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Wang X, Dewancker BJ, Tian D, Zhuang S. Exploring the Burden of PM2.5-Related Deaths and Economic Health Losses in Beijing. TOXICS 2024; 12:377. [PMID: 38922057 PMCID: PMC11209575 DOI: 10.3390/toxics12060377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024]
Abstract
Air pollution is one of the major global public health challenges. Using annual fine particulate matter (PM2.5) concentration data from 2016 to 2021, along with the global exposure mortality model (GEMM), we estimated the multi-year PM2.5-pollution-related deaths divided by different age groups and diseases. Then, using the VSL (value of statistical life) method, we assessed corresponding economic losses and values. The number of deaths attributed to PM2.5 in Beijing in 2021 fell by 33.74 percent from 2016, while health economic losses would increase by USD 4.4 billion as per capita disposable income increases year by year. In 2021, the average annual concentration of PM2.5 in half of Beijing's municipal administrative districts is less than China's secondary ambient air quality standard (35 μg/m3), but it can still cause 48,969 deaths and corresponding health and economic losses of USD 16.31 billion, equivalent to 7.9 percent of Beijing's GDP. Therefore, it is suggested that more stringent local air quality standards should be designated to protect public health in Beijing.
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Affiliation(s)
- Xiaoqi Wang
- Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan;
| | - Bart Julien Dewancker
- Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan;
| | - Dongwei Tian
- School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
| | - Shao Zhuang
- School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China;
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2
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Li X, Guo M, Niu Y, Xie M, Liu X. Secular trends of asthma mortality in China and the United States from 1990 to 2019. Chin Med J (Engl) 2024; 137:273-282. [PMID: 37882090 PMCID: PMC10836907 DOI: 10.1097/cm9.0000000000002855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Asthma imposes a large healthcare burden in China and the United States (US). However, the trends of asthma mortality and the relative risk factors have not been comparatively analyzed between the countries. The aim of this study was to compare the mortality and risk factors between China and the US. METHODS The deaths, and mortality rates of asthma in China and the US during 1990-2019 were obtained from the Global Burden of Disease Study 2019. The age-period-cohort model was used to estimate these mortality rates based on a log-linear scale with additive age, period, and cohort effects. The population attributable fractions of risk factors for asthma were estimated. RESULTS In 1990-2019, the asthma mortality rate was higher in China than in the US. The crude and age-standardized asthma mortality rates trended downward in both China and the US from 1990 to 2019. The decline in mortality was more obvious in China. Mortality gap between the two countries was narrowing. A sex difference in asthma mortality was observed with higher mortality in males in China and females in the US. The age effects showed that mortality increased with age in adults older than 20 years, particularly in the elderly. Downward trends were generally observed in the period and cohort rate ratios in both countries, with China experiencing a more obvious decrease. Smoking and high body mass index (BMI) were the leading risk factors for asthma mortality in China and the US, respectively. Mortality attributable to occupational asthmagens and smoking decreased the most in China and the US, respectively. CONCLUSIONS In 1990-2019, the asthma mortality rate was higher in China than in the US; however, the mortality gap has narrowed. Mortality increased with age in adults. The improvements in asthma death risk with period and birth cohort were more obvious in China than in the US. Smoking, high BMI, and aging are major health problems associated with asthma control. The role of occupational asthmagens in asthma mortality underscores the importance of management and prevention of occupational asthma.
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Affiliation(s)
- Xiaochen Li
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
- NHC Key Laboratory of Respiratory Diseases, Wuhan, Hubei 430030, China
| | - Mingzhou Guo
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
- NHC Key Laboratory of Respiratory Diseases, Wuhan, Hubei 430030, China
| | - Yang Niu
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
- NHC Key Laboratory of Respiratory Diseases, Wuhan, Hubei 430030, China
| | - Min Xie
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
- NHC Key Laboratory of Respiratory Diseases, Wuhan, Hubei 430030, China
| | - Xiansheng Liu
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
- NHC Key Laboratory of Respiratory Diseases, Wuhan, Hubei 430030, China
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3
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Wu C, He G, Wu W, Meng R, Zhou C, Bai G, Yu M, Gong W, Huang B, Xiao Y, Hu J, Xiao J, Zeng F, Yang P, Liu D, Zhu Q, Chen Z, Yu S, Huang C, Du Y, Liang X, Liu T, Ma W. Ambient PM 2.5 and cardiopulmonary mortality in the oldest-old people in China: A national time-stratified case-crossover study. MED 2024; 5:62-72.e3. [PMID: 38218176 DOI: 10.1016/j.medj.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/03/2023] [Accepted: 12/07/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND Evidence on the associations of fine particulate matter (PM2.5) with cardiopulmonary mortality in the oldest-old (aged 80+ years) people remains limited. METHODS We conducted a time-stratified case-crossover study of 1,475,459 deaths from cardiopulmonary diseases in China to estimate the associations between short-term exposure to ambient PM2.5 and cardiopulmonary mortality among the oldest-old people. FINDINGS Each 10 μg/m3 increase in PM2.5 concentration (6-day moving average [lag05]) was associated with higher mortality from cardiopulmonary diseases (excess risks [ERs] = 1.69%, 95% confidence interval [CI]: 1.54%, 1.84%), cardiovascular diseases (ER = 1.72%, 95% CI: 1.54%, 1.90%), and respiratory diseases (ER = 1.62%, 95% CI: 1.33%, 1.91%). Compared to the other groups, females (ER = 1.94%, 95% CI: 1.73%, 2.15%) (p for difference test = 0.043) and those aged 95-99 years (ER = 2.31%, 95% CI: 1.61%, 3.02%) (aged 80-85 years old was the reference, p for difference test = 0.770) presented greater mortality risks. We found 14 specific cardiopulmonary causes associated with PM2.5, out of which emphysema (ER = 3.20%, 95% CI: 1.57%, 4.86%) had the largest association. Out of the total deaths, 6.27% (attributable fraction [AF], 95% CI: 5.72%, 6.82%) were ascribed to short-term PM2.5 exposure. CONCLUSIONS This study provides evidence of PM2.5-induced cardiopulmonary mortality and calls for targeted prevention actions for the oldest-old people. FUNDING This work was supported by the National Key Research and Development Program of China, the National Natural Science Foundation of China, the Foreign Expert Program of the Ministry of Science and Technology, the Natural Science Foundation of Guangdong, China, and the Science and Technology Program of Guangzhou.
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Affiliation(s)
- Cuiling Wu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
| | - Guanhao He
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
| | - Wei Wu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Ruilin Meng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Chunliang Zhou
- Department of Environment and Health, Hunan Provincial Center for Disease Control and Prevention, Changsha 450001, China
| | - Guoxia Bai
- Institute of Non-communicable Diseases Prevention and Control, Tibet Center for Disease Control and Prevention, Lhasa 850000, China
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, Zhejiang 310051, China
| | - Weiwei Gong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, Zhejiang 310051, China
| | - Biao Huang
- Health Hazard Factors Control Department, Jilin Provincial Center for Disease Control and Prevention, Changchun 130062, China
| | - Yize Xiao
- Yunnan Center for Disease Control and Prevention, Kunming 650022, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Fangfang Zeng
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
| | - Pan Yang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
| | - Dan Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
| | - Qijiong Zhu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
| | - Zhiqing Chen
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
| | - Siwen Yu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| | - Yaodong Du
- Guangdong Provincial Climate Center, Guangzhou 510080, China
| | - Xiaofeng Liang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China.
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
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Wang Y, Li Q, Luo Z, Zhao J, Lv Z, Deng Q, Liu J, Ezzati M, Baumgartner J, Liu H, He K. Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing. COMMUNICATIONS EARTH & ENVIRONMENT 2023; 4:451. [PMID: 38130441 PMCID: PMC7615407 DOI: 10.1038/s43247-023-01119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023]
Abstract
With the decreasing regional-transported levels, the health risk assessment derived from fine particulate matter (PM2.5) has become insufficient to reflect the contribution of local source heterogeneity to the exposure differences. Here, we combined the both ultra-high-resolution PM2.5 concentration with population distribution to provide the personal daily PM2.5 internal dose considering the indoor/outdoor exposure difference. A 30-m PM2.5 assimilating method was developed fusing multiple auxiliary predictors, achieving higher accuracy (R2 = 0.78-0.82) than the chemical transport model outputs without any post-simulation data-oriented enhancement (R2 = 0.31-0.64). Weekly difference was identified from hourly mobile signaling data in 30-m resolution population distribution. The population-weighted ambient PM2.5 concentrations range among districts but fail to reflect exposure differences. Derived from the indoor/outdoor ratio, the average indoor PM2.5 concentration was 26.5 μg/m3. The internal dose based on the assimilated indoor/outdoor PM2.5 concentration shows high exposure diversity among sub-groups, and the attributed mortality increased by 24.0% than the coarser unassimilated model.
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Affiliation(s)
- Yongyue Wang
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Qiwei Li
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Zhenyu Luo
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Junchao Zhao
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Zhaofeng Lv
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Qiuju Deng
- Centre for Clinical and Epidemiologic Research, Beijing An Zhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China
| | - Jing Liu
- Centre for Clinical and Epidemiologic Research, Beijing An Zhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China
| | - Majid Ezzati
- School of Public Health, Imperial College London, London SW72AZ, UK
| | - Jill Baumgartner
- School of Population and Global Health, McGill University, Montréal, QC H3A0G4, Canada
| | - Huan Liu
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
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5
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Li Y, Ma L, Ni M, Bai Y, Li C. Drivers of ozone-related premature mortality in China: Implications for historical and future scenarios. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 345:118663. [PMID: 37487454 DOI: 10.1016/j.jenvman.2023.118663] [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: 05/03/2023] [Revised: 07/15/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
Long-term exposure to ambient ozone (O3) poses a severe public health threat in China. However, the drivers of premature mortality caused by O3 pollution are still poorly constrained, despite being prerequisites for addressing the threat. Here, we demonstrate the contributions of historical and future changes in peak-season O3, population size, age structure, and baseline mortality to China's O3-related mortality using decomposition analysis. From 2013 to 2021, O3-related mortality decreased dramatically from 78.8 (40.8-124.6) to 68.7 (36.0-107.2) thousand, especially in densely populated areas with high pollution. Variations in peak-season O3, population size, age structure, and baseline mortality led to changes in O3-related mortality of +27.3 (14.8-41.3), +2.6 (1.4-4.1), +22.3 (11.5-35.2), and -40.3 (20.9-63.7) thousand, respectively. The influence of peak-season O3 on O3-related mortality shifted from positive during 2013-2019 (+8.4% per year) to negative during 2019-2021 (-8.8% per year), which highly regulated the interannual trend of mortality. From 2021 to 2035, O3-related mortality is expected to increase by 31% in the current context of peak-season O3 levels, primarily caused by increased aging. Even reducing peak-season O3 to the WHO interim target 1 (IT-1) would only reduce O3-related mortality by 3.9%, while a more rigorous standard (IT-2) would prevent 83.7% of mortality. These findings suggest that improving ambient O3 can lead to significant health benefits, but substantial mitigation strategies are merited given the future trend of population aging.
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Affiliation(s)
- Yong Li
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, 550025, China
| | - Lu Ma
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, 550025, China.
| | - Maofei Ni
- College of Eco-Environmental Science and Engineering, Guizhou Minzu University, Guiyang, 550025, China
| | - Yun Bai
- School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China
| | - Chuan Li
- School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China.
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Roger Chen YH, Lee WC, Liu BC, Yang PC, Ho CC, Hwang JS, Huang TH, Lin HH, Lo WC. Quantifying the potential effects of air pollution reduction on population health and health expenditure in Taiwan. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 336:122405. [PMID: 37597736 DOI: 10.1016/j.envpol.2023.122405] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/12/2023] [Accepted: 08/16/2023] [Indexed: 08/21/2023]
Abstract
Air pollution, particularly ambient fine particulate matter (PM2.5) pollution, poses a significant risk to public health, underscoring the importance of comprehending the long-term impact on health burden and expenditure at national and subnational levels. Therefore, this study aims to quantify the disease burden and healthcare expenditure associated with PM2.5 exposure in Taiwan and assess the potential benefits of reducing pollution levels. Using a comparative risk assessment framework that integrates an auto-aggressive integrated moving average model, we evaluated the avoidable burden of cardiopulmonary diseases (including ischemic heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, and diabetes mellitus) and related healthcare expenditure under different air quality target scenarios, including status quo and target scenarios of 15, 10, and 5 μg/m3 reduction in PM2.5 concentration. Our findings indicate that reducing PM2.5 exposure has the potential to significantly alleviate the burden of multiple diseases. Comparing the estimated attributable disease burden and healthcare expenditure between reference and target scenarios from 2022 to 2050, the avoidable disability-adjusted life years were 0.61, 1.83, and 3.19 million for the 15, 10, and 5 μg/m3 target scenarios, respectively. Correspondingly, avoidable healthcare expenditure ranged from US$ 0.63 to 3.67 billion. We also highlighted the unequal allocation of resources and the need for policy interventions to address health disparities due to air pollution. Notably, in the 5 μg/m3 target scenario, Kaohsiung City stands to benefit the most, with 527,368 disability-adjusted life years avoided and US$ 0.53 billion saved from 2022 to 2050. Our findings suggest that adopting stricter emission targets can effectively reduce the health burden and associated healthcare expenditure in Taiwan. Overall, this study provides policymakers in Taiwan with valuable insights for mitigating the negative effects of air pollution by establishing a comprehensive framework for evaluating the co-benefits of air pollution reduction on healthcare expenditure and disease burden.
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Affiliation(s)
- Yi-Hsuan Roger Chen
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, USA; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Wan-Chen Lee
- Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Bo-Chen Liu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Po-Chieh Yang
- Department of Industrial Economics, Tamkang University, Taipei, Taiwan
| | - Chi-Chang Ho
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | | | - Tzu-Hsuan Huang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; AbbVie Inc. North Chicago, Illinois, USA
| | - Hsien-Ho Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Global Health Program, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Wei-Cheng Lo
- Master Program in Applied Epidemiology, College of Public Health, Taipei Medical University, Taipei, Taiwan; Taipei Medical University Research Center of Urology and Kidney, Taipei Medical University, Taipei, Taiwan.
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7
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Huang W, Zhou Y, Chen X, Zeng X, Knibbs LD, Zhang Y, Jalaludin B, Dharmage SC, Morawska L, Guo Y, Yang X, Zhang L, Shan A, Chen J, Wang T, Heinrich J, Gao M, Lin L, Xiao X, Zhou P, Yu Y, Tang N, Dong G. Individual and joint associations of long-term exposure to air pollutants and cardiopulmonary mortality: a 22-year cohort study in Northern China. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 36:100776. [PMID: 37547049 PMCID: PMC10398602 DOI: 10.1016/j.lanwpc.2023.100776] [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: 12/05/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 08/08/2023]
Abstract
Background Evidence on the associations between long-term exposure to multiple air pollutants and cardiopulmonary mortality is limited, especially for developing regions with higher pollutant levels. We aimed to characterise the individual and joint (multi-pollutant) associations of long-term exposure to air pollutants with cardiopulmonary mortality, and to identify air pollutant that primarily contributes to the mortality risk. Methods We followed 37,442 participants with a mean age of 43.5 years in four cities in northern China (Tianjin, Shenyang, Taiyuan, and Rizhao) from January 1998 to December 2019. Annual particulate matter (PM) with diameters ≤2.5 μm (PM2.5), ≤10 μm (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) were estimated using daily average values from satellite-derived machine learning models and monitoring stations. Time-varying Cox proportional hazards model was used to evaluate the individual association between air pollutants and mortality from non-accidental causes, cardiovascular diseases (CVDs), non-malignant respiratory diseases (RDs) and lung cancer, accounting for demographic and socioeconomic factors. Effect modifications by age, sex, income and education level were also examined. Quantile-based g-Computation integrated with time-to-event data was additionally applied to evaluate the co-effects and the relative weight of contributions for air pollutants. Findings During 785,807 person-years of follow-up, 5812 (15.5%) died from non-accidental causes, among which 2932 (7.8%) were from all CVDs, 479 (1.3%) from non-malignant RDs, and 552 (1.4%) from lung cancer. Long-term exposure to PM10 (mean [baseline]: 136.5 μg/m3), PM2.5 (mean [baseline]: 70.2 μg/m3), SO2 (mean [baseline]: 113.0 μg/m3) and NO2 (mean [baseline]: 39.2 μg/m3) were adversely and consistently associated with all mortality outcomes. A 10 μg/m3 increase in PM2.5 was associated with higher mortality from non-accidental causes (hazard ratio 1.20; 95% confidence interval 1.17-1.23), CVDs (1.23; 1.19-1.28), non-malignant RDs (1.37; 1.25-1.49) and lung cancer (1.14; 1.05-1.23). A monotonically increasing curve with linear or supra-linear shape with no evidence of a threshold was observed for the exposure-response relationship of mortality with individual or joint exposure to air pollutants. PM2.5 consistently contributed most to the elevated mortality risks related to air pollutant mixture, followed by SO2 or PM10. Interpretation There was a strong and positive association of long-term individual and joint exposure to PM10, PM2.5, SO2, and NO2 with mortalities from non-accidental causes, CVDs, non-malignant RDs and lung cancer in high-exposure settings, with PM2.5 potentially being the main contributor. The shapes of associations were consistent with a linear or supra-linear exposure-response relationship, with no lower threshold observed within the range of concentrations in this study. Funding National Key Research and Development Program of China, the China Scholarship Council, the National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province.
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Affiliation(s)
- Wenzhong Huang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Yang Zhou
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China
| | - Xi Chen
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin 300070, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin 300070, China
| | - Xiaowen Zeng
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Luke D. Knibbs
- Faculty of Medicine and Health, School of Public Health, The University of Sydney, NSW 2006, Australia
- Public Health Research Analytics and Methods for Evidence, Public Health Unit, Sydney Local Health District, Camperdown, NSW 2050, Australia
| | - Yunting Zhang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Bin Jalaludin
- Centre for Air Quality and Health Research and Evaluation, Glebe, NSW 2037, Australia
- Ingham Institute for Applied Medial Research, Liverpool, NSW 2170, Australia
- School of Public Health and Community Medicine, The University of New South Wales, Kensington, NSW 2052, Australia
| | - Shyamali C. Dharmage
- Allergy and Lung Health Unit, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Xueli Yang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin 300070, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin 300070, China
| | - Liwen Zhang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin 300070, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin 300070, China
| | - Anqi Shan
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin 300070, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin 300070, China
| | - Jie Chen
- Department of Occupational and Environmental Health, School of Public Health, China Medical University, Shenyang 110122, China
| | - Tong Wang
- School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Joachim Heinrich
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich 80336, Germany
| | - Meng Gao
- Department of Geography, Hong Kong Baptist University, Hong Kong SAR, China
| | - Lizi Lin
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiang Xiao
- Department of Geography, Hong Kong Baptist University, Hong Kong SAR, China
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Peien Zhou
- Department of Public Health & Primary Care, University of Cambridge, Cambridge CB2 1TN, United Kingdom
| | - Yunjiang Yu
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China
| | - Naijun Tang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
| | - Guanghui Dong
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
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Bai H, Wu H, Gao W, Wang S, Cao Y. Influence of spatial resolution of PM 2.5 concentrations and population on health impact assessment from 2010 to 2020 in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 326:121505. [PMID: 36965685 DOI: 10.1016/j.envpol.2023.121505] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 06/18/2023]
Abstract
Ambient PM2.5 pollution is a leading environmental health risk factor worldwide. The spatial resolution of PM2.5 concentrations and population strongly impacts PM2.5-related health impact estimates. However, long-term variations and regional differences in this impact have rarely been explored, particularly in China. Here, by aggregating satellite-derived PM2.5 concentration and population datasets at 1-km resolution in China to coarser resolutions (10, 50, and 100 km), we evaluated decadal changes in the impact of resolution on health assessments at national and local scales. For the sensitivity of population-weighted mean (PWM) PM2.5 concentrations to resolution, we found that the national PWM PM2.5 concentration decreased with coarser resolutions; this pattern was widely observed and was more obvious in southern and central China and the Sichuan Basin. The results showed that the sensitivity of national PWM PM2.5 concentrations to resolution continuously weakened from 2010 to 2020, likely due to a reduction in the spatial heterogeneity of PM2.5 concentrations in regions with high sensitivity. This weakness caused a large underestimation of the long-term trend of national PWM PM2.5 using a 100-km resolution, which was 7% lower than the trend at 1 km. Regarding the sensitivity of PM2.5-attributable mortality to resolution, most of China exhibited a pattern in which attributable mortality decreased with coarser resolution. The sensitivity of the estimated PM2.5-attributable mortality to resolution also weakened over time on a national scale and in most parts of China. Nevertheless, the weakness for mortality sensitivity was not as apparent as for PWM PM2.5 sensitivity. This was likely because different drivers played distinct roles in the temporal variation of the mortality sensitivity: population aging enhanced the sensitivity, and variations in PM2.5 concentrations and population distribution both weakened the sensitivity. However, the national attributable mortality trend at a 100-km resolution was still underestimated by 1.75% relative to the 1-km resolution.
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Affiliation(s)
- Heming Bai
- Research Center for Intelligent Information Technology, Nantong University, Nantong, 226019, China.
| | - Huiqun Wu
- Department of Medical Informatics, Medical School of Nantong University, Nantong, 226019, China
| | - Wenkang Gao
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Shuai Wang
- China National Environmental Monitoring Center, Beijing, 100012, China
| | - Yang Cao
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
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9
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Gao S, Zou BJ, Shi S, Wei YF, Du ZD, Zheng G, Wang R, Yin JL, Zhao JQ, Yan S, Qin X, Xiao Q, Gong TT, Chen RJ, Zhao YH, Wu QJ. PM 2.5 exposure and its interaction of oxidative balance score on ovarian cancer survival: A prospective cohort study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 256:114877. [PMID: 37037107 DOI: 10.1016/j.ecoenv.2023.114877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 03/24/2023] [Accepted: 04/04/2023] [Indexed: 06/19/2023]
Abstract
Recent evidence advises particles with a diameter of 2.5 µm or less (PM2.5) might be a prognostic factor for ovarian cancer (OC) survival. The oxidative balance score (OBS) incorporates diet-lifestyle factors to estimate individuals' anti-oxidant exposure status which may be relevant to cancer prognosis. We aimed to investigate the roles of PM2.5, and OBS and their interaction in OC prognosis. 663 patients with OC were enrolled in the current study. Satellite-derived annual average exposures to PM2.5 based on patients' residential locations. The OBS was calculated based on 16 different diet-lifestyle components derived using an acknowledged self-reported questionnaire. The Cox regression model was performed to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for overall survival (OS). We also assessed the effect of modification between PM2.5 and OS by OBS via interaction terms. During a median follow-up of 37.57 (interquartile:35.27-40.17) months, 123 patients died. Compared to low-concentration PM2.5 exposure, high PM2.5 during 1 year before diagnosis was associated with worse OC survival (HR= 1.19, 95% CI = 1.01-1.42). We observed an improved OS with the highest compared with the lowest OBS (HR = 0.46, 95% CI = 0.27-0.79, P for trend < 0.05). Notably, we also found an additive interaction between low OBS and high exposure to PM2.5, with the corresponding associations of PM2.5 being more pronounced among participants with lower OBS (HR = 1.42, 95% CI = 1.09-1.86). PM2.5 may blunt OC survival, but high OBS represented an antioxidative performance that could alleviate the adverse association of PM2.5 and OS.
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Affiliation(s)
- Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Bing-Jie Zou
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Su Shi
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Yi-Fan Wei
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Zong-Da Du
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Gang Zheng
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Rang Wang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jia-Li Yin
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jun-Qi Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Shi Yan
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xue Qin
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qian Xiao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Ren-Jie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Yu-Hong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China; Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China; Key Laboratory of Reproductive and Genetic Medicine (China Medical University), National Health Commission, Shenyang, China.
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10
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Guo Y, Yang L, Li H, Qiu L, Wang L, Zhang L. County level study of the interaction effect of PM 2.5 and climate sustainability on mortality in China. Front Public Health 2023; 10:1036272. [PMID: 36684965 PMCID: PMC9853058 DOI: 10.3389/fpubh.2022.1036272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 12/06/2022] [Indexed: 01/09/2023] Open
Abstract
Introduction PM2.5 and climate change are two major public health concerns, with majority of the research on their interaction focused on the synergistic effect, particularly for extreme events such as hot or cold temperatures. The climate sustainability index (CLS) was introduced to comprehensively explore the impact of climate change and the interactive effect on human health with air pollution. Methods In this study, a county-level panel data in China was collected and used. The generalized additive model (GAM) and geographically and temporally weighted regression (GTWR) was used to explore the interactive and spatial effect on mortality between CLS and PM2.5. Results and discussions Individually, when CLS is higher than 150 or lower than 50, the mortality is higher. Moreover, when PM2.5 is more than 35 μg/m3, the influence on mortality is significantly increased as PM2.5 concentration rises; when PM2.5 is above 70 μg/m3, the trend is sharp. A nonlinear antagonistic effect between CLS and PM2.5 was found in this study, proving that the combined adverse health effects of climate change and air pollution, especially when CLS was lower (below 100) and PM2.5 was higher (above 35 μg/m3), the antagonistic effect was much stronger. From a spatial perspective, the impact of CLS and PM2.5 on mortality varies in different geographical regions. A negative and positive influence of CLS and PM2.5 was found in east China, especially in the northeastern and northern regions, -which were heavily polluted. This study illustrated that climate sustainability, at certain level, could mitigate the adverse health influence of air pollution, and provided a new perspective on health risk mitigation from pollution reduction and climate adaptation.
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Affiliation(s)
- Yanan Guo
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Linsheng Yang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Hairong Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Leijie Qiu
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Li Wang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Lantian Zhang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
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11
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Chung CY, Yang J, Yang X, He J. Mathematical modeling in the health risk assessment of air pollution-related disease burden in China: A review. Front Public Health 2022; 10:1060153. [PMID: 36504933 PMCID: PMC9727382 DOI: 10.3389/fpubh.2022.1060153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 11/08/2022] [Indexed: 11/24/2022] Open
Abstract
This review paper covers an overview of air pollution-related disease burden in China and a literature review on the previous studies which have recently adopted a mathematical modeling approach to demonstrate the relative risk (RR) of air pollution-related disease burden. The associations between air pollution and disease burden have been explored in the previous studies. Therefore, it is necessary to quantify the impact of long-term exposure to ambient air pollution by using a suitable mathematical model. The most common way of estimating the health risk attributable to air pollution exposure in a population is by employing a concentration-response function, which is often based on the estimation of a RR model. As most of the regions in China are experiencing rapid urbanization and industrialization, the resulting high ambient air pollution is influencing more residents, which also increases the disease burden in the population. The existing RR models, including the integrated exposure-response (IER) model and the global exposure mortality model (GEMM), are critically reviewed to provide an understanding of the current status of mathematical modeling in the air pollution-related health risk assessment. The performances of different RR models in the mortality estimation of disease are also studied and compared in this paper. Furthermore, the limitations of the existing RR models are pointed out and discussed. Consequently, there is a need to develop a more suitable RR model to accurately estimate the disease burden attributable to air pollution in China, which contributes to one of the key steps in the health risk assessment. By using an updated RR model in the health risk assessment, the estimated mortality risk due to the impacts of environment such as air pollution and seasonal temperature variation could provide a more realistic and reliable information regarding the mortality data of the region, which would help the regional and national policymakers for intensifying their efforts on the improvement of air quality and the management of air pollution-related disease burden.
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Affiliation(s)
- Chee Yap Chung
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang Province, China,*Correspondence: Chee Yap Chung
| | - Jie Yang
- Department of Mathematics, University of Hull, Hull, United Kingdom
| | - Xiaogang Yang
- Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang Province, China,Xiaogang Yang
| | - Jun He
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang Province, China
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12
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Xiao Q, Geng G, Xue T, Liu S, Cai C, He K, Zhang Q. Tracking PM 2.5 and O 3 Pollution and the Related Health Burden in China 2013-2020. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:6922-6932. [PMID: 34941243 DOI: 10.1021/acs.est.1c04548] [Citation(s) in RCA: 82] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/), we characterized the spatiotemporal variations in PM2.5 and O3 exposures and quantified the long- and short-term exposure related premature deaths during 2013-2020 with respect to the two-stage clean air actions (2013-2017 and 2018-2020). We find a 48% decrease in national PM2.5 exposure during 2013-2020, although the decrease rate has slowed after 2017. At the same time, O3 pollution worsened, with the average April-September O3 exposure increased by 17%. The improved air quality led to 308 thousand and 16 thousand avoided long- and short-term exposure related deaths, respectively, in 2020 compared to the 2013 level, which was majorly attributed to the reduction in ambient PM2.5 concentration. It is also noticed that with smaller PM2.5 reduction, the avoided long-term exposure associated deaths in 2017-2020 (13%) was greater than that in 2013-2017 (9%), because the exposure-response curve is nonlinear. As a result of the efforts in reducing PM2.5-polluted days with the daily average PM2.5 higher than 75 μg/m3 and the considerable increase in O3-polluted days with the daily maximum 8 h average O3 higher than 160 μg/m3, deaths attributable to the short-term O3 exposure were greater than those due to PM2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM2.5 and O3 are urgently needed.
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Affiliation(s)
- Qingyang Xiao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100080, China
| | - Shigan Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Cilan Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
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13
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Zhu Q, Bi J, Liu X, Li S, Wang W, Zhao Y, Liu Y. Satellite-Based Long-Term Spatiotemporal Patterns of Surface Ozone Concentrations in China: 2005-2019. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:27004. [PMID: 35138921 PMCID: PMC8827621 DOI: 10.1289/ehp9406] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
BACKGROUND Although short-term ozone (O3) exposure has been associated with a series of adverse health outcomes, research on the health effects of chronic O3 exposure is still limited, especially in developing countries because of the lack of long-term exposure estimates. OBJECTIVES The present study aimed to estimate the spatiotemporal distribution of monthly mean daily maximum 8-h average O3 concentrations in China from 2005 to 2019 at a 0.05° spatial resolution. METHODS We developed a machine learning model with a satellite-derived boundary-layer O3 column, O3 precursors, meteorological conditions, land-use information, and proxies of anthropogenic emissions as predictors. RESULTS The random, spatial, and temporal cross-validation R2 of our model were 0.87, 0.86, and 0.76, respectively. Model-predicted spatial distribution of ground-level O3 concentrations showed significant differences across seasons. The highest summer peak of O3 occurred in the North China Plain, whereas southern regions were the most polluted in winter. Most large urban centers showed elevated O3 levels, but their surrounding suburban areas may have even higher O3 concentrations owing to nitrogen oxides titration. The annual trend of O3 concentrations fluctuated over 2005-2013, but a significant nationwide increase was observed afterward. DISCUSSION The present model had enhanced performance in predicting ground-level O3 concentrations in China. This national data set of O3 concentrations would facilitate epidemiological studies to investigate the long-term health effect of O3 in China. Our results also highlight the importance of controlling O3 in China's next round of the Air Pollution Prevention and Control Action Plan. https://doi.org/10.1289/EHP9406.
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Affiliation(s)
- Qingyang Zhu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Jianzhao Bi
- Department of Environmental & Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, USA
| | - Xiong Liu
- Harvard–Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USA
| | - Shenshen Li
- State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
| | - Wenhao Wang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Yu Zhao
- State Key Laboratory of Pollution Control and Resource Reuse and School of the Environment, Nanjing University, Nanjing, Jiangsu Province, China
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
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14
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Yang X, Wang Y, Zhao C, Fan H, Yang Y, Chi Y, Shen L, Yan X. Health risk and disease burden attributable to long-term global fine-mode particles. CHEMOSPHERE 2022; 287:132435. [PMID: 34606897 DOI: 10.1016/j.chemosphere.2021.132435] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 08/11/2021] [Accepted: 09/15/2021] [Indexed: 06/13/2023]
Abstract
Particulate matter 2.5 (PM2.5) pollution has long been a global environmental problem and still poses a great threat to public health. This study investigates global spatiotemporal variations in PM2.5 using the newly developed satellite-derived PM2.5 dataset from 1998 to 2018. An integrated exposure-response (IER) model was employed to examine the characteristics of PM2.5-related deaths caused by chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), lung cancer (LC), and stroke in adults (age≥25), as well as lower respiratory infection (LRI) in children (age≤5). The results showed that high annual PM2.5 concentrations were observed mainly in East Asia and South Asia. Over the 19-year period, PM2.5 concentrations constantly decreased in developed regions, but increased in most developing regions. Approximately 84% of the population lived in regions where PM2.5 concentrations exceeded 10 μg/m3. Meanwhile, the vast majority of the population (>60%) in East and South Asia was consistently exposed to PM2.5 levels above 35 μg/m3. PM2.5 exposure was linked to 3.38 (95% UI: 3.05-3.70) million premature deaths globally in 2000, a number that increased to 4.11 (95% UI: 3.55-4.69) million in 2018. Premature deaths related to PM2.5 accounted for 6.54%-7.79% of the total cause of deaths worldwide, with a peak in 2011. Furthermore, developing regions contributed to the majority (85.95%-95.06%) of PM2.5-related deaths worldwide, and the three highest-ranking regions were East Asia, South Asia, and Southeast Asia. Globally, IHD and stroke were the two main contributors to total PM2.5-related deaths, followed by COPD, LC, and LRI.
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Affiliation(s)
- Xingchuan Yang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Yuan Wang
- Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Chuanfeng Zhao
- State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
| | - Hao Fan
- State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Yikun Yang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Yulei Chi
- State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Lixing Shen
- State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Xing Yan
- State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
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15
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Qiu B, Zhou M, Qiu Y, Ma Y, Ma C, Tu J, Li S. An Integration Method for Regional PM 2.5 Pollution Control Optimization Based on Meta-Analysis and Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 19:ijerph19010344. [PMID: 35010605 PMCID: PMC8750964 DOI: 10.3390/ijerph19010344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/10/2021] [Accepted: 12/22/2021] [Indexed: 05/06/2023]
Abstract
PM2.5 pollution in China is becoming increasingly severe, threatening public health. The major goal of this study is to evaluate the mortality rate attributed to PM2.5 pollution and design pollution mitigation schemes in a southern district of China through a two-objective optimization model. The mortality rate is estimated by health effect evaluation model. Subjected to limited data information, it is assumed that the meta-analysis method, through summarizing and combining the research results on the same subject, was suitable to estimate the percentage of deaths caused by PM2.5 pollution. The critical parameters, such as the total number of deaths and the background concentration of PM2.5, were obtained through on-site survey, data collection, literature search, policy analysis, and expert consultation. The equations for estimating the number of deaths caused by PM2.5 pollution were established by incorporating the relationship coefficient of exposure to reaction, calculated residual PM2.5 concentration of affected region, and statistical total base number of deaths into a general framework. To balance the cost from air quality improvement and human health risks, a two-objective optimization model was developed. The first objective is to minimize the mortality rate attributable to PM2.5 pollution, and the second objective is to minimize the total system cost over three periods. The optimization results demonstrated that the combination of weights assigned to the two objectives significantly influenced the model output. For example, a high weight value assigned to minimizing the number of deaths results in the increased use of treatment techniques with higher efficiencies and a dramatic decrease in pollutant concentrations. In contrast, a model weighted more toward minimizing economic loss may lead to an increase in the death toll due to exposure to higher air pollution levels. The effective application of this model in the Nanshan District of Shenzhen City, China, is expected to serve as a basis for similar work in other parts of the world in the future.
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Affiliation(s)
- Bingkui Qiu
- Department of Tourism Management, Jin Zhong University, Jinzhong 033619, China;
| | - Min Zhou
- College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.M.); (C.M.); (J.T.); (S.L.)
- Correspondence: ; Tel./Fax: +86-27-87543047
| | - Yang Qiu
- Department of Economics, University of Warwick, Coventry CV4 7AL, UK;
| | - Yuxiang Ma
- College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.M.); (C.M.); (J.T.); (S.L.)
| | - Chaonan Ma
- College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.M.); (C.M.); (J.T.); (S.L.)
| | - Jiating Tu
- College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.M.); (C.M.); (J.T.); (S.L.)
| | - Siqi Li
- College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.M.); (C.M.); (J.T.); (S.L.)
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Wang C, Wang Y, Shi Z, Sun J, Gong K, Li J, Qin M, Wei J, Li T, Kan H, Hu J. Effects of using different exposure data to estimate changes in premature mortality attributable to PM 2.5 and O 3 in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 285:117242. [PMID: 33957508 DOI: 10.1016/j.envpol.2021.117242] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 04/20/2021] [Accepted: 04/23/2021] [Indexed: 06/12/2023]
Abstract
The assessment of premature mortality associated with the dramatic changes in fine particulate matter (PM2.5) and ozone (O3) has important scientific significance and provides valuable information for future emission control strategies. Exposure data are particularly vital but may cause great uncertainty in health burden assessments. This study, for the first time, used six methods to generate the concentration data of PM2.5 and O3 in China between 2014 and 2018, and then quantified the changes in premature mortality due to PM2.5 and O3 using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that PM2.5-related premature mortality in China decreases by 263 (95% confidence interval (CI95): 142-159) to 308 (CI95: 213-241) thousands from 2014 to 2018 by using different concentration data, while O3-related premature mortality increases by 67 (CI95: 26-104) to 103 (CI95: 40-163) thousands. The estimated mean changes are up to 40% different for the PM2.5-related mortality, and up to 30% for the O3-related mortality if different exposure data are chosen. The most significant difference due to the exposure data is found in the areas with a population density of around 103 people/km2, mostly located in Central China, for both PM2.5 and O3. Our results demonstrate that the exposure data source significantly affects mortality estimations and should thus be carefully considered in health burden assessments.
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Affiliation(s)
- Chunlu 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
| | - 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
| | - Zhihao Shi
- 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
| | - Jinjin Sun
- 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
| | - Kangjia Gong
- 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
| | - Jingyi Li
- 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
| | - Momei Qin
- 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
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20740, USA
| | - Tiantian Li
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, Fudan University, Shanghai, 200032, 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.
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17
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Amodio E, Zarcone M, Casuccio A, Vitale F. Trends in epidemiology: the role of denominator fluctuation in population based estimates. AIMS Public Health 2021; 8:500-506. [PMID: 34395700 PMCID: PMC8334628 DOI: 10.3934/publichealth.2021040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 07/05/2021] [Indexed: 11/28/2022] Open
Abstract
Background Population estimates are of paramount importance for calculating occurrence and association measures although they can be affected by problems of accuracy and completeness. This study has performed a simulation of the impact of Italian population size variability on incidence rates. Methods Data have been obtained by the Italian National Institute of Statistics. For each year expected cases were calculated at increasing fixed rates (up to 1,000/100,000) and were considered constant in the “following year”, calculating statistical differences (P < 0.05). Results In Italy and in other regions, statistically significant higher RRs were found in 2012 vs. 2011 whereas statistically significant lower RRs were found in 2013 vs. 2012 and in 2014 vs. 2013. Contribution The simulation confirms that significant differences due to population fluctuation could be found between consecutive years when investigating diseases with medium-high rates. Researchers should be encouraged to implement actions for reducing the risk of biased population denominators.
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Affiliation(s)
- Emanuele Amodio
- University of Palermo, Department of Health Promotion Sciences, Maternal and Infant Care, Internal Medicine and Medical Specialties "G. D'Alessandro". Via Del Vespro 133, Palermo, Italy
| | - Maurizio Zarcone
- University of Palermo, Department of Health Promotion Sciences, Maternal and Infant Care, Internal Medicine and Medical Specialties "G. D'Alessandro". Via Del Vespro 133, Palermo, Italy
| | - Alessandra Casuccio
- University of Palermo, Department of Health Promotion Sciences, Maternal and Infant Care, Internal Medicine and Medical Specialties "G. D'Alessandro". Via Del Vespro 133, Palermo, Italy
| | - Francesco Vitale
- University of Palermo, Department of Health Promotion Sciences, Maternal and Infant Care, Internal Medicine and Medical Specialties "G. D'Alessandro". Via Del Vespro 133, Palermo, Italy
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Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM2.5 Concentrations in Major Chinese Cities between 2005 and 2015. ENERGIES 2021. [DOI: 10.3390/en14113232] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Deteriorating air quality is one of the most important environmental factors posing significant health risks to urban dwellers. Therefore, an exploration of the factors influencing air pollution and the formulation of targeted policies to address this issue are critically needed. Although many studies have used semi-parametric geographically weighted regression and geographically weighted regression to study the spatial heterogeneity characteristics of influencing factors of PM2.5 concentration change, due to the fixed bandwidth of these methods and other reasons, those studies still lack the ability to describe and explain cross-scale dynamics. The multi-scale geographically weighted regression (MGWR) method allows different variables to have different bandwidths, which can produce more realistic and useful spatial process models. By applying the MGWR method, this study investigated the spatial heterogeneity and spatial scales of impact of factors influencing PM2.5 concentrations in major Chinese cities during the period 2005–2015. This study showed the following: (1) Factors influencing changes in PM2.5 concentrations, such as technology, foreign investment levels, wind speed, precipitation, and Normalized Difference Vegetation Index (NDVI), evidenced significant spatial heterogeneity. Of these factors, precipitation, NDVI, and wind speed had small-scale regional effects, whose bandwidth ratios are all less than 20%, while foreign investment levels and technologies had medium-scale regional effects, whose bandwidth levels are 23% and 32%, respectively. Population, urbanization rates, and industrial structure demonstrated weak spatial heterogeneity, and the scale of their influence was predominantly global. (2) Overall, the change of NDVI was the most influential factor, which can explain 15.3% of the PM2.5 concentration change. Therefore, an enhanced protection of urban surface vegetation would be of universal significance. In some typical areas, dominant factors influencing pollution were evidently heterogeneous. Change in wind speed is a major factor that can explain 51.6% of the change in PM2.5 concentration in cities in the Central Plains, and change in foreign investment levels is the dominant influencing factor in cities in the Yunnan-Guizhou Plateau and the Sichuan Basin, explaining 30.6% and 44.2% of the PM2.5 concentration change, respectively. In cities located within the lower reaches of the Yangtze River, NDVI is a key factor, reducing PM2.5 concentrations by 9.7%. Those results can facilitate the development of region-specific measures and tailored urban policies to reduce PM2.5 pollution levels in different regions such as Northeast China and the Sichuan Basin.
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Zhang Y. All-Cause Mortality Risk and Attributable Deaths Associated with Long-Term Exposure to Ambient PM 2.5 in Chinese Adults. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:6116-6127. [PMID: 33870687 DOI: 10.1021/acs.est.0c08527] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Several recent studies in China have associated raised mortality risks with chronic exposure to ambient PM2.5. However, cohort evidence covering general populations and more homogeneous regions is extensively scarce. We conceived a nationwide perspective cohort study from 2010 through 2018, by enrolling 30 946 adult men and women aged 16-110 years from 25 provincial regions in mainland China. Cox proportional hazards models with time-varying exposures were adopted to quantify longitudinal association of PM2.5 exposure with all-cause mortality. A total of 1762 death events occurred during a median follow-up of 8.1 years. Participants were exposed to a broad range of annual mean PM2.5 concentrations (2.4-112 μg/m3), with an average estimate of 47.5 μg/m3. A 10-μg/m3 increase in annual average of PM2.5 exposure was associated with an hazard ratio of 1.055 (95% confidence interval: 1.022-1.088, p < 0.001) for all-cause mortality. We estimated totally 2.68 million deaths attributable to ambient PM2.5 in 2015, yielding a remarkable reduction of 36.7 thousand compared to the estimate in 2010 (2.72 million deaths). This study added nationally representative evidence regarding concentration-response function for long-term PM2.5-mortality association in Chinese adults, which may significantly contribute to national and global assessments of PM2.5-attributable death burden.
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Affiliation(s)
- Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
- Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan 430065, China
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