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Tong M, Lu H, Xu H, Fan X, Zhang JJ, Kelly FJ, Gong J, Han Y, Li P, Wang R, Li J, Zhu T, Xue T. Reduced human fecundity attributable to ambient fine particles in low- and middle-income countries. ENVIRONMENT INTERNATIONAL 2024; 189:108784. [PMID: 38852259 DOI: 10.1016/j.envint.2024.108784] [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: 01/23/2024] [Revised: 05/09/2024] [Accepted: 05/27/2024] [Indexed: 06/11/2024]
Abstract
BACKGROUND Exposure to ambient fine particulate matter (PM2.5) has been associated with reduced human fecundity. However, the attributable burden has not been estimated for low- and middle-income countries (LMICs), where the exposure-response function between PM2.5 and the infertility rate has been insufficiently studied. OBJECTIVE This study examined the associations between long-term exposure to PM2.5 and human fecundity indicators, namely the expected time to pregnancy (TTP) and 12-month infertility rate (IR), and then estimated PM2.5-attributable burden of infertility in LMICs. METHODS We analyzed 164,593 eligible women from 100 Demographic and Health Surveys conducted in 49 LMICs between 1999 and 2021. We assessed PM2.5 exposures during the 12 months before a pregnancy attempt using the global satellite-derived PM2.5 estimates produced by Atmospheric Composition Analysis Group (ACAG). First, we created a series of pseudo-populations with balanced covariates, given different levels of PM2.5 exposure, using a matching approach based on the generalized propensity score. For each pseudo-population, we used 2-stage generalized Gamma models to derive TTP or IR from the probability distribution of the questionnaire-based duration time for the pregnancy attempt before the interview. Second, we used spline regressions to generate nonlinear PM2.5 exposure-response functions for each of the two fecundity indicators. Finally, we applied the exposure-response functions to estimate number of infertile couples attributable to PM2.5 exposure in 118 LMICs. RESULTS Based on the Gamma models, each 10 µg/m3 increment in PM2.5 exposure was associated with a TTP increase by 1.7 % (95 % confidence interval [CI]: -2.3 %-6.0 %) and an IR increase by 2.3 % (95 %CI: 0.6 %-3.9 %). The nonlinear exposure-response function suggested a robust effect of an increased IR for high-concentration PM2.5 exposure (>75 µg/m3). Based on the PM2.5-IR function, across the 118 LMICs, the number of infertile couples attributable to PM2.5 exposure exceeding 35 µg/m3 (the first-stage interim target recommended by the World Health Organization global air quality guidelines) was 0.66 million (95 %CI: 0.061-1.43), accounting for 2.25 % (95 %CI: 0.20 %-4.84 %) of all couples affected by infertility. Among the 0.66 million, 66.5 % were within the top 10 % high-exposure infertile couples, mainly from South Asia, East Asia, and West Africa. CONCLUSION PM2.5 contributes significantly to human infertility in places with high levels of air pollution. PM2.5-pollution control is imperative to protect human fecundity in LMICs.
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Affiliation(s)
- Mingkun Tong
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Center, Beijing, China
| | - Hong Lu
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Center, Beijing, China
| | - Huiyu Xu
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China
| | - Xinguang Fan
- Department of Sociology, Peking University, Beijing, China
| | - Junfeng Jim Zhang
- Nicholas School of the Environment, & Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Frank J Kelly
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Jicheng Gong
- SKL-ESPC & SEPKL-AERM, College of Environmental Sciences and Engineering, Center for Environment and Health, Peking University, Beijing, China
| | - Yiqun Han
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Pengfei Li
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang, China
| | - Ruohan Wang
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jiajianghui Li
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Center, Beijing, China
| | - Tong Zhu
- SKL-ESPC & SEPKL-AERM, College of Environmental Sciences and Engineering, Center for Environment and Health, Peking University, Beijing, China
| | - Tao Xue
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Center, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang, China; Center for Environment and Health, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
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Tang Y, Wang Y, Chen X, Liang J, Li S, Chen G, Chen Z, Tang B, Zhu J, Li X. Diurnal emission variation of ozone precursors: Impacts on ozone formation during Sep. 2019. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172591. [PMID: 38663597 DOI: 10.1016/j.scitotenv.2024.172591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/08/2024] [Accepted: 04/17/2024] [Indexed: 04/30/2024]
Abstract
With the issue of ozone (O3) pollution having increasingly gained visibility and prominence in China, the Chinese government explored various policies to mitigate O3 pollution. In some provinces and cities, diurnal regulations of O3 precursor were implemented, such as shifting O3 precursor emission processes to nighttime and offering preferential refueling at night. However, the effectiveness of these policies remains unverified, and their impact on the O3 generation process requires further elucidation. In this study, we utilized a regional climate and air quality model (WRF-Chem, v4.5) to test three scenarios aimed at exploring the impact of diurnal industry emission variation of O3 precursors on O3 formation. Significant O3 variations were observed mainly in urban areas. Shifting volatile organic compounds (VOCs) to nighttime have slight decreased daytime O3 levels while moving nitrogen oxides (NOx) to nighttime elevates O3 levels. Simultaneously moving both to nighttime showed combined effects. Process analysis indicates that the diurnal variation in O3 was mainly attributed to chemical process and vertical mixing in urban areas, while advection becomes more important in non-urban areas, contributing to the changes in O3 and O3 precursors levels through regional transportation. Further photochemical analysis reveals that the O3 photochemical production in urban areas was affected by reduced daytime O3 precursors emissions. Specifically, decreasing VOCs lowered the daytime O3 production by reducing the ROx radicals (ROx = HO + HO˙2 + RO˙2), whereas decreasing NOx promoted the daytime O3 production by weakening ROx radical loss. Our results demonstrate that diurnal regulation of O3 precursors will disrupt the ROx radical and O3 formation in local areas, resulting in a change in O3 concentration and atmospheric oxidation capacity, which should be considered in formulating new relevant policies.
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Affiliation(s)
- Yifan Tang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Yuchen Wang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China; State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China
| | - Xuwu Chen
- School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha 410205, PR China
| | - Jie Liang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Shuai Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Gaojie Chen
- College of Mathematics and Econometrics, Hunan University, Changsha 410082, PR China
| | - Zuo Chen
- College of Information Science and Technology, Hunan University, Changsha 410082, PR China
| | - Binxu Tang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Jiesong Zhu
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Xiaodong Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
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Zhu L, Fang J, Yao Y, Yang Z, Wu J, Ma Z, Liu R, Zhan Y, Ding Z, Zhang Y. Long-term ambient ozone exposure and incident cardiovascular diseases: National cohort evidence in China. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134158. [PMID: 38636234 DOI: 10.1016/j.jhazmat.2024.134158] [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: 08/26/2023] [Revised: 03/07/2024] [Accepted: 03/27/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Long-term ozone (O3) exposure has been associated with cardiovascular disease (CVD) mortality in mounting cohort evidence, yet its relationship with incident CVD was poorly understood, especially in low- and middle-income countries (LMICs) experiencing high ambient air pollution. METHODS We carried out a nationwide perspective cohort study from 2010 through 2018 by dynamically enrolling 36948 participants across Chinese mainland. Warm-season (April-September) O3 concentrations were estimated using satellite-based machine-learning models with national coverage. Cox proportional hazards model with time-varying exposures was employed to evaluate the association of long-term O3 exposure with incident CVD (overall CVD, hypertension, stroke, and coronary heart disease [CHD]). Assuming causality, a counterfactual framework was employed to estimate O3-attributable CVD burden based on the exposure-response (E-R) relationship obtained from this study. Decomposition analysis was utilized to quantify the contributions of four key direct driving factors (O3 exposure, population size, age structure, and incidence rate) to the net change of O3-related CVD cases between 2010 and 2018. RESULTS A total of 4428 CVD, 2600 hypertension, 1174 stroke, and 337 CHD events were reported during 9-year follow-up. Each 10-μg/m³ increase in warm-season O3 was associated with an incident risk of 1.078 (95% confidence interval [CI]: 1.050-1.106) for overall CVD, 1.098 (95% CI: 1.062-1.135) for hypertension, 1.073 (95% CI: 1.019-1.131) for stroke, and 1.150 (95% CI: 1.038-1.274) for CHD, respectively. We observed no departure from linear E-R relationships of O3 exposure with overall CVD (Pnonlinear= 0.22), hypertension (Pnonlinear= 0.19), stroke (Pnonlinear= 0.70), and CHD (Pnonlinear= 0.44) at a broad concentration range of 60-160 µg/m3. Compared with rural dwellers, those residing in urban areas were at significantly greater O3-associated incident risks of overall CVD, hypertension, and stroke. We estimated 1.22 million (10.6% of overall CVD in 2018) incident CVD cases could be attributable to ambient O3 pollution in 2018, representing an overall 40.9% growth (0.36 million) compared to 2010 (0.87 million, 9.7% of overall CVD in 2010). This remarkable rise in O3-attributable CVD cases was primary driven by population aging (+24.0%), followed by increase in O3 concentration (+10.5%) and population size (+6.7%). CONCLUSIONS Long-term O3 exposure was associated with an elevated risk and burden of incident CVD in Chinese adults, especially among urban dwellers. Our findings underscored policy priorities of implementing joint control measures for fine particulate matter and O3 in the context of accelerated urbanization and population aging in China.
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Affiliation(s)
- Lifeng Zhu
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Jiaying Fang
- Huadu District People's Hospital of Guangzhou, Guangzhou 510800, China
| | - Yao Yao
- China Center for Health Development Studies, Peking University, Beijing 100871, China
| | - Zhiming Yang
- School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
| | - Jing Wu
- China Center for Health Development Studies, Peking University, Beijing 100871, China
| | - Zongwei Ma
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Riyang Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Zan Ding
- Baoan Central Hospital of Shenzhen, Shenzhen 518102, China.
| | - Yunquan Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China.
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Hu Y, Wang Y, Zhao Z, Zhao B. Reconsidering gas as clean energy: Switching to electricity for household cooking to reduce NO 2-attributed disease burden. ECO-ENVIRONMENT & HEALTH 2024; 3:174-182. [PMID: 38638171 PMCID: PMC11021829 DOI: 10.1016/j.eehl.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 10/17/2023] [Accepted: 10/25/2023] [Indexed: 04/20/2024]
Abstract
Nitrogen dioxide (NO2) is a prevalent air pollutant in urban areas, originating from outdoor sources, household gas consumption, and secondhand smoke. The limited evaluation of the disease burden attributable to NO2, encompassing different health effects and contributions from various sources, impedes our understanding from a public health perspective. Based on modeled NO2 exposure concentrations, their exposure-response relationships with lung cancer, chronic obstructive pulmonary disease, and diabetes mellitus, and baseline disability-adjusted life years (DALYs), we estimated that 1,675 (655-2,624) thousand DALYs were attributable to NO2 in urban China in 2019 [138 (54-216) billion Chinese yuan (CNY) economic losses]. The transition from gas to electricity for household cooking was estimated to reduce the attributable economic losses by 35%. This reduction falls within the range of reductions achieved when outdoor air meets the World Health Organization interim target 3 and air quality guidelines for annual NO2, highlighting the significance of raising awareness of gas as a polluting household energy for cooking. These findings align with global sustainable development initiatives, providing a sustainable solution to promote public health while potentially mitigating climate change.
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Affiliation(s)
- Ying Hu
- Department of Building Science, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Ye Wang
- Department of Building Science, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Zhuohui Zhao
- School of Public Health, Fudan University, Shanghai 200433, China
- Key Laboratory of Public Health Safety of the Ministry of Education, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200433, China
- Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, WMO/IGAC MAP-AQ Asian Office Shanghai, Fudan University, Shanghai 200433, China
| | - Bin Zhao
- Department of Building Science, School of Architecture, Tsinghua University, Beijing 100084, China
- Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
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5
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Zhu R, Luo W, Grieneisen ML, Zuoqiu S, Zhan Y, Yang F. A novel approach to deriving the fine-scale daily NO 2 dataset during 2005-2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment. ENVIRONMENTAL RESEARCH 2024; 249:118381. [PMID: 38331142 DOI: 10.1016/j.envres.2024.118381] [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: 12/02/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/10/2024]
Abstract
Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005-2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 μg/m3, while cross-validation across China during 2013-2020 showed an R2 of 0.73 and RMSE of 9.6 μg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 μg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012-2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China.
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Affiliation(s)
- Rongxin Zhu
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China; College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Wenfeng Luo
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Michael L Grieneisen
- Department of Land, Air, and Water Resources, University of California, Davis, CA, 95616, United States
| | - Sophia Zuoqiu
- Pittsburgh Institute, Sichuan University, Chengdu, Sichuan, 610207, China
| | - Yu Zhan
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Fumo Yang
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan, 610065, China
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Xue T, Wang R, Wang M, Wang Y, Tong D, Meng X, Huang C, Ai S, Li F, Cao J, Tong M, Ni X, Liu H, Deng J, Lu H, Wan W, Gong J, Zhang S, Zhu T. Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions: progress in efficacy and geographic equality. Natl Sci Rev 2024; 11:nwad263. [PMID: 38213522 PMCID: PMC10776362 DOI: 10.1093/nsr/nwad263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 09/13/2023] [Accepted: 10/08/2023] [Indexed: 01/13/2024] Open
Abstract
Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs.
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Affiliation(s)
- Tao Xue
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
- Advanced Institute of Information Technology, Peking University, Hangzhou311215, China
| | - Ruohan Wang
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY14214, USA
| | - Yanying Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
| | - Dan Tong
- Department of Earth System Science, Tsinghua University, Beijing100084, China
| | - Xia Meng
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai200433, China
| | - Conghong Huang
- College of Land Management, Nanjing Agricultural University, Nanjing 210095, China
- National & Local Joint Engineering, Research Center for Rural Land Resources Use and Consolidation, Nanjing 210095, China
| | - Siqi Ai
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Fangzhou Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Jingyuan Cao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Mingkun Tong
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Xueqiu Ni
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Hengyi Liu
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Jianyu Deng
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Hong Lu
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Wei Wan
- Clean Air Asia, Beijing100600, China
| | - Jicheng Gong
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Shiqiu Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Tong Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
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Zheng H, Kong S, Seo J, Yan Y, Cheng Y, Yao L, Wang Y, Zhao T, Harrison RM. Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022. ENVIRONMENT INTERNATIONAL 2024; 183:108361. [PMID: 38091821 DOI: 10.1016/j.envint.2023.108361] [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: 07/03/2023] [Revised: 11/02/2023] [Accepted: 11/29/2023] [Indexed: 01/25/2024]
Abstract
Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr-1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr-1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018-2020 compared to that during 2014-2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations.
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Affiliation(s)
- Huang Zheng
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Shaofei Kong
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of the China Meteorological Administration, PREMIC, Nanjing University of Information Science &Technology, Nanjing, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China.
| | - Jihoon Seo
- Climate and Environmental Research Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Yingying Yan
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Yi Cheng
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
| | - Liquan Yao
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
| | - Yanxin Wang
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Tianliang Zhao
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of the China Meteorological Administration, PREMIC, Nanjing University of Information Science &Technology, Nanjing, China
| | - Roy M Harrison
- School of Geography, Earth and Environment Sciences, University of Birmingham, Birmingham B15 2TT, UK; Department of Environmental Sciences, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, PO Box 80203, Jeddah, Saudi Arabia.
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8
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Zhu Y, Liu Y, Liu X, Wang H. Carbon mitigation and health effects of fleet electrification in China's Yangtze River Delta. ENVIRONMENT INTERNATIONAL 2023; 180:108203. [PMID: 37717521 DOI: 10.1016/j.envint.2023.108203] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 09/19/2023]
Abstract
Fleet electrification is one of the most promising strategies to mitigate carbon emissions and improve air quality. This study provides a comprehensive analysis of the currently unclear CO2 mitigation and human health benefits from electric vehicle (EV) adoption and energy decarbonization in the Yangtze River Delta (YRD) region by integrating fleet modeling, emission projection, air quality modeling and health risk assessment. Based on future socioeconomic trajectories, we project that the total vehicle stock in the YRD region will peak at 107-117 million around 2045-2050. The transition to EVs combined with largely renewable energy in the YRD region can potentially reduce CO2 emissions by 870 Tg in 2060 and brings along substantial health co-benefits with ∼360 avoided premature deaths per million from reduced PM2.5 and O3 concentrations. This study further explores the NO2-attributable burden from road transportation and reveals that fleet electrification could yield greater NO2-attributable health benefits than those from reduced PM2.5 and O3, especially in traffic-dense urban areas. Those findings indicate that China's near-term energy development plans (35% renewable energy) have created the conditions for large-scale EV adoption. Our results imply that the benefits of EVs exhibit substantial spatial heterogeneity, underscoring the importance of region-specific EV incentive policies, and hint that policymakers should prioritize densely populated megacities to maximize the potential for public health gains.
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Affiliation(s)
- Yijing Zhu
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Yifan Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Xiang Liu
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Haikun Wang
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China; Collaborative Innovation Center of Climate Change, Nanjing 210023, China; Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing 210023, China.
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9
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Wang K, Yuan Y, Wang Q, Yang Z, Zhan Y, Wang Y, Wang F, Zhang Y. Incident risk and burden of cardiovascular diseases attributable to long-term NO 2 exposure in Chinese adults. ENVIRONMENT INTERNATIONAL 2023; 178:108060. [PMID: 37478679 DOI: 10.1016/j.envint.2023.108060] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/29/2023] [Accepted: 06/21/2023] [Indexed: 07/23/2023]
Abstract
BACKGROUND A number of studies suggested a nexus between long-term exposure to nitrogen dioxide (NO2) and the incidence of cardiovascular disease (CVD), while population-based cohort evidence in low- and middle-income countries was extensively sparse. METHODS We carried out an 8-year longitudinal study (2010-2018) in a nationwide dynamic cohort of 36,948 Chinese adult participants, who were free of CVD at baseline. Annual average estimates of NO2 exposure were predicted using a well-validated spatiotemporal model and assigned to study participants based on their residential counties. Considering death as a competing risk event, Fine-Gray competing risk models with time-varying exposures at an annual scale were used to quantify incident risks of overall CVD, hypertension, and stroke associated with a 10-μg/m3 rise in NO2 exposure. Using the meta-analysis approach, we performed a pooled analysis of hazard ratio (HR) drawn from this and prior multinational cohort studies for the assessment of attributable burden. NO2-attributable overall CVD incidents in China were evaluated by city and province for years 2010 and 2018, referring to a counterfactual exposure level of 10 μg/m3 (2021 World Health Organization [WHO] air quality guidelines). A decomposition method was used to decompose net change in NO2-attributable CVD incidents during 2010 and 2018 into 3 primary contributions of driving factors (i.e., changes in NO2 exposure, population size, and incidence rate). RESULTS A total of 4428 overall CVD events (hypertension 2448, stroke 1044) occurred during a median follow-up period of 6.1 years. Annual mean NO2 concentration from 2010 to 2018 was 20.0 μg/m3 (range: 6.9-57.4 μg/m3). An increase of 10-µg/m3 in NO2 was associated with an HR of 1.558 (95% confidence interval [CI]: 1.477, 1.642) for overall CVD, 1.521 (95% CI: 1.419, 1.631) for hypertension, and 1.664 (95% CI: 1.485, 1.865) for stroke. Longitudinal associations of NO2 exposure with incident CVD were nearly linear over the exposure range, suggesting no discernible thresholds. Subgroup analyses indicated significantly higher NO2-associated risks of incident CVD among urban residents and overweight/obese individuals. According to pooled HR of NO2-CVD association (1.108, 95% CI: [1.007, 1.219]) from 10 multinational cohort studies, we estimated totally 1.44 million incident CVD cases attributable to NO2 exposure in 2018, representing a substantial decrease of 0.41 million compared to the estimate in 2010 (1.85 million) in mainland of China. Nationally, from 2010 to 2018, the attributable incident cases greatly dropped by 22.4%, which was dominantly driven by declined NO2 concentration (-47.1%) that had offset far from the rise of CVD incidence rate (+19.6%) and population growth (+5.1%). CONCLUSIONS This study provided nationwide cohort evidence for elevated risks of CVD incidence associated with long-term ambient NO2 exposure among Chinese adults, particularly in urban areas and among overweight/obese individuals. Our findings highlighted that reducing NO2 exposure below 2021 WHO guideline could help prevent a substantial portion of incident CVD cases in China.
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Affiliation(s)
- Kai Wang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, Hubei, 430065, China
| | - Yang Yuan
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, Hubei, 430065, China
| | - Qun Wang
- School of Public Health, Hubei University of Medicine, Shiyan, Hubei, 442000, China
| | - Zhiming Yang
- School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Yaqi Wang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, Hubei, 430065, China
| | - Fang Wang
- School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
| | - Yunquan Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, Hubei, 430065, China.
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Huang K, Zhu Q, Lu X, Gu D, Liu Y. Satellite-Based Long-Term Spatiotemporal Trends in Ambient NO 2 Concentrations and Attributable Health Burdens in China From 2005 to 2020. GEOHEALTH 2023; 7:e2023GH000798. [PMID: 37206379 PMCID: PMC10190124 DOI: 10.1029/2023gh000798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/27/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023]
Abstract
Despite the recent development of using satellite remote sensing to predict surface NO2 levels in China, methods for estimating reliable historical NO2 exposure, especially before the establishment of NO2 monitoring network in 2013, are still rare. A gap-filling model was first adopted to impute the missing NO2 column densities from satellite, then an ensemble machine learning model incorporating three base learners was developed to estimate the spatiotemporal pattern of monthly mean NO2 concentrations at 0.05° spatial resolution from 2005 to 2020 in China. Further, we applied the exposure data set with epidemiologically derived exposure response relations to estimate the annual NO2 associated mortality burdens in China. The coverage of satellite NO2 column densities increased from 46.9% to 100% after gap-filling. The ensemble model predictions had good agreement with observations, and the sample-based, temporal and spatial cross-validation (CV) R 2 were 0.88, 0.82, and 0.73, respectively. In addition, our model can provide accurate historical NO2 concentrations, with both by-year CV R 2 and external separate year validation R 2 achieving 0.80. The estimated national NO2 levels showed a increasing trend during 2005-2011, then decreased gradually until 2020, especially in 2012-2015. The estimated annual mortality burden attributable to long-term NO2 exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. This satellite-based ensemble model could provide reliable long-term NO2 predictions at a high spatial resolution with complete coverage for environmental and epidemiological studies in China. Our results also highlighted the heavy disease burden by NO2 and call for more targeted policies to reduce the emission of nitrogen oxides in China.
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Affiliation(s)
- Keyong Huang
- Department of EpidemiologyFuwai Hospital, National Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- Key Laboratory of Cardiovascular EpidemiologyChinese Academy of Medical SciencesBeijingChina
| | - Qingyang Zhu
- Gangarosa Department of Environmental HealthRollins School of Public HealthEmory UniversityAtlantaGAUSA
| | - Xiangfeng Lu
- Department of EpidemiologyFuwai Hospital, National Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- Key Laboratory of Cardiovascular EpidemiologyChinese Academy of Medical SciencesBeijingChina
| | - Dongfeng Gu
- Department of EpidemiologyFuwai Hospital, National Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- Key Laboratory of Cardiovascular EpidemiologyChinese Academy of Medical SciencesBeijingChina
- School of MedicineSouthern University of Science and TechnologyShenzhenChina
| | - Yang Liu
- Gangarosa Department of Environmental HealthRollins School of Public HealthEmory UniversityAtlantaGAUSA
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