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Ren N, Huang H, Liu B, Wu C, Xiang J, Zhou Q, Kang S, Zhang X, Jiang Y. Interactive effects of atmospheric oxidising pollutants and heat waves on the risk of residential mortality. Glob Health Action 2024; 17:2313340. [PMID: 38381455 PMCID: PMC10883108 DOI: 10.1080/16549716.2024.2313340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 01/29/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND The impact of heat waves and atmospheric oxidising pollutants on residential mortality within the framework of global climate change has become increasingly important. OBJECTIVE In this research, the interactive effects of heat waves and oxidising pollutants on the risk of residential mortality in Fuzhou were examined. Methods We collected environmental, meteorological, and residential mortality data in Fuzhou from 1 January 2016, to 31 December 2021. We then applied a generalised additive model, distributed lagged nonlinear model, and bivariate three-dimensional model to investigate the effects and interactions of various atmospheric oxidising pollutants and heat waves on the risk of residential mortality. RESULTS Atmospheric oxidising pollutants increased the risk of residential mortality at lower concentrations, and O3 and Ox were positively associated with a maximum risk of 2.19% (95% CI: 0.74-3.66) and 1.29% (95% CI: 0.51-2.08). The risk of residential mortality increased with increasing temperature, with a strong and long-lasting effect and a maximum cumulative lagged effect of 1.11% (95% CI: 1.01, 1.23). Furthermore, an interaction between atmospheric oxidising pollutants and heat waves may have occurred: the larger effects in the longest cumulative lag time on residential mortality per 10 µg/m3 increase in O3, NO2 and Ox during heat waves compared to non-heat waves were [-3.81% (95% CI: -14.82, 8.63)]; [-0.45% (95% CI: -2.67, 1.81)]; [67.90% (95% CI: 11.55, 152.71)]; 16.37% (95% CI: 2.43, 32.20)]; [-3.00% (95% CI: -20.80, 18.79)]; [-0.30% (95% CI: -3.53, 3.04)]. The risk on heat wave days was significantly higher than that on non-heat wave days and higher than the separate effects of oxidising pollutants and heat waves. CONCLUSIONS Overall, we found some evidence suggesting that heat waves increase the impact of oxidising atmospheric pollutants on residential mortality to some extent.
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Affiliation(s)
- Nan Ren
- Department of Preventive Medicine, Fujian Provincial Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Huimin Huang
- Department of Preventive Medicine, Fujian Provincial Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Baoying Liu
- Department of Preventive Medicine, Fujian Provincial Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Chuancheng Wu
- Department of Preventive Medicine, Fujian Provincial Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Jianjun Xiang
- Department of Preventive Medicine, Fujian Provincial Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Quan Zhou
- Department of Public Health, Fuzhou Center for Disease Control and Prevention, Fuzhou, China
| | - Shuling Kang
- Department of Public Health, Fuzhou Center for Disease Control and Prevention, Fuzhou, China
| | - Xiaoyang Zhang
- Department of Public Health, Fuzhou Center for Disease Control and Prevention, Fuzhou, China
| | - Yu Jiang
- Department of Preventive Medicine, Fujian Provincial Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou, China
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Hua C, Ma W, Zheng F, Zhang Y, Xie J, Ma L, Song B, Yan C, Li H, Liu Z, Liu Q, Kulmala M, Liu Y. Health risks and sources of trace elements and black carbon in PM 2.5 from 2019 to 2021 in Beijing. J Environ Sci (China) 2024; 142:69-82. [PMID: 38527897 DOI: 10.1016/j.jes.2023.05.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 05/12/2023] [Accepted: 05/14/2023] [Indexed: 03/27/2024]
Abstract
A comprehensive health risk assessment of PM2.5 is meaningful to understand the current status and directions regarding further improving air quality from the perspective of human health. In this study, we evaluated the health risks of PM2.5 as well as highly toxic inorganic components, including heavy metals (HMs) and black carbon (BC) based on long-term observations in Beijing from 2019 to 2021. Our results showed that the relative risks of chronic obstructive pulmonary disease, lung cancer, acute lower respiratory tract infection, ischemic heart disease, and stroke decreased by 4.07%-9.30% in 2020 and 2.12%-6.70% in 2021 compared with 2019. However, they were still at high levels ranging from 1.26 to 1.77, in particular, stroke showed the highest value in 2021. Mn had the highest hazard quotient (HQ, from 2.18 to 2.56) for adults from 2019 to 2021, while Ni, Cr, Pb, As, and BC showed high carcinogenic risks (CR > 1.0×10-6) for adults. The HQ values of Mn and As and the CR values of Pb and As showed constant or slight upwards trends during our observations, which is in contrast to the downward trends of other HMs and PM2.5. Mn, Cr, and BC are crucial toxicants in PM2.5. A significant shrink of southern region sourcesof HMs and BCshrank suggests the increased importance of local sources. Industry, dust, and biomass burning are the major contributors to the non-carcinogenic risks, while traffic emissions and industry are the dominant contributors to the carcinogenic risks in Beijing.
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Affiliation(s)
- Chenjie Hua
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Wei Ma
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Feixue Zheng
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yusheng Zhang
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jiali Xie
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Li Ma
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Boying Song
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Chao Yan
- Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Hongyan Li
- School of Environment and Safety, Taiyuan University of Science and Technology, Taiyuan 030024, China
| | - Zhen Liu
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Qian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Markku Kulmala
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China; Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Yongchun Liu
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
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Li W, Zong X, He YS, Liu X, Zhao C, Wang Y, Zhang J, Pan HF. The effect of air pollution exposure on the risk of outpatient visits for periodontitis: a time-series study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2024; 34:2418-2429. [PMID: 37652674 DOI: 10.1080/09603123.2023.2253738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/26/2023] [Indexed: 09/02/2023]
Abstract
This study aimed to determine if air pollution affected the risk of periodontitis outpatient visits. We collected the records of 56,456 periodontitis outpatient visits in Hefei, China, from January 1, 2014 to December 31, 2021. The relationship between air pollution and periodontitis outpatient visits was evaluated using distributed lag nonlinear and generalized linear models. Additional analyses were performed, stratifying the data by age, season, and sex. Subgroup analyses showed a significantly higher risk of periodontitis outpatient visits due to NO2 exposure during the warm season compared with the cold season. Moreover, O3 exposure was associated with a lower risk of periodontitis outpatient visits in the cold season. The findings suggest that NO2 exposure is associated with an increased risk of periodontitis outpatient visits, whereas O3 exposure is associated with a decreased risk of periodontitis outpatient visits. Season is found to be an effect modifier in these associations.
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Affiliation(s)
- Wuli Li
- College & Hospital of Stomatology, Anhui Medical University, Key Laboratory of Oral Diseases Research of Anhui Province, Hefei, China
| | - Xirun Zong
- College & Hospital of Stomatology, Anhui Medical University, Key Laboratory of Oral Diseases Research of Anhui Province, Hefei, China
| | - Yi-Sheng He
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Xinpai Liu
- College & Hospital of Stomatology, Anhui Medical University, Key Laboratory of Oral Diseases Research of Anhui Province, Hefei, China
| | - Chunhui Zhao
- College & Hospital of Stomatology, Anhui Medical University, Key Laboratory of Oral Diseases Research of Anhui Province, Hefei, China
| | - Yuanyin Wang
- College & Hospital of Stomatology, Anhui Medical University, Key Laboratory of Oral Diseases Research of Anhui Province, Hefei, China
| | - Jing Zhang
- College & Hospital of Stomatology, Anhui Medical University, Key Laboratory of Oral Diseases Research of Anhui Province, Hefei, China
| | - Hai-Feng Pan
- College & Hospital of Stomatology, Anhui Medical University, Key Laboratory of Oral Diseases Research of Anhui Province, Hefei, China
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
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Shen Y, Jiang F, Feng S, Xia Z, Zheng Y, Lyu X, Zhang L, Lou C. Increased diurnal difference of NO 2 concentrations and its impact on recent ozone pollution in eastern China in summer. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:159767. [PMID: 36341852 DOI: 10.1016/j.scitotenv.2022.159767] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/23/2022] [Accepted: 10/23/2022] [Indexed: 06/16/2023]
Abstract
Nitrogen dioxide (NO2) is a key tropospheric O3 precursor. Since 2013, efforts to decrease air pollution in China have driven substantial declines in annual NO2 concentrations, whereas ozone (O3) concentrations have increased. Based on nationwide NO2 observations and a regional air quality model (WRF-CMAQ), we analyzed trends in the diurnal difference (DD, the difference between nighttime and daytime concentrations) of NO2 concentrations across eastern China and in five national urban agglomerations (UAs) from 2014 to 2021, and explored the factors underlying such changes and the potential impacts on O3 pollution. We found that the observed DD of NO2 has increased in most cities and UAs, and that this trend can be primarily attributed to changes in anthropogenic emissions, based on comparison with DDs simulated with fixed anthropogenic emissions, which generally showed much weaker trends and little interannual variation. A sensitivity analysis using the WRF-CMAQ model was conducted to investigate the impact of a modified diurnal cycle of nitrogen oxides (NOx) emissions on O3 concentrations. The result revealed that enhancing the DD of NO2 would increase O3 concentrations in the morning and the daily maximum 8-h O3 concentrations in the cities with high NOx concentrations, as well as downwind areas of cities, indicating that greater DDs in NO2 is one of the reasons that have led to the enhanced China's O3 pollution in recent years.
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Affiliation(s)
- Yang Shen
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
| | - Fei Jiang
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China; Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing 210023, China.
| | - Shuzhuang Feng
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
| | - Zheng Xia
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Yanhua Zheng
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
| | - Xiaopu Lyu
- Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong
| | - LingYu Zhang
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
| | - Chenxi Lou
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
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Huang C, Liu K, Zhou L. Spatio-temporal trends and influencing factors of PM 2.5 concentrations in urban agglomerations in China between 2000 and 2016. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:10988-11000. [PMID: 33108644 DOI: 10.1007/s11356-020-11357-z] [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: 08/13/2020] [Accepted: 10/20/2020] [Indexed: 06/11/2023]
Abstract
An urban agglomeration (UA), similar to a megalopolis or a metropolitan area, is a region where cities and people are concentrated, and where air pollution has adversely impacted on sustainable and high quality development. Studies on the spatio-temporal trends and the factors which influence PM2.5 concentrations may be used as a reference to support air pollution control policy for major UAs throughout the world. Nineteen UAs in China covering the years 2000-2016 were chosen as the research object, the PM2.5 concentrations being used to reflect air pollution and being estimated from analysis of remote sensing images. The Exploratory Spatial Data Analysis method was used to study the spatio-temporal trends for PM2.5 concentrations, and the Geodetector method was used to examine the factors influencing the PM2.5 concentrations. The results revealed that (i) the temporal trend for the average values of the PM2.5 concentrations in the UAs followed an inverted U-shaped curve and the inflection points of the curve occurred in 2007. (ii) The PM2.5 concentrations in the UAs exhibited significant global spatial autocorrelation with the high-high type and the low-low type being the main categories. (iii) The rate of land urbanization and the structure of energy consumption were the main factors which influenced the PM2.5 concentrations in the UAs.
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Affiliation(s)
- Caihong Huang
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China
| | - Kai Liu
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
- Collaborative Innovation Center of Human-Nature and Green Development in Universities of Shandong, Shandong Normal University, Jinan, 250358, China.
| | - Liang Zhou
- Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, 730070, China
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Zhang A, Lin J, Chen W, Lin M, Lei C. Spatial-Temporal Distribution Variation of Ground-Level Ozone in China's Pearl River Delta Metropolitan Region. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:872. [PMID: 33498400 PMCID: PMC7908513 DOI: 10.3390/ijerph18030872] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/15/2021] [Accepted: 01/18/2021] [Indexed: 11/29/2022]
Abstract
Long-term exposure to ozone pollution will cause severe threats to residents' physical and mental health. Ground-level ozone is the most severe air pollutant in China's Pearl River Delta Metropolitan Region (PRD). It is of great significance to accurately reveal the spatial-temporal distribution characteristics of ozone pollution exposure patterns. We used the daily maximum 8-h ozone concentration data from PRD's 55 air quality monitoring stations in 2015 as input data. We used six models of STK and ordinary kriging (OK) for the simulation of ozone concentration. Then we chose a better ozone pollution prediction model to reveal the ozone exposure characteristics of the PRD in 2015. The results show that the Bilonick model (BM) model had the highest simulation precision for ozone in the six models for spatial-temporal kriging (STK) interpolation, and the STK model's simulation prediction results are significantly better than the OK model. The annual average ozone concentrations in the PRD during 2015 showed a high spatial variation in the north and east and low in the south and west. Ozone concentrations were relatively high in summer and autumn and low in winter and spring. The center of gravity of ozone concentrations tended to migrate to the north and west before moving to the south and then finally migrating to the east. The ozone's spatial autocorrelation was significant and showed a significant positive correlation, mainly showing high-high clustering and low-low clustering. The type of clustering undergoes temporal migration and conversion over the four seasons, with spatial autocorrelation during winter the most significant.
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Affiliation(s)
- An Zhang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (A.Z.); (C.L.)
| | - Jinhuang Lin
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
| | - Wenhui Chen
- College of Geographical Science, Fujian Normal University, Fuzhou 350007, China;
| | - Mingshui Lin
- College of Tourism, Fujian Normal University, Fuzhou 350117, China
| | - Chengcheng Lei
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (A.Z.); (C.L.)
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National Forest Parks in China: Origin, Evolution, and Sustainable Development. FORESTS 2019. [DOI: 10.3390/f10040323] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The concept of National Forest Park (NFP) is mainly used in mainland China. Originating in 1982, NFP embodies a “top-down” concept and associated program launched by the Chinese government. It is aimed at promoting forest-based tourism and economic development under the premise of protecting forest resources. After 30 years of development, NFPs have made great achievements in protecting specific forest resources, promoting forest-based tourists, promoting regional economic development, and they have gained popularity worldwide. However, due to the fast pace of NFP expansion, lack of predictable planning and innovative thinking, and ineffective governance, some problems like overexploitation, scenic pollution, monotonous development patterns, and ecological degradation associated with NFP constrain its sustainable development. In order to solve these problems effectively, a holistic review of the status of NFPs in China is needed. To help meet this need, the origin, evolution, and current status of NFPs in China were analyzed. The presented research also included retrospective analyses of challenges and opportunities for NFPs sustainable development in China. Results show that from 1982 to 2015, the number of NFPs grew dramatically, and this development occurred in four phases. In addition, NFP development has been unbalanced in regional distribution. When analyzing the evolution of NFPs, the main issues to date have included failure to implement Master Plans in practice, unclear supervisory responsibilities, ambiguous classification, unbalanced distribution, destruction of natural resource and ecosystems, insufficient cultural protection, weak awareness of nature education, lack of resource statistics, monotonous planning, and weak marketing. Study findings can contribute to promoting the sustainable future development of NFPs and support the forest-based tourism industry.
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