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Naimi N, Sarkhosh M, Nabavi BF, Najafpoor A, Musa Farkhani E. Estimating the burden of diseases attributed to PM 2.5 using the AirQ + software in Mashhad during 2016-2021. Sci Rep 2024; 14:24462. [PMID: 39424839 PMCID: PMC11489694 DOI: 10.1038/s41598-024-74328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 09/25/2024] [Indexed: 10/21/2024] Open
Abstract
The study used the AirQ + software developed by the World Health Organization (WHO) to evaluate the health impacts associated with long-term exposure to PM2.5 in Mashhad, Iran. For this purpose, we analyzed the daily average concentrations of PM2.5 (with a diameter of 2.5 micrometers or less) registered by the air quality monitoring stations from 2016 to 2021. The levels of PM2.5 surpassed the Air Quality Guidelines (AQG) limit value of 5 µg/m3 (annual value) established by WHO. The findings revealed that the burden of mortality (from all-natural causes) at people above 30 years old associated with PM2.5 exposures was 2093 [95% confidence interval [CI]: 1627-2314] deaths in 2016 and 2750 [95% CI: 2139-3038] deaths in 2021. In general, the attributable mortality from specific causes of deaths (e.g., COPD (chronic obstructive pulmonary diseases), IHD (ischemic heart diseases) and stroke) in people above 25 years old increased between the years, but the mortality from lung cancer was stable at 46 [95% CI: 33-59] deaths in 2016 and 48 [95% CI: 34-61] deaths in 2021. The attributable mortality from ALRI (Acute Lower Respiratory Infection) in children below 5 years old increased between the years. We also found differences in mortality cases from IHD and stroke among the age groups and between the years 2016 and 2021. It was concluded that burden of disease methodologies are suitable tools for regional and national policymakers, who must take decisions to prevent and to control air pollution and to analyze the cost-effectiveness of interventions.
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Affiliation(s)
- Nayera Naimi
- Student Research Committee, Department of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Maryam Sarkhosh
- Department of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Bibi Fatemeh Nabavi
- Student Research Committee, Department of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Aliasghar Najafpoor
- Department of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ehsan Musa Farkhani
- Department of Epidemiology, Faculty of Health, Mashhad University of Medical Sciences, Mashhad, Iran
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2
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Liu S, Li X, Wei J, Shu L, Jin J, Fu TM, Yang X, Zhu L. Short-Term Exposure to Fine Particulate Matter and Ozone: Source Impacts and Attributable Mortalities. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:11256-11267. [PMID: 38885093 PMCID: PMC11223482 DOI: 10.1021/acs.est.4c00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 06/20/2024]
Abstract
Short-term exposure to particles with aerodynamic diameters less than 2.5 μm (PM2.5) and ozone (O3) are important risk factors for human health. Despite the awareness of reducing attributable health burden, region-specific and source-specific strategies remain less explored due to the gap between precursor emissions and health effects. In this study, we isolate the health burden of individual sector sources of PM2.5 and O3 precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), across the globe. Specifically, we estimate mortalities attributable to short-term exposure using machine-learning-based daily exposure estimates and quantify sectoral impacts using chemical transport model simulations. Globally, short-term exposure to PM2.5 and O3 result in 713.5 (95% Confidence Interval: 598.8-843.3) thousand and 496.3 (371.3-646.1) thousand mortalities in 2019, respectively, of which 12.5% are contributed by fuel-related NOx emissions from transportation, energy, and industry. Sectoral impacts from anthropogenic NOx and VOC emissions on health burden vary significantly among seasons and regions, requiring a target shift from transportation in winter to industry in summer for East Asia, for instance. Emission control and health management are additionally complicated by unregulated natural influences during climatic events. Fire-sourced NOx and VOC emissions, respectively, contribute to 8.5 (95% CI: 6.2-11.7) thousand and 4.8 (3.6-5.9) thousand PM2.5 and O3 mortalities, particularly for tropics with high vulnerability to climate change. Additionally, biogenic VOC emissions during heatwaves contribute to 1.8 (95% CI: 1.5-2.2) thousand O3-introduced mortalities, posing challenges in urban planning for high-income regions, where biogenic contributions to health burden during heatwaves are 13% of anthropogenic contributions annually. Our study provides important implications for temporally dynamic and sector-targeted emission control and health management strategies, which are of urgency under the projection of continuously increasing energy consumption and changing climate.
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Affiliation(s)
- Song Liu
- School
of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Collaborative
Innovation Center of Atmospheric Environment and Equipment Technology,
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution
Control (AEMPC), Nanjing University of Information
Science and Technology, Nanjing 210044, China
| | - Xicheng Li
- School
of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jing Wei
- Department
of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary
Center, University of Maryland, College Park, Maryland 20742-5031, United
States
| | - Lei Shu
- School
of Geographical Sciences, Fujian Normal
University, Fuzhou 350117, China
| | - Jianbing Jin
- Jiangsu
Key Laboratory of Atmospheric Environment Monitoring and Pollution
Control, Collaborative Innovation Center of Atmospheric Environment
and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Tzung-May Fu
- School
of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong
Provincial Observation and Research Station for Coastal Atmosphere
and Climate of the Greater Bay Area, Shenzhen 518055, China
- Shenzhen
Key Laboratory of Precision Measurement and Early Warning Technology
for Urban Environmental Health Risks, School of Environmental Science
and Engineering, Southern University of
Science and Technology, Shenzhen 518055, China
| | - Xin Yang
- School
of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong
Provincial Observation and Research Station for Coastal Atmosphere
and Climate of the Greater Bay Area, Shenzhen 518055, China
- Shenzhen
Key Laboratory of Precision Measurement and Early Warning Technology
for Urban Environmental Health Risks, School of Environmental Science
and Engineering, Southern University of
Science and Technology, Shenzhen 518055, China
| | - Lei Zhu
- School
of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong
Provincial Observation and Research Station for Coastal Atmosphere
and Climate of the Greater Bay Area, Shenzhen 518055, China
- Shenzhen
Key Laboratory of Precision Measurement and Early Warning Technology
for Urban Environmental Health Risks, School of Environmental Science
and Engineering, Southern University of
Science and Technology, Shenzhen 518055, China
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3
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Fang H, Wang W, Wang R, Xu H, Zhang Y, Wu T, Zhou R, Zhang J, Ruan Z, Li F, Wang X. Ozone and its precursors at an urban site in the Yangtze River Delta since clean air action plan phase II in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 347:123769. [PMID: 38499173 DOI: 10.1016/j.envpol.2024.123769] [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: 11/01/2023] [Revised: 02/05/2024] [Accepted: 03/09/2024] [Indexed: 03/20/2024]
Abstract
In response to regional ozone (O3) pollution, Chinese government has implemented air pollution control measures in recent years. Here, a case study was performed at an O3-polluted city, Wuhu, in Yangtze River Delta region of China to investigate O3 variation trend and the relationship to its precursors after implementation of Clean Air Action Plan Phase II, which aims to reduce O3 pollution. The results showed that peak O3 concentration was effectively reduced since Clean Air Action Plan Phase II. Due to significant NOx reduction, O3 formation tended to shift from volatile organic compound (VOC)-limited regimes to NOx-limited regimes during 2018-2022. VOC/NOx ratios measured in 2022 revealed that peak O3 concentration tended to respond positively to NOx. Apart from high-O3 period, Wuhu was still in a VOC-limited regime. The relationship of maximum daily 8-h ozone average and NO2 followed a lognormal distribution with an inflection point at 20 μg m-3 of NO2, suggesting that Wuhu should conduct joint control of VOC and NOx with a focus on VOC reduction before the inflection point. Alkenes and aromatics were suggested to be preferentially controlled due to their higher ozone formation potentials. Using random forest meteorological normalization method, meteorology had a positive effect on O3 concentration in 2018, 2019 and 2022, but a negative effect in 2020 and 2021. The meteorology could explain 44.0 ± 19.1% of the O3 variation during 2018-2022. High temperature favors O3 production and O3 pollution occurred more easily when temperature was over 25 °C, while high relative humidity inhibits O3 generation and no O3 pollution was found at relative humidity above 70%. This study unveils some new insights into the trend of urban O3 pollution in Yangtze River Delta region since Clean Air Action Plan Phase II and the findings provide important references for formulating control strategies against O3 pollution.
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Affiliation(s)
- Hua Fang
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China; Center of Cooperative Innovation for Recovery and Reconstruction of Degraded Ecosystem in Wanjiang City Belt, Wuhu, 241000, China.
| | - Wenjing Wang
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Ran Wang
- Wuhu Institute of Ecological Environmental Sciences, Wuhu, 241000, China
| | - Hongling Xu
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Ying Zhang
- Wuhu Ecological and Environmental Monitoring Center of Anhui Province, Wuhu, 241005, China
| | - Ting Wu
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China; Center of Cooperative Innovation for Recovery and Reconstruction of Degraded Ecosystem in Wanjiang City Belt, Wuhu, 241000, China.
| | - Ruicheng Zhou
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Jianxi Zhang
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Zhirong Ruan
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Feng Li
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Xinming Wang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou, 510640, China
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4
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Du S, He C, Zhang L, Zhao Y, Chu L, Ni J. Policy implications for synergistic management of PM 2.5 and O 3 pollution from a pattern-process-sustainability perspective in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 916:170210. [PMID: 38246366 DOI: 10.1016/j.scitotenv.2024.170210] [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: 11/17/2023] [Revised: 01/03/2024] [Accepted: 01/14/2024] [Indexed: 01/23/2024]
Abstract
In recent years, the pattern of air pollution in China has changed profoundly, and PM2.5 and surface ozone (O3) have become the main air pollutants affecting the air quality of cities and regions in China. The synergistic control of the two has become the key to the sustainable improvement of air quality in China. In this study, we investigated and analyzed the spatial and temporal distribution patterns, exposure health risks, key drivers, and sustainable characteristics of PM2.5 and O3 concentrations in China from 2013 to 2022 at the national and city cluster scales by combining methodological models such as spatial statistics, trend analysis, exposure-response function, Hurst index, and multi-scale geographically weighted regression (MGWR) model. Ultimately, a synergistic management system for PM2.5 and O3 pollution was proposed. The results showed that: (1) The PM2.5 concentration decreased at a rate of 1.45 μg/m3 per year (p < 0.05), while the O3 concentration increased at a rate of 2.54 μg/m3 per year (p < 0.05). The trends of the two concentrations showed significant differences in spatial distribution. (2) Population exposure risks to pollutants showed an increasing trend, with PM2.5 and O3 increasing by 55.1 % and 42.7 %, respectively. The annual deaths associated with exposure to PM2.5 and O3 demonstrated a decreasing and inverted U-shaped trend, respectively, with annual average deaths of 1.312 million and 98,000. Significant regional disparities in health risks from these pollutants were influenced by socio-economic factors such as industrial activities and population density. In the future, it is expected that more than half of China's regions will be exposed to rising risks of PM2.5 and O3 population exposure. (3) Key drivers of regional exacerbation in PM2.5 and O3 levels include the number of industrial enterprises above designated size (NSIE) and population agglomeration (PA), while the disposable income of urban residents (URDI), technological innovation (TI), and government attention level (GAL) emerged as primary factors in controlling pollution hotspots, ranked in order of influence from greatest to least as TI > GAL > URDI. Overall, this study sheds light on the current status of air pollution and health risk sustainability in China and enhances the understanding of future air pollution dynamics in China. The results of the study may help to develop effective targeted control measures to synergize the management of PM2.5 and O3 in different regions.
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Affiliation(s)
- Shenwen Du
- College of Resources and Environment, Yangtze University, Wuhan 430100, China; Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
| | - Chao He
- College of Resources and Environment, Yangtze University, Wuhan 430100, China; Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China.
| | - Lu Zhang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Yue Zhao
- College of Resources and Environment, Yangtze University, Wuhan 430100, China; Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
| | - Lilin Chu
- College of Resources and Environment, Yangtze University, Wuhan 430100, China; Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
| | - Jinmian Ni
- College of Resources and Environment, Yangtze University, Wuhan 430100, China; Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
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5
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Ji JS, Xia Y, Liu L, Zhou W, Chen R, Dong G, Hu Q, Jiang J, Kan H, Li T, Li Y, Liu Q, Liu Y, Long Y, Lv Y, Ma J, Ma Y, Pelin K, Shi X, Tong S, Xie Y, Xu L, Yuan C, Zeng H, Zhao B, Zheng G, Liang W, Chan M, Huang C. China's public health initiatives for climate change adaptation. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 40:100965. [PMID: 38116500 PMCID: PMC10730322 DOI: 10.1016/j.lanwpc.2023.100965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/01/2023] [Accepted: 11/01/2023] [Indexed: 12/21/2023]
Abstract
China's health gains over the past decades face potential reversals if climate change adaptation is not prioritized. China's temperature rise surpasses the global average due to urban heat islands and ecological changes, and demands urgent actions to safeguard public health. Effective adaptation need to consider China's urbanization trends, underlying non-communicable diseases, an aging population, and future pandemic threats. Climate change adaptation initiatives and strategies include urban green space, healthy indoor environments, spatial planning for cities, advance location-specific early warning systems for extreme weather events, and a holistic approach for linking carbon neutrality to health co-benefits. Innovation and technology uptake is a crucial opportunity. China's successful climate adaptation can foster international collaboration regionally and beyond.
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Affiliation(s)
- John S. Ji
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Yanjie Xia
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Linxin Liu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Weiju Zhou
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and National School of Public Health, Health Commission Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Guanghui Dong
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Qinghua Hu
- Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and National School of Public Health, Health Commission Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Tiantian Li
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yi Li
- Public Meteorological Service Centre, China Meteorological Administration, Beijing, China
| | - Qiyong Liu
- National Institute of Infectious Diseases at China, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanxiang Liu
- Public Meteorological Service Centre, China Meteorological Administration, Beijing, China
| | - Ying Long
- School of Architecture, Tsinghua University, Beijing, China
| | - Yuebin Lv
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jian Ma
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Yue Ma
- School of Architecture, Tsinghua University, Beijing, China
| | - Kinay Pelin
- School of Climate Change and Adaptation, University of Prince Edward Island, Prince Edward Island, Canada
| | - Xiaoming Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shilu Tong
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Public Health, Queensland University of Technology, Brisbane, Australia
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, China
| | - Lei Xu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Changzheng Yuan
- School of Public Health, Zhejiang University, Hangzhou, China
| | - Huatang Zeng
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| | - Bin Zhao
- Department of Building Science, School of Architecture, Tsinghua University, Beijing, China
| | - Guangjie Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | - Wannian Liang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Margaret Chan
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
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6
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Ma Y, Zhang Y, Wang W, Qin P, Li H, Jiao H, Wei J. Estimation of health risk and economic loss attributable to PM 2.5 and O 3 pollution in Jilin Province, China. Sci Rep 2023; 13:17717. [PMID: 37853161 PMCID: PMC10584970 DOI: 10.1038/s41598-023-45062-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 10/15/2023] [Indexed: 10/20/2023] Open
Abstract
Ambient pollutants, particularly fine particulate matter (PM2.5) and ozone (O3), pose significant risks to both public health and economic development. In recent years, PM2.5 concentration in China has decreased significantly, whereas that of O3 has increased rapidly, leading to considerable health risks. In this study, a generalized additive model was employed to establish the relationship of PM2.5 and O3 exposure with non-accidental mortality across 17 districts and counties in Jilin Province, China, over 2015-2016. The health burden and economic losses attributable to PM2.5 and O3 were assessed using high-resolution satellite and population data. According to the results, per 10 µg/m3 increase in PM2.5 and O3 concentrations related to an overall relative risk (95% confidence interval) of 1.004 (1.001-1.007) and 1.009 (1.005-1.012), respectively. In general, the spatial distribution of mortality and economic losses was uneven. Throughout the study period, a total of 23,051.274 mortalities and 27,825.015 million Chinese Yuan (CNY) in economic losses were attributed to O3 exposure, which considerably surpassing the 5,450.716 mortalities and 6,553,780 million CNY in economic losses attributed to PM2.5 exposure. The O3-related health risks and economic losses increased by 3.75% and 9.3% from 2015 to 2016, while those linked to PM2.5 decreased by 23.33% and 18.7%. Sensitivity analysis results indicated that changes in pollutant concentrations were the major factors affecting mortality rather than baseline mortality and population.
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Affiliation(s)
- Yuxia Ma
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China.
| | - Yifan Zhang
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Wanci Wang
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Pengpeng Qin
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Heping Li
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Haoran Jiao
- Meteorological Observatory, Liaoning Provincial Meteorological Bureau, Shenyang, 110000, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, 20740, USA
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7
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Yu P, Zhang Y, Meng J, Liu W. Statistical significance of PM 2.5 and O 3 trends in China under long-term memory effects. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 892:164598. [PMID: 37271384 DOI: 10.1016/j.scitotenv.2023.164598] [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: 03/25/2023] [Revised: 05/05/2023] [Accepted: 05/29/2023] [Indexed: 06/06/2023]
Abstract
Over the past decade, the Chinese government has implemented the "Clean Air Action" measures to enhance the atmospheric environmental quality, primarily focusing on curbing PM2.5 and O3 concentrations. The efficacy of these strategies and the underlying causes (human factors or natural variability) of any observed increases or decreases in PM2.5 and O3 concentrations are of great importance. Examining the hourly PM2.5 and O3 concentration time series from six representative regions in China between 2015 and 2021 revealed an overall downward trend in PM2.5 concentrations. However, the O3 concentration time series indicated upward trends in some regions, except for the Northeast area (NE) and Sichuan Basin (SCB). In the context of conventional significance tests, the assumption is typically that the time series' samples are independent and therefore memoryless. However, in situations where the time series exhibits strong autocorrelation and limited sample size, this assumption can lead to an overestimation of the statistical significance of the linear trend. To account for this, we utilized a long-term memory model that can reproduce the long-term persistence of pollutant records to improve the accuracy of significance tests. By comparing the P-values of real and surrogate data generated by the long-term memory model, we found that only PM2.5 concentrations in the Pearl River Delta (PRD) were slightly insignificant. For the remaining five regions, the P-values of PM2.5 concentrations were smaller than the significant level of 0.05, suggesting that the observed downward trends in PM2.5 concentrations are not due to natural variability, thereby confirming the effectiveness of the government's policies aimed at curbing atmospheric particulate matter in recent years. Our results show that O3 pollution is significantly increasing only in the Beijing-Tianjin-Hebei (BTH) region, beyond natural variability. In contrast, the trends of O3 pollution in many regions of China are markedly impacted by natural and climate variability.
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Affiliation(s)
- Ping Yu
- Data Science Research Center, Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Yongwen Zhang
- Data Science Research Center, Faculty of Science, Kunming University of Science and Technology, Kunming, China.
| | - Jun Meng
- School of Science, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Wenqi Liu
- Data Science Research Center, Faculty of Science, Kunming University of Science and Technology, Kunming, China
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8
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Wang J, Gao A, Li S, Liu Y, Zhao W, Wang P, Zhang H. Regional joint PM 2.5-O 3 control policy benefits further air quality improvement and human health protection in Beijing-Tianjin-Hebei and its surrounding areas. J Environ Sci (China) 2023; 130:75-84. [PMID: 37032044 DOI: 10.1016/j.jes.2022.06.036] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/12/2022] [Accepted: 06/25/2022] [Indexed: 06/19/2023]
Abstract
Beijing-Tianjin-Hebei and its surrounding areas (hereinafter referred to as "2+26" cities) are one of the most severe air pollution areas in China. The fine particulate matter (PM2.5) and surface ozone (O3) pollution have aroused a significant concern on the national scale. In this study, we analyzed the pollution characteristics of PM2.5 and O3 in "2+26" cities, and then estimated the health burden and economic loss before and after the implementation of the joint PM2.5-O3 control policy. During 2017-2019, PM2.5 concentration reduced by 19% while the maximum daily 8 hr average (MDA8) O3 stayed stable in "2+26" cities. Spatially, PM2.5 pollution in the south-central area and O3 pollution in the central region were more severe than anywhere else. With the reduction in PM2.5 concentration, premature deaths from PM2.5 decreased by 18% from 2017 to 2019. In contrast, premature deaths from O3 increased by 5%. Noticeably, the huge potential health benefits can be gained after the implementation of a joint PM2.5-O3 control policy. The premature deaths attributed to PM2.5 and O3 would be reduced by 91.6% and 89.1%, and the avoidable economic loss would be 60.8 billion Chinese Yuan (CNY), and 68.4 billion CNY in 2035 compared with that in 2019, respectively. Therefore, it is of significance to implement the joint PM2.5-O3 control policy for improving public health and economic development.
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Affiliation(s)
- Junyi Wang
- Hebei GEO University, Hebei Center for Ecological and Environmental Geology Research, Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources, Hebei Province Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure, Shijiazhuang 050031, China
| | - Aifang Gao
- Hebei GEO University, Hebei Center for Ecological and Environmental Geology Research, Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources, Hebei Province Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure, Shijiazhuang 050031, China.
| | - Shaorong Li
- Hebei GEO University, Hebei Center for Ecological and Environmental Geology Research, Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources, Hebei Province Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure, Shijiazhuang 050031, China
| | - Yuehua Liu
- Hebei GEO University, Hebei Center for Ecological and Environmental Geology Research, Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources, Hebei Province Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure, Shijiazhuang 050031, China
| | - Weifeng Zhao
- Hebei Provincial Academy of Environmental Science, Shijiazhuang 050037, China
| | - Peng Wang
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China; Shanghai Qi Zhi Institute, Shanghai 200232, China; IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China.
| | - Hongliang Zhang
- IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China; Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China; Institute of Eco-Chongming (SIEC), Shanghai 200062, China
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9
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Zhang Y, Liu X, Shi X, Xue W, Liu Z, Wang Y, Yan G. Health impacts under different ozone mitigation pathways in Beijing-Tianjin-Hebei and its surroundings. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 882:163436. [PMID: 37059152 DOI: 10.1016/j.scitotenv.2023.163436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/10/2023] [Accepted: 04/07/2023] [Indexed: 06/01/2023]
Abstract
To evaluate the effects of the various ozone (O3) control approaches on environmental health and health inequalities, 121 reduction scenarios for nitrogen oxides (NOx) and volatile organic compounds (VOCs) were developed, and their environmental health impacts were calculated. With the target of achieving the 90th percentile of the daily maximum 8 h mean O3 concentration (MDA8-90th) of 160 μg/m3 in Beijing-Tianjin-Hebei and its surroundings ("2 + 26" cities), three typical scenarios namely, High-NOx reduction ratio (HN, NOx/VOCs = 6:1), High-VOCs reduction ratio (HV, NOx/VOCs = 3:7), and Balanced reduction ratio (Balanced, NOx/VOCs = 1:1) were investigated. The results show that O3 formation is currently NOx-limited at the regional scale, while some developed cities are VOC-limited, indicating that NOx mitigation should be the core for achieving the targeted concentration (160 μg/m3) at the regional scale, whereas cities such as Beijing in the short term should focus on VOCs mitigation. The population-weighted O3 concentrations in the HN, Balanced, and HV scenarios were 159.19, 159.19, and 158.44 μg/m3, respectively. In addition, the O3-related premature mortality was 41,320 in "2 + 26" cities; control measures under HN, Balanced, and HV could potentially decrease O3-related premature deaths by 59.94 %, 60.25 %, and 71.48 %, respectively. The HV scenario has been found to be more advantageous in lowering the O3-related environmental health impacts than the HN and Balanced scenarios. It was further found that premature deaths avoided by the HN scenario were mainly concentrated in economically unadvanced regions, whereas those prevented by the HV scenario were mainly concentrated in developed cities. This may lead to geographical inequities in environmental health. As ozone pollution in large cities with high population density is primarily VOC-limited, decrease in VOCs should be focused on in the short term to avoid more O3-related premature deaths, whereas NOx control may be more important in decreasing ozone concentrations and ozone-related mortality in the future.
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Affiliation(s)
- Yu Zhang
- College of Chemistry, Zhengzhou University, 450001 Zhengzhou, China
| | - Xin Liu
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, 100043 Beijing, China
| | - Xurong Shi
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, 100043 Beijing, China
| | - Wenbo Xue
- College of Chemistry, Zhengzhou University, 450001 Zhengzhou, China; Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, 100043 Beijing, China.
| | - Zeyuan Liu
- College of Environmental & Resource Sciences, Zhejiang University, 310058 Hangzhou, China
| | - Yihao Wang
- Chinese Research Academy of Environmental Sciences, 100012 Beijing, China
| | - Gang Yan
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, 100043 Beijing, China.
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10
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Bui LT, Nguyen NHT, Nguyen PH. Chronic and acute health effects of PM 2.5 exposure and the basis of pollution control targets. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:79937-79959. [PMID: 37291347 DOI: 10.1007/s11356-023-27936-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/22/2023] [Indexed: 06/10/2023]
Abstract
Ho Chi Minh City (HCMC) is changing and expanding quickly, leading to environmental consequences that seriously threaten human health. PM2.5 pollution is one of the main causes of premature death. In this context, studies have evaluated strategies to control and reduce air pollution; such pollution-control measures need to be economically justified. The objective of this study was to assess the socio-economic damage caused by exposure to the current pollution scenario, taking 2019 as the base year. A methodology for calculating and evaluating the economic and environmental benefits of air pollution reduction was implemented. This study aimed to simultaneously evaluate the impacts of both short-term (acute) and long-term (chronic) PM2.5 pollution exposure on human health, providing a comprehensive overview of economic losses attributable to such pollution. Spatial partitioning (inner-city and suburban) on health risks of PM2.5 and detailed construction of health impact maps by age group and sex on a spatial resolution grid (3.0 km × 3.0 km) was performed. The calculation results show that the economic loss from premature deaths due to short-term exposure (approximately 38.86 trillion VND) is higher than that from long-term exposure (approximately 14.89 trillion VND). As the government of HCMC has been developing control and mitigation solutions for the Air Quality Action Plan towards short- and medium-term goals in 2030, focusing mainly on PM2.5, the results of this study will help policymakers develop a roadmap to reduce the impact of PM2.5 during 2025-2030.
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Affiliation(s)
- Long Ta Bui
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam.
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam.
| | - Nhi Hoang Tuyet Nguyen
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
| | - Phong Hoang Nguyen
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
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11
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Liu Y, Geng G, Cheng J, Liu Y, Xiao Q, Liu L, Shi Q, Tong D, He K, Zhang Q. Drivers of Increasing Ozone during the Two Phases of Clean Air Actions in China 2013-2020. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37276527 DOI: 10.1021/acs.est.3c00054] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In response to the severe air pollution issue, the Chinese government implemented two phases (Phase I, 2013-2017; Phase II, 2018-2020) of clean air actions since 2013, resulting in a significant decline in fine particles (PM2.5) during 2013-2020, while the warm-season (April-September) mean maximum daily 8 h average ozone (MDA8 O3) increased by 2.6 μg m-3 yr-1 in China during the same period. Here, we derived the drivers behind the rising O3 concentrations during the two phases of clean air actions by using a bottom-up emission inventory, a regional chemical transport model, and a multiple linear regression model. We found that both meteorological variations (3.6 μg m-3) and anthropogenic emissions (6.7 μg m-3) contributed to the growth of MDA8 O3 from 2013 to 2020, with the changes in anthropogenic emissions playing a more important role. The anthropogenic contributions to the O3 rise during 2017-2020 (1.2 μg m-3) were much lower than that in 2013-2017 (5.2 μg m-3). The lack of volatile organic compound (VOC) control and the decline in nitrogen oxides (NOx) emissions were responsible for the O3 increase in 2013-2017 due to VOC-limited regimes in most urban areas, while the synergistic control of VOC and NOx in Phase II initially worked to mitigate O3 pollution during 2018-2020, although its effectiveness was offset by the penalty of PM2.5 decline. Future mitigation efforts should pay more attention to the simultaneous control of VOC and NOx to improve O3 air quality.
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Affiliation(s)
- Yuxi Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, 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
| | - Jing Cheng
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Yang Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Qingyang Xiao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Liangke Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Qinren Shi
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Dan Tong
- 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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12
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Xu J, Yin X, Jiang T, Wang S, Wang D. Effects of air pollution control policies on intracerebral hemorrhage mortality among residents in Tianjin, China. BMC Public Health 2023; 23:858. [PMID: 37170126 PMCID: PMC10173217 DOI: 10.1186/s12889-023-15735-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 04/22/2023] [Indexed: 05/13/2023] Open
Abstract
BACKGROUND Exposure to air pollution is an important risk factor for intracerebral hemorrhage (ICH), which is a major cause of death worldwide. However, the relationship between ICH mortality and air quality improvement has been poorly studied. This study aims to evaluate the impact of the air pollution control policies in the Beijing-Tianjin-Hebei region on ICH mortality among Tianjin residents. METHODS This study used an interrupted time series analysis. We fitted autoregressive integrated moving average (ARIMA) models to assess the changes in ICH deaths before and after the interventions of air pollution control policies based on the data of ICH deaths in Tianjin collected by the Tianjin Center for Disease Control and Prevention. RESULTS Between 2009 and 2020, there were 63,944 ICH deaths in Tianjin, and there was an overall decreasing trend in ICH mortality. The intervention conducted in June 2014 resulted in a statistically significant (p = 0.03) long-term trend change, reducing the number of deaths from ICH by 0.69 (95% confidence interval [CI]: -1.30 to -0.07) per month. The intervention in October 2017 resulted in a statistically significant (p = 0.04) immediate decrease of 25.74 (95% CI: -50.62 to -0.85) deaths from ICH in that month. The intervention in December 2017 caused a statistically significant (p = 0.04) immediate reduction of 26.58 (95% CI: -52.02 to -1.14) deaths from ICH in that month. The intervention in March 2018 resulted in a statistically significant (p = 0.02) immediate decrease of 30.40 (95% CI: -56.41 to -4.40) deaths from ICH in that month. No significant differences were observed in the changes of male ICH mortality after any of the four interventions. However, female ICH deaths showed statistically significant long-term trend change after the intervention in June 2014 and immediate changes after the interventions in December 2017 and March 2018. Overall, the interventions prevented an estimated 5984.76 deaths due to ICH. CONCLUSION During the study period, some interventions of air pollution control policies were significantly associated with the reductions in the number of deaths from ICH among residents in Tianjin. ICH survivors and females were more sensitive to the protective effects of the interventions. Interventions for air pollution control can achieve public health gains in cities with high levels of air pollution.
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Affiliation(s)
- Jiahui Xu
- School of Public Health, Tianjin Medical University, Tianjin, China
- NCDs Preventive Department, Tianjin Centers for Disease Control and Prevention, No. 6 Huayue Road, Hedong District, Tianjin, 300011, China
| | - Xiaolin Yin
- School of Public Health, Tianjin Medical University, Tianjin, China
- NCDs Preventive Department, Tianjin Centers for Disease Control and Prevention, No. 6 Huayue Road, Hedong District, Tianjin, 300011, China
| | - Tingting Jiang
- School of Public Health, Tianjin Medical University, Tianjin, China
- NCDs Preventive Department, Tianjin Centers for Disease Control and Prevention, No. 6 Huayue Road, Hedong District, Tianjin, 300011, China
| | - Shiyu Wang
- School of Public Health, Tianjin Medical University, Tianjin, China
- NCDs Preventive Department, Tianjin Centers for Disease Control and Prevention, No. 6 Huayue Road, Hedong District, Tianjin, 300011, China
| | - Dezheng Wang
- School of Public Health, Tianjin Medical University, Tianjin, China.
- NCDs Preventive Department, Tianjin Centers for Disease Control and Prevention, No. 6 Huayue Road, Hedong District, Tianjin, 300011, China.
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13
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Gao C, Zhang F, Fang D, Wang Q, Liu M. Spatial characteristics of change trends of air pollutants in Chinese urban areas during 2016-2020: The impact of air pollution controls and the COVID-19 pandemic. ATMOSPHERIC RESEARCH 2023; 283:106539. [PMID: 36465231 PMCID: PMC9701570 DOI: 10.1016/j.atmosres.2022.106539] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/07/2022] [Accepted: 11/22/2022] [Indexed: 05/26/2023]
Abstract
Air pollution is a threat to public health in China, and several actions and plans have been implemented by Chinese authorities in recent years to mitigate it. This study examined the spatial distribution of changes in urban air pollutants (UAP) in 336 Chinese cities from 2016 to 2020 and their responses to air pollution controls and the COVID-19 pandemic. Based on the harmonic model, decreases in fine particles (PM2.5), inhalable particles (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) levels were found in 90.7%, 91.9%, 75.2%, 94.3%, and 88.7% of cities, respectively, while an increase in ozone (O3) was found in 87.2% of cities. Notable spatial heterogeneity was observed in the air pollution trends. The greatest improvement in air quality occurred mainly in areas with poor air quality, such as Hebei province and its surrounding cities. However, some areas (i.e., Yunnan and Hainan provinces) with good air quality showed a worsening trend. During the 13th Five-Year Plan period (2016-2020), the remarkable effects of PM2.5 and SO2 pollution control plans were confirmed. Additionally, economic growth in 74.2% of the Chinese provinces decoupled from air quality after implementing pollution control measures. In 2020, several Chinese cities were locked down to reduce the spread of COVID-19. Except for SO2, the national air pollution in 2020 improved to a greater extent than that in 2016-2019; In particularly, the contribution of simulated COVID-19 pandemic to NO2 reduction was 66.7%. Overall, air pollution control actions improved urban PM2.5, PM10, SO2, and CO, whereas NO2 was reduced primarily because of the COVID-19 pandemic.
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Affiliation(s)
- Chanchan Gao
- College of Geography and Tourism, Hengyang Normal University, Hengyang 421000, China
- Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China
| | - Fengying Zhang
- China National Environmental Monitoring Center, Beijing 100012, China
| | - Dekun Fang
- China National Environmental Monitoring Center, Beijing 100012, China
| | - Qingtao Wang
- School of Landscape and Ecological Engineering, Hebei University of Engineering, Handan 056038, Hebei Province, China
| | - Min Liu
- Key Lab of Forensic Science, Ministry of Justice, China (Academy of Forensic Science), Shanghai 200063, China
- Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China
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14
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Yang L, Hong S, Mu H, Zhou J, He C, Wu Q, Gong X. Ozone exposure and health risks of different age structures in major urban agglomerations in People's Republic of China from 2013 to 2018. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:42152-42164. [PMID: 36645592 DOI: 10.1007/s11356-022-24809-5] [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: 06/09/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
High concentration of surface ozone (O3) will cause health risks to people. In order to analyze the spatiotemporal characteristics of O3 and assess O3 exposure and health risks for different age groups in China, we applied multiple methods including standard deviation ellipse, spatial autocorrelation, and exposure-response functions. Results show that O3 concentrations increased in 64.5% of areas in China from 2013 to 2018. The central plain urban agglomeration (CPU), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) witnessed the greatest incremental rates of O3 by 16.7%, 14.3%, and 13.1%. Spatially, the trend of O3 shows a significant positive autocorrelation, and high trend values primarily in central and east China. The proportion of the total population exposed to high O3 (above 160 μg/m3) increased annually. Compared to 2013, the proportion of the young, adult, and old populations exposed to high O3 increased to different extents in 2018 by 26.8%, 29.6%, and 27.2%, respectively. The extent of population exposure risk areas in China expanded in size, particularly in north and east China. The total premature respiratory mortalities attributable to long-term O3 exposure in six urban agglomerations were about 177,000 in 2018 which has increased by 16.4% compared to that in 2013. Among different age groups, old people are more vulnerable to O3 pollution, so we need to strengthen their relevant health protection of them.
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Affiliation(s)
- Lu Yang
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - Song Hong
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China.
| | - Hang Mu
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - Jingwei Zhou
- Wageningen Institute for Environment and Climate Research, Wageningen University & Research, 6700 HB, Wageningen, Gelderland, Netherlands
| | - Chao He
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Qian Wu
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - Xi Gong
- School of Low Carbon Economics, Hubei University of Economics, Wuhan, 430205, China
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15
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Chen L, Liao H, Zhu J, Li K, Bai Y, Yue X, Yang Y, Hu J, Zhang M. Increases in ozone-related mortality in China over 2013-2030 attributed to historical ozone deterioration and future population aging. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:159972. [PMID: 36356763 DOI: 10.1016/j.scitotenv.2022.159972] [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: 07/31/2022] [Revised: 10/18/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
We systematically examine historical and future changes in premature respiratory mortalities attributable to ozone (O3) exposure (O3-mortality) in China and identify the leading cause of respective change for the first time. The historical assessment for 2013-2019 is based on gridded O3 concentrations generated by a multi-source-data-fusion algorithm; the future prediction for 2019-2030 uses gridded O3 concentrations projected by four Coupled Model Intercomparison Project Phase 6 (CMIP6) models under three Shared Socioeconomic Pathways (SSP) scenarios. During 2013-2019, national annual O3-mortality is 176.3 thousand (95%CI: 123.5-224.0 thousand) averaged over 2013-2019 with an increasing trend of 14.1 thousand yr-1 (95%CI: 10.2-17.4 thousand yr-1); sensitivity experiments show that the O3-mortality varies at a rate of +12.7 (95%CI: 9.2-15.6), +5.8 (95%CI: 4.0-7.4), +1.0 (95%CI: 0.7-1.2), -5.4 (95%CI: -6.9 to -3.7) thousand yr-1, owing to changes in O3 concentration, population age structure, population size, mortality rate for respiratory disease, respectively. The deterioration of O3 air quality, shown as significant increase in O3 concentration, is identified as the primary factor which contributes 90.1 % of 2013-2019 O3-mortality rise. Compared with O3-mortality estimated in this study, the widely-used O3-mortality assessment method based on urban-site-dominant O3 measurements generates close national O3-mortality but overestimates (underestimates) provincial O3-mortality in coastal (central) provinces. From 2019 to 2030, national O3-mortality is projected to increase by 50.4-103.7 thousand under different SSP scenarios. The change in age structure (i.e. population aging) alone will result in significant O3-mortality rises of 137.9-160.5 thousand. Compared with 2013-2019 rapid O3 increase (+2.5 μg m-3 yr-1 at national level), O3 concentrations are projected to increase at a lower rate (+0.4 μg m-3 yr-1 in SSP5-8.5) or even decrease (-0.7 μg m-3 yr-1 in SSP1-2.6) from 2019 to 2030. Therefore, population aging, in place of O3 air quality deterioration, will become the leading cause of future O3-mortality rises during the coming decade.
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Affiliation(s)
- Lei Chen
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Jia Zhu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Ke Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Yang Bai
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Xu Yue
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Yang Yang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Meigen Zhang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
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16
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Yuan Y, Zhang X, Zhao J, Shen F, Nie D, Wang B, Wang L, Xing M, Hegglin MI. Characteristics, health risks, and premature mortality attributable to ambient air pollutants in four functional areas in Jining, China. Front Public Health 2023; 11:1075262. [PMID: 36741959 PMCID: PMC9893643 DOI: 10.3389/fpubh.2023.1075262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/03/2023] [Indexed: 01/20/2023] Open
Abstract
Air pollution is one of the leading causes for global deaths and understanding pollutant emission sources is key to successful mitigation policies. Air quality data in the urban, suburban, industrial, and rural areas (UA, SA, IA, and RA) of Jining, Shandong Province in China, were collected to compare the characteristics and associated health risks. The average concentrations of PM2.5, PM10, SO2, NO2, and CO show differences of -3.87, -16.67, -19.24, -15.74, and -8.37% between 2017 and 2018. On the contrary, O3 concentrations increased by 4.50%. The four functional areas exhibited the same seasonal variations and diurnal patterns in air pollutants, with the highest exposure excess risks (ERs) resulting from O3. More frequent ER days occurred within the 25-30°C, but much larger ERs are found within the 0-5°C temperature range, attributed to higher O3 pollution in summer and more severe PM pollution in winter. The premature deaths attributable to six air pollutants can be calculated in 2017 and 2018, respectively. Investigations on the potential source show that the ER of O3 (r of 0.86) had the tightest association with the total ER. The bivariate polar plots indicated that the highest health-based air quality index (HAQI) in IA influences the HAQI in UA and SA by pollution transport, and thus can be regarded as the major pollutant emission source in Jining. The above results indicate that urgent measures should be taken to reduce O3 pollution taking into account the characteristics of the prevalent ozone formation regime, especially in IA in Jining.
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Affiliation(s)
- Yue Yuan
- Jining Meteorological Bureau, Shandong, China
| | - Xi Zhang
- Jining Meteorological Bureau, Shandong, China
| | | | - Fuzhen Shen
- Institute of Energy and Climate Research, IEK-7: Stratosphere, Forschungszentrum Jülich, Jülich, Germany,Department of Meteorology, University of Reading, Reading, United Kingdom,*Correspondence: Fuzhen Shen ✉
| | - Dongyang Nie
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Bing Wang
- Henley Business School, University of Reading, Reading, United Kingdom
| | - Lei Wang
- Jining Bureau of Ecology and Environment, Shandong, China
| | - Mengyue Xing
- Business School, Dalian University of Foreign Languages, Liaoning, China
| | - Michaela I. Hegglin
- Institute of Energy and Climate Research, IEK-7: Stratosphere, Forschungszentrum Jülich, Jülich, Germany,Department of Meteorology, University of Reading, Reading, United Kingdom,Michaela I. Hegglin ✉
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17
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Lyu Y, Wu Z, Wu H, Pang X, Qin K, Wang B, Ding S, Chen D, Chen J. Tracking long-term population exposure risks to PM 2.5 and ozone in urban agglomerations of China 2015-2021. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 854:158599. [PMID: 36089013 DOI: 10.1016/j.scitotenv.2022.158599] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/24/2022] [Accepted: 09/04/2022] [Indexed: 06/15/2023]
Abstract
China has experienced severe air pollution in the past decade, especially PM2.5 and emerging ozone pollution recently. In this study, we comprehensively analyzed long-term population exposure risks to PM2.5 and ozone in urban agglomerations of China during 2015-2021 regarding two-stage clean-air actions based on the Ministry of Ecology and the Environment (MEE) air monitoring network. Overall, the ratio of the population living in the regions exceeding the Chinese National Ambient Air Quality Standard (35 μg/m3) decreases by 29.9 % for PM2.5 from 2015 to 2021, driven by high proportions in the Middle Plain (MP, 42.3 %) and Lan-Xi (35.0 %) regions. However, this ratio almost remains unchanged for ozone and even increases by 1.5 % in the MP region. As expected, the improved air quality leads to 234.7 × 103 avoided premature mortality (ΔMort), mainly ascribed to the reduction in PM2.5 concentration. COVID-19 pandemic may influence the annual variation of PM2.5-related ΔMort as it affects the shape of the population exposure curve to become much steeper. Although all eleven urban agglomerations share stroke (43.6 %) and ischaemic heart disease (IHD, 30.1 %) as the two largest contributors to total ΔMort, cause-specific ΔMort is highly regional heterogeneous, in which ozone-related ΔMort is significantly higher (21 %) in the Tibet region than other urban agglomeration. Despite ozone-related ΔMort being one order of magnitude lower than PM2.5-related ΔMort from 2015 to 2021, ozone-related ΔMort is predicted to increase in major urban agglomerations initially along with a continuous decline for PM2.5-related ΔMort from 2020 to 2060, highlighting the importance of ozone control. Coordinated controls of PM2.5 and O3 are warranted for reducing health burdens in China during achieving carbon neutrality.
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Affiliation(s)
- Yan Lyu
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, China; School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China; Shaoxing Research Institute, Zhejiang University of Technology, Shaoxing 312077, China.
| | - Zhentao Wu
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, China
| | - Haonan Wu
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, China
| | - Xiaobing Pang
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, China.
| | - Kai Qin
- School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
| | - Baozhen Wang
- Green intelligence Environmental School, Yangtze Normal University, Chongqing 408100, China
| | - Shimin Ding
- Green intelligence Environmental School, Yangtze Normal University, Chongqing 408100, China
| | - Dongzhi Chen
- School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316000, China
| | - Jianmeng Chen
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, China; School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316000, China
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18
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Guan Y, Xiao Y, Zhang N, Chu C. Tracking short-term health impacts attributed to ambient PM 2.5 and ozone pollution in Chinese cities: an assessment integrates daily population. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:91176-91189. [PMID: 35881283 PMCID: PMC9315092 DOI: 10.1007/s11356-022-22067-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
Joint and synergistic control of PM2.5 and ozone pollution is an urgent need in China and a global-widely concerned issue. Health impact assessment could provide a comprehensive perspective for PM2.5-ozone coordinated control strategies. For a detailed understanding of the seasonality and regionality of the health impacts attributed to PM2.5 and ozone in China, this study extended the classic health impact function by daily population and assessed the short-term (daily) health impacts in 335 Chinese cities in 2021. Population migration indexes from Baidu were introduced to estimate the cities' daily population. Using this method, we quantitatively investigated the influence of population on short-term health impact assessment and identified which was significant in the Pearl River Delta (PRD) region and other populous cities. Although the annual sums of PM2.5- and ozone-related daily health impacts were close for all Chinese cities, the PM2.5-related health impact was equivalent to 333.96% and 32.07% of that ozone-related, during the cold and warm periods. The correlation and local spatial association analysis found significant city-specific and city-cluster associations of daily health impacts during the warm period and in Beijing-Tianjin-Hebei and surrounding regions (BTHS) and the Yangtze River Delta (YRD). Policymakers could promote period- and pollutant-targeted control actions for the major city groups, especially the BTHS, YRD, and PRD. Our methods and findings investigated the various influences of the population on short-term health impact assessment and proposed the PM2.5-ozone collaborative control idea for key regions and city groups.
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Affiliation(s)
- Yang Guan
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, 28 Beiyuan Road, Chaoyang District, Beijing, 100012, China
- The Center for Beautiful China, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Yang Xiao
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, 28 Beiyuan Road, Chaoyang District, Beijing, 100012, China
- The Center for Beautiful China, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Nannan Zhang
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, 28 Beiyuan Road, Chaoyang District, Beijing, 100012, China.
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
| | - Chengjun Chu
- Center of Environmental Status and Plan Assessment, Chinese Academy of Environmental Planning, Beijing, 100012, China
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19
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Zhang N, Guan Y, Jiang Y, Zhang X, Ding D, Wang S. Regional demarcation of synergistic control for PM 2.5 and ozone pollution in China based on long-term and massive data mining. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:155975. [PMID: 35588824 DOI: 10.1016/j.scitotenv.2022.155975] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 05/06/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Implementing an inter-regional synergistic control policy for fine particulate matter (PM2.5) and ground-level ozone (O3) could improve regional air quality. However, little is known about the effectiveness and accuracy of synergistic control region delineation. This study aimed to construct a network model and apply it to a case study of regional delineation in China at different scales to quantify the interactions between regions. Firstly, the Cumulative Risk Index (CRI) was proposed and quantified from a health risk perspective based on the daily mean PM2.5 and daily maximum 8-h average O3 concentrations from 2015 to 2020 in China. Then, the complex network topology parameters were introduced to determine the optimal threshold for different network constructions, and the Girvan-Newman (GN) algorithm was used to divide the network into independent regions. Results showed that the correlation between cities is more robust than that between provinces. There are four-seven major provincial-scale regions with strong synchronicity in CRI, suggesting that PM2.5 and O3 synergistic control policies shall be implemented jointly within these demarcated regions. Moreover, urban-scale CRI network analysis indicated that the existing key control areas (2 + 26 cities) need to be expanded to 40-50 cities and refined into seven independent urban regions. Meanwhile, the Fen-Wei Plain can be focused on six cities: Xi'an, Baoji, Xianyang, Weinan, Yuncheng, and Tongchuan. This study could improve our understanding of the synergistic control regions for PM2.5 and O3 pollution, and the results could be used to develop joint control policies for both pollutants.
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Affiliation(s)
- Nannan Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yang Guan
- Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xuya Zhang
- Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Dian Ding
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
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20
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Hou X, Guo Q, Hong Y, Yang Q, Wang X, Zhou S, Liu H. Assessment of PM 2.5-related health effects: A comparative study using multiple methods and multi-source data in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 306:119381. [PMID: 35500711 DOI: 10.1016/j.envpol.2022.119381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 06/14/2023]
Abstract
In China, PM2.5 pollution has caused extensive death and economic loss. Thus, an accurate assessment of the spatial distribution of these losses is crucial for delineating priority areas for air pollution control in China. In this study, we assessed the PM2.5 exposure-related health effects according to the integrated exposure risk function and non-linear power law (NLP) function in 338 prefecture-level cities in China by utilizing online monitoring data and the PM2.5 Hindcast Database (PHD). Our results revealed no significant difference between the monitoring data and PHD (p value = 0.66 > 0.05). The number of deaths caused by PM2.5-related Stroke (cerebrovascular disease), ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer at the national level estimated through the NLP function was 0.27 million (95% CI: 0.06-0.50), 0.23 million (95% CI: 0.08-0.38), 0.31 million (95% CI: 0.04-0.57), and 0.31 million (95% CI: 0.16-0.40), respectively. The total economic cost at the national level in 2016 was approximately US$80.25 billion (95% CI: 24.46-132.25). Based on a comparison of Z statistics, we propose that the evaluation results obtained using the NLP function and monitoring data are accurate. Additionally, according to scenario simulations, Beijing, Chongqing, Tianjin, and other cities should be priority areas for PM2.5 pollution control to achieve considerable health benefits. Our statistics can help improve the accuracy of PM2.5-related health effect assessments in China.
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Affiliation(s)
- Xiaoyun Hou
- School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou, 310016, China; Zhejiang Academy of Ecological Civilization, Hangzhou, 310016, China
| | - Qinghai Guo
- School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou, 310016, China; Zhejiang Academy of Ecological Civilization, Hangzhou, 310016, China.
| | - Yan Hong
- School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou, 310016, China
| | - Qiaowei Yang
- School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou, 310016, China
| | - Xinkui Wang
- Dongying Development and Reform Commission, Dongying, 370502, China
| | - Siyang Zhou
- School of Environment, State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing, 100875, China
| | - Haiqiang Liu
- School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou, 310016, China
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21
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Spatiotemporal Distribution Patterns and Exposure Risks of PM2.5 Pollution in China. REMOTE SENSING 2022. [DOI: 10.3390/rs14133173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The serious pollution of PM2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns of PM2.5 in China. The regional and population exposure risks of the nation and of urban agglomerations were evaluated by exceedance frequency and population weight. The results indicated that the PM2.5 concentrations of urban agglomerations decreased sharply from 2014 to 2020. The region with PM2.5 concentrations less than 35 μg·m−3 accounted for 80.27% in China, and the average PM2.5 concentrations in 8 urban agglomerations were less than 35 μg·m−3 in 2020. The spatial distribution pattern of PM2.5 concentrations in China revealed higher concentrations to the east of the Hu Line and lower concentrations to the west. The annual regional exposure risk (RER) in China was at a high level, with a national average of 0.75, while the average of 14 urban agglomerations was as high as 0.86. Among the 14 urban agglomerations, the average annual RER was the highest in the Shandong Peninsula (0.99) and lowest in the Northern Tianshan Mountains (0.76). The RER in China has obvious seasonality; the most serious was in winter, and the least serious was in summer. The population exposure risk (PER) east of the Hu Line was significantly higher than that west of the Hu Line. The average PER was the highest in Beijing-Tianjin-Hebei (4.09) and lowest in the Northern Tianshan Mountains (0.71). The analysis of air pollution patterns and exposure risks in China and urban agglomerations in this study could provide scientific guidance for cities seeking to alleviate air pollution and prevent residents’ exposure risks.
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22
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Cao J, Qiu X, Peng L, Gao J, Wang F, Yan X. Impacts of the differences in PM 2.5 air quality improvement on regional transport and health risk in Beijing-Tianjin-Hebei region during 2013-2017. CHEMOSPHERE 2022; 297:134179. [PMID: 35247451 DOI: 10.1016/j.chemosphere.2022.134179] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 02/21/2022] [Accepted: 02/28/2022] [Indexed: 05/16/2023]
Abstract
Due to the implementation of different air pollution control measures in the Beijing-Tianjin-Hebei (BTH) region during 2013-2017, the air quality exhibited varied improvements in each province, indicating substantial changes in the interprovincial regional transport of PM2.5. In this study, we investigated these changes by using the Community Multiscale Air Quality (CMAQ) model coupled with the integrated source apportionment method (ISAM) during this period. The results showed that the concentrations of primary particles, SO42-, and NO3- decreased by 41.5, 40.8, and 1.8%, respectively due to the air pollutants emission reduction. Local air pollutant emissions were the predominant contributors of PM2.5 for each region in BTH, accounting for 41.3-47.6, 38.1-40.6, 50.6-53.6, and 54.0-57.1% of PM2.5 in Beijing, Tianjin, and northern and southern Hebei, respectively. Total PM2.5 has been mitigated by 7.1-12.3 and 5.1-11.7 μg/m3 from local and regional emission reduction, respectively in the BTH. Moreover, diverse local meteorological conditions variation increased the PM2.5 concentration by 5.3 μg/m3 in Tianjin and decreased it by 7.6, 2.0, and 4.9 μg/m3 in Beijing, and northern and southern Hebei, respectively. Estimation by integrated exposure-response function revealed that the number of premature deaths attributable to PM2.5 exposure decreased by approximately 3000 in the BTH region during 2013-2017. Additional policies that focus on PM2.5-O3 coordinated control and stringent regional joint air pollution regulation are required to substantially reduce the health impacts, especially in southern Hebei.
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Affiliation(s)
- Jingyuan Cao
- College of Environmental Sciences and Engineering, North China Electric Power University, Beijing, 102206, China; Key Laboratory of Resources and Environmental Systems Optimization, Ministry of Education, College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China
| | - Xionghui Qiu
- College of Environmental Sciences and Engineering, North China Electric Power University, Beijing, 102206, China; Key Laboratory of Resources and Environmental Systems Optimization, Ministry of Education, College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Lin Peng
- College of Environmental Sciences and Engineering, North China Electric Power University, Beijing, 102206, China; Key Laboratory of Resources and Environmental Systems Optimization, Ministry of Education, College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China
| | - Jian Gao
- Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Fangyuan Wang
- College of Environmental Sciences and Engineering, North China Electric Power University, Beijing, 102206, China; Key Laboratory of Resources and Environmental Systems Optimization, Ministry of Education, College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China
| | - Xiao Yan
- Beijing Municipal Research Institute of Environmental Protection, Beijing, 100037, China
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23
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Duan W, Wang X, Cheng S, Wang R. Regional collaboration to simultaneously mitigate PM 2.5 and O 3 pollution in Beijing-Tianjin-Hebei and the surrounding area: Multi-model synthesis from multiple data sources. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 820:153309. [PMID: 35065107 DOI: 10.1016/j.scitotenv.2022.153309] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 12/28/2021] [Accepted: 01/17/2022] [Indexed: 06/14/2023]
Abstract
Beijing-Tianjin-Hebei and the surrounding area (BTHSA) shows the poorest air quality in China, reflected in sub-standard PM2.5 and increasingly pronounced O3 pollution, stressing the urgency for regional cooperation and collaborative control of PM2.5 and O3. With the aim to explore the cooperative regions and response mechanisms of PM2.5 and O3 in BTHSA, this study applied multiple mathematical models and analytical indicators to multiple data sources, including applying self-organizing map (SOM), response surface model (RSM), random forest (RF), distributed lag nonlinear models (DLNMs), and meta-analysis, on ground observations of air quality and meteorology, ozone monitoring instrument (OMI) observations, and air pollutant emission inventory. The results revealed that BTHSA exhibited clear regional characteristics of air pollution and can be divided into four clusters for enhanced intercity cooperation. Over 2015-2020, anthropogenic factors played more important roles than meteorological ones on the alleviation of PM2.5 and the deterioration of O3. RSM based on observations and RF based on emissions both suggested that, in the near future, strengthened abatement of SO2, PM2.5 and VOC can be beneficial for controlling PM2.5 and O3 pollution, while intensive NOx reduction in PM2.5-dominant months and mitigatory NOx reduction in O3-dominant months should be formulated before certifying an obvious transition of O3-NOx-VOC sensitivity. This study, with multi-model and multi-data fusion, can be expected to provide synthesized fact- and science-based guidance for the next-stage collaborative control of PM2.5 and O3 in BTHSA.
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Affiliation(s)
- Wenjiao Duan
- Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing 100124, China
| | - Xiaoqi Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing 100124, China.
| | - Shuiyuan Cheng
- Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing 100124, China.
| | - Ruipeng Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing 100124, China
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24
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Deng C, Tian S, Li Z, Li K. Spatiotemporal characteristics of PM 2.5 and ozone concentrations in Chinese urban clusters. CHEMOSPHERE 2022; 295:133813. [PMID: 35114261 DOI: 10.1016/j.chemosphere.2022.133813] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/25/2022] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Abstract
Despite China's public commitment to emphasise air pollution investigation and control, trends in PM2.5 and ozone concentrations in Chinese urban clusters remain unclear. This study quantifies the spatiotemporal variations in PM2.5 and surface ozone at the scale of Chinese urban clusters by using a long-term integrated dataset from 2015 to 2020. Nonlinear Granger causality testing was used to explore the spatial association patterns of PM2.5 and ozone pollution in five megacity cluster regions. The results show a significant downward trend in annual mean PM2.5 concentrations from 2015 to 2020, with a decline rate of 2.8 μg m-3 yr-1. By contrast, surface ozone concentrations increased at a rate of 2.1 μg m-3 yr-1 over the 6 years. The annual mean PM2.5 concentrations in urban clusters show significant spatial clustering characteristics, mainly in Beijing-Tianjin-Hebei (BTH), Fenwei Plain (FWP), Northern slope of Tianshan Mountains urban cluster (NSTM), Sichuan Basin urban cluster (SCB), and Yangtze River Delta (YRD). Surface ozone shows severe summertime pollution and distributional variability, with increased ozone pollution in major urban clusters. The highest increases were observed in BTH, Yangtze River midstream urban cluster (YRMR), YRD, and Pearl River Delta (PRD). Nonlinear Granger causality tests showed that PM2.5 was a nonlinear Granger cause of ozone, further supporting the literature's findings that PM2.5 reduction promoted photochemical reaction rates and stimulated ozone production. The nonlinear test statistic passed the significance test in magnitude and statistical significance. FWP was an exception, with no significant long-term nonlinear causal link between PM2.5 and ozone. This study highlights the challenges of compounded air pollution caused primarily by ozone and secondary PM2.5. These results have implications for the design of synergistic pollution abatement policies for coupled urban clusters.
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Affiliation(s)
- Chuxiong Deng
- School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, 410081, PR China.
| | - Si Tian
- School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, 410081, PR China.
| | - Zhongwu Li
- School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, 410081, PR China.
| | - Ke Li
- Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education of China), Key Laboratory of Applied Statistics and Data Science, School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan, 410081, PR China.
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25
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Zhou Y, Duan W, Chen Y, Yi J, Wang B, Di Y, He C. Exposure Risk of Global Surface O 3 During the Boreal Spring Season. EXPOSURE AND HEALTH 2022; 14:431-446. [PMID: 35128147 PMCID: PMC8800438 DOI: 10.1007/s12403-022-00463-7] [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: 09/02/2021] [Revised: 12/06/2021] [Accepted: 01/13/2022] [Indexed: 06/14/2023]
Abstract
UNLABELLED Surface ozone (O3) is an oxidizing gaseous pollutant; long-term exposure to high O3 concentrations adversely affects human health. Based on daily surface O3 concentration data, the spatiotemporal characteristics of O3 concentration, exposure risks, and driving meteorological factors in 347 cities and 10 major countries (China, Japan, India, South Korea, the United States, Poland, Spain, Germany, France, and the United Kingdom) worldwide were analyzed using the MAKESENS model, Moran' I analysis, and Generalized additive model (GAM). The results indicated that: in the boreal spring season from 2015 to 2020, the global O3 concentration exhibited an increasing trend at a rate of 0.6 μg/m3/year because of the volatile organic compounds (VOCs) and NOx changes caused by human activities. Due to the lockdown policies after the outbreak of COVID-19, the average O3 concentration worldwide showed an inverted U-shaped growth during the study period, increasing from 21.9 μg/m3 in 2015 to 27.3 μg/m3 in 2019, and finally decreasing to 25.9 μg/m3 in 2020. According to exposure analytical methods, approximately 6.32% of the population (31.73 million people) in the major countries analyzed reside in rapidly increasing O3 concentrations. 6.53% of the population (32.75 million people) in the major countries were exposed to a low O3 concentration growth environment. Thus, the continuous increase of O3 concentration worldwide is an important factor leading to increasing threats to human health. Further we found that mean wind speed, maximum temperature, and relative humidity are the main factors that determine the change of O3 concentration. Our research results are of great significance to the continued implementation of strict air quality policies and prevention of population hazards. However, due to data limitations, this research can only provide general trends in O3 and human health, and more detailed research will be carried out in the follow-up. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12403-022-00463-7.
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Affiliation(s)
- Yiqi Zhou
- University of Chinese Academy of Science, Beijing, 100049 China
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011 China
| | - Weili Duan
- University of Chinese Academy of Science, Beijing, 100049 China
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011 China
| | - Yaning Chen
- University of Chinese Academy of Science, Beijing, 100049 China
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011 China
| | - Jiahui Yi
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079 China
| | - Bin Wang
- College of Computer Science, Chongqing University, Chongqing, 400044 China
| | - Yanfeng Di
- College of Environment and Resources, Guangxi Normal University, Guilin, 541006 China
| | - Chao He
- College of Resources and Environment, Yangtze University, Wuhan, 430100 China
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26
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Guan Y, Xiao Y, Wang Y, Zhang N, Chu C. Assessing the health impacts attributable to PM 2.5 and ozone pollution in 338 Chinese cities from 2015 to 2020. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 287:117623. [PMID: 34171728 DOI: 10.1016/j.envpol.2021.117623] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 05/13/2023]
Abstract
China has effectively reduced the fine particulate (PM2.5) pollution from 2015 to 2020. Ozone pollution and related health impacts have become severe contemporaneously. The coordinated control of PM2.5 and ozone is becoming a new issue for China's air pollution control. This study quantitatively assessed the health impacts attributed to PM2.5 and ozone pollution in 338 Chinese cities from 2015 to 2020 and estimated the possible health benefits from achieving dual concentration targets during 2021-2025. Results show PM2.5 caused a total health impact of 2.45 × 107 disability-adjusted life years (DALYs) in 2020. All-cause and respiratory ozone-related health impact in 2020 was 1.04 × 107 DALYs and 1.56 × 106 DALYs. Between 2015 and 2020, the PM2.5-related health impacts decreased by 14.97%, while those ozone-related increased by 94.61% and 96.54% for all-cause and respiratory. Cities in the North China Plain have suffered higher health impacts attributable to PM2.5 and ozone pollution, indicating that the two-pollutant coordinated control is primarily needed. By achieving aggressive concentration target (decreasing 10%) between 2020 and 2025, China will reduce the PM2.5-related health impacts in 338 cities by 1.56 × 106 DALYs (improving 6.37%). By achieving general target (decreasing 10% or within the Interim target-1 of World Health Organization), the PM2.5-related health benefit will be 7.98 × 105 DALYs (improving 3.25%). The deteriorating ozone health risks will also be improved. Controlling air pollution in large cities and regional center cities can achieve remarkable health benefits. Due to the inter-region, inter-province, and inter-city difference of health impacts, targeted and differentiated pollution prevention and control need to be implemented.
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Affiliation(s)
- Yang Guan
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Yang Xiao
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Yameng Wang
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Nannan Zhang
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100012, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
| | - Chengjun Chu
- Center of Environmental Status and Plan Assessment, Chinese Academy of Environmental Planning, Beijing, 100012, China
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Short-term effect of fine particulate matter and ozone on non-accidental mortality and respiratory mortality in Lishui district, China. BMC Public Health 2021; 21:1661. [PMID: 34517854 PMCID: PMC8439017 DOI: 10.1186/s12889-021-11713-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/29/2021] [Indexed: 11/29/2022] Open
Abstract
Background In recent years, air pollution has become an imminent problem in China. Few studies have investigated the impact of air pollution on the mortality of the middle-aged and elderly people. Therefore, this study aims to evaluate the impact of PM2.5 (fine particulate matter) and O3 (ozone) on non-accidental mortality and respiratory mortality of the middle-aged and elderly people in Lishui District of Nanjing and provide the evidence for potential prevention and control measures of air pollution. Method Using daily mortality and atmospheric monitoring data from 2015 to 2019, we applied a generalized additive model with time-series analysis to evaluate the association of PM2.5 and O3 exposure with daily non-accidental mortality and respiratory mortality in Lishui District. Using the population attributable fractions to estimate the death burden caused by short-term exposure to O3 and PM2.5。. Result For every 10 μg/m3 increase in PM2.5, non-accidental mortality increased 0.94% with 95% confidence interval (CI) between 0.05 and 1.83%, and PM2.5 had a more profound impact on females than males. For every 10 μg/m3 increase in O3, respiratory mortality increased 1.35% (95% CI: 0.05, 2.66%) and O3 had a more profound impact on males than females. Compared with the single pollutant model, impact of the two-pollutant model on non-accidental mortality and respiratory mortality slightly decreased. In summer and winter as opposed to the other seasons, O3 had a more obvious impact on non-accidental mortality. The population attributable fractions of non-accidental mortality were 0.84% (95% CI:0.00, 1.63%) for PM2.5 and respiratory mortality were 0.14% (95% CI:0.01, 0.26%) for O3. For every 10 μg/m3 decrease in PM2.5, 122 (95% CI: 6, 237) non-accidental deaths could be avoided. For every 10 μg/m3 decrease in O3, 10 (95% CI: 1, 38) respiratory deaths could be avoided. Conclusion PM2.5 and O3 could significantly increase the risk of non-accidental and respiratory mortality in the middle-aged and elderly people in Lishui District of Nanjing. Exposed to air pollutants, men were more susceptible to O3 damage, and women were more susceptible to PM2.5 damage. Reduction of PM2.5 and O3 concentration in the air may have the potential to avoid considerable loss of lives.
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Shao L, Ge S, Jones T, Santosh M, Silva LFO, Cao Y, Oliveira MLS, Zhang M, BéruBé K. The role of airborne particles and environmental considerations in the transmission of SARS-CoV-2. GEOSCIENCE FRONTIERS 2021; 12:101189. [PMID: 38620834 PMCID: PMC8020609 DOI: 10.1016/j.gsf.2021.101189] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 05/06/2023]
Abstract
Corona Virus Disease 2019 (COVID-19) caused by the novel coronavirus, results in an acute respiratory condition coronavirus 2 (SARS-CoV-2) and is highly infectious. The recent spread of this virus has caused a global pandemic. Currently, the transmission routes of SARS-CoV-2 are being established, especially the role of environmental transmission. Here we review the environmental transmission routes and persistence of SARS-CoV-2. Recent studies have established that the transmission of this virus may occur, amongst others, in the air, water, soil, cold-chain, biota, and surface contact. It has also been found that the survival potential of the SARS-CoV-2 virus is dependent on different environmental conditions and pollution. Potentially important pathways include aerosol and fecal matter. Particulate matter may also be a carrier for SARS-CoV-2. Since microscopic particles can be easily absorbed by humans, more attention must be focused on the dissemination of these particles. These considerations are required to evolve a theoretical platform for epidemic control and to minimize the global threat from future epidemics.
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Affiliation(s)
- Longyi Shao
- State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), Beijing 100083, China
- College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
| | - Shuoyi Ge
- State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), Beijing 100083, China
- College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
| | - Tim Jones
- School of Earth and Environmental Sciences, Cardiff University, Museum Avenue, Cardiff, CF10 3YE, UK
| | - M Santosh
- School of Earth Sciences and Resources, China University of Geosciences Beijing, Beijing 100083, China
- Department of Earth Science, University of Adelaide, Adelaide, SA 5005, Australia
| | - Luis F O Silva
- Department of Civil and Environmental, Universidad de la Costa, Calle 58 #55-66, 080002 Barranquilla, Atlántico, Colombia
| | - Yaxin Cao
- State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), Beijing 100083, China
- College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
| | - Marcos L S Oliveira
- Department of Civil and Environmental, Universidad de la Costa, Calle 58 #55-66, 080002 Barranquilla, Atlántico, Colombia
- Departamento de Ingeniería Civil y Arquitectura, Universidad de Lima, Avenida Javier Prado Este 4600 - Santiago de, Surco 1503, Peru
| | - Mengyuan Zhang
- State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), Beijing 100083, China
- College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
| | - Kelly BéruBé
- School of Biosciences, Cardiff University, Museum Avenue, Cardiff CF10 3AX, Wales, UK
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29
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Yin M, Zhang X, Li Y, Fan K, Li H, Gao R, Li J. Ambient ozone pollution at a coal chemical industry city in the border of Loess Plateau and Mu Us Desert: characteristics, sensitivity analysis and control strategies. PeerJ 2021; 9:e11322. [PMID: 33987008 PMCID: PMC8086580 DOI: 10.7717/peerj.11322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 03/31/2021] [Indexed: 11/20/2022] Open
Abstract
In this study, ambient ozone (O3) pollution characteristics and sensitivity analysis were carried out in Yulin, a city in the central area of the Loess Plateau during 2017 to 2019 summer. O3 concentrations increased for 2017 to 2019. Correlation and statistics analysis indicated high temperature (T > 25 °C, low relative humidity (RH < 60%), and low wind speed (WS < 3 m/s) were favorable for O3 formation and accumulation, and the O3 pollution days (MDA8 O3 > 160 µg/m3) were predominantly observed when the wind was traveling from the easterly and southerly. O3 concentration in urban area of Yulin was higher than that in background. The pollution air masses from Fenwei Plain increase the level and duration of O3 pollution. In order to clarify the formation mechanism and source of O3, online measurements of volatile organic compounds (VOCs) were conducted from 7 July to 10 August in 2019. The average of VOCs concentration was 26 ± 12 ppbv, and large amounts of alkenes followed by aromatics, characteristic pollutants of the coal chemical industry, were detected in the ambient air. To further measure the sensitivity, the observation-based model (OBM) simulation was conducted. Empirical Kinetic Modeling Approach (EKMA) plot and relative incremental reactivity (RIR) value indicated Yulin located on VOCs-limited regime. That implied a slight decrease of NOx may increase O3 concentration. When the emission reduction ratio of anthropogenic VOCs/NOx higher than 1:1, the O3 will decrease. O3 control strategies analysis shows that the O3 targets of 5% and 10% O3 concentration reductions are achievable through precursor control, but more effort is needed to reach the 30% and 40% reduction control targets.
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Affiliation(s)
- Manfei Yin
- College of Resource and Environment Engineering, Guizhou University, Guiyang, China.,State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Xin Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China.,Environment Research Institute, Shandong University, Jinan, China
| | - Yunfeng Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China.,Environment Research Institute, Shandong University, Jinan, China
| | - Kai Fan
- Yulin Municipal Ecology and Environment Bureau, Yulin, China
| | - Hong Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Rui Gao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Jinjuan Li
- College of Resource and Environment Engineering, Guizhou University, Guiyang, China
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