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Du P, Du H, Zhang W, Lu K, Zhang C, Ban J, Wang Y, Liu T, Hu J, Li T. Unequal Health Risks and Attributable Mortality Burden of Source-Specific PM 2.5 in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:10897-10909. [PMID: 38843119 DOI: 10.1021/acs.est.3c08789] [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: 06/11/2024]
Abstract
Anthropogenic emissions, originating from human activities, stand as the primary contributors to PM2.5, which is recognized as a global health threat. The disease burden associated with PM2.5 has been extensively documented. However, the prevailing estimations have predominantly relied on PM2.5 exposure-response functions, neglecting the distinct risks posed by PM2.5 from various sources. China has experienced a significant reduction in the PM2.5 concentration due to stringent emission controls. With diverse sources and abundant mortality data, this situation provides a unique opportunity to estimate short-term source-specific attributable mortality. Our approach involves an integrated unequal health risk-oriented modeling in China, incorporating a source-oriented Community Multiscale Air Quality model, an adjustment and downscaling method for exposure measurement, a generalized linear model with random-effects meta-analysis, and premature mortality estimation. Adhering to the unequal health risk concept, we calculated the attributable mortality of multiple PM2.5 sources by determining the source risk-adjusted factor. In this study, we observed varying excess risks associated with multiple PM2.5 sources, with transportation-related PM2.5 exhibiting the most substantial association. An interquartile range increase (7.65 μg/m3) was linked to a 1.98% higher daily nonaccidental mortality. Residential use- and transportation-related PM2.5 emerged as the two principal sources of premature mortality. In 2018, a remarkable 53,381 avoiding deaths were estimated compared to 2013, and over 67% of these were attributed to reductions in coal-dependent sources. Notably, transportation-related PM2.5 emerged as the largest contributor to premature mortality in 2018. This study underscores the significance of a new source-oriented health risk assessment to support actions aimed at reducing air pollution. It strongly advocates for heightened attention to PM2.5 reductions in the transportation sector in China.
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Affiliation(s)
- Peng Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Hang Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Wenjing 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
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Kailai Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Can 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
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Jie Ban
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yiyi Wang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Ting Liu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
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Karim N, Hod R, Wahab MIA, Ahmad N. Projecting non-communicable diseases attributable to air pollution in the climate change era: a systematic review. BMJ Open 2024; 14:e079826. [PMID: 38719294 PMCID: PMC11086555 DOI: 10.1136/bmjopen-2023-079826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
OBJECTIVES Climate change is a major global issue with significant consequences, including effects on air quality and human well-being. This review investigated the projection of non-communicable diseases (NCDs) attributable to air pollution under different climate change scenarios. DESIGN This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 flow checklist. A population-exposure-outcome framework was established. Population referred to the general global population of all ages, the exposure of interest was air pollution and its projection, and the outcome was the occurrence of NCDs attributable to air pollution and burden of disease (BoD) based on the health indices of mortality, morbidity, disability-adjusted life years, years of life lost and years lived with disability. DATA SOURCES The Web of Science, Ovid MEDLINE and EBSCOhost databases were searched for articles published from 2005 to 2023. ELIGIBILITY CRITERIA FOR SELECTING STUDIES The eligible articles were evaluated using the modified scale of a checklist for assessing the quality of ecological studies. DATA EXTRACTION AND SYNTHESIS Two reviewers searched, screened and selected the included studies independently using standardised methods. The risk of bias was assessed using the modified scale of a checklist for ecological studies. The results were summarised based on the projection of the BoD of NCDs attributable to air pollution. RESULTS This review included 11 studies from various countries. Most studies specifically investigated various air pollutants, specifically particulate matter <2.5 µm (PM2.5), nitrogen oxides and ozone. The studies used coupled-air quality and climate modelling approaches, and mainly projected health effects using the concentration-response function model. The NCDs attributable to air pollution included cardiovascular disease (CVD), respiratory disease, stroke, ischaemic heart disease, coronary heart disease and lower respiratory infections. Notably, the BoD of NCDs attributable to air pollution was projected to decrease in a scenario that promotes reduced air pollution, carbon emissions and land use and sustainable socioeconomics. Contrastingly, the BoD of NCDs was projected to increase in a scenario involving increasing population numbers, social deprivation and an ageing population. CONCLUSION The included studies widely reported increased premature mortality, CVD and respiratory disease attributable to PM2.5. Future NCD projection studies should consider emission and population changes in projecting the BoD of NCDs attributable to air pollution in the climate change era. PROSPERO REGISTRATION NUMBER CRD42023435288.
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Affiliation(s)
- Norhafizah Karim
- Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala lumpur, Malaysia
| | - Rozita Hod
- Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala lumpur, Malaysia
| | - Muhammad Ikram A Wahab
- Center of Toxicology and Health Risk Studies (CORE), Universiti Kebangsaan Malaysia Fakulti Sains Kesihatan, Kuala Lumpur, Wilayah Persekutuan, Malaysia
| | - Norfazilah Ahmad
- Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala lumpur, Malaysia
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Anita WM, Uttajug A, Seposo XT, Sudo K, Nakata M, Takemura T, Takano H, Fujiwara T, Ueda K. Interplay of Climate Change and Air Pollution- Projection of the under-5 mortality attributable to ambient particulate matter (PM2.5) in South Asia. ENVIRONMENTAL RESEARCH 2024; 248:118292. [PMID: 38266897 DOI: 10.1016/j.envres.2024.118292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/20/2023] [Accepted: 01/20/2024] [Indexed: 01/26/2024]
Abstract
Ambient fine particulate matter (PM2.5) pollution is a leading health risk factor for children under- 5 years, especially in developing countries. South Asia is a PM2.5 hotspot, where climate change, a potential factor affecting PM2.5 pollution, adds a major challenge. However, limited evidence is available on under-5 mortality attributable to PM2.5 under different climate change scenarios. This study aimed to project under-5 mortality attributable to long-term exposure to ambient PM2.5 under seven air pollution and climate change mitigation scenarios in South Asia. We used a concentration-risk function obtained from a previous review to project under-5 mortality attributable to ambient PM2.5. With a theoretical minimum risk exposure level of 2.4 μg/m3, this risk function was linked to gridded annual PM2.5 concentrations from atmospheric modeling to project under-5 mortality from 2010 to 2049 under different climate change mitigation scenarios. The scenarios were developed from the Aim/Endues global model based on end-of-pipe (removing the emission of air pollutants at the source, EoP) and 2 °C target measures. Our results showed that, in 2010-2014, about 306.8 thousand under-5 deaths attributable to PM2.5 occurred in South Asia under the Reference (business as usual) scenario. The number of deaths was projected to increase in 2045-2049 by 36.6% under the same scenario and 7.7% under the scenario where EoP measures would be partially implemented by developing countries (EoPmid), and was projected to decrease under other scenarios, with the most significant decrease (81.2%) under the scenario where EoP measures would be fully enhanced by all countries along with the measures to achieve 2 °C target (EoPmaxCCSBLD) across South Asia. Country-specific projections of under-5 mortality varied by country. The current emission control strategy would not be sufficient to reduce the number of deaths in South Asia. Robust climate change mitigation and air pollution control policy implementation is required.
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Affiliation(s)
| | - Athicha Uttajug
- Department of Hygiene, Graduate School of Medicine, Hokkaido University, Japan.
| | | | - Kengo Sudo
- Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan; Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.
| | - Makiko Nakata
- Faculty of Applied Sociology Kindai University, Osaka, Japan.
| | - Toshihiko Takemura
- Research Institute for Applied Mechanics, Kyushu University, Kyushu, Japan.
| | - Hirohisa Takano
- Graduate School of Global Environmental Studies, Kyoto University, Japan; Graduate School of Engineering, Kyoto University, Japan.
| | - Taku Fujiwara
- Graduate School of Global Environmental Studies, Kyoto University, Japan; Graduate School of Engineering, Kyoto University, Japan.
| | - Kayo Ueda
- Graduate School of Global Environmental Studies, Kyoto University, Japan; Department of Hygiene, Graduate School of Medicine, Hokkaido University, Japan.
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Ban J, Cheng J, Zhang C, Lu K, Zhou Z, Liu Z, Chen Y, Wang C, Cai W, Gong P, Luo Y, Tong D, Hu J, Guo X, Hao J, Li T. China's carbon-neutral policies will reduce short-term PM 2.5-associated excess incidence of cardiovascular diseases. ONE EARTH (CAMBRIDGE, MASS.) 2024; 7:497-505. [PMID: 38532982 PMCID: PMC10962059 DOI: 10.1016/j.oneear.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 10/31/2023] [Accepted: 01/08/2024] [Indexed: 03/28/2024]
Abstract
China's carbon-neutral target could have benefits for ambient fine particulate matter (PM2.5)-associated mortality. Although previous studies have researched such benefits, the potential impact on cardiovascular disease incidence burden is yet to be investigated thoroughly. Here, we first estimate the association between short-term PM2.5 exposure and the incidence of stroke and coronary heart disease (CHD) via a case-crossover study before projecting future changes in short-term PM2.5-associated excess incidence across China from 2025 to 2060 under three different emission scenarios. We find that, compared to the 2015-2020 baseline, average PM2.5 concentrations nationwide in 2060 under SSP119 (an approximation of a carbon-neutral scenario) are projected to decrease by 81.07%. The short-term PM2.5-related excess incidence of stroke and CHD is projected to be reduced to 3,352 cases (95% confidence interval: 939, 5,738)-compared with 34,485 cases under a medium-emissions scenario (SSP245)-and is expected to be accompanied by a 95% reduction in the related economic burden. China's carbon-neutral policies are likely to bring health benefits for cardiovascular disease by reducing short-term PM2.5-related incidence burden.
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Affiliation(s)
- Jie Ban
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, 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 & Technology, Nanjing 210044, China
| | - Jing Cheng
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
| | - Can Zhang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Kailai Lu
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Zhen Zhou
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Zhao Liu
- School of Linkong Economics and Management, Beijing Institute of Economics and Management, Beijing 100024, China
| | - Yidan Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China
| | - Can Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China
| | - Wenjia Cai
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
| | - Peng Gong
- Department of Earth Sciences and Geography, University of Hong Kong, Hong Kong Special Administrative Region 999077, China
| | - Yong Luo
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
| | - Dan Tong
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
| | - Jianlin Hu
- 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 & Technology, Nanjing 210044, China
| | - Xinbiao Guo
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, No. 38 Xueyuan Road, Beijing 100191, China
| | - Junwei Hao
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
- Key Laboratory for Neurodegenerative Diseases of Ministry of Education, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
- Beijing Municipal Geriatric Medical Research Center, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
| | - Tiantian Li
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- 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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5
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Niu S, Tang J, Hu K, Xu X, Dong J, Chu J, Guo X, Wang H, Cao Y, Zhao Q, Fornah L, Lu Z, Ma W. The combined effect of air pollution and non-optimal temperature on mortality in Shandong Province, China: establishment of air health index. Int Arch Occup Environ Health 2024; 97:189-197. [PMID: 38156995 DOI: 10.1007/s00420-023-02036-w] [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: 09/25/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE The air health index (AHI) captures the combined effects of air pollution and non-optimal temperatures and helps assess the atmospheric environment's overall health risk. Shandong Province is a crucial industrial base in China, and the health effects of air pollution and non-optimal temperature cannot be ignored. To construct an AHI for Shandong Province and assess the district-level mortality burden due to AHI in the study area. METHODS Daily district-specific mortality, meteorological, and air pollution data over 2013-2018 were collected in Shandong Province, China. The AHI construction eventually incorporated PM2.5 and NO2, O3, and non-optimal temperatures. Attributable fraction (AF) and attributable number (AN) were used to estimate the district-specific mortality burden attributable to AHI. RESULTS The average AHI value observed in Shandong Province was 6. Our research revealed a positive association between the total AHI and total mortality, with an overall trend of a slow increase followed by a rapid increase. The exposure-response curves, when stratified by gender, age, and cause of death, were approximately consistent with the overall trend. The provincial attributable fraction (AF) was 5.31% (95% CI 4.58%, 5.91%), and the attributable number (AN) was 188,246 (95% CI 162,396, 209,533). Overall, higher ANs mainly appeared in the southwestern area, while higher values of AF were observed in the central-eastern and central-northern areas. CONCLUSIONS The air health index performs well in predicting death burden and can convey health risks related to exposure to the ambient environment to the public.
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Affiliation(s)
- Shuyue Niu
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 West Wenhua Road, Jinan, 250012, Shandong, People's Republic of China
- Shandong University Climate Change and Health Center, Jinan, People's Republic of China
| | - Junli Tang
- Shandong Center for Disease Control and Prevention, No.16992, Jingshi Road, Jinan, 250014, People's Republic of China
| | - Kejia Hu
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, People's Republic of China
| | - Xiaohui Xu
- Shandong Center for Disease Control and Prevention, No.16992, Jingshi Road, Jinan, 250014, People's Republic of China
| | - Jing Dong
- Shandong Center for Disease Control and Prevention, No.16992, Jingshi Road, Jinan, 250014, People's Republic of China
| | - Jie Chu
- Shandong Center for Disease Control and Prevention, No.16992, Jingshi Road, Jinan, 250014, People's Republic of China
| | - Xiaolei Guo
- Shandong Center for Disease Control and Prevention, No.16992, Jingshi Road, Jinan, 250014, People's Republic of China
| | - Haitao Wang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 West Wenhua Road, Jinan, 250012, Shandong, People's Republic of China
- Shandong University Climate Change and Health Center, Jinan, People's Republic of China
| | - Yanwen Cao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 West Wenhua Road, Jinan, 250012, Shandong, People's Republic of China
- Shandong University Climate Change and Health Center, Jinan, People's Republic of China
| | - Qi Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 West Wenhua Road, Jinan, 250012, Shandong, People's Republic of China
- Shandong University Climate Change and Health Center, Jinan, People's Republic of China
| | - Lovel Fornah
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 West Wenhua Road, Jinan, 250012, Shandong, People's Republic of China
| | - Zilong Lu
- Shandong Center for Disease Control and Prevention, No.16992, Jingshi Road, Jinan, 250014, People's Republic of China.
| | - Wei Ma
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 West Wenhua Road, Jinan, 250012, Shandong, People's Republic of China.
- Shandong University Climate Change and Health Center, Jinan, People's Republic of China.
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Guan Y, Rong B, Kang L, Zhang N, Qin C. Measuring the urban-rural and spatiotemporal heterogeneity of the drivers of PM 2.5-attributed health burdens in China from 2008 to 2021 using high-resolution dataset. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 346:118940. [PMID: 37741197 DOI: 10.1016/j.jenvman.2023.118940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/15/2023] [Accepted: 09/04/2023] [Indexed: 09/25/2023]
Abstract
Urbanization has been considered a driver of PM2.5 pollution and the attributed health burden. This study systematically measured the spatiotemporal and urban-rural heterogeneity of PM2.5-attributed health burden drivers, including income, population, baseline mortality rate, and PM2.5 level. The results reveal the significantly positive contribution of disposable income and the periodical and urban-rural differentiation of population contribution to PM2.5-attributed health burden. The difference in driver performance due to socioeconomic development and urbanization stages might be an important determinant for different or even opposite results of previous studies. Policymaking for mitigating PM2.5-attributed health risk could incorporate the re-assessment and driver determination for PM2.5-attributed health burden into the construction and development plan from the overall urbanization perspective. The urbanization-perspective driver decomposition could be synergized with the flow analysis, equality evaluation, and policy benefit estimation to achieve further direction-determining and quantitative assessment of the urban-rural PM2.5 health risk management strategies.
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Affiliation(s)
- Yang Guan
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300191, China; Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Bing Rong
- Center of Environmental Status and Plan Assessment, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Lei Kang
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Nannan Zhang
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100041, China.
| | - Changbo Qin
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100041, China; The Center for Beautiful China, Chinese Academy of Environmental Planning, Beijing, 100041, China.
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Weber E, Downward GS, Ebi KL, Lucas PL, van Vuuren D. The use of environmental scenarios to project future health effects: a scoping review. Lancet Planet Health 2023; 7:e611-e621. [PMID: 37438002 DOI: 10.1016/s2542-5196(23)00110-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 05/04/2023] [Accepted: 05/19/2023] [Indexed: 07/14/2023]
Abstract
Environmental risks are a substantial factor in the current burden of disease, and their role is likely to increase in the future. Model-based scenario analysis is used extensively in environmental sciences to explore the potential effects of human activities on the environment. In this Review, we examine the literature on scenarios modelling environmental effects on health to identify the most relevant findings, common methods used, and important research gaps. Health outcomes and measures related to climate change (n=106) and air pollution (n=30) were most frequently studied. Studies examining future disease burden due to changes or policies related to dietary risks were much less common (n=10). Only a few studies assessed more than two environmental risks (n=3), even though risks can accumulate and interact with each other. Studies predominantly covered high-income countries and Asia. Sociodemographic, vulnerability, and health-system changes were rarely accounted for; thus, assessing the full effect of future environmental changes in an integrative way is not yet possible. We recommend that future models incorporate a broader set of determinants of health to more adequately capture their effect, as well as the effect of mitigation and adaptation efforts.
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Affiliation(s)
- Eartha Weber
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht University, Utrecht, Netherlands.
| | - George S Downward
- Department of Global Public Health and Bioethics, Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Kristie L Ebi
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Paul L Lucas
- PBL Netherlands Environmental Assessment Agency, The Hague, Netherlands
| | - Detlef van Vuuren
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht University, Utrecht, Netherlands
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Xiang S, Guo X, Kou W, Zeng X, Yan F, Liu G, Zhu Y, Xie Y, Lin X, Han W, Gao Y. Substantial short- and long-term health effect due to PM 2.5 and the constituents even under future emission reductions in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 874:162433. [PMID: 36841405 DOI: 10.1016/j.scitotenv.2023.162433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Heavy pollution events of fine particulate matter (PM2.5) frequently occur in China, seriously affecting the human health. However, how meteorological factors and anthropogenic emissions affect PM2.5 and the major constituents, as well as the subsequent health effect, remains unclear. Here, based on regional climate and air quality models Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ), the PM2.5 and major constituents in China at present and mid-century under the carbon neutral scenario Shared Socioeconomic Pathways (SSP)1-2.6 are simulated. Due to anthropogenic emission reduction, concentrations of PM2.5 and the constituents decrease substantially in SSP1-2.6. The long-term exposure premature deaths at present are 2.23 million per year in mainland China, which is projected to increase by 76 % under SSP1-2.6 despite emission reduction, primarily attributable to aging which strikingly offsets the effect of air quality improvement. The number of annual premature deaths resulting from short-term exposure is 228,104 in mainland China at present, which is projected to decrease in the future. Using North China Plain as an example, we identify that among the major constituents of PM2.5, organic carbon leads to the most short-term exposure deaths considering the largest exposure-response coefficient. Regarding the abnormally meteorological conditions, we find, relative to low relative humidity (RH) and non-stagnation, the compound events, defined as concurrence of high RH and atmospheric stagnation, exhibit an amplified role inducing larger premature deaths compared to the additive effect of the individual event of high RH and atmospheric stagnation. This nonlinear effect occurs at both present and future, but diminished in future due to emission reductions. Our study highlights the importance of considering both the long- and short-term premature deaths associated with PM2.5 and the constituents, as well as the critical effect of extreme weather events.
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Affiliation(s)
- Shengnan Xiang
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China
| | - Xiuwen Guo
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China
| | - Wenbin Kou
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China
| | - Xinran Zeng
- Zhejiang Institute of Meteorological Sciences, Hangzhou 310008, China
| | - Feifan Yan
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China
| | - Guangliang Liu
- Shandong Provincial Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250101, China
| | - Yuanyuan Zhu
- China National Environmental Monitoring Centre, Beijing 100012, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing 100191, China
| | - Xiaopei Lin
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China
| | - Wei Han
- Department of Pulmonary and Critical Care Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao 266100, China
| | - Yang Gao
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China.
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Ma X, Zhang B, Duan H, Wu H, Dong J, Guo X, Lu Z, Ma J, Xi B. Estimating future PM 2.5-attributed acute myocardial infarction incident cases under climate mitigation and population change scenarios in Shandong Province, China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 256:114893. [PMID: 37059016 DOI: 10.1016/j.ecoenv.2023.114893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/07/2023] [Accepted: 04/08/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND The effects of fine particulate matter (PM2.5) on acute myocardial infarction (AMI) have been widely recognized. However, no studies have comprehensively evaluated future PM2.5-attributed AMI burdens under different climate mitigation and population change scenarios. We aimed to quantify the PM2.5-AMI association and estimate the future change in PM2.5-attributed AMI incident cases under six integrated scenarios in 2030 and 2060 in Shandong Province, China. METHODS Daily AMI incident cases and air pollutant data were collected from 136 districts/counties in Shandong Province from 2017 - 2019. A two-stage analysis with a distributed lag nonlinear model was conducted to quantify the baseline PM2.5-AMI association. The future change in PM2.5-attributed AMI incident cases was estimated by combining the fitted PM2.5-AMI association with the projected daily PM2.5 concentrations under six integrated scenarios. We further analyzed the factors driving changes in PM2.5-related AMI incidence using a decomposition method. RESULTS Each 10 μg/m3 increase in PM2.5 exposure at lag05 was related to an excess risk of 1.3 % (95 % confidence intervals: 0.9 %, 1.7 %) for AMI incidence from 2017 - 2019 in Shandong Province. The estimated total PM2.5-attributed AMI incident cases would increase by 10.9-125.9 % and 6.4-244.6 % under Scenarios 1 - 3 in 2030 and 2060, whereas they would decrease by 0.9-5.2 % and 33.0-46.2 % under Scenarios 5 - 6 in 2030 and 2060, respectively. Furthermore, the percentage increases in PM2.5-attributed female cases (2030: -0.3 % to 135.1 %; 2060: -33.2 % to 321.5 %) and aging cases (2030: 15.2-171.8 %; 2060: -21.5 % to 394.2 %) would wholly exceed those in male cases (2030: -1.8 % to 133.2 %; 2060: -41.1 % to 264.3 %) and non-aging cases (2030: -41.0 % to 45.7 %; 2060: -89.5 % to -17.0 %) under six scenarios in 2030 and 2060. Population aging is the main driver of increased PM2.5-related AMI incidence under Scenarios 1 - 3 in 2030 and 2060, while improved air quality can offset these negative effects of population aging under the implementation of the carbon neutrality and 1.5 °C targets. CONCLUSION The combination of ambitious climate policies (i.e., 1.5 °C warming limits and carbon neutrality targets) with stringent clean air policies is necessary to reduce the health impacts of air pollution in Shandong Province, China, regardless of population aging.
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Affiliation(s)
- Xiaoyun Ma
- Department of Epidemiology, School of Public Health, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Bingyin Zhang
- Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, Shandong, China
| | - Haiping Duan
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, Shandong, China
| | - Han Wu
- Department of Epidemiology, School of Public Health, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Jing Dong
- Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, Shandong, China
| | - Xiaolei Guo
- Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, Shandong, China
| | - Zilong Lu
- Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, Shandong, China
| | - Jixiang Ma
- Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, Shandong, China.
| | - Bo Xi
- Department of Epidemiology, School of Public Health, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
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Oak YJ, Park RJ, Lee JT, Byun G. Future air quality and premature mortality in Korea. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 865:161134. [PMID: 36587681 DOI: 10.1016/j.scitotenv.2022.161134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/22/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
We simulate air quality in Korea for the present, the near-term, and the long-term future conditions under the Shared Socioeconomic Pathways (SSP1: most sustainable pathway with strong emissions control, SSP3: most challenging pathway with mild emissions control) using a chemical transport model. Simulated future concentrations of NO2, SO2, and fine particulate matter (PM2.5), show, in general, lower values compared to the present with varying degrees depending on SSP scenarios. Significant reductions in precursor emissions result in a decrease in O3 concentrations under a NOx-limited environment in the long-term future under SSP1. Under SSP3, O3 increases in the future under a VOC-limited regime, driven by increased CH4 levels and biogenic VOC emissions under the warming climate. Concentrations of PM2.5 and its components, including sulfate, nitrate, ammonium, and organic aerosols (OA), generally decrease in the long-term future under both scenarios. However, the contribution of biogenic secondary OA (BSOA) to PM2.5 will increase in the future. Simulated results are used to estimate cardiorespiratory mortality changes with concentration-response factors from epidemiologic studies in Korea based on national health surveys and Korean cohorts, using projected population structures from the SSP database. The cardiorespiratory health burden of long-term exposure to O3, NO2, SO2, and PM2.5 is estimated to be 10,419 (95 % confidence interval: 1271-17,142), 8630 (0-18,713), 3958 (0-9272), and 10,431 (1411-20,643) deaths in 2019. We find that the total cardiorespiratory excess mortality due to air pollutants under SSP1 decreases by 8 % and 95 % in 2045 and 2095, respectively. Under SSP3, excess mortality increases by 80 % in 2045, and decreases by 22 % in 2095, resulting in a substantial difference in the health outcomes depending on the emission scenario. We also find that the BSOA contribution to total PM2.5 will differ by region, emphasizing the potential health impact of BSOA on a local scale in the future.
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Affiliation(s)
- Yujin J Oak
- School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
| | - Rokjin J Park
- School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea.
| | - Jong-Tae Lee
- School of Health Policy and Management, College of Health Science, Korea University, Seoul, South Korea; Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea
| | - Garam Byun
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea
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Li Y, Li B, Liao H, Zhou BB, Wei J, Wang Y, Zang Y, Yang Y, Liu R, Wang X. Changes in PM 2.5-related health burden in China's poverty and non-poverty areas during 2000-2020: A health inequality perspective. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 861:160517. [PMID: 36464040 DOI: 10.1016/j.scitotenv.2022.160517] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/30/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
China suffers from severe PM2.5 pollution that has resulted in a huge health burden. Such PM2.5-related health burden has long been suspected to differ between China's poverty-stricken areas (PAs) and non-poverty-stricken areas (NPAs). Yet, evidence-based examination of this long-held belief, which is critical as a barrier of environmental injustice to advancing China's sustainability, is still missing. Here our study shows that the PM2.5 pollution is more serious in China's NPAs than PAs-with their annual averages being respectively 54.83 μg/m3 and 43.63 μg/m3-causing higher premature mortality in the NPAs. Compared to economic inequality, China's total PM2.5-related premature mortality was relatively evenly distributed during 2000-2015 across regions of varying levels of gross domestic product (GDP) per capita but increased slightly in 2015-2020 owing to the dramatic change in age structure. The elderly population increased by 31 %. PM2.5-related premature deaths were more severe for populations of low socioeconomic status, and such environmental health inequalities could be amplified by population aging. Additionally, population migration from China's PAs to developed cities contributed to 638, 779, 303, 954, and 896 premature deaths in 2000, 2005, 2010, 2015, and 2020, respectively. Changes in the age structure (53 %) and PM2.5 concentration (28 %) had the greatest impact on premature deaths, followed by changes in population (12 %) and baseline mortality (8 %). The contribution rate of changes in the age structure and PM2.5 concentration was higher in PAs than in NPAs. Our findings provide insight into PM2.5-related premature death and environmental inequality, and may inform more equitable clean air policies to achieve China's sustainable development goals.
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Affiliation(s)
- Yan Li
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Baojie Li
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Hong Liao
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Bing-Bing Zhou
- School of International Affairs and Public Administration, Ocean University of China, Qingdao 266100, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States
| | - Yuxia Wang
- School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Yuzhu Zang
- School of Public Administration, China University of Geosciences, Wuhan 430074, China
| | - Yang Yang
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Rui Liu
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Xiaorui Wang
- Jiangsu Provincial Land Development and Consolidation Center, Nanjing 210017, China
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Nishikawa H, Seposo XT, Madaniyazi L, Kim Y, Tobías A, Yamagami M, Kim SE, Takami A, Sugata S, Honda Y, Ueda K, Hashizume M, Ng CFS. Long-term trends in mortality risk associated with short-term exposure to air pollution in 10 Japanese cities between 1977 and 2015. ENVIRONMENTAL RESEARCH 2023; 219:115108. [PMID: 36549488 DOI: 10.1016/j.envres.2022.115108] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/15/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND AIM Short-term associations between air pollution and mortality have been well reported in Japan, but the historical changes in mortality risk remain unknown. We examined temporal changes in the mortality risks associated with short-term exposure to four criteria air pollutants in selected Japanese cities. METHODS We collected daily mortality data for non-accidental causes (n = 5,748,206), cardiovascular (n = 1,938,743) and respiratory diseases (n = 777,266), and air pollutants (sulfur dioxide [SO2], nitrogen dioxide [NO2], suspended particulate matter [SPM], and oxidants [Ox]) in 10 cities from 1977 to 2015. We performed two-stage analysis with 5-year stratification to estimate the relative risk (RR) of mortality per 10-unit increase in the 2-day moving average of air pollutant concentrations. In the first stage, city-specific associations were assessed using a quasi-Poisson generalized linear regression model. In the second stage, city-specific estimates were pooled using a random-effects meta-analysis. Linear trend and ratio of relative risks (RRR) were computed to examine temporal changes. RESULTS When stratifying the analysis by every 5 years, average concentrations in each sub-period decreased for SO2, NO2, and SPM (14.2-2.3 ppb, 29.4-17.5 ppb, 52.1-20.6 μg/m3, respectively) but increased for Ox (29.1-39.1 ppb) over the study period. We found evidence of a negative linear trend in the risk of cardiovascular mortality associated with SPM across sub-periods. However, the risks of non-accidental and respiratory mortality per 10-unit increase in SPM concentration were significantly higher in the most recent period than in the earliest period. Other gaseous pollutants did not show such temporal risk change. The risks posed by these pollutants were slightly to moderately heterogeneous in the different cities. CONCLUSIONS The mortality risks associated with short-term exposure to SPM changed, with different trends by cause of death, in 10 cities over 39 years whereas the risks for other gaseous pollutants were relatively stable.
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Affiliation(s)
- Hironori Nishikawa
- Department of Global Health, School of Tropical Medicine and Global Health, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan; Department of Global Health Policy, School of International Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Xerxes Tesoro Seposo
- Department of Global Health, School of Tropical Medicine and Global Health, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan; Department of Hygiene, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan
| | - Lina Madaniyazi
- Department of Global Health, School of Tropical Medicine and Global Health, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan
| | - Yoonhee Kim
- Department of Global Environmental Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Aurelio Tobías
- Department of Global Health, School of Tropical Medicine and Global Health, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan; Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), C/ Jordi Girona 18-26, E-08034, Barcelona, Spain
| | - Makiko Yamagami
- Nagoya City Institute for Environmental Sciences, 16-8, Toyoda 5-chome, Minami-ku, Nagoya, 457-0841, Japan
| | - Satbyul Estella Kim
- Faculty of Health and Sport Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8577, Japan
| | - Akinori Takami
- Regional Environment Conservation Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
| | - Seiji Sugata
- Regional Environment Conservation Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
| | - Yasushi Honda
- Department of Global Health, School of Tropical Medicine and Global Health, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan; Center for Climate Change Adaptation, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
| | - Kayo Ueda
- Department of Hygiene, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan
| | - Masahiro Hashizume
- Department of Global Health Policy, School of International Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Institute of Tropical Medicine (Nekken), Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan
| | - Chris Fook Sheng Ng
- Department of Global Health, School of Tropical Medicine and Global Health, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan; Department of Global Health Policy, School of International Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
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Zhao Z, Chu J, Xu X, Cao Y, Schikowski T, Geng M, Chen G, Bai G, Hu K, Xia J, Ma W, Liu Q, Lu Z, Guo X, Zhao Q. Association between ambient cold exposure and mortality risk in Shandong Province, China: Modification effect of particulate matter size. Front Public Health 2023; 10:1093588. [PMID: 36684922 PMCID: PMC9850236 DOI: 10.3389/fpubh.2022.1093588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/06/2022] [Indexed: 01/06/2023] Open
Abstract
Introduction Numerous studies have reported the modification of particulate matters (PMs) on the association between cold temperature and health. However, it remains uncertain whether the modification effect may vary by size of PMs, especially in Shandong Province, China where the disease burdens associated with cold temperature and PMs are both substantial. This study aimed to examine various interactive effects of cold exposure and ambient PMs with diameters ≤1/2.5 μm (PM1 and PM2.5) on premature deaths in Shandong Province, China. Methods In the 2013-2018 cold seasons, data on daily mortality, PM1 and PM2.5, and weather conditions were collected from the 1822 sub-districts of Shandong Province. A time-stratified case-crossover study design was performed to quantify the cumulative association between ambient cold and mortality over lag 0-12 days, with a linear interactive term between temperature and PM1 and PM2.5 additionally added into the model. Results The mortality risk increased with temperature decline, with the cumulative OR of extreme cold (-16.9°C, the 1st percentile of temperature range) being 1.83 (95% CI: 1.66, 2.02), compared with the minimum mortality temperature. The cold-related mortality risk was 2.20 (95%CI: 1.83, 2.64) and 2.24 (95%CI: 1.78, 2.81) on high PM1 and PM2.5 days, which dropped to 1.60 (95%CI: 1.39, 1.84) and 1.60 (95%CI: 1.37, 1.88) on low PM1 and PM2.5 days. PM1 showed greater modification effect for per unit concentration increase than PM2.5. For example, for each 10?g/m3 increase in PM1 and PM2.5, the mortality risk associated with extreme cold temperature increased by 7.6% (95% CI: 1.3%, 14.2%) and 2.6% (95% CI: -0.7%, 5.9%), respectively. Discussion The increment of smaller PMs' modification effect varied by population subgroups, which was particularly strong in the elderly aged over 75 years and individuals with middle school education and below. Specific health promotion strategies should be developed towards the greater modification effect of smaller PMs on cold effect.
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Affiliation(s)
- Zhonghui Zhao
- Department of Epidemiology, School of Public Health, Shandong University, Jinan, China,Shandong University Climate Change and Health Center, Jinan, China
| | - Jie Chu
- Shandong Center for Disease Control and Prevention, Jinan, China,Academy of Preventive Medicine, Shandong University, Jinan, China
| | - Xiaohui Xu
- Shandong Center for Disease Control and Prevention, Jinan, China,Academy of Preventive Medicine, Shandong University, Jinan, China
| | - Yanwen Cao
- Department of Epidemiology, School of Public Health, Shandong University, Jinan, China,Shandong University Climate Change and Health Center, Jinan, China
| | - Tamara Schikowski
- Department of Epidemiology, Leibniz Institute for Environmental Medicine (IUF)-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | - Mengjie Geng
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Gongbo Chen
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Guannan Bai
- Department of Child Health Care, The Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Kejia Hu
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | - Jingjing Xia
- School of Life Sciences, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
| | - Wei Ma
- Department of Epidemiology, School of Public Health, Shandong University, Jinan, China,Shandong University Climate Change and Health Center, Jinan, China
| | - Qiyong Liu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zilong Lu
- Shandong Center for Disease Control and Prevention, Jinan, China,Academy of Preventive Medicine, Shandong University, Jinan, China
| | - Xiaolei Guo
- Shandong Center for Disease Control and Prevention, Jinan, China,Academy of Preventive Medicine, Shandong University, Jinan, China,Xiaolei Guo ✉
| | - Qi Zhao
- Department of Epidemiology, School of Public Health, Shandong University, Jinan, China,Shandong University Climate Change and Health Center, Jinan, China,Department of Epidemiology, Leibniz Institute for Environmental Medicine (IUF)-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany,*Correspondence: Qi Zhao ✉
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Pan R, Zhang Y, Xu Z, Yi W, Zhao F, Song J, Sun Q, Du P, Fang J, Cheng J, Liu Y, Chen C, Lu Y, Li T, Su H, Shi X. Exposure to fine particulate matter constituents and cognitive function performance, potential mediation by sleep quality: A multicenter study among Chinese adults aged 40-89 years. ENVIRONMENT INTERNATIONAL 2022; 170:107566. [PMID: 36219911 DOI: 10.1016/j.envint.2022.107566] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/02/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Although exposure to fine particulate matter (PM2.5) has been associated with cognitive decline, little is known about which PM2.5 constituents are more harmful. Recent study on the association between PM2.5 and sleep quality prompted us to propose that sleep quality may mediate the adverse effects of PM2.5 components on cognitive decline. Understanding the association between PM2.5 constituents and cognitive function, as well as the mediating role of sleep quality provides a future intervention target for improving cognitive function. Using data involving 1834 participants from a multicenter cross-sectional study in nine cities of the Beijing-Tianjin-Hebei (BTH) region in China, we undertook multivariable linear regression analyses to quantify the association of annual moving-average PM2.5 and its chemical constituents with cognitive function and to assess the modifying role of exposure characteristic in this association. Besides, we examined the extent to which this association of PM2.5 constituents with cognitive function was mediated via sleep quality by a mediation analysis. We observed significantly negative associations between an increase of one interquartile range increase in PM2.5 [-0.876 (95 % CI: -1.205, -0.548)], organic carbon [-0.481 (95 % CI: -0.744, -0.219)], potassium [-0.344 (95 % CI: -0.530, -0.157)], iron [-0.468 (95 % CI: -0.646, -0.291)], and ammonium ion [-0.125 (95 % CI: -0.197, -0.052)] and cognitive decline. However, we didn't find any individual components more harmful than PM2.5. Poor sleep quality partially mediated the estimated associations, which were explained ranging from 2.28 % to 11.99 %. Stratification analyses showed that people living in areas with lower greenspace were more susceptible to specific PM2.5 components. Our study suggests that the adverse effect of suffering from PM2.5 components is more pronounced among individuals with poor sleep quality, amplifying environmental inequalities in health. Besides reducing environmental pollution, improving sleep quality may be another measure worth considering to improve cognition if our research is confirmed in the future.
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Affiliation(s)
- Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Yi 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
| | - Zhiwei Xu
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Qinghua Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Peng Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Jianlong Fang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Yingchun Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yifu Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China.
| | - Xiaoming Shi
- 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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