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Yan Q, Liu X, Kong S, Zhang W, Gao Q, Zhang Y, Li H, Wang H, Xiao T, Li J. Hourly emission amounts and concentration of water-soluble ions in primary particles from residential coal burning in rural northern China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 359:124641. [PMID: 39122172 DOI: 10.1016/j.envpol.2024.124641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 07/27/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
Residential coal burning (RCB) stands as an important contributor to ambient pollutants in China. For the effective execution of air pollution control policies, it is essential to maintain precise emission inventories of RCB. The absence of hourly emission factors (EFs) combined with the inaccuracies in the spatial-temporal distribution of activity data, constrained the quality of residential coal combustion emission inventories, thereby impeding the estimation of air pollutant emissions. This study revised the hourly EFs for PM2.5 and water-soluble ions (WSIs) emitted from RCB in China. The hourly emission inventories for PM2.5 and WSIs derived from RCB illustrate the diurnal fluctuations in emission patterns. This study found that the emissions of PM2.5, NH4+, Cl-, and SO42- showed similar emission features with emission of 106.8 Gg, 1417.6, 356.8, and 5868.5 ton in erupt period. The results provide basic data for evaluating RCB emission reduction policies, simulating particles, and preventing air pollution in both sub-regions and time periods. The spatial emission and simulated concentration distribution of PM2.5 and WSIs indicated that emission hotspot shifted from North China Plain (NCP) to Northeast region in China. The emissions in China were well-controlled in '2 + 26' region (R28) priority region, with hotspots decreasing by 99.6% in BTH region. The RCB became the dominant contributor to ambient PM2.5 with a ratio in the range of 16.2-23.7% in non-priority region.
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Affiliation(s)
- Qin Yan
- Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing, China; Department of Atmospheric Sciences, School of Environmental Sciences, China University of Geosciences, Wuhan, China
| | - Xi Liu
- School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
| | - Shaofei Kong
- Department of Atmospheric Sciences, School of Environmental Sciences, China University of Geosciences, Wuhan, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan, China.
| | - Wenjie Zhang
- Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing, China.
| | - Qingxian Gao
- Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Yuzhe Zhang
- Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Hui Li
- Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Han Wang
- Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Tingyu Xiao
- Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Junhong Li
- Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing, China
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Bai Y, Liu M. Multi-scale spatiotemporal trends and corresponding disparities of PM 2.5 exposure in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 340:122857. [PMID: 37925009 DOI: 10.1016/j.envpol.2023.122857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 11/06/2023]
Abstract
Despite the effectiveness of targeted measures to mitigate air pollution, China-a developing country with high PM2.5 concentration and dense population, faces a high risk of PM2.5-related mortality. However, existing studies on long-term PM2.5 exposure in China have not reached a consensus as to which year it peaked during the "initially pollution, then mitigation" process. Furthermore, analyses in these studies were rarely undertaken from multi-spatial scales. In this study, a piecewise linear regression model was employed to detect the turning point of population-weighted exposure (PWE) to PM2.5 for the period 2000-2020. Multi-scale spatiotemporal patterns of PM2.5 exposure were evaluated during upward and downward periods at the province, city and county levels, and their corresponding disparities were estimated using the Gini index. The results showed that 2013 was the breakpoint year for PM2.5 PWE across China from 2000 to 2020. Cities and counties where PM2.5 PWE displayed increasing trends during the mitigation stage (2013-2020) basically became the heaviest PM2.5 exposure regions in 2020. High PM2.5 exposure was observed in Beijing-Tianjin-Hebei, Central China, and the Tarim Basin in Xinjiang, whereas lower PM2.5 exposure regions were mainly concentrated in Hainan Province, the Hengduan Mountains, and northern Xinjiang. These cross-provincial patterns might have been overlooked when conducting macro-scale analyses. Province-level PM2.5 exposure inequality was less than the city- and county-levels estimations, and regional inequalities were high in eastern and western China. In this study, multi-scale PM2.5 exposure trends and their disparities over a prolonged period were investigated, and the findings provide a reference for pollution mitigation and regional inequality reduction.
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Affiliation(s)
- Yu Bai
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Menghang Liu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
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Shi S, Wang W, Li X, Xu C, Lei J, Jiang Y, Zhang L, He C, Xue T, Chen R, Kan H, Meng X. Evolution in disparity of PM 2.5 pollution in China. ECO-ENVIRONMENT & HEALTH (ONLINE) 2023; 2:257-263. [PMID: 38435353 PMCID: PMC10902506 DOI: 10.1016/j.eehl.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/06/2023] [Accepted: 08/28/2023] [Indexed: 03/05/2024]
Abstract
The spatial disparity of air pollutants is one of the key influential factors for environmental inequality. We quantitatively evaluated the evolution of PM2.5 spatial disparity in China during 2013-2020, and investigated the associations between PM2.5 spatial disparity and economic indicators. Differences in PM2.5 between more- and less-polluted cities declined over time, suggesting decreased absolute disparity. However, the more polluted cities in 2013 remained so in 2017 and 2020, and vice versa, indicating persistent relative disparity. PM2.5 pollution levels increased with higher GDP per capita in less-developed areas of China, but such negative effects weakened over time, while economic development tended to promote cleaner air in developed areas of China. Therefore, policies to improve air quality and promote economic development simultaneously are needed in China to reduce the disparity of air pollution and promote all people to enjoy environmental equality.
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Affiliation(s)
- Su Shi
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Weidong Wang
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Xinyue Li
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Chang Xu
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Jian Lei
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Yixuan Jiang
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Lina Zhang
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Cheng He
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
- Institute of Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health (GmbH), Munich D-85764, Germany
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Renjie Chen
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Haidong Kan
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Xia Meng
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
- Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China
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Effects of indoor and outdoor temperatures on blood pressure and central hemodynamics in a wintertime longitudinal study of Chinese adults. J Hypertens 2022; 40:1950-1959. [PMID: 35969204 DOI: 10.1097/hjh.0000000000003198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES We aimed to estimate the effects of indoor and outdoor temperature on wintertime blood pressure (BP) among peri-urban Beijing adults. METHODS We enrolled 1279 adults (ages: 40-89 years) and conducted measurements in two winter campaigns in 2018-2019 and 2019-2020. Study staff traveled to participant homes to administer a questionnaire and measure brachial and central BP. Indoor temperature was measured in the 5 min prior to BP measurement. Outdoor temperature was estimated from regional meteorological stations. We used multivariable mixed-effects regression models to estimate the within-individual and between-individual effects of indoor and outdoor temperatures on BP. RESULTS Indoor and outdoor temperatures ranged from 0.0 to 28 °C and -14.3 to 6.4 °C, respectively. In adjusted models, a 1 °C increase in indoor temperature was associated with decreased SBP [-0.4 mmHg, 95% confidence interval (CI): -0.7 to -0.1 (between-individual; brachial and central BP); -0.5 mmHg, 95% CI: -0.8 to -0.2 (within-individual, brachial BP); -0.4 mmHg, 95% CI: -0.7 to -0.2 (within-individual, central BP)], DBP [-0.2 mmHg, 95% CI:-0.4 to -0.03 (between-individual); -0.3 mmHg, 95% CI: -0.5 to -0.04 (within-individual)], and within-individual pulse pressure [-0.2 mmHg, 95% CI: -0.4 to -0.04 (central); -0.3 mmHg, 95% CI: -0.4 to -0.1 (brachial)]. Between-individual SBP estimates were larger among participants with hypertension. There was no evidence of an effect of outdoor temperature on BP. CONCLUSION Our results support previous findings of inverse associations between indoor temperature and BP but contrast with prior evidence of an inverse relationship with outdoor temperature. Wintertime home heating may be a population-wide intervention strategy for high BP and cardiovascular disease in China.
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Li X, Baumgartner J, Harper S, Zhang X, Sternbach T, Barrington‐Leigh C, Brehmer C, Robinson B, Shen G, Zhang Y, Tao S, Carter E. Field measurements of indoor and community air quality in rural Beijing before, during, and after the COVID-19 lockdown. INDOOR AIR 2022; 32:e13095. [PMID: 36040277 PMCID: PMC9538603 DOI: 10.1111/ina.13095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/15/2022] [Accepted: 07/31/2022] [Indexed: 06/15/2023]
Abstract
The coronavirus (COVID-19) lockdown in China is thought to have reduced air pollution emissions due to reduced human mobility and economic activities. Few studies have assessed the impacts of COVID-19 on community and indoor air quality in environments with diverse socioeconomic and household energy use patterns. The main goal of this study was to evaluate whether indoor and community air pollution differed before, during, and after the COVID-19 lockdown in homes with different energy use patterns. Using calibrated real-time PM2.5 sensors, we measured indoor and community air quality in 147 homes from 30 villages in Beijing over 4 months including periods before, during, and after the COVID-19 lockdown. Community pollution was higher during the lockdown (61 ± 47 μg/m3 ) compared with before (45 ± 35 μg/m3 , p < 0.001) and after (47 ± 37 μg/m3 , p < 0.001) the lockdown. However, we did not observe significantly increased indoor PM2.5 during the COVID-19 lockdown. Indoor-generated PM2.5 in homes using clean energy for heating without smokers was the lowest compared with those using solid fuel with/without smokers, implying air pollutant emissions are reduced in homes using clean energy. Indoor air quality may not have been impacted by the COVID-19 lockdown in rural settings in China and appeared to be more impacted by the household energy choice and indoor smoking than the COVID-19 lockdown. As clean energy transitions occurred in rural households in northern China, our work highlights the importance of understanding multiple possible indoor sources to interpret the impacts of interventions, intended or otherwise.
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Affiliation(s)
- Xiaoying Li
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Department of Civil and Environmental EngineeringColorado State UniversityFort CollinsColoradoUSA
| | - Jill Baumgartner
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Institute for Health and Social PolicyMcGill UniversityMontrealQuebecCanada
| | - Sam Harper
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Institute for Health and Social PolicyMcGill UniversityMontrealQuebecCanada
| | - Xiang Zhang
- Department of GeographyMcGill UniversityMontrealQuebecCanada
| | - Talia Sternbach
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Institute for Health and Social PolicyMcGill UniversityMontrealQuebecCanada
| | - Christopher Barrington‐Leigh
- Institute for Health and Social PolicyMcGill UniversityMontrealQuebecCanada
- Bieler School of EnvironmentMcGill UniversityMontrealQuebecCanada
| | - Collin Brehmer
- Department of Civil and Environmental EngineeringColorado State UniversityFort CollinsColoradoUSA
| | - Brian Robinson
- Department of GeographyMcGill UniversityMontrealQuebecCanada
| | - Guofeng Shen
- Laboratory for Earth Surface Processes, Sino‐French Institute for Earth System Science, College of Urban and Environmental SciencesPeking UniversityBeijingChina
| | - Yuanxun Zhang
- College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijingChina
- CAS Center for Excellence in Regional Atmospheric EnvironmentChinese Academy of SciencesXiamenChina
| | - Shu Tao
- Laboratory for Earth Surface Processes, Sino‐French Institute for Earth System Science, College of Urban and Environmental SciencesPeking UniversityBeijingChina
| | - Ellison Carter
- Department of Civil and Environmental EngineeringColorado State UniversityFort CollinsColoradoUSA
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