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Meng W, Kiesewetter G, Zhang S, Schöpp W, Rafaj P, Klimont Z, Tao S. Costs and Benefits of Household Fuel Policies and Alternative Strategies in the Jing-Jin-Ji Region. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21662-21672. [PMID: 38079372 DOI: 10.1021/acs.est.3c01622] [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: 12/27/2023]
Abstract
Air pollution is still one of the most severe problems in northern China, especially in the Jing-Jin-Ji region around Beijing. In recent years, China has implemented many stringent policies to address the air quality issue, including promoting energy transition toward cleaner fuels in residential sectors. But until 2020, even in the Jing-Jin-Ji region, nearly half of the rural households still use solid fuels for heating. For residents who are not covered by the clean heating campaign, we analyze five potential mitigation strategies and evaluate their environmental effects as well as the associated health benefits and costs. We estimate that substitution with electricity or gas would reduce air pollution and premature mortality more strongly, while the relatively low investment costs of implementing clean coal or biomass pellet lead to a larger benefit-cost ratio, indicating higher cost efficiency. Hence, clean coal or biomass pellet could be transitional substitution options for the less developed or remote areas which cannot afford a total transition toward electricity or natural gas in the short term.
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Affiliation(s)
- Wenjun Meng
- Institute of Carbon Neutrality, 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
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
| | - Gregor Kiesewetter
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
| | - Shaohui Zhang
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
- School of Economics and Management, Beihang University, Beijing 100191, P. R. China
| | - Wolfgang Schöpp
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
| | - Peter Rafaj
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
| | - Zbigniew Klimont
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
| | - Shu Tao
- Institute of Carbon Neutrality, 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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2
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Shao Y, Liu R, Yang J, Liu M, Fang W, Hu L, Bi J, Ma Z. Economic Growth Facilitates Household Fuel Use Transition to Reduce PM 2.5-Related Deaths in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:12663-12673. [PMID: 37558636 DOI: 10.1021/acs.est.3c03276] [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: 08/11/2023]
Abstract
Exposure to ambient and indoor particle matter (PM2.5) leads to millions of premature deaths in China. In recent years, indoor air pollution and premature deaths associated with polluting fuel cooking demonstrate an abrupt decline. However, the driving forces behind the mortality change are still unclear due to the uncertainty in household fuel use prediction. Here, we propose an integrated approach to estimate the fuel use fractions and PM2.5-related deaths from outdoor and indoor sources during 2000-2020 across China. Our model estimated 1.67 and 1.21 million premature deaths attributable to PM2.5 exposure in 2000 and 2020, respectively. We find that the residential energy transition is associated with a substantial reduction in premature deaths from indoor sources, with 100,000 (95% CI: 76,000-122,000) for urban and 265,000 (228,000-300,000) for rural populations during 2000-2020. Economic growth is the dominant driver of fuel use transition and avoids 21% related deaths (357,000, 315,000-402,000) from polluting fuel cooking since 2000, which offsets the adverse impact of ambient emissions contributed by economic growth. Our findings give an insight into the coupled impact of socioeconomic factors in reshaping health burden in exposure pathways.
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Affiliation(s)
- Yanchuan Shao
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Riyang Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Jianxun Yang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Miaomiao Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Wen Fang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Litiao Hu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Jun Bi
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Zongwei Ma
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China
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3
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Ai J, Qin W, Chen J, Sun Y, Yu Q, Xin K, Huang H, Zhang L, Ahmad M, Liu X. Pollution characteristics and light-driven evolution of environmentally persistent free radicals in PM 2.5 in two typical northern cities of China. JOURNAL OF HAZARDOUS MATERIALS 2023; 454:131466. [PMID: 37099909 DOI: 10.1016/j.jhazmat.2023.131466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/06/2023] [Accepted: 04/20/2023] [Indexed: 05/19/2023]
Abstract
Environmentally persistent free radicals (EPFRs) in PM2.5 can pose significant health risks by generating reactive oxygen species (ROS). In this study, Beijing and Yuncheng were chosen as two representative northern cities of China that mainly relied on natural gas and coal respectively as the energy source for domestic heating in winter. The pollution characteristics and exposure risks of EPFRs in PM2.5 around the heating season of 2020 were investigated and compared between the two cities. Through laboratory simulation experiments, the decay kinetics and secondary formation of EPFRs in PM2.5 collected in both cities were also studied. EPFRs in PM2.5 collected in Yuncheng in the heating period showed longer lifetime and lower reactivity, suggesting that EPFRs originated from coal combustion were more stable in the atmosphere. However, the generation rate of hydroxyl radical (·OH) by the newly formed EPFRs in PM2.5 in Beijing under ambient conditions was 4.4 times of that in Yuncheng, suggesting higher oxidative potential of EPFRs from the atmospheric secondary processes. Accordingly, the control strategies of EPFRs and their health risks were raised for the two cities, which would also have direct implication for the control of EPFRs in other areas of similar atmospheric emission and reaction patterns.
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Affiliation(s)
- Jing Ai
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center of Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Weihua Qin
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center of Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Jing Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center of Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
| | - Yuewei Sun
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center of Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Qing Yu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center of Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Ke Xin
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center of Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Huiying Huang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center of Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Lingyun Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center of Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Mushtaq Ahmad
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Department of Environmental Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
| | - Xingang Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center of Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, 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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Liu S, Liu H, Mauzerall DL. Improving Building Envelope Efficiency Lowers Costs and Emissions from Rural Residential Heating in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:595-605. [PMID: 36490291 DOI: 10.1021/acs.est.2c06863] [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: 06/17/2023]
Abstract
In 2017, the Chinese government launched a clean heating campaign that replaced millions of rural coal stoves with various clean heaters. The clean heating program contributed to remarkable improvements in air quality. However, the benefits of reducing heating demand by improving building envelope efficiency were not sufficiently considered. This study provides a needed quantitative assessment of potential energy-savings, costs, greenhouse gas emission reductions, and adoption strategies for improving building envelope efficiency in Chinese rural residential buildings. We find that different strategies must be employed in existing and new buildings to achieve desired outcomes. For existing buildings, to encourage easy and beneficial building retrofits (e.g., air sealing, efficient windows), current fuel subsidies should be replaced with retrofit subsidies. Building retrofits can reduce the size and hence capital costs of new clean heaters. They can also reduce operating costs, hence reducing the likelihood of backsliding to coal. For new construction, whole-home insulation and heat pumps would best avoid carbon lock-in. These efficient technologies have high upfront costs but decrease heating costs and significantly reduce carbon emissions relative to current policies. Hence, subsidies and policies that encourage improvements in building envelopes as well as the uptake of clean and efficient heaters are critical.
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Affiliation(s)
- Shangwei Liu
- Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey 08544, United States
| | - Hongxun Liu
- School of Economics and Finance, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Denise L Mauzerall
- Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey 08544, United States
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
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6
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Li Z, Liu J, Zhai Z, Liu C, Ren Z, Yue Z, Yang D, Hu Y, Zheng H, Kong S. Heterogeneous changes of chemical compositions, sources and health risks of PM 2.5 with the "Clean Heating" policy at urban/suburban/industrial sites. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 854:158871. [PMID: 36126707 DOI: 10.1016/j.scitotenv.2022.158871] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/05/2022] [Accepted: 09/15/2022] [Indexed: 06/15/2023]
Abstract
China has enacted the "Clean Heating" (CH) policy in north China. The domain-specific impacts on PM2.5 constituents and sources in small cities are still lacking, which obstruct the further policy optimization. Here, we performed an intensive observation covering the heating period (HP) and pre-heating period (PHP) in winter of 2017 at urban (UR), industrial (IS), and suburban (SUR) sites in one of the "2 + 26" cities. The mean PM2.5 concentrations at UR and IS decreased by 15.2 % and 4.6 %, while increased by 9.8 % at SUR in the HP compared with the PHP, indicating the heterogeneous responses. The lowest contribution percentages of coal combustion (14.6 %) and industrial emissions (17.1 %) to PM2.5 at UR in the HP implied the CH policy played more effective role. The most increase in NO3-/SO42- ratio by 26.8 % and the highest NO3- concentration at UR in the HP were linked mainly with the thermal-NOx emitted from natural gas (NG) burning in view of NOx emission reductions from other sources. The highest concentrations of OC, SO42-, K+, and Cl-, and contribution percentages of biomass burning (20.0 %) and coal combustion (24.8 %) to PM2.5 at SUR in the HP evidenced the enhanced usage of biomass/coal. Coal banning in the HP at IS and UR led to the obvious decreases in OC, SO42-, As, and Sb. Secondary nitrate became the largest PM2.5 source at IS and UR in the HP. Coal banning, emission control on large-size enterprises and ignored control on small-size enterprises efficiently modified the concentrations and health risks of heavy metals. The lowest carcinogenic risks moved from SUR in the PHP to UR in the HP. The policies on de-NOx of NG-burning related enterprises, reduction of biomass/coal usage in suburban area, and strict regulation of small-size enterprises were urgently need to further improve the air quality.
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Affiliation(s)
- Zhiyong Li
- Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China; MOE Key Laboratory of Resources and Environmental Systems Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China.
| | - Jixiang Liu
- Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China
| | - Zhen Zhai
- Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China
| | - Chen Liu
- Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China
| | - Zhuangzhuang Ren
- Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China
| | - Ziyuan Yue
- Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China
| | - Dingyuan Yang
- Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China
| | - Yao Hu
- Department of Atmospheric Sciences, School of Environmental Sciences, China University of Geosciences, Wuhan 430074,China
| | - Huang Zheng
- Department of Atmospheric Sciences, School of Environmental Sciences, China University of Geosciences, Wuhan 430074,China
| | - Shaofei Kong
- Department of Atmospheric Sciences, School of Environmental Sciences, China University of Geosciences, Wuhan 430074,China.
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7
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Shen G, Xiong R, Tian Y, Luo Z, Jiangtulu B, Meng W, Du W, Meng J, Chen Y, Xue B, Wang B, Duan Y, Duo J, Fan F, Huang L, Ju T, Liu F, Li S, Liu X, Li Y, Wang M, Nan Y, Pan B, Pan Y, Wang L, Zeng E, Zhan C, Chen Y, Shen H, Cheng H, Tao S. Substantial transition to clean household energy mix in rural China. Natl Sci Rev 2022; 9:nwac050. [PMID: 35854783 PMCID: PMC9283105 DOI: 10.1093/nsr/nwac050] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 03/07/2022] [Accepted: 03/10/2022] [Indexed: 11/14/2022] Open
Abstract
The household energy mix has significant impacts on human health and climate, as it contributes greatly to many health- and climate-relevant air pollutants. Compared to the well-established urban energy statistical system, the rural household energy statistical system is incomplete and is often associated with high biases. Via a nationwide investigation, this study revealed high contributions to energy supply from coal and biomass fuels in the rural household energy sector, while electricity comprised ∼20%. Stacking (the use of multiple sources of energy) is significant, and the average number of energy types was 2.8 per household. Compared to 2012, the consumption of biomass and coals in 2017 decreased by 45% and 12%, respectively, while the gas consumption amount increased by 204%. Increased gas and decreased coal consumptions were mainly in cooking, while decreased biomass was in both cooking (41%) and heating (59%). The time-sharing fraction of electricity and gases (E&G) for daily cooking grew, reaching 69% in 2017, but for space heating, traditional solid fuels were still dominant, with the national average shared fraction of E&G being only 20%. The non-uniform spatial distribution and the non-linear increase in the fraction of E&G indicated challenges to achieving universal access to modern cooking energy by 2030, particularly in less-developed rural and mountainous areas. In some non-typical heating zones, the increased share of E&G for heating was significant and largely driven by income growth, but in typical heating zones, the time-sharing fraction was <5% and was not significantly increased, except in areas with policy intervention. The intervention policy not only led to dramatic increases in the clean energy fraction for heating but also accelerated the clean cooking transition. Higher income, higher education, younger age, less energy/stove stacking and smaller family size positively impacted the clean energy transition.
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Affiliation(s)
- Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Rui Xiong
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Yanlin Tian
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Zhihan Luo
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Bahabaike Jiangtulu
- Institute of Reproductive and Child Health, Peking University, Beijing 100191, China
| | - Wenjun Meng
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Wei Du
- Laboratory of Geographic Information Science, School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Jing Meng
- The Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, UK
| | - Yuanchen Chen
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, China
| | - Bing Xue
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Bin Wang
- Institute of Reproductive and Child Health, Peking University, Beijing 100191, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Yonghong Duan
- College of Resources and Environment, Shanxi Agricultural University, Jinzhong 030801, China
| | - Jia Duo
- Xinjiang Key Laboratory of Environmental Pollution and Bioremediation, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
- National Engineering Technology Research Center for Desert-Oasis Ecological Construction, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
- Universityof Chinese Academy of Sciences, Beijing 100049, China
| | - Fenggui Fan
- School of Geography and Tourism, Anhui University, Wuhu 241000, China
| | - Lei Huang
- School of Environment, Nanjing University, Nanjing 210033, China
| | - Tianzhen Ju
- College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
| | - Fenggui Liu
- College of Geographical Science, Qinghai Normal University, Xining 810008, China
- Academy of Plateau Science and Sustainability, Xining 810008, China
| | - Shunxin Li
- College of Chemistry, Chemical Engineering and Environment, Minnan Normal University, Zhangzhou 363000, China
| | - Xianli Liu
- School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China
| | - Yungui Li
- Department of Environmental Engineering, Southwest University of Science and Technology, Mianyang 621010, China
| | - Mu Wang
- College of Food Science, Tibet Agricultural and Animal Husbandry University, Linzhi 860000, China
| | - Ying Nan
- College of Geography and Ocean Sciences, Yanbian University, Yanji 133002, China
| | - Bo Pan
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Yanfang Pan
- College of Geography and Environmental Science, Henan University, Kaifeng 475001, China
| | - Lizhi Wang
- College of Ecology and Environment, Hainan University, Haikou 570228, China
| | - Eddy Zeng
- School of Environment, Jinan University, Guangzhou 510632, China
| | - Chao Zhan
- Institute of Coastal Research, Ludong University, Yantai 264025, China
| | - Yilin Chen
- College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China
| | - Huizhong Shen
- College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Shu Tao
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China
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8
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Conibear L, Reddington CL, Silver BJ, Chen Y, Arnold SR, Spracklen DV. Emission Sector Impacts on Air Quality and Public Health in China From 2010 to 2020. GEOHEALTH 2022; 6:e2021GH000567. [PMID: 35765413 PMCID: PMC9207900 DOI: 10.1029/2021gh000567] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/22/2022] [Accepted: 03/31/2022] [Indexed: 06/15/2023]
Abstract
Anthropogenic emissions and ambient fine particulate matter (PM2.5) concentrations have declined in recent years across China. However, PM2.5 exposure remains high, ozone (O3) exposure is increasing, and the public health impacts are substantial. We used emulators to explore how emission changes (averaged per sector over all species) have contributed to changes in air quality and public health in China over 2010-2020. We show that PM2.5 exposure peaked in 2012 at 52.8 μg m-3, with contributions of 31% from industry and 22% from residential emissions. In 2020, PM2.5 exposure declined by 36% to 33.5 μg m-3, where the contributions from industry and residential sources reduced to 15% and 17%, respectively. The PM2.5 disease burden decreased by only 9% over 2012 where the contributions from industry and residential sources reduced to 15% and 17%, respectively 2020, partly due to an aging population with greater susceptibility to air pollution. Most of the reduction in PM2.5 exposure and associated public health benefits occurred due to reductions in industrial (58%) and residential (29%) emissions. Reducing national PM2.5 exposure below the World Health Organization Interim Target 2 (25 μg m-3) would require a further 80% reduction in residential and industrial emissions, highlighting the challenges that remain to improve air quality in China.
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Affiliation(s)
- Luke Conibear
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Carly L. Reddington
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Ben J. Silver
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Ying Chen
- College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK
| | - Stephen R. Arnold
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Dominick V. Spracklen
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
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9
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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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Tao S, Shen G, Cheng H, Ma J. Toward Clean Residential Energy: Challenges and Priorities in Research. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:13602-13613. [PMID: 34597039 DOI: 10.1021/acs.est.1c02283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Solid fuels used for cooking, heating, and lighting are major emission sources of many air pollutants, specifically PM2.5 and black carbon, resulting in adverse environmental and health impacts. At the same time, the transition from using residential solid fuels toward using cleaner energy sources can result in significant health benefits. Here, we briefly review recent research progress on the emissions of air pollutants from the residential sector and the impacts of emissions on ambient and indoor air quality, population exposure, and health consequences. The major challenges and future research priorities are identified and discussed.
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Affiliation(s)
- Shu Tao
- College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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11
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Xu H, Ren Y, Zhang W, Meng W, Yun X, Yu X, Li J, Zhang Y, Shen G, Ma J, Li B, Cheng H, Wang X, Wan Y, Tao S. Updated Global Black Carbon Emissions from 1960 to 2017: Improvements, Trends, and Drivers. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:7869-7879. [PMID: 34096723 DOI: 10.1021/acs.est.1c03117] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Accurate estimation of black carbon (BC) emissions is essential for assessing the health and climate impact of this pollutant. Past emission inventories were associated with high uncertainty due to data limitations, and recent information has provided a unique updating opportunity. Moreover, understanding the drivers that cause temporal emission changes is of research value. Here, we update the global BC emission estimates using new data on the activities and emission factors (EFs). The new inventory covers 73 detailed sources at 0.1° × 0.1° spatial resolution and monthly temporal resolution from 1960 to 2017. The estimated annual emissions were 32% higher than the average of several previous inventories, which was primarily due to field-measured EFs for residential stoves and differentiated EFs for motor vehicles. In addition, the updated emissions show an inverse U-shaped temporal trend, which was mainly driven by the interaction between the positive effects of population growth, per capita energy consumption, and vehicle fleet and the negative effects of residential energy switching, stove upgrading, phasing out of beehive coke ovens, and reduced EFs for vehicles and industrial processes. Urbanization caused a significant increase in urban emissions accompanied by a more significant decline in rural emissions.
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Affiliation(s)
- Haoran Xu
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China
| | - Yu'ang Ren
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, 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, China
| | - Wenjun Meng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China
| | - Xiao Yun
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China
| | - Xinyuan Yu
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, 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, 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, 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
| | - 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
| | - Bengang Li
- 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
| | - Xilong Wang
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, 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, 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
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Conibear L, Reddington CL, Silver BJ, Chen Y, Knote C, Arnold SR, Spracklen DV. Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China. GEOHEALTH 2021; 5:e2021GH000391. [PMID: 33977182 PMCID: PMC8095364 DOI: 10.1029/2021gh000391#gh2231-bib-0010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/15/2021] [Accepted: 04/17/2021] [Indexed: 06/13/2023]
Abstract
Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51%-94% of first-order sensitivity index), industrial (7%-31%), and agricultural emissions (0%-24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68%-81%, down to 15.3-25.9 μg m-3, remaining above the World Health Organization annual guideline of 10 μg m-3. The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m-3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors.
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Affiliation(s)
- Luke Conibear
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Carly L. Reddington
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Ben J. Silver
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Ying Chen
- College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK
| | | | - Stephen R. Arnold
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Dominick V. Spracklen
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
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13
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Conibear L, Reddington CL, Silver BJ, Chen Y, Knote C, Arnold SR, Spracklen DV. Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China. GEOHEALTH 2021; 5:e2021GH000391. [PMID: 33977182 PMCID: PMC8095364 DOI: 10.1029/2021gh000391] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/15/2021] [Accepted: 04/17/2021] [Indexed: 05/25/2023]
Abstract
Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51%-94% of first-order sensitivity index), industrial (7%-31%), and agricultural emissions (0%-24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68%-81%, down to 15.3-25.9 μg m-3, remaining above the World Health Organization annual guideline of 10 μg m-3. The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m-3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors.
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Affiliation(s)
- Luke Conibear
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Carly L. Reddington
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Ben J. Silver
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Ying Chen
- College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK
| | | | - Stephen R. Arnold
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Dominick V. Spracklen
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
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