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Wan X, Zeng W, Gu G. Coking exhaust contributes to airborne particulate matter in the Beijing-Tianjin-Hebei region. Heliyon 2024; 10:e31359. [PMID: 38803864 PMCID: PMC11129094 DOI: 10.1016/j.heliyon.2024.e31359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/29/2024] Open
Abstract
Coking was regarded as a predominant source of air pollution. Despite the adoption of more environmentally friendly equipment, whether the coking enterprises in the Beijing-Tianjin-Hebei (BTH) region are still causing regional air pollution is worthy of study, which is essential for the control of coking enterprises in this area. To improve the prediction accuracy of large-scale air pollutant distribution, the air particle distribution in the BTH region was simulated via land use regression (LUR) combined with Bayesian maximum entropy (BME); then, the distribution was correlated with the exhaust gas emitted from coking enterprises. Results indicated that the R2 of the "LUR + BME" method reached 0.95, higher than 0.82 using LUR alone. The air quality distribution presented a pattern of "low in the northern mountains and high in the southern plains", similar to the distribution of coking enterprises in BTH region. A significant correlation was found between exhaust emissions from coking enterprises and air quality in the BTH region, confirming the contribution of coking emissions to air pollution in this region, and the necessity to continue the strict control on coking enterprises in BTH area.
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Affiliation(s)
- Xiaoming Wan
- Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100089, China
| | - Weibin Zeng
- Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100089, China
| | - Gaoquan Gu
- Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100089, China
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2
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Ma X, Zou B, Deng J, Gao J, Longley I, Xiao S, Guo B, Wu Y, Xu T, Xu X, Yang X, Wang X, Tan Z, Wang Y, Morawska L, Salmond J. A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023. ENVIRONMENT INTERNATIONAL 2024; 183:108430. [PMID: 38219544 DOI: 10.1016/j.envint.2024.108430] [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: 09/03/2023] [Revised: 11/26/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024]
Abstract
Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.
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Affiliation(s)
- Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China; College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China.
| | - Jun Deng
- College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; Shaanxi Key Laboratory of Prevention and Control of Coal Fire, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Jay Gao
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
| | - Ian Longley
- National Institute of Water and Atmospheric Research, Auckland 1010, New Zealand
| | - Shun Xiao
- School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yarui Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Tingting Xu
- School of Software Engineering, Chongqing University of Post and Telecommunications, Chongqing 400065, China
| | - Xin Xu
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Xiaosha Yang
- Shandong Nova Fitness Co., Ltd., Baoji, Shaanxi 722404, China
| | - Xiaoqi Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Zelei Tan
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yifan Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Jennifer Salmond
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
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3
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Lyu T, Tang Y, Cao H, Gao Y, Zhou X, Zhang W, Zhang R, Jiang Y. Estimating the geographical patterns and health risks associated with PM 2.5-bound heavy metals to guide PM 2.5 control targets in China based on machine-learning algorithms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 337:122558. [PMID: 37714401 DOI: 10.1016/j.envpol.2023.122558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/02/2023] [Accepted: 09/12/2023] [Indexed: 09/17/2023]
Abstract
PM2.5 is the main component of haze, and PM2.5-bound heavy metals (PBHMs) can induce various toxic effects via inhalation. However, comprehensive macroanalyses on large scales are still lacking. In this study, we compiled a substantial dataset consisting of the concentrations of eight PBHMs, including As, Cd, Cr, Cu, Mn, Ni, Pb and Zn, across different cities in China. To improve prediction accuracy, we enhanced the traditional land-use regression (LUR) model by incorporating emission source-related variables and employing the best-fitted machine-learning algorithm, which was applied to predict PBHM concentrations, analyze geographical patterns and assess the health risks associated with metals under different PM2.5 control targets. Our model exhibited excellent performance in predicting the concentrations of PBHMs, with predicted values closely matching measured values. Noncarcinogenic risks exist in 99.4% of the estimated regions, and the carcinogenic risks in all studied regions of the country are within an acceptable range (1 × 10-5-1 × 10-6). In densely populated areas such as Henan, Shandong, and Sichuan, it is imperative to control the concentration of PBHMs to reduce the number of patients with cancer. Controlling PM2.5 effectively decreases both carcinogenic and noncarcinogenic health risks associated with PBHMs, but still exceed acceptable risk level, suggesting that other important emission sources should be given attention.
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Affiliation(s)
- Tong Lyu
- Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Yilin Tang
- Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Hongbin Cao
- Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
| | - Yue Gao
- Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Xu Zhou
- Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Wei Zhang
- Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Ruidi Zhang
- Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Yanxue Jiang
- College of Environment and Ecology, Chongqing University, Chongqing, 400045, China
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Shan M, Wang Y, Wang Y, Qiao Z, Ping L, Lee LC, Sun Y, Pan Z. Health burden evaluation of industrial parks caused by PM 2.5 pollution at city scale. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:101267-101279. [PMID: 37644274 DOI: 10.1007/s11356-023-29417-5] [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: 04/16/2023] [Accepted: 08/17/2023] [Indexed: 08/31/2023]
Abstract
Industrial park is an important emission sector of PM2.5 pollution. Previous studies have provided valuable information on the impact of PM2.5 from industrial parks on human health, but relevant studies at city scale are limited. In this study, the health burden of industrial parks was evaluated based on PM2.5-related premature deaths and economic contributions. The premature deaths were calculated in terms of a novel research model by integrating the Bayesian maximum entropy (BME) model, weighted concentration-weighted trajectory (WCWT), and integrated exposure-response function (IER). Take Tianjin City for example, it was found that since the main diffusion direction of PM2.5 in Tianjin is from south to north, the industrial parks in the south of Tianjin and close to the central city with high population density have high health burden. These industrial parks need to be focused on or even relocated in the future. The research model can provide scientific basis for the health burden evaluation of industrial parks at city scale, so as to help local governments optimize the layout of industrial parks and formulate environmental responsibility management policies for industrial parks.
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Affiliation(s)
- Mei Shan
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Yanwei Wang
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Yuan Wang
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China.
| | - Zhi Qiao
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Liying Ping
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Lien-Chieh Lee
- School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi, 435003, Hubei, China
| | - Yun Sun
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Zhou Pan
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
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5
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Liu L, Tian X, Zhao Y, Zhao Z, Luo L, Luo H, Han Z, Kang X, Wang X, Liu X, Guo X, Tao L, Luo Y. Long-term exposure to PM 2.5 and PM 10 and chronic kidney disease: the Beijing Health Management Cohort, from 2013 to 2018. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:17817-17827. [PMID: 36203044 DOI: 10.1007/s11356-022-23251-x] [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: 06/08/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
Long-term exposure to ambient particulate pollutants (PM2.5 and PM10) may increase the risk of chronic kidney disease (CKD), but the results of previous research were limited and inconsistent. The purpose of this study was to assess the relationships of PM2.5 and PM10 with CKD. This study was a cohort study based on the physical examination data of 2082 Beijing residents from 2013 to 2018 in the Beijing Health Management Cohort (BHMC). A land-use regression model was used to estimate the individual exposure concentration of air pollution based on the address provided by each participant. CKD events were identified based on self-report or medical evaluation (estimated glomerular filtration rate, eGFR less than 60 ml/min/1.73 m2). Finally, the associations of PM2.5 and PM10 with CKD were calculated using univariate and multivariate logistic regression models. During the research period, we collected potentially confounding information. After adjusting for confounders, each 10 μg/m3 increase in PM2.5 and PM10 exposure was associated with an 84% (OR: 1.84; 95% CI: 1.45, 2.33) and 37% (OR: 1.37; 95% CI: 1.15, 1.63) increased risk of CKD. Adjusting for the four common gaseous air pollutants (CO, NO2, SO2, O3), the effect of PM2.5 and PM10 on CKD was significantly enhanced, but the effect of PM10 was no longer significant in the multi-pollutant model. The results of the stratified analysis showed that PM2.5 and PM10 were more significant in males, middle-aged and elderly people over 45 years old, smokers, drinkers, BMI ≥ 24 kg/m2, and abnormal metabolic components. In conclusion, long-term exposure to ambient PM2.5 and PM10 was associated with an increased risk of CKD.
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Affiliation(s)
- Lulu Liu
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Xue Tian
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Yuhan Zhao
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Zemeng Zhao
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Lili Luo
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Hui Luo
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Ze Han
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Xiaoping Kang
- Beijing Xiaotangshan Hospital, No. 390 Wenquan Street, Xiaotangshan Town, Changping District, Beijing, 102211, China
| | - Xiaonan Wang
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Xiangtong Liu
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Xiuhua Guo
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Lixin Tao
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China
| | - Yanxia Luo
- School of Public Health, Capital Medical University & Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 10 Xitoutiao, You'anmen Wai Street, Fengtai District, Beijing, 100069, China.
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6
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Shi G, Liu J, Zhong X. Spatial and temporal variations of PM 2.5 concentrations in Chinese cities during 2015-2019. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2022; 32:2695-2707. [PMID: 34643444 DOI: 10.1080/09603123.2021.1987394] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 09/25/2021] [Indexed: 06/13/2023]
Abstract
The study analyzed the current status and changing trends of PM2.5 pollution, and used Kriging spatial interpolation, spatial autocorrelation analysis, and scan statistics to explore the spatiotemporal characteristics and identify hotspots. The results showed that PM2.5 pollution during 2015-2019 displayed a downward trend year by year, with a pronounced seasonal difference of higher concentrations in winter and lower concentrations in summer. By 2019, there were still 110 cities (n = 194) failed to meet China's annual grade II air quality standard (35 μg/m3). The spatial distribution of PM2.5 was characterized by marked heterogeneity, with a significant spatial dependence and clustering characteristics. The pollution hotspots of PM2.5 were mainly concentrated in eastern and central China, especially in the Beijing-Tianjin-Hebei region and its surrounding area. The results of this study will assist Chinese authorities in developing strategies for preventing and controlling air pollution, especially in hotspot regions and during peak periods.
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Affiliation(s)
- Guiqian Shi
- School of Public Health and Management, Chongqing Medical University, Chongqing, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China
| | - Jiaxiu Liu
- School of Public Health and Management, Chongqing Medical University, Chongqing, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China
| | - Xiaoni Zhong
- School of Public Health and Management, Chongqing Medical University, Chongqing, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China
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7
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Cui C, Liu Y, Chen L, Liang S, Shan M, Zhao J, Liu Y, Yu S, Sun Y, Mao J, Zhang H, Gao S, Ma Z. Assessing public health and economic loss associated with black carbon exposure using monitoring and MERRA-2 data. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 313:120190. [PMID: 36122658 DOI: 10.1016/j.envpol.2022.120190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 09/10/2022] [Accepted: 09/12/2022] [Indexed: 06/15/2023]
Abstract
Black carbon (BC) exposure in China continues to be relatively high, prompting researchers to assess BC exposure levels using data from monitoring sites, satellite remote sensing, and models. However, data regarding the application of a combined strategy comprising the analysis of monitoring data and various types of data to simulate BC exposure levels are lacking. Hence, the current study seeks to estimate short- and long-term BC exposure levels by combining national monitoring data with data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). Furthermore, this study attempts to improve the spatio-temporal resolution of BC exposure levels using Bayesian maximum entropy (BME). The BME model performed well in terms of estimating short- (R2 = 0.74 and RMSE = 1.76 μg/m3) and long-term (R2 = 0.76 and RMSE = 1.3 μg/m3) exposure. Premature mortalities and economic losses were also assessed by applying localised concentration-response coefficients simulated in China. A total of 74,500 (95% confidence interval (CI): 23,900-124,500) and 538,400 (95% CI: 495,000-581,300) all-cause premature mortality cases were found to be associated with short- and long-term BC exposure, respectively. Meanwhile, short-term BC exposure was associated with economic losses ranging from 7.5 to 13.2 billion US dollars (USD) (1 USD = 6.36 RMB on January 19, 2022) based on amended human capital (AHC) and willingness to pay (WTP), accounting for 0.06%-0.1% of China's total gross domestic product (GDP) in 2017 (1.2 × 104 billion USD), respectively. The economic losses for long-term exposure varied from 53 to 93.2 billion USD based on AHC and WTP, accounting for 0.4%-0.8% of China's total GDP in 2017, respectively.
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Affiliation(s)
- Chen Cui
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Yusi Liu
- State Key Laboratory of Severe Weather & Key Laboratory for Atmospheric Chemistry of China Meteorology Administration, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Li Chen
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China.
| | - Shuang Liang
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Mei Shan
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Jingwen Zhao
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Yaxin Liu
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Shunbang Yu
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Yanling Sun
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Jian Mao
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Hui Zhang
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Shuang Gao
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Zhenxing Ma
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
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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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9
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Xu J, Yang Z, Han B, Yang W, Duan Y, Fu Q, Bai Z. A unified empirical modeling approach for particulate matter and NO 2 in a coastal city in China. CHEMOSPHERE 2022; 299:134384. [PMID: 35337823 DOI: 10.1016/j.chemosphere.2022.134384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/27/2022] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
Modeling air pollutants on a fine spatiotemporal scale is necessary for health studies that focus on critical short-term exposure windows. A unified empirical modeling approach is useful for health studies; however, it is unclear whether this approach can be used in a coastal city for air pollutants driven by local emissions and regional meteorological factors. An advanced empirical modeling approach was used to develop exposure models from October 2012 to December 2019, for particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 μm (PM2.5 and PM10) and nitrogen dioxide (NO2) in the coastal city of Shanghai, China. Air pollutant concentrations were obtained from daily measurements at 55 administrative monitoring sites that were integrated into three-day average concentrations. Data on a large array of geographic variables were collected, and their dimensions were reduced using the partial least squares regression method. A geostatistical model using the land-use regression approach in a universal kriging framework was developed to estimate short-term exposure concentrations. The prediction ability of the models were determined by leave-one (site)-out cross-validation (LOOCV) and external validation (EV). Compared to the LOOCV results, the EV results for PM2.5 and PM10 were consistently reliable, but the EV for NO2 had a larger root mean squared error. The temporal random effects involved in the model structure were interpreted using sensitivity analyses. This affected the short-term PM2.5 and PM10 model predictions. This unified empirical modeling approach was successfully used for particulate matter in Shanghai, where air pollution is affected by complex regional and meteorological conditions. These exposure models are going to be applied for making exposure predictions at residential locations for short-term exposure predictions in the "Growth trajectories and air pollution" (GAAP) study in Shanghai that focuses on maternal and early life exposure to air pollutants.
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Affiliation(s)
- Jia Xu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Zhenchun Yang
- Duke Global Health Institute, Duke University, Durham, NC, 27708, United States
| | - Bin Han
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Wen Yang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
| | - Yusen Duan
- Shanghai Environmental Monitoring Center, Shanghai, China.
| | - Qingyan Fu
- Shanghai Environmental Monitoring Center, Shanghai, China
| | - Zhipeng Bai
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
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10
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Kim AR, Bang JH, Lee SH, Lee J. What Is the Role of Night-Time Noise Exposure in Childhood Allergic Disease? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052748. [PMID: 35270440 PMCID: PMC8910617 DOI: 10.3390/ijerph19052748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/17/2022] [Accepted: 02/24/2022] [Indexed: 01/25/2023]
Abstract
The cause of the allergic disease is known to be multifactorial, and there is growing evidence of environmental factors triggering the disease. Indeed, it is essential to find modifiable environmental factors related to allergic disease. Noise is an environmental pollutant causing various health problems, especially when exposed during the night-time. This study assessed the impact of night-time noise exposure in allergic disease. Subjects were selected from a panel data survey containing questions on allergic disease and related factors. Incidence of allergic disease, covariates, and addresses was derived from survey questionnaires. By applying the Land Use Regression modeling method, each subject’s night-time noise estimates were elicited based on the night-time noise level collected from the noise monitoring site. Association between night-time noise difference rate and incidence of asthma were analyzed by Cox proportional hazard regression. Incidence of allergic disease increased when night-time noise difference was positive compared to the negative difference. Additionally, the incidence of allergic disease increased by per interquartile range of night-time noise difference rate. The result showed that exposure to night-time noise tends to increase the risk of allergic disease. With further studies, the result of our study may serve as supplementary data when determining noise limits.
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Affiliation(s)
- A-Ram Kim
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Korea; (A.-R.K.); (S.-H.L.)
| | - Jin-Hee Bang
- Environmental Health Center, University of Ulsan College of Medicine, Ulsan 44033, Korea;
| | - Sung-Hee Lee
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Korea; (A.-R.K.); (S.-H.L.)
| | - Jiho Lee
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Korea; (A.-R.K.); (S.-H.L.)
- Correspondence: ; Tel.: +82-52-250-7288
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11
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Zhang P, Ma W, Wen F, Liu L, Yang L, Song J, Wang N, Liu Q. Estimating PM 2.5 concentration using the machine learning GA-SVM method to improve the land use regression model in Shaanxi, China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 225:112772. [PMID: 34530262 DOI: 10.1016/j.ecoenv.2021.112772] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 08/19/2021] [Accepted: 09/08/2021] [Indexed: 06/13/2023]
Abstract
With rapid economic growth, urbanization and industrialization, fine particulate matter with aerodynamic diameters ≤ 2.5 µm (PM2.5) has become a major pollutant and shows adverse effects on both human health and the atmospheric environment. Many studies on estimating PM2.5 concentrations have been performed using statistical regression models and satellite remote sensing. However, the accuracy of PM2.5 concentration estimates is limited by traditional regression models; machine learning methods have high predictive power, but fewer studies have been performed on the complementary advantages of different approaches. This study estimates PM2.5 concentrations from satellite remote sensing-derived aerosol optical depth (AOD) products, meteorological data, terrain data and other predictors in 2015 in Shaanxi, China, using a combined genetic algorithm-support vector machine (GA-SVM) method, after which the spatial clustering pattern was explored at the season and year levels. The results indicated that temperature (r = -0.684), precipitation (r = -0.602) and normalized difference vegetation index (NDVI) (r = -0.523) were significantly negatively correlated with the PM2.5 concentration, while AOD (r = 0.337) was significantly positively correlated with the PM2.5 concentration. Compared to conventional land use regression (LUR) and SVM models and previous related studies, the GA-SVM method demonstrated a significantly better prediction accuracy of PM2.5 concentration, with a higher 10-fold cross-validation coefficient of determination (R2) of 0.84 and lower root mean square error (RMSE) and mean absolute error (MAE) of 12.1 μg/m3 and 10.07 μg/m3, respectively. Y-scrambling test shows that the models have no chance correlation. The central and southern parts of Shaanxi have high PM2.5 concentrations, which are mainly due to the pollutant emissions and meteorological and topographical conditions in those areas. There was a positive spatial agglomeration characteristic of regional PM2.5 pollution, and the spatial spillover effect of PM2.5 pollution for seasonal and annual variations does exist. In general, the GA-SVM method is robust and accurately estimates PM2.5 concentrations via a novel modeling framework application and high-quality spatiotemporal information. It also has great significance for the exploration of PM2.5 pollution estimation and high-precision mapping methods, especially early warning in high-risk areas. Finally, the prevention and control of atmospheric pollution should take pollution control measures from major cities and surrounding cities, and focus on the joint pollution control measures for plain cities.
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Affiliation(s)
- Ping Zhang
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China; Shaanxi Key Laboratory of Land Consolidation, Xi'an 710075, China; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
| | - Wenjie Ma
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
| | - Feng Wen
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
| | - Lei Liu
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
| | - Lianwei Yang
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
| | - Jia Song
- School of Information Science and Technology, Yunnan Normal University, Kunming 650000, China
| | - Ning Wang
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China.
| | - Qi Liu
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
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12
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When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method. REMOTE SENSING 2021. [DOI: 10.3390/rs13214324] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.
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13
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Mo Y, Booker D, Zhao S, Tang J, Jiang H, Shen J, Chen D, Li J, Jones KC, Zhang G. The application of land use regression model to investigate spatiotemporal variations of PM 2.5 in Guangzhou, China: Implications for the public health benefits of PM 2.5 reduction. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 778:146305. [PMID: 34030351 DOI: 10.1016/j.scitotenv.2021.146305] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 02/28/2021] [Accepted: 03/02/2021] [Indexed: 06/12/2023]
Abstract
Understanding the intra-city variation of PM2.5 is important for air quality management and exposure assessment. In this study, to investigate the spatiotemporal variation of PM2.5 in Guangzhou, we developed land use regression (LUR) models using data from 49 routine air quality monitoring stations. The R2, adjust R2 and 10-fold cross validation R2 for the annual PM2.5 LUR model were 0.78, 0.72 and 0.66, respectively, indicating the robustness of the model. In all the LUR models, traffic variables (e.g., length of main road and the distance to nearest ancillary) were the most common variables in the LUR models, suggesting vehicle emission was the most important contributor to PM2.5 and controlling vehicle emissions would be an effective way to reduce PM2.5. The predicted PM2.5 exhibited significant variations with different land uses, with the highest value for impervious surfaces, followed by green land, cropland, forest and water areas. Guangzhou as the third largest city that PM2.5 concentration has achieved CAAQS Grade II guideline in China, it represents a useful case study city to examine the health and economic benefits of further reduction of PM2.5 to the lower concentration ranges. So, the health and economic benefits of reducing PM2.5 in Guangzhou was further estimated using the BenMAP model, based on the annual PM2.5 concentration predicted by the LUR model. The results showed that the avoided all cause mortalities were 992 cases (95% CI: 221-2140) and the corresponding economic benefits were 1478 million CNY (95% CI: 257-2524) (willingness to pay approach) if the annual PM2.5 concentration can be reduced to the annual CAAQS Grade I guideline value of 15 μg/m3. Our results are expected to provide valuable information for further air pollution control strategies in China.
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Affiliation(s)
- Yangzhi Mo
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China; National Air Quality Testing Services, Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom
| | - Douglas Booker
- National Air Quality Testing Services, Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom; Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom
| | - Shizhen Zhao
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China
| | - Jiao Tang
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China
| | - Hongxing Jiang
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China
| | - Jin Shen
- Guangdong Environmental Protection Key Laboratory of Secondary Air Pollution Research, Guangdong Environmental Monitoring Center, Guangzhou, China
| | - Duohong Chen
- Guangdong Environmental Protection Key Laboratory of Secondary Air Pollution Research, Guangdong Environmental Monitoring Center, Guangzhou, China
| | - Jun Li
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China
| | - Kevin C Jones
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom
| | - Gan Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China.
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14
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Liu Z, Guan Q, Lin J, Yang L, Luo H, Wang N. A new buffer selection strategy for land use regression model of PM 2.5 in Xi'an, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:21245-21255. [PMID: 33411307 DOI: 10.1007/s11356-020-11770-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 11/18/2020] [Indexed: 06/12/2023]
Abstract
In order to calculate the spatial distribution of high-resolution air-pollutant levels, the land use regression (LUR) model can be an effective method due to the comprehensive consideration of various factors. Traditional LUR models mostly use predefined buffers, which have the disadvantage of not matching high-resolution data well. In order to get a better-fitting model, a few researches have proposed new buffer selection methods. To solve this problem, we propose a new optimal buffer selection method based on the dichotomy to improve the correlation between predicted variables and pollutant concentration. For some socioeconomic data with high spatial resolution that cannot be obtained, for example, building data is used instead of population density data. Compared with the model with the predefined buffers, the model with our buffer selection strategy explained additional 5% variability in measured concentrations, in terms of the R2 of the final model. Our model explained 98% of the samples, and the deviation (1.78%) and root mean square error (5.17 μg/m) were small. It means that the LUR model with our buffer selection strategy can be used as a fit method to better describe spatial variability in atmospheric pollutant levels, which will be conducive to epidemiological research and urban environmental planning.
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Affiliation(s)
- Zeyu Liu
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Qingyu Guan
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
| | - Jinkuo Lin
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Liqin Yang
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Haiping Luo
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Ning Wang
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
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15
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Zhang Y, Li Z, Bai K, Wei Y, Xie Y, Zhang Y, Ou Y, Cohen J, Zhang Y, Peng Z, Zhang X, Chen C, Hong J, Xu H, Guang J, Lv Y, Li K, Li D. Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives. FUNDAMENTAL RESEARCH 2021. [DOI: 10.1016/j.fmre.2021.04.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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16
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Zhu C, Lee CC. The internal and external effects of air pollution on innovation in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:9462-9474. [PMID: 33146820 DOI: 10.1007/s11356-020-11439-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 10/26/2020] [Indexed: 06/11/2023]
Abstract
China is now the world's largest energy consumer, but severe air pollution problems have brought greater pressure to the production and development of its domestic economy. As an unavoidable result of air pollution, PM2.5 emissions are increasing. Previous literature has focused more on the impact of PM2.5 on the micro-level such as resident health and company location, yet macro-pattern studies between PM2.5 and innovation are inadequate. To bridge this gap, our research uses a spatial dynamic panel data model to systematically investigate the internal and external effects of PM2.5 concentration on innovation in China during the period 2001-2016. After forming a dataset of real-time PM2.5 concentration from satellite detection and using an innovation index instead of patents, we find a stronger spatial linkage between PM2.5 concentration and innovation. Thus, PM2.5 inhibits regional innovation significantly, and this result still exists after using the air mobility index as an instrument variable to alleviate endogenous problems. Lastly, PM2.5 concentration in neighboring regions also impedes local innovation considerably, indicating a spatial ripple effect of PM2.5.
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Affiliation(s)
- Chen Zhu
- School of Economics, Hefei University of Technology, Hefei, China
| | - Chien-Chiang Lee
- Research Center of the Central China for Economic and Social Development, Nanchang University, Nanchang, China.
- School of Economics and Management, Nanchang University, Nanchang, Jiangxi, China.
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17
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Shi T, Hu Y, Liu M, Li C, Zhang C, Liu C. Land use regression modelling of PM 2.5 spatial variations in different seasons in urban areas. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 743:140744. [PMID: 32663682 DOI: 10.1016/j.scitotenv.2020.140744] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 06/12/2020] [Accepted: 07/02/2020] [Indexed: 06/11/2023]
Abstract
As one of the principal components of haze, fine particulate matter (PM2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52-61% of the variation in the PM2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution.
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Affiliation(s)
- Tuo Shi
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang 110016, China; College of Resources and Environment, University of Chinese Academy of Sciences, No. 19, Yuquan Road, Beijing 100049, China
| | - Yuanman Hu
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang 110016, China
| | - Miao Liu
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang 110016, China.
| | - Chunlin Li
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang 110016, China.
| | - Chuyi Zhang
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang 110016, China; College of Resources and Environment, University of Chinese Academy of Sciences, No. 19, Yuquan Road, Beijing 100049, China
| | - Chong Liu
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang 110016, China; College of Resources and Environment, University of Chinese Academy of Sciences, No. 19, Yuquan Road, Beijing 100049, China
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18
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Investigation of the Impact of Land-Use Distribution on PM 2.5 in Weifang: Seasonal Variations. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17145135. [PMID: 32708629 PMCID: PMC7400403 DOI: 10.3390/ijerph17145135] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/13/2020] [Accepted: 07/13/2020] [Indexed: 11/17/2022]
Abstract
As air pollution becomes highly focused in China, the accurate identification of its influencing factors is critical for achieving effective control and targeted environmental governance. Land-use distribution is one of the key factors affecting air quality, and research on the impact of land-use distribution on air pollution has drawn wide attention. However, considerable studies have mostly used linear regression models, which fail to capture the nonlinear effects of land-use distribution on PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) and to show how impacts on PM2.5 vary with land-use magnitudes. In addition, related studies have generally focused on annual analyses, ignoring the seasonal variability of the impact of land-use distribution on PM2.5, thus leading to possible estimation biases for PM2.5. This study was designed to address these issues and assess the impacts of land-use distribution on PM2.5 in Weifang, China. A machine learning statistical model, the boosted regression tree (BRT), was applied to measure nonlinear effects of land-use distribution on PM2.5, capture how land-use magnitude impacts PM2.5 across different seasons, and explore the policy implications for urban planning. The main conclusions are that the air quality will significantly improve with an increase in grassland and forest area, especially below 8% and 20%, respectively. When the distribution of construction land is greater than around 10%, the PM2.5 pollution can be seriously substantially increased with the increment of their areas. The impact of gardens and farmland presents seasonal characteristics. It is noted that as the weather becomes colder, the inhibitory effect of vegetation distribution on the PM2.5 concentration gradually decreases, while the positive impacts of artificial surface distributions, such as construction land and roads, are aggravated because leaves drop off in autumn (September-November) and winter (December-February). According to the findings of this study, it is recommended that Weifang should strengthen pollution control in winter, for instance, expand the coverage areas of evergreen vegetation like Pinus bungeana Zucc. and Euonymus japonicus Thunb, and increase the width and numbers of branches connecting different main roads. The findings also provide quantitative and optimal land-use planning and strategies to minimize PM2.5 pollution, referring to the status of regional urbanization and greening construction.
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19
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Yang Q, Yuan Q, Yue L, Li T. Investigation of the spatially varying relationships of PM 2.5 with meteorology, topography, and emissions over China in 2015 by using modified geographically weighted regression. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 262:114257. [PMID: 32146364 DOI: 10.1016/j.envpol.2020.114257] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 02/21/2020] [Accepted: 02/21/2020] [Indexed: 06/10/2023]
Abstract
PM2.5 pollution is caused by multiple factors and determining how these factors affect PM2.5 pollution is important for haze control. In this study, we modified the geographically weighted regression (GWR) model and investigated the relationships between PM2.5 and its influencing factors. Experiments covering 368 cities and 9 urban agglomerations were conducted in China in 2015 and more than 20 factors were considered. The modified GWR coefficients (MGCs) were calculated for six variables, including two emission factors (SO2 and NO2 concentrations), two meteorological factors (relative humidity and lifted index), and two topographical factors (woodland percentage and elevation). Then the spatial distribution of MGCs was analyzed at city, cluster, and region scales. Results showed that the relationships between PM2.5 and the different factors varied with location. SO2 emission positively affected PM2.5, and the impact was the strongest in the Beijing-Tianjin-Hebei (BTH) region. The impact of NO2 was generally smaller than that of SO2 and could be important in coastal areas. The impact of meteorological factors on PM2.5 was complicated in terms of spatial variations, with relative humidity and lifted index exerting a strong positive impact on PM2.5 in Pearl River Delta and Central China, respectively. Woodland percentage mainly influenced PM2.5 in regions of or near deserts, and elevation was important in BTH and Sichuan. The findings of this study can improve our understanding of haze formation and provide useful information for policy-making.
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Affiliation(s)
- Qianqian Yang
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China
| | - Qiangqiang Yuan
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China; Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, 430079, Hubei, China.
| | - Linwei Yue
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, Hubei, 430074, China
| | - Tongwen Li
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, 430079, China
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20
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Zhang L, Yang G, Li X. Mining sequential patterns of PM2.5 pollution between 338 cities in China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2020; 262:110341. [PMID: 32250817 DOI: 10.1016/j.jenvman.2020.110341] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 02/16/2020] [Accepted: 02/24/2020] [Indexed: 05/22/2023]
Abstract
Serious PM2.5 air pollution has persistently plagued and endangered most urban areas in China in recent years, and targeted policies are necessary to improve urban air quality ranging from macro policy (national level) to medium policy (city level) to micro policy (site specific). However, the macro-pattern study of air pollution between Chinese cities is inadequate, and not conducive to the formulation of macro-policy. To bridge this gap, we applied a sequential pattern mining algorithm to analyze the spatial-temporal patterns of PM2.5 pollution across Chinese cities during the period 2015 to 2018. The sequential patterns were collected from three levels of granularity on geographic areas and ten temporal scenarios covering time intervals from 10 to 100 h. Many underlying associative relationships were revealed between different cities by the mined patterns. The patterns were heterogeneous and presented five characteristics (i.e., clustering, symmetry, imbalance, decay, and stability). Each of the urban areas under investigation at different granularities was analyzed to identify the occurrence of associative relationships between it and other urban areas; moreover, we determined the degree of severity of such relationships. Our research results provide solid data that can be used as a reference by the various levels of Chinese governments for decision-making; overall, they can be used to improve the design of joint policies to prevent and control PM2.5 pollution in Chinese urban areas.
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Affiliation(s)
- Liankui Zhang
- Institute of Systems Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian, 116024, China
| | - Guangfei Yang
- Institute of Systems Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian, 116024, China.
| | - Xianneng Li
- Institute of Systems Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian, 116024, China
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21
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Chen J, Yin J, Zang L, Zhang T, Zhao M. Stacking machine learning model for estimating hourly PM 2.5 in China based on Himawari 8 aerosol optical depth data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 697:134021. [PMID: 31484095 DOI: 10.1016/j.scitotenv.2019.134021] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/03/2019] [Accepted: 08/19/2019] [Indexed: 06/10/2023]
Abstract
Aerosol optical depth (AOD) from polar orbit satellites and meteorological factors have been widely used to estimate concentrations of surface particulate matter with an aerodynamic diameter <2.5 μm (PM2.5). However, estimations with high temporal resolution remain lacking because of the limitations of satellite observations. Here, we used AOD data with a temporal resolution of 1 h provided by a geostationary satellite called Himawari 8 to overcome this problem. We developed a stacking model, which contained three submodels of machine learning, namely, AdaBoost, XGBoost and random forest, stacked through a multiple linear regression model. Then, we estimated the hourly concentrations of PM2.5 in Central and Eastern China. The accuracy evaluation showed that the proposed stacking model performed better than the single models when applied to the test set, with an average coefficient of determination (R2) of 0.85 and a root-mean-square error (RMSE) of 17.3 μg/m3. Model precision reached its peak at 14:00 (local time), with an R2 (RMSE) of 0.92 (12.9 μg/m3). In addition, the spatial and temporal distributions of PM2.5 in Central and Eastern China were plotted in this study. The North China Plain was determined to be the most polluted area in China, with an annual mean PM2.5 concentration of 58 μg/m3 during daytime. Moreover, the pollution level of PM2.5 was the highest in winter, with an average concentration of 73 μg/m3.
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Affiliation(s)
- Jiangping Chen
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Jianhua Yin
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
| | - Lin Zang
- Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
| | - Taixin Zhang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Mengdi Zhao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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22
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Jin L, Berman JD, Warren JL, Levy JI, Thurston G, Zhang Y, Xu X, Wang S, Zhang Y, Bell ML. A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China. ENVIRONMENTAL RESEARCH 2019; 177:108597. [PMID: 31401375 DOI: 10.1016/j.envres.2019.108597] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 07/15/2019] [Accepted: 07/19/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings. OBJECTIVE A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings. METHODS In each of four seasons in 2016-2017, NO2 was measured using Ogawa badges for 2 weeks at 75 ground-level sites. PM2.5 was measured using DataRAM for shorter time intervals at a subset (N = 38) of the 75 sites. Vertical profile measurements were conducted on 9 stories at 2 high-rise buildings (N = 18), with one building facing traffic and another facing away from traffic. The average seasonal concentrations of NO2 and PM2.5 at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models. RESULTS We developed robust LUR models at the ground level for estimated annual averages of NO2 (R2: 0.71, adjusted R2: 0.67, and Leave-One-Out Cross Validation (LOOCV) R2: 0.64) and PM2.5 (R2: 0.77, adjusted R2: of 0.73, and LOOCV R2: 0.67) in the urban core of Lanzhou, China. The LUR models for the estimated seasonal averages of NO2 showed similar patterns. Vertical variation of NO2 and PM2.5 differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations. CONCLUSIONS Ground-level NO2 and PM2.5 showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.
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Affiliation(s)
- Lan Jin
- School of Forestry and Environmental Studies, Yale University, 195 Prospect St, New Haven, CT, 06511, USA.
| | - Jesse D Berman
- Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, USA
| | - Joshua L Warren
- School of Public Health, Yale University, 60 College St, New Haven, CT, 06510, USA
| | - Jonathan I Levy
- School of Public Health, Boston University, 715 Albany St Talbot Building, Boston, MA, 02118, USA
| | - George Thurston
- Department of Environmental Medicine, New York University, 57 Old Forge Rd, Tuxedo Park, NY, 10987, USA
| | - Yawei Zhang
- School of Public Health, Yale University, 60 College St, New Haven, CT, 06510, USA
| | - Xibao Xu
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing, 210008, China
| | - Shuxiao Wang
- School of Environment, Tsinghua University, Haidian District, Beijing, 100091, China
| | - Yaqun Zhang
- Gansu Academy of Environmental Sciences, 225 Yanerwan Rd, Chengguan District, Lanzhou, Gansu, 730000, China
| | - Michelle L Bell
- School of Forestry and Environmental Studies, Yale University, 195 Prospect St, New Haven, CT, 06511, USA
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23
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Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method. SUSTAINABILITY 2019. [DOI: 10.3390/su11143793] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In order to evaluate the atmospheric environment sustainability in the provinces of Northeast China, this paper has constructed a comprehensive evaluation model based on the rough set and entropy weight methods. This paper first constructs a Pressure-State-Response (PSR) model with a pressure layer, state layer and response layer, as well as an atmospheric environment evaluation system consisting of 17 indicators. Then, this paper obtains the weight of different indicators by using the rough set method and conducts equal-width discrete analysis and clustering analysis by using SPSS software. This paper has found that different discrete methods will end up with different reduction sets and multiple indicators sharing the same weight. Therefore, this paper has further introduced the entropy weight method based on the weight solution determined by rough sets and solved the attribute reduction sets of different layers by using the Rosetta software. Finally, this paper has further proved the rationality of this evaluation model for atmospheric environment sustainability by comparing the results with those of the entropy weight method alone and those of the rough set method alone. The results show that the sustainability level of the atmospheric environment in Northeast China provinces has first improved, and then worsened, with the atmospheric environment sustainability level reaching the highest level of 0.9275 in 2014, while dropping to the lowest level of 0.6027 in 2017. Therefore, future efforts should focus on reducing the pressure layer and expanding the response layer. Based on analysis of the above evaluation results, this paper has further offered recommendations and solutions for the improvement of atmospheric environment sustainability in the three provinces of Northeast China.
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Fei X, Christakos G, Xiao R, Ren Z, Liu Y, Lv X. Improved heavy metal mapping and pollution source apportionment in Shanghai City soils using auxiliary information. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 661:168-177. [PMID: 30669049 DOI: 10.1016/j.scitotenv.2019.01.149] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Revised: 01/13/2019] [Accepted: 01/13/2019] [Indexed: 06/09/2023]
Abstract
Soil heavy metal pollution can be a serious threat to human health and the environment. The accurate mapping of the spatial distribution of soil heavy metal pollutant concentrations enables the detection of high pollution areas and facilitates pollution source apportionment and control. To make full use of auxiliary soil properties information and show that they can improve mapping, a synthesis of the Bayesian Maximum Entropy (BME) theory and the Geographically Weighted Regression (GWR) model is proposed and implemented in the study of the Shanghai City soils (China). The results showed that, compared to traditional techniques, the proposed BME-GWR synthesis has certain important advantages: (a) it integrates heavy metal measurements and auxiliary information on a sound theoretical basis, and (b) it performs better in terms of both prediction accuracy and implementation flexibility (including the assimilation of multiple data sources). Based on the heavy metal concentration maps generated by BME-GWR, we found that the As, Cr and Pb concentration levels are high in the eastern part of Shanghai, whereas high Cd concentration levels were observed in the northwestern part of the city. Organic carbon and pH were significantly correlated with most of the heavy metals in Shanghai soils. We concluded that Cd pollution is mainly the result of agricultural activities, and that the Cr pollution is attributed to natural sources, whereas Pb and As have compound pollution sources. Future studies should investigate the implementation of BME-GWR in the case of space-time heavy metal mapping and its ability to integrate human activity information and soil category variables.
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Affiliation(s)
- Xufeng Fei
- Zhejiang Academy of Agriculture Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China.
| | - George Christakos
- Ocean College, Zhejiang University, Zhoushan, China; Department of Geography, San Diego State University, San Diego, CA, USA
| | - Rui Xiao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
| | - Zhouqiao Ren
- Zhejiang Academy of Agriculture Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Yue Liu
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
| | - Xiaonan Lv
- Zhejiang Academy of Agriculture Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
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25
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Association between Exposure to Ambient Air Pollution and Rheumatoid Arthritis in Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16071227. [PMID: 30959862 PMCID: PMC6480037 DOI: 10.3390/ijerph16071227] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 04/03/2019] [Accepted: 04/04/2019] [Indexed: 12/13/2022]
Abstract
Environmental factors may play roles in the development of rheumatoid arthritis (RA), and some studies have shown that air pollution was associated with the development of autoimmune disease. This study was designed to investigate the effect of air pollutants on the development of adult RA. A nested case-control cohort study was performed using the National Health Insurance Service-National Sample Cohort during 2002⁻2014 in Korea. Air pollution data were collected from the National Ambient Air Monitoring System (NAMIS), and exposure levels were extrapolated using geographic information systems. The group with RA (n = 444) was compared with a propensity score-matched control group (n = 1776), and one-year average concentrations of air pollution were predicted at each patient's residence. The adjusted binary logistic regression analysis showed a positive association between O₃ exposure and the incidence risk of RA for the third (odds ratios (OR) = 1.45, 95% confidence intervals (CI): 1.08⁻1.96) and fourth (OR = 1.35, 95% CI: 1.00⁻1.83) quartiles in adults over 20 years of age. The third quartile CO exposure was also associated with an increased risk of RA (OR = 1.57, 95% CI: 1.16⁻2.12). The results of this nationwide population-based study showed that a one-year exposure to CO and O₃ in adults was associated with an increased risk of RA.
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