; 385:386-390. [PMID: 39052781 DOI: 10.1126/science.adp7832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 06/17/2024] [Indexed: 07/27/2024]
Abstract
The most up-to-date estimate of the global burden of disease indicates that ambient air pollution, including fine particulate matter and ozone, contributes to an estimated 5.2 million deaths each year. In this review, we highlight the challenges in estimating population exposure to air pollution and attributable health risks, particularly in low- and middle-income countries and among vulnerable populations. To protect public health, the evidence so far confirms urgent needs to prioritize interdisciplinary research on air pollution exposure and risk assessment and to develop evidence-based intervention policies and risk communication strategies. Here, we synthesize the emerging evidence supporting the monitoring and evaluation of the progress in implementation of the Global Air Quality Guidelines prepared by the World Health Organization.
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Affiliation(s)
- Wei Huang
- Department of Occupational and Environmental Health, Peking University School of Public Health, and Peking University Institute of Environmental Medicine, Beijing, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
- Peking University Institute for Global Health, Beijing, China
| | - Hongbing Xu
- Department of Occupational and Environmental Health, Peking University School of Public Health, and Peking University Institute of Environmental Medicine, Beijing, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
| | - Jing Wu
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Minghui Ren
- Peking University Institute for Global Health, Beijing, China
- Department of Global Health, Peking University School of Public Health, and China Center for Health Development Studies, Peking University, Beijing, China
| | - Yang Ke
- Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jie Qiao
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
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7
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Weng Z, Dong Z, Zhao Y, Xu M, Xie Y, Lu F. Cleaner heating policies contribute significantly to health benefits and cost-savings: A case study in Beijing, China. iScience 2024; 27:110249. [PMID: 39027367 PMCID: PMC11254592 DOI: 10.1016/j.isci.2024.110249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/20/2024] [Accepted: 06/07/2024] [Indexed: 07/20/2024] Open
Abstract
Cleaner heating policies aim to reduce air pollution and may bring about health benefits to individuals. Based on a fixed-effect model focusing on Beijing, this study found that after the onset of air pollution, daily clinic visits, hospitalization days, and hospitalization expenses increased several days after the occurrence of air pollution. These hospitalization changes were observed in males and females and three different age groups. A difference-in-differences (DID) model was constructed to identify the influences of cleaner heating policies on health consequences. The study revealed that the policy positively affects health outcomes, with an average decrease of 3.28 thousand clinic visits for all diseases. The total hospitalization days and expenses tend to decrease by 0.22 thousand days and 0.34 million CNY (Chinese Yuan), respectively. Furthermore, implementing the policy significantly reduced the number of daily clinic visits for respiratory diseases, asthma, stroke, diabetes, and chronic obstructive pulmonary diseases (COPDs).
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Affiliation(s)
- Zhixiong Weng
- Institute of Circular Economy, Beijing University of Technology, Beijing 100124, China
| | - Zhaomin Dong
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Yi Zhao
- School of Economics and Management, Beihang University, Beijing 100191, China
| | - Meng Xu
- School of Management, Wuhan Institute of Technology, Wuhan 430205, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing 100191, China
| | - Feng Lu
- Beijing Municipal Health Commission Information Center, Beijing 100034, China
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Wang X, Li C, Zhou L, Liu L, Qiu X, Huang D, Liu S, Zeng X, Wang L. Associations of prenatal exposure to PM 2.5 and its components with offsprings' neurodevelopmental and behavioral problems: A prospective cohort study from China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 282:116739. [PMID: 39029225 DOI: 10.1016/j.ecoenv.2024.116739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 07/10/2024] [Accepted: 07/13/2024] [Indexed: 07/21/2024]
Abstract
Prenatal exposure to fine particulate matter (PM2.5) has been linked with increased neurodevelopmental disorders. However, the most detrimental component of PM2.5 and the most vulnerable exposure time windows remain undetermined, especially in areas with high PM2.5 levels. In a prospective cohort study involving 4494 mother-child dyads, we examined the associations of prenatal exposure to PM2.5 and its four main components with children's neurodevelopmental and behavioral problems (NBPs), separately in three pregnancy trimesters. Poisson regression and generalized additive models were used to depict the linear and nonlinear associations, respectively. Weighted quantile sum and Bayesian kernel machine regression models were applied to examine the effects of exposure to both mixed and individual components. Results showed that exposure to PM2.5 and its components throughout the three trimesters increased the risk of children's NBPs (Risk ratio for PM2.5: 1.16, 95 % confidence interval 1.14-1.18 per μg/m3 in the first trimester; 1.15, 1.12-1.17 in the second trimester; 1.06, 1.04-1.08 in the third trimester), with associations gradually diminishing as pregnancy progressed (P values for trends < 0.05). Among the four main components of PM2.5, exposure to SO42- posed the highest risks on children's NBPs, while organic matter contributed the largest proportion to the overall impacts of PM2.5 exposure. These results underscore the significance of mitigating PM2.5 exposure in pregnant women to reduce the risk of neurodevelopmental disorders in offspring. Our findings would inform risk assessment of PM2.5 exposure and facilitate the development of precision preventive strategies targeting specific components of PM2.5 in similar areas with high levels of exposure.
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Affiliation(s)
- Xiaogang Wang
- Department of Epidemiology, School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, PR China
| | - Chanhua Li
- Department of Epidemiology, School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, PR China
| | - Lihong Zhou
- Department of Epidemiology, School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, PR China
| | - Lili Liu
- Department of Epidemiology, School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, PR China
| | - Xiaoqiang Qiu
- Department of Epidemiology, School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, PR China
| | - Dongping Huang
- Department of Sanitary Chemistry, School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, PR China
| | - Shun Liu
- Department of Child and Adolescent Health & Maternal and Child Health, School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, PR China
| | - Xiaoyun Zeng
- Department of Epidemiology, School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, PR China; Department of Epidemiology and Health Statistics, School of Public Health, Guilin Medical University, No. 1 Zhiyuan Road, Lingui District, Guilin, Guangxi, PR China.
| | - Lijun Wang
- Department of Epidemiology, School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, PR China.
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Dong Z, Li S, Jiang Y, Wang S, Xing J, Ding D, Zheng H, Wang H, Huang C, Yin D, Zhao B, Hao J. Health-Oriented Emission Control Strategy of Energy Utilization and Its Co-CO 2 Benefits: A Case Study of the Yangtze River Delta, China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:12320-12329. [PMID: 38973717 DOI: 10.1021/acs.est.3c10693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
Reducing air pollutants and CO2 emissions from energy utilization is crucial for achieving the dual objectives of clean air and carbon neutrality in China. Thus, an optimized health-oriented strategy is urgently needed. Herein, by coupling a CO2 and air pollutants emission inventory with response surface models for PM2.5-associated mortality, we shed light on the effectiveness of protecting human health and co-CO2 benefit from reducing fuel-related emissions and generate a health-oriented strategy for the Yangtze River Delta (YRD). Results reveal that oil consumption is the primary contributor to fuel-related PM2.5 pollution and premature deaths in the YRD. Significantly, curtailing fuel consumption in transportation is the most effective measure to alleviate the fuel-related PM2.5 health impact, which also has the greatest cobenefits for CO2 emission reduction on a regional scale. Reducing fuel consumption will achieve substantial health improvements especially in eastern YRD, with nonroad vehicle emission reductions being particularly impactful for health protection, while on-road vehicles present the greatest potential for CO2 reductions. Scenario analysis confirms the importance of mitigating oil consumption in the transportation sector in addressing PM2.5 pollution and climate change.
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Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dian Ding
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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10
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Zhang T, Yan B, Henneman L, Kinney P, Hopke PK. Regulation-driven changes in PM 2.5 sources in China from 2013 to 2019, a critical review and trend analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 934:173091. [PMID: 38729379 DOI: 10.1016/j.scitotenv.2024.173091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/15/2024] [Accepted: 05/07/2024] [Indexed: 05/12/2024]
Abstract
Identifying changes in source-specific fine particles (PM2.5) over time is essential for evaluating the effectiveness of regulatory measures and informing future policy decisions. After the extreme haze events in China during 2013-14, more comprehensive and stringent policies were implemented to combat PM2.5 pollution. To determine the effectiveness of these policies, it is necessary to assess the changes in the specific source types to which the regulations pertain. Multiple studies have been conducted over the past decade to apportion PM2.5. The purpose of this study was to explore the available literature and conduct a critical review of the reliable results. In total, 5008 articles were screened, but only 48 studies were included for further analysis given our inclusion criteria including covering a monitoring period of ≥1 year and having enough speciation data to provide mass closure. Using these studies, we analyzed temporal and spatial trends across China from 2013 to 2019. We observed the overall decrease in the concentration contributions from all main source categories. The reductions from industry, coal and heavy oil combustion, and the related secondary sulfate were more notable, especially from 2013 to 2016-17. The contributions from biomass burning initially decreased but then increased slightly after 2016 in some locations despite new constraints on agricultural and household burning practices. Although the contributions from vehicle emissions and related secondary nitrate decreased, they gradually became the primary contributors to PM2.5 by ∼2017. Despite the substantial improvements achieved by the air pollution regulation implementations, further improvements in air quality will require additional aggressive actions, especially those targeting vehicular emissions. Ultimately, source apportionment studies based on extended duration, fixed-site sampling are recommended to provide a more thorough understanding of the sources impacting areas and transformations in PM2.5 sources prompted by regulatory actions.
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Affiliation(s)
- Ting Zhang
- Sid and Reva Dewberry Dept. of Civil, Environmental, & Infrastructure Engineering, George Mason University, USA.
| | - Beizhan Yan
- Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA
| | - Lucas Henneman
- Sid and Reva Dewberry Dept. of Civil, Environmental, & Infrastructure Engineering, George Mason University, USA
| | - Patrick Kinney
- Boston University School of Public Health, Boston, MA 02118, USA
| | - Philip K Hopke
- Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA; Institute for a Sustainable Environment, Clarkson University, Potsdam, NY 13699, USA
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11
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Wang Z, Deng M, Zhang S, Zhang Z, Hu C, Yue H, Huang H, Wang D, Li X, Cheng H. Variations in the oxidation potential of PM 2.5 in an old industrial city in China from 2015 to 2018. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 948:174639. [PMID: 39019281 DOI: 10.1016/j.scitotenv.2024.174639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/19/2024] [Accepted: 07/07/2024] [Indexed: 07/19/2024]
Abstract
PM2.5 pollution in China has decreased dramatically, but how its health effects change is not clear. There are 120 old industrial cities in China, where the sources, composition, and health effects of PM2.5 may be significantly different with other cities. Huangshi, an old industrial city in central China, underwent intense green transformations from 2015 to 2018. In this study, we collected ambient PM2.5 samples in 2015 and 2018 at an urban site in Huangshi. The average PM2.5 concentration decreased from 83.44 ± 48.04 μg/m3 in 2015 to 68.03 ± 39.41 μg/m3 in 2018. However, the average volume-normalized dithiothreitol (DTTv) of PM2.5 increased from 1.38 ± 0.45 nmol/min/m3 to 2.14 ± 1.31 nmol/min/m3 and the DTT normalized by particulate mass (DTTm) increased from 20.6 ± 10.1 pmol/min/μg to 40.07 ± 21.9 pmol/min/μg, indicating increased exposure risk and inherent toxicity. The increased toxicity of PM2.5 might be related to the increased trace elements (TEs) concentrations. The positive matrix factorization and multiple linear regression methods were employed to quantify the contributions of emission sources to PM2.5 and DTTv. The results showed that the contribution of coal combustion, industry, and dust to PM2.5 decreased significantly from 2015 to 2018, while that of vehicle emission and secondary sources increased. Despite the decreased fraction of coal combustion and industry sources, their contribution to DTTv increased slightly, which was caused by the increased intrinsic toxicity. The increased intrinsic toxicity was possibly caused by increased TEs, such as Pb, Cu, and V. Besides, the contribution of vehicle emission to DTTv also increased. Overall, these results provide valuable insights into the effectiveness of controlling strategies in reducing particulate health impacts in old industrial cities, and stress the necessity of formulating toxicity-oriented controlling strategies, with special attention to TEs from coal combustion and industry sources as well as vehicle emissions.
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Affiliation(s)
- Zhaoqi Wang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Mengjie Deng
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Shen Zhang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Zhihao Zhang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Caijiao Hu
- Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan 430072, China
| | - Han Yue
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Haibin Huang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Dengtai Wang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Xiaoxiao Li
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China.
| | - Hairong Cheng
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China.
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12
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Gong Y, Zhou H, Chun X, Wan Z, Wang J, Liu C. Response of PM 2.5 chemical composition to the emission reduction and meteorological variation during the COVID-19 lockdown. CHEMOSPHERE 2024; 363:142844. [PMID: 39004145 DOI: 10.1016/j.chemosphere.2024.142844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 06/11/2024] [Accepted: 07/12/2024] [Indexed: 07/16/2024]
Abstract
PM2.5 is a main atmospheric pollutant with various sources and complex chemical compositions, which are influenced by various factors, such as anthropogenic emissions (AE) and meteorological conditions (MC). MC have a significant impacts on variations in atmospheric pollutant; therefore, emission reduction policies and ambient air quality are non-linearly correlated, which hinders the accurate assessment of the effectiveness of control measures. In this study, we conducted online observations of PM2.5 and its chemical composition in Hohhot, China, from December 1, 2019, to February 29, 2020, to investigate how the chemical compositions of PM2.5 respond to the variations in AE and MC. Moreover, the random forest (RF) model was used to quantify the contributions of AE and MC to PM2.5 and its chemical composition during severe hazes and the COVID-19 pandemic lockdown period. During the clean period, MC reduced PM2.5 concentrations by 124%, while MC incresed PM2.5 concentrations by 49% during severe pollution episode. Inorganic aerosols (SO42-, NO3-, and NH4+) showed the strongest response to MC. MC significantly contributed to PM2.5 (36%), SO42- (32%), NO3- (29%), NH4+ (28%), OC (22%), and SOC (17%) levels during pollution episodes. From the pre-lockdown to lockdown period, AE (MC) contributed 52% (48%), 81% (19%), 48% (52%), 68% (32%), 59% (41%), and 288% (-188%) to the PM2.5, SO42-, NO3-, NH4+, OC, and SOC reductions, respectively. The variations in MC (especially the increase in relative humidity) rapidly generated meteorologically sensitive species (SO42-, NO3-, and NH4+), which led to severe winter pollution. This study provides a reference for assessing the net benefits of emission reduction measures for PM2.5 and its chemical compositions.
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Affiliation(s)
- Yitian Gong
- College of Geographical Sciences, Inner Mongolia Normal University, Hohhot, 010022, China; Key Laboratory of Mongolian Plateau's Climate System at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot, 010022, China
| | - Haijun Zhou
- College of Geographical Sciences, Inner Mongolia Normal University, Hohhot, 010022, China; Key Laboratory of Mongolian Plateau's Climate System at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot, 010022, China; Inner Mongolia Repair Engineering Laboratory of Wetland Eco-environment System, Inner Mongolia Normal University, Hohhot, 010022, China.
| | - Xi Chun
- College of Geographical Sciences, Inner Mongolia Normal University, Hohhot, 010022, China; Key Laboratory of Mongolian Plateau's Climate System at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot, 010022, China; Inner Mongolia Repair Engineering Laboratory of Wetland Eco-environment System, Inner Mongolia Normal University, Hohhot, 010022, China
| | - Zhiqiang Wan
- College of Geographical Sciences, Inner Mongolia Normal University, Hohhot, 010022, China; Key Laboratory of Mongolian Plateau's Climate System at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot, 010022, China; Inner Mongolia Repair Engineering Laboratory of Wetland Eco-environment System, Inner Mongolia Normal University, Hohhot, 010022, China
| | - Jingwen Wang
- College of Geographical Sciences, Inner Mongolia Normal University, Hohhot, 010022, China; Key Laboratory of Mongolian Plateau's Climate System at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot, 010022, China
| | - Chun Liu
- College of Geographical Sciences, Inner Mongolia Normal University, Hohhot, 010022, China; Key Laboratory of Mongolian Plateau's Climate System at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot, 010022, China
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13
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Chen Y, Shen H, Shen G, Ma J, Cheng Y, Russell AG, Zhao S, Hakami A, Tao S. Substantial differences in source contributions to carbon emissions and health damage necessitate balanced synergistic control plans in China. Nat Commun 2024; 15:5880. [PMID: 38997317 PMCID: PMC11245606 DOI: 10.1038/s41467-024-50327-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 07/04/2024] [Indexed: 07/14/2024] Open
Abstract
China's strategy to concurrently address climate change and air pollution mitigation is hindered by a lack of comprehensive information on source contributions to health damage and carbon emissions. Here we show notable discrepancies between source contributions to CO2 emissions and fine particulate matter (PM2.5)-related mortality by using adjoint emission sensitivity modeling to attribute premature mortality in 2017 to 53 sector and fuel/process combinations with high spatial resolution. Our findings reveal that monetized PM2.5 health damage exceeds climate impacts in over half of the analyzed subsectors. In addition to coal-fired energy generators and industrial boilers, the combined health and climate costs from energy-intensive processes, diesel-powered vehicles, domestic coal combustion, and agricultural activities exceed 100 billion US dollars, with health-related costs predominating. This research highlights the critical need to integrate the social costs of health damage with climate impacts to develop more balanced mitigation strategies toward these dual goals, particularly during fuel transition and industrial structure upgrading.
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Affiliation(s)
- Yilin Chen
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Huizhong Shen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Yafang Cheng
- Max Planck Institute for Chemistry, Mainz, 55128, Germany
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Shunliu Zhao
- Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON, K1S5B6, Canada
| | - Amir Hakami
- Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON, K1S5B6, Canada
| | - Shu Tao
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
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14
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Guo Q, Wang Y, Zheng J, Zhu M, Sha Q, Huang Z. Temporal evolution of speciated volatile organic compound (VOC) emissions from solvent use sources in the Pearl River Delta Region, China (2006-2019). THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 933:172888. [PMID: 38697531 DOI: 10.1016/j.scitotenv.2024.172888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/08/2024] [Accepted: 04/28/2024] [Indexed: 05/05/2024]
Abstract
Volatile organic compounds (VOCs) emitted from solvent use sources constitute an important part of ozone (O3) and secondary organic aerosols (SOA) in the Pearl River Delta (PRD) region, China. While stringent control measures targeting VOCs have been implemented in recent years, an assessment of historical trends is imperative to evaluate their effectiveness. In this study, trends of VOC emissions, compositions, and reactivity from solvent use sources in the PRD region from 2006 to 2019 were estimated using a developed methodology, which considered the improvement of manufacturing equipment and removal efficiency. Results showed that total VOC emissions from solvent use sources displayed an overall increase from 277 kt in 2006 to 400 kt in 2019 despites some fluctuations, with metal products contributing more than 20 % each year. Aromatics and oxygenated VOCs (OVOCs) accounted for over 70 % of total VOC emissions, increasing by 21 kt and 52 kt respectively. OFP and SOAFP increased by 40 % and 23 % respectively from 2006 to 2019. Specific aromatic species, including m/p-xylene, toluene, 1,2,3,5-tetramethylbenzene, o-xylene and ethylbenzene were identified as key species in both VOC emission amount and reactivity. This study aims to facilitate the understanding of VOC emission evolution from solvent use sources in the region and provide insights into the impact of enacted measures, aiding in the future development of more targeted and efficient strategies in the PRD region.
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Affiliation(s)
- Qing Guo
- College of Environment and Climate, Institute for Environmental and Climate Change, Jinan University, Guangzhou 511436, China; School of Earth and Atmospheric Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Yuzheng Wang
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
| | - Junyu Zheng
- Sustainable Energy and Environment Trust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511458, China.
| | - Manni Zhu
- College of Environment and Climate, Institute for Environmental and Climate Change, Jinan University, Guangzhou 511436, China.
| | - Qing'e Sha
- College of Environment and Climate, Institute for Environmental and Climate Change, Jinan University, Guangzhou 511436, China
| | - Zhijiong Huang
- College of Environment and Climate, Institute for Environmental and Climate Change, Jinan University, Guangzhou 511436, China
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15
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Li Y, Ye C, Ma X, Tan Z, Yang X, Zhai T, Liu Y, Lu K, Zhang Y. Radical chemistry and VOCs-NO x-O 3-nitrate sensitivity in the polluted atmosphere of a suburban site in the North China Plain. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174405. [PMID: 38960186 DOI: 10.1016/j.scitotenv.2024.174405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 06/29/2024] [Accepted: 06/29/2024] [Indexed: 07/05/2024]
Abstract
In this study, the chemical mechanisms of O3 and nitrate formation as well as the control strategy were investigated based on extensive observations in Tai'an city in the NCP and an observation-constrained box model. The results showed that O3 pollution was severe with the maximum hourly O3 concentration reaching 150 ppb. Higher O3 concentration was typically accompanied by higher PM2.5 concentrations, which could be ascribed to the common precursors of VOCs and NOx. The modeled averaged peak concentrations of OH, HO2, and RO2 were relatively higher compared to previous observations, indicating strong atmospheric oxidation capacity in the study area. The ROx production rate increased from 2.8 ppb h-1 to 5 ppb h-1 from the clean case to the heavily polluted case and was dominated by HONO photolysis, followed by HCHO photolysis. The contribution of radical-self combination to radical termination gradually exceeded NO2 + OH from clean to polluted cases, indicating that O3 formation shifted to a more NOx-limited regime. The O3 production rate increased from 14 ppb h-1 to 22 ppb h-1 from clean to heavily polluted cases. The relative incremental reactivity (RIR) results showed that VOCs and NOx had comparable RIR values during most days, which suggested that decreasing VOCs or NOx was both effective in alleviating O3 pollution. In addition, HCHO, with the largest RIR value, made important contribution to O3 production. The Empirical Kinetic Modeling Approach (EKMA) revealed that synergistic control of O3 and nitrate can be achieved by decreasing both NOx and VOCs emissions (e.g., alkenes) with the ratio of 3:1. This study emphasized the importance of NOx abatement for the synergistic control of O3 and nitrate pollution in the Tai'an area as the sustained emissions control has shifted the O3 and nitrate formation to a more NOx-limited regime.
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Affiliation(s)
- Yang Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Can Ye
- School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China.
| | - Xuefei Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Zhaofeng Tan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Xinping Yang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Tianyu Zhai
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yuhan Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Keding Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
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16
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Xie W, Yu Q, Fang W, Zhang X, Geng J, Tang J, Jing W, Liu M, Ma Z, Yang J, Bi J. Data-driven approaches linking wastewater and source estimation hazardous waste for environmental management. Nat Commun 2024; 15:5432. [PMID: 38926394 PMCID: PMC11208539 DOI: 10.1038/s41467-024-49817-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 06/19/2024] [Indexed: 06/28/2024] Open
Abstract
Industrial enterprises are major sources of contaminants, making their regulation vital for sustainable development. Tracking contaminant generation at the firm-level is challenging due to enterprise heterogeneity and the lack of a universal estimation method. This study addresses the issue by focusing on hazardous waste (HW), which is difficult to monitor automatically. We developed a data-driven methodology to predict HW generation using wastewater big data which is grounded in the availability of this data with widespread application of automatic sensors and the logical assumption that a correlation exists between wastewater and HW generation. We created a generic framework that used representative variables from diverse sectors, exploited a data-balance algorithm to address long-tail data distribution, and incorporated causal discovery to screen features and improve computation efficiency. Our method was tested on 1024 enterprises across 10 sectors in Jiangsu, China, demonstrating high fidelity (R² = 0.87) in predicting HW generation with 4,260,593 daily wastewater data.
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Affiliation(s)
- Wenjun Xie
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China
| | - Qingyuan Yu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China
| | - Wen Fang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China.
| | - Xiaoge Zhang
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Jinghua Geng
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China
| | - Jiayi Tang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China
| | - Wenfei Jing
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China
| | - Miaomiao Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China.
| | - Zongwei Ma
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China
| | - Jianxun Yang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China
| | - Jun Bi
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China.
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17
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Lizhi H, Zhenghui X, Cheng X, Yongquan L. Characteristics of surface ozone pollution and its correlations with meteorological factors: the case of Xiangtan. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:658. [PMID: 38916763 DOI: 10.1007/s10661-024-12830-9] [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: 03/29/2024] [Accepted: 06/15/2024] [Indexed: 06/26/2024]
Abstract
Based on ozone (O3) monitoring data for Xiangtan and meteorological observation data for 2020-2022, we examined ozone pollution characteristics and the effects of meteorological factors on daily maximum 8-h average ozone (O3-8h) concentrations in Xiangtan. Thus, we observed significant increases as well as notable seasonal variations in O3-8h concentrations in Xiangtan during the period considered. The ozone and temperature change response slope (KO3-T) indicated that local emissions had no significant effect on O3-8h generation. Further, average O3-8h concentration and maximum temperature (Tmax) values showed a polynomial distribution. Specifically, at Tmax < 27 °C, it increased almost linearly with increasing temperature, and at Tmax between 27 and 37 °C, it showed an upward curvilinear trend as temperature increased, but at a much lower rate. Then, at Tmax > 37 °C, it decreased with increasing temperature. With respect to relative humidity (RH), the average O3-8h concentration primarily exceeded the standard value when RH varied in the range of 45-65%, which is the key humidity range for O3 pollution, and the inflection point for the correlation curve between O3-8h concentration and RH appeared at ~55%. Furthermore, at wind speeds (WSs) below 1.5 m∙s-1, O3-8h concentration increased rapidly, and at WSs in the 1.5-2 m∙s-1 range, it increased at a much faster rate. However, at WSs > 2 m∙s-1, it decreased slowly with increasing WS. O3-8h concentration also showed the tendency to exceed the standard value when the dominant wind directions in Xiangtan were easterly or southeasterly.
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Affiliation(s)
- He Lizhi
- Xiangtan Ecological Environment Monitoring Center of Hunan Province, Xiangtan, 411100, China.
| | - Xiao Zhenghui
- School of Earth Science and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
| | - Xie Cheng
- Xiangtan Ecological Environment Monitoring Center of Hunan Province, Xiangtan, 411100, China
| | - Long Yongquan
- School of Earth Science and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
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Huang W, Ye X, Lv Z, Yao Y, Chen Y, Zhou Y, Chen J. Dual isotopic evidence of δ 15N and δ 18O for priority control of vehicle emissions in a megacity of East China: Insight from measurements in summer and winter. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 931:172918. [PMID: 38697522 DOI: 10.1016/j.scitotenv.2024.172918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 03/31/2024] [Accepted: 04/29/2024] [Indexed: 05/05/2024]
Abstract
The source apportionment and main formation pathway of nitrate aerosols in China are not yet fully understood. In this study, PM2.5 samples were collected in Shanghai in the summer and winter of 2019. Water-soluble inorganic ions and isotopic signatures of stable nitrogen (δ15N-NO3-) and stable oxygen (δ18O-NO3-) in PM2.5 were determined. The results showed that NO3- was less important in summer (NO3-/SO42- = 0.4 ± 0.8), while it became the dominant species in winter (52.1 %). The average values of δ15N-NO3- and δ18O-NO3- in summer were + 2.0 ± 6.1 ‰ and 63.3 ± 9.4 ‰ respectively, which were significantly lower than those in winter (+7.2 ± 3.4 ‰ and 88.3 ± 12.1 ‰), indicating discrepancies between NOx sources and nitrate formation pathways. Both δ15N-NO3- and δ18O-NO3- were elevated at night, demonstrating that N2O5 hydrolysis contributed to the nocturnal nitrate increase even in summer. The contribution of the OH oxidation pathway to nitrate aerosols averaged at 70.5 ± 17.0 % in summer and N2O5 hydrolysis dominated the nitrate production in winter (approximately 80 %). On average, vehicle exhaust, coal combustion, natural gas burning, and soil emission contributed 50.7 %, 21.5 %, 15.9 %, and 11.9 %, respectively, to nitrate aerosols in summer, and contributed 56.8 %, 23.9 %, 13.6 %, and 5.7 %, respectively, to nitrate production in winter. Notably, natural gas burning is a non-negligible source of nitrate aerosols in Shanghai. In contrast to an inverse correlation between δ15N-NO3- and PM2.5, the value of δ18O-NO3- was positively correlated with nitrate concentration and aerosol liquid water content (ALWC) in winter, suggesting that explosive growth of nitrate was driven by continuous accumulation of N-depleted NOx and rapid N2O5 hydrolysis under calm and humid conditions. To continuously improve air quality, priority control should be given to vehicle emissions as the dominant source of NOx and volatile organic compounds (VOCs) in Shanghai.
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Affiliation(s)
- Weijie Huang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Xingnan Ye
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China; Institute of Eco-Chongming (IEC), Chongming District, Shanghai 202162, China.
| | - Zhixiao Lv
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Yinghui Yao
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Yanan Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Yuanqiao Zhou
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Jianmin Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China; Institute of Eco-Chongming (IEC), Chongming District, Shanghai 202162, China
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19
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Wei G, Yang Y, Li R, Liu Y, He BJ. Digital economy exhibits varying degrees of mitigation of air pollution in China: Total cities-economic subdivisions-urban agglomerations. iScience 2024; 27:110091. [PMID: 38952684 PMCID: PMC11215294 DOI: 10.1016/j.isci.2024.110091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/06/2024] [Accepted: 05/21/2024] [Indexed: 07/03/2024] Open
Abstract
Air pollution is a challenge for many cities. The digital economy enhances support for environmental pollution management, while the mechanisms and scaling heterogeneity remain unclear. This study explored the contribution of digital economy development to PM2.5 concentrations control in China and driving mechanisms in different economic subregions and urban agglomerations. Results show that the spillover transfer effect on air pollution mitigation far exceeded the direct effect at different scales. At the national scale, the air pollution mitigation effect of digital economy was mainly through empowering industrial structure optimization and green technology innovation, while it also affected economic subregions and urban agglomerations through varying scenario combinations of pathways with structural optimization, green production, resource allocation, and technology innovation. Research findings provide support for cross-regional joint management strategies of digital economy and air quality and designing regionally differentiated pollution control pathways in the digital economy dimension.
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Affiliation(s)
- Guoen Wei
- School of Resources and Environment, Nanchang University, Nanchang 330031, China
- Central China Research Center for Economic and Social Development, Nanchang University, Nanchang 330031, China
| | - Yiting Yang
- Central China Research Center for Economic and Social Development, Nanchang University, Nanchang 330031, China
- School of Economic and Management, Nanchang University, Nanchang 330031, China
| | - Ruzi Li
- Central China Research Center for Economic and Social Development, Nanchang University, Nanchang 330031, China
- School of Economic and Management, Nanchang University, Nanchang 330031, China
| | - Yaobin Liu
- Central China Research Center for Economic and Social Development, Nanchang University, Nanchang 330031, China
- School of Economic and Management, Nanchang University, Nanchang 330031, China
| | - Bao-Jie He
- Centre for Climate–Resilient and Low–Carbon Cities, School of Architecture and Urban Planning, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, China
- Institute for Smart City of Chongqing University in Liyang, Chongqing University, Liyang, Jiangsu 213300, China
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20
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Zhang R, Zhu S, Zhang Z, Zhang H, Tian C, Wang S, Wang P, Zhang H. Long-term variations of air pollutants and public exposure in China during 2000-2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 930:172606. [PMID: 38642757 DOI: 10.1016/j.scitotenv.2024.172606] [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: 01/15/2024] [Revised: 04/12/2024] [Accepted: 04/17/2024] [Indexed: 04/22/2024]
Abstract
Since 2000, China has faced severe air pollution challenges,prompting the initiation of comprehensive emission control measures post-2013. The subsequent implementation of these measures has led to remarkable enhancements in air quality. This study aims to enhance our understanding of the long-term trends in fine particulate matter (PM2.5) and gaseous pollutants of ozone (O3) and nitrogen dioxide (NO2) across China from 2000 to 2020. Utilizing the Community Multiscale Air Quality (CMAQ) model, we conducted a nationwide analysis of air quality, systematically quantifying model predictions against observations for pollutants. The CMAQ model effectively captured the trends of air pollutants, meeting recommended performance benchmarks. The findings reveal variations in pollutant concentrations, with initial increases in PM2.5 followed by a decline after 2013. The proportion of the population living in high PM2.5 concentrations (>75 μg/m3) decreased to <5 % after 2015. However, during the period from 2017 to 2020, around 40 % of the population continued to live in regions that did not meet the criteria for Chinese air quality standards (35 μg/m3). From 2000 to 2019, fewer than 20 % of the population met the WHO standard (100 μg/m3) for MDA8 O3. In 2000, 77 % of the population met the NO2 standard (<20 μg/m3), a figure that declined to 60 % between 2005 and 2014, nearly reaching 70 % in 2020. This study offers a comprehensive analysis of the changes in pollutants and public exposure in 2000-2020. It serves as a foundational resource for future efforts in air pollution control and health research.
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Affiliation(s)
- Ruhan Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai, China
| | - Shengqiang Zhu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai, China
| | - Zhaolei Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai, China
| | - Haoran Zhang
- School of Atmospheric Sciences, Nanjing University, Nanjing, China
| | - Chunfeng Tian
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, China
| | - Shuai Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai, China
| | - Peng Wang
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, China; Shanghai Key Laboratory of Ocean-land-atmosphere Boundary Dynamics and Climate Change, China; IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China.
| | - Hongliang Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai, China; IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China; Institute of Eco-Chongming, Shanghai, China.
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21
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Peng B, Cai Q, Shi X, Wang Z, Yan J, Xu M, Wang M, Shi Z, Niu Z, Guo X, Yang Y. Metal-containing nanoparticles in road dust from a Chinese megacity over the last decade: Spatiotemporal variation and driving factors. JOURNAL OF HAZARDOUS MATERIALS 2024; 476:134970. [PMID: 38905977 DOI: 10.1016/j.jhazmat.2024.134970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/17/2024] [Accepted: 06/17/2024] [Indexed: 06/23/2024]
Abstract
As a crucial sink of metal-containing nanoparticles (MNPs), road dust can record their spatiotemporal variations in urban environments. In this study, taking Shanghai as a representative megacity in China, a total of 272 dust samples were collected in the winter and summer of 2013 and 2021/2022 to understand the spatiotemporal variations and driving factors of MNPs. The number concentrations of Fe-, Ti-, and Zn-containing NPs were 3.8 × 106 - 8.4 × 108, 2.3 × 106-1.4 × 108, and 6.0 × 105-2.3 × 108 particles/mg, respectively, according to single particle (sp)ICP-MS analysis. These MNPs showed significantly higher number concentrations in summer than in winter. Hotspots of Fe-containing NPs were more concentrated in industrial and traffic areas, Zn-containing NPs were mainly distributed in the central urban areas, while Ti-containing NPs were abundant in areas receiving high rainfall. The structural equation model results indicates that substantial rainfall in summer can help remove MNPs from atmospheric PM2.5 into dust, while in winter industrial and traffic activities were the primary contributors for MNPs. Moreover, the contribution of traffic emissions to MNPs has surpassed industrial one over the last decade, highlighting the urgency to control traffic-sourced MNPs, especially those from non-exhaust emissions by electric vehicles.
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Affiliation(s)
- Bo Peng
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
| | - Qiuyu Cai
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
| | - Xu Shi
- Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center Co. Ltd., 68 South Yutian Road, Shanghai 201805, China
| | - Zhiyan Wang
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
| | - Jia Yan
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
| | - Miao Xu
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
| | - Mengyuan Wang
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
| | - Zhiqiang Shi
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
| | - Zuoshun Niu
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
| | - Xingpan Guo
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
| | - Yi Yang
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China; State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China; Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China.
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22
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Wen Z, Ma X, Xu W, Si R, Liu L, Ma M, Zhao Y, Tang A, Zhang Y, Wang K, Zhang Y, Shen J, Zhang L, Zhao Y, Zhang F, Goulding K, Liu X. Combined short-term and long-term emission controls improve air quality sustainably in China. Nat Commun 2024; 15:5169. [PMID: 38886390 PMCID: PMC11183230 DOI: 10.1038/s41467-024-49539-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 06/10/2024] [Indexed: 06/20/2024] Open
Abstract
The effectiveness of national policies for air pollution control has been demonstrated, but the relative effectiveness of short-term emission reduction measures in comparison with national policies has not. Here we show that short-term abatement measures during important international events substantially reduced PM2.5 concentrations, but air quality rebounded to pre-event levels after the measures ceased. Long-term adherence to strict emission reduction policies led to successful decreases of 54% in PM2.5 concentrations in Beijing, and 23% in atmospheric nitrogen deposition in China from 2012 to 2020. Incentivized by "blue skies" type campaigns, economic development and reactive nitrogen pollution are quickly decoupled, showing that a combination of inspiring but aggressive short-term measures and effective but durable long-term policies delivers sustainable air quality improvement. However, increased ammonia concentrations, transboundary pollutant flows, and the complexity to achieving reduction targets under climate change scenarios, underscore the need for the synergistic control of multiple pollutants and inter-regional action.
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Affiliation(s)
- Zhang Wen
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
- State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Xin Ma
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Wen Xu
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Ruotong Si
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Lei Liu
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Mingrui Ma
- State Key Laboratory of Pollution Control & Resource Reuse and School of Environment, Nanjing University, Nanjing, 210008, China
| | - Yuanhong Zhao
- College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China
| | - Aohan Tang
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Yangyang Zhang
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Kai Wang
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Ying Zhang
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Jianlin Shen
- Instute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Yu Zhao
- State Key Laboratory of Pollution Control & Resource Reuse and School of Environment, Nanjing University, Nanjing, 210008, China
| | - Fusuo Zhang
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Keith Goulding
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, AL5 2JQ, UK
| | - Xuejun Liu
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China.
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23
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Tang Y, Wang Y, Chen X, Liang J, Li S, Chen G, Chen Z, Tang B, Zhu J, Li X. Diurnal emission variation of ozone precursors: Impacts on ozone formation during Sep. 2019. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172591. [PMID: 38663597 DOI: 10.1016/j.scitotenv.2024.172591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/08/2024] [Accepted: 04/17/2024] [Indexed: 04/30/2024]
Abstract
With the issue of ozone (O3) pollution having increasingly gained visibility and prominence in China, the Chinese government explored various policies to mitigate O3 pollution. In some provinces and cities, diurnal regulations of O3 precursor were implemented, such as shifting O3 precursor emission processes to nighttime and offering preferential refueling at night. However, the effectiveness of these policies remains unverified, and their impact on the O3 generation process requires further elucidation. In this study, we utilized a regional climate and air quality model (WRF-Chem, v4.5) to test three scenarios aimed at exploring the impact of diurnal industry emission variation of O3 precursors on O3 formation. Significant O3 variations were observed mainly in urban areas. Shifting volatile organic compounds (VOCs) to nighttime have slight decreased daytime O3 levels while moving nitrogen oxides (NOx) to nighttime elevates O3 levels. Simultaneously moving both to nighttime showed combined effects. Process analysis indicates that the diurnal variation in O3 was mainly attributed to chemical process and vertical mixing in urban areas, while advection becomes more important in non-urban areas, contributing to the changes in O3 and O3 precursors levels through regional transportation. Further photochemical analysis reveals that the O3 photochemical production in urban areas was affected by reduced daytime O3 precursors emissions. Specifically, decreasing VOCs lowered the daytime O3 production by reducing the ROx radicals (ROx = HO + HO˙2 + RO˙2), whereas decreasing NOx promoted the daytime O3 production by weakening ROx radical loss. Our results demonstrate that diurnal regulation of O3 precursors will disrupt the ROx radical and O3 formation in local areas, resulting in a change in O3 concentration and atmospheric oxidation capacity, which should be considered in formulating new relevant policies.
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Affiliation(s)
- Yifan Tang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Yuchen Wang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China; State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China
| | - Xuwu Chen
- School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha 410205, PR China
| | - Jie Liang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Shuai Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Gaojie Chen
- College of Mathematics and Econometrics, Hunan University, Changsha 410082, PR China
| | - Zuo Chen
- College of Information Science and Technology, Hunan University, Changsha 410082, PR China
| | - Binxu Tang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Jiesong Zhu
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Xiaodong Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
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24
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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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25
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Popovic I, Soares Magalhaes R, Yang S, Yang Y, Yang BY, Dong GH, Wei X, Van Buskirk J, Fox G, Ge E, Marks G, Knibbs L. Long-term exposure to ambient fine particulate matter (PM 2.5) and attributable pulmonary tuberculosis notifications in Ningxia Hui Autonomous Region, China: a health impact assessment. BMJ Open 2024; 14:e082312. [PMID: 38834325 PMCID: PMC11163650 DOI: 10.1136/bmjopen-2023-082312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 05/16/2024] [Indexed: 06/06/2024] Open
Abstract
INTRODUCTION Long-term exposure to fine particulate matter (≤2.5 µm (PM2.5)) has been associated with pulmonary tuberculosis (TB) notifications or incidence in recent publications. Studies quantifying the relative contribution of long-term PM2.5 on TB notifications have not been documented. We sought to perform a health impact assessment to estimate the PM2.5- attributable TB notifications during 2007-2017 in Ningxia Hui Autonomous Region (NHAR), China. METHODS PM2.5 attributable TB notifications were estimated at township level (n=358), stratified by age group and summed across NHAR. PM2.5-associated TB-notifications were estimated for total and anthropogenic PM2.5 mass and expressed as population attributable fractions (PAFs). The main analysis used effect and uncertainty estimates from our previous study in NHAR, defining a counterfactual of the lowest annual PM2.5 (30 µg/m3) level, above which we assumed excess TB notifications. Sensitivity analyses included counterfactuals based on the 5th (31 µg/m3) and 25th percentiles (38 µg/m3), and substituting effect estimates from a recent meta-analysis. We estimated the influence of PM2.5 concentrations, population growth and baseline TB-notification rates on PM2.5 attributable TB notifications. RESULTS Over 2007-2017, annual PM2.5 had an estimated average PAF of 31.2% (95% CI 22.4% to 38.7%) of TB notifications while the anthropogenic PAF was 12.2% (95% CI 9.2% to 14.5%). With 31 and 38 µg/m3 as counterfactuals, the PAFs were 29.2% (95% CI 20.9% to 36.3%) and 15.4% (95% CI 10.9% to 19.6%), respectively. PAF estimates under other assumptions ranged between 6.5% (95% CI 2.9% to 9.6%) and 13.7% (95% CI 6.2% to 19.9%) for total PM2.5, and 2.6% (95% CI 1.2% to 3.8%) to 5.8% (95% CI 2.7% to 8.2%) for anthropogenic PM2.5. Relative to 2007, overall changes in PM2.5 attributable TB notifications were due to reduced TB-notification rates (-23.8%), followed by decreasing PM2.5 (-6.2%), and population growth (+4.9%). CONCLUSION We have demonstrated how the potential impact of historical or hypothetical air pollution reduction scenarios on TB notifications can be estimated, using public domain, PM2.5 and population data. The method may be transferrable to other settings where comparable TB-notification data are available.
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Affiliation(s)
- Igor Popovic
- Faculty of Medicine, School of Public Health, The University of Queensland, Herston, Queensland, Australia
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton, Queensland, Australia
| | - Ricardo Soares Magalhaes
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton, Queensland, Australia
- Children's Health and Environment Program, UQ Children's Health Research Center, The University of Queensland, South Brisbane, Queensland, Australia
| | - Shukun Yang
- Department of Radiology, The First People's Hospital in Yinchuan, The Second Affiliated Hospital of Ningxia Medical University, Yinchuan, Ningsia, China
| | - Yurong Yang
- Department of Pathogenic Biology & Medical Immunology, School of Basic Medical Science, Ningxia Medical University, Yinchuan, Ningxia, China
| | - Bo-Yi Yang
- Environmental Epidemiology, Sun Yat-Sen University, Guangzhou, China
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xiaolin Wei
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Joseph Van Buskirk
- Public Health Unit, Sydney Local Health District, Camperdown, New South Wales, Australia
- School of Public Health, The University of Sydney Faculty of Medicine and Health, Sydney, New South Wales, Australia
| | - Gregory Fox
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia
| | - Erjia Ge
- University of Toronto Dalla Lana School of Public Health, Toronto, Ontario, Canada
| | - Guy Marks
- South Western Sydney Clinical School, University of New South Wales, The University of Sydney, Liverpool, New South Wales, Australia
- Woolcock Institute of Medical Research, Glebe, New South Wales, Australia
| | - Luke Knibbs
- Public Health Research Analytics and Methods for Evidence, Public Health Unit, Sydney Local Health District, Camperdown, New South Wales, Australia
- Faculty of Medicine and Health, School of Public Health, The University of Sydney, Sydney, New South Wales, Australia
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26
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Yue H, Worrell E, Crijns-Graus W, Wagner F, Zhang S, Hu J. Air Quality and Health Implications of Coal Power Retirements Attributed to Industrial Electricity Savings in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9187-9199. [PMID: 38691631 DOI: 10.1021/acs.est.3c09517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
The coal-dominated electricity system, alongside increasing industrial electricity demand, places China into a dilemma between industrialization and environmental impacts. A practical solution is to exploit air quality and health cobenefits of industrial energy efficiency measures, which has not yet been integrated into China's energy transition strategy. This research examines the pivotal role of industrial electricity savings in accelerating coal plant retirements and assesses the nexus of energy-pollution-health by modeling nationwide coal-fired plants at individual unit level. It shows that minimizing electricity needs by implementing more efficient technologies leads to the phaseout of 1279 hyper-polluting units (subcritical, <300 MW) by 2040, advancing the retirement of these units by an average of 7 years (3-16 years). The retirements at different locations yield varying levels of air quality improvements (9-17%), across six power grids. Reduced exposure to PM2.5 could avoid 123,100 pollution-related cumulative deaths over the next 20 years from 2020, of which ∼75% occur in the Central, East, and North grids, particularly coal-intensive and populous provinces (e.g., Shandong and Jiangsu). These findings provide key indicators to support geographically specific policymaking and lay out a rationale for decision-makers to incorporate multiple benefits into early coal phaseout strategies to avoid lock-in risk.
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Affiliation(s)
- Hui Yue
- School of Management, Zhengzhou University, Science Avenue 100, 450001 Zhengzhou, China
- Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
| | - Ernst Worrell
- Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
| | - Wina Crijns-Graus
- Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
| | - Fabian Wagner
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Xueyuan Road 37, 100191 Beijing, China
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
| | - Jing Hu
- Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
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27
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Chen Q, Miao R, Geng G, Shrivastava M, Dao X, Xu B, Sun J, Zhang X, Liu M, Tang G, Tang Q, Hu H, Huang RJ, Wang H, Zheng Y, Qin Y, Guo S, Hu M, Zhu T. Widespread 2013-2020 decreases and reduction challenges of organic aerosol in China. Nat Commun 2024; 15:4465. [PMID: 38796477 PMCID: PMC11127919 DOI: 10.1038/s41467-024-48902-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 05/17/2024] [Indexed: 05/28/2024] Open
Abstract
High concentrations of organic aerosol (OA) occur in Asian countries, leading to great health burdens. Clean air actions have resulted in significant emission reductions of air pollutants in China. However, long-term nation-wide trends in OA and their causes remain unknown. Here, we present both observational and model evidence demonstrating widespread decreases with a greater reduction in primary OA than in secondary OA (SOA) in China during the period of 2013 to 2020. Most of the decline is attributed to reduced residential fuel burning while the interannual variability in SOA may have been driven by meteorological variations. We find contrasting effects of reducing NOx and SO2 on SOA production which may have led to slight overall increases in SOA. Our findings highlight the importance of clean energy replacements in multiple sectors on achieving air-quality targets because of high OA precursor emissions and fluctuating chemical and meteorological conditions.
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Affiliation(s)
- Qi Chen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China.
- Institute of Carbon Neutrality, Peking University, Beijing, China.
| | - Ruqian Miao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Guannan Geng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | | | - Xu Dao
- China National Environmental Monitoring Centre, Beijing, China
| | - Bingye Xu
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Zhejiang Province Environment Monitoring Centre, Hangzhou, China
| | - Jiaqi Sun
- China National Environmental Monitoring Centre, Beijing, China
| | - Xian Zhang
- China National Environmental Monitoring Centre, Beijing, China
| | - Mingyuan Liu
- China National Environmental Monitoring Centre, Beijing, China
| | - Guigang Tang
- China National Environmental Monitoring Centre, Beijing, China
| | - Qian Tang
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Zhejiang Province Environment Monitoring Centre, Hangzhou, China
| | - Hanwen Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | - Ru-Jin Huang
- State Key Laboratory of Loess and Quaternary Geology, Center for Excellence in Quaternary Science and Global Change, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China
| | - Hao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yan Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yue Qin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
| | - Song Guo
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
| | - Min Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
| | - Tong Zhu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
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28
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Tan Z, Feng M, Liu H, Luo Y, Li W, Song D, Tan Q, Ma X, Lu K, Zhang Y. Atmospheric Oxidation Capacity Elevated during 2020 Spring Lockdown in Chengdu, China: Lessons for Future Secondary Pollution Control. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8815-8824. [PMID: 38733566 DOI: 10.1021/acs.est.3c08761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
This study presents the measurement of photochemical precursors during the lockdown period from January 23, 2020, to March 14, 2020, in Chengdu in response to the coronavirus (COVID-19) pandemic. To derive the lockdown impact on air quality, the observations are compared to the equivalent periods in the last 2 years. An observation-based model is used to investigate the atmospheric oxidation capacity change during lockdown. OH, HO2, and RO2 concentrations are simulated, which are elevated by 42, 220, and 277%, respectively, during the lockdown period, mainly due to the reduction in nitrogen oxides (NOx). However, the radical turnover rates, i.e., OH oxidation rate L(OH) and local ozone production rate P(O3), which determine the secondary intermediates formation and O3 formation, only increase by 24 and 48%, respectively. Therefore, the oxidation capacity increases slightly during lockdown, which is partly attributed to unchanged alkene concentrations. During the lockdown, alkene ozonolysis seems to be a significant radical primary source due to the elevated O3 concentrations. This unique data set during the lockdown period highlights the importance of controlling alkene emission to mitigate secondary pollution formation in Chengdu and may also be applicable in other regions of China given an expected NOx reduction due to the rapid transformation to electrified fleets in the future.
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Affiliation(s)
- Zhaofeng Tan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Miao Feng
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Hefan Liu
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Yina Luo
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Wei Li
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Danlin Song
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Qinwen Tan
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Xuefei Ma
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Keding Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
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29
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Cui H, Li J, Sun Y, Milne R, Tao Y, Ren J. A novel framework for quantitative attribution of particulate matter pollution mitigation to natural and socioeconomic drivers. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171910. [PMID: 38522549 DOI: 10.1016/j.scitotenv.2024.171910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 03/26/2024]
Abstract
Quantifying drivers contributing to air quality improvements is crucial for pollution prevention and optimizing local policies. Despite advances in machine learning for air quality analysis, their limited interpretability hinders attribution on global and local scales, vital for informed city management. Our study introduces an innovative framework quantifying socioeconomic and natural impacts on mitigation of particulate matter pollution in 31 Chinese major cities from 2014 to 2021. Two indices, formulated based on the additivity of Shapley additive explanations, are proposed to measure driver contributions globally and locally. Our analysis explores the self-contained and interactive effects of these drivers on particulate levels, pinpointing critical threshold values where these drivers trigger shifts in particulate matter levels. It is revealed that SO2, NOx, and dust emission reductions collectively account for 51.58 % and 51.96 % of PM2.5 and PM10 decreases at the global level. Moreover, our findings unveil a significant heterogeneity in driver contributions to pollutant mitigation across distinct cities, which can be instrumental in crafting location-specific policy recommendations.
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Affiliation(s)
- Hao Cui
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Jian Li
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Yutong Sun
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Russell Milne
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton T6G 2G1, Alberta, Canada
| | - Yiwen Tao
- School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, Henan, China.
| | - Jingli Ren
- School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, Henan, China.
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30
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Fu W, Zhao T, Sun X, Bai Y, Yang Q, Shen L, Liang D, Tan C, Luo Y, Yang K, Zhang Y, Wang J. Recent-year variations in O 3 pollution with high-temperature suppression over central China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 349:123932. [PMID: 38583796 DOI: 10.1016/j.envpol.2024.123932] [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: 01/15/2024] [Revised: 03/31/2024] [Accepted: 04/04/2024] [Indexed: 04/09/2024]
Abstract
By analyzing environmental and meteorological monitoring data over recent years of 2015-2022, the Twain-Hu Basin (THB) in central China was identified as a regional O3 pollution center over China with the highest increasing trend at 1.10 %⸱yr-1 in interannual variations of O3 concentrations with deteriorating O3 pollution over recent years. We explored the spatiotemporal variations in O3 pollution in the THB with ozone suppression (OS) under high air temperature over metropolitan, small urban, and mountainous areas. The bipolarized interannual trends in interannual O3 variations in urban and mountainous areas over central China were characterized with the increasing and decreasing 90th percentiles of the daily maximum 8-h (MDA8-90) O3 concentrations respectively in polluted urban areas and clean mountainous areas over recent eight years. The changes of the near-surface O3 concentrations with air temperature exhibited the inflection points of OS from increasing to decreasing O3 at air temperature of 30.5 °C in mountainous areas, 32.5 °C in small urban areas, and 34.5 °C in metropolitan areas, and the intensity of OS was estimated in the ranking with mountainous areas (-2.30 μg⸱m-3⸱°C-1) > small urban areas (-1.96 μg⸱m-3⸱°C-1) > metropolitan areas (-1.54 μg⸱m-3⸱°C-1), indicating that the OS was more significant over the lower-O3 mountainous areas. This study has implications for understanding O3 pollution variations with the meteorological drivers.
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Affiliation(s)
- Weikang Fu
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Tianliang Zhao
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
| | - Xiaoyun Sun
- Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei, 230031, China
| | - Yongqing Bai
- Institute of Heavy Rain, China Meteorological Administration, Wuhan, 430205, China
| | - Qingjian Yang
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Lijuan Shen
- School of Atmosphere and Remote Sensing, Wuxi University, Wuxi, 214105, China
| | - Dingyuan Liang
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, 999077, 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
| | - Yuehan Luo
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Kai Yang
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Yuqing Zhang
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Junyu Wang
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing, 210044, China
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31
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Meng W, Cheng Y, Shen G, Shen H, Su H, Tao S. The Long Hazy Tail: Analysis of the Impacts and Trends of Severe Outdoor and Indoor Air Pollution in North China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8326-8335. [PMID: 38696616 DOI: 10.1021/acs.est.4c02778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
China, especially the densely populated North China region, experienced severe haze events in the past decade that concerned the public. Although the most extreme cases have been largely eliminated through recent mitigation measures, severe outdoor air pollution persists and its environmental impact needs to be understood. Severe indoor pollution draws less public attention due to the short visible distance indoors, but its public health impacts cannot be ignored. Herein, we assess the trends and impacts of severe outdoor and indoor air pollution in North China from 2014 to 2021. Our results demonstrate the uneven contribution of severe hazy days to ambient and exposure concentrations of particulate matter with an aerodynamic diameter <2.5 (PM2.5). Although severe indoor pollution contributes to indoor PM2.5 concentrations (23%) to a similar extent as severe haze contributes to ambient PM2.5 concentrations (21%), the former's contribution to premature deaths was significantly higher. Furthermore, residential emissions contributed more in the higher PM2.5 concentration range both indoors and outdoors. Notably, severe haze had greater health impacts on urban residents, while severe indoor pollution was more impactful in rural areas. Our findings suggest that, besides reducing severe haze, mitigating severe indoor pollution is an important aspect of combating air pollution, especially toward improving public health.
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Affiliation(s)
- Wenjun Meng
- Institute of Carbon Neutrality, Peking University, 100871 Beijing, China
- Laboratory for Earth Surface Processes and College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China
- Minerva Research Group, Max Planck Institute for Chemistry, 55128 Mainz, Germany
| | - Yafang Cheng
- Minerva Research Group, Max Planck Institute for Chemistry, 55128 Mainz, Germany
| | - Guofeng Shen
- Institute of Carbon Neutrality, Peking University, 100871 Beijing, China
- Laboratory for Earth Surface Processes and College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, 518055 Shenzhen, China
| | - Hang Su
- Institute for Atmospheric Physics, Chinese Academy of Science, 100029 Beijing, China
| | - Shu Tao
- Institute of Carbon Neutrality, Peking University, 100871 Beijing, China
- Laboratory for Earth Surface Processes and College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China
- School of Environmental Science and Engineering, Southern University of Science and Technology, 518055 Shenzhen, China
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32
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Dai H, Liao H, Wang Y, Qian J. Co-occurrence of ozone and PM 2.5 pollution in urban/non-urban areas in eastern China from 2013 to 2020: Roles of meteorology and anthropogenic emissions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 924:171687. [PMID: 38485008 DOI: 10.1016/j.scitotenv.2024.171687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/25/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024]
Abstract
We applied a three-dimensional (3-D) global chemical transport model (GEOS-Chem) to evaluate the influences of meteorology and anthropogenic emissions on the co-occurrence of ozone (O3) and fine particulate matter (PM2.5) pollution day (O3-PM2.5PD) in urban and non-urban areas of the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions during the warm season (April-October) from 2013 to 2020. The model captured the observed O3-PM2.5PD trends and spatial distributions well. From 2013 to 2020, with changes in both anthropogenic emissions and meteorology, the simulated values of O3-PM2.5PD in the urban (non-urban) areas of the BTH and YRD regions were 424.8 (330.1) and 309.3 (286.9) days, respectively, suggesting that pollution in non-urban areas also warrants attention. The trends in the simulated values of O3-PM2.5PD were -0.14 and -0.15 (+1.18 and +0.81) days yr-1 in the BTH (YRD) urban and non-urban areas, respectively. Sensitivity simulations revealed that changes in anthropogenic emissions decreased the occurrence of O3-PM2.5PD, with trends of -0.99 and -1.23 (-1.47 and -1.92) days yr-1 in the BTH (YRD) urban and non-urban areas, respectively. Conversely, meteorological conditions could exacerbate the frequency of O3-PM2.5PD, especially in the urban YRD areas, but less notably in the urban BTH areas, with trends of +2.11 and +0.30 days yr-1, respectively, owing to changes in meteorology only. The increases in T2m_max and T2m were the main meteorological factors affecting O3-PM2.5PD in most BTH and YRD areas. Furthermore, by conducting sensitivity experiments with different levels of pollutant precursor reductions in 2020, we found that volatile organic compound (VOC) reductions primarily benefited O3-PM2.5PD decreases in urban areas and that NOx reductions more notably influenced those in non-urban areas, especially in the YRD region. Simultaneously, reducing VOC and NOx emissions by 50 % resulted in considerable O3-PM2.5PD decreases (58.8-72.6 %) in the urban and non-urban areas of the BTH and YRD regions. The results of this study have important implications for the control of O3-PM2.5PD in the urban and non-urban areas of the BTH and YRD regions.
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Affiliation(s)
- Huibin Dai
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Ye Wang
- Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jing Qian
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
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33
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Lei Y, Yin Z, Lu X, Zhang Q, Gong J, Cai B, Cai C, Chai Q, Chen H, Chen R, Chen S, Chen W, Cheng J, Chi X, Dai H, Feng X, Geng G, Hu J, Hu S, Huang C, Li T, Li W, Li X, Liu J, Liu X, Liu Z, Ma J, Qin Y, Tong D, Wang X, Wang X, Wu R, Xiao Q, Xie Y, Xu X, Xue T, Yu H, Zhang D, Zhang N, Zhang S, Zhang S, Zhang X, Zhang X, Zhang Z, Zheng B, Zheng Y, Zhou J, Zhu T, Wang J, He K. The 2022 report of synergetic roadmap on carbon neutrality and clean air for China: Accelerating transition in key sectors. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 19:100335. [PMID: 37965046 PMCID: PMC10641488 DOI: 10.1016/j.ese.2023.100335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/19/2023] [Accepted: 10/19/2023] [Indexed: 11/16/2023]
Abstract
China is now confronting the intertwined challenges of air pollution and climate change. Given the high synergies between air pollution abatement and climate change mitigation, the Chinese government is actively promoting synergetic control of these two issues. The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators. The Synergetic Roadmap 2022 report is the first annual update, featuring 20 indicators across five aspects: synergetic governance system and practices, progress in structural transition, air pollution and associated weather-climate interactions, sources, sinks, and mitigation pathway of atmospheric composition, and health impacts and benefits of coordinated control. Compared to the comprehensive review presented in the 2021 report, the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones. These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time, a decline in the production of crude steel and cement after years of growth, and the surging penetration of electric vehicles. Additionally, in 2022, China issued the first national policy that synergizes abatements of pollution and carbon emissions, marking a new era for China's pollution-carbon co-control. These changes highlight China's efforts to reshape its energy, economic, and transportation structures to meet the demand for synergetic control and sustainable development. Consequently, the country has witnessed a slowdown in carbon emission growth, improved air quality, and increased health benefits in recent years.
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Affiliation(s)
- Yu Lei
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Zhicong Yin
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Xi Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jicheng Gong
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Bofeng Cai
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Cilan Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Qimin Chai
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Huopo Chen
- Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, 200032, China
| | - Shi Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Wenhui Chen
- School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Jing Cheng
- Department of Earth System Science, University of California, Irvine, CA, 92697, USA
| | - Xiyuan Chi
- National Meteorological Center, China Meteorological Administration, Beijing, 100081, China
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Xiangzhao Feng
- Policy Research Center for Environment and Economy, Ministry of Ecology and Environment of the People's Republic of China, Beijing, 100029, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Shan Hu
- Building Energy Research Center, School of Architecture, Tsinghua University, Beijing, 100084, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, 100084, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Wei Li
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xiaomei Li
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Jun Liu
- Department of Environmental Engineering, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xin Liu
- Energy Foundation China, Beijing, 100004, China
| | - Zhu Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jinghui Ma
- Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai, 200030, China
| | - Yue Qin
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Dan Tong
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xuhui Wang
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Xuying Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Rui Wu
- Transport Planning and Research Institute (TPRI) of the Ministry of Transport, Beijing, 100028, China
| | - Qingyang Xiao
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Xiaolong Xu
- China Association of Building Energy Efficiency, Beijing, 100029, China
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100080, China
| | - Haipeng Yu
- Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Da Zhang
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Ning Zhang
- Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Xian Zhang
- The Administrative Centre for China's Agenda 21 (ACCA21), Ministry of Science and Technology (MOST), Beijing, 100038, China
| | - Xin Zhang
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Zengkai Zhang
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Xiamen University, Xiamen, 361102, China
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Yixuan Zheng
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Jian Zhou
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Tong Zhu
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Jinnan Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
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An T, Li Y, Wang R, Jing S, Gao Y, Liu S, Huang D, Zhou M, Dai H, Huang C, Lu J, Wang H, Fu Q. Characteristics of typical intermediate and semi volatile organic compounds in Shanghai during China International Import Expo event. CHEMOSPHERE 2024; 355:141779. [PMID: 38537709 DOI: 10.1016/j.chemosphere.2024.141779] [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: 11/10/2023] [Revised: 03/16/2024] [Accepted: 03/22/2024] [Indexed: 04/01/2024]
Abstract
To ensure good air quality during the China International Import Expo (CIIE) event, stringent emission-reduction measures were implemented in Shanghai. To assess the efficacy of these measures, this study measured typical categories of intermediate/semi volatile organic compounds (I/SVOCs), including alkanes (C10-C26 n-alkanes and pristane), EPA-priority polycyclic aromatic hydrocarbons (PAHs), alkylnaphthalenes, benzothiazole (BTH) and chlorobenzenes (CBs), at an urban site of Shanghai before and during two CIIE events (2019 and 2020; non-CIIE versus CIIE). The average concentrations of alkanes and PAHs during both 2019 and 2020 CIIE events decreased by approximately 41% and 17%, respectively, compared to non-CIIE periods. However, the decline in BTH and CBs was only observed during CIIE-2019. Secondary organic aerosol (SOA) formation from alkanes, PAHs and BTH was evaluated under atmospheric conditions, revealing considerable SOA contributions from dimethylnaphthalenes and BTH. Positive matrix factorization (PMF) analysis further revealed that life-related sources, such as cooking and residential emissions, make a noticeable contribution (21.6%) in addition to the commonly concerned gasoline-vehicle sources (31.5%), diesel-related emissions (20.8%), industrial emissions (18.6%) and ship emissions (7.5%). These findings provide valuable insights into the efficacy of the implemented measures in reducing atmospheric I/SVOCs levels. Moreover, our results highlight the significance of exploring additional individual species of I/SVOCs and life-related sources for further research and policy development.
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Affiliation(s)
- Taikui An
- College of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China; State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Yingjie Li
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China.
| | - Rui Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Sheng'ao Jing
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Yaqin Gao
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China; Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Shuyu Liu
- College of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
| | - Dandan Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Min Zhou
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Haixia Dai
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Jun Lu
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Qingyan Fu
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
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35
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Yoo JW, Park SY, Jo HY, Jeong Y, Lee HJ, Cheol-Hee Kim, Lee SH. Assessing the role of cold front passage and synoptic patterns on air pollution in the Korean Peninsula. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 348:123803. [PMID: 38521399 DOI: 10.1016/j.envpol.2024.123803] [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: 01/13/2024] [Revised: 02/26/2024] [Accepted: 03/14/2024] [Indexed: 03/25/2024]
Abstract
Various numerical experiments using WRF (Weather Research & Forecasting Model) and CMAQ (Community Multiscale Air Quality Modeling System) were performed to analyze the phenomenon of rapidly high concentration PM2.5 after the passage of a cold front in an area with limited local emissions. The episode period was from January 14 to 23, 2018, and analysis was conducted by dividing it into two stages according to the characteristics of changes in PM2.5 concentrations during the period. Through the analysis of observational data during the episode period, we confirmed meteorological impacts (decrease in temperature, increase in wind speed and relative humidity) and an increase in air pollution (PM10 and PM2.5) attributed to the passage of a cold front. Using CMAQ's IPR (Integrated Process Rate) analysis, the contribution of the horizontal advection process was observed in transporting PM2.5 to Gangneung at higher altitudes, and the PM2.5 concentrations at the surface increased because the vertical advection process was influenced by the terrain. Notably, in Stage 2 (64 μg·m-3), a higher contribution of the vertical advection process compared to Stage 1 (35 μg·m-3) was observed, which is attributed to the differences in synoptic patterns following the passage of the cold front. During Stage 2, following the cold front, atmospheric stability (dominance of high-pressure system) led to air subsidence and the presence of a temperature inversion layer, creating favorable meteorological conditions for the accumulation of air pollutants. This study offers the mechanisms of air pollution over the Korean Peninsula under non-stationary meteorological conditions, particularly in relation to the passage of the cold front (low-pressure system). Notably, the influence of a cold front can vary according to the synoptic patterns that develop following its passage.
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Affiliation(s)
- Jung-Woo Yoo
- Institute of Environmental Studies, Pusan National University, Busan, 46241, Republic of Korea
| | - Soon-Young Park
- Department of Science Education, Daegu National University of Education, Daegu, 42411, Republic of Korea
| | - Hyun-Young Jo
- Institute of Environmental Studies, Pusan National University, Busan, 46241, Republic of Korea
| | - Yeomin Jeong
- Institute of Environmental Studies, Pusan National University, Busan, 46241, Republic of Korea
| | - Hyo-Jung Lee
- Institute of Environmental Studies, Pusan National University, Busan, 46241, Republic of Korea; Department of Atmospheric Sciences, Pusan National University, Busan, 46241, Republic of Korea
| | - Cheol-Hee Kim
- Institute of Environmental Studies, Pusan National University, Busan, 46241, Republic of Korea; Department of Atmospheric Sciences, Pusan National University, Busan, 46241, Republic of Korea
| | - Soon-Hwan Lee
- Institute of Environmental Studies, Pusan National University, Busan, 46241, Republic of Korea; Department of Earth Science Education, Pusan National University, Busan, 46241, Republic of Korea.
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36
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Huang Z, Bai Y, Ali M, Fang Z. "Two Mountains concept" leading the green transformation of China's economic society. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 359:120960. [PMID: 38678897 DOI: 10.1016/j.jenvman.2024.120960] [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: 10/04/2023] [Revised: 02/19/2024] [Accepted: 04/19/2024] [Indexed: 05/01/2024]
Abstract
The rapid urbanization are serious threats to global sustainable development, making the green transformation of socio-economy and industry a must for global efforts. The theory of ecological and economic harmonization in ecological economics has gained attention. However the Two Mountains concept, i.e., "Lucid waters and lush mountains are invaluable assets", has been mostly neglected as a practical demonstration of the theory. In this study an equal weights method is used to construct an index system for testing the effectiveness of the ongoing practices and demonstrations of the Two Mountains concept, and whether it can achieve the expected green transformation objectives. A total of 421 pilot cases and 208 surrounding non-pilot cases in China from 2010 to 2020 are selected for analysis. The results indicate that: (1) From 2010 to 2020, overall 98.33% of pilots show positive improvement in comprehensive effectiveness; (2) Strong evidence indicate that the positive externality of the demonstrations extends to their surrounding region, mainly manifested in the impact of industrial structure change, inspiring collaboration between cities; (3) Such ambitious green transformation has a significant impact on landscape characteristics, which emphasizes the role of landscape management and monitoring. Therefore, this study proposes an industrial integration framework to enhance the transformation of ecosystem service values, to facilitate transition to a green economy in various regions globally. It provides significant managerial insights and practical expertise. The demonstration of China's Two Mountains concept can offer reliable empirical cases to enrich the theory of ecological economics and global sustainable development.
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Affiliation(s)
- Zhongde Huang
- Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Yunnan International Joint Laboratory of Southeast Asia Biodiversity Conservation, Menglun, 666303, China
| | - Yang Bai
- Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Yunnan International Joint Laboratory of Southeast Asia Biodiversity Conservation, Menglun, 666303, China.
| | - Maroof Ali
- Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, China; Yunnan International Joint Laboratory of Southeast Asia Biodiversity Conservation, Menglun, 666303, China
| | - Zhou Fang
- Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, China; Yunnan International Joint Laboratory of Southeast Asia Biodiversity Conservation, Menglun, 666303, China
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37
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Tan Y, Zhang Y, Wang T, Chen T, Mu J, Xue L. Dissecting Drivers of Ozone Pollution during the 2022 Multicity Lockdowns in China Sheds Light on Future Control Direction. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6988-6997. [PMID: 38592860 DOI: 10.1021/acs.est.4c01197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
In 2022, many Chinese cities experienced lockdowns and heatwaves. We analyzed ground and satellite data using machine learning to elucidate chemical and meteorological drivers of changes in O3 pollution in 27 major Chinese cities during lockdowns. We found that there was an increase in O3 concentrations in 23 out of 27 cities compared with the corresponding period in 2021. Random forest modeling indicates that emission reductions in transportation and other sectors, as well as the changes in meteorology, increased the level of O3 in most cities. In cities with over 80% transportation reductions and temperature fluctuations within -2 to 2 °C, the increases in O3 concentrations were mainly attributable to reductions in nitrogen oxide (NOx) emissions. In cities that experienced heatwaves and droughts, increases in the O3 concentrations were primarily driven by increases in temperature and volatile organic compound (VOC) emissions, and reductions in NOx concentrations from ground transport were offset by increases in emissions from coal-fired power generation. Despite 3-99% reduction in passenger volume, most cities remained VOC-limited during lockdowns. These findings demonstrate that to alleviate urban O3 pollution, it will be necessary to further reduce industrial emissions along with transportation sources and to take into account the climate penalty and the impact of heatwaves on O3 pollution.
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Affiliation(s)
- Yue Tan
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077,China
| | - Yingnan Zhang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077,China
| | - Tao Wang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077,China
| | - Tianshu Chen
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077,China
| | - Jiangshan Mu
- Environment Research Institute, Shandong University, Qingdao, Shandong 266237, China
| | - Likun Xue
- Environment Research Institute, Shandong University, Qingdao, Shandong 266237, China
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Zhang ZE, Li J, Zhang R, Tian C, Sun Z, Li T, Han M, Yu K, Zhang G. Increase in Agricultural-Derived NH x and Decrease in Coal Combustion-Derived NO x Result in Atmospheric Particulate N-NH 4+ Surpassing N-NO 3- in the South China Sea. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6682-6692. [PMID: 38547356 DOI: 10.1021/acs.est.3c09173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
The atmospheric deposition of anthropogenic active nitrogen significantly influences marine primary productivity and contributes to eutrophication. The form of nitrogen deposition has been evolving annually, alongside changes in human activities. A disparity arises between observation results and simulation conclusions due to the limited field observation and research in the ocean. To address this gap, our study undertook three field cruises in the South China Sea in 2021, the largest marginal sea of China. The objective was to investigate the latest atmospheric particulate inorganic nitrogen deposition pattern and changes in nitrogen sources, employing nitrogen-stable isotopes of nitrate (δ15N-NO3-) and ammonia (δ15N-NH4+) linked to a mixing model. The findings reveal that the N-NH4+ deposition generally surpasses N-NO3- deposition, attributed to a decline in the level of NOx emission from coal combustion and an upswing in the level of NHx emission from agricultural sources. The disparity in deposition between N-NH4+ and N-NO3- intensifies from the coast to the offshore, establishing N-NH4+ as the primary contributor to oceanic nitrogen deposition, particularly in ocean background regions. Fertilizer (33 ± 21%) and livestock (20 ± 6%) emerge as the primary sources of N-NH4+. While coal combustion continues to be a significant contributor to marine atmospheric N-NO3-, its proportion has diminished to 22 (Northern Coast)-35% (background area) due to effective NOx emission controls by the countries surrounding the South China Sea, especially the Chinese Government. As coal combustion's contribution dwindles, the significance of vessel and marine biogenic emissions grows. The daytime higher atmospheric N-NO3- concentration and lower δ15N-NO3- compared with nighttime further underscore the substantial role of marine biogenic emissions.
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Affiliation(s)
- Zheng-En Zhang
- State Key Laboratory of Organic Geochemistry, Guangdong Province Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Jun Li
- State Key Laboratory of Organic Geochemistry, Guangdong Province Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
| | - Ruijie Zhang
- Guangxi Laboratory on the Study of Coral Reefs in the South China Sea; Coral Reef Research Center of China; School of Marine Sciences, Guangxi University, Nanning 530004, P. R. China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, P. R. China
| | - Chongguo Tian
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China
| | - Zeyu Sun
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Tingting Li
- State Key Laboratory of Organic Geochemistry, Guangdong Province Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Minwei Han
- Guangxi Laboratory on the Study of Coral Reefs in the South China Sea; Coral Reef Research Center of China; School of Marine Sciences, Guangxi University, Nanning 530004, P. R. China
| | - Kefu Yu
- Guangxi Laboratory on the Study of Coral Reefs in the South China Sea; Coral Reef Research Center of China; School of Marine Sciences, Guangxi University, Nanning 530004, P. R. China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, P. R. China
| | - Gan Zhang
- State Key Laboratory of Organic Geochemistry, Guangdong Province Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
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Deng M, Wang C, Yang C, Li X, Cheng H. Nitrogen and oxygen isotope characteristics, formation mechanism, and source apportionment of nitrate aerosols in Wuhan, Central China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 921:170715. [PMID: 38331296 DOI: 10.1016/j.scitotenv.2024.170715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 01/20/2024] [Accepted: 02/03/2024] [Indexed: 02/10/2024]
Abstract
Understanding the sources and formation mechanisms of nitrate in PM2.5 is important for effective and precise prevention and control of particulate matter pollution. In this study, we detected stable nitrogen and oxygen isotope signatures of NO- 3 (expressed as δ15N-NO- 3 and δ18O-NO3-) in PM2.5 samples in Wuhan, the largest city in central China. The sources and formation pathways of NO3- were quantitatively analyzed using the modified version of the Bayesian isotope mixing (MixSIR) model, and the regional transport characteristics of NO3- were analyzed using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model and concentration-weighted trajectory (CWT) method. The results showed that NO3- significantly contributed to the ambient PM2.5 pollution and its driving effect increased with the gradient of pollution level. The average δ15N-NO3- and δ18O-NO3- values were 4.7 ± 0.9 ‰ and 79.7 ± 2.9 ‰, respectively. δ15N-NO3- and δ18O-NO3- were more enriched in winter and increased dramatically in heavily polluted days. The reaction pathway of NO2 + OH dominated nitrate formation in summer, while the reaction pathway of N2O5+ H2O dominated in other seasons and contributed more in polluted days than clean days. The contributions of vehicle emission, coal combustion, biomass burning, biogenic soil emission, and ship emission sources to NO3- were 26.4 %, 23.4 %, 22.8 %, 15.3 %, and 12.1 %, respectively. In addition to local emissions, air mass transport from the northern China had a significant impact on particulate NO3- in Wuhan. Overall, we should pay special attention to vehicle and ship emissions and winter coal combustion emissions in future policymaking.
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Affiliation(s)
- Mengjie Deng
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Cimou Wang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Chunmian Yang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Xiaoxiao Li
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China.
| | - Hairong Cheng
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China.
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40
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Liu R, Shao M, Wang Q. Multi-timescale variation characteristics of PM 2.5 in different regions of China during 2014-2022. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:171008. [PMID: 38369160 DOI: 10.1016/j.scitotenv.2024.171008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/31/2024] [Accepted: 02/14/2024] [Indexed: 02/20/2024]
Abstract
Over the past decade, China has achieved a significant reduction in PM2.5 concentrations. Due to the diversity of natural and artificial factors, regional differences are remarkable in the variation characteristics and have not been well addressed in previous studies. Based on hourly observed PM2.5 concentrations from 2014 to 2022, this study conducted a comprehensive analysis of variation characteristics on annual, seasonal, and diurnal scales, with a special focus on differences across major regions. Driving factors of the variations, the effectiveness of air pollution control efforts as well as future priorities were discussed. The annual PM2.5 concentrations in all regions showed an overall downward trend from 2014 to 2022, but the decline rates differed notably across the regions, with the maximum value nearly two times higher than the minimum value. The seasonal decline rates also differ from region to region, which could be partially attributed to the burning of crop residues and dust events. Northeast China was significantly affected by the burning of crop residues and experienced a big drop in the number of fire points in autumn, but a remarkable increase in spring. The spring dust events may greatly contribute to PM2.5 concentrations in northern and western China. For diurnal variation, nighttime concentrations were generally greater than daytime concentrations, and the nighttime concentrations were likely to increase in eastern regions and decrease in western regions. Furthermore, the daytime and nighttime ratios (calculated by daytime/nighttime concentration divided by the daily-mean concentration) exhibited different interannual trends, with the daytime ratios decreasing and nighttime ratios increasing, especially in the northeastern and western regions. The findings indicate that the air pollution control efforts have been generally successful, but with large regional disparities, and highlight the importance of controlling crop residue burning, dust events, and nighttime emissions for specific seasons and regions.
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Affiliation(s)
- Rui Liu
- State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Min Shao
- School of Environment, Nanjing Normal University, Nanjing 210046, China
| | - Qin'geng Wang
- State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing 210023, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
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41
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Cheng Y, Zhong YJ, Liu JM, Cao XB, Zhang Q, He KB. Response of Harbin aerosol to latest clean air actions in China. JOURNAL OF HAZARDOUS MATERIALS 2024; 467:133728. [PMID: 38335619 DOI: 10.1016/j.jhazmat.2024.133728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/20/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024]
Abstract
Cities in Northeast China, e.g., Harbin, were brought to the forefront of air pollution control by a national-level policy promulgated in 2021, i.e., the Circular on Further Promoting the Pollution Prevention and Control Battle (the FP3CB Circular) which aimed at eliminating heavy or severe air pollution events. In this study, we explored the response of Harbin aerosol to the FP3CB Circular, based on observational results from two campaigns conducted during 2020-2021 and 2021-2022. A clear decreasing trend was identified for the impact of domestic biomass burning between the two winters, presumably driven by the clean heating actions. The 2021-2022 winter was also characterized by reduced formation of secondary organic aerosol but enhanced production of nitrate, which could be attributed to the less humid conditions but higher temperatures, respectively, compared to the 2020-2021 winter. The overall effect of these changes was a decrease in the contribution of organic species to wintertime aerosol in Harbin. In addition, the number of heavy or severe pollution days rebounded in the 2021-2022 winter compared to 2020-2021 (5 vs. 3), indicating that the emissions of primary particles and gaseous precursors must be further reduced to achieve the ambitious goals of the FP3CB Circular.
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Affiliation(s)
- Yuan Cheng
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Ying-Jie Zhong
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Jiu-Meng Liu
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
| | - Xu-Bing Cao
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Ke-Bin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
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42
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Wang Y, Liang L, Xu W, Liu C, Cheng H, Liu Y, Zhang G, Xu X, Yu D, Wang P, Song Q, Liu J, Cheng Y. Influence of meteorological factors on open biomass burning at a background site in Northeast China. J Environ Sci (China) 2024; 138:1-9. [PMID: 38135377 DOI: 10.1016/j.jes.2023.02.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 12/24/2023]
Abstract
Biomass burning (BB) is a very important emission source that significantly adversely impacts regional air quality. BB produces a large number of primary organic aerosol (POA) and black carbon (BC). Besides, BB also provides many precursors for secondary organic aerosol (SOA) generation. In this work, the ratio of levoglucosan (LG) to organic carbon (OC) and the fire hotspots map was used to identify the open biomass burning (OBB) events, which occurred in two representative episodes, October 13 to November 30, 2020, and April 1 to April 30, 2021. The ratio of organic aerosol (OA) to reconstructed PM2.5 concentration (PM2.5*) increased with the increase of LG/OC. When LG/OC ratio is higher than 0.03, the highest OA/PM2.5* ratio can reach 80%, which means the contribution of OBB to OA is crucial. According to the ratio of LG to K+, LG to mannosan (MN) and the regional characteristics of Longfengshan, it can be determined that the crop residuals are the main fuel. The occurrence of OBB coincides with farmers' preferred choices, i.e., burning biomass in "bright weather". The "bright weather" refers to the meteorological conditions with high temperature, low humidity, and without rain. Meteorological factors indirectly affect regional biomass combustion pollution by influencing farmers' active choices.
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Affiliation(s)
- Yulong Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Linlin Liang
- State Key Laboratory of Severe Weather & Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Wanyun Xu
- State Key Laboratory of Severe Weather & Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Chang Liu
- State Key Laboratory of Severe Weather & Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Hongbing Cheng
- State Key Laboratory of Severe Weather & Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yusi Liu
- State Key Laboratory of Severe Weather & Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Gen Zhang
- State Key Laboratory of Severe Weather & Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xiaobin Xu
- State Key Laboratory of Severe Weather & Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Dajiang Yu
- Longfengshan Regional Background Station, China Meteorological Administration, Heilongjiang 150200, China
| | - Peng Wang
- Longfengshan Regional Background Station, China Meteorological Administration, Heilongjiang 150200, China
| | - Qingli Song
- Heilongjiang Climate Center, Heilongjiang 150030, China
| | - Jiumeng Liu
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
| | - Yuan Cheng
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
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Kong L, Song M, Li X, Liu Y, Lu S, Zeng L, Zhang Y. Analysis of China's PM 2.5 and ozone coordinated control strategy based on the observation data from 2015 to 2020. J Environ Sci (China) 2024; 138:385-394. [PMID: 38135404 DOI: 10.1016/j.jes.2023.03.030] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 03/16/2023] [Accepted: 03/19/2023] [Indexed: 12/24/2023]
Abstract
The coordinated control of PM2.5 and ozone has become the strategic goal of national air pollution control. Considering the gradual decline in PM2.5 concentration and the aggravation of ozone pollution, a better understanding of the coordinated control of PM2.5 and ozone is urgently needed. Here, we collected and sorted air pollutant data for 337 cities from 2015 to 2020 to explore the characteristics of PM2.5 and ozone pollution based on China's five major air pollution regions. The results show that it is necessary to continue to strengthen the emission reduction in PM2.5 and ozone precursors, and control NOx and VOCs while promoting a dramatic emission reduction in PM2.5. The primary method of curbing ozone pollution is to strengthen the emission control of VOCs, with a long-term strategy of achieving substantial emission reductions in NOx, because VOCs and NOx are also precursors to PM2.5; hence, their reductions also contribute to the reduction in PM2.5. Therefore, the implementation of a multipollutant emission reduction control strategy aimed at the prevention and control of PM2.5 and ozone pollution is the only means to realize the coordinated control of PM2.5 and ozone.
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Affiliation(s)
- Liuwei Kong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Mengdi Song
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Xin Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
| | - Ying Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Sihua Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Limin Zeng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
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Wang J, Zhang H, Liu Y, Li Z, Liu Z. Unexpected PM 2.5-related emissions and accompanying environmental-economic inequalities driven by "clean" tertiary industry in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 919:170823. [PMID: 38342464 DOI: 10.1016/j.scitotenv.2024.170823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 02/13/2024]
Abstract
The tertiary industry, led by service sectors, usually have "clean" production processes and thus is ignored by current PM2.5 pollution mitigation strategies in China. Actually, the tertiary industry heavily relies on the supplies from its upstream industries, resulting in pollutant emissions and economic benefits transferring among different regions. With the application of the multiregional input-output (MRIO) model, our study explores the emission contribution from the tertiary industry's consumption activities in China and analyses the accompanying emission-economy relationship. We find that the production process of tertiary industry (with the sector Transportation excluded) contributes only ∼1 % of China's PM2.5-related emissions in 2017. However, its consumption-based emission contributions could increase to 11 %-17 %, among which >95 % are indirectly contributed. More than 40 % of tertiary industry consumption-based emissions, accompanied by 25 % of the consumption-based value added, are transferred via interprovincial trade. The proportion of transferred emissions even exceeds 50 % for the top 10 importers. The spatial pattern of value-added flows is nearly opposite to that of emission flows. Our results also reveal that among the 30 provinces and 870 interprovincial trading pairs, 6 provinces are experiencing environmental-economic win, 7 provinces are experiencing environmental-economic loss, and in detail 326 trading pairs are experiencing environmental-economic win or loss. To reduce the unexpected emissions and inequalities embodied in seemingly "clean" industries, consumption activities should be considered and strengthened in China's new-stage environmental policies.
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Affiliation(s)
- Jingxu Wang
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China.
| | - Haoyu Zhang
- College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Yu Liu
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; Institute of Carbon Neutrality, Peking University, 100871, Beijing, China.
| | - Zhongyi Li
- College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Zhengzhong Liu
- College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
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Li T, Chen C, Zhang M, Zhao L, Liu Y, Guo Y, Wang Q, Du H, Xiao Q, Liu Y, He MZ, Kinney PL, Cohen AJ, Tong S, Shi X. Accountability analysis of health benefits related to National Action Plan on Air Pollution Prevention and Control in China. PNAS NEXUS 2024; 3:pgae142. [PMID: 38689709 PMCID: PMC11060103 DOI: 10.1093/pnasnexus/pgae142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 03/22/2024] [Indexed: 05/02/2024]
Abstract
China is one of the largest producers and consumers of coal in the world. The National Action Plan on Air Pollution Prevention and Control in China (2013-2017) particularly aimed to reduce emissions from coal combustion. Here, we show whether the acute health effects of PM2.5 changed from 2013 to 2018 and factors that might account for any observed changes in the Beijing-Tianjin-Hebei (BTH) and the surrounding areas where there were major reductions in PM2.5 concentrations. We used a two-stage analysis strategy, with a quasi-Poisson regression model and a random effects meta-analysis, to assess the effects of PM2.5 on mortality in the 47 counties of BTH. We found that the mean daily PM2.5 levels and the SO42- component ratio dramatically decreased in the study period, which was likely related to the control of coal emissions. Subsequently, the acute effects of PM2.5 were significantly decreased for total and circulatory mortality. A 10 μg/m3 increase in PM2.5 concentrations was associated with a 0.16% (95% CI: 0.08, 0.24%) and 0.02% (95% CI: -0.09, 0.13%) increase in mortality from 2013 to 2015 and from 2016 to 2018, respectively. The changes in air pollution sources or PM2.5 components appeared to have played a core role in reducing the health effects. The air pollution control measures implemented recently targeting coal emissions taken in China may have resulted in significant health benefits.
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Affiliation(s)
- Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- School of Public Health, Nanjing Medical University, No.101 Longmian Avenue, Jiangning District, Nanjing 211166, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
| | - Mengxue Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- School of Public Health, Nanjing Medical University, No.101 Longmian Avenue, Jiangning District, Nanjing 211166, China
| | - Liang Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
| | - Yuanyuan Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
| | - Yafei Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
| | - Qing Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
| | - Hang Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
| | - Qingyang Xiao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Haidian District, Tsinghua University, Beijing 100084, China
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Mike Z He
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
| | - Patrick L Kinney
- Department of Environmental Health, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA
| | - Aaron J Cohen
- Health Effects Institute, 75 Federal Street, Boston, MA 02110, USA
| | - Shilu Tong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- School of Public Health and Social Work, Queensland University of Technology, 2 George Street, Brisbane, QLD 4001, Australia
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No.7 Panjiayuan South, Chaoyang District, Beijing 100021, China
- School of Public Health, Nanjing Medical University, No.101 Longmian Avenue, Jiangning District, Nanjing 211166, China
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Qu Y, Liu H, Zhang T, Su H, Wang N, Zhou Y, Shi J, Wang L, Wang Q, Liu S, Zhu C, Cao J. Source-specific light absorption and radiative effects decreases and indications due to the lockdown. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 356:120600. [PMID: 38547823 DOI: 10.1016/j.jenvman.2024.120600] [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: 11/20/2023] [Revised: 02/13/2024] [Accepted: 03/10/2024] [Indexed: 04/07/2024]
Abstract
The 'extreme' emission abatement during the lockdown (from the end of 2019 to the early 2020) provided an experimental period to investigate the corresponding source-specific effects of aerosol. In this study, the variations of source-specific light absorption (babs) and direct radiative effect (DRE) were obtained during and after the lockdown period by using the artificial neural network (ANN) and source apportionment environmental receptor model. The results showed that the babs decreased for all sources during the two periods. The most reductions were observed with ∼90% for traffic-related emissions (during the lockdown) and ∼85% for coal combustion (after the lockdown), respectively. Heightened babs (370 nm) values were obtained for coal and biomass burning during the lockdown, which was attributed to the enhanced atmospheric oxidization capacity. Nevertheless, the variations of babs (880 nm) after the lockdown was mainly due to the weakening of oxidation and reduced emissions of secondary precursors. The present study indicated that the large-scale emission reduction can promote both reductions of babs (370 nm) and DRE (34-68%) during the lockdown. The primary emissions decrease (e.g., Traffic emission) may enhance atmosphere oxidation, increase the ultraviolet wavelength light absorption and DRE efficiencies. The source-specific emission reduction may be contributed to various radiation effects, which is beneficial for the adopting of control strategies.
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Affiliation(s)
- Yao Qu
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Huikun Liu
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China
| | - Ting Zhang
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China
| | - Hui Su
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China; Xi'an Institute for Innovative Earth Environment Research, Xi'an, 710061, China
| | - Nan Wang
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China; Xi'an Institute for Innovative Earth Environment Research, Xi'an, 710061, China
| | - Yue Zhou
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China
| | - Julian Shi
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China; Xi'an Institute for Innovative Earth Environment Research, Xi'an, 710061, China
| | - Luyao Wang
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China; Xi'an Institute for Innovative Earth Environment Research, Xi'an, 710061, China
| | - Qiyuan Wang
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China
| | - Suixin Liu
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China
| | - Chongshu Zhu
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, Xi'an, 710499, China.
| | - Junji Cao
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
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Yue H, He C, Huang Q, Zhang D, Shi P, Moallemi EA, Xu F, Yang Y, Qi X, Ma Q, Bryan BA. Substantially reducing global PM 2.5-related deaths under SDG3.9 requires better air pollution control and healthcare. Nat Commun 2024; 15:2729. [PMID: 38548716 PMCID: PMC10978932 DOI: 10.1038/s41467-024-46969-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/14/2024] [Indexed: 04/01/2024] Open
Abstract
The United Nations' Sustainable Development Goal (SDG) 3.9 calls for a substantial reduction in deaths attributable to PM2.5 pollution (DAPP). However, DAPP projections vary greatly and the likelihood of meeting SDG3.9 depends on complex interactions among environmental, socio-economic, and healthcare parameters. We project potential future trends in global DAPP considering the joint effects of each driver (PM2.5 concentration, death rate of diseases, population size, and age structure) and assess the likelihood of achieving SDG3.9 under the Shared Socioeconomic Pathways (SSPs) as quantified by the Scenario Model Intercomparison Project (ScenarioMIP) framework with simulated PM2.5 concentrations from 11 models. We find that a substantial reduction in DAPP would not be achieved under all but the most optimistic scenario settings. Even the development aligned with the Sustainability scenario (SSP1-2.6), in which DAPP was reduced by 19%, still falls just short of achieving a substantial (≥20%) reduction by 2030. Meeting SDG3.9 calls for additional efforts in air pollution control and healthcare to more aggressively reduce DAPP.
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Affiliation(s)
- Huanbi Yue
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China
- School of International Affairs and Public Administration, Ocean University of China, Qingdao, China
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
| | - Chunyang He
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China.
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China.
- Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management & Ministry of Education, Beijing Normal University, Beijing, China.
- Academy of Plateau Science and Sustainability, People's Government of Qinghai Province & Beijing Normal University, Xining, China.
| | - Qingxu Huang
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
| | - Da Zhang
- College of Geography and Ocean Sciences, Yanbian University, Yanji, China.
| | - Peijun Shi
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
- Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management & Ministry of Education, Beijing Normal University, Beijing, China
- Academy of Plateau Science and Sustainability, People's Government of Qinghai Province & Beijing Normal University, Xining, China
| | - Enayat A Moallemi
- Commonwealth Scientific and Industrial Research Organization (CSIRO), Melbourne, Victoria, Australia
| | - Fangjin Xu
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Yang Yang
- School of International Affairs and Public Administration, Ocean University of China, Qingdao, China
- Institute of Marine Development, Ocean University of China, Qingdao, China
| | - Xin Qi
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ocean University of China, Qingdao, China
| | - Qun Ma
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai, China
| | - Brett A Bryan
- School of Life and Environmental Sciences, Deakin University, Melbourne, Victoria, Australia
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Chen X, Li K, Yang T, Yang Z, Wang X, Zhu B, Chen L, Yang Y, Wang Z, Liao H. Trends and drivers of aerosol vertical distribution over China from 2013 to 2020: Insights from integrated observations and modeling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170485. [PMID: 38296080 DOI: 10.1016/j.scitotenv.2024.170485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/21/2024] [Accepted: 01/24/2024] [Indexed: 02/04/2024]
Abstract
Understanding aerosol vertical distribution is of great importance to climate change and atmospheric chemistry, but there is a dearth of systematical analysis for aerosol vertical distribution amid rapid emission decline after 2013 in China. Here, the GEOS-Chem model and multiple-sourced observations were applied to quantify the changes of aerosol vertical distributions in response to clean air actions. In 2013-2020, the MODIS aerosol optical depth (AOD) presented extensive decreasing trends by -7.9 %/yr to -4.2 %/yr in summer and -6.1 %/yr to -5.8 %/yr in winter in polluted regions. Vertically, the aerosol extinction coefficient (AEC) from CALIPSO decreased by -8.0 %/yr to -5.5 %/yr below ~1 km, but the trends weakened significantly with increasing altitude. Compared with available measurements, the model can reasonably reproduce 2013-2020 trends and seasonality in AOD and vertical AEC. Model simulations confirm that emission reduction was the dominant driver of the 2013-2020 decline in AOD, while the effect of meteorology varied seasonally, with contributions ranging from -2 % to 13 % in summer and -67 % to -2 % in winter. Vertical distributions of emission-driven AEC trends strongly depended on emission reductions, local planetary boundary layer height, and relative humidity. For aerosol components, sulfate accounted for ~50 % of the AOD decline during summer, followed by ammonium and organic aerosol, while in winter the contribution of organic aerosol doubled (24 %-35 %), and nitrate exhibited a weak increasing trend. Chemical production and meteorological conditions (e.g., relative humidity) primarily drove the nitrate contribution, but emission reduction and hygroscopicity were decisive for other components. This work provides an integrated observational and modeling effort to better understand rapid changes in aerosol vertical distribution over China.
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Affiliation(s)
- Xi Chen
- Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Ke Li
- Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Ting Yang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Zhenjiang Yang
- Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Xueqing Wang
- Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Bin Zhu
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Lei Chen
- Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Yang Yang
- Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Zifa Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Hong Liao
- Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
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49
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Chi X, Li Z, Liu H, Chen J, Gao J. Predicting air pollutant emissions of the foundry industry: Based on the electricity big data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170323. [PMID: 38278260 DOI: 10.1016/j.scitotenv.2024.170323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/10/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024]
Abstract
Industrial enterprises are one of the largest sources of air pollution. However, the existing means of monitoring air pollutant emissions are narrow in coverage, high in cost, and low in accuracy. To bridge these gaps, this study explored a predicting model for air pollutant emissions from foundry industries based on high-accuracy electricity consumption data and continuous emission monitoring system (CEMS). The model has then been applied to the calculation of air pollutant emissions from foundries without CEMS and the optimization of air pollutant emission temporal allocation factors. The results reveal that electricity consumption and PM emissions during the 2022 Beijing Winter Olympics have the same ascending and descending relationship. Furthermore, a cubic polynomial model between electricity consumption and flue gas flow is established based on the whole year data of 2021 (R2 = 0.85). The relative errors between the PM emissions calculated by the model and the emission factor method are small (-17.09-24.12 %), and the results from the two methods revealed a strong correlation (r = 0.93, p < 0.01). In addition, the monthly PM emissions from foundries are mainly concentrated in spring and winter, and the daily emissions on weekends are significantly lower than those on workdays. These results can be useful for environmental regulation and optimization of air pollutant emission inventories of foundry industry.
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Affiliation(s)
- Xiangyu Chi
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Zheng Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Hanqing Liu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Jianhua Chen
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Jian Gao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
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Jia W, Zhang X, Wang D, Wang J, Yang Y, Wang H, Liu H, Wang Y. Impacts of emissions and meteorological conditions in three different phases of aerosol pollution during 2013-2022 in Anhui, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 925:171582. [PMID: 38494029 DOI: 10.1016/j.scitotenv.2024.171582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/20/2024] [Accepted: 03/06/2024] [Indexed: 03/19/2024]
Abstract
The PM2.5 concentrations in Anhui, which links the Yangtze River Delta region, China's fastest growing economy area, with the Beijing-Tianjin-Hebei (BTH) region, China's most polluted region, are influenced not only by emissions, but also by variation of meteorological conditions. A comprehensive understanding of the relative impacts of meteorology and emissions on heavy pollution in Anhui at three phases (i.e., phase1: from 2013 to 2017; phase2: from 2018 to 2020; phase 3: from 2021 to 2022) from 2013 to 2022, which can provide suggestions for pollution prevention and control in the future. The decrease in pollutant concentrations from 2013 to 2022 is mainly attributed to the continued reduction in emissions, while the year-to-year fluctuations in pollutant concentrations are largely influenced by meteorological conditions. Although emissions are decreasing, the proportions of residential biofuel combustion and cement are increasing. In addition to the effects of prevailing northeasterly and northwesterly winds (i.e., Type1 and Type2), there is also concern about the influences of static weather and neighboring regional transport (i.e., Type5 and Type6), especially in 2016. The contribution of emissions is greater in phase 2 and phase 3, with a 17 % increase compared to phase 1. Overall, approximately 57 % of explosive growth in PM2.5 concentration during the cumulative stage (CS) can be regarded as the feedback effect of the deteriorating meteorological conditions. Therefore, statistical analyses show that limiting PM2.5 concentrations below about 73 μg m-3 would weaken the feedback effects, which in turn would avoid most of the explosive growth processes in the CS of the 60 heavy pollution processes, which can provide a reference for the government to set a target for sustained emission reduction.
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Affiliation(s)
- Wenxing Jia
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China; Center for Excellence in Regional Atmospheric Environment, IUE, Chinese Academy of Sciences, Xiamen 361021, China.
| | - Deying Wang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Jizhi Wang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yuanqin Yang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Hong Wang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Hongli Liu
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yaqiang Wang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
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