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Neupane BK, Acharya BK, Cao C, Xu M, Bhattarai H, Yang Y, Wang S. A systematic review of spatial and temporal epidemiological approaches, focus on lung cancer risk associated with particulate matter. BMC Public Health 2024; 24:2945. [PMID: 39448953 DOI: 10.1186/s12889-024-20431-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 10/16/2024] [Indexed: 10/26/2024] Open
Abstract
BACKGROUND Particulate matter (PM), including the major risk factor for lung cancer (LC), greatly impacts human health. Although numerous studies have highlighted spatiotemporal patterns and PM-LC associations, these studies have not been well-reviewed. Thus, we examined epidemiological studies linked with PM-LC and provided concise, up-to-date data. METHODS We used certain keywords to review articles published in PubMed, Web of Science, Scopus, and Google Scholar until 30th June 2024 and identified 1474 research articles. We then filtered the research articles based on our criteria and ultimately dropped down to 30 for this review. RESULTS Out of the thirty reviewed studies on the PM-LC relation, twenty-four focused on PM2.5, four on PM10, and two on both, indicating that approximately 80% of the respondents were inclined toward fine particles and their health impacts. The study revealed that 22 studies used visualization, 12 used exploration, and 15 used modeling methods. A strong positive relationship was reported between LC and PM2.5, ranging from 1.04 to 1.60 (95% CI) for a 10 µg/m3 increase in PM2.5 exposure. However, compared to PM2.5, PM10 was found to have a significantly less positive association. CONCLUSIONS Very few studies have used advanced spatiotemporal methods to examine the association between LC and PM. Advanced spatiotemporal analysis techniques should be employed to explore this association in specific geographical locations. Further research should utilize spatiotemporal epidemiological approaches to study the link between PM and lung cancer.
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Affiliation(s)
- Basanta Kumar Neupane
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100094, China
| | | | - Chunxiang Cao
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
| | - Min Xu
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Hemraj Bhattarai
- Earth and Environmental Sciences Program and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
| | - Yujie Yang
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100094, China
| | - Shaohua Wang
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
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Yu H, Wang Y, Huang J, Yue X, Chu J, Sun G, Gao H, Yang M, Zhang H. Effect of forest cover on lung cancer incidence: a case study in Southwest China. Front Public Health 2024; 12:1466462. [PMID: 39430708 PMCID: PMC11486646 DOI: 10.3389/fpubh.2024.1466462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Accepted: 09/23/2024] [Indexed: 10/22/2024] Open
Abstract
Introduction Forests are closely linked to human health, particularly about lung cancer incidence. However, there is currently limited research on how forest coverage and different types of forests influence lung cancer rates. This study aims to address this gap by examining how the coverage of various forest types impacts lung cancer incidence in Southwest China, thereby providing theoretical support for health-oriented forest structure planning. Methods We focused on 438 counties in Southwest China, employing spatial autocorrelation analysis (Moran's I) and spatial regression models [including Spatial Lag Model (SLM), Spatial Error Model (SEM), and Spatial Durbin Model (SDM)] to explore the effects of forest coverage and internal forest structure on lung cancer incidence. We used ArcGIS to visualize lung cancer incidence and forest coverage rates across the study area. Results The study found a significant negative correlation between forest coverage and lung cancer incidence. Specifically, for every 1% increase in forest coverage, lung cancer incidence decreased by 0.017 levels. Evergreen forests and mixed forests showed a significant negative impact on lung cancer rates, with evergreen forests having a particularly strong effect; a 1% increase in evergreen forest coverage was associated with a 0.027 level decrease in lung cancer incidence. In contrast, deciduous forests had no significant impact. Additionally, the study revealed a marked spatial heterogeneity in lung cancer incidence and forest coverage across Southwest China: higher lung cancer rates were observed in the eastern regions, while forest coverage was predominantly concentrated in the western and southern regions. Discussion This study demonstrates that increasing forest coverage, particularly of evergreen and mixed forests, can help reduce lung cancer incidence. This effect may be related to the ability of forests to absorb harmful gasses and particulate matter from the air. Furthermore, the spatial heterogeneity in lung cancer incidence suggests that regional economic development levels and urbanization processes may also play significant roles in the spatial distribution of lung cancer rates. The findings provide empirical support for the development of targeted forest conservation and development policies aimed at optimizing regional forest structures to reduce the risk of lung cancer.
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Affiliation(s)
- Haishi Yu
- Yunnan Normal University Hospital, Yunnan Normal University, Kunming, China
| | - Yang Wang
- Faculty of Geography, Yunnan Normal University, Kunming, China
| | - Jinyu Huang
- Faculty of Geography, Yunnan Normal University, Kunming, China
| | - Xiaoli Yue
- Faculty of Geography, Yunnan Normal University, Kunming, China
| | - Jun Chu
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
| | - Guiquan Sun
- Faculty of Geography, Yunnan Normal University, Kunming, China
| | - Han Gao
- Faculty of Geography, Yunnan Normal University, Kunming, China
| | - Min Yang
- Faculty of Geography, Yunnan Normal University, Kunming, China
| | - Hong’ou Zhang
- Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China
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Tian L, Zhao S, Zhang R, Lv S, Chen D, Li J, Jones KC, Sweetman AJ, Peng P, Zhang G. Tire Wear Chemicals in the Urban Atmosphere: Significant Contributions of Tire Wear Particles to PM 2.5. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 39264297 DOI: 10.1021/acs.est.4c04378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Tire wear particles (TWPs) containing tire wear chemicals (TWCs) are of global concern due to their large emissions and potential toxicity. However, TWP contributions to urban fine particles are poorly understood. Here, 72 paired gas-phase and PM2.5 samples were collected in the urban air of the Pearl River Delta, China. The concentrations of 54 compounds were determined, and 28 TWCs were detected with total concentrations of 3130-317,000 pg/m3. Most p-phenylenediamines (PPDs) were unstable in solvent, likely leading to their low detection rates. The TWCs were mainly (73 ± 26%) in the gas phase. 2-OH-benzothiazole contributed 82 ± 21% of the gas-phase TWCs and benzothiazole-2-sulfonic acid contributed 74 ± 18% of the TWCs in PM2.5. Guangzhou and Foshan were "hotspots" for atmospheric TWCs. Most TWC concentrations significantly correlated with the road length nearby. More particulate TWCs were observed than model predictions, probably due to the impacts of nonexchangeable portion and sampling artifacts. Source apportionment combined with characteristic molecular markers indicated that TWPs contributed 13 ± 7% of urban PM2.5. Our study demonstrates that TWPs are important contributors to urban air pollution that could pose risks to humans. There is an urgent need to develop strategies to decrease TWP emissions, along with broader urban air quality improvement strategies.
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Affiliation(s)
- Lele Tian
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shizhen Zhao
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control and Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou 510640, China
| | - Ruiling Zhang
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Shaojun Lv
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Duohong Chen
- Environmental Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong Environmental Monitoring Center, Guangzhou 510308, China
| | - Jun Li
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control and Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou 510640, China
| | - Kevin C Jones
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, U.K
| | - Andrew J Sweetman
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, U.K
| | - Ping'an Peng
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control and Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou 510640, China
| | - Gan Zhang
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control and Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou 510640, China
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Yu H, Wang Y, Yue X, Zhang H. Influence of the atmospheric environment on spatial variation of lung cancer incidence in China. PLoS One 2024; 19:e0305345. [PMID: 38889132 PMCID: PMC11185477 DOI: 10.1371/journal.pone.0305345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
Conducting this research contributes to a deeper understanding of the correlation between atmospheric environmental quality and lung cancer incidence, and provides the scientific basis for formulating effective environmental protection and lung cancer prevention and control strategies. Lung cancer incidence in China has strong spatial variation. However, few studies have systematically revealed the characteristics of the spatial variation in lung cancer incidence, and have explained the causes of this spatial variation in lung cancer incidence from the perspectives of multiple components of the atmospheric environment to explain this spatial variation in lung cancer incidence. To address research limitations, we first analyze the spatial variation and spatial correlation characteristics of lung cancer incidence in China. Then, we build a spatial regression model using GeoDa software with lung cancer incidence as the dependent variable, five atmospheric environment factors-particulate matter 2.5 (PM2.5) concentration, temperature, atmospheric pressure, and elevation as explanatory variables, and four socio-economic characteristics as control variables to systematically analyze the influence and intensity of these factors on lung cancer incidence. The results show that lung cancer incidence in China has apparent changes in geographical and spatial unevenness, and spatial autocorrelation characteristics. In China, the lung cancer incidence is relatively high in Northeast China, while some areas of high lung cancer incidence still exist in Central China, Southwest China and South China, although the overall lung cancer incidence is relatively low. The atmospheric environment significantly affects lung cancer incidence. Different elements of the atmospheric environment vary in the direction and extent of their influence on the development of lung cancer. A 1% increase in PM2.5 concentration is associated with a level of 0.002975 increase in lung cancer incidence. Atmospheric pressure positively affects lung cancer incidence, and an increase in atmospheric pressure by 1% increases lung cancer incidence by a level of 0.026061. Conversely, a 1% increase in temperature is linked to a level of 0.006443 decreases in lung cancer incidence, and a negative correlation exists between elevation and lung cancer incidence, where an increase in elevation by 1% correlates with a decrease in lung cancer incidence by a level of 0.000934. The core influencing factors of lung cancer incidence in the seven geographical divisions of China exhibit variations. This study facilitates our understanding of the spatial variation characteristics of lung cancer incidence in China on a finer scale, while also offering a more diverse perspective on the impact of the atmospheric environment on lung cancer incidence.
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Affiliation(s)
- Haishi Yu
- Yunnan Normal University Hospital, Kunming, Yunnan, China
| | - Yang Wang
- Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, China
| | - Xiaoli Yue
- Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, China
| | - Hong’ou Zhang
- Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, Guangdong, China
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Wang J, Liu Y, Chen L, Liu Y, Mi K, Gao S, Mao J, Zhang H, Sun Y, Ma Z. Validation and calibration of aerosol optical depth and classification of aerosol types based on multi-source data over China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166603. [PMID: 37660811 DOI: 10.1016/j.scitotenv.2023.166603] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/12/2023] [Accepted: 08/25/2023] [Indexed: 09/05/2023]
Abstract
A refined classification of aerosol types is essential to identify and control air pollution sources. This study focused on improving the resolution and accuracy of aerosol optical depth (AOD) and further refining the classification of aerosol types in China. We validated the accuracy of the AOD acquired using the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) and Copernicus Atmosphere Monitoring Service (CAMS) by comparing it with that acquired using from the Aeronet Robotic Network (AERONET). We simulated the AOD with high spatial resolution and accuracy based on the extremely randomized trees (ERT), adaptive boosting (AdaBoost), and gradient boosting decision trees (GBDT) models and identified aerosol types based on the Angstrom Exponent (AE) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the calibrated AOD. The results showed that CAMS overestimates AOD (21.4 %) and MERRA2 underestimates AOD (-17.3 %). Among the three machine learning models, the ERT model performed best, with a determination coefficient (R2) of 0.825 and the root-mean-square error (RMSE) of 0.174. Biomass burning/urban-industrial aerosols dominated China, with the largest contributions to southern, eastern, and central China in spring and summer. Clean continental aerosols contributed the most to southwestern China in fall and winter, whereas desert dust aerosols contributed the most to northwestern and eastern China in spring.
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Affiliation(s)
- Jing Wang
- 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.
| | - Yaxin Liu
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Ke Mi
- 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
| | - 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
| | - Yanling Sun
- 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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Xu X, Zhang W, Shi X, Su Z, Cheng W, Wei Y, Ma H, Li T, Wang Z. China's air quality improvement strategy may already be having a positive effect: evidence based on health risk assessment. Front Public Health 2023; 11:1250572. [PMID: 37927881 PMCID: PMC10624126 DOI: 10.3389/fpubh.2023.1250572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/15/2023] [Indexed: 11/07/2023] Open
Abstract
Aiming to investigate the health risk impact of PM2.5 pollution on a heavily populated province of China. The exposure response function was used to assess the health risk of PM2.5 pollution. Results shows that the total number of premature deaths and diseases related to PM2.5 pollution in Shandong might reach 159.8 thousand people based on the new WHO (2021) standards. The health effects of PM2.5 pollution were more severe in men than in women. Five of the 16 cities in Shandong had higher health risks caused by PM2.5 pollution, including LinYi, HeZe, JiNing, JiNan, and WeiFang. PM2.5 pollution resulted in nearly 7.4 billions dollars in healthy economic cost, which accounted for 0.57% of GDP in Shandong in 2021. HeZe, LiaoCheng, ZaoZhuang, and LinYi were the cities where the health economic loss was more than 1% of the local GDP, accounted for 1.30, 1.26, 1.08, and 1.04%. Although the more rigorous assessment criteria, the baseline concentration was lowered by 30 μg/m3 compared to our previous study, there was no significant increase in health risks and economic losses. China's air quality improvement strategy may already be having a positive effect.
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Affiliation(s)
- Xianmang Xu
- Heze Branch, Biological Engineering Technology Innovation Center of Shandong Province, Qilu University of Technology (Shandong Academy of Sciences), Heze, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, China
| | - Wen Zhang
- Department of Clinical Medicine, Heze Medical College, Heze, China
| | - Xiaofeng Shi
- Department of Clinical Medicine, Heze Medical College, Heze, China
| | - Zhi Su
- Heze Ecological Environment Monitoring Center of Shandong Province, Heze, China
| | - Wei Cheng
- Heze Branch, Biological Engineering Technology Innovation Center of Shandong Province, Qilu University of Technology (Shandong Academy of Sciences), Heze, China
| | - Yinuo Wei
- Heze Branch, Biological Engineering Technology Innovation Center of Shandong Province, Qilu University of Technology (Shandong Academy of Sciences), Heze, China
| | - He Ma
- Heze Branch, Biological Engineering Technology Innovation Center of Shandong Province, Qilu University of Technology (Shandong Academy of Sciences), Heze, China
| | - Tinglong Li
- Heze Branch, Biological Engineering Technology Innovation Center of Shandong Province, Qilu University of Technology (Shandong Academy of Sciences), Heze, China
| | - Zhenhua Wang
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
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7
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Liu Y, Xu Y, Li Y, Wei H. Identifying the Environmental Determinants of Lung Cancer: A Case Study of Henan, China. GEOHEALTH 2023; 7:e2023GH000794. [PMID: 37275567 PMCID: PMC10234758 DOI: 10.1029/2023gh000794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/30/2023] [Accepted: 04/26/2023] [Indexed: 06/07/2023]
Abstract
Lung cancer has become one of the most prevalent cancers in the last several decades. Studies have documented that most cases of lung cancer are caused by inhaling environmental carcinogens while how external environmental factors lead to individual lung cancer is still an open issue as the pathogenesis may come from the combined action of multiple environmental factors, and such pathogenic mechanism may vary from region to region. Based on the data of lung cancer cases from hospitals at the county level in Henan from 2016 to 2020, we analyzed the response relationship between lung cancer incidence and physical ambient factors (air quality, meteorological conditions, soil vegetation) and socioeconomic factors (occupational environment, medical level, heating mode, smoking behavior). We used a Bayesian spatio-temporal interaction model to evaluate the relative risk of disease in different regions. The results showed that smoking was still the primary determinant of lung cancer, but the influence of air quality was increasing year by year, with meteorological conditions and occupational environment playing a synergistic role in this process. The high-risk areas were concentrated in the plains of East and Central Henan and the basin of South Henan, while the low-risk areas were concentrated in the hilly areas of North and West Henan, which were related to the topography of Henan. Our study provides a better understanding of the environmental determinants of lung cancer which will help refine existing prevention strategies and recognize the areas where actions are required to prevent environment and occupation related lung cancer.
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Affiliation(s)
- Yan Liu
- School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina
| | - Yanqing Xu
- School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina
| | - Yuchen Li
- MRC Epidemiology UnitSchool of Clinical MedicineUniversity of CambridgeCambridgeUK
| | - Haitao Wei
- The School of the Geo‐Science & TechnologyZhengzhou UniversityZhengzhouChina
- Joint Laboratory of Eco‐MeteorologyZhengzhou UniversityZhengzhouChina
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8
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Lu Y, Wang Y, Liao Y, Wang J, Shan M, Jiang H. Public Concern about Haze and Ozone in the Era of Their Coordinated Control in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:911. [PMID: 36673669 PMCID: PMC9859249 DOI: 10.3390/ijerph20020911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 12/29/2022] [Accepted: 12/31/2022] [Indexed: 06/17/2023]
Abstract
In China, due to the implementation of the Action Plan for Prevention and Control of Air Pollution (APPCAP), the concentrations of PM2.5 (fine particulate matter) and severe haze in most cities have decreased significantly. However, at present, haze pollution in China has not been completely mitigated, and the problem of O3 (ozone) has become prominent. Therefore, the prevention and control of haze and O3 pollution have become important and noticeable issues in the field of atmospheric management. We used the Baidu search indices of "haze" and "ozone" to reflect public concerns about air quality and uncover different correlations between level of concern and level of pollution, and then we identified regions in China that require public attention. The results showed that (1) over the last decade, the search index of haze had a rapid trend of variation in line with changes in haze pollution, but that of O3 had a relatively slowly increasing trend; (2) the lag days between the peaks of public concern and the peaks of air pollution became increasingly shorter according to daily data analysis; and (3) 96 polluted cities did not receive sufficient public attention. Although periods of heavily haze-polluted weather, which affects visibility, have generated much public concern, periods of slight pollution have not received enough public attention. Public health protection and environmental participation regarding these periods of slight pollution in China deserve appropriate levels of attention.
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Affiliation(s)
- Yaling Lu
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
- The Center of Enterprise Green Governance, Chinese Academy for Environmental Planning, Beijing 100012, China
| | - Yuan Wang
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Yujie Liao
- Hebei Key Laboratory of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China
| | - Jiantong Wang
- The Center of Enterprise Green Governance, Chinese Academy for Environmental Planning, Beijing 100012, China
| | - Mei Shan
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Hongqiang Jiang
- The Center of Enterprise Green Governance, Chinese Academy for Environmental Planning, Beijing 100012, China
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Jin S, Wang W, Ostic D, Zhang C, Lu N, Wang D, Ni W. Air quality and health benefits of increasing carbon mitigation tech-innovation in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:6786-6804. [PMID: 36006537 DOI: 10.1007/s11356-022-22602-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Most studies on the short-term local benefits of carbon mitigation technologies on air quality improvement and health focus on specific technologies such as biofuels or carbon sequestration technologies, while ignoring the overall role of the growing scale of low-carbon technologies. Based on STIRPAT model and EKC hypothesis, this paper takes 30 provinces in China from 2004 to 2016 as research samples. We builded the panel double fixed effect model to empirical analysis of climate change on carbon mitigation tech-innovation suppressing the influence of haze pollution, on this basis, the mediating effect model was used to explore the mediation function of industrial structure and energy structure. Meanwhile, we drawed on the existing studies on air quality and health benefits, and quantify the co-benefits of carbon mitigation tech-innovation on health through the equivalent substitution formula. It shows that a 1% increase in the number of low-carbon patent applications can reduce haze pollution by 0.066%. According to this estimate, to 2029, China's carbon mitigation tech-innovation could reduce PM2.5 concentration to 15 μg/m3 preventing 5.597 million premature deaths. Moreover, carbon mitigation tech-innovation can also indirectly inhibit haze pollution by triggering more systematic economic structure changes such as energy and industrial structure. Additionally, we found that the role of gray tech-innovation (GT) related to improving the efficiency of fossil energy is stronger than that of clean technology (CT) related to the use of renewable energy. This suggests that for a large economy such as China, where coal is still the dominant source of energy consumption, the short-term local benefits of improving air quality and health through the use of gray tech-innovation to improve energy and industrial structure are still important to balance the cost of carbon mitigation.
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Affiliation(s)
- Shunlin Jin
- School of Finance and Economics, Jiangsu University, Zhenjiang, China
| | - Weidong Wang
- School of Finance and Economics, Jiangsu University, Zhenjiang, China.
| | - Dragana Ostic
- School of Finance and Economics, Jiangsu University, Zhenjiang, China
| | - Caijing Zhang
- College of Public Administration, Nanjing Agricultural University, Nanjing, China
| | - Na Lu
- School of Finance and Economics, Jiangsu University, Zhenjiang, China
| | - Dong Wang
- School of Finance and Economics, Jiangsu University, Zhenjiang, China
| | - Wenli Ni
- School of Finance and Economics, Jiangsu University, Zhenjiang, China
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Morphology and Dust-Suppression Evaluation of Fugitive Dust Particles in Beijing. SCI 2022. [DOI: 10.3390/sci4030027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Fugitive dust particles are important contributors to urban ambient particulate matter (PM), while their emissions have been ignored or greatly underestimated in previous studies, leading to the underestimation of PM concentrations and health impacts. Thus, studying the morphology of fugitive dust, taking appropriate dust-suppression measures, and evaluating dust-suppression effects are crucial to the prevention and control of fugitive dust. In this study, we investigated the morphology and composition of dust particles from different dust sources, including bare land, stock dump, construction, and road dust. Afterwards, different dust-suppression measures including fence interception nets, bare ground mesh nets, and road dust-suppressants were undertaken to simulate and analyze their dust-suppression effects. Finally, the height concentration profiling method was used to comprehensively evaluate the on-site dust-suppression effect, which can not only accurately evaluate the dust-suppression effect, but also predict the dust-suppression ability in a wide range. Gaining insights into the morphology and composition of dust from representative sources is an important step forward to prevent and control fugitive dust, and selecting an appropriate dust-suppression effect evaluation method will provide a beneficial guide for effectively controlling PM pollution in the future.
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11
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Zeng B, Yang Y, Gou X. Research on physical health early warning based on GM(1,1). Comput Biol Med 2022; 143:105256. [PMID: 35124440 DOI: 10.1016/j.compbiomed.2022.105256] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 11/03/2022]
Abstract
At present, hundreds of millions of Chinese people face increasingly serious health risks, and health checks have undoubtedly played a significant role in finding health risks. However, the current health check in China mainly judges the quality of physical functions by a single index value without dynamic analysis of the changing trends of the index, which may lead to unreasonable diagnostic conclusions. In this paper, the data characteristics of physical indicators are systematically analyzed, and grey system models dedicated to data with the characteristics are applied to simulate and predict the changing trends of body indicators. On this basis, possible pathological changes in body organs were identified. Specifically, this paper analyses the state of human kidney functions by grey prediction models. The results showed that even when the renal function index (serum creatinine) is within the normal range, the human renal function might be abnormal. The grey model analysis of the change trends of serum creatinine can predict the potential health hazards of renal functions.
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Affiliation(s)
- Bo Zeng
- School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China.
| | - Yingjie Yang
- Centre for Computational Intelligence, De Montfort University, Leicester, LE1 9BH, UK.
| | - Xiaoyi Gou
- School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China
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12
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Fan W, Xu L, Zheng H. Using Multisource Data to Assess PM 2.5 Exposure and Spatial Analysis of Lung Cancer in Guangzhou, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052629. [PMID: 35270346 PMCID: PMC8910196 DOI: 10.3390/ijerph19052629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/21/2022] [Indexed: 02/01/2023]
Abstract
Elevated air pollution, along with rapid urbanization, have imposed higher health risks and a higher disease burden on urban residents. To accurately assess the increasing exposure risk and the spatial association between PM2.5 and lung cancer incidence, this study integrated PM2.5 data from the National Air Quality Monitoring Platform and location-based service (LBS) data to introduce an improved PM2.5 exposure model for high-precision spatial assessment of Guangzhou, China. In this context, the spatial autocorrelation method was used to evaluate the spatial correlation between lung cancer incidence and PM2.5. The results showed that people in densely populated areas suffered from higher exposure risk, and the spatial distribution of population exposure risk was highly consistent with the dynamic distribution of the population. In addition, areas with PM2.5 roughly overlapped with areas with high lung cancer incidence, and the lung cancer incidence in different locations was not randomly distributed, confirming that lung cancer incidence was significantly associated with PM2.5 exposure. Therefore, dynamic population distribution has a great impact on the accurate assessment of environmental exposure and health burden, and it is necessary to use LBS data to improve the exposure assessment model. More mitigation controls are needed in highly populated and highly polluted areas.
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Affiliation(s)
| | - Linyu Xu
- Correspondence: ; Tel.: +86-10-5880-0618
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13
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Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models. REMOTE SENSING 2022. [DOI: 10.3390/rs14030599] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Due to the country’s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city.
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14
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Fan H, Wang Y, Wang Y, Coyte PC. The impact of environmental pollution on the physical health of middle-aged and older adults in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:4219-4231. [PMID: 34403062 DOI: 10.1007/s11356-021-15832-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 08/02/2021] [Indexed: 06/13/2023]
Abstract
While several studies have demonstrated the negative impacts of environmental pollution on population health, in general, few studies have examined the potential differential effects on the physical health of middle-aged and older populations, i.e., 45 years and older. Given the twin concerns of environmental pollution and population aging in China, this article employed a fixed effects model to infer the impact of environmental pollution on public health with a particular focus on middle-aged and older adults. The analyses were based on data from the 2011 to 2018 waves of the CHARLS and pollutant data from prefecture-level cities. The results showed that both the level and intensity of environmental pollution significantly increased the risk of chronic diseases and negatively impacted the physical health of middle-aged and older adults. Environmental pollution had its greatest negative effect on the physical health of the elderly, urban residents, residents of the Eastern region, and those with lower incomes than their counterparts. We further found that the potential channels of health effect were through reduced physical exercise and sleep duration and an increase in depressive symptoms, and the pollution prevention actions alleviated the health deterioration of environmental pollution for the middle-aged and the elderly. It is imperative for the government to urgently reinforce policy enforcement to decrease air and water pollution and enhance the ability to circumvent pollution for the lower socioeconomic groups.
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Affiliation(s)
- Hongli Fan
- School of Insurance, Shandong University of Finance and Economics, 40 Shungeng Street, Jinan, 250000, Shandong, China.
| | - Yingcheng Wang
- School of Insurance, Shandong University of Finance and Economics, 40 Shungeng Street, Jinan, 250000, Shandong, China
| | - Ying Wang
- School of Finance, Shandong University of Finance and Economics, Jinan, Shandong, China
| | - Peter C Coyte
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
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15
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Qayyum F, Mehmood U, Tariq S, Haq ZU, Nawaz H. Particulate matter (PM 2.5) and diseases: an autoregressive distributed lag (ARDL) technique. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:67511-67518. [PMID: 34255259 DOI: 10.1007/s11356-021-15178-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/24/2021] [Indexed: 05/22/2023]
Abstract
Air pollution can be attributed to the reduction in visibility, less agricultural activity, more health issues, and long-term destruction to infrastructure. This paper aimed to examine the validity of association among the Particulate matter (PM2.5) and number of acute upper respiratory infection (ARI) and Asthma (AS) patients using an autoregressive distributed lag (ARDL) approach. This ARDL model study was conducted in Lahore, Pakistan. We used monthly data of ARI and AS patients acquired from Directorate General Health Services Punjab and PM2.5 from Air Visual-IQAir during the period January 2018-August 2019. ARDL bound testing technique was used to investigate the association between number of AS, ARI patients and PM2.5. In the short run, the PM2.5 has substantial positive impact on number of AS patients in Lahore. The values of short-run coefficient depicts that the association between PM2.5 and ARI patients is stronger than AS. The effect of PM2.5 on number of patients in short term is more than that in the long-term. For both AS and ARI, in the long run, PM2.5 has negative impact on number of patients.
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Affiliation(s)
- Fazzal Qayyum
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan
| | - Usman Mehmood
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan.
| | - Salman Tariq
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Department of Space Science, University of the Punjab, Lahore, Pakistan
| | - Zia Ul Haq
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan
| | - Hasan Nawaz
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan
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16
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Wu M, Wang F, Chen J, Zhang H, Zeng H, Liu J. Interactions of model airborne particulate matter with dipalmitoyl phosphatidylcholine and a clinical surfactant Calsurf. J Colloid Interface Sci 2021; 607:1993-2009. [PMID: 34798708 DOI: 10.1016/j.jcis.2021.09.193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 09/29/2021] [Accepted: 09/30/2021] [Indexed: 12/31/2022]
Abstract
HYPOTHESIS Lung surfactant protects lung tissue and reduces the surface tension in the alveoli during respiration. Particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5), which invades primely through inhalation, can deposit on and interact with the surfactant layer, leading to changes in the biophysical and morphological properties of the lung surfactant. EXPERIMENTS Langmuir monolayers of 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC) and clinical surfactant Calsurf were investigated with a PM2.5 model injected into the water subphase, which were characterized by surface pressure-area isotherms, Brewster angle microscopy, atomic force microscopy, fluorescent microscopy, and x-ray photoelectron spectroscopy. The binding between DPPC/Calsurf and PM2.5 was studied using isothermal titration calorimetry. FINDINGS PM2.5 induced the expansion of the monolayers at low surface pressure (п) and film condensation at high п. Aggregation of PM2.5 mainly occurred at the interface of liquid expanded/liquid condensed (LE/LC) phases. PM2.5 led to slimmer and ramified LC domains on DPPC and the reduction of nano-sized condensed domains on Calsurf. Both DPPC and Calsurf showed fast binding with PM2.5 through complex binding modes attributed to the heterogeneity and amphiphilic property of PM2.5. This study improves the fundamental understanding of PM2.5-lung surfactant interaction and shows useful implications of the toxicity of PM2.5 through respiration process.
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Affiliation(s)
- Min Wu
- The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510700, China; Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Feifei Wang
- The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510700, China; Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Jingsi Chen
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Hao Zhang
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Hongbo Zeng
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
| | - Jifang Liu
- The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510700, China.
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17
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Akdi Y, Gölveren E, Ünlü KD, Yücel ME. Modeling and forecasting of monthly PM 2.5 emission of Paris by periodogram-based time series methodology. ENVIRONMENTAL MONITORING AND ASSESSMENT 2021; 193:622. [PMID: 34477984 DOI: 10.1007/s10661-021-09399-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/17/2021] [Indexed: 06/13/2023]
Abstract
In this study, monthly particulate matter (PM2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.
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Affiliation(s)
- Yılmaz Akdi
- Department of Statistics, Faculty of Science, Ankara University, Ankara, Turkey
| | - Elif Gölveren
- Department of Econometrics, Faculty of Economics and Administrative Sciences, Ataturk University, Erzurum, Turkey
| | | | - Mustafa Eray Yücel
- Department of Economics, Faculty of Economics, Administrative and Social Sciences, İhsan Dogramaci Bilkent University, Ankara, Turkey
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18
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Alahabadi A, Fazeli I, Rakhshani MH, Najafi ML, Alidadi H, Miri M. Spatial distribution and health risk of exposure to BTEX in urban area: a comparison study of different land-use types and traffic volumes. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2021; 43:2871-2885. [PMID: 33411121 DOI: 10.1007/s10653-020-00799-6] [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: 09/29/2020] [Accepted: 12/15/2020] [Indexed: 06/12/2023]
Abstract
Many previous studies have investigated BTEX concentrations in urban areas; however, the available evidence on the association of different land-use types and BTEX concentrations is still scarce. In this study, the BTEX concentrations were measured and compared in different land-use types and traffic volumes of Mashhad metropolis, Iran. Sampling was conducted in summer and winter of 2018 based on NIOSH 1501 method in six land-use types, including Residential, Commercial/official, Industrial, Greenspace, Transportation, and Tourism. The spatial autocorrelation model was used to investigate the emission pattern. The Monte Carlo simulation technique and sensitivity analysis were used to assess the health risk of exposure to BTEX compounds. The median [interquartile range (IQR)] of benzene, toluene, ethylbenzene m-xylene, o-xylene and total BTEX concentrations based on overall mean were 4 (2.23), 8.37 (4.48), 1.2 (1.46), 0.89 (2.59), 0.8 (1.73) and 17.7 (8.19) µg/m3, respectively. Benzene and toluene had clustered emission patterns (z-score > 1.96). Exposure to benzene in the study area had a carcinogenic risk for inhabitants. The concentration of BTEX compounds was significantly different based on land-use type. The maximum and minimum concentrations of BTEX were observed in Transportation and Greenspace land uses, respectively. The BTEX concentrations in summer were significantly higher than in winter, and traffic had a significant effect on BTEX concentrations. Overall, our results supported a significant relationship between land-use type and BTEX concentrations in the urban area. Moreover, ambient benzene concentration had a carcinogenic risk potential for inhabitants of study area.
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Affiliation(s)
- Ahmad Alahabadi
- Department of Environmental Health, School of Public Health, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Iman Fazeli
- Department of Environmental Health, School of Public Health, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Mohammad Hassan Rakhshani
- Department of Biostatistics and Epidemiology, School of Health, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Moslem Lari Najafi
- Pharmaceutical Sciences and Cosmetic Products Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Hossein Alidadi
- Department of Environmental & Occupational Health, School of Public Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad Miri
- Non-Communicable Diseases Research Center, Department of Environmental Health, School of Public Health, Sabzevar University of Medical Sciences, PO Box 319, Sabzevar, Iran.
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19
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Al Noaimi G, Yunis K, El Asmar K, Abu Salem FK, Afif C, Ghandour LA, Hamandi A, Dhaini HR. Prenatal exposure to criteria air pollutants and associations with congenital anomalies: A Lebanese national study. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 281:117022. [PMID: 33813197 DOI: 10.1016/j.envpol.2021.117022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 02/19/2021] [Accepted: 03/22/2021] [Indexed: 06/12/2023]
Abstract
Maternal exposure to air pollution has been associated with a higher birth defect (BD) risk. Previous studies suffer from inaccurate exposure assessment methods, confounding individual-level variations, and classical analytical modelling. This study aimed to examine the association between maternal exposure to criteria air pollutants and BD risk. A total of 553 cases and 10,214 controls were identified from private and public databases. Two subgroups were then formed: one for a matched case-control design, and another for Feature Selection (FS) analysis. Exposure assessment was based on the mean air pollutant-specific levels in the mother's residential area during the specific BD gestational time window of risk (GTWR) and other time intervals. Multivariate regression models outcomes consistently showed a significant protective effect for folic acid intake and highlighted parental consanguinity as a strong BD risk factor. After adjusting for these putative risk factors and other covariates, results show that maternal exposure to PM2.5 during the first trimester is significantly associated with a higher overall BD risk (OR:1.05, 95%CI:1.01-1.09), and with a higher risk of genitourinary defects (GUD) (OR:1.06, 95%CI:1.01-1.11) and neural tube defects (NTD) (OR:1.10, 95%CI:1.03-1.17) during specific GTWRs. Maternal exposure to NO2 during GTWR exhibited a significant protective effect for NTD (OR:0.94, 95%CI:0.90-0.99), while all other examined associations were not statistically significant. Additionally, maternal exposure to SO2 during GTWR showed a significant association with a higher GUD risk (OR:1.17, 95%CI:1.08-1.26). When limiting selection to designated monitor coverage radiuses, PM2.5 maintained significance with BD risk and showed a significant gene-environment interaction for GUD (p = 0.018), while NO2 protective effect expanded to other subtypes. On the other hand, FS analysis confirmed maternal exposure to PM2.5 and NO2 as important features for GUD, CHD, and NTD. Our findings, set the basis for building a novel BD risk prediction model.
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Affiliation(s)
- Ghaliya Al Noaimi
- Department of Environmental Health, Faculty of Health Sciences, American University of Beirut, Lebanon.
| | - Khalid Yunis
- Department of Pediatrics and Adolescent Medicine, Faculty of Medicine, American University of Beirut, Lebanon.
| | - Khalil El Asmar
- Department of Epidemiology and Population Health, Faculty of Health Sciences, American University of Beirut, Lebanon.
| | - Fatima K Abu Salem
- Department of Computer Science, Faculty of Arts and Sciences, American University of Beirut, Lebanon.
| | - Charbel Afif
- EMMA Laboratory, Center for Analysis and Research, Faculty of Science, Saint-Joseph University, Beirut, Lebanon; Climate and Atmosphere Research Center, The Cyprus Institute, Nicosia, Cyprus.
| | - Lilian A Ghandour
- Department of Epidemiology and Population Health, Faculty of Health Sciences, American University of Beirut, Lebanon.
| | - Ahmad Hamandi
- Department of Computer Science, Faculty of Arts and Sciences, American University of Beirut, Lebanon.
| | - Hassan R Dhaini
- Department of Environmental Health, Faculty of Health Sciences, American University of Beirut, Lebanon.
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20
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Is Compact Urban Form Good for Air Quality? A Case Study from China Based on Hourly Smartphone Data. LAND 2021. [DOI: 10.3390/land10050504] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In previous studies, planners have debated extensively whether compact development can improve air quality in urban areas. Most of them estimated pollution exposure with stationary census data that linked exposures solely to residential locations, therefore overlooking residents’ space–time inhalation of air pollutants. In this study, we conducted an air pollution exposure assessment by scrutinizing one-hour resolution population distribution maps derived from hourly smartphone data and air pollutant concentrations derived from inverse distance weighted interpolation. We selected Wuhan as the study area and used Pearson correlation analysis to explore the effect of compactness on population-weighted concentrations. The results showed that even if a compact urban form helps to reduce pollution concentrations by decreasing vehicle traveling miles and tailpipe emissions, higher levels of building density and floor area ratios may increase population-weighted exposure. With regard to downtown areas with high population density, compact development may locate more people in areas with excessive air pollution. In all, reducing density in urban public centers and developing a polycentric urban structure may aid in the improvement of air quality in cities with compact urban forms.
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21
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Ouyang X, Wei X, Li Y, Wang XC, Klemeš JJ. Impacts of urban land morphology on PM 2.5 concentration in the urban agglomerations of China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 283:112000. [PMID: 33508555 DOI: 10.1016/j.jenvman.2021.112000] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 01/13/2021] [Accepted: 01/14/2021] [Indexed: 06/12/2023]
Abstract
Accurate understanding of the relationship between urban land morphology and the concentration of PM2.5 is essential for achieving high-quality development of urban agglomerations. Based on a mechanism framework of "Internal-External driving force", 19 Chinese urban agglomerations at different development levels were analysed using the geographically weighted regression model to evaluate the impacts of urban land morphology on PM2.5 concentrations in years 2000-2017. The results show: (1) The PM2.5 average concentrations of all 19 urban agglomerations continue to increase from 30 μg/m3 in 2000 to 52 μg/m3 in 2007 but decreased to 34 μg/m3 in 2017. The changes in PM2.5 concentrations vary for urban agglomerations at different development levels. Spatial differences in PM2.5 concentrations are significant, forming a pattern that decreases from the centre to the periphery regions; (2) The urban land morphology of the entire urban agglomeration areas has undergone significant changes. The fractal dimension index (from 4.150 to 2.731) and the compactness (from 0.647 to 0.635) showed a downward trend, while the shape indices (from 1.421 to 1.606) demonstrated an increasing trend. National-level urban agglomerations are more compact and more complex in shape, while more fragmented are regional and local urban agglomerations; (3) Different parameters of urban land morphology have varying effects on PM2.5 concentration varies and at different development levels of urban agglomerations. The combination of urban land morphology, socio-economic factors, and natural elements has a complex effect on PM2.5 concentrations. It can contribute to understanding the linkage between urban land morphology and PM2.5, providing references for future studies.
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Affiliation(s)
- Xiao Ouyang
- Hunan Institute of Economic Geography, Hunan University of Finance and Economics, Changsha, 410205, China; Hunan Key Laboratory of Land Resources Evaluation and Utilization, Changsha, 410007, China; School of Engineering Management, Hunan University of Finance and Economics, Changsha, 410205, China
| | - Xiao Wei
- Hunan Institute of Economic Geography, Hunan University of Finance and Economics, Changsha, 410205, China
| | - Yonghui Li
- School of Engineering Management, Hunan University of Finance and Economics, Changsha, 410205, China
| | - Xue-Chao Wang
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69, Brno, Czech Republic.
| | - Jiří Jaromír Klemeš
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69, Brno, Czech Republic
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22
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Liu H, Yan G, Duan Z, Chen C. Intelligent modeling strategies for forecasting air quality time series: A review. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106957] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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23
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Yan JW, Tao F, Zhang SQ, Lin S, Zhou T. Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18052222. [PMID: 33668193 PMCID: PMC7967664 DOI: 10.3390/ijerph18052222] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/11/2021] [Accepted: 02/19/2021] [Indexed: 01/04/2023]
Abstract
As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.
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Affiliation(s)
- Jin-Wei Yan
- School of Geographical Sciences, Nantong University, Nantong 226007, China; (J.-W.Y.); (S.-Q.Z.); (S.L.)
| | - Fei Tao
- School of Geographical Sciences, Nantong University, Nantong 226007, China; (J.-W.Y.); (S.-Q.Z.); (S.L.)
- Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
- Key Laboratory of Virtual Geographical Environment, MOE, Nanjing Normal University, Nanjing 210046, China
- Correspondence: (F.T.); (T.Z.); Tel.: +86-137-7692-3762 (F.T.); +86-135-8521-7135 (T.Z.)
| | - Shuai-Qian Zhang
- School of Geographical Sciences, Nantong University, Nantong 226007, China; (J.-W.Y.); (S.-Q.Z.); (S.L.)
| | - Shuang Lin
- School of Geographical Sciences, Nantong University, Nantong 226007, China; (J.-W.Y.); (S.-Q.Z.); (S.L.)
| | - Tong Zhou
- School of Geographical Sciences, Nantong University, Nantong 226007, China; (J.-W.Y.); (S.-Q.Z.); (S.L.)
- Correspondence: (F.T.); (T.Z.); Tel.: +86-137-7692-3762 (F.T.); +86-135-8521-7135 (T.Z.)
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24
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A spatial multi-resolution multi-objective data-driven ensemble model for multi-step air quality index forecasting based on real-time decomposition. COMPUT IND 2021. [DOI: 10.1016/j.compind.2020.103387] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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25
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Zhang Y, Chen YJ, Song Y, Dong C, Cai Z. Atmospheric pressure gas chromatography-tandem mass spectrometry analysis of fourteen emerging polycyclic aromatic sulfur heterocycles in PM2.5. CHINESE CHEM LETT 2021. [DOI: 10.1016/j.cclet.2020.07.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Song Q, Zheng YJ, Sheng WG, Yang J. Tridirectional Transfer Learning for Predicting Gastric Cancer Morbidity. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:561-574. [PMID: 32275615 DOI: 10.1109/tnnls.2020.2979486] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Our previous study has constructed a deep learning model for predicting gastrointestinal infection morbidity based on environmental pollutant indicators in some regions in central China. This article aims to adapt the prediction model for three purposes: 1) predicting the morbidity of a different disease in the same region; 2) predicting the morbidity of the same disease in a different region; and 3) predicting the morbidity of a different disease in a different region. We propose a tridirectional transfer learning approach, which achieves the abovementioned three purposes by: 1) developing a combined univariate regression and multivariate Gaussian model for establishing the relationship between the morbidity of the target disease and that of the source disease together with the high-level pollutant features in the current source region; 2) using mapping-based deep transfer learning to extend the current model to predict the morbidity of the source disease in both source and target regions; and 3) applying the pattern of the combined model in the source region to the extended model to derive a new combined model for predicting the morbidity of the target disease in the target region. We select gastric cancer as the target disease and use the proposed transfer learning approach to predict its morbidity in the source region and three target regions. The results show that, given only a limited number of labeled samples, our approach achieves an average prediction accuracy of over 80% in the source region and up to 78% in the target regions, which can contribute considerably to improving medical preparedness and response.
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Calculation Methods of Emission Factors and Emissions of Fugitive Particulate Matter in South Korean Construction Sites. SUSTAINABILITY 2020. [DOI: 10.3390/su12239802] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Recently, efforts to effectively reduce particulate matter by identifying its sources and trends have become necessary due to the sustained damage it has caused in East Asia. In the case of South Korea, damage due to fugitive dust generated at construction sites in densely populated downtown areas is significant, and particulate matter in such fugitive dust directly influences the health of nearby residents and construction workers. Accordingly, the purpose of the present study was to develop a method for calculating emission factors for PM10 and PM2.5 emission amounts in the fugitive dust generated in construction sites and to derive emission amount trends for major variables to predict the amounts of generated particulate matter. To this end, South Korean emission factors for PM10 and PM2.5 for different construction equipment and activities that generate fugitive dust were derived and a method for calculating the amount of particulate matter using the derived emission factors was proposed. In addition, the calculated total emissions using these factors were compared to those calculated using construction site fugitive dust equations developed for the United States, Europe, and South Korea, and the trend analysis of total emissions according to the major emission factor variables was conducted.
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Wang N, Mengersen K, Tong S, Kimlin M, Zhou M, Liu Y, Hu W. County-level variation in the long-term association between PM 2.5 and lung cancer mortality in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 738:140195. [PMID: 32806350 DOI: 10.1016/j.scitotenv.2020.140195] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/11/2020] [Accepted: 06/11/2020] [Indexed: 06/11/2023]
Abstract
INTRODUCTION The relative risk (RR) of long-term exposure to PM2.5 in lung cancer mortality (LCM) may vary spatially in China. However, previous studies applying global regression have been unable to capture such variation. We aimed to employ a geographically weighted Poisson regression (GWPR) to estimate the RRs of LCM among the elderly (≥65 years) related to long-term exposure to PM2.5 and the LCM attributable to PM2.5 at the county level in China. METHODS We obtained annual LCM in the elderly between 2013 and 2015 from the National Death Surveillance. We linked annual mean concentrations of PM2.5 between 2000 and 2004 with LCM using GWPR model at 148 counties across mainland China, adjusting for smoking and socioeconomic covariates. We used county-specific GWPR models to estimate annual average LCM in the elderly between 2013 and 2015 attributable to PM2.5 exposure between 2000 and 2004. RESULTS The magnitude of the association between long-term exposure to PM2.5 and LCM varied with county. The median of county-specific RRs of LCM among elderly men and women was 1.52 (range: 0.90, 2.40) and 1.49 (range: 0.88, 2.56) for each 10 μg/m3 increment in PM2.5, respectively. The RRs were positively significant (P < 0.05) at 95% (140/148) of counties among both elderly men and women. Higher RRs of PM2.5 among elderly men were located at Southwest and South China, and higher RRs among elderly women were located at Northwest, Southwest, and South China. There were 99,967 and 54,457 lung cancer deaths among elderly men and women that could be attributed to PM2.5, with the attributable fractions of 31.4% and 33.8%, respectively. CONCLUSIONS The relative importance of long-term exposure to PM2.5 in LCM differed by county. The results could help the government design tailored and efficient interventions. More stringent PM2.5 control is urgently needed to reduce LCM in China.
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Affiliation(s)
- Ning Wang
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kerrie Mengersen
- School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Shilu Tong
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia; Shanghai Children's Medical Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, China
| | - Michael Kimlin
- Health Research Institute, University of the Sunshine Coast, Sippy Downs, Queensland, Australia; School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Maigeng Zhou
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
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Spatiotemporal Differences and Dynamic Evolution of PM2.5 Pollution in China. SUSTAINABILITY 2020. [DOI: 10.3390/su12135349] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Air pollution, especially the urban haze, has become an urgent issue affecting the sustainable development of cities. Based on the PM2.5 concentration data of 225 Chinese cities collected by satellite remote sensing from 1998 to 2016, we quantitatively analyzed the spatiotemporal distribution characteristics and dynamic evolution trends of PM2.5 concentration in the four regions of China, namely the East, the Central, the West and the Northeast, by using statistical classification, GIS visualization, Dagum Gini coefficient decomposition and kernel density estimation. The results are as follows: First, the PM2.5 pollution in China showed a trend of fluctuation, which appeared to be increasing first and then decreasing, with the year 2007 as an important turning point for PM2.5 pollution changes across the country, as well as in the eastern and central regions. Second, PM2.5 pollution in China had significant spatial agglomeration. The intra-regional difference within the eastern region was the largest, and the inter-regional differences were the main source of overall differences. Third, kernel density estimation showed that the absolute difference of PM2.5 concentration distribution in China was expanding, with a significant phenomenon of polarization and the characteristics of spatial imbalance. This paper aimed to provide a scientific basis and effective reference for further advancing the sustainable development strategy of China in the new era.
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Liao WB, Ju K, Zhou Q, Gao YM, Pan J. Forecasting PM 2.5-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM 2.5 data from 1998 to 2016: a modeling study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:22946-22955. [PMID: 32328997 PMCID: PMC7293676 DOI: 10.1007/s11356-020-08843-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 04/13/2020] [Indexed: 05/28/2023]
Abstract
The serious ambient fine particulate matter (PM2.5) is one of the key risk factors for lung cancer. However, existing studies on the health effects of PM2.5 in China were less considered the regional transport of PM2.5 concentration. In this study, we aim to explore the association between lung cancer and PM2.5 and then forecast the PM2.5-induced lung cancer morbidity and mortality in China. Ridge regression (RR), partial least squares regression (PLSR), model tree-based (MT) regression, regression tree (RT) approach, and the combined forecasting model (CFM) were alternative forecasting models. The result of the Pearson correlation analysis showed that both local and regional scale PM2.5 concentration had a significant association with lung cancer mortality and morbidity and compared with the local lag and regional lag exposure to ambient PM2.5; the regional lag effect (0.172~0.235 for mortality; 0.146~0.249 for morbidity) was not stronger than the local lag PM2.5 exposure (0.249~0.294 for mortality; 0.215~0.301 for morbidity). The overall forecasting lung cancer morbidity and mortality were 47.63, 47.86, 39.38, and 39.76 per 100,000 population. The spatial distributions of lung cancer morbidity and mortality share a similar spatial pattern in 2015 and 2016, with high lung cancer morbidity and mortality areas mainly located in the central to east coast areas in China. The stakeholders would like to implement a cross-regional PM2.5 control strategy for the areas characterized as a high risk of lung cancer.
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Affiliation(s)
- Wei-Bin Liao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 17, Section 3, Ren Min Nan Road, Chengdu, 610041, Sichuan, China
| | - Ke Ju
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 17, Section 3, Ren Min Nan Road, Chengdu, 610041, Sichuan, China
| | - Qian Zhou
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 17, Section 3, Ren Min Nan Road, Chengdu, 610041, Sichuan, China
| | - Ya-Min Gao
- Medical College, Northwest Minzu University, Lanzhou, China
| | - Jay Pan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 17, Section 3, Ren Min Nan Road, Chengdu, 610041, Sichuan, China.
- West China Research Center for Rural Health Development, Sichuan University, Chengdu, China.
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31
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Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China. REMOTE SENSING 2020. [DOI: 10.3390/rs12101684] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Reliable aerosol optical depth (AOD) data with high spatial and temporal resolutions are needed for research on air pollution in China. AOD products from the Advanced Himawari Imager (AHI) onboard the geostationary Himawari-8 satellite and reanalysis datasets make it possible to capture diurnal variations of aerosol loadings. However, due to the different retrieval methods, their applicability may vary with different space and time. Thus, in this study, taking the measured AOD at the Aerosol Robotic NETwork (AERONET) stations as the gold standard, the performance of the latest AHI hourly AOD product (i.e., L3 AOD) was evaluated and then compared with that of two reanalysis AOD datasets offered by Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS), respectively, covering from July 2015 to December 2017 over China. For all the matchups, AHI AOD shows the highest robustness with a high correlation (R) of 0.82, low root-mean-square error (RMSE) of 0.23, and moderate mean absolute relative error (MARE) of 0.56. Although MERRA-2 and CAMS products both have lower R values (0.74, 0.72) and higher RMSE (0.28, 0.26), the former is slightly better than the latter. Accuracy of AOD products could be mainly affected by the pollution level and less affected by particle size distribution. Comparisons among these AOD products imply that AHI AOD is more reliable in regions with high pollution levels, such as central and eastern China, while in the northern and western part, MERRA-2 AOD seems more satisfying. The performance of all the three AOD products presents a significant diurnal variety, as indicated by the highest accuracy in the morning for AHI and at noon for reanalysis data. Moreover, due to various pollution distribution patterns and meteorological conditions, there are distinct seasonal characteristics in the performance of AOD products for different regions.
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Zhu M, Shen L, Tam VWY, Liu Z, Shu T, Luo W. A load-carrier perspective examination on the change of ecological environment carrying capacity during urbanization process in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 714:136843. [PMID: 32018981 DOI: 10.1016/j.scitotenv.2020.136843] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 01/16/2020] [Accepted: 01/20/2020] [Indexed: 06/10/2023]
Abstract
Urbanization has prompted a dramatic social and economic development during the past decades in China. As a long-term national strategy, urbanization can only be implemented effectively with sufficient and sustainable ecological environment resources. By appreciating that the ecological environment carrying capacity (EECC) is a yardstick for guiding the practice of sustainable urban development, it is therefore pressing to examine the change of EECC adequately, so that the sustainable urbanization can be addressed appropriately. This paper develops a new method from load-carrier perspective to explore the change of EECC performance in the rapid urbanizing China. The EECC performance on water, land, atmosphere and overall perspectives were measured for 30 provinces in China based on the established method. The results show that most provinces in China are experiencing an improving EECC performance during the urbanization process, particularly with an obvious progress in land dimension. In referring to the spatial difference of overall EECC performance, the gap between 30 provinces has been narrowing during surveyed years. However, few provinces including Chongqing, Shandong and Jiangxi have undergone a degradation in overall EECC performance. The EECC performance in atmosphere dimension is still considered as a challenge faced by most provinces, evidenced by high level of PM2.5 concentration. These research findings provide valuable references not only for Chinese governments to formulate effective policy instruments and strategy measures for improving ecological environmental carrying status, but also for researchers to further study in the ecological environment carrying capacity in the context of other countries.
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Affiliation(s)
- Mengcheng Zhu
- School of Management Science and Real Estate, Chongqing University, Chongqing, PR China; International Research Center for Sustainable Built Environment, Chongqing University, Chongqing, PR China.
| | - Liyin Shen
- School of Management Science and Real Estate, Chongqing University, Chongqing, PR China; International Research Center for Sustainable Built Environment, Chongqing University, Chongqing, PR China.
| | - Vivian W Y Tam
- School of Computing, Engineering and Mathematics, Western Sydney University, NSW 2751, Australia.
| | - Zhi Liu
- School of Management Science and Real Estate, Chongqing University, Chongqing, PR China; International Research Center for Sustainable Built Environment, Chongqing University, Chongqing, PR China.
| | - Tianheng Shu
- School of Management Science and Real Estate, Chongqing University, Chongqing, PR China; International Research Center for Sustainable Built Environment, Chongqing University, Chongqing, PR China.
| | - Wenzhu Luo
- School of Management Science and Real Estate, Chongqing University, Chongqing, PR China; International Research Center for Sustainable Built Environment, Chongqing University, Chongqing, PR China.
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Spatio-Temporal Variations of Satellite-Based PM 2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17062157. [PMID: 32213893 PMCID: PMC7143496 DOI: 10.3390/ijerph17062157] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 03/19/2020] [Accepted: 03/20/2020] [Indexed: 02/01/2023]
Abstract
With the aggravation of air pollution in recent years, a great deal of research on haze episodes is mainly concentrated on the east-central China. However, fine particulate matter (PM2.5) pollution in northwest China has rarely been discussed. To fill this gap, based on the standard deviational ellipse analysis and spatial autocorrelation statistics method, we explored the spatio-temporal variation and aggregation characteristics of PM2.5 concentrations in Xinjiang from 2001 to 2016. The result showed that annual average PM2.5 concentration was high both in the north slope of Tianshan Mountain and the western Tarim Basin. Furthermore, PM2.5 concentrations on the northern slope of the Tianshan Mountain increased significantly, while showing an obviously decrease in the western Tarim Basin during the period of 2001–2016. Based on the result of the geographical detector method (GDM), population density was the most dominant factor of the spatial distribution of PM2.5 concentrations (q = 0.550), followed by road network density (q = 0.423) and GDP density (q = 0.413). During the study period (2001–2016), the driving force of population density on the distribution of PM2.5 concentrations showed a gradual downward trend. However, other determinants, like DEM (Digital elevation model), NSL (Nighttime stable light), LCT (Land cover type), and NDVI (Normalized Difference Vegetation Index), show significant increased trends. Therefore, further effort is required to reveal the role of landform and vegetation in the spatio-temporal variations of PM2.5 concentrations. Moreover, the local government should take effective measures to control urban sprawl while accelerating economic development.
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Guo H, Li W, Wu J. Ambient PM2.5 and Annual Lung Cancer Incidence: A Nationwide Study in 295 Chinese Counties. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17051481. [PMID: 32106556 PMCID: PMC7084498 DOI: 10.3390/ijerph17051481] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 02/13/2020] [Accepted: 02/21/2020] [Indexed: 12/20/2022]
Abstract
Most studies have examined PM2.5 effects on lung cancer mortalities, while few nationwide studies have been conducted in developing countries to estimate the effects of PM2.5 on lung cancer incidences. To fill this gap, this work aims to examine the effects of PM2.5 exposure on annual incidence rates of lung cancer for males and females in China. We performed a nationwide analysis in 295 counties (districts) from 2006 to 2014. Two regression models were employed to analyse data controlling for time, location and socioeconomic characteristics. We also examined whether the estimates of PM2.5 effects are sensitive to the adjustment of health and behaviour covariates, and the issue of the changing cancer registries each year. We further investigated the modification effects of region, temperature and precipitation. Generally, we found significantly positive associations between PM2.5 and incidence rates of lung cancer for males and females. If concurrent PM2.5 changes by 10 g/m3, then the incidence rate relative to its baseline significantly changes by 4.20% (95% CI: 2.73%, 5.88%) and 2.48% (95% CI: 1.24%, 4.14%) for males and females, respectively. The effects of exposure to PM2.5 were still significant when further controlling for health and behaviour factors or using 5 year consecutive data from 91 counties. We found the evidence of long-term lag effects of PM2.5. We also found that temperature appeared to positively modify the effects of PM2.5 on the incidence rates of lung cancer for males. In conclusion, there were significantly adverse effects of PM2.5 on the incidence rates of lung cancer for both males and females in China. The estimated effect sizes might be considerably lower than those reported in developed countries. There were long-term lag effects of PM2.5 on lung cancer incidence in China.
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Affiliation(s)
- Huagui Guo
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong 999077, China;
- Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, China
| | - Weifeng Li
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong 999077, China;
- Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, China
- Correspondence: ; Tel.: +86-(852)-39172566
| | - Jiansheng Wu
- Key Laboratory for Urban Habitat Environmental Science and Technology, Shenzhen Graduate School, Peking University, Shenzhen 518055, China;
- Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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Ning J, Li P, Zhang B, Han B, Su X, Wang Q, Wang X, Li B, Kang H, Zhou L, Chu C, Zhang N, Pang Y, Niu Y, Zhang R. miRNAs deregulation in serum of mice is associated with lung cancer related pathway deregulation induced by PM2.5. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 254:112875. [PMID: 31377334 DOI: 10.1016/j.envpol.2019.07.043] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 06/23/2019] [Accepted: 07/09/2019] [Indexed: 05/28/2023]
Abstract
Ambient fine particulate matter (PM2.5) as an environmental pollution has been associated with the lung cancer. However, the mechanism of epigenetics such as miRNAs deregulation between PM2.5-exposure and lung cancer has not been elucidated clearly. Twenty C57BL/6 mice were divided randomly into 2 groups and exposed to the filtered air (FA) and the concentrated air (CA), respectively. The FA mice were exposed to filtered air in chambers with a high-efficient particulate air filter (HEPA-filter), and the CA mice were exposed to concentration ambient PM2.5. The total duration of exposure was performed 6 h per day from December 1st, 2017 to January 27th, 2018. The mice exposed 900.21 μg/m3 PM2.5 for 6 h per day in CA chamber, which was nearly equaled to 225.05 μg/m3 for 24-h calculatingly. After exposure, the serum miRNAs levels were detected by microarray. Genetic and pathological alterations in lung of mice with/without PM2.5 exposure were detected. 38 differential miRNAs in serum of mice were found after PM2.5 exposure for 8 weeks. Among of them, 13 miRNAs related with lung cancer were consistent in serum and lung of mice. The target genes of 13 deregulated miRNAs including CRK, NR2F2, VIM, RASSF1, CCND2, PRKCA, SIRT1, CDK6, MAP3K7, HIF1A, UBE2V2, ATG10, BAX, E2F1, RASSF5 and CTNNB1, could involve in the pathway of lung cancer developing. Compared with the FA group, the significantly increases of histopathological changes, ROS and DNA damage were observed in lung of mice in CA group. Our study suggested that miRNAs in serum could be identified as candidate biomarkers to predict the lung cancer development during early PM2.5 exposure.
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Affiliation(s)
- Jie Ning
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Peiyuan Li
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Boyuan Zhang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Bin Han
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Xuan Su
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Qian Wang
- Experimental Center, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Xiurong Wang
- Department of Immunology, School of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Binghua Li
- Department of Occupation Health and Environmental Health, School of Public Health, Hebei Medical University, Shijiazhuang, 050051, PR China
| | - Hui Kang
- Department of Occupation Health and Environmental Health, School of Public Health, Hebei Medical University, Shijiazhuang, 050051, PR China
| | - Lixiao Zhou
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Chen Chu
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Ning Zhang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Yaxian Pang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China
| | - Yujie Niu
- Department of Occupation Health and Environmental Health, School of Public Health, Hebei Medical University, Shijiazhuang, 050051, PR China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang, 050017, PR China
| | - Rong Zhang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, PR China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang, 050017, PR China.
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Guo H, Chang Z, Wu J, Li W. Air pollution and lung cancer incidence in China: Who are faced with a greater effect? ENVIRONMENT INTERNATIONAL 2019; 132:105077. [PMID: 31415963 DOI: 10.1016/j.envint.2019.105077] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 07/29/2019] [Accepted: 07/31/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Whether socioeconomic indicators modify the relationship between air pollution exposure and health outcomes remains uncertain, especially in developing countries. OBJECTIVE This work aims to examine modification effects of socioeconomic indicators on the association between PM2.5 and annual incidence rate of lung cancer for males in China. METHODS We performed a nationwide analysis in 295 counties (districts) from 2006 to 2014. Using multivariable linear regression models controlling for weather conditions and socioeconomic indicators, we examined modification effects in the stratified and combined datasets according to the tertile and binary divisions of socioeconomic indicators. We also extensively investigated whether the roles of socioeconomic modifications were sensitive to the further adjustment of demographic factors, health and behaviour covariates, household solid fuel consumption, the different operationalization of socioeconomic indicators and PM2.5 exposure with single and moving average lags. RESULTS We found a stronger relationship between PM2.5 and incidence rate of male lung cancer in urban areas, in the lower economic or lower education counties (districts). If PM2.5 changes by 10 μg/m3, then the shift in incidence rate relative to its mean was significantly higher by 3.97% (95% CI: 2.18%, 4.96%, p = 0.000) in urban than in rural areas. With regard to economic status, if PM2.5 changes by 10 μg/m3, then the change in incidence rate relative to its mean was significantly lower by 0.99% (95% CI: -2.18%, 0.20%, p = 0.071) and 1.39% (95% CI: -2.78%, 0.00%, p = 0.037) in the middle and high economic groups than in the low economic group, respectively. The change in incidence rate relative to its mean was significantly lower by 1.98% (95% CI: -3.18%, -0.79%, p = 0.001) and 2.78% (95% CI: -4.17%, -1.39%, p = 0.000) in the middle and high education groups compared with the low education group, respectively, if PM2.5 changes by 10 μg/m3. We found no robust modification effects of employment rate and urbanisation growth rate. CONCLUSION Male residents in urban areas, in the lower economic or lower education counties are faced with a greater effect of PM2.5 on the incidence rate of lung cancer in China. The findings emphasize the need for public health intervention and urban planning initiatives targeting the urban-rural, educational or economic disparities in health associated with air pollution exposure. Future prediction on air pollution-induced health effects should consider such socioeconomic disparities, especially for the dominant urban-rural disparity in China.
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Affiliation(s)
- Huagui Guo
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, SAR, PR China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China.
| | - Zheng Chang
- Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, SAR, PR China.
| | - Jiansheng Wu
- Key Laboratory for Urban Habitat Environmental Science and Technology, Shenzhen Graduate School, Peking University, Shenzhen 518055, PR China; Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, PR China.
| | - Weifeng Li
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, SAR, PR China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China.
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Chen S, Zhang X, Lin J, Huang J, Zhao D, Yuan T, Huang K, Luo Y, Jia Z, Zang Z, Qiu Y, Xie L. Fugitive Road Dust PM 2.5 Emissions and Their Potential Health Impacts. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:8455-8465. [PMID: 31117536 DOI: 10.1021/acs.est.9b00666] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Fugitive road dust (FRD) particles emitted by traffic-generated turbulence are an important contributor to urban ambient fine particulate matter (PM2.5). Especially in urban areas of developing countries, FRD PM2.5 emissions are a serious environmental threat to air quality and public health. FRD PM2.5 emissions have been neglected or substantially underestimated in previous study, resulting in the underestimation of modeling PM concentrations and estimating their health impacts. This study constructed the FRD PM2.5 emissions inventory in a major inland city in China (Lanzhou) in 2017 at high-resolution (500 × 500 m2), investigated the spatiotemporal characteristics of the FRD emissions in different urban function zones, and quantified their health impacts. The FRD PM2.5 emission was approximately 1141 ± 71 kg d-1, accounting for 24.6% of total PM2.5 emission in urban Lanzhou. Spatially, high emissions exceeding 3 × 104 μg m-2 d-1 occurred over areas with smaller particle sizes, larger traffic intensities, and more frequent construction activities. The estimated premature mortality burden induced by FRD PM2.5 exposure was 234.5 deaths in Lanzhou in 2017. Reducing FRD emissions are an important step forward to protect public health in many developing urban regions.
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Affiliation(s)
- Siyu Chen
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education , Lanzhou University , Lanzhou 730000 , P. R. China
| | - Xiaorui Zhang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education , Lanzhou University , Lanzhou 730000 , P. R. China
| | - Jintai Lin
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics , Peking University , Beijing 100871 , P. R. China
| | - Jianping Huang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education , Lanzhou University , Lanzhou 730000 , P. R. China
| | - Dan Zhao
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education , Lanzhou University , Lanzhou 730000 , P. R. China
| | - Tiangang Yuan
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education , Lanzhou University , Lanzhou 730000 , P. R. China
| | - Kangning Huang
- Yale School of Forestry and Environmental Studies , Yale University , New Haven , Connecticut 06511 , United States
| | - Yuan Luo
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education , Lanzhou University , Lanzhou 730000 , P. R. China
| | - Zhuo Jia
- College of Earth and Environmental Sciences , Lanzhou University , Lanzhou 730000 , P. R. China
| | - Zhou Zang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education , Lanzhou University , Lanzhou 730000 , P. R. China
| | - Yue'an Qiu
- School of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-Simulation , Sun Yat-sen University , Guangzhou 510275 , P. R. China
| | - Li Xie
- Gansu Provincial Maternity and Child Care Hospital , Lanzhou 730050 , P. R. China
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A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China. SUSTAINABILITY 2019. [DOI: 10.3390/su11143832] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM10, SO2 and NO2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.
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Han X, Guo Y, Gao H, Ma J, Sang M, Zhou S, Huang T, Mao X. Estimating the spatial distribution of environmental suitability for female lung cancer mortality in China based on a novel statistical method. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2019; 26:10083-10096. [PMID: 30756355 DOI: 10.1007/s11356-019-04444-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Accepted: 01/30/2019] [Indexed: 06/09/2023]
Abstract
Lung cancer as one of the major causes of cancer mortality has been demonstrated to be closely related to the ambient atmospheric environment, but little has been done in the synthetic evaluation of the linkage between cancer mortality and combined impact of ambient air pollution and meteorological conditions. The present study determined the environmental suitability for female lung cancer mortality associated with air contaminants and meteorological variables. A novel fuzzy matter-element method was applied to identify the spatial distribution and regions for the environmental suitability for the female lung cancer mortality across China in 2013. The membership functions between the cancer mortality and 6 environmental factors, including PM2.5, NO2, SO2, PM10, the annual mean wind speed, and mean temperature, were generated and the weights of each of the environmental factors were established by the maximum entropy (MaxEnt) model. We categorized the environmental suitability combined with GIS spatial analysis into three zones, including low-suitable, medium-suitable, and high-suitable region where the cancer mortality ranging from low to high rate was identified. These three zones were quantified by the MaxEnt model taking different air pollutants and meteorological variables into consideration. We identified that NO2 was a most significant factor among the 6 environmental factors with the weight of 24.88%, followed by the annual mean wind speed, SO2, and PM2.5. The high-suitable area, mainly in the North China Plain which is a most heavily contaminated region by air pollution in China, covers 1.6195 million square kilometers, accounting for 17.85% of the total area investigated in this study. Identification of the impact of various environmental factors on cancer mortality in the different suitable area provides a scientific basis for the environmental management, risk assessment, and lung cancer control.
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Affiliation(s)
- Xiao Han
- Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Yanlong Guo
- Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, Gansu, China
| | - Hong Gao
- Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, Gansu, China.
| | - Jianmin Ma
- Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, Gansu, China
- College of Urban and Environmental Science, Peking University, Beijing, 100000, China
| | - Manjie Sang
- Research Center for Eco-Environment Sciences in Shanxi, Taiyuan, 030000, Shanxi, China
| | - Sheng Zhou
- Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Tao Huang
- Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Xiaoxuan Mao
- Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, Gansu, China
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40
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Guo L, Luo J, Yuan M, Huang Y, Shen H, Li T. The influence of urban planning factors on PM 2.5 pollution exposure and implications: A case study in China based on remote sensing, LBS, and GIS data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 659:1585-1596. [PMID: 31096368 DOI: 10.1016/j.scitotenv.2018.12.448] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 12/24/2018] [Accepted: 12/29/2018] [Indexed: 04/14/2023]
Abstract
In recent years, haze pollution has become a serious environmental problem affecting cities in China. Reducing PM2.5 concentrations through urban planning is a promising method that has been a focus of recent multidisciplinary research. Most existing studies only analyze the relationship between urban planning factors and PM2.5 concentration, and it is difficult to accurately reflect residents' actual air pollution exposure without considering their space-time behaviors. This study uses satellite remote sensing and location service data to measure PM2.5 pollution exposure in Wuhan metropolitan area and explores the effects of urban spatial structure, land use, spatial form, transportation, and green space on pollution exposure. The results show that spatial structure, building density, road density, and green space coverage have a significant impact on PM2.5 pollution exposure. In addition, this study proposes corresponding implications for urban planning to improve public respiratory health.
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Affiliation(s)
- Liang Guo
- School of Architecture and Urban Planning, Huazhong University of Science and Technology, China
| | - Jia Luo
- School of Architecture and Urban Planning, Huazhong University of Science and Technology, China
| | - Man Yuan
- School of Architecture and Urban Planning, Huazhong University of Science and Technology, China.
| | - Yaping Huang
- School of Architecture and Urban Planning, Huazhong University of Science and Technology, China
| | - Huanfeng Shen
- School of Resource and Environmental Science, Wuhan University, China
| | - Tongwen Li
- School of Resource and Environmental Science, Wuhan University, China
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41
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Yang Y, Li J, Zhu G, Yuan Q. Spatio⁻Temporal Relationship and Evolvement of Socioeconomic Factors and PM 2.5 in China During 1998⁻2016. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E1149. [PMID: 30935066 PMCID: PMC6480332 DOI: 10.3390/ijerph16071149] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 02/28/2019] [Accepted: 03/28/2019] [Indexed: 01/03/2023]
Abstract
A comprehensive understanding of the relationships between PM2.5 concentration and socioeconomic factors provides new insight into environmental management decision-making for sustainable development. In order to identify the contributions of socioeconomic development to PM2.5, their spatial interaction and temporal variation of long time series are analyzed in this paper. Unary linear regression method, Spearman's rank and bivariate Moran's I methods were used to investigate spatio⁻temporal variations and relationships of socioeconomic factors and PM2.5 concentration in 31 provinces of China during the period of 1998⁻2016. Spatial spillover effect of PM2.5 concentration and the impact of socioeconomic factors on PM2.5 concentration were analyzed by spatial lag model. Results demonstrated that PM2.5 concentration in most provinces of China increased rapidly along with the increase of socioeconomic factors, while PM2.5 presented a slow growth trend in Southwest China and a descending trend in Northwest China along with the increase of socioeconomic factors. Long time series analysis revealed the relationships between PM2.5 concentration and four socioeconomic factors. PM2.5 concentration was significantly positive spatial correlated with GDP per capita, industrial added value and private car ownership, while urban population density appeared a negative spatial correlation since 2006. GDP per capita and industrial added values were the most important factors to increase PM2.5, followed by private car ownership and urban population density. The findings of the study revealed spatial spillover effects of PM2.5 between different provinces, and can provide a theoretical basis for sustainable development and environmental protection.
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Affiliation(s)
- Yi Yang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
| | - Jie Li
- School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China.
| | - Guobin Zhu
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
| | - Qiangqiang Yuan
- School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China.
- Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China.
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42
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Shen Y, Zhang L, Fang X, Ji H, Li X, Zhao Z. Spatiotemporal patterns of recent PM 2.5 concentrations over typical urban agglomerations in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 655:13-26. [PMID: 30469058 DOI: 10.1016/j.scitotenv.2018.11.105] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2018] [Revised: 11/07/2018] [Accepted: 11/07/2018] [Indexed: 05/24/2023]
Abstract
China experiences severe particulate matter pollution associated with rapid economic growth and accelerated urbanization. In this study, concentrations of PM2.5 (fine particulate matter with an aerodynamic diameter ≤ 2.5 μm) throughout China, and specifically in nine typical urban agglomerations and one economic region, were statistically analyzed using high-resolution ground-based PM2.5 observations from June 2014 to May 2018. The spatial variation of PM2.5 was also explored via spatial autocorrelation analysis. High annual mean PM2.5 concentrations were predominantly concentrated in the Beijing-Tianjin-Hebei, Central Plain, Northern Slope of Tianshan Mountain, and Cheng-Yu urban agglomerations, as well as the Huaihai Economic Region. The proportion of air quality nationwide monitoring sites where annual average PM2.5 concentrations exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II annual standard were 82.8%, 77.1%, and 70.8% in 2015, 2016, and 2017, respectively. Moreover, the frequency of PM2.5 concentrations meeting the CAAQS Grade I 24-h standard increased in five national-level urban agglomerations, and the average annual PM2.5 decreased from 2015 to 2017 with a reduction rate of over 20%. The southern Beijing-Tianjin-Hebei agglomeration and surrounding areas revealed the highest PM2.5 pollution in four seasons. Monthly mean PM2.5 typically exhibited a characteristic "U" shape. Diurnal mean PM2.5 concentrations were generally consistent with typical urban agglomerations, with maximum and minimum PM2.5 values occurring at approximately 08:00-12:00 and 15:00-17:00, respectively, except for the Northern Slope of Tianshan Mountain urban agglomeration (NSTM-UA) (14:00 and 08:00, respectively). A positive spatial autocorrelation of PM2.5 concentrations was observed in all urban agglomerations (except NSTM-UA); high-high agglomeration centers of PM2.5 pollution were located far inland with a circular distribution, and low-low agglomeration centers formed at the periphery of the high-high agglomeration region. This study is key for understanding the difference in PM2.5 concentrations among urban agglomerations and region-oriented air pollution control strategies are highly suggested.
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Affiliation(s)
- Yang Shen
- School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
| | - Lianpeng Zhang
- School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China.
| | - Xing Fang
- School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
| | - Hanyu Ji
- School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
| | - Xing Li
- School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
| | - Zhuowen Zhao
- Jiangsu Provincial Bureau of Surveying Mapping and Geoinformation, Nanjing 210013, China
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43
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He Q, Gu Y, Zhang M. Spatiotemporal patterns of aerosol optical depth throughout China from 2003 to 2016. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 653:23-35. [PMID: 30399558 DOI: 10.1016/j.scitotenv.2018.10.307] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 10/19/2018] [Accepted: 10/22/2018] [Indexed: 06/08/2023]
Abstract
With China's rapid economic growth, particle pollution, especially fine particulate matter (PM2.5), which is known to have adverse health impacts, has become an increasingly serious issue. Satellite aerosol optical depth (AOD), an important physical property of aerosol particles, can serve as a proxy for investigating particle pollution because it can provide observations with comprehensive spatial and temporal coverage compared with ground-level measurements. This study used an improved 14-year high-resolution AOD dataset to examine the spatial characteristics and temporal dynamics of the dominant pollutants in China from 2003 to 2016 using advanced statistical methods. The improved AOD dataset combines the Moderate Resolution Imaging Spectroradiometer (MODIS) 3-km dark target AOD and 10-km deep blue AOD datasets, which enables a comparison of aerosol loading between eastern and western China. Pixel-based analysis indicates a significant difference between eastern and western China: high AOD values were generally observed in the east with a notable decrease, while low aerosol loadings were found in western China with no distinct change. The most particle polluted areas were the North China Plain, Hubei-Hunan region, Sichuan Basin, and Guangxi-Guangdong region in eastern China and western Qinghai and Tarim Basin in western China, with changes in the national AOD average center shifting to the northwest from 2013 to 2016. The impact factor analysis based on geographically weighted regression indicates that the effect of topography on the spatial characteristics of AOD is negative and more important in eastern China, which has low elevations. Built-up areas significantly exacerbate air pollution in the areas between eastern and western China, and there is no apparent AOD-vegetation relation dominates the country. This study thus provides a comprehensive understanding of the spatiotemporal variations of particle concentrations and can facilitate environmental management, policies to alleviate particle pollution, and health risk assessment studies.
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Affiliation(s)
- Qingqing He
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China; Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong.
| | - Yefu Gu
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong
| | - Ming Zhang
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China.
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Miri M, Alahabadi A, Ehrampush MH, Rad A, Lotfi MH, Sheikhha MH, Sakhvidi MJZ. Mortality and morbidity due to exposure to ambient particulate matter. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2018; 165:307-313. [PMID: 30205333 DOI: 10.1016/j.ecoenv.2018.09.012] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 07/19/2018] [Accepted: 09/01/2018] [Indexed: 05/21/2023]
Abstract
The aim of this study was to investigate spatial variation and health risk of the exposure to PM2.5 (particulate matter with a diameter of 2.5 µm or less) and PM10 (particulate matter with a diameter of 10 µm or less) in Sabzevar, Iran. PM2.5 and PM10 were measured during three campaigns from April to November 2017, in 26 sampling points. Spatial analysis was performed using kriging and autocorrelations (Moran's index) model in Arc GIS software. Relationship between exposure to the PM2.5 and PM10 and their health impacts were investigated by AirQ 2.2.3 software. The mean concentrations (and standard deviation) of PM 2.5 and PM10 over the entire study period were 32.54 (37.28) and 42.61 (47.76) μg/m3, respectively, which were higher than the guideline of World Health Organization. According to the spatial analysis, the maximum concentrations of PM2.5 and PM10 were around the main highway (beltway) which placed all over the south of Sabzevar. According to the Moran's index, the emission patterns for PM2.5 (Z-score = 2.53; P-value = 0.011) and PM10 (Z-score = 2.82; P-value = 0.004) were clustered. The attributable percentage (AP) of total mortality related to PM2.5 and PM10 were 3.544% (95% confidence interval (CI): 2.623-4.447%) and 2.055% (95% CI: 1.379-2.721%) per increasing each 10 μg/m3 of these pollutants, respectively. According to observed results, it is suggested that the beltway and other pollution sources, such as industries, should be placed at a greater distance from the city, to reduce PM amounts in residential areas.
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Affiliation(s)
- Mohammad Miri
- Environmental Science and Technology Research Center, Department of Environmental Health, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; Department of Environmental Health, School of Public Health, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Ahmad Alahabadi
- Department of Environmental Health, School of Public Health, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Mohammad Hassan Ehrampush
- Environmental Science and Technology Research Center, Department of Environmental Health, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
| | - Abolfazl Rad
- Cellular and Molecular Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Mohammad Hassan Lotfi
- Department of Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Mohammad Hassan Sheikhha
- Research and Clinical Center for Infertility, Yazd Reproductive Sciences Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Mohammad Javad Zare Sakhvidi
- Occupational Health Research Center, Department of Occupational Health, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
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Lu Y, Wang Y, Zuo J, Jiang H, Huang D, Rameezdeen R. Characteristics of public concern on haze in China and its relationship with air quality in urban areas. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 637-638:1597-1606. [PMID: 29801253 DOI: 10.1016/j.scitotenv.2018.04.382] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 04/27/2018] [Accepted: 04/27/2018] [Indexed: 06/08/2023]
Abstract
Severe air pollution associated with the rapid urbanization is a pressing issue in China. Moreover, the public awareness of environmental protection in China is awakening, which poses enormous pressure on governments to enforce environmental regulations. The study of environmental problems from the public perspective plays a crucial role in effective environmental governance. The Baidu search engine is the China's largest search engine. The search index of haze based on Baidu search engine reflects the public concern on air quality in China. The aim of this study is to uncover important relationships between public concern and air quality monitoring data based on the case study of haze pollution crisis in China. The results indicate that: (1) the year 2013 is the turning point of the public concern on air quality in China; (2) according to daily data analysis, the search index of haze has increased progressively with increased PM2.5 concentration with a time lag of 0-4 days and the lag time has a declining tendency from 2013 to 2017; (3) according to annual data analysis, the public concern showed a weak correlation with air quality and they showed an opposite temporal trend. However, when the long-term annual trend was removed, the strong positive correlation emerges between the fluctuation parts of the search index of haze and monitoring data of air quality. This indicates the public is more sensitive to the short-term fluctuation of air quality. The results of this paper provide statistical evidence on the evolution of public concern on air quality from 2013 to 2017. This study will help policy makers to better understand the patterns of the public's perception of environmental problems and consequently improve the government's capability to deal with these challenges.
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Affiliation(s)
- Yaling Lu
- China-Australia Centre for Sustainable Urban Development, School of Environmental Science and Engineering, Tianjin University, Tianjin, China; State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy for Environmental Planning, Beijing, China
| | - Yuan Wang
- China-Australia Centre for Sustainable Urban Development, School of Environmental Science and Engineering, Tianjin University, Tianjin, China
| | - Jian Zuo
- School of Architecture & Built Environment, Entrepreneurship, Commercialisation and Innovation Centre (ECIC), The University of Adelaide, SA 5005, Australia
| | - Hongqiang Jiang
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy for Environmental Planning, Beijing, China.
| | - Dacang Huang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, China
| | - Raufdeen Rameezdeen
- School of Natural and Built Environments, University of South Australia, Adelaide 5000, Australia
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46
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Wang N, Mengersen K, Kimlin M, Zhou M, Tong S, Fang L, Wang B, Hu W. Lung cancer and particulate pollution: A critical review of spatial and temporal analysis evidence. ENVIRONMENTAL RESEARCH 2018; 164:585-596. [PMID: 29626820 DOI: 10.1016/j.envres.2018.03.034] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Revised: 02/14/2018] [Accepted: 03/21/2018] [Indexed: 05/02/2023]
Abstract
BACKGROUND Particulate matter (PM) has been recognized as one of the key risk factors of lung cancer. However, spatial and temporal patterns of this association remain unclear. Spatiotemporal analyses incorporate the spatial and temporal structure of the data within random effects models, generating more accurate evaluations of PM-lung cancer associations at a scale that can better inform lung cancer prevention programs. METHODS We conducted a critical review of spatial and temporal analyses of PM and lung cancer. The databases of PubMed, Web of Science and Scopus were searched for potential articles published until September 30, 2017. We included studies that applied spatial and temporal analyses to evaluate the associations of PM2.5 (inhalable particles with diameters that are 2.5 µm and smaller) and PM10 (inhalable particles with diameters that are 10 µm and smaller) with lung cancer. RESULTS We identified 17 articles eligible for the review. Of these, 11 focused on PM2.5, five on PM10, and one on both. These studies suggested a significant positive association between PM2.5 exposure and the risk of lung cancer. Relative risks of lung cancer mortality ranged from 1.08 (95% confidence interval (CI): 1.07-1.09) to 1.60 (95%CI: 1.09-2.33) for 10 µg/m3 increase in PM2.5 exposure. The association between PM10 and lung cancer had been less well researched and the results were not consistent. In terms of the analysis methods, 16 papers undertook spatial analysis and one paper employed temporal analysis. No paper included spatial and temporal analyses simultaneously and considered spatiotemporal uncertainty into model predictions. Among the 16 papers with spatial analyses, thirteen studies presented maps, while only five and 11 studies utilized spatial exploration and modeling methods, respectively. CONCLUSIONS Advanced spatial and temporal epidemiological methods were seldom applied to PM-lung cancer associations. Further research is urgently needed to develop and employ robust and comprehensive spatiotemporal analysis methods for the evaluation of PM-lung cancer associations and the support of lung cancer prevention strategies.
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Affiliation(s)
- Ning Wang
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kerrie Mengersen
- School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Michael Kimlin
- Health Research Institute, University of the Sunshine Coast, Sippy Downs, Queensland, Australia; Cancer Council Queensland, Brisbane, Queensland, Australia
| | - Maigeng Zhou
- National Center for Chronic Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shilu Tong
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia; Shanghai Children's Medical Centre, Shanghai Jiao Tong University, Shanghai, China; School of Public Health, Anhui Medical University, Hefei, China
| | - Liwen Fang
- National Center for Chronic Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Baohua Wang
- National Center for Chronic Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
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Yang Z, Shen J, Gao Z. Ventilation and Air Quality in Student Dormitories in China: A Case Study during Summer in Nanjing. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:E1328. [PMID: 29941805 PMCID: PMC6068894 DOI: 10.3390/ijerph15071328] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2018] [Revised: 06/14/2018] [Accepted: 06/21/2018] [Indexed: 11/17/2022]
Abstract
The Air quality in student dormitories can have a major impact on the health of millions of students in China. This study aims to investigate the ventilation and air quality in student dormitories. Questionnaire survey was conducted in eight dormitory buildings and field measurements were conducted in one dormitory during the summer in Nanjing. The survey result reveals that most students thought the indoor and outdoor air quality was neutral and the correlation between indoor and outdoor perceived air quality is statistically significant. There are few indoor PM2.5 and ozone sources in dormitories and natural ventilation is the most common form of ventilation. However, there is no statistically significant correlation between window opening behaviors and the perceptions of indoor and outdoor air quality. The field measurement result shows the measured I/O ratios of PM2.5 and ozone over 37 days are in the range of 0.42⁻0.79 and 0.21⁻1.00, respectively. The I/O ratios for PM2.5 and ozone are 0.49 ± 0.05 and 0.26 ± 0.05 in the case of the window being closed, and the I/O ratios for PM2.5 and ozone are 0.65 ± 0.08 and 0.50 ± 0.15 in the case of the window being open. The outdoor and indoor ozone concentrations show pronounced diurnal periodic variations, while the PM2.5 concentrations do not. Finally, recommended open/close window strategies are discussed to reduce indoor pollutant levels. Understanding the indoor/outdoor PM2.5 and ozone concentrations in different window patterns can be a guidance to preventing high indoor PM2.5 and ozone exposure in student dormitories.
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Affiliation(s)
- Zhe Yang
- School of Architecture and Urban Planning, Nanjing University, 22 Hankou Road, Nanjing 210093, China.
| | - Jialei Shen
- School of Architecture and Urban Planning, Nanjing University, 22 Hankou Road, Nanjing 210093, China.
| | - Zhi Gao
- School of Architecture and Urban Planning, Nanjing University, 22 Hankou Road, Nanjing 210093, China.
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Wang R, Guo Y, Liu H, Chen Y, Shang Y, Liu H. The effect of chitin nanoparticles on surface behavior of DPPC/DPPG Langmuir monolayers. J Colloid Interface Sci 2018; 519:186-193. [DOI: 10.1016/j.jcis.2018.02.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 02/04/2018] [Accepted: 02/05/2018] [Indexed: 12/27/2022]
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