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Gong Y, Tong Y, Jiang H, Xu N, Yin J, Wang J, Huang J, Chen Y, Jiang Q, Li S, Zhou Y. Three Gorges Dam: Potential differential drivers and trend in the spatio-temporal evolution of the change in snail density based on a Bayesian spatial-temporal model and 5-year longitudinal study. Parasit Vectors 2023; 16:232. [PMID: 37452398 PMCID: PMC10349508 DOI: 10.1186/s13071-023-05846-6] [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: 04/27/2023] [Accepted: 06/21/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Snail abundance varies spatially and temporally. Few studies have elucidated the different effects of the determinants affecting snail density between upstream and downstream areas of the Three Gorges Dam (TGD). We therefore investigated the differential drivers of changes in snail density in these areas, as well as the spatial-temporal effects of these changes. METHODS A snail survey was conducted at 200 sites over a 5-year period to monitor dynamic changes in snail abundance within the Yangtze River basin. Data on corresponding variables that might affect snail abundance, such as meteorology, vegetation, terrain and economy, were collected from multiple data sources. A Bayesian spatial-temporal modeling framework was constructed to explore the differential determinants driving the change in snail density and the spatial-temporal effects of the change. RESULTS Volatility in snail density was unambiguously detected in the downstream area of the TGD, while a small increment in volatility was detected in the upstream area. Regarding the downstream area of the TGD, snail density was positively associated with the average minimum temperature in January of the same year, the annual Normalized Difference Vegetation Index (NDVI) of the previous year and the second, third and fourth quartile, respectively, of average annual relative humidity of the previous year. Snail density was negatively associated with the average maximum temperature in July of the previous year and annual nighttime light of the previous year. An approximately inverted "U" curve of relative risk was detected among sites with a greater average annual ground surface temperature in the previous year. Regarding the upstream area, snail density was positively associated with NDVI and with the second, third and fourth quartile, respectively, of total precipitation of the previous year. Snail density was negatively associated with slope. CONCLUSIONS This study demonstrated a rebound in snail density between 2015 and 2019. In particular, temperature, humidity, vegetation and human activity were the main drivers affecting snail abundance in the downstream area of the TGD, while precipitation, slope and vegetation were the main drivers affecting snail abundance in the upstream area. These findings can assist authorities to develop and perform more precise strategies for surveys and control of snail populations.
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Affiliation(s)
- Yanfeng Gong
- Fudan University School of Public Health, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
| | - Yixin Tong
- Fudan University School of Public Health, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
| | - Honglin Jiang
- Fudan University School of Public Health, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
| | - Ning Xu
- Fudan University School of Public Health, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
| | - Jiangfan Yin
- Fudan University School of Public Health, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
| | - Jiamin Wang
- Fudan University School of Public Health, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
| | - Junhui Huang
- Fudan University School of Public Health, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
| | - Yue Chen
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, 600 Peter Morand Crescent, Ottawa, ON K1G 5Z3 Canada
| | - Qingwu Jiang
- Fudan University School of Public Health, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
| | - Shizhu Li
- Chinese Center for Disease Control and Prevention, NHC Key Laboratory of Parasite and Vector Biology, National Institute of Parasitic Diseases, Chinese Center for Tropical Diseases Research, Shanghai, 200025 China
| | - Yibiao Zhou
- Fudan University School of Public Health, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong’an Road, Xuhui District, Shanghai, 200032 China
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Wright N, Newell K, Chan KH, Gilbert S, Hacker A, Lu Y, Guo Y, Pei P, Yu C, Lv J, Chen J, Li L, Kurmi O, Chen Z, Lam KBH, Kartsonaki C. Long-term ambient air pollution exposure and cardio-respiratory disease in China: findings from a prospective cohort study. Environ Health 2023; 22:30. [PMID: 36973808 PMCID: PMC10041804 DOI: 10.1186/s12940-023-00978-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 03/07/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND Existing evidence on long-term ambient air pollution (AAP) exposure and risk of cardio-respiratory diseases in China is mainly on mortality, and based on area average concentrations from fixed-site monitors for individual exposures. Substantial uncertainty persists, therefore, about the shape and strength of the relationship when assessed using more personalised individual exposure data. We aimed to examine the relationships between AAP exposure and risk of cardio-respiratory diseases using predicted local levels of AAP. METHODS A prospective study included 50,407 participants aged 30-79 years from Suzhou, China, with concentrations of nitrogen dioxide (NO2), sulphur dioxide (SO2), fine (PM2.5), and inhalable (PM10) particulate matter, ozone (O3) and carbon monoxide (CO) and incident cases of cardiovascular disease (CVD) (n = 2,563) and respiratory disease (n = 1,764) recorded during 2013-2015. Cox regression models with time-dependent covariates were used to estimate adjusted hazard ratios (HRs) for diseases associated with local-level concentrations of AAP exposure, estimated using Bayesian spatio-temporal modelling. RESULTS The study period of 2013-2015 included a total of 135,199 person-years of follow-up for CVD. There was a positive association of AAP, particularly SO2 and O3, with risk of major cardiovascular and respiratory diseases. Each 10 µg/m3 increase in SO2 was associated with adjusted hazard ratios (HRs) of 1.07 (95% CI: 1.02, 1.12) for CVD, 1.25 (1.08, 1.44) for COPD and 1.12 (1.02, 1.23) for pneumonia. Similarly, each 10 µg/m3 increase in O3 was associated with adjusted HR of 1.02 (1.01, 1.03) for CVD, 1.03 (1.02, 1.05) for all stroke, and 1.04 (1.02, 1.06) for pneumonia. CONCLUSIONS Among adults in urban China, long-term exposure to ambient air pollution is associated with a higher risk of cardio-respiratory disease.
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Affiliation(s)
- Neil Wright
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute Building, Old Road Campus, OX3 7LF, Oxford, UK
| | - Katherine Newell
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute Building, Old Road Campus, OX3 7LF, Oxford, UK
| | - Ka Hung Chan
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute Building, Old Road Campus, OX3 7LF, Oxford, UK
- Oxford British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Simon Gilbert
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute Building, Old Road Campus, OX3 7LF, Oxford, UK
| | - Alex Hacker
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute Building, Old Road Campus, OX3 7LF, Oxford, UK
| | - Yan Lu
- NCDs Prevention and Control Department, Suzhou CDC, Jiangsu, China
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
| | - Canqing Yu
- Peking University Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Peking University Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Junshi Chen
- National Center for Food Safety Risk Assessment, Beijing, China
| | - Liming Li
- Peking University Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Om Kurmi
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, UK
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute Building, Old Road Campus, OX3 7LF, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kin Bong Hubert Lam
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute Building, Old Road Campus, OX3 7LF, Oxford, UK.
| | - Christiana Kartsonaki
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute Building, Old Road Campus, OX3 7LF, Oxford, UK.
- MRC Population Health Research Unit, University of Oxford, Oxford, UK.
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