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Wang Y, Ni J, Xu K, Zhang H, Gong X, He C. Intricate synergistic effects between air pollution and carbon emission: An emerging evidence from China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 349:123851. [PMID: 38527582 DOI: 10.1016/j.envpol.2024.123851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 02/29/2024] [Accepted: 03/22/2024] [Indexed: 03/27/2024]
Abstract
Due to global climate change and intensifying anthropogenic pollution, China confronts the dual challenge of controlling particulate matter 2.5 μm (PM2.5) pollution and reducing carbon emissions. Quantifying the characteristics of PM2.5 concentrations and CO2 emissions, as well as identifying the driving factors and synergistic effects of PM2.5 reduction and CO2 mitigation, are crucial steps in promoting sustainable urban development and achieving the Sustainable Development Goals (SDGs) in China. In this study, we selected 168 cities as our case-study, and quantified spatial characteristics of PM2.5 concentrations and CO2 emissions from 2015 to 2020 in China. Then we analyzed driving factors affecting the spatial heterogeneity of PM2.5 reduction and CO2 mitigation applying Multi-scale Geographically Weighted Regression (MGWR) model. By employing coupling coordination degree (CCD) model, we further detected the spatiotemporal evolution patterns of the synergistic effects between PM2.5 reduction and CO2 mitigation in key Chinese cities. The result showed that: (a) From 2015 to 2020, PM2.5 concentrations experienced a significant reduction from 59.78 μg/m3 to 49.83 μg/m3, while CO2 emissions increased from 44.88 × 106 t in 2015 to 45.77 × 106 t in 2020; (b) Green economy efficiency (gee), government attention (gover), and environmental regulation (envir) demonstrate the most pronounced synergistic effect on pollution reduction and carbon mitigation, with the drivers exhibiting obvious spatial heterogeneity; (c) The overall coupling coordination level of PM2.5 pollution and CO2 emissions in China dropped from 0.49 in 2015 to 0.46 in 2020, and the coupling coordination grade in northern cities was notably higher than that in southern cities. The result enhances our understanding of spatiotemporal patterns of synergistic effects between PM2.5 reduction and CO2 mitigation, and provides the theoretical basis for policy decision-making to realize pollution decrease and carbon neutral and regional environment governance.
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Affiliation(s)
- Yanwen Wang
- School of Economics and Management, China University of Geosciences, Wuhan, 430074, 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
| | - Kewei Xu
- School of Economics and Management, China University of Geosciences, Wuhan, 430074, China
| | - Hao Zhang
- School of Economics and Management, China University of Geosciences, Wuhan, 430074, China
| | - Xusheng Gong
- Hubei University of Science and Technology, Xianning, 437100, China
| | - Chao He
- Hubei University of Science and Technology, Xianning, 437100, China.
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Zhao X, Wu T, Zhou W, Han L, Neophytou AM. Reducing air pollution does not necessarily reduce related adults' mortality burden: Variations in 177 countries with different economic levels. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 933:173037. [PMID: 38740214 DOI: 10.1016/j.scitotenv.2024.173037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/08/2024] [Accepted: 05/05/2024] [Indexed: 05/16/2024]
Abstract
Prolonged exposure to PM2.5 is associated with increased mortality. However, reducing air pollution concentrations does not necessarily reduce the related burden of deaths. Here, we aim to estimate the variations in PM2.5-related mortality due to contributions from key factors - PM2.5 concentration, population exposure, and healthcare levels - for 177 countries from 2000 to 2018 at the 1-km grid scale according to the Global Mortality Exposure Model (GEMM) model. We find that global reductions in PM2.5-related deaths mainly come from high and upper-middle income countries, where lowered air pollutant concentration and better healthcare can offset mortality burdens caused by increasing exposed populations. Changes in population exposure to PM2.5 contribute the most (54 %) to change in global related deaths over the examined period, followed by changes in healthcare (-42 %) and pollution concentrations (4 %). The impacts vary across countries and regions within them due to other drivers, which are significantly influenced by development status. Policies aiming at reducing PM2.5 associated health risks need to account for country-specific balances of these key socioeconomic drivers.
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Affiliation(s)
- Xiuling Zhao
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing 100085, China; Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, USA
| | - Tong Wu
- The Natural Capital Project, Stanford University, Stanford, CA 94305, USA
| | - Weiqi Zhou
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing 100085, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China; Beijing Urban Ecosystem Research Station, 18 Shuangqing Road, Haidian District, Beijing 100085, China.
| | - Lijian Han
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing 100085, China
| | - Andreas M Neophytou
- Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, USA
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Sun J, Dang Y, Wang J, Hua C. Spatiotemporal characteristics analysis of multi-factorial air pollution in the Jing-Jin-Ji region based on improved sequential ICI method and novel grey spatiotemporal incidence models. ENVIRONMENTAL RESEARCH 2024; 252:118948. [PMID: 38649013 DOI: 10.1016/j.envres.2024.118948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/27/2024] [Accepted: 04/14/2024] [Indexed: 04/25/2024]
Abstract
Air pollution shares the attributes of multi-factorial influence and spatiotemporal complexity, leading to air pollution control assistance models easily falling into a state of failure. To address this issue, we design a framework containing improved data fusion method, novel grey incidence models and air pollution spatiotemporal analysis to analyze the complex characteristics of air pollution under the fusion of multiple factors. Firstly, we improve the existing data fusion method for multi-factor fusion. Subsequently, we construct two grey spatiotemporal incidence models to examine the spatiotemporal characteristics of multi-factorial air pollution in network relationships and changing trends. Furthermore, we propose two new properties that can manifest the performance of grey incidence analysis, and we provide detailed proof of the properties of the new models. Finally, in the Jing-Jin-Ji region, the novel models are used to study the network relationships and trend changes of air pollution. The findings are as follows: (1) Two highly polluted belts in the region require attention. (2) Although the air pollution network under multi-factorial fusion obeys the first law of geography, the network density and node density exhibit significant variations. (3) From 2013 to 2021, all pollutants except O3 show improvement. (4) Recommendations for responses are presented based on the above-mentioned results. (5) The parameter analyses, model comparisons, Monte Carlo experiments and model feature summaries illustrate that the proposed models are practical, interpretable and considerably outperform various prevailing competitors with remarkable universality.
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Affiliation(s)
- Jing Sun
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 211100, China
| | - Yaoguo Dang
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 211100, China
| | - Junjie Wang
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 211100, China.
| | - Chao Hua
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 211100, China
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Soltaninia S, Eskandaripour M, Ahmadi Z, Ahmadi S, Eslamian S. The hidden threat of heavy metal leaching in urban runoff: Investigating the long-term consequences of land use changes on human health risk exposure. ENVIRONMENTAL RESEARCH 2024; 251:118668. [PMID: 38467359 DOI: 10.1016/j.envres.2024.118668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/23/2024] [Accepted: 03/08/2024] [Indexed: 03/13/2024]
Abstract
This study evaluated the potential effects of long-term land use and climate change on the quality of surface runoff and the health risks associated with it. The land use change projection 2030 was derived from the main changes in land use from 2009 to 2019, and rainfall data was obtained from the Long Ashton Research Station Weather Generator (LARS-WG) model. The Long-Term Hydrological Impact Assessment (L-THIA) model was then utilized to calculate the rate of runoff heavy metal (HM) pollutant loading from the urban catchment. It was found that areas with heavy development posed a significantly greater public health risk associated with runoff, with higher risks observed in high-development and traffic areas compared to industrial, residential, and commercial areas. Additionally, exposure to Lead (Pb), Mercury (Hg), and Arsenic (As) was found to contribute significantly to overall non-carcinogenic health risks for possible consumers of runoff. Carcinogenic risk values of As, Cadmium (Cd), and Pb were also observed to increase, particularly in high-development and traffic areas, by 2030. This investigation offers important insight into the health risks posed by metals present in surface runoff in urban catchment areas under different land use and climate change scenarios.
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Affiliation(s)
- Shahrokh Soltaninia
- Department of Environmental Sciences, University of Hertfordshire, College Lane, Hatfield, Hertfordshire, AL10 9AB, UK.
| | | | - Zahra Ahmadi
- Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Sara Ahmadi
- Department of Chemistry, Islamic Azad University, Shahreza, 86481-46411, Iran
| | - Saeid Eslamian
- Department of Agricultural Engineering, Isfahan University of Technology (IUT), Isfahan, Iran
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Cai B, Tang R, Wang H, Sun J, Zhao M, Huang X, Song X, Han Z, Fan Z. Impact of economic development on soil trace metal(loid)s pollution: A case study of China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 344:123319. [PMID: 38185361 DOI: 10.1016/j.envpol.2024.123319] [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: 09/20/2023] [Revised: 12/20/2023] [Accepted: 01/04/2024] [Indexed: 01/09/2024]
Abstract
Recently, intensive anthropogenic activities, while promoting economic growth, have also exacerbated soil trace metal(loid) (TM) pollution. To explore the impact of economic development on soil TM pollution, a time-weighted method was introduced to calculate the average concentrations of eight TMs in Chinese topsoil from 2001 to 2020, and panel data on TMs and economic factors of 31 provinces were used for regression analysis. The results revealed that the average concentrations of soil TMs all exceeded their respective soil background values. Meanwhile, the spatial distribution of soil TMs was characterized by obvious regional heterogeneity, with economically developed areas being heavily polluted and having high ecological risks. In addition, the results derived from panel data models showed that the relationship between soil TM pollution and economic development in China presented a continuous growth curve, but with an N-shaped pattern in eastern China, a U-shaped pattern in central China, and a positive linearity in western China. Four control variables were also introduced to evaluate their impact on TM pollution, and the results indicated that the proportion of secondary industry and the road area per capita were the major influencing factors. Ultimately, the inflection point estimation results suggested that the soil TM pollution level will increase in eastern China, central China and western China with ongoing economic growth. Our findings contribute to the current understanding of the relationship between soil TM pollution and anthropogenic activities, and provide a scientific basis for adjusting and planning industrial development and layout according to the characteristics of soil TM pollution.
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Affiliation(s)
- Boya Cai
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Rui Tang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Huijuan Wang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Jiaxun Sun
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China; Department of Geographical Science, University of Maryland, College Park 20742, United States
| | - Menglu Zhao
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Xinmiao Huang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Xiaoyong Song
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Zilin Han
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Zhengqiu Fan
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China.
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Li B, Ni J, Liu J, Zhao Y, Liu L, Jin J, He C. Spatiotemporal patterns of surface ozone exposure inequality in China. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:265. [PMID: 38351419 DOI: 10.1007/s10661-024-12426-3] [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: 09/16/2023] [Accepted: 02/02/2024] [Indexed: 02/16/2024]
Abstract
Rising surface ozone (O3) levels in China are increasingly emphasizing the potential threats to public health, ecological balance, and economic sustainability. Using a 1 km × 1 km dataset of O3 concentrations, this research employs subpopulation demographic data combined with a population-weighted quality model. Its aim is to evaluate quantitatively the differences in O3 exposure among various subpopulations within China, both at a provincial and urban cluster level. Additionally, an exposure disparity indicator was devised to establish unambiguous exposure risks among significant urban agglomerations at varying O3 concentration levels. The findings reveal that as of 2018, the population-weighted average concentration of O3 for all subgroups has experienced a significant uptick, surpassing the average O3 concentration (118 μg/m3). Notably, the middle-aged demographic exhibited the highest O3 exposure level at 135.7 μg/m3, which is significantly elevated compared to other age brackets. Concurrently, there exists a prominent positive correlation between educational attainment and O3 exposure levels, with the medium-income bracket showing the greatest susceptibility to O3 exposure risks. From an industrial vantage point, the secondary sector demographic is the most adversely impacted by O3 exposure. In terms of urban-rural structure, urban groups in all regions had higher levels of exposure to O3 than rural areas, with North and East China having the most significant levels of exposure. These findings not only emphasize the intricate interplay between public health and environmental justice but further highlight the indispensability of segmented subgroup strategies in environmental health risk assessment. Moreover, this research furnishes invaluable scientific groundwork for crafting targeted public health interventions and sustainable air quality management policies.
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Affiliation(s)
- Bin Li
- 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
| | - Jianhua Liu
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, 430100, 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
| | - Lijun Liu
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, 430100, China
| | - Jiming Jin
- 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.
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7
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Shi S, Wang W, Li X, Xu C, Lei J, Jiang Y, Zhang L, He C, Xue T, Chen R, Kan H, Meng X. Evolution in disparity of PM 2.5 pollution in China. ECO-ENVIRONMENT & HEALTH (ONLINE) 2023; 2:257-263. [PMID: 38435353 PMCID: PMC10902506 DOI: 10.1016/j.eehl.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/06/2023] [Accepted: 08/28/2023] [Indexed: 03/05/2024]
Abstract
The spatial disparity of air pollutants is one of the key influential factors for environmental inequality. We quantitatively evaluated the evolution of PM2.5 spatial disparity in China during 2013-2020, and investigated the associations between PM2.5 spatial disparity and economic indicators. Differences in PM2.5 between more- and less-polluted cities declined over time, suggesting decreased absolute disparity. However, the more polluted cities in 2013 remained so in 2017 and 2020, and vice versa, indicating persistent relative disparity. PM2.5 pollution levels increased with higher GDP per capita in less-developed areas of China, but such negative effects weakened over time, while economic development tended to promote cleaner air in developed areas of China. Therefore, policies to improve air quality and promote economic development simultaneously are needed in China to reduce the disparity of air pollution and promote all people to enjoy environmental equality.
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Affiliation(s)
- Su Shi
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Weidong Wang
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Xinyue Li
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Chang Xu
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Jian Lei
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Yixuan Jiang
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Lina Zhang
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Cheng He
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
- Institute of Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health (GmbH), Munich D-85764, Germany
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Renjie Chen
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Haidong Kan
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Xia Meng
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
- Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China
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Cai X, Hu H, Liu C, Tan Z, Zheng S, Qiu S. The effect of natural and socioeconomic factors on haze pollution from global and local perspectives in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:68356-68372. [PMID: 37120500 DOI: 10.1007/s11356-023-27134-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 04/17/2023] [Indexed: 05/27/2023]
Abstract
Analyzing the factors that cause haze and the regional differences in the influence of factors on haze is the premise and critical to precise prevention and control of haze pollution. This paper explores the global effects of haze pollution drivers and the spatial heterogeneity of factors on haze pollution using global and local regression models. The results show that, from a global perspective, a 1 μg/m3 increase in the average PM2.5 concentration of a city's neighbors will increase the city's PM2.5 concentration by 0.965 μg/m3. Temperature, atmospheric pressure, population density, and green coverage of built-up areas are positively associated with haze, while GDP per capita is the opposite. From a local perspective, each factor has different influencing scales on haze pollution. Specifically, technical support is on a global scale, and for every 1 unit increase in technical support level, the PM2.5 concentration will decrease by 0.106-0.102 μg/m3. The influencing scales of other drivers are local. In southern China, the concentration of PM2.5 decreases by 0.001-0.075 μg/m3 for every 1 °C increase in temperature, while in northern China, the concentration of PM2.5 increases by 0.001-0.889 μg/m3. In the region around the Bohai Sea in eastern China, the concentration of PM2.5 will decrease by 0.001-0.889 μg/m3 for every 1 m/s increase in wind speed. Population density positively impacts haze pollution, and the impact intensity gradually increases from 0.097 to 1.140 from south to north. For every 1% increase in the proportion of the secondary industry in southwest China, the PM2.5 concentration will increase by 0.001-0.284 μg/m3. For cities in northeast China, for every 1% increase in the urbanization rate, the PM2.5 concentration will decrease by 0.001-0.203 μg/m3. These findings help policymakers develop targeted joint prevention and control policies for haze pollution, considering regional differences.
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Affiliation(s)
- Xiaomei Cai
- School of Management, China University of Mining and Technology, No. 11 Xueyuan Road, Haidian District, Beijing, 100083, China
| | - Han Hu
- School of Management, China University of Mining and Technology, No. 11 Xueyuan Road, Haidian District, Beijing, 100083, China
| | - Chan Liu
- School of Management, China University of Mining and Technology, No. 11 Xueyuan Road, Haidian District, Beijing, 100083, China.
| | - Zhanglu Tan
- School of Management, China University of Mining and Technology, No. 11 Xueyuan Road, Haidian District, Beijing, 100083, China
| | - Shuxian Zheng
- School of Management, China University of Mining and Technology, No. 11 Xueyuan Road, Haidian District, Beijing, 100083, China
| | - Shuohan Qiu
- China Electronics Standardization Institute, Beijing, 100007, China
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Li S, Qu M. Spatiotemporal variations and mechanism of PM 2.5 pollution in urban area: The case of Guiyang, Guizhou, China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 341:118030. [PMID: 37172348 DOI: 10.1016/j.jenvman.2023.118030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/26/2022] [Accepted: 04/25/2023] [Indexed: 05/14/2023]
Abstract
PM2.5 has been a hot concern in the recent decade. Many studies have focused on metropolises or those areas with poor air quality, but the PM2.5 of more widespread areas is less considered. Considering the challenges of rapid economic growth and environmental problems against a developing region, we took Guiyang as a study case to assess the spatiotemporal variations and mechanism of PM2.5 pollution in an urban area from 2000 to 2020 in an extended sense. Based on PM2.5 concentration data from 14 monitoring points in Guiyang, spatiotemporal variations and formation mechanism were assessed using wavelet, moving maximal information coefficients, and spatial correlation analysis. The urban Nighttime light data was selected to evaluate the impacts of socioeconomic factors on PM2.5 concentration using spatial correlation analysis. Further, wavelet and statistical analysis were adopted to analyze multi-dimensional temporal variations of PM2.5 hourly concentration and the relationship with pressure, temperature, vapor pressure, relative humidity, wind, and visibility. The PM2.5 hourly concentration was obtained from the monitoring points in downtown Guiyang according to data continuity and availability. PM2.5 had different temporal variations at daily, monthly, seasonal, and annual levels, and interannual variation was the most obvious. The temperature was the main factor leading to the interannual temporal variation of PM2.5. Wind and pressure were more significant for the responses of a shorter period variation with -0.76 and -0.80 of the minimum of correlation coefficient, respectively. Meanwhile, human activities significantly influenced spatiotemporal variations of PM2.5. A spatial correlation analysis between PM2.5 and the related influencing factors from 2000 to 2018 was implemented based on a geographic information system. Besides, the landcovers within a buffer zone with a radius of 1 km on 14 monitoring points were visually interpreted to analyze the relationship between PM2.5 and landcovers. Moreover, multivariate wavelet coherence analysis revealed the PM2.5 interaction among monitoring points. The PM2.5 concentration in Guiyang dropped from 49 μg/m3 in 2012 to about 27 μg/m3 in 2018, and the air quality greatly improved. As in most cities, Guiyang has a significant PM2.5 pollution island effect, with traffic and building land density contributing to higher PM2.5 concentrations. There were some typical nonlinear spatiotemporal variations between PM2.5 and its influencing factors, and these variations varied with the selected scale.
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Affiliation(s)
- Song Li
- School of Geography and Resources, Guizhou Education University, Guiyang, Guizhou, 550018, China; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Mingxin Qu
- Guiyang Municipal Environmental Monitoring Center, Guiyang, Guizhou, 550007, China
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Hou Y, Yan W, Guo L, Li G, Sang N. Prenatal PM 2.5 exposure impairs spatial learning and memory in male mice offspring: from transcriptional regulation to neuronal morphogenesis. Part Fibre Toxicol 2023; 20:13. [PMID: 37081511 PMCID: PMC10116824 DOI: 10.1186/s12989-023-00520-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 03/12/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND As one of the environmental risk factors for human health, atmospheric fine particulate matter (PM2.5) contributes to cognitive deterioration in addition to respiratory and cardiovascular injuries. Recently, increasing evidence implicates that PM2.5 inhalation can affect neurological functions in offspring, but the sex-specific outcomes and the underlying biological processes are largely unknown. OBJECTIVES To observe the influence of prenatal PM2.5 exposure on cognitive performance in offspring, to elucidate the neuronal morphological alterations and possible transcriptional regulation based on mRNA-sequencing (mRNA-Seq) data after birth, and to determine the key components of PM2.5 contributing to the adverse effects. METHODS Pregnant C57BL/6J mice were exposed to sterile saline or PM2.5 suspension. Morris water maze test was used to assess the cognitive function in weanling offspring. Microscopic observation was applied to detect neuronal morphogenesis in vivo and in vitro. The cortex tissues from male offspring were collected on postnatal days (PNDs) 1, 7, and 21 for mRNA-Seq analysis. The organic and inorganic components of PM2.5 were separated to assess their contributions using primary cultured neurons. RESULTS Prenatal PM2.5 exposure impaired spatial learning and memory in weanling male mice, but not female mice. The sex-specific outcomes were associated with mRNA expression profiles of the cortex during postnatal critical windows, and the annotations in Gene Ontology (GO) of differentially expressed genes (DEGs) revealed that the exposure persistently disrupted the expression of genes involved in neuronal features in male offspring. Consistently, axonal growth impairment and dendritic complexity reduction were observed. Importantly, Homeobox A5 (Hoxa5), a critical transcription factor regulating all of the neuronal morphogenesis-associated hub genes on PNDs 1, 7, and 21, significantly decreased in the cortex of male offspring following PM2.5 exposure. In addition, both inorganic and organic components were harmful to axonal and dendritic growth, with organic components exhibiting stronger inhibition than inorganic ones. CONCLUSION Prenatal PM2.5 exposure affected spatial learning and memory in male mice by disrupting Hoxa5-mediated neuronal morphogenesis, and the organic components, including polycyclic aromatic hydrocarbons (PAHs), posed more adverse effects than the inorganic components.
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Affiliation(s)
- Yanwen Hou
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Taiyuan, Shanxi, 030006, PR China
| | - Wei Yan
- Xuzhou Engineering Research Center of Medical Genetics and Transformation, Key Laboratory of Genetic Foundation and Clinical Application, Department of Genetics, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, PR China
| | - Lin Guo
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Taiyuan, Shanxi, 030006, PR China
| | - Guangke Li
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Taiyuan, Shanxi, 030006, PR China.
| | - Nan Sang
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Taiyuan, Shanxi, 030006, PR China.
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11
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Yao Y, Wang W, Ma K, Tan H, Zhang Y, Fang F, He C. Transmission paths and source areas of near-surface ozone pollution in the Yangtze River delta region, China from 2015 to 2021. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 330:117105. [PMID: 36610191 DOI: 10.1016/j.jenvman.2022.117105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/29/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
Near-ground ozone in the Yangtze River Delta (YRD) region has become one of the main air pollutants that threaten the health of residents. However, to date, the transport behavior and source areas of ozone in the YRD region have not been systematically analyzed. In this study, by combining the ozone observational record with a HYSPLIT (hybrid single-particle Lagrangian integrated trajectory) model, we tried to reveal the spatiotemporal regularity of the airflow transport trajectory of ozone. Spatially, high ozone concentrations mainly clustered in industrial cities and resource-based cities. Temporally, the center of the ozone pollution shifted westward of Nanjing from 2015 to 2021. With the passage of time, the influence of meteorological elements on the ozone concentration in the YRD region gradually weakened. Marine atmosphere had the most significant impact on the transmission path of ozone in Shanghai, of which the trajectory frequency in 2021 accounted for 64.21% of the total frequency. The transmission trajectory of ozone in summer was different from that in other seasons, and its transmission trajectory was mainly composed of four medium-distance transmission paths: North China-Bohai Sea, East China Sea-West Pacific Ocean, Philippine Sea, and South China Sea-South China. The contribution source areas mainly shifted to the southeast, and the emission of pollutants from the Shandong Peninsula, the Korean Peninsula-Japan, and the Philippine Sea-Taiwan area increased the impact of ozone pollution in the Shanghai area from 2019 to 2021. This study identified the regional transport path of ozone in the YRD region and provided a scientific reference for the joint prevention and control of ozone pollution in this area.
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Affiliation(s)
- Youru Yao
- Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Anhui Province, School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China.
| | - Wei Wang
- Nanjing Institute of Environmental Sciences, Ministry of Ecological Environment, Nanjing, 210042, China.
| | - Kang Ma
- Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Anhui Province, School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China.
| | - Huarong Tan
- Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Anhui Province, School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China.
| | - Yong Zhang
- Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, 35487, USA.
| | - Fengman Fang
- Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Anhui Province, School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China.
| | - Cheng He
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, 85764, Germany.
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12
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Lv P, Zhang H, Li X. Spatio-Temporal Distribution Characteristics and Drivers of PM 2.5 Pollution in Henan Province, Central China, before and during the COVID-19 Epidemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4788. [PMID: 36981695 PMCID: PMC10049534 DOI: 10.3390/ijerph20064788] [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: 01/17/2023] [Revised: 03/02/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
PM2.5 is the main cause of haze pollution, and studying its spatio-temporal distribution and driving factors can provide a scientific basis for prevention and control policies. Therefore, this study uses air quality monitoring information and socioeconomic data before and during the COVID-19 outbreak in 18 prefecture-level cities in Henan Province from 2017 to 2020, using spatial autocorrelation analysis, ArcGIS mapping, and the spatial autocorrelation analysis. ArcGIS mapping and the Durbin model were used to reveal the characteristics of PM2.5 pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM2.5 concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM2.5 concentrations in Henan Province in 2017-2020 are positively autocorrelated spatially, with an obvious spatial spillover effect. Areas characterized by a high concentration saw an increase between 2017 and 2019, and a decrease in 2020; values in low-concentration areas remained stable, and the spatial range showed a decreasing trend. (3) The coefficients of socio-economic factors that increased the PM2.5 concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM2.5 concentrations were negatively correlated with precipitation and temperature, and positively correlated with humidity. Traffic and production restrictions during the COVID-19 epidemic also improved air quality.
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13
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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: 0] [Impact Index Per Article: 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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14
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He C, Wu Q, Li B, Liu J, Gong X, Zhang L. Surface ozone pollution in China: Trends, exposure risks, and drivers. Front Public Health 2023; 11:1131753. [PMID: 37026118 PMCID: PMC10071862 DOI: 10.3389/fpubh.2023.1131753] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 03/03/2023] [Indexed: 04/08/2023] Open
Abstract
Introduction Within the context of the yearly improvement of particulate matter (PM) pollution in Chinese cities, Surface ozone (O3) concentrations are increasing instead of decreasing and are becoming the second most important air pollutant after PM. Long-term exposure to high concentrations of O3 can have adverse effects on human health. In-depth investigation of the spatiotemporal patterns, exposure risks, and drivers of O3 is relevant for assessing the future health burden of O3 pollution and implementing air pollution control policies in China. Methods Based on high-resolution O3 concentration reanalysis data, we investigated the spatial and temporal patterns, population exposure risks, and dominant drivers of O3 pollution in China from 2013 to 2018 utilizing trend analysis methods, spatial clustering models, exposure-response functions, and multi-scale geographically weighted regression models (MGWR). Results The results show that the annual average O3 concentration in China increased significantly at a rate of 1.84 μg/m3/year from 2013 to 2018 (160 μg/m3) in China increased from 1.2% in 2013 to 28.9% in 2018, and over 20,000 people suffered premature death from respiratory diseases attributed to O3 exposure each year. Thus, the sustained increase in O3 concentrations in China is an important factor contributing to the increasing threat to human health. Furthermore, the results of spatial regression models indicate that population, the share of secondary industry in GDP, NOx emissions, temperature, average wind speed, and relative humidity are important determinants of O3 concentration variation and significant spatial differences are observed. Discussion The spatial differences of drivers result in the spatial heterogeneity of O3 concentration and exposure risks in China. Therefore, the O3 control policies adapted to various regions should be formulated in the future O3 regulation process in China.
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Affiliation(s)
- Chao He
- College of Resources and Environment, Yangtze University, Wuhan, China
| | - Qian Wu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
| | - Bin Li
- College of Resources and Environment, Yangtze University, Wuhan, China
| | - Jianhua Liu
- College of Resources and Environment, Yangtze University, Wuhan, China
| | - Xi Gong
- School of Low Carbon Economics, Hubei University of Economics, Wuhan, China
- Collaborative Innovation Center for Emissions Trading System Co-constructed by the Province and Ministry, Wuhan, China
- *Correspondence: Xi Gong
| | - Lu Zhang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
- Lu Zhang
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15
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Huang H, He Z, Li M, Zhou Y, Zhang J, Jin X, Chen J. Influence of exposure history on the particle retention capacity and physiological responses of Euonymus japonicus Thunb. var. aurea-marginatus Hort. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 316:120593. [PMID: 36336181 DOI: 10.1016/j.envpol.2022.120593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/18/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
Green plants in urban environments experience cyclical particulate matter stress. And this history of exhaust exposure may generate stress memory in plants, which may alter their subsequent response. Studies combining urban pollution characteristics and stress memory are limited. Therefore, we selected E. japonicus var. aurea-marginatus, a common urban greening tree species in the Yangtze River Delta, and conducted an experiment in three periods: the initial pollution period (S1: 28 days), the recovery period (R: 14 days) and the secondary pollution period (S2: 28 days). The experimental design consisted of an elevated pollution treatment (173 μg•cm-3) and an ambient control (34 μg•cm-3) with three replicates. In S2, the net total particle retention and saturated particle retention decreased by 11.5% and 19.3%, respectively, while PM10 and PM2.5 did not change significantly. E. japonicus var. aurea-marginatus exhibited recovery of chlorophyll levels, slower degradation of carotenoid, faster accumulation of ASA, lower accumulation of MDA, reduced activity of SOD under the second pollution period, and the period had a significant effect on the physiological indicators. Collectively, the effect of autoexhaust exposure history on the particle retention capacity of selected plant varied across particle sizes, and stress memory may confer plant resistance to recurrent exhaust pollution via combined regulations of physiological responses. Fine particles which pose a great risk to human health arise predominantly from vehicular traffic and energy production. So, E. japonicus tends to play a stabilising role in particle retention in industrial, traffic and residential areas.
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Affiliation(s)
- Hanhan Huang
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin'an, 311300, China
| | - Zhengxuan He
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin'an, 311300, China
| | - Ming Li
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin'an, 311300, China
| | - Yuanhong Zhou
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin'an, 311300, China
| | - Jing Zhang
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin'an, 311300, China
| | - Xinjie Jin
- College of Life and Environmental Sciences, Wenzhou University, Wenzhou, 325035, China
| | - Jian Chen
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin'an, 311300, China.
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16
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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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17
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Hassan MA, Mehmood T, Lodhi E, Bilal M, Dar AA, Liu J. Lockdown Amid COVID-19 Ascendancy over Ambient Particulate Matter Pollution Anomaly. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13540. [PMID: 36294120 PMCID: PMC9603700 DOI: 10.3390/ijerph192013540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/10/2022] [Accepted: 10/15/2022] [Indexed: 06/16/2023]
Abstract
Air is a diverse mixture of gaseous and suspended solid particles. Several new substances are being added to the air daily, polluting it and causing human health effects. Particulate matter (PM) is the primary health concern among these air toxins. The World Health Organization (WHO) addressed the fact that particulate pollution affects human health more severely than other air pollutants. The spread of air pollution and viruses, two of our millennium's most serious concerns, have been linked closely. Coronavirus disease 2019 (COVID-19) can spread through the air, and PM could act as a host to spread the virus beyond those in close contact. Studies on COVID-19 cover diverse environmental segments and become complicated with time. As PM pollution is related to everyday life, an essential awareness regarding PM-impacted COVID-19 among the masses is required, which can help researchers understand the various features of ambient particulate pollution, particularly in the era of COVID-19. Given this, the present work provides an overview of the recent developments in COVID-19 research linked to ambient particulate studies. This review summarizes the effect of the lockdown on the characteristics of ambient particulate matter pollution, the transmission mechanism of COVID-19, and the combined health repercussions of PM pollution. In addition to a comprehensive evaluation of the implementation of the lockdown, its rationales-based on topographic and socioeconomic dynamics-are also discussed in detail. The current review is expected to encourage and motivate academics to concentrate on improving air quality management and COVID-19 control.
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Affiliation(s)
- Muhammad Azher Hassan
- Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Tariq Mehmood
- College of Ecology and Environment, Hainan University, Haikou 570228, China
- Department of Environmental Engineering, Helmholtz Centre for Environmental Research—UFZ, D-04318 Leipzig, Germany
| | - Ehtisham Lodhi
- The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Muhammad Bilal
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
| | - Afzal Ahmed Dar
- School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Xi’an 710000, China
| | - Junjie Liu
- Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
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18
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Tu P, Tian Y, Hong Y, Yang L, Huang J, Zhang H, Mei X, Zhuang Y, Zou X, He C. Exposure and Inequality of PM 2.5 Pollution to Chinese Population: A Case Study of 31 Provincial Capital Cities from 2000 to 2016. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912137. [PMID: 36231437 PMCID: PMC9564533 DOI: 10.3390/ijerph191912137] [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: 08/12/2022] [Revised: 09/18/2022] [Accepted: 09/21/2022] [Indexed: 05/02/2023]
Abstract
Fine particulate matter (PM2.5) exposure has been linked to numerous adverse health effects, with some disadvantaged subgroups bearing a disproportionate exposure burden. Few studies have been conducted to estimate the exposure and inequality of different subgroups due to a lack of adequate characterization of disparities in exposure to air pollutants in urban areas, and a mechanistic understanding of the causes of these exposure inequalities. Based on a long-term series of PM2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM2.5 in 31 provincial capital cities of China from 2000 to 2016 using the coefficient of variation and trend analyses. A health risk assessment of human exposure to PM2.5 from 2000 to 2016 was then undertaken. A cumulative population-weighted average concentration method was applied to investigate exposures and inequality for education level, job category, age, gender and income population subgroups. The relationships between socioeconomic factors and PM2.5 exposure concentrations were quantified using the geographically and temporally weighted regression model (GTWR). Results indicate that the PM2.5 concentrations in most of the capital cities in the study experienced an increasing trend at a rate of 0.98 μg m-3 per year from 2000 to 2016. The proportion of the population exposed to high PM2.5 (above 35 μg m-3) increased annually, mainly due to the increase of population migrating into north, east, south and central China. The higher educated, older, higher income and urban secondary industry share (SIS) subgroups suffered from the most significant environmental inequality, respectively. The per capita GDP, population size, and the share of the secondary industry played an essential role in unequal exposure to PM2.5.
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Affiliation(s)
- Peiyue Tu
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
| | - Ya Tian
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
| | - Yujia Hong
- Wuhan Britain-China School, Wuhan 430034, China
| | - Lu Yang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
| | - Jiayi Huang
- Woodsworth College, University of Toronto, Toronto, ON M5S1A9, Canada
| | - Haoran Zhang
- Department of Geography, University of Washington, Seattle, WA 98195, USA
- Correspondence: (H.Z.); (C.H.); Tel.: +86-15727359013 (C.H.); Fax: +86-2769111990 (C.H.)
| | - Xin Mei
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
| | - Yanhua Zhuang
- Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
| | - Xin Zou
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
| | - Chao He
- College of Resources and Environment, Yangtze University, Wuhan 430100, China
- Correspondence: (H.Z.); (C.H.); Tel.: +86-15727359013 (C.H.); Fax: +86-2769111990 (C.H.)
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19
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Cui Z, Ren FR, Wei Q, Xi Z. What drives the spatio-temporal distribution and spillover of air quality in China’s three urban agglomerations? Evidence from a two-stage approach. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.977598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Beijing-Tianjin-Hebei urban agglomeration (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are the most important economic hinterlands in China, offering high levels of economic development. In 2020, their proportion of China’s total GDP reached 39.28%. Over the 5 years of 2014–2018, the annual maximum air quality index (AQI) of the three major urban agglomerations was greater than 100, thus maintaining a grade III light pollution (100 < AQI < 200) in Chinese air standards. This research thus uses a two-stage empirical analysis method to explore the spatial-temporal dispersal physiognomies and spillover effects of air quality in these three major urban agglomerations. In the first stage, the Kriging interpolation method regionally estimates and displays the air quality monitoring sampling data. The results show that the air quality of these three major urban agglomerations is generally good from 2014 to 2018, the area of good air is gradually expanding, the AQI value is constantly decreasing, the air pollution of YRD is shifting from southeast to northwest, and the air pollution of PRD is increasing. The dyeing industry shows a trend of concentration from northwest to south-central. In the second stage, Moran’s I and Spatial Durbin Model (SDM) explore the spatial autocorrelation and spillover effects of air quality related variables. The results show that Moran’s I values in the spatial autocorrelation analysis all pass the significance test. Moreover, public transport, per capita GDP, science and technology expenditure, and the vegetation index all have a significant influence on the spatial dispersal of air quality in the three urban agglomerations, among which the direct effect of public transport and the indirect effect and total effect of the vegetation index are the most significant. Therefore, the China’s three major urban agglomerations (TMUA) ought to adjust the industrial structure, regional coordinated development, and clean technology innovation.
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20
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Does the Clean Air Action Really Affect Labor Demand in China? JOURNAL OF GLOBAL INFORMATION MANAGEMENT 2022. [DOI: 10.4018/jgim.292478] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
How to balance resources, environment, and economic growth to achieve sustainable development is a challenge for developing countries. In 2013, China implemented a high-stringency environmental regulation—the Clean Air Action, which has effectively controlled air pollution. To explore the economic cost of environmental regulation, this paper investigates the policy effect on employment in the industrial sector. However, there are still controversies about whether environmental regulations impact employment. Based on the city-level data and firm-level data, this study applied a quasi-natural experiment for policy evaluation and used the mediating effect model for mechanism analysis. The difference-in-difference estimation results show that environmental regulation has a significant impact on employment. The mechanism analysis verifies that output adjustment, capital input, and green innovation are the main channels, by which environmental regulation distresses employment. The findings of this paper could be extended to other countries at a similar stage of development.
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21
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Schmidt S, Kinne J, Lautenbach S, Blaschke T, Lenz D, Resch B. Greenwashing in the US metal industry? A novel approach combining SO 2 concentrations from satellite data, a plant-level firm database and web text mining. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 835:155512. [PMID: 35489485 DOI: 10.1016/j.scitotenv.2022.155512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/15/2022] [Accepted: 04/21/2022] [Indexed: 06/14/2023]
Abstract
This study deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO2), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO2 concentrations is through satellite data from the Sentinel-5P programme, which represents a major advance due to its unprecedented spatial resolution. In this paper, Sentinel-5P remote sensing data was combined with a plant-level firm database to investigate the relationship between the US metal industry and SO2 concentrations using a spatial regression analysis. Additionally, this study considered web text data, classifying companies based on their websites in order to depict their self-portrayal on the topic of sustainability. In doing so, we investigated the topic of greenwashing, i.e. whether or not a positive self-portrayal regarding sustainability is related to lower local SO2 concentrations. Our results indicated a general, positive correlation between the number of employees in the metal industry and local SO2 concentrations. The web-based analysis showed that only 8% of companies in the metal industry could be classified as engaged in sustainability based on their websites. The regression analyses indicated that these self-reported "sustainable" companies had a weaker effect on local SO2 concentrations compared to their "non-sustainable" counterparts, which we interpreted as an indication of the absence of general greenwashing in the US metal industry. However, the large share of firms without a website and lack of specificity of the text classification model were limitations to our methodology.
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Affiliation(s)
- Sebastian Schmidt
- Department of Geoinformatics - Z_GIS, University of Salzburg, 5020 Salzburg, Austria; ISTARI.AI, 68163 Mannheim, Germany.
| | - Jan Kinne
- ISTARI.AI, 68163 Mannheim, Germany; Department of Economics of Innovation and Industrial Dynamics, Centre for European Economic Research, 68161 Mannheim, Germany
| | - Sven Lautenbach
- Heidelberg Institute for Geoinformation Technology at Heidelberg University, 69118 Heidelberg, Germany; GIScience department, Heidelberg University, 69120 Heidelberg, Germany
| | - Thomas Blaschke
- Department of Geoinformatics - Z_GIS, University of Salzburg, 5020 Salzburg, Austria
| | - David Lenz
- ISTARI.AI, 68163 Mannheim, Germany; Department of Statistics and Econometrics, Justus-Liebig-University, 35394 Giessen, Germany
| | - Bernd Resch
- Department of Geoinformatics - Z_GIS, University of Salzburg, 5020 Salzburg, Austria; Center for Geographic Analysis, Harvard University, 9VGM+R8 Cambridge, USA
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22
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Deng C, Qin C, Li Z, Li K. Spatiotemporal variations of PM 2.5 pollution and its dynamic relationships with meteorological conditions in Beijing-Tianjin-Hebei region. CHEMOSPHERE 2022; 301:134640. [PMID: 35439486 DOI: 10.1016/j.chemosphere.2022.134640] [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: 02/06/2022] [Revised: 04/01/2022] [Accepted: 04/13/2022] [Indexed: 05/16/2023]
Abstract
Identifying the effects of meteorological conditions on PM2.5 pollution is of great significance to explore methods to reduce atmospheric pollution. This study attempts to analyze the spatiotemporal variations of PM2.5 pollution and its dynamic nexus with meteorological factors in the Beijing-Tianjin-Hebei (BTH) region from 2015 to 2020 using standard deviation ellipse (SDE) and panel vector autoregressive (PVAR) model. The results indicate that: (1) In 2015-2020, PM2.5 pollution decreased significantly, indicating air pollution control policies in China have taken effect; Also, it showed a cumulative effect, or there was the path dependence of air pollution. (2) PM2.5 pollution presented a distribution pattern from northeast to southwest, while the directionality of air pollution has weakened. Based on SDE, PM2.5 pollution in Cangzhou can reflect the average level in the BTH; (3) Meteorological conditions exhibited a lagged and sustained effect on PM2.5 pollution. Specifically, the effects of meteorological factors on PM2.5 presented disequilibrium over time. In the long run, precipitation and temperature mainly showed negative impacts on PM2.5 pollution, while wind speed, relative humidity and sunshine duration aggravated PM2.5 pollution in the BTH. This study contributes to extending the study on the spatiotemporal evolution of PM2.5 pollution and its links with meteorological conditions.
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Affiliation(s)
- Chuxiong Deng
- School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, 410081, PR China; Hunan institute for carbon peaking and carbon neutrality, Changsha, Hunan 410081, PR China.
| | - Chunyan Qin
- School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, 410081, PR China; Hunan institute for carbon peaking and carbon neutrality, Changsha, Hunan 410081, PR China.
| | - Zhongwu Li
- School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, 410081, PR China; Hunan institute for carbon peaking and carbon neutrality, Changsha, Hunan 410081, PR China.
| | - Ke Li
- School of Mathematics & Statistics, Hunan Normal University, Changsha, Hunan, 410081, PR China; Hunan institute for carbon peaking and carbon neutrality, Changsha, Hunan 410081, PR China.
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23
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Wang Y, Wang Y, Xu H, Zhao Y, Marshall JD. Ambient Air Pollution and Socioeconomic Status in China. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:67001. [PMID: 35674427 PMCID: PMC9175641 DOI: 10.1289/ehp9872] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 04/26/2022] [Accepted: 04/29/2022] [Indexed: 05/02/2023]
Abstract
BACKGROUND Air pollution disparities by socioeconomic status (SES) are well documented for the United States, with most literature indicating an inverse relationship (i.e., higher concentrations for lower-SES populations). Few studies exist for China, a country accounting for 26% of global premature deaths from ambient air pollution. OBJECTIVE Our objective was to test the relationship between ambient air pollution exposures and SES in China. METHODS We combined estimated year 2015 annual-average ambient levels of nitrogen dioxide (NO 2 ) and fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 )] with national demographic information. Pollution estimates were derived from a national empirical model for China at 1 -km spatial resolution; demographic estimates were derived from national gridded gross national product (GDP) per capita at 1 -km resolution, and (separately) a national representative sample of 21,095 individuals from the China Health and Retirement Longitudinal Study (CHARLS) 2015 cohort. Our use of global data on population density and cohort data on where people live helped avoid the spatial imprecision found in publicly available census data for China. We quantified air pollution disparities among individual's rural-to-urban migration status; SES factors (education, occupation, and income); and minority status. We compared results using three approaches to SES measurement: individual SES score, community-averaged SES score, and gridded GDP per capita. RESULTS Ambient NO 2 and PM 2.5 levels were higher for higher-SES populations than for lower-SES population, higher for long-standing urban residents than for rural-to-urban migrant populations, and higher for the majority ethnic group (Han) than for the average across nine minority groups. For the three SES measurements (individual SES score, community-averaged SES score, gridded GDP per capita), a 1-interquartile range higher SES corresponded to higher concentrations of 6 - 9 μ g / m 3 NO 2 and 3 - 6 μ g / m 3 PM 2.5 ; average concentrations for the highest and lowest 20th percentile of SES differed by 41-89% for NO 2 and 12-25% for PM 2.5 . This pattern held in rural and urban locations, across geographic regions, across a wide range of spatial resolution, and for modeled vs. measured pollution concentrations. CONCLUSIONS Multiple analyses here reveal that in China, ambient NO 2 and PM 2.5 concentrations are higher for high-SES than for low-SES individuals; these results are robust to multiple sensitivity analyses. Our findings are consistent with the idea that in China's current industrialization and urbanization stage, economic development is correlated with both SES and air pollution. To our knowledge, our study provides the most comprehensive picture to date of ambient air pollution disparities in China; the results differ dramatically from results and from theories to explain conditions in the United States. https://doi.org/10.1289/EHP9872.
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Affiliation(s)
- Yuzhou Wang
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Yafeng Wang
- Institute of Social Survey Research, Peking University, Beijing, China
| | - Hao Xu
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Yaohui Zhao
- National School of Development, Peking University, Beijing, China
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
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24
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Do We Need More Urban Green Space to Alleviate PM2.5 Pollution? A Case Study in Wuhan, China. LAND 2022. [DOI: 10.3390/land11060776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Urban green space can help to reduce PM2.5 concentration by absorption and deposition processes. However, few studies have focused on the historical influence of green space on PM2.5 at a fine grid scale. Taking the central city of Wuhan as an example, this study has analyzed the spatiotemporal trend and the relationship between green space and PM2.5 in the last two decades. The results have shown that: (1) PM2.5 concentration reached a maximum value (139 μg/m3) in 2010 and decreased thereafter. Moran’s I index values of PM2.5 were in a downward trend, which indicates a sparser distribution; (2) from 2000 to 2019, the total area of green space decreased by 25.83%. The reduction in larger patches, increment in land cover diversity, and less connectivity led to fragmented spatial patterns of green space; and (3) the regression results showed that large patches of green space significantly correlated with PM2.5 concentration. The land use/cover diversity negatively correlated with the PM2.5 concentration in the ordinary linear regression. In conclusion, preserving large native natural habitats can be a supplemental measure to enlarge the air purification function of the green space. For cities in the process of PM2.5 reduction, enhancing the landscape patterns of green space provides a win-win solution to handle air pollution and raise human well-being.
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25
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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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26
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Examining the Potential Scaling Law in Urban PM2.5 Pollution Risks along with the Nationwide Air Environmental Effort in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19084460. [PMID: 35457331 PMCID: PMC9027287 DOI: 10.3390/ijerph19084460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/30/2022] [Accepted: 04/06/2022] [Indexed: 12/10/2022]
Abstract
Urban scaling law provides a quantitative understanding of the fundamental nonlinear properties of how cities work. Addressing this, this study intended to examine the potential scaling law that may lie in urban air pollution. With ground-monitored PM2.5 data and statistical socioeconomic factors in 265 Chinese cities (2015–2019), a targeted analysis, based on the scaling power-law model and scale-adjusted metropolitan indicator (SAMI) was conducted. The main findings of this study were summarized as follows: (1) A significant sublinear scaling relationship between PM2.5 and urban population size indicated that air quality degradation significantly lagged behind urban growth, affirming the remarkable effectiveness of national efforts on atmospheric environment improvement. (2) SAMI analysis expressed the relative conflict risk between PM2.5 pollution and urbanization and showed significant spatial cluster characteristics. Cities in central China showed higher potential risk than other regions, and there was a clear southward tendency for the city clusters with increasing SAMIs during the study period. (3) During the study period, urbanization was not the reason affecting the human-land conflict in terms of air pollution. This study is significant in that it marked the first innovative incorporation of the scaling law model into an urban environmental risk study. It also offered a new perspective from which to reframe the urban PM2.5 pollution risk, along with the nationwide air environmental effort in China, which will benefit future research on multi-types of urban environmental issues.
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27
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Xu W, Wang Y, Sun S, Yao L, Li T, Fu X. Spatiotemporal heterogeneity of PM2.5 and its driving difference comparison associated with urbanization in China's multiple urban agglomerations. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:29689-29703. [PMID: 34993793 DOI: 10.1007/s11356-021-17929-x] [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: 09/11/2021] [Accepted: 11/30/2021] [Indexed: 06/14/2023]
Abstract
The development of urban agglomeration further deteriorates the air pollution status arising from urbanization. However, disparities in the urbanization process across different urban agglomerations may shape unique regional air pollution characters, and further complicate its driving mechanism. In this study, 11 urban agglomerations with different urbanization levels in China thus were chosen as the case study areas, to explore the spatiotemporal heterogeneity of PM2.5 and its potential driving difference related to the urbanization process from a multi-urban agglomeration perspective. The ground-monitored PM2.5 data and socioeconomic panel data (2015-2018) were processed using multiple statistical analysis methods, and the main findings of this study can be generated as followed: (1) significant spatial heterogeneity characteristics of PM2.5 pollution were recognized across the study area. And even though obvious improvement in the air quality during the study period, PM2.5 concentration remains at a high level for most of the urban agglomerations. (2) Urbanization process has a substantial contribution to regional PM2.5 pollution, and significant differences of the urbanization factors on PM2.5 concentration across the urban agglomerations assigned with various urbanization levels were emphasized. The significance of this study is to provide insight into the relationship of the urbanization process on PM2.5 pollution in different urban agglomerations and to offer a scientific basis for regional cooperation for air pollution regulation among multiple urban agglomerations.
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Affiliation(s)
- Wentian Xu
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Yixu Wang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Shuo Sun
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Lei Yao
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China.
| | - Tong Li
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Xuecheng Fu
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
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28
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Study on the Spatial and Temporal Distribution Characteristics and Influencing Factors of Particulate Matter Pollution in Coal Production Cities in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063228. [PMID: 35328922 PMCID: PMC8950844 DOI: 10.3390/ijerph19063228] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/04/2022] [Accepted: 03/08/2022] [Indexed: 02/06/2023]
Abstract
In recent years, with the continuous advancement of China's urbanization process, regional atmospheric environmental problems have become increasingly prominent. We selected 12 cities as study areas to explore the spatial and temporal distribution characteristics of atmospheric particulate matter in the region, and analyzed the impact of socioeconomic and natural factors on local particulate matter levels. In terms of time variation, the particulate matter in the study area showed an annual change trend of first rising and then falling, a monthly change trend of "U" shape, and an hourly change trend of double-peak and double-valley distribution. Spatially, the concentration of particulate matter in the central and southern cities of the study area is higher, while the pollution in the western region is lighter. In terms of social economy, PM2.5 showed an "inverted U-shaped" quadratic polynomial relationship with Second Industry and Population Density, while it showed a U-shaped relationship with Generating Capacity and Coal Output. The results of correlation analysis showed that PM2.5 and PM10 were significantly positively correlated with NO2, SO2, CO and air pressure, and significantly negatively correlated with O3 and air temperature. Wind speed was significantly negatively correlated with PM2.5, and significantly positively correlated with PM10. In terms of pollution transmission, the southwest area of Taiyuan City is a high potential pollution source area of fine particles, and the long-distance transport of PM2.5 in Xinjiang from the northwest also has a certain contribution to the pollution of fine particles. This study is helpful for us to understand the characteristics and influencing factors of particulate matter pollution in coal production cities.
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29
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PM 2.5 Concentration Exposure over the Belt and Road Region from 2000 to 2020. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052852. [PMID: 35270546 PMCID: PMC8910040 DOI: 10.3390/ijerph19052852] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/18/2022] [Accepted: 02/25/2022] [Indexed: 11/30/2022]
Abstract
Ambient fine particulate matter (PM2.5) can cause respiratory and heart diseases, which have a great negative impact on human health. While, as a fast-developing region, the Belt and Road (B&R) has suffered serious air pollution, more detailed information has not been revealed. This study aims to investigate the evolutionary relationships between PM2.5 air pollution and its population-weighted exposure level (PWEL) over the B&R based on satellite-derived PM2.5 concentration and to identify the key regions for exposure control in the future. For this, the study focused on the B&R region, covering 51 countries, ranging from developed to least developed levels, extensively evaluated the different development levels of PM2.5 concentrations during 2000–2020 by spatial-temporal trend analysis and bivariate spatial correlation, then identified the key regions with high risk under different levels of Air Quality Guidelines (AQG). Results show that the overall PM2.5 and PWEL of PM2.5 concentration remained stable. Developing countries presented with the heaviest PM2.5 pollution and highest value of PWEL of PM2.5 concentration, while least developed countries presented with the fastest increase of both PM2.5 and PWEL of PM2.5 concentration. Areas with a high level and rapid increase PWEL of PM2.5 concentration were mainly located in the developing countries of India, Bangladesh, Nepal, and Pakistan, the developed country of Saudi Arabia, and least developed countries of Yemen and Myanmar. The key regions at high risk were mainly on the Indian Peninsula, Arabian Peninsula, coastal area of the Persian Gulf, northwestern China, and North China Plain. The findings of this research would be beneficial to identify the spatial distributions of PM2.5 concentration exposure and offer suggestions for formulating policies for the prevention and control PM2.5 air pollution at regional scale by the governments.
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30
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Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China. ATMOSPHERE 2022. [DOI: 10.3390/atmos13030375] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
In recent years, air pollution has become a serious threat, causing adverse health effects and millions of premature deaths in China. This study examines the spatial-temporal characteristics of ambient air quality in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) from January 2015 to December 2018. For this purpose, surface-level aerosol pollutants, including particulate matter (PMx, x = 2.5 and 10) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3)) were obtained from China National Environmental Monitoring Center (CNEMC). The results showed that fine particulate matter (PM2.5), coarse particulate matter (PM10), SO2, NO2, and CO decreased by 28.2%, 32.7%, 41.9%, 6.2%, and 27.3%, respectively, while O3 increased by 3.96% in NWC during 2018 as compared with 2015. The particulate matter (PM2.5 and PM10) levels exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II standards as well as the WHO recommended Air Quality Guidelines, while SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In addition, the average air quality index (AQI), calculated from ground-based data, improved by 21.3%, the proportion of air quality Class I (0–50) improved by 114.1%, and the number of pollution days decreased by 61.8% in NWC. All the pollutants’ (except ozone) AQI and PM2.5/PM10 ratios showed the highest pollution levels in winter and lowest in summer. AQI was strongly positively correlated with PM2.5, PM10, SO2, NO2, and CO, while negatively correlated with O3. PM10 was the primary pollutant, followed by O3, PM2.5, NO2, CO, and SO2, with different spatial and temporal variations. The proportion of days with PM2.5, PM10, SO2, and CO as the primary pollutants decreased but increased for NO2 and O3. This study provides useful information and a valuable reference for future research on air quality in northwest China.
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31
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Xing H, Zhu L, Chen B, Niu J, Li X, Feng Y, Fang W. Spatial and temporal changes analysis of air quality before and after the COVID-19 in Shandong Province, China. EARTH SCIENCE INFORMATICS 2022; 15:863-876. [PMID: 35106098 PMCID: PMC8793823 DOI: 10.1007/s12145-021-00739-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/02/2021] [Indexed: 05/13/2023]
Abstract
Due to the COVID-19 pandemic outbreak, the home quarantine policy was implemented to control the spread of the pandemic, which may have a positive impact on the improvement of air quality in China. In this study, Google Earth Engine (GEE) cloud computing platform was used to obtain CO, NO2, SO2 and aerosol optical depth (AOD) data from December 2018-March 2019, December 2019-March 2020, and December 2020-March 2021 in Shandong Province. These data were used to study the spatial and temporal distribution of air quality changes in Shandong Province before and after the pandemic and to analyze the reasons for the changes. The results show that: (1) Compared with the same period, CO and NO2 showed a decreasing trend from December 2019 to March 2020, with an average total change of 4082.36 mol/m2 and 167.25 mol/m2, and an average total change rate of 4.80% and 38.11%, respectively. SO2 did not have a significant decrease. This is inextricably linked to the reduction of human travel production activities with the implementation of the home quarantine policy. (2) The spatial and temporal variation of AOD was similar to that of pollutants, but showed a significant increase in January 2020, with an average total amount increase of 1.69 × 107 up about 2.54% from December 2019 to March 2020. This is attributed to urban heating and the reduction of pollutants such as NOx. (3) Pollutants and AOD were significantly correlated with meteorological data (e.g., average temperature, average humidity, average wind speed, average precipitation, etc.). This study provides data support for atmospheric protection and air quality monitoring in Shandong Province, as well as theoretical basis and technical guidance for policy formulation and urban planning.
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Affiliation(s)
- Huaqiao Xing
- School of Surveying and Geo-informatics, Shandong Jianzhu University, Jinan, Shandong Province China
- The Key Laboratory of Digital Simulation in Spatial Design of Architecture and Urban-Rural, Shandong Provincial Education Department, Jinan, Shandong Province China
| | - Linye Zhu
- School of Surveying and Geo-informatics, Shandong Jianzhu University, Jinan, Shandong Province China
- The Key Laboratory of Digital Simulation in Spatial Design of Architecture and Urban-Rural, Shandong Provincial Education Department, Jinan, Shandong Province China
| | - Bingyao Chen
- School of Surveying and Geo-informatics, Shandong Jianzhu University, Jinan, Shandong Province China
- The Key Laboratory of Digital Simulation in Spatial Design of Architecture and Urban-Rural, Shandong Provincial Education Department, Jinan, Shandong Province China
| | - Jingge Niu
- School of Surveying and Geo-informatics, Shandong Jianzhu University, Jinan, Shandong Province China
- The Key Laboratory of Digital Simulation in Spatial Design of Architecture and Urban-Rural, Shandong Provincial Education Department, Jinan, Shandong Province China
| | - Xuehan Li
- University of Sydney, Sydney, Australia
| | - Yongyu Feng
- Shandong Geographical Institute of Land Spatial Data and Remote Sensing Technology Center, Jinan, Shandong Province China
| | - Wenbo Fang
- The Key Laboratory of Digital Simulation in Spatial Design of Architecture and Urban-Rural, Shandong Provincial Education Department, Jinan, Shandong Province China
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32
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High Spatial-Temporal PM2.5 Modeling Utilizing Next Generation Weather Radar (NEXRAD) as a Supplementary Weather Source. REMOTE SENSING 2022. [DOI: 10.3390/rs14030495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PM2.5, a type of fine particulate with a diameter equal to or less than 2.5 micrometers, has been identified as a major source of air pollution, and is associated with many health issues. Research on utilizing various data sources, such as remote sensing and in situ sensors, for PM2.5 concentrations modeling remains a hot topic. In this study, the Next Generation Weather Radar (NEXRAD) is used as a supplementary weather data source, along with European Centre for Medium-Range Weather Forecasts (ECMWF), solar angles, and Geostationary Operational Environmental Satellite (GOES16) Aerosol Optical Depth (AOD) to model high spatial-temporal PM2.5 concentrations. PM2.5 concentrations as well as in situ weather condition variables are collected from the 31 sensors that are deployed in the Dallas Metropolitan area. Four machine learning models with different predictor variables are developed based on an ensemble approach. Since in situ weather observations are not widely available, ECMWF is used as an alternative data source for weather conditions in studies. Hence, the four established models are compared in three groups. Both models in this first group use weather variables collected from deployed sensors, but one uses NEXRAD and the other does not. In the second group, the two models use weather variables retrieved from ECMWF, one using NEXRAD and one without. In the third group, one model uses weather variables from ECMWF, and the other uses in situ weather variables, both without NEXRAD. The first two environmental groups investigate how NEXRAD can enhance model performances with weather variables collected from in situ observations and ECMWF, respectively. The third group explores how effective using ECMWF as an alternative source of weather conditions. Based on the results, the incorporation of NEXRAD achieves an R2 score of 0.86 and 0.83 for groups 1 and 2, respectively, for an improvement of 2.8% and 9.6% over those models without NEXRAD. For group three, the use of ECMWF as an alternative source of in situ weather observations results in a 0.13 R2 drop. For PM2.5 estimation, weather variables including precipitation, temperature, pressure, and surface pressure from ECMWF and deployed sensors, as well as NEXRAD velocity, are shown to be significant factors.
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33
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Zhao X, Zhou W, Wu T, Han L. The impacts of urban structure on PM 2.5 pollution depend on city size and location. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 292:118302. [PMID: 34626714 DOI: 10.1016/j.envpol.2021.118302] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/22/2021] [Accepted: 10/05/2021] [Indexed: 06/13/2023]
Abstract
Many cities across the world face the challenge of severe fine particulate matter (PM2.5) pollution. Among the many factors that affect PM2.5 pollution, there is an increasing interest in the impacts of urban structure. However, quantifying these impacts in China has been difficult due to differences of study area and scale in existing research, as well as limited sample sizes. Here, we conducted a continental study focusing on 301 prefectural cities in mainland China to investigate the effects of urban structure, including urban size and urban compactness, on PM2.5 concentrations. Based on PM2.5 raster and land cover data, we used quantile regression and a general multilinear model to estimate the effects and relative contributions of urban size and urban compactness on urban PM2.5 pollution, with explicit consideration for pollution level, urban size and geographical location. We found: (1) nationwide, the larger and more compact that cities were, the heavier the PM2.5 pollution tended to be. Additionally, this relationship became stronger with increasing levels of pollution. (2) In general, urban size played a more important role than urban form, and there were no significant interactive effects between the two metrics on urban PM2.5 concentrations at the national scale. (3) The impacts of urban size and form varied by city size and geographical location. The impacts of urban size were only significant for small or medium-large cities but not for large cities. Among large cities, only urban form had a significantly positive effect on urban PM2.5 concentrations. The further north and west that cities were, the more dependent PM2.5 pollution was on urban form, whereas the further south and east that cities were, the greater the impact of urban size. These results provide insights into how urban design and planning can be used to alleviate air pollution.
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Affiliation(s)
- Xiuling Zhao
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, China; School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, 443 Huangshan Road, Shushan District, Hefei, 230027, China
| | - Weiqi Zhou
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China; Beijing Urban Ecosystem Research Station, 18 Shuangqing Road, Haidian District, Beijing, 100085, China.
| | - Tong Wu
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, China
| | - Lijian Han
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, China
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Sarmadi M, Rahimi S, Rezaei M, Sanaei D, Dianatinasab M. Air quality index variation before and after the onset of COVID-19 pandemic: a comprehensive study on 87 capital, industrial and polluted cities of the world. ENVIRONMENTAL SCIENCES EUROPE 2021; 33:134. [PMID: 34900511 PMCID: PMC8645297 DOI: 10.1186/s12302-021-00575-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/20/2021] [Indexed: 05/06/2023]
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) pandemic provided an opportunity for the environment to reduce ambient pollution despite the economic, social and health disruption to the world. The purpose of this study was to investigate the changes in the air quality indexes (AQI) in industrial, densely populated and capital cities in different countries of the world before and after 2020. In this ecological study, we used AQI obtained from the free available databases such as the World Air Quality Index (WAQI). Bivariate correlation analysis was used to explore the correlations between meteorological and AQI variables. Mean differences (standard deviation: SD) of AQI parameters of different years were tested using paired-sample t-test or Wilcoxon signed-rank test as appropriate. Multivariable linear regression analysis was conducted to recognize meteorological variables affecting the AQI parameters. RESULTS AQI-PM2.5, AQI-PM10 and AQI-NO2 changes were significantly higher before and after 2020, simultaneously with COVID-19 restrictions in different cities of the world. The overall changes of AQI-PM2.5, AQI-PM10 and AQI-NO2 in 2020 were - 7.36%, - 17.52% and - 20.54% compared to 2019. On the other hand, these results became reversed in 2021 (+ 4.25%, + 9.08% and + 7.48%). In general, the temperature and relative humidity were inversely correlated with AQI-PM2.5, AQI-PM10 and AQI-NO2. Also, after adjusting for other meteorological factors, the relative humidity was inversely associated with AQI-PM2.5, AQI-PM10 and AQI-NO2 (β = - 1.55, β = - 0.88 and β = - 0.10, P < 0.01, respectively). CONCLUSIONS The results indicated that air quality generally improved for all pollutants except carbon monoxide and ozone in 2020; however, changes in 2021 have been reversed, which may be due to the reduction of some countries' restrictions. Although this quality improvement was temporary, it is an important result for planning to control environmental pollutants.
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Affiliation(s)
- Mohammad Sarmadi
- Department of Environmental Health Engineering, School of Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
- Health Sciences Research Center, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
| | - Sajjad Rahimi
- Department of Environmental Health Engineering, School of Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
- Health Sciences Research Center, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
| | - Mina Rezaei
- Health Sciences Research Center, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
| | - Daryoush Sanaei
- Department of Environmental Health Engineering, Faculty of Public Health and Safety, Shahid Beheshti University of Medical Science, Tehran, Iran
| | - Mostafa Dianatinasab
- Department of Complex Genetics and Epidemiology, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
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Wang H, Tan Y, Zhang L, Shen L, Zhao T, Dai Q, Guan T, Ke Y, Li X. Characteristics of air quality in different climatic zones of China during the COVID-19 lockdown. ATMOSPHERIC POLLUTION RESEARCH 2021; 12:101247. [PMID: 34720609 PMCID: PMC8548732 DOI: 10.1016/j.apr.2021.101247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/25/2021] [Accepted: 10/25/2021] [Indexed: 05/16/2023]
Abstract
The diverse climate types and the complex anthropogenic source emissions in China lead to the great regional differences of air pollution mechanisms. The COVID-19 lockdown has given us a precious opportunity to understand the effect of weather conditions and anthropogenic sources on the distribution of air pollutants in different climate zones. In this study, to understand the impact of meteorological and socio-economic factors on air pollution during COVID-19 lockdown, we divided 358 Chinese cities into eight climate regions. Temporal, spatial and diurnal variations of six major air pollutants from January 1 to April 18, 2020 were analyzed. The differences in the characteristics of air pollutants in different climate zones were obvious. PM2.5 reduced by 59.0%-64.2% in cold regions (North-East China (NEC) and North-Western (NW)), while O3 surged by 99.0%-99.9% in warm regions (Central South (CS) and Southern Coast (SC)). Diurnal variations of atmospheric pollutants were also more prominent in cold regions. Moreover, PM2.5, PM10, CO and SO2 showed more prominent reductions (20.5%-64.2%) in heating regions (NEC, NW, NCP and MG) than no-heating regions (0.8%-48%). Climate has less influence on NO2, which dropped by 41.2%-57.1% countrywide during the lockdown. The influences of weather conditions on the atmospheric pollutants in different climate zones were different. The wind speed was not the primary reason for the differences in air pollutants in different climate zones. Temperature, precipitation, and air pollution emissions led to prominent regional differences in air pollutants throughout the eight climates. The effect of temperature on PM, SO2, CO, and NO2 varied obviously with the latitude, at which condition temperature was negatively correlated to PM, SO2, CO, and NO2 in the north but positively in the south. The temperature was positively correlated to ozone in different climate zones, and the correlation was the highest in NEC and the lowest in SC. The rainfall has a strong removal effect on atmospheric pollutants in the climate regions with more precipitation, but it increases the pollutant concentrations in the climate regions with less precipitation. In regions with more emission sources, air pollutants experienced more significant variations and returned to pre-lockdown levels earlier.
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Affiliation(s)
- Honglei Wang
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &Technology, Nanjing, 210044, China
- State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Yue Tan
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &Technology, Nanjing, 210044, China
| | - Lianxia Zhang
- Ordos Meteorological Bureau of Inner Mongolia, Ordos, 017000, China
| | - Lijuan Shen
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &Technology, Nanjing, 210044, China
| | - Tianliang Zhao
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &Technology, Nanjing, 210044, China
| | - Qihang Dai
- Department of Earth and Environmental Sciences, The University of Manchester, Manchester, United Kingdom
| | - Tianyi Guan
- Department of Earth and Environmental Sciences, The University of Manchester, Manchester, United Kingdom
| | - Yue Ke
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &Technology, Nanjing, 210044, China
| | - Xia Li
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &Technology, Nanjing, 210044, China
- State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
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Tan H, Chen Y, Wilson JP, Zhou A, Chu T. Self-adaptive bandwidth eigenvector spatial filtering model for estimating PM 2.5 concentrations in the Yangtze River Delta region of China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:67800-67813. [PMID: 34268695 DOI: 10.1007/s11356-021-15196-4] [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: 04/12/2021] [Accepted: 06/25/2021] [Indexed: 06/13/2023]
Abstract
PM2.5 concentrations are commonly estimated using geographically weighted regression (GWR) models, but these models may suffer from multi-collinearity and over-focus on local feature problems. To overcome these shortcomings, a self-adaptive bandwidth eigenvector spatial filtering (SA-ESF) model utilizing the golden section search (GO-ESF) and genetic algorithm (GA-ESF) was proposed. The SA-ESF model was applied to estimate ground PM2.5 concentrations in the Yangtze River Delta (YRD) region of China both seasonally and annually from December 2015 to November 2016 using remotely sensing data, factory locations, and road networks. The results of the original eigenvector spatial filtering (ESF), GO-ESF, GA-ESF, and GWR models show that the GA-ESF model offers better performance and exhibits a better average adjusted R2 which is 26.6%, 15.3%, and 10.8% higher than for the ESF, GO-ESF, and GWR models, respectively. We next calculated stochastic site indicators that can describe characteristics of regional concentration from interpolated concentration maps derived from the GA-ESF and GWR models. The concentration maps and stochastic site indicators point to major differences in the PM2.5 concentrations in mountainous areas. There are notably high concentrations in those areas using the GWR model, in contrast with the GA-ESF results, indicating that there may be overfitting problems using the GWR model. Overall, the proposed SA-ESF model with the genetic algorithm technique can capture both global and local features and achieve promising results.
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Affiliation(s)
- Huangyuan Tan
- School of Resource and Environment Sciences, Wuhan University, 129 Luoyu Road, Wuhan, 430079, Hubei, China
| | - Yumin Chen
- School of Resource and Environment Sciences, Wuhan University, 129 Luoyu Road, Wuhan, 430079, Hubei, China.
| | - John P Wilson
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA, 90089-0374, USA
| | - Annan Zhou
- School of Resource and Environment Sciences, Wuhan University, 129 Luoyu Road, Wuhan, 430079, Hubei, China
| | - Tianyou Chu
- School of Resource and Environment Sciences, Wuhan University, 129 Luoyu Road, Wuhan, 430079, Hubei, China
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Jiang W, Gao W, Gao X, Ma M, Zhou M, Du K, Ma X. Spatio-temporal heterogeneity of air pollution and its key influencing factors in the Yellow River Economic Belt of China from 2014 to 2019. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 296:113172. [PMID: 34225044 DOI: 10.1016/j.jenvman.2021.113172] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/10/2021] [Accepted: 06/26/2021] [Indexed: 05/14/2023]
Abstract
The Yellow River Economic Belt (YREB) plays an important role in China's socio-economic development and ecological security. However, this region has suffered from serious atmospheric pollution in recent years due to intense human activity. Identifying and qualifying the spatio-temporal characteristics of the region's air pollution and its driving forces would help in the formulation of effective mitigation policies. Here, the YREB's spatio-temporal characteristics of air quality were meticulously investigated using air pollution observation, synchronous meteorological, and socio-economic data from 103 cities, for the period of 2014-2019. Furthermore, the factors influencing air pollution were analyzed and qualified. Although air quality improved in the cities of the YREB following the implementation of the Air Pollution Action Plan, the region's quality index (AQI) remained higher than the national average. Annual variations of AQI in the YREB followed a U-shaped pattern, being high in autumn and winter and low in spring and summer; this U shape became shallower following improvements in air quality during autumn and winter. From 2014 to 2019, the annual average AQI values of cities in the eastern, middle, and western YREB dropped from 109.66, 111.70, and 94.65 to 92.00, 103.85, and 73.95, respectively. The air pollution trends of cities revealed obvious spatial agglomeration, and those cities with poor air quality were primarily the western cities of Shandong province, most cities in Henan province, and the eastern cities of Shanxi province. Due to the improvement of air quality in eastern cities, the pollution center of gravity moved gradually from Changzhi (113°3411"E, 36°040"N) to Linfen (110°5222″E, 36°2344″N). The results of the spatial Durbin model (SDM) indicated that air pollution had an apparent spillover effect in the YREB at the watershed scale, and that government technical expenditure, gross domestic product (GDP) per capita, population density, annual wind speed, and relative humidity all had significant negative overall effects on the AQI values of cities. The green cover rate, ratio of secondary industry, and temperature, meanwhile, all had significant positive total effects. Due to differences the natural conditions and stages of socio-economic development between the eastern, middle, and western cities of the YREB, the impact directions and functional strengths of their key factors differed greatly.
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Affiliation(s)
- Wei Jiang
- School of Water Conservancy and Environ'ment, University of Jinan, Jinan, 250022, China; College of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
| | - Weidong Gao
- School of Water Conservancy and Environ'ment, University of Jinan, Jinan, 250022, China
| | - Xiaomei Gao
- School of Water Conservancy and Environ'ment, University of Jinan, Jinan, 250022, China
| | - Mingchun Ma
- School of Civil Engineering and Architecture, University of Jinan, Jinan, 250022, China
| | - Mimi Zhou
- School of Water Conservancy and Environ'ment, University of Jinan, Jinan, 250022, China
| | - Ke Du
- School of Water Conservancy and Environ'ment, University of Jinan, Jinan, 250022, China
| | - Xiao Ma
- School of Water Conservancy and Environ'ment, University of Jinan, Jinan, 250022, China
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Haze Pollution Levels, Spatial Spillover Influence, and Impacts of the Digital Economy: Empirical Evidence from China. SUSTAINABILITY 2021. [DOI: 10.3390/su13169076] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
With the development of digital technologies such as the Internet and digital industries such as e-commerce, the digital economy has become a new form of economic and social development, which has brought forth a new perspective for environmental governance, energy conservation, and emission reduction. Based on data from 30 Chinese provinces from 2011 to 2018, this study applies the space and threshold models to empirically examine the digital economy’s influence on haze pollution and its spatial spillover. Furthermore, it investigates the spatial diffusion effect of regional digital economic development and haze pollution by constructing a spatial weight matrix. Subsequently, an instrumental variable robustness test is performed. Results indicate the following: (1) Haze pollution has spatial spillover effects and high emission aggregation characteristics, with haze pollution in neighbouring provinces significantly aggravating pollution levels in the focal province. (2) China’s digital economy has positively impacted haze pollution, with digital economic development having a significant effect (i.e., most prominent in eastern China) on reducing haze pollution. (3) Changing the energy structure and supporting innovation can restrain haze pollution, and the digital economy can reduce the path mechanism of haze pollution through the mediating effect of an advanced industrial structure. It shows a non-linear characteristic that the influence of haze reduction continues to weaken. Thus, policymakers should include the digital economy as a mechanism for ecologically sustainable development in haze pollution control.
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Ding S, He J, Liu D. Investigating the biophysical and socioeconomic determinants of China tropospheric O 3 pollution based on a multilevel analysis approach. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2021; 43:2835-2849. [PMID: 33411122 PMCID: PMC7789902 DOI: 10.1007/s10653-020-00797-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 12/15/2020] [Indexed: 06/12/2023]
Abstract
Severe tropospheric O3 pollution has swept across China in recent years. Consequently, investigation of tropospheric O3 concentration influencing mechanism is of significance for O3 pollution control in China. Previous studies have rarely detected combined impacts of natural factors and anthropogenic activities behind tropospheric O3 concentration in China at a national scale. Moreover, there is significant spatiotemporal heterogeneity of O3 pollution distribution in China due to the temporal and regional differences of socioeconomic and natural environmental condition in the vast territory. The targeted O3 control recommendations for different regions and seasons should be put forward in terms of the spatiotemporal heterogeneity of O3 concentration determinants. In this context, a three-level regression model integrating multi-scale biophysical and socioeconomic variables was proposed to explore the determinants of O3 pollution in China. The results showed that the tropospheric O3 concentration in the eastern and southeastern regions of China was strongly affected by meteorological conditions. In contrast, tropospheric O3 pollution concentrated in inland areas mainly depended on the emission intensity from anthropogenic sources.
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Affiliation(s)
- Su Ding
- School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou, 311300 China
- State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou, 311300 China
- Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Hangzhou, 311300 China
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079 China
| | - Jianhua He
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079 China
- Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, 430079 China
| | - Dianfeng Liu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079 China
- Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, 430079 China
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Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China. REMOTE SENSING 2021. [DOI: 10.3390/rs13153011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Fine particulate matter in the lower atmosphere (PM2.5) continues to be a major public health problem globally. Identifying the key contributors to PM2.5 pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM2.5 values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM2.5 in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM2.5 varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) > socioeconomic factors (e.g., gross domestic product, GDP) > topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM2.5 concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM2.5 concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM2.5 values and offers a reliable reference for pollution control strategies.
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Qiu W, He H, Xu T, Jia C, Li W. The air quality changes and related mortality benefits during the coronavirus disease 2019 pandemic in China: Results from a nationwide forecasting study. JOURNAL OF CLEANER PRODUCTION 2021; 308:127327. [PMID: 34690451 PMCID: PMC8525877 DOI: 10.1016/j.jclepro.2021.127327] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 04/25/2021] [Accepted: 04/27/2021] [Indexed: 06/13/2023]
Abstract
Air quality changes during the coronavirus disease 2019 (COVID-19) pandemic in China has attracted increasing attention. However, more details in the changes, future air quality trends, and related death benefits on a national scale are still unclear. In this study, a total of 352 Chinese cities were included. We collected air pollutants (including fine particulate matter [PM2.5], inhalable particulate matter [PM10], nitrogen dioxide [NO2], and ozone [O3]) data for each city from January 2015 to July 2020. Convolutional neural network-quantile regression (CNN-QR) forecasting model was used to predict pollutants concentrations from February 2020 to January 2021 and the changes in air pollutants were compared. The relationships between the socioeconomic factors and the changes and the avoided mortality due to the changes were further estimated. We found sharp declines in all air pollutants from February 2020 to January 2021. Specifically, PM2.5, PM10, NO2, and O3 would drop by 3.86 μg/m3 (10.81%), 4.84 μg/m3 (7.65%), 0.55 μg/m3 (2.18%), and 3.14 μg/m3 (3.36%), respectively. The air quality changes were significantly related to many of the socioeconomic factors, including the size of built-up area, gross regional product, population density, gross regional product per capita, and secondary industry share. And the improved air quality would avoid a total of 7237 p.m.2.5-related deaths (95% confidence intervals [CI]: 4935, 9209), 9484 p.m.10-related deaths (95%CI: 5362, 13604), 4249 NO2-related deaths (95%CI: 3305, 5193), and 6424 O3-related deaths (95%CI: 3480, 9367), respectively. Our study shows that the interventions to control COVID-19 would improve air quality, which had significant relationships with some socioeconomic factors. Additionally, improved air quality would reduce the number of non-accidental deaths.
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Affiliation(s)
- Weihong Qiu
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, And State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Heng He
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Tao Xu
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, And State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Chengyong Jia
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, And State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Wending Li
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, And State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
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Marinello S, Butturi MA, Gamberini R. How changes in human activities during the lockdown impacted air quality parameters: A review. ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2021; 40:e13672. [PMID: 34221243 PMCID: PMC8237064 DOI: 10.1002/ep.13672] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/26/2021] [Accepted: 05/02/2021] [Indexed: 05/14/2023]
Abstract
The health emergency linked to the spread of COVID-19 has led to important reduction in industrial and logistics activities, as well as to a drastic changes in citizens' behaviors and habits. The restrictions on working activities, journeys and relationships imposed by the lockdown have had important consequences, including for environmental quality. This review aims to provide a structured and critical evaluation of the recent scientific bibliography that analyzed and described the impact of lockdown on human activities and on air quality. The results indicate an important effect of the lockdown during the first few months of 2020 on air pollution levels, compared to previous periods. The concentrations of particulate matter, nitrogen dioxide, sulfur dioxide and carbon monoxide have decreased. Tropospheric ozone, on the other hand, has significantly increased. These results are important indicators that can become decision drivers for future policies and strategies in industrial and logistics activities (including the mobility sector) aimed at their environmental sustainability. The scenario imposed by COVID-19 has supported the understanding of the link between the reduction of polluting emissions and the state of air quality and will be able to support strategic choices for the future sustainable growth of the industrial and logistics sector.
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Affiliation(s)
- Samuele Marinello
- En&Tech Interdipartimental Center of the University of Modena and Reggio EmiliaReggio EmiliaItaly
| | - Maria Angela Butturi
- Department of Sciences and Methods for EngineeringUniversity of Modena and Reggio EmiliaReggio EmiliaItaly
| | - Rita Gamberini
- En&Tech Interdipartimental Center of the University of Modena and Reggio EmiliaReggio EmiliaItaly
- Department of Sciences and Methods for EngineeringUniversity of Modena and Reggio EmiliaReggio EmiliaItaly
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Impacts of Industrial Restructuring and Technological Progress on PM 2.5 Pollution: Evidence from Prefecture-Level Cities in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18105283. [PMID: 34065663 PMCID: PMC8156493 DOI: 10.3390/ijerph18105283] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/14/2021] [Accepted: 05/14/2021] [Indexed: 11/16/2022]
Abstract
PM2.5 pollution has produced adverse effects all over the world, especially in fast-developing China. PM2.5 pollution in China is widespread and serious, which has aroused widespread concern of the government, the public and scholars. This paper evaluates the evolution trend and spatial pattern of PM2.5 pollution in China based on the data of 281 prefecture-level cities in China from 2007 to 2017, and reveals the pollution situation of PM2.5 and its relationship with industrial restructuring and technological progress by using spatial dynamic panel model. The results show that China's PM2.5 pollution has significant path dependence and spatial correlation, and the industrial restructuring and technological progress have significant positive effects on alleviating PM2.5 pollution. As a decomposition item of technological progress, technical change effectively alleviates PM2.5 pollution. Another important discovery is that the interaction between industrial restructuring and technological progress will aggravate PM2.5 pollution. Finally, in order to effectively improve China's air quality, while advocating the Chinese government to pursue high-quality development, this paper puts forward a regional joint prevention mechanism.
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Zhou M, Huang Y, Li G. Changes in the concentration of air pollutants before and after the COVID-19 blockade period and their correlation with vegetation coverage. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:23405-23419. [PMID: 33447974 PMCID: PMC7808704 DOI: 10.1007/s11356-020-12164-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/17/2020] [Indexed: 05/23/2023]
Abstract
In order to control the spread of COVID-19, China had implemented strict lockdown measures. The closure of cities had had a huge impact on human production and consumption activities, which had greatly reduced population mobility. This article used air pollutant data from 341 cities in mainland China and divided these cities into seven major regions based on geographic conditions and climatic environment. The impact of urban blockade on air quality during COVID-19 was studied from the perspectives of time, space, and season. In addition, this article used Normalized Difference Vegetation Index (NDVI) to systematically analyze the characteristics of air pollution in the country and used the Pearson correlation coefficient to explore the relationship between NDVI and the air pollutant concentrations during the COVID-19 period. Then, linear regression was used to find the quantitative relationship between NDVI and AQI, and the fitting effect of the model was found to be significant through t test. Finally, some countermeasures were proposed based on the analysis results, and suggestions were provided for improving air quality. This paper has drawn the following conclusions: (1) the concentration of pollutants varied greatly in different regions, and the causes of their pollution sources were also different. The region with the largest decline in AQI was the Northeast China (60.01%), while the AQI in the southwest China had the smallest change range, and its value had increased by 1.72%. In addition, after the implementation of the city blockade, the concentration of NO2 in different regions dropped the most, but the increase in O3 was more obvious. (2) Higher vegetation coverage would have a beneficial impact on the atmospheric environment. Areas with higher NDVI values have relatively low AQI. There is a negative correlation between NDVI and AQI, and an average increase of 0.1 in NDVI will reduce AQI by 3.75 (95% confidence interval). In the case of less human intervention, the higher the vegetation coverage, the lower the local pollutant concentration will be. Therefore, the degree of vegetation coverage would have a direct or indirect impact on air pollution.
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Affiliation(s)
- Manguo Zhou
- Jiangxi University of Science and Technology School of Electrical Engineering and Automation, Ganzhou, China
| | - Yanguo Huang
- Jiangxi University of Science and Technology School of Electrical Engineering and Automation, Ganzhou, China.
| | - Guilan Li
- Jiangxi University of Science and Technology School of Electrical Engineering and Automation, Ganzhou, China
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Abstract
SARS-CoV-2 was discovered in Wuhan (Hubei) in late 2019 and covered the globe by March 2020. To prevent the spread of the SARS-CoV-2 outbreak, China imposed a countrywide lockdown that significantly improved the air quality. To investigate the collective effect of SARS-CoV-2 on air quality, we analyzed the ambient air quality in five provinces of northwest China (NWC): Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX) and Qinghai (QH), from January 2019 to December 2020. For this purpose, fine particulate matter (PM2.5), coarse particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were obtained from the China National Environmental Monitoring Center (CNEMC). In 2020, PM2.5, PM10, SO2, NO2, CO, and O3 improved by 2.72%, 5.31%, 7.93%, 8.40%, 8.47%, and 2.15%, respectively, as compared with 2019. The PM2.5 failed to comply in SN and XJ; PM10 failed to comply in SN, XJ, and NX with CAAQS Grade II standards (35 µg/m3, 70 µg/m3, annual mean). In a seasonal variation, all the pollutants experienced significant spatial and temporal distribution, e.g., highest in winter and lowest in summer, except O3. Moreover, the average air quality index (AQI) improved by 4.70%, with the highest improvement in SN followed by QH, GS, XJ, and NX. AQI improved in all seasons; significant improvement occurred in winter (December to February) and spring (March to May) when lockdowns, industrial closure etc. were at their peak. The proportion of air quality Class I improved by 32.14%, and the number of days with PM2.5, SO2, and NO2 as primary pollutants decreased while they increased for PM10, CO, and O3 in 2020. This study indicates a significant association between air quality improvement and the prevalence of SARS-CoV-2 in 2020.
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Wang Y, Liu C, Wang Q, Qin Q, Ren H, Cao J. Impacts of natural and socioeconomic factors on PM 2.5 from 2014 to 2017. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 284:112071. [PMID: 33561762 DOI: 10.1016/j.jenvman.2021.112071] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 01/22/2021] [Accepted: 01/26/2021] [Indexed: 05/27/2023]
Abstract
The State Council of China had issued the Air Pollution Prevention and Control Action Plan (abbreviated as "Clean Air Actions"), which ended in 2017. To evaluate the implementation effect of the clean air actions and provide the scientific basis on the future control policy, a Geographical Detector was used to quantify the impact of natural and socioeconomic factors on the PM2.5 concentration and its reductions in China from the years of 2014-2017. In terms of the impact on PM2.5 reduction, the industrial sulfur dioxide (SO2) and industrial soot emissions are the only two factors shown significant influences. So the controls of industrial emission were the major policies during the implementation of the Clean Air Actions. In terms of the impact on the PM2.5 concentrations, industrial emission was the strongest socioeconomic factor in the beginning of the Clean Air Actions, but its dominance was then declining. In contrast, the influences of population density had been enhancing and became the greatest factor in the final year. So the new control measures should focus on the urbanization regulation. In addition, the interactions between different socioeconomic factors are proved to bivariate enhance the influences on the PM2.5 concentration levels. Multiple factors should thus be taken into account when any new control policies are going to be established.
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Affiliation(s)
- Yichen Wang
- School of Public Policy and Administration, Northwestern Polytechnical University, Xi'an, 710129, China
| | - ChenGuang Liu
- School of Public Policy and Administration, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Qiyuan Wang
- Key Laboratory of Aerosol Chemistry and Physics, State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; CAS Center for Excellence in Quaternary Science and Global Change, Xi'an, 710061, China.
| | - Quande Qin
- College of Management, Shenzhen University, Shenzhen, 518060, China
| | - Honghao Ren
- School of Public Policy and Administration, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Junji Cao
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China.
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47
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Lin L, Li T, Sun M, Liang Q, Ma Y, Wang F, Duan J, Sun Z. Effect of particulate matter exposure on the prevalence of allergic rhinitis in children: A systematic review and meta-analysis. CHEMOSPHERE 2021; 268:128841. [PMID: 33172665 DOI: 10.1016/j.chemosphere.2020.128841] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 10/08/2020] [Accepted: 10/29/2020] [Indexed: 05/22/2023]
Abstract
Among various air pollutants, particulate matter (PM) is the most harmful and representative pollutant. At the same time, allergic rhinitis (AR) is getting more and more attention, so we explore the relationship between PM and the prevalence of AR among children. Then, PubMed, Web of Science, Google Scholar was used to search for relevant studies up to January 2020. Literature quality assessment was processed using the Newcastle-Ottawa Scale (NOS) evaluation scale. Adjusted odds ratio (OR) with corresponding 95% confidence interval (CI) was retrieved from individual studies and pooled to generate a summary effect via STATA software. Besides, we test the result stability by Egger's test and funnel plot, and using the trim-and-fill method to modify the possible asymmetric funnel graph. 21 studies were included in the meta-analysis. 9 articles reported about PM2.5 on childhood AR (1.09, 95%CI: 1.01, 1.17, per 10 μg/m3 increase). 15 articles reported about PM10 on childhood AR (1.06, 95%CI: 1.02,1.11, per 10 μg/m3 increase), PM2.5 exposure has a bigger effect on children AR than PM10. In addition, a series of subgroup analysis was performed, and we found that PM2.5 and PM10 have different performances in different subgroups. In addition to this, we analyzed the sources of heterogeneity of the study. Apart from the results we got all have good stability without publication bias. Therefore, it can be concluded that exposure to PM may increase the prevalence of AR among children.
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Affiliation(s)
- Lisen Lin
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Tianyu Li
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Mengqi Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Qingqing Liang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Yuexiao Ma
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Fenghong Wang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Junchao Duan
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China.
| | - Zhiwei Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China.
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Sulaymon ID, Zhang Y, Hopke PK, Zhang Y, Hua J, Mei X. COVID-19 pandemic in Wuhan: Ambient air quality and the relationships between criteria air pollutants and meteorological variables before, during, and after lockdown. ATMOSPHERIC RESEARCH 2021; 250:105362. [PMID: 33199931 PMCID: PMC7657938 DOI: 10.1016/j.atmosres.2020.105362] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 11/06/2020] [Accepted: 11/10/2020] [Indexed: 05/02/2023]
Abstract
As a result of the lockdown (LD) control measures enacted to curtail the COVID-19 pandemic in Wuhan, almost all non-essential human activities were halted beginning on January 23, 2020 when the total lockdown was implemented. In this study, changes in the concentrations of the six criteria air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) in Wuhan were investigated before (January 1 to 23, 2020), during (January 24 to April 5, 2020), and after the COVID-19 lockdown (April 6 to June 20, 2020) periods. Also, the relationships between the air pollutants and meteorological variables during the three periods were investigated. The results showed that there was significant improvement in air quality during the lockdown. Compared to the pre-lockdown period, the concentrations of NO2, PM2.5, PM10, and CO decreased by 50.6, 41.2, 33.1, and 16.6%, respectively, while O3 increased by 149% during the lockdown. After the lockdown, the concentrations of PM2.5, CO and SO2 declined by an additional 19.6, 15.6, and 2.1%, respectively. However, NO2, O3, and PM10 increased by 55.5, 25.3, and 5.9%, respectively, compared to the lockdown period. Except for CO and SO2, WS had negative correlations with the other pollutants during the three periods. RH was inversely related with all pollutants. Positive correlations were observed between temperature and the pollutants during the lockdown. Easterly winds were associated with peak PM2.5 concentrations prior to the lockdown. The highest PM2.5 concentrations were associated with southwesterly wind during the lockdown, and northwesterly winds coincided with the peak PM2.5 concentrations after the lockdown. Although, COVID-19 pandemic had numerous negative effects on human health and the global economy, the reductions in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits. This study improves the understanding of the mechanisms that lead to air pollution under diverse meteorological conditions and suggest effective ways of reducing air pollution in Wuhan.
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Affiliation(s)
- Ishaq Dimeji Sulaymon
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanxun Zhang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Philip K Hopke
- Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699, USA
- Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA
| | - Yang Zhang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinxi Hua
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaodong Mei
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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49
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He C, Hong S, Zhang L, Mu H, Xin A, Zhou Y, Liu J, Liu N, Su Y, Tian Y, Ke B, Wang Y, Yang L. Global, continental, and national variation in PM 2.5, O 3, and NO 2 concentrations during the early 2020 COVID-19 lockdown. ATMOSPHERIC POLLUTION RESEARCH 2021; 12:136-145. [PMID: 33584105 PMCID: PMC7867708 DOI: 10.1016/j.apr.2021.02.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 05/21/2023]
Abstract
Lockdowns implemented in response to COVID-19 have caused an unprecedented reduction in global economic and transport activity. In this study, variation in the concentration of health-threatening air pollutants (PM2.5, NO2, and O3) pre- and post-lockdown was investigated at global, continental, and national scales. We analyzed ground-based data from >10,000 monitoring stations in 380 cities across the globe. Global-scale results during lockdown (March to May 2020) showed that concentrations of PM2.5 and NO2 decreased by 16.1% and 45.8%, respectively, compared to the baseline period (2015-2019). However, O3 concentration increased by 5.4%. At the continental scale, concentrations of PM2.5 and NO2 substantially dropped in 2020 across all continents during lockdown compared to the baseline, with a maximum reduction of 20.4% for PM2.5 in East Asia and 42.5% for NO2 in Europe. The maximum reduction in O3 was observed in North America (7.8%), followed by Asia (0.7%), while small increases were found in other continents. At the national scale, PM2.5 and NO2 concentrations decreased significantly during lockdown, but O3 concentration showed varying patterns among countries. We found maximum reductions of 50.8% for PM2.5 in India and 103.5% for NO2 in Spain. The maximum reduction in O3 (22.5%) was found in India. Improvements in air quality were temporary as pollution levels increased in cities since lockdowns were lifted. We posit that these unprecedented changes in air pollutants were mainly attributable to reductions in traffic and industrial activities. Column reductions could also be explained by meteorological variability and a decline in emissions caused by environmental policy regulations. Our results have implications for the continued implementation of strict air quality policies and emission control strategies to improve environmental and human health.
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Affiliation(s)
- Chao He
- 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
| | - Lu Zhang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, Hubei, China
| | - Hang Mu
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - Aixuan Xin
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - 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
| | - Jinke Liu
- University of Chinese Academy of Science, Beijing, 100049, China
- Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, Gansu, China
| | - Nanjian Liu
- University of Chinese Academy of Science, Beijing, 100049, China
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, Shaanxi, China
| | - Yuming Su
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - Ya Tian
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - Biqin Ke
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - Yanwen Wang
- Economics and Management College, China University of Geosciences, 430074, Wuhan, China
| | - Lu Yang
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
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50
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Gan T, Yang H, Liang W, Liao X. Do economic development and population agglomeration inevitably aggravate haze pollution in China? New evidence from spatial econometric analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:5063-5079. [PMID: 32959322 DOI: 10.1007/s11356-020-10847-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 09/13/2020] [Indexed: 06/11/2023]
Abstract
With sustained economic development, China's ecological environment is becoming increasingly fragile and the problem of haze pollution is becoming increasingly prominent, which has affected the normal life of human beings and the stable development of society. In this paper, 287 cities' panel data from 1998 to 2016 are used, PM2.5 is used to represent haze pollution, and the spatial Durbin model is used to explore the role of the economy and population agglomeration on smog pollution. The empirical results show that (1) haze pollution has obvious spatial spillover. From the perspective of China as a whole, the relationship between the economy and smog pollution is an inverted U shape. (2) China is divided into three economic regions, i.e., the east, the middle, and the west. In the east and middle regions, it is found that economic development also shows an inverted U-shaped relationship with haze pollution. (3) Regardless of the country or the three major economic regions, population agglomeration is the primary factor that aggravates haze pollution; the progress of technology and the optimization of the industrial structure can improve haze pollution. (4) Through further analysis of the indirect effects of haze in China, it is found that there is a significant spatial spillover effect. According to the results of this research, policy suggestions are put forward.
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Affiliation(s)
- Ting Gan
- Business School, University of Jinan, Jinan, 250002, China
| | - Huachao Yang
- Business School, University of Jinan, Jinan, 250002, China
| | - Wei Liang
- Business School, University of Jinan, Jinan, 250002, China.
- Shandong Longshan Green Economic Research Center, Jinan, 250022, China.
| | - Xianchun Liao
- Shandong Longshan Green Economic Research Center, Jinan, 250022, China
- Institute of Green Development, University of Jinan, Jinan, 250022, China
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