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Li Z, Meng F, Wu S, Afthanorhan A, Hao Y. Guiding clean energy transitions in rural households: Insights from China's pilot low-carbon policies. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 370:122782. [PMID: 39369521 DOI: 10.1016/j.jenvman.2024.122782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2024] [Revised: 09/25/2024] [Accepted: 09/29/2024] [Indexed: 10/08/2024]
Abstract
The impact of Low Carbon Pilot Policies (LCPPs) on carbon reduction and energy efficiency has been extensively studied. However, the potential of these policies to promote clean energy transition (CET) in rural households remains underexplored. This article constructed a staggered-DID model using data from the China Family Panel Studies (CFPS) to investigate the impact and mechanisms of LCPPs on rural households' CET. The findings indicate that LCPPs significantly enhance the CET among rural households. Moreover, the effects of LCPPs vary across cities, while differences within communities and households are less pronounced. Mechanism analysis reveals that LCPPs facilitate rural households' CET through income effects, infrastructure improvements, and enhanced low-carbon awareness. Notably, the income and low-carbon awareness effects are heterogeneous. Additionally, LCPPs have increased rural households' expenditures on home-cooked meals. We estimate the average fixed cost of the CET for rural households to be approximately $404.495. These insights provide valuable empirical evidence that can guide other countries and regions in promoting CET in rural areas.
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Affiliation(s)
- Zhichao Li
- School of Economics, Beijing Institute of Technology, Beijing, 100081, China.
| | - Fanchen Meng
- Faculty of Economics, Shenzhen MSU-BIT University, Shenzhen, 518172, China.
| | - Shaohui Wu
- School of Economics, Beijing Institute of Technology, Beijing, 100081, China.
| | - Asyraf Afthanorhan
- Operation Research & Management Sciences Research Group, Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Kuala Nerus, Malaysia.
| | - Yu Hao
- School of Economics, Beijing Institute of Technology, Beijing, 100081, China; Faculty of Economics, Shenzhen MSU-BIT University, Shenzhen, 518172, China; Digital Economy and Policy Intelligentization Key Laboratory of Ministry of Industry and Information Technology, Beijing, 100081, China; Operation Research & Management Sciences Research Group, Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Kuala Nerus, Malaysia.
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2
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Li M, Geng Y, Zhou S, Sarkis J. Clean energy transitions and health. Heliyon 2023; 9:e21250. [PMID: 38027842 PMCID: PMC10654143 DOI: 10.1016/j.heliyon.2023.e21250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Clean energy can lead to significant health benefits. Making it accessible throughout the world can address many ills. We delve deeply into one example-the transition toward clean residential heating and its relationship to health benefits-in China. We find that the health benefits can outweigh costs from energy expenses in northern provinces. Low-income households enjoy larger health benefits but also experience a higher expense increase, suggesting that extra subsidies or stimuli are needed to help them benefit from clean energy. Our findings suggest that clean energy transitions should be promoted in developing economies due to improved social health, lessened medical costs, and significant environmental improvements.
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Affiliation(s)
- Meng Li
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yong Geng
- School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China
| | - Shaojie Zhou
- School of Public Policy and Management, Tsinghua University, Beijing, China
| | - Joseph Sarkis
- Business School, Worcester Polytechnic Institute, Worcester, United States
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3
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Wang Z, Liu J, Wang B, Zhang B, Deng N. Health benefits from risk information of air pollution in China. Sci Rep 2023; 13:15432. [PMID: 37723248 PMCID: PMC10507042 DOI: 10.1038/s41598-023-42502-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/11/2023] [Indexed: 09/20/2023] Open
Abstract
Risk-related information regarding air pollution can help people understand the risk involved and take preventive measures to reduce health loss. However, the health benefits through these protective behaviors and the health threat of information inequality have not been systematically measured. This article reports the health gains and losses caused by the interaction of "air pollution-air pollution information-human", and studies the heterogeneity and impact of this interaction. Based on field investigations and transfer learning algorism, this study compiled the first nationwide city-level risk-related information (ERI) response parameter set in China. Then, we developed a Information-Behavioral Equivalent PM2.5 Exposure Model (I-BEPEM) model to project the health benefits caused by the impact of environmental risk-related information on residents' protective behaviors under different scenarios. The protective behavior led by air pollution risk information reduces 5.7% PM2.5-related premature deaths per year. With a 1% increase in regional ERI reception, PM2.5-related premature mortality decreases by 0.1% on average; If the level of information perception and behavioral protection in all cities is the same as that in Beijing, PM2.5-related premature deaths will decrease by 6.9% annually in China. Further, changing the air quality standard issued by China to the American standard can reduce the overall PM2.5-related premature deaths by 9.9%. Meanwhile, compared with men, other age groups and rural residents, women, older persons, and urban residents are more likely to conceive risk information and adopt protective behaviors to reduce the risk of premature death from air pollution. Air pollution risk information can significantly reduce people's health loss. Changing the real-time air quality monitoring information indicator standard to a more stringent level can quickly and effectively enhance this effect. However, the uneven distribution of this information in regions and populations has resulted in the inequality of health gains and losses.
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Affiliation(s)
- Zhaohua Wang
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China
- Research Center for Sustainable Development and Intelligent Decision, Beijing Institute of Technology, Beijing, China
| | - Jie Liu
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China
- Research Center for Sustainable Development and Intelligent Decision, Beijing Institute of Technology, Beijing, China
| | - Bo Wang
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China.
- Research Center for Sustainable Development and Intelligent Decision, Beijing Institute of Technology, Beijing, China.
| | - Bin Zhang
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China
- Research Center for Sustainable Development and Intelligent Decision, Beijing Institute of Technology, Beijing, China
| | - Nana Deng
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China
- Research Center for Sustainable Development and Intelligent Decision, Beijing Institute of Technology, Beijing, China
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4
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Meng W, Zhu L, Liang Z, Xu H, Zhang W, Li J, Zhang Y, Luo Z, Shen G, Shen H, Chen Y, Cheng H, Ma J, Tao S. Significant but Inequitable Cost-Effective Benefits of a Clean Heating Campaign in Northern China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37256786 DOI: 10.1021/acs.est.2c07492] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Residential emissions significantly contribute to air pollution. To address this issue, a clean heating campaign was implemented to replace coal with electricity or natural gas among 13.9 million rural households in northern China. Despite great success, the cost-benefits and environmental equity of this campaign have never been fully investigated. Here, we modeled the environmental and health benefits, as well as the total costs of the campaign, and analyzed the inequality and inequity. We found that even though the campaign decreased only 1.1% of the total energy consumption, PM2.5 emissions and PM2.5 exposure experienced 20% and 36% reduction, respectively, revealing the amplification effects along the causal pathway. Furthermore, the number of premature deaths attributable to residential emissions reduced by 32%, suggesting that the campaign was highly beneficial. Governments and residents shared the cost of 2,520 RMB/household. However, the benefits and the costs were unevenly distributed, as the residents in mountainous areas were not only less benefited from the campaign but also paid more because of the higher costs, resulting in a notably lower cost-effectiveness. Moreover, villages in less developed areas tended to choose natural gas with a lower initial investment but a higher total cost (2,720 RMB/household) over electricity (2,190 RMB/household). With targeted investment and subsidies in less developed areas and the promotion of electricity and other less expensive alternatives, the multidevelopment goals of improved air quality, reduced health impacts, and reduced inequity in future clean heating interventions could be achieved.
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Affiliation(s)
- Wenjun Meng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Lei Zhu
- School of Economics and Management, Beihang University, Beijing 100191, P. R. China
| | - Zhuang Liang
- School of Economics and Management, Beihang University, Beijing 100191, P. R. China
| | - Haoran Xu
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Wenxiao Zhang
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Jin Li
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Yuanzheng Zhang
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Zhihan Luo
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
| | - Yilin Chen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Shu Tao
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
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5
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Dai Q, Chen J, Wang X, Dai T, Tian Y, Bi X, Shi G, Wu J, Liu B, Zhang Y, Yan B, Kinney PL, Feng Y, Hopke PK. Trends of source apportioned PM 2.5 in Tianjin over 2013-2019: Impacts of Clean Air Actions. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 325:121344. [PMID: 36878277 DOI: 10.1016/j.envpol.2023.121344] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/03/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
A long-term (2013-2019) PM2.5 speciation dataset measured in Tianjin, the largest industrial city in northern China, was analyzed with dispersion normalized positive matrix factorization (DN-PMF). The trends of source apportioned PM2.5 were used to assess the effectiveness of source-specific control policies and measures in support of the two China's Clean Air Actions implemented nationwide in 2013-2017 and 2018-2020, respectively. Eight sources were resolved from the DN-PMF analysis: coal combustion (CC), biomass burning (BB), vehicular emissions, dust, steelmaking and galvanizing emissions, a mixed sulfate-rich factor and secondary nitrate. After adjustment for meteorological fluctuations, a substantial improvement in PM2.5 air quality was observed in Tianjin with decreases in PM2.5 at an annual rate of 6.6%/y. PM2.5 from CC decreased by 4.1%/y. The reductions in SO2 concentration, PM2.5 contributed by CC, and sulfate demonstrated the improved control of CC-related emissions and fuel quality. Policies aimed at eliminating winter-heating pollution have had substantial success as shown by reduced heating-related SO2, CC, and sulfate from 2013 to 2019. The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries. BB reduced significantly by 2016 and remained low due to the no open field burning policy. Vehicular emissions and road/soil dust declined over the Action's first phase followed by positive upward trends, showing that further emission controls are needed. Nitrate concentrations remained constant although NOX emissions dropped significantly. The lack of a decrease in nitrate may result from increased ammonia emissions from enhanced vehicular NOX controls. The port and shipping emissions were evident implying their impacts on coastal air quality. These results affirm the effectiveness of the Clean Air Actions in reducing primary anthropogenic emissions. However, further emission reductions are needed to meet global health-based air quality standards.
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Affiliation(s)
- Qili Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Jiajia Chen
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Xuehan Wang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Tianjiao Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Yingze Tian
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Xiaohui Bi
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Guoliang Shi
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Jianhui Wu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Baoshuang Liu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Yufen Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Beizhan Yan
- Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, 10964, USA
| | - Patrick L Kinney
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China.
| | - 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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6
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Wang J, Wang S, Xu X, Li X, He P, Qiao Y, Chen Y. The diminishing effects of winter heating on air quality in northern China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 325:116536. [PMID: 36326523 DOI: 10.1016/j.jenvman.2022.116536] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 10/10/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Cleaner winter heating has been promoted to abate the winter air pollution in northern China. Although improvements in air quality have been observed, the effectiveness and mechanism of cleaner heating measures on air quality have not been examined on the empirical ground. In this study, we estimate the annual effects of winter heating policy on air quality from 2014 to 2017 using a regression discontinuity design (RDD) and dynamic regression model. The results show that winter heating aggravates Air Quality Index (AQI). Specifically, the AQI raised by winter heating reduce from 85.3 in 2014 to 24.1 in 2017, indicating diminishing effects of winter heating with the implementation of clean heating measures. The heterogeneous characteristics of winter heating in terms of different pollutants and city scales are further quantified. The effects of clean heating are more evident for particulate pollutants (PM2.5 and PM10) than for SO2, NO2, CO and O3. The promotion of clean heating is more effective in larger cities. These findings provided insights into the diminishing air pollution change with continuous advancement in clean heating policy and the heterogeneity among cities and pollutants should be taken into account when formulating future policies in response to energy transition and climate change.
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Affiliation(s)
- Junfeng Wang
- College of Environmental Science and Engineering, Nankai University, Tianjin, 300500, China; Research Center for Resource, Energy and Environmental Policy, Nankai University, Tianjin, 300500, China.
| | - Shimeng Wang
- College of Environmental Science and Engineering, Nankai University, Tianjin, 300500, China; Research Center for Resource, Energy and Environmental Policy, Nankai University, Tianjin, 300500, China
| | - Xiaoya Xu
- College of Environmental Science and Engineering, Nankai University, Tianjin, 300500, China; Research Center for Resource, Energy and Environmental Policy, Nankai University, Tianjin, 300500, China
| | - Xiao Li
- School of Public Policy and Administration, Xi'an Jiaotong University, No.28 Xianning West Road, Xi'an, Shaanxi, 710049, China
| | - Pan He
- School of Earth and Environmental Sciences, Cardiff University, Cardiff, CF10 3AT, UK
| | - Yuanbo Qiao
- Institute for Studies in County Development, Shandong University, No.49 Zhenhua Street, Qingdao, Shandong, 266200, China
| | - Ying Chen
- Laboratory of Atmospheric Chemistry, Paul Scherrer Institute (PSI), Forschungsstrasse 111, 5232, Villigen, Switzerland
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7
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Zheng S, Shen H, Shen G, Chen Y, Ma J, Cheng H, Tao S. Vertically-resolved indoor measurements of air pollution during Chinese cooking. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2022; 12:100200. [PMID: 36157347 PMCID: PMC9500372 DOI: 10.1016/j.ese.2022.100200] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/24/2022] [Accepted: 06/28/2022] [Indexed: 05/07/2023]
Abstract
Chinese cooking features several unique processes, e.g., stir-frying and pan-frying, which represent important sources of household air pollution. However, factors affecting household air pollution and the vertical variations of indoor pollutants during Chinese cooking are less clear. Here, using low-cost sensors with high time resolutions, we measured concentrations of five gas species and particulate matter (PM) in three different sizes at multiple heights in a kitchen during eighteen different Chinese cooking events. We found indoor gas species were elevated by 21%-106% during cooking, compared to the background, and PMs were elevated by 44%-159%. Vertically, the pollutants concentrations were highly variable during cooking periods. Gas species generally showed a monotonic increase with height, while PMs changed more diversely depending on the cooking activity's intensity. Intense cooking, e.g., stir-frying, pan-frying, or cooking on high heat, tended to shoot PMs to the upper layers, while moderate ones left PMs within the breathing zone. Individuals with different heights would be subject to different levels of household air pollution exposure during cooking. The high vertical variability challenges the current indoor standard that presumes a uniform pollution level within the breathing zone and thus has important implications for public health and policy making.
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Affiliation(s)
- Shuxiu Zheng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Huizhong Shen
- 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, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Yilin Chen
- 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, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Shu Tao
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
- 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, Southern University of Science and Technology, Shenzhen, 518055, China
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8
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Ren J, Li X, Zhu S, Yin B, Guo Z, Cui Q, Song J, Pei H, Ma Y. Sesamin Ameliorates Fine Particulate Matter (PM 2.5)-Induced Lung Injury via Suppression of Apoptosis and Autophagy in Rats. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:9489-9498. [PMID: 35881548 DOI: 10.1021/acs.jafc.2c02470] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Lung damage can be caused by fine particulate matter (PM2.5). Thus, effective prevention strategies for PM2.5-induced lung injury are urgently required. Sesamin (Ses) is a natural polyphenolic compound that has attracted considerable attention of researchers because of its wide range of pharmacological activities. The present study aims to elucidate whether Ses pretreatment could alleviate PM2.5-induced lung damage and identify its possible mechanisms. Sprague-Dawley rats were orally dosed with 0.5% carboxymethylcellulose (CMC) and different concentrations of Ses once a day for 21 days. Then, the rats of the PM2.5 exposure group and Ses-treated group were exposed to PM2.5 by intratracheal instillation every 2 days for 1 week. Biomarkers associated with lung injury were detected in bronchoalveolar lavage fluid (BALF). Lung tissue was collected for histology, inflammation, oxidative stress, immunohistochemistry, and Western blot. Our results showed that PM2.5 exposure could cause pathological changes in lung tissue and increase levels of TP, AKP, and ALB in BALF. Meanwhile, exposure to PM2.5 can cause oxidative stress and inflammation in the lungs. In addition, Ses pretreatment could ameliorate histopathological injury, oxidative stress, and inflammation caused by PM2.5 exposure. It could also inhibit PM2.5-induced apoptosis and upregulation of autophagy-associated proteins. Collectively, our study indicated that Ses pretreatment could ameliorate PM2.5-induced lung damage via inhibiting apoptosis and autophagy in rats.
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Affiliation(s)
- Jingyi Ren
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, 050017, China
| | - Xiang Li
- Undergraduate of College of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Siqi Zhu
- Undergraduate of College of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Bowen Yin
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, 050017, China
| | - Zihao Guo
- Undergraduate of College of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Qiqi Cui
- Undergraduate of College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Jianshi Song
- Undergraduate of College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Huanting Pei
- Undergraduate of College of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yuxia Ma
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, 050017, China
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9
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Cai F, Yin K, Hao M. COVID-19 Pandemic, Air Quality, and PM2.5 Reduction-Induced Health Benefits: A Comparative Study for Three Significant Periods in Beijing. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.885955] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Previous studies have estimated the influence of control measures on air quality in the ecological environment during the COVID-19 pandemic. However, few have attached importance to the comparative study of several different periods and evaluated the health benefits of PM2.5 decrease caused by COVID-19. Therefore, we aimed to estimate the control measures' impact on air pollutants in 16 urban areas in Beijing and conducted a comparative study across three different periods by establishing the least squares dummy variable model and difference-in-differences model. We discovered that restriction measures did have an apparent impact on most air pollutants, but there were discrepancies in the three periods. The Air Quality Index (AQI) decreased by 7.8%, and SO2, NO2, PM10, PM2.5, and CO concentrations were lowered by 37.32, 46.76, 53.22, 34.07, and 19.97%, respectively, in the first period, while O3 increased by 36.27%. In addition, the air pollutant concentrations in the ecological environment, including O3, reduced significantly, of which O3 decreased by 7.26% in the second period. Furthermore, AQI and O3 concentrations slightly increased compared to the same period in 2019, while other pollutants dropped, with NO2 being the most apparent decrease in the third period. Lastly, we employed health effects and environmental value assessment methods to evaluate the additional public health benefits of PM2.5 reduction owing to the restriction measures in three periods. This research not only provides a natural experimental basis for governance actions of air pollution in the ecological environment, but also points out a significant direction for future control strategies.
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10
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Weng Z, Wang Y, Yang X, Cheng C, Tan X, Shi L. Effect of cleaner residential heating policy on air pollution: A case study in Shandong Province, China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 311:114847. [PMID: 35272159 DOI: 10.1016/j.jenvman.2022.114847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 02/23/2022] [Accepted: 03/04/2022] [Indexed: 06/14/2023]
Abstract
Coal-fired heating in winter is a primary source of air pollution in many countries. In northern China, the use of scattered coal for winter heating has led to severe environmental issues. In this study, we use a quasi-natural experiment in Shandong Province, China, to investigate the effectiveness of a cleaner heating transition policy. Specifically, we use a difference-in-differences approach to identify the effects of the cleaner heating transition policy on air pollution using high-resolution hourly data. Our findings indicate that implementation of the policy could effectively reduce air pollution by decreasing a PM2.5 by 7.32%, PM10 by 2.62%, SO2 by 3.98%, and NO2 by 4.67%. In addition, we used event study and a series of robustness checks to further support our findings. Notably, our findings indicate that implementation of the policy includes a spatial spillover effect, which differs according to the level of compulsory implementation and the distance to a city centre. Overall, our findings can help promote the application of a cleaner transitioning policy for the entire country and offer guidance for further policy development regarding the effective reduction of winter air pollution in the developing world.
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Affiliation(s)
- Zhixiong Weng
- Institute of Circular Economy, Beijing University of Technology, Beijing, 100124, China.
| | - Yue Wang
- School of Environment & Natural Resources, Renmin University of China, Beijing, 100872, China.
| | - Xuan Yang
- School of Environment & Natural Resources, Renmin University of China, Beijing, 100872, China.
| | - Cuiyun Cheng
- Chinese Academy for Environmental Planning, Beijing, 100045, China.
| | - Xue Tan
- State Grid Energy Research Institute Co., Ltd., Beijing, 102209, China.
| | - Lei Shi
- School of Environment & Natural Resources, Renmin University of China, Beijing, 100872, China.
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11
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Meng W, Shen G, Shen H, Chen Y, Ma J, Liu J, Cheng H, Hu J, Wan Y, Tao S. Source contributions and drivers of physiological and psychophysical cobenefits from major air pollution control actions in North China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:2225-2235. [PMID: 35119844 DOI: 10.1021/acs.est.1c07171] [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: 06/14/2023]
Abstract
North China is among the most polluted regions in the country, and human exposure to PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) in this region has led to severe health consequences. The region has also benefited the most from emission reductions in recent years. It is of interest to understand to what extent and through which paths emissions from different sectors cause adverse health impacts. Here, we present the results of a full evaluation of the health benefits of emission control actions implemented in recent years based on segregated emission inventories with an emphasis on residential emissions. Two major causal paths, one from residential emissions to indoor air pollution, exposure, and premature deaths, and the other from nonresidential emissions to ambient air pollution and psychophysical impacts, were identified and quantified. From 2014 to 2019, both ambient (33%) and indoor (39%) PM2.5 decreased significantly, leading to decreasing trends in exposure (36%), premature deaths (10%), and psychophysical impacts (21%). The Air Pollution Prevention and Control Action Plan, the Clean Heating Campaign, and spontaneous residential shifts to clean energy contributed significantly to these reductions when the effects of other drivers, such as population and economic growth, were excluded.
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Affiliation(s)
- Wenjun Meng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
| | - Yilin Chen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Junfeng Liu
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Jianying Hu
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Yi Wan
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Shu Tao
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
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12
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Shupler M, Hystad P, Birch A, Chu YL, Jeronimo M, Miller-Lionberg D, Gustafson P, Rangarajan S, Mustaha M, Heenan L, Seron P, Lanas F, Cazor F, Jose Oliveros M, Lopez-Jaramillo P, Camacho PA, Otero J, Perez M, Yeates K, West N, Ncube T, Ncube B, Chifamba J, Yusuf R, Khan A, Liu Z, Wu S, Wei L, Tse LA, Mohan D, Kumar P, Gupta R, Mohan I, Jayachitra KG, Mony PK, Rammohan K, Nair S, Lakshmi PVM, Sagar V, Khawaja R, Iqbal R, Kazmi K, Yusuf S, Brauer M. Multinational prediction of household and personal exposure to fine particulate matter (PM 2.5) in the PURE cohort study. ENVIRONMENT INTERNATIONAL 2022; 159:107021. [PMID: 34915352 DOI: 10.1016/j.envint.2021.107021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM2.5). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM2.5 levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM2.5 exposure models. METHODS The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM2.5 kitchen concentrations (n = 2,365) and male and/or female PM2.5 exposure monitoring (n = 910) in a subset of households in Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania and Zimbabwe. PURE-AIR measurements were combined with survey data on cooking environment characteristics in hierarchical Bayesian log-linear regression models. Model performance was evaluated using leave-one-out cross validation. Predictive models were applied to survey data from the larger PURE cohort (22,480 households; 33,554 individuals) to quantitatively estimate PM2.5 exposures. RESULTS The final models explained half (R2 = 54%) of the variation in kitchen PM2.5 measurements (root mean square error (RMSE) (log scale):2.22) and personal measurements (R2 = 48%; RMSE (log scale):2.08). Primary cooking fuel type, heating fuel type, country and season were highly predictive of PM2.5 kitchen concentrations. Average national PM2.5 kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20 μg/m3 (Chile); 55 μg/m3 (China)) and 12-fold among households primarily cooking with wood (36 μg/m3 (Chile)); 427 μg/m3 (Pakistan)). Average PM2.5 kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM2.5 female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile). CONCLUSION Using survey data to estimate PM2.5 exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM2.5 exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.
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Affiliation(s)
- Matthew Shupler
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada; Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, United Kingdom.
| | - Perry Hystad
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, United States
| | - Aaron Birch
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Yen Li Chu
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matthew Jeronimo
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Paul Gustafson
- Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sumathy Rangarajan
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Maha Mustaha
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Laura Heenan
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Pamela Seron
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | | | | | | | | | - Paul A Camacho
- Fundación Oftalmológica de Santander (FOSCAL), Floridablanca, Colombia
| | - Johnna Otero
- Universidad Militar Nueva Granada, Bogota, Colombia
| | | | - Karen Yeates
- Department of Medicine, Queen's University, Kingston, Ontario, Canada; Department of Biomedical Sciences, University of Zimbabwe, Harare, Zimbabwe
| | - Nicola West
- Pamoja Tunaweza Research Centre, Moshi, Tanzania
| | - Tatenda Ncube
- Department of Biomedical Sciences, University of Zimbabwe, Harare, Zimbabwe
| | - Brian Ncube
- Department of Biomedical Sciences, University of Zimbabwe, Harare, Zimbabwe
| | - Jephat Chifamba
- Department of Biomedical Sciences, University of Zimbabwe, Harare, Zimbabwe
| | - Rita Yusuf
- School of Life Sciences, Independent University, Dhaka, Bangladesh
| | - Afreen Khan
- School of Life Sciences, Independent University, Dhaka, Bangladesh
| | - Zhiguang Liu
- Beijing An Zhen Hospital of the Capital University of Medical Sciences, China
| | - Shutong Wu
- Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, China
| | - Li Wei
- Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, China
| | - Lap Ah Tse
- Jockey Club School of Public Health and Primary Care, the Chinese University of Hong Kong, HKSAR, China
| | - Deepa Mohan
- Madras Diabetes Research Foundation, Chennai, India
| | | | - Rajeev Gupta
- Eternal Heart Care Centre & Research Institute, Jaipur, India
| | - Indu Mohan
- Mahatma Gandhi University of Medical Sciences and Technology, Jaipur, India
| | - K G Jayachitra
- St. John's Medical College & Research Institute, Bangalore, India
| | - Prem K Mony
- St. John's Medical College & Research Institute, Bangalore, India
| | - Kamala Rammohan
- Health Action By People, Government Medical College, Trivandrum, India
| | - Sanjeev Nair
- Health Action By People, Government Medical College, Trivandrum, India
| | - P V M Lakshmi
- Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Vivek Sagar
- Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Rehman Khawaja
- Department of Community Health Science, Aga Khan University Hospital, Karachi, Pakistan
| | - Romaina Iqbal
- Department of Community Health Science, Aga Khan University Hospital, Karachi, Pakistan
| | - Khawar Kazmi
- Department of Community Health Science, Aga Khan University Hospital, Karachi, Pakistan
| | - Salim Yusuf
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Michael Brauer
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
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13
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Ren JY, Yin BW, Li X, Zhu SQ, Deng JL, Sun YT, Zhang ZA, Guo ZH, Pei HT, Zhang F, Li RQ, Chen FG, Ma YX. Sesamin attenuates PM 2.5-induced cardiovascular injury by inhibiting ferroptosis in rats. Food Funct 2021; 12:12671-12682. [PMID: 34825691 DOI: 10.1039/d1fo02913d] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Objective: This study aimed to elucidate the pharmacological effects of sesamin (Ses) and its mechanism of action towards PM2.5-induced cardiovascular injuries. Method: Forty Sprague Dawley (SD) rats were randomly divided into five groups: a saline control group; a PM2.5 exposure group; and low-, middle-, and high-dose Ses pretreatment groups. The SD rats were pretreated with different concentrations of Ses for 21 days. Afterward, the rats were exposed to ambient PM2.5 by intratracheal instillation every other day for a total of three times. The levels of inflammatory markers, including tumor necrosis factor-alpha (TNF-α), interleukin-1beta (IL-1β), and interleukin-6 (IL-6), and indicators related to oxidative responses, such as total superoxide dismutase (SOD), reduced glutathione (GSH), glutathione peroxidase (GSH-Px), and malondialdehyde (MDA), were measured in the blood and heart. The expression of ferroptosis-related proteins in heart tissues was determined via western blot and immunohistochemistry. Results: Ses pretreatment substantially ameliorated cardiovascular injuries in rats as evidenced by the decrease in the pathological score and collagen area. The decreased levels of SOD, GSH, and GSH-Px in the heart and serum were inhibited by Ses. In addition, Ses not only notably increased the activity of antioxidant enzymes but also reduced the levels of MDA, CK, LDH, CK-MB, IL-6, TNF-α, IL-1β, and IL-6. Furthermore, Ses pretreatment upregulated the expression levels of GPX4, SLC7A11, TFRC, and FPN1 and inhibited the expression levels of FTH1 and FTL. Conclusion: Ses pretreatment could ameliorate PM2.5-induced cardiovascular injuries perhaps by inhibiting ferroptosis. Therefore, Ses pretreatment may be a novel strategy for the prevention and treatment of PM2.5-induced cardiovascular injury.
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Affiliation(s)
- Jing-Yi Ren
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, 050017, China.
| | - Bo-Wen Yin
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, 050017, China.
| | - Xiang Li
- Undergraduate of College of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Si-Qi Zhu
- Undergraduate of College of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Jin-Liang Deng
- Undergraduate of College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yi-Ting Sun
- Undergraduate of College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Zhen-Ao Zhang
- Undergraduate of College of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Zi-Hao Guo
- Undergraduate of College of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Huan-Ting Pei
- Undergraduate of College of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Fan Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, 050017, China.
| | - Rui-Qiang Li
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, 050017, China.
| | - Feng-Ge Chen
- Shijiazhuang Center for Disease Control and Prevention, Shijiazhuang, 050017, China
| | - Yu-Xia Ma
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, 050017, China.
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14
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Yang X, Wang Y, Chen D, Tan X, Tian X, Shi L. Does the "Blue Sky Defense War Policy" Paint the Sky Blue?-A Case Study of Beijing-Tianjin-Hebei Region, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182312397. [PMID: 34886123 PMCID: PMC8657255 DOI: 10.3390/ijerph182312397] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/22/2021] [Accepted: 11/22/2021] [Indexed: 11/16/2022]
Abstract
Improving air quality is an urgent task for the Beijing-Tianjin-Hebei (BTH) region in China. In 2018, utilizing 365 days' daily concentration data of six air pollutants (including PM2.5, PM10, SO2, NO2, CO and O3) at 947 air quality grid monitoring points of 13 cities in the BTH region and controlling the meteorological factors, this paper takes the implementation of the Blue Sky Defense War (BSDW) policy as a quasi-natural experiment to examine the emission reduction effect of the policy in the BTH region by applying the difference-in-difference method. Results show that the policy leads to the significant reduction of the daily average concentration of PM2.5, PM10, SO2, O3 by -1.951 μg/m3, -3.872 μg/m3, -1.902 μg/m3, -7.882 μg/m3 and CO by -0.014 mg/m3, respectively. The results of the robustness test support the aforementioned conclusions. However, this paper finds that the concentration of NO2 increases significantly (1.865 μg/m3). In winter heating seasons, the concentration of SO2, CO and O3 decrease but PM2.5, PM10 and NO2 increase significantly. Besides, resource intensive cities, non-key environmental protection cities and cities in the north of the region have great potential for air pollutant emission reduction. Finally, policy suggestions are recommended; these include setting specific goals at the city level, incorporating more cities into the list of key environmental protection cities, refining the concrete indicators of domestic solid fuel, and encouraging and enforcing clean heating diffusion.
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Affiliation(s)
- Xuan Yang
- School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China; (X.Y.); (Y.W.); (D.C.); (X.T.)
| | - Yue Wang
- School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China; (X.Y.); (Y.W.); (D.C.); (X.T.)
| | - Di Chen
- School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China; (X.Y.); (Y.W.); (D.C.); (X.T.)
| | - Xue Tan
- Energy Strategy and Planning Research Department, State Grid Energy Research Institute Co., Ltd., Beijing 102209, China;
| | - Xue Tian
- School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China; (X.Y.); (Y.W.); (D.C.); (X.T.)
| | - Lei Shi
- School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China; (X.Y.); (Y.W.); (D.C.); (X.T.)
- Correspondence: ; Tel.: +86-10-82502696
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15
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Zhu YD, Fan L, Wang J, Yang WJ, Li L, Zhang YJ, Yang YY, Li X, Yan X, Yao XY, Wang XL. Spatiotemporal variation in residential PM2.5 and PM10 concentrations in China: National on-site survey. ENVIRONMENTAL RESEARCH 2021; 202:111731. [PMID: 34297935 DOI: 10.1016/j.envres.2021.111731] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/10/2021] [Accepted: 07/16/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Significant efforts have been directed toward addressing the adverse health effects of particulate matter, while few data exist to evaluate indoor exposure nationwide in China. OBJECTIVES This study aimed to investigate dwellings particulate matter levels in the twelve cities in China and provide large data support for policymakers to accelerate the legislative process. METHODS The current study was based on the CIEHS 2018 study and conducted in 12 cities of China. A total of 2128 air samples were collected from 610 residential households during the summer and winter. Both PM10 and PM2.5 were detected with a light-scattering dust meter in both the living room and bedroom. The Wilcoxon rank-sum test was performed to evaluate the correlations between PM2.5 and PM10 concentrations and both sampling season and site. Ratios of the living room to bedroom were calculated to evaluate the particulate matter variation between rooms. Hierarchical clustering was used to probe the question of whether the concentration varies between cities throughout China. RESULTS The geometric means of the PM2.5 in living rooms and bedrooms were 39.80 and 36.55 μg/m3 in the summer, and 70.97 and 67.99 μg/m3 in the winter, respectively. In the summer, approximately 70 % of indoor dwelling PM2.5 exceeded the limit of 25 μg/m3, and for PM10 approximately 60 % of dwellings demonstrated levels higher than 50 μg/m3; the corresponding values were over 90 % and 80 % in winter, respectively. In Shijiazhuang, Lanzhou, Luoyang and Qingdao, the geometric means of the PM2.5 concentrations were observed to be 1.5 to 4.3 times higher during winter than during summer; similar concentrations in summer and winter were observed in Harbin, Wuxi, and Shenzhen, while the PM2.5 concentrations in Panjin were approximately 1.5 times higher in summer than in winter. There was no significant difference in particulate matter concentrations between the living rooms and bedrooms. Scatter plots showed that cities with low GDP and a small population had higher concentrations, while Shenzhen, which has a higher GDP and a large permanent population, had a relatively low concentration of particulate matter. CONCLUSIONS Our results suggest that indoor air pollution is a severe problem in China. It is necessary to continue monitoring indoor air quality to observe the changing trend under the tremendous effort of the Chinese government.
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Affiliation(s)
- Yuan-Duo Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Lin Fan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Jiao Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Wen-Jing Yang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Li 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
| | - Yu-Jing Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Yu-Yan Yang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Xu 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
| | - Xu Yan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Xiao-Yuan Yao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Xian-Liang Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China.
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16
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Lu M, Tang X, Feng Y, Wang Z, Chen X, Kong L, Ji D, Liu Z, Liu K, Wu H, Liang S, Zhou H, Hu K. Nonlinear response of SIA to emission changes and chemical processes over eastern and central China during a heavy haze month. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 788:147747. [PMID: 34034193 DOI: 10.1016/j.scitotenv.2021.147747] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 05/06/2021] [Accepted: 05/09/2021] [Indexed: 06/12/2023]
Abstract
This study used a chemical transport model to investigate the response of secondary inorganic aerosols (SIA) to chemical processes and its precursor emissions over northern and southern city-clusters of China in January 2014. Unexpectedly, SIA concentrations with low levels of precursor emissions were much higher over the southern regions than those over the northern region with high levels of precursor emissions, based on ground observations and high-precision simulations. The sensitivity analysis of chemical processes suggests that the gas-phase chemistry was a critical factor determining the SIA pattern, especially the higher efficiency of nitrogen conversion to nitrate in southern cities controlled by favorable meteorological elements than that in northern city. However, the heterogeneous process led to the decrease of SIA in southern regions by 3% to 36% and the increasing of SIA in NCP by 26.9%, mainly attributing to the impact on nitrate. The reason was that sulfate enhancement by the heterogeneous reactions can compete ammonia (NH3) and the excessive nitric acid converted into nitrogen oxide (NOx), leading to nitrate decrease in southern regions under NH3-deficient regimes. Moreover, through sensitivity experiments of precursor emission reduction by 20%, NH3 control was found to be the most effective for reducing SIA concentrations comparing to sulfur dioxide (SO2) and NOx reduction and a more remarkable decrease of SIA was in southern regions by 10% to 15% than that in northern region by 6.7%. The effect of the synergy control of precursors emission varied in different city-clusters, inferring that the control strategy aimed at improving air quality should be implemented based on specific characteristics of precursors emission in different regions of China.
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Affiliation(s)
- Miaomiao Lu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU, Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
| | - Xiao Tang
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU, Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China.
| | - Zifa Wang
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
| | - Xueshun Chen
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Lei Kong
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongsheng Ji
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Zirui Liu
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Kexin Liu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU, Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
| | - Huangjian Wu
- Guanghua School of Management, Peking University, Beijing 100871, China
| | - Shengwen Liang
- Wuhan Environmental Monitoring Center, Wuhan 430015, China
| | - Hui Zhou
- Hunan Meteorological Observatory, Changsha 410118, China
| | - Ke Hu
- Wuhan Environmental Monitoring Center, Wuhan 430015, China
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17
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Alves CA, Vicente ED, Evtyugina M, Vicente AMP, Sainnokhoi TA, Kováts N. Cooking activities in a domestic kitchen: Chemical and toxicological profiling of emissions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 772:145412. [PMID: 33581534 DOI: 10.1016/j.scitotenv.2021.145412] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/18/2021] [Accepted: 01/21/2021] [Indexed: 05/09/2023]
Abstract
To obtain emission factors and cooking-related chemical signatures, a monitoring campaign was carried out in a modern kitchen where different dishes of the Latin cuisine were prepared. Particulate matter (PM10, PM2.5 and PM1) and total volatile organic compounds (TVOCs) were continuously measured. Passive tubes for carbonyls and a high volume PM10 sampler were simultaneously used. PM10 filters were analysed for organic and elemental carbon and for multiple organic compounds, including polyaromatic hydrocarbons (PAHs). The toxic potential of PM10 was evaluated using a bioluminescence inhibition bioassay. Acrolein was never detected, while formaldehyde and acetaldehyde levels were comparable to those in the background air. The protection limit for TVOCs was always exceeded. Fine particles comprised more than 86% of the PM10 mass concentrations. PM10 emission rates ranged from 124 to 369 μg min-1. Relatively low PAH concentrations were obtained. PM10 encompassed alcohols, acids, plasticisers, alkyl esters, sterols, sugars, polyols, glyceridic compounds, phenolics, among others. Total concentrations were 1.9-5.3 times higher during cooking than in the background air but, for some compounds, differences of tens or hundreds of times were registered. PM10 from grilled pork was found to contribute to non-negligible cancer risks and to be very toxic, while samples from other dishes were categorised as toxic.
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Affiliation(s)
- Célia A Alves
- Department of Environment and Planning, Centre for Environmental and Marine Studies (CESAM), University of Aveiro, 3810-193 Aveiro, Portugal.
| | - Estela D Vicente
- Department of Environment and Planning, Centre for Environmental and Marine Studies (CESAM), University of Aveiro, 3810-193 Aveiro, Portugal
| | - Margarita Evtyugina
- Department of Environment and Planning, Centre for Environmental and Marine Studies (CESAM), University of Aveiro, 3810-193 Aveiro, Portugal
| | - Ana M P Vicente
- Department of Environment and Planning, Centre for Environmental and Marine Studies (CESAM), University of Aveiro, 3810-193 Aveiro, Portugal
| | - Tsend-Ayush Sainnokhoi
- Centre for Environmental Sciences, University of Pannonia, Egyetem str. 10, 8200 Veszprém, Hungary
| | - Nora Kováts
- Centre for Environmental Sciences, University of Pannonia, Egyetem str. 10, 8200 Veszprém, Hungary
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18
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Zhang Y, Chen X, Yu S, Wang L, Li Z, Li M, Liu W, Li P, Rosenfeld D, Seinfeld JH. City-level air quality improvement in the Beijing-Tianjin-Hebei region from 2016/17 to 2017/18 heating seasons: Attributions and process analysis. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 274:116523. [PMID: 33508716 DOI: 10.1016/j.envpol.2021.116523] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 12/27/2020] [Accepted: 01/14/2021] [Indexed: 05/21/2023]
Abstract
With the implementation of clean air strategies, PM2.5 pollution abatement has been observed in the "2 + 26" cities in the Beijing-Tianjin-Hebei (BTH) region (referred to as the BTH2+26) and their surrounding areas. To identify the drivers for PM2.5 concentration decreases in the BTH2+26 cites from the 2016/17 heating season (HS1617) to the 2017/18 heating season (HS1718), we investigated the contributions of meteorological conditions and emission-reduction measures by Community Multi-Scale Air Quality (CMAQ) model simulations. The source apportionments of five sector sources (i.e., agriculture, industry, power plants, traffic and residential), and regional sources (i.e., local, within-BTH: other cities within the BTH2+26 cities, outside-BTH, and boundary conditions (BCON)) to the PM2.5 decreases in the BTH2+26 cities were estimated with the Integrated Source Apportionment Method (ISAM). Mean PM2.5 concentrations in the BTH2+26 cities substantially decreased from 77.4 to 152.5 μg m-3 in HS1617 to 52.9-101.9 μg m-3 in HS1718, with the numbers of heavy haze (daily PM2.5 ≥150 μg m-3) days decreasing from 17-77 to 5-30 days. The model simulation results indicated that the PM2.5 concentration decreases in most of the BTH2+26 cities were attributed to emission reductions (0.4-55.0 μg m-3, 2.3-81.6% of total), but the favorable meteorological conditions also played important roles (1.9-25.4 μg m-3, 18.4-97.7%). Residential sources dominated the PM2.5 reductions, leading to decreases in average PM2.5 concentrations by more than 30 μg m-3 in severely polluted cities (i.e., Shijiazhuang, Baoding, Xingtai, and Beijing). Regional source analyses showed that both local and within-BTH sources were significant contributors to PM2.5 concentrations for most cities. Emission controls in local and within-BTH sources in HS1718 decreased the average PM2.5 concentrations by 0.1-47.2 μg m-3 and 0.3-22.1 μg m-3, respectively, relative to those in HS1617. Here we demonstrate that a combination of favorable meteorological conditions and anthropogenic emission reductions contributed to the improvement of air quality from HS1617 to HS1718 in the BTH2+26 cities.
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Affiliation(s)
- Yibo Zhang
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, PR China
| | - Xue Chen
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, PR China
| | - Shaocai Yu
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, PR China; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
| | - Liqiang Wang
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, PR China
| | - Zhen Li
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, PR China
| | - Mengying Li
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, PR China
| | - Weiping Liu
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, PR China
| | - Pengfei Li
- College of Science and Technology, Hebei Agricultural University, Baoding, Hebei, 071000, PR China
| | - Daniel Rosenfeld
- Institute of Earth Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
| | - John H Seinfeld
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
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19
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Huang Y, Partha DB, Harper K, Heyes C. Impacts of Global Solid Biofuel Stove Emissions on Ambient Air Quality and Human Health. GEOHEALTH 2021; 5:e2020GH000362. [PMID: 33778310 PMCID: PMC7983341 DOI: 10.1029/2020gh000362] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 01/29/2021] [Indexed: 05/14/2023]
Abstract
Global solid biofuel stove emissions strongly impact air quality, climate change, and human health. However, investigations of the impacts of global solid biofuel stove emissions on human health associated with PM2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) and ozone (O3) are limited. Here, we quantify the impacts of global solid biofuel stove emissions on ambient PM2.5 and O3 air quality and the associated human health effects for the year 2010, using the Community Atmosphere Model coupled with Chemistry version 5.3. Annual mean surface PM2.5 concentrations from global solid biofuel stove emissions averaged over 2006-2010 are up to 23.1 μg m-3, with large impacts found over China, India, sub-Saharan Africa, and eastern and central Europe. For surface O3 impacts, we find that global solid biofuel stove emissions lead to increases in surface O3 concentrations by up to 5.7 ppbv for China, India, and sub-Saharan Africa, and negligible impacts or reductions of up to 0.5 ppbv for the US, Europe, and parts of South America. Global solid biofuel stove emissions for the year 2010 contribute to 382,000 [95% confidence interval (95CI): 349,000-409,000] annual premature deaths associated with PM2.5 and O3 exposure, with the corresponding years of life lost as 8.10 million years (95CI: 7.38-8.70 million years). Our study highlights air quality and human health benefits of mitigating emissions from the global solid biofuel stove sector, especially over populous regions of low-income and middle-income countries, through promoting clean household energy programs for the residential energy supply.
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Affiliation(s)
- Yaoxian Huang
- Department of Civil and Environmental EngineeringWayne State UniversityDetroitMIUSA
| | - Debatosh B. Partha
- Department of Civil and Environmental EngineeringWayne State UniversityDetroitMIUSA
| | - Kandice Harper
- Earth and Life InstituteUniversité catholique de LouvainLouvain‐la‐NeuveBelgium
| | - Chris Heyes
- International Institute for Applied Systems AnalysisLaxenburgAustria
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20
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Dust Transport from Inland Australia and Its Impact on Air Quality and Health on the Eastern Coast of Australia during the February 2019 Dust Storm. ATMOSPHERE 2021. [DOI: 10.3390/atmos12020141] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Dust storms originating from Central Australia and western New South Wales frequently cause high particle concentrations at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affected air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model—Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in the WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints, namely mortality, respiratory and cardiac disease hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. Generally, the health impact is similar in order of magnitude to that caused by biomass burning events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory disease hospitalisation for this dust event (161) compared to the fire event (24).
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21
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Wang J, Xu X, Wang S, He S, Li X, He P. Heterogeneous effects of COVID-19 lockdown measures on air quality in Northern China. APPLIED ENERGY 2021; 282:116179. [PMID: 33199939 PMCID: PMC7657037 DOI: 10.1016/j.apenergy.2020.116179] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/17/2020] [Accepted: 10/30/2020] [Indexed: 05/18/2023]
Abstract
In response to the spread of COVID-19, China implemented a series of control measures. The causal effect of these control measures on air quality is an important consideration for extreme air pollution control in China. Here, we established a difference-in-differences model to quantitatively estimate the lockdown effect on air quality in the Beijing-Tianjin-Hebei (BTH) region. We found that the lockdown measures did have an obvious effect on air quality. The air quality index (AQI) was reduced by 15.2%, the concentration of NO2, PM10, PM2.5, and CO were reduced by 37.8%, 33.6%, 21.5%, and 20.4% respectively. At the same time, we further explored the heterogeneous effects of travel restrictions and the control measure intensity on air quality. We found that the traffic restrictions, especially the restriction of intra-city travel intensity (TI), exhibited a significant heterogeneous effect on NO2 with a decrease of approximately 13.6%, and every one-unit increase in control measures intensity reduced the concentration of air pollutants by approximately 2-4%. This study not only provides a natural, experimental basis for control measures on air quality but also indicates an important direction for future control strategies. Importantly, determining the estimated effect helps formulate accurate and effective intervention measures on the differentiated level of air pollution, especially on extreme air pollution.
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Affiliation(s)
- Junfeng Wang
- College of Environmental Science and Engineering, Nankai University, Tianjin 300500, China
- Research Center for Resource, Energy and Environmental Policy, Nankai University, Tianjin 300500, China
| | - Xiaoya Xu
- College of Environmental Science and Engineering, Nankai University, Tianjin 300500, China
- Research Center for Resource, Energy and Environmental Policy, Nankai University, Tianjin 300500, China
| | - Shimeng Wang
- College of Environmental Science and Engineering, Nankai University, Tianjin 300500, China
- Research Center for Resource, Energy and Environmental Policy, Nankai University, Tianjin 300500, China
| | - Shutong He
- Institute for Environmental Studies, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081HV Amsterdam, the Netherlands
| | - Xiao Li
- Department of Earth System Science, Tsinghua University, Beijing 100048, China
| | - Pan He
- Department of Earth System Science, Tsinghua University, Beijing 100048, China
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22
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Zhang M, Jordaan SM, Peng W, Zhang Q, Miller SM. Potential Uses of Coal Methane in China and Associated Benefits for Air Quality, Health, and Climate. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:12447-12455. [PMID: 32845142 DOI: 10.1021/acs.est.0c01207] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
China is the world's largest producer and consumer of coal, but the country has recently set ambitious targets for cleaner energy sources. These include goals to capture and utilize methane from coal seams as a source of unconventional natural gas. We investigate the impacts of using coal methane to displace coal power plants and residential coal combustion across northern China. We compare the greenhouse gas emissions, air quality, and public health impacts of several scenarios for coal methane utilization. We find that China's existing goals would decrease the country's total carbon emissions by ∼2.3% (284 million tons CO2eq). Furthermore, these reductions are dominated by mitigated methane emissions and therefore confer a much larger climate benefit than would be expected from other forms of natural gas. Our results also indicate that the air quality and health impacts strongly depend on how the methane is utilized. Using the methane to displace coal-fired electricity would reduce annual mean ambient PM2.5 concentrations by up to >2.5 μg/m3 and prevent up to 9290 premature mortalities annually (95% confidence interval: 7862-9992). By contrast, utilizing coal methane in home heating yields smaller changes to ambient air quality (∼0.6 μg/m3), but improvements to indoor air quality could produce comparable reductions in premature mortality.
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Affiliation(s)
- Mingyang Zhang
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore 21218, Maryland, United States
| | - Sarah M Jordaan
- School of Advanced International Studies, Johns Hopkins University, Washington 21218-2625, District of Columbia, United States
| | - Wei Peng
- School of International Affairs and Department of Civil and Environmental Engineering, Pennsylvania State University, State College, Pennsylvania 16802-1800, United States
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Scot M Miller
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore 21218, Maryland, United States
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23
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Lin J, Lin B. Does integrated efficiency improvement of the heating industry matter for air quality in China? THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 717:137020. [PMID: 32065895 DOI: 10.1016/j.scitotenv.2020.137020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 12/28/2019] [Accepted: 01/29/2020] [Indexed: 06/10/2023]
Abstract
Residential and industrial heating demand is increasing sharply in China. Coal as the main fuel for the heating industry exerts heavy burdens on China's environment. This paper aims to figure out with constant technological progress, whether the improvement of the total-factor unified integrated efficiency of the heating industry helps improve the air quality in 29 provinces in China over 2003-2014. It is found that the total-factor unified integrated efficiency has no correlation with the air quality. Whether in the north or south, technological catch-up in the heating industry in China is weak. Technical progress of the heating industry in the north is limited in improving the integrated efficiency. Besides, the effects of technological progress and efficiency improvements may be greatly reduced without reasonable energy price.
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Affiliation(s)
- Jing Lin
- School of Economics and Management, Shanghai Institute of Technology, Shanghai 200235, PR China
| | - Boqiang Lin
- School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Xiamen, Fujian 361005, PR China.
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24
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Zhang Q, Zheng Y, Tong D, Shao M, Wang S, Zhang Y, Xu X, Wang J, He H, Liu W, Ding Y, Lei Y, Li J, Wang Z, Zhang X, Wang Y, Cheng J, Liu Y, Shi Q, Yan L, Geng G, Hong C, Li M, Liu F, Zheng B, Cao J, Ding A, Gao J, Fu Q, Huo J, Liu B, Liu Z, Yang F, He K, Hao J. Drivers of improved PM 2.5 air quality in China from 2013 to 2017. Proc Natl Acad Sci U S A 2019; 116:24463-24469. [PMID: 31740599 PMCID: PMC6900509 DOI: 10.1073/pnas.1907956116] [Citation(s) in RCA: 691] [Impact Index Per Article: 138.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population-weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3-70.0) to 42.0 µg/m3 (95% CI: 35.7-48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9-7.1), 4.4- (95% CI: 3.8-4.9), 2.8- (95% CI: 2.5-3.0), and 2.2- (95% CI: 2.0-2.5) µg/m3 declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35-0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China's recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.
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Affiliation(s)
- Qiang Zhang
- Department of Earth System Science, Tsinghua University, 100084 Beijing, China;
| | - Yixuan Zheng
- Department of Earth System Science, Tsinghua University, 100084 Beijing, China
| | - Dan Tong
- Department of Earth System Science, Tsinghua University, 100084 Beijing, China
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Min Shao
- College of Environmental Sciences and Engineering, Peking University, 100871 Beijing, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Yuanhang Zhang
- College of Environmental Sciences and Engineering, Peking University, 100871 Beijing, China
| | - Xiangde Xu
- Chinese Academy of Meteorological Sciences, China Meteorological Administration, 100081 Beijing, China
| | - Jinnan Wang
- Chinese Academy for Environmental Planning, 100012 Beijing, China
| | - Hong He
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 100085 Beijing, China
| | - Wenqing Liu
- Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, 230031 Hefei, China
| | - Yihui Ding
- National Climate Center, China Meteorological Administration, 100081 Beijing, China
| | - Yu Lei
- Chinese Academy for Environmental Planning, 100012 Beijing, China
| | - Junhua Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Zifa Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029 Beijing, China
| | - Xiaoye Zhang
- Chinese Academy of Meteorological Sciences, China Meteorological Administration, 100081 Beijing, China
| | - Yuesi Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029 Beijing, China
| | - Jing Cheng
- Department of Earth System Science, Tsinghua University, 100084 Beijing, China
| | - Yang Liu
- Department of Earth System Science, Tsinghua University, 100084 Beijing, China
| | - Qinren Shi
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Liu Yan
- Department of Earth System Science, Tsinghua University, 100084 Beijing, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Chaopeng Hong
- Department of Earth System Science, Tsinghua University, 100084 Beijing, China
| | - Meng Li
- Department of Earth System Science, Tsinghua University, 100084 Beijing, China
| | - Fei Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Bo Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Junji Cao
- Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, 710061 Xi'an, China
| | - Aijun Ding
- School of Atmospheric Sciences, Nanjing University, 210023 Nanjing, China
| | - Jian Gao
- Chinese Research Academy of Environmental Sciences, 100012 Beijing, China
| | - Qingyan Fu
- Shanghai Environmental Monitoring Center, 200030 Shanghai, China
| | - Juntao Huo
- Shanghai Environmental Monitoring Center, 200030 Shanghai, China
| | - Baoxian Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
- Beijing Key Laboratory of Airborne Particulate Matter Monitoring Technology, Beijing Municipal Environmental Monitoring Center, 100048 Beijing, China
| | - Zirui Liu
- Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029 Beijing, China
| | - Fumo Yang
- Department of Environmental Science and Engineering, College of Architecture and Environment, Sichuan University, 610065 Chengdu, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China;
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China;
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25
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Zhao H, Geng G, Zhang Q, Davis SJ, Li X, Liu Y, Peng L, Li M, Zheng B, Huo H, Zhang L, Henze DK, Mi Z, Liu Z, Guan D, He K. Inequality of household consumption and air pollution-related deaths in China. Nat Commun 2019; 10:4337. [PMID: 31554811 PMCID: PMC6761204 DOI: 10.1038/s41467-019-12254-x] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 08/29/2019] [Indexed: 01/06/2023] Open
Abstract
Substantial quantities of air pollution and related health impacts are ultimately attributable to household consumption. However, how consumption pattern affects air pollution impacts remains unclear. Here we show, of the 1.08 (0.74-1.42) million premature deaths due to anthropogenic PM2.5 exposure in China in 2012, 20% are related to household direct emissions through fuel use and 24% are related to household indirect emissions embodied in consumption of goods and services. Income is strongly associated with air pollution-related deaths for urban residents in which health impacts are dominated by indirect emissions. Despite a larger and wealthier urban population, the number of deaths related to rural consumption is higher than that related to urban consumption, largely due to direct emissions from solid fuel combustion in rural China. Our results provide quantitative insight to consumption-based accounting of air pollution and related deaths and may inform more effective and equitable clean air policies in China.
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Affiliation(s)
- Hongyan Zhao
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Guannan Geng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
| | - Steven J Davis
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
- Department of Earth System Science, University of California, Irvine, CA, 92697, USA
- Department of Civil and Environmental Engineering, University of California, Irvine, CA, 92697, USA
| | - Xin Li
- Department of Environmental Science and Engineering, Beijing Technology and Business University, Beijing, 100048, China
| | - Yang Liu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Liqun Peng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Meng Li
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Bo Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Hong Huo
- Institute of Energy, Environment and Economy, Tsinghua University, Beijing, 100084, China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Daven K Henze
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Zhifu Mi
- The Bartlett School of Construction and Project Management, University College London, London, WC1E 7HB, UK
| | - Zhu Liu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Dabo Guan
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Kebin He
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
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Meng W, Zhong Q, Chen Y, Shen H, Yun X, Smith KR, Li B, Liu J, Wang X, Ma J, Cheng H, Zeng EY, Guan D, Russell AG, Tao S. Energy and air pollution benefits of household fuel policies in northern China. Proc Natl Acad Sci U S A 2019; 116:16773-16780. [PMID: 31383761 PMCID: PMC6708357 DOI: 10.1073/pnas.1904182116] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
In addition to many recent actions taken to reduce emissions from energy production, industry, and transportation, a new campaign substituting residential solid fuels with electricity or natural gas has been launched in Beijing, Tianjin, and 26 other municipalities in northern China, aiming at solving severe ambient air pollution in the region. Quantitative analysis shows that the campaign can accelerate residential energy transition significantly, and if the planned target can be achieved, more than 60% of households are projected to remove solid fuels by 2021, compared with fewer than 20% without the campaign. Emissions of major air pollutants will be reduced substantially. With 60% substitution realized, emission of primary PM2.5 and contribution to ambient PM2.5 concentration in 2021 are projected to be 30% and 41% of those without the campaign. With 60% substitution, average indoor PM2.5 concentrations in living rooms in winter are projected to be reduced from 209 (190 to 230) μg/m3 to 125 (99 to 150) μg/m3 The population-weighted PM2.5 concentrations can be reduced from 140 μg/m3 in 2014 to 78 μg/m3 or 61 μg/m3 in 2021 given that 60% or 100% substitution can be accomplished. Although the original focus of the campaign was to address ambient air quality, exposure reduction comes more from improved indoor air quality because ∼90% of daily exposure of the rural population is attributable to indoor air pollution. Women benefit more than men.
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Affiliation(s)
- Wenjun Meng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, 100871 Beijing, China
| | - Qirui Zhong
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, 100871 Beijing, China
| | - Yilin Chen
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| | - Huizhong Shen
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| | - Xiao Yun
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, 100871 Beijing, China
| | - Kirk R Smith
- School of Public Health, University of California, Berkeley, CA 94720;
- Collaborative Clean Air Policy Centre, 110003 New Delhi, India
| | - Bengang Li
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, 100871 Beijing, China
| | - Junfeng Liu
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, 100871 Beijing, China
| | - Xilong Wang
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, 100871 Beijing, China
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, 100871 Beijing, China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, 100871 Beijing, China
| | - Eddy Y Zeng
- School of Environment, Guangzhou Key Laboratory of Environmental Exposure and Health, Jinan University, 510632 Guangzhou, China
- School of Environment, Guangdong Key Laboratory of Environmental Pollution and Health, Jinan University, 510632 Guangzhou, China
| | - Dabo Guan
- School of International Development, University of East Anglia, NR4 7TJ Norwich, United Kingdom
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| | - Shu Tao
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, 100871 Beijing, China;
- Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101 Beijing, China
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Shupler M, Hystad P, Gustafson P, Rangarajan S, Mushtaha M, Jayachtria KG, Mony PK, Mohan D, Kumar P, Lakshmi PVM, Sagar V, Gupta R, Mohan I, Nair S, Varma RP, Li W, Hu B, You K, Ncube T, Ncube B, Chifamba J, West N, Yeates K, Iqbal R, Khawaja R, Yusuf R, Khan A, Seron P, Lanas F, Lopez-Jaramillo P, Camacho PA, Puoane T, Yusuf S, Brauer M. Household, Community, Sub-National and Country-level Predictors of Primary Cooking Fuel Switching in Nine Countries from the PURE Study. ENVIRONMENTAL RESEARCH LETTERS : ERL [WEB SITE] 2019; 14:085006. [PMID: 33777170 PMCID: PMC7995525 DOI: 10.1088/1748-9326/ab2d46] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
INTRODUCTION Switching from polluting (e.g. wood, crop waste, coal) to clean cooking fuels (e.g. gas, electricity) can reduce household air pollution (HAP) exposures and climate-forcing emissions. While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. METHODS We examined longitudinal survey data from 24,172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology (PURE) study. We assessed household-level primary cooking fuel switching during a median of 10 years of follow up (~2005-2015). We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. RESULTS One-half of study households (12,369) reported changing their primary cooking fuels between baseline and follow up surveys. Of these, 61% (7,582) switched from polluting (wood, dung, agricultural waste, charcoal, coal, kerosene) to clean (gas, electricity) fuels, 26% (3,109) switched between different polluting fuels, 10% (1,164) switched from clean to polluting fuels and 3% (522) switched between different clean fuels. Among the 17,830 households using polluting cooking fuels at baseline, household-level factors (e.g. larger household size, higher wealth, higher education level) were most strongly associated with switching from polluting to clean fuels in India; in all other countries, community-level factors (e.g. larger population density in 2010, larger increase in population density between 2005-2015) were the strongest predictors of polluting-to-clean fuel switching. CONCLUSIONS The importance of community and sub-national factors relative to household characteristics in determining polluting-to-clean fuel switching varied dramatically across the nine countries examined. This highlights the potential importance of national and other contextual factors in shaping large-scale clean cooking transitions among rural communities in low- and middle-income countries.
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Affiliation(s)
- Matthew Shupler
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Perry Hystad
- College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon, United States
| | - Paul Gustafson
- Department of Statistics, University of British Columbia, Vancouver, British Columbia
| | - Sumathy Rangarajan
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Maha Mushtaha
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - KG Jayachtria
- St. John’s Medical College & Research Institute, Bangalore, India
| | - Prem K. Mony
- St. John’s Medical College & Research Institute, Bangalore, India
| | - Deepa Mohan
- Madras Diabetes Research Foundation, Chennai, India
| | | | - PVM Lakshmi
- School of Public Health, PGIMER, Chandigarh, India
| | - Vivek Sagar
- School of Public Health, PGIMER, Chandigarh, India
- Department of Community Medicine, PGIMER, Chandigarh, India
| | - Rajeev Gupta
- Eternal Heart Care Centre and Research Institute, Jaipur, India
| | - Indu Mohan
- Eternal Heart Care Centre and Research Institute, Jaipur, India
| | - Sanjeev Nair
- Health Action By People, Thiruvananthapuram and Medical College, Trivandrum, India
| | - Ravi Prasad Varma
- Health Action By People, Thiruvananthapuram and Medical College, Trivandrum, India
- Achutha Menon Centre for Health Science Studies, Trivandrum India
| | - Wei Li
- Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Beijing, China
| | - Bo Hu
- Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Beijing, China
| | - Kai You
- Shunyi District Center for Disease Prevention and Control, Beijing, China
| | - Tatenda Ncube
- Department of Physiology, University of Zimbabwe, Harare, Zimbabwe
| | - Brian Ncube
- Department of Physiology, University of Zimbabwe, Harare, Zimbabwe
| | - Jephat Chifamba
- Department of Physiology, University of Zimbabwe, Harare, Zimbabwe
| | - Nicola West
- Pamoja Tunaweza Research Centre, Moshi, Tanzania
| | - Karen Yeates
- Pamoja Tunaweza Research Centre, Moshi, Tanzania
- Department of Medicine, Queen’s University, Kingston, Ontario, Canada
| | - Romaina Iqbal
- Department of Community Health Science, Aga Khan University Hospital, Karachi, Pakistan
| | - Rehman Khawaja
- Department of Community Health Science, Aga Khan University Hospital, Karachi, Pakistan
| | - Rita Yusuf
- School of Life Sciences, Independent University, Dhaka, Bangladesh
| | - Afreen Khan
- School of Life Sciences, Independent University, Dhaka, Bangladesh
| | | | | | - Patricio Lopez-Jaramillo
- Research Department, FOSCAL and Medical School, Universidad de Santander (UDES), Bucaramanga, Colombia
| | - Paul A. Camacho
- Research Department, FOSCAL and Medical School, Universidad Autonoma de Bucaramanga (UNAB), Colombia
| | - Thandi Puoane
- School of Public Health, University of the Western Cape, Bellville, South Africa
| | - Salim Yusuf
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Michael Brauer
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
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Lin P, He W, Nie L, Schauer JJ, Wang Y, Yang S, Zhang Y. Comparison of PM 2.5 emission rates and source profiles for traditional Chinese cooking styles. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2019; 26:21239-21252. [PMID: 31115821 DOI: 10.1007/s11356-019-05193-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/15/2019] [Indexed: 06/09/2023]
Abstract
The number of restaurants is increasing rapidly in recent years, especially in urban cities with dense populations. Particulate matter emitted from commercial and residential cooking is a significant contributor to both indoor and outdoor aerosols. The PM2.5 emission rates and source profiles are impacted by many factors (cooking method, food type, oil type, fuel type, additives, cooking styles, cooking temperature, source surface area, pan, and ventilation) discussed in previous studies. To determine which cooking activities are most influential on PM2.5 emissions and work towards cleaner cooking, an experiment design based on multi-factor and level orthogonal tests was conducted in a laboratory that is specifically designed to resemble a professional restaurant kitchen. In this cooking test, four main parameters (the proportion of meat in ingredients, flavor, cooking technique, oil type) were chosen and five levels for each parameter were selected to build up 25 experimental dishes. Concentrations of PM2.5 emission rates, organic carbon/elemental carbon (OC/EC), water-soluble ions, elements, and main organic species (PAHs, n-alkanes, alkanoic acids, fatty acids, dicarboxylic acids, polysaccharides, and sterols) were investigated across 25 cooking tests. The statistical significance of the data was analyzed by analysis of variance (ANOVA) with ranges calculated to determine the influence orders of the 4 parameters. The PM2.5 emission rates of 25 experimental dishes ranged from 0.1 to 9.2 g/kg of ingredients. OC, EC, water-soluble ions (WSI), and elements accounted for 10.49-94.85%, 0-1.74%, 10.09-40.03%, and 0.04-3.93% of the total PM2.5, respectively. Fatty acids, dicarboxylic acids, n-alkanes, alkanoic acids, and sterols were the most abundant organic species and accounted for 2.32-93.04%, 0.84-60.36%, 0-45.05%, and 0-25.42% of total PM2.5, respectively. There was no significant difference between the 4 parameters on PM2.5 emission rates, while a significant difference was found in WSI, elements, n-alkanes, and dicarboxylic acids according to ANOVA. Cooking technique was found to be the most influential factor for PM2.5 source profiles, followed by the proportion of meat in ingredients and oil type which resulted in significant difference of 183.19, 185.14, and 115.08 g/kg of total PM2.5 for dicarboxylic acids, n-alkanes, and WSI, respectively. Strong correlations were found among PM2.5 and OC (r = 0.854), OC and sterols (r = 0.919), PAHs and n-alkanes (r = 0.850), alkanoic acids and fatty acids (r = 0.877), and many other species of PM2.5.
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Affiliation(s)
- Pengchuan Lin
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wanqing He
- Beijing Key Laboratory of Urban Atmospheric Volatile Organic Compounds Pollution Control and Application, Beijing Municipal Research Institute of Environmental Protection, Beijing, 100037, China
| | - Lei Nie
- Beijing Key Laboratory of Urban Atmospheric Volatile Organic Compounds Pollution Control and Application, Beijing Municipal Research Institute of Environmental Protection, Beijing, 100037, China
| | - James J Schauer
- Environmental Chemistry and Technology Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI, 53718, USA
| | - Yuqin Wang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
- College of Environmental Science and Engineering, Shaanxi University of Science and Technology, Xi'an, 710021, China
| | - Shujian Yang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuanxun Zhang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
- CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen, 361021, China.
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29
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Aunan K, Ma Q, Lund MT, Wang S. Population-weighted exposure to PM 2.5 pollution in China: An integrated approach. ENVIRONMENT INTERNATIONAL 2018; 120:111-120. [PMID: 30077943 DOI: 10.1016/j.envint.2018.07.042] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 07/19/2018] [Accepted: 07/27/2018] [Indexed: 05/22/2023]
Abstract
Fine particulate matter air pollution (PM2.5) is a major risk factor for premature death globally. Studies of the PM2.5 health burden usually treat exposure to ambient air pollution (AAP) and household air pollution from solid fuels (HAP) as separate risk factors. AAP and HAP can, however, be closely interrelated. Taking as the starting point that the total exposure to PM2.5 is what matters for health, and recognizing the curvilinear form of exposure-response functions for important health effects, we develop a method for estimating the total annual mean population-weighted personal exposure, denoted integrated population-weighted exposure (IPWE). To establish the IPWE in China, we used recent emission inventories, Chemical Transport Models, China Census data on population and residential fuel use, and estimates of the PM2.5 exposure among solid fuel users. We found an IPWE of 151 [123-179] μg/m3, of which 62-74% was attributable to residential solid fuels through HAP exposure and the residential sector emissions' contribution to AAP. We found large disparities in the PM2.5 exposure burden, with an estimated IPWE in rural populations nearly twice the level in urban populations. Using the IPWE metric, we estimated that 1.15 [1.09-1.19] million premature deaths were attributable to PM2.5 exposure annually in the period 2010-2013. Using the same data set, but calculating premature deaths from AAP and HAP in isolation, the estimated number was nearly 50% higher. The IPWE metric enables integration across AAP and HAP in policy analyses and could mitigate the concern of a potential double counting of the health burden that may arise from treating AAP and HAP as separate health risk factors.
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Affiliation(s)
- Kristin Aunan
- Center for International Climate Research (CICERO), P.O. Box 1129 Blindern, N-0318 Oslo, Norway.
| | - Qiao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Marianne T Lund
- Center for International Climate Research (CICERO), P.O. Box 1129 Blindern, N-0318 Oslo, Norway
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
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30
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Chen Y, Shen H, Smith KR, Guan D, Chen Y, Shen G, Liu J, Cheng H, Zeng EY, Tao S. Estimating household air pollution exposures and health impacts from space heating in rural China. ENVIRONMENT INTERNATIONAL 2018; 119:117-124. [PMID: 29957353 DOI: 10.1016/j.envint.2018.04.054] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 04/16/2018] [Accepted: 04/28/2018] [Indexed: 05/24/2023]
Abstract
Exposure to and the related burden of diseases caused by pollution from solid fuel cooking, known as household air pollution (HAP), has been incorporated in the assessment of the Global Burden of Diseases (GBD) project. In contrast, HAP from space heating using solid fuels, prevalent in countries at middle or high altitudes, is less studied and missing from the GBD assessment. China is an ideal example to estimate the bias of exposure and burden of diseases assessment when space heating is neglected, considering its remarkably changing demands for heating from the north to the south and a large solid-fuel-dependent rural population. In this study, based on a meta-analysis of 27 field measurement studies in rural China, we derive the indoor PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 μm) concentration for both the heating and non-heating seasons. Combining this dataset with time-activity patterns and percentage of households using solid fuels, we assess the population-weighted annual mean exposure to PM2.5 (PWE) and the health impacts associated with HAP in mainland rural China by county for the year 2010. We find that ignoring heating impacts leads to an underestimation in PWE estimates by 38 μg/m3 for the nationwide rural population (16 to 40 as interquartile range) with substantial negative bias in northern provinces. Correspondingly, premature deaths and disability-adjusted life years will be underestimated by approximately 30 × 103 and 60 × 104 in 2010, respectively. Our study poses the need for incorporating heating effects into HAP risk assessments in China as well as globally.
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Affiliation(s)
- Yilin Chen
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Huizhong Shen
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Kirk R Smith
- School of Public Health, University of California, Berkeley, CA 94720, United States
| | - Dabo Guan
- Water Security Research Centre, School of International Development, University of East Anglia, Norwich NR4 7TJ, UK
| | - Yuanchen Chen
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Guofeng Shen
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Junfeng Liu
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Hefa Cheng
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Eddy Y Zeng
- School of Environment, Jinan University, Guangzhou, Guangdong 510632, China
| | - Shu Tao
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
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31
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Carter E, Shan M, Zhong Y, Ding W, Zhang Y, Baumgartner J, Yang X. Development of Renewable, Densified Biomass for Household Energy in China. ENERGY FOR SUSTAINABLE DEVELOPMENT : THE JOURNAL OF THE INTERNATIONAL ENERGY INITIATIVE 2018; 46:42-52. [PMID: 32863650 PMCID: PMC7453936 DOI: 10.1016/j.esd.2018.06.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Recent national strategic plans in China have set renewable energy targets for rural household energy programs, including those that advance the production of densified biomass fuels (e.g. pellets, briquettes) for use in household cooking and heating stoves. There is presently very little information on potential barriers to the successful development of densified biomass for household cooking and heating in China, but such knowledge may be informative in settings that aim to replace unprocessed coal and other polluting fuels with renewable, cleaner-burning energy sources. We designed a case study to coordinate data-gathering efforts at rural field sites in southwestern Sichuan province and northeastern Jilin and Heilongjiang provinces, where production of densified biomass fuels is under development for household end-users. We conducted interviews with factory personnel, local administrative leaders, and sector experts involved in the production and distribution of densified fuel, including pellets and briquettes, for household use. Results from our qualitative textual data analysis yielded several recommendations for improving development of densified biomass fuels for household end-use. These included reducing heterogeneity of feedstocks, increasing financial support for operational costs (e.g. collection, transport, and storage of raw materials; storage and distribution of final products), improving household perceptions of and subsequent demand for densified biomass fuels, and increasing enforcement of national and provincial policies banning the use of coal and open-field biomass burning. Collection and storage of raw materials and the final densified fuel product were consistently noted as critical challenges to scaling up production at all three sites. Finally, the perspectives of factory managers and local village administrators that we present also indicated that production of densified biomass fuels would most likely be more successful and reproducible in places where national-level policies are viewed as obligatory.
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Affiliation(s)
- Ellison Carter
- Colorado State University, Civil and Environmental Engineering, Fort Collins, CO, USA
| | - Ming Shan
- Tsinghua University, Building Science, Beijing, China
| | - Yuan Zhong
- Shandong University, Civil and Environmental Engineering, Shandong, China
| | - Weimeng Ding
- McGill University, Epidemiology, Occupational Health, and Biostatistics, Montreal, Canada
- McGill University, Institute of Health and Social Policy, Montreal, Canada
| | - Yichen Zhang
- Colorado State University, Civil and Environmental Engineering, Fort Collins, CO, USA
| | - Jill Baumgartner
- McGill University, Epidemiology, Occupational Health, and Biostatistics, Montreal, Canada
- McGill University, Institute of Health and Social Policy, Montreal, Canada
| | - Xudong Yang
- Tsinghua University, Building Science, Beijing, China
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Conibear L, Butt EW, Knote C, Arnold SR, Spracklen DV. Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India. Nat Commun 2018; 9:617. [PMID: 29434294 PMCID: PMC5809377 DOI: 10.1038/s41467-018-02986-7] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 01/10/2018] [Indexed: 01/08/2023] Open
Abstract
Exposure to ambient fine particulate matter (PM2.5) is a leading contributor to diseases in India. Previous studies analysing emission source attributions were restricted by coarse model resolution and limited PM2.5 observations. We use a regional model informed by new observations to make the first high-resolution study of the sector-specific disease burden from ambient PM2.5 exposure in India. Observed annual mean PM2.5 concentrations exceed 100 μg m−3 and are well simulated by the model. We calculate that the emissions from residential energy use dominate (52%) population-weighted annual mean PM2.5 concentrations, and are attributed to 511,000 (95UI: 340,000–697,000) premature mortalities annually. However, removing residential energy use emissions would avert only 256,000 (95UI: 162,000–340,000), due to the non-linear exposure–response relationship causing health effects to saturate at high PM2.5 concentrations. Consequently, large reductions in emissions will be required to reduce the health burden from ambient PM2.5 exposure in India. Exposure to ambient particulate matter is a key contributor to disease in India and source attribution is vital for pollution control. Here the authors use a high-resolution regional model to show residential emissions dominate particulate matter concentrations and associated premature mortality.
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Affiliation(s)
- Luke Conibear
- Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training (CDT) in Bioenergy, University of Leeds, Leeds, LS2 9JT, UK. .,Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK.
| | - Edward W Butt
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
| | - Christoph Knote
- Meteorological Institute, Ludwig-Maximilians-University Munich, Theresienstr. 37, 80333, Munich, Germany
| | - Stephen R Arnold
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
| | - Dominick V Spracklen
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
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33
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Peng W, Yang J, Wagner F, Mauzerall DL. Substantial air quality and climate co-benefits achievable now with sectoral mitigation strategies in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 598:1076-1084. [PMID: 28482455 DOI: 10.1016/j.scitotenv.2017.03.287] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Revised: 03/16/2017] [Accepted: 03/31/2017] [Indexed: 04/14/2023]
Abstract
China is the world's top carbon emitter and suffers from severe air pollution. We examine near-term air quality and CO2 co-benefits of various current sector-based policies in China. Using a 2015 base case, we evaluate the potential benefits of four sectoral mitigation strategies. All scenarios include a 20% increase in conventional air pollution controls as well as the following sector-specific fuel switching or technology upgrade strategies. Power sector (POW): 80% replacement of small coal power plants with larger more efficient ones; Industry sector (IND): 10% improvement in energy efficiency; Transport sector (TRA): replacement of high emitters with average vehicle fleet emissions; and Residential sector (RES): replacement of 20% of coal-based stoves with stoves using liquefied petroleum gas (LPG). Conducting an integrated assessment using the regional air pollution model WRF-Chem, we find that the IND scenario reduces national air-pollution-related deaths the most of the four scenarios examined (27,000, 24,000, 13,000 and 23,000 deaths reduced annually in IND, POW, TRA and RES, respectively). In addition, the IND scenario reduces CO2 emissions more than 8times as much as any other scenario (440, 53, 0 and 52Mt CO2 reduced in IND, POW, TRA and RES, respectively). We also examine the benefits of an industrial efficiency improvement of just 5%. We find the resulting air quality and health benefits are still among the largest of the sectoral scenarios, while the carbon mitigation benefits remain more than 3 times larger than any other scenario. Our analysis hence highlights the importance of even modest industrial energy efficiency improvements and air pollution control technology upgrades for air quality, health and climate benefits in China.
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Affiliation(s)
- Wei Peng
- Science, Technology and Environmental Policy Program, Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, United States
| | - Junnan Yang
- Science, Technology and Environmental Policy Program, Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, United States
| | - Fabian Wagner
- Science, Technology and Environmental Policy Program, Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, United States; Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ, United States
| | - Denise L Mauzerall
- Science, Technology and Environmental Policy Program, Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, United States; Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States.
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34
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Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia. Sci Rep 2016; 6:37074. [PMID: 27848989 PMCID: PMC5111049 DOI: 10.1038/srep37074] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 10/24/2016] [Indexed: 11/08/2022] Open
Abstract
Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153-17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality.
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