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Xu N, Zhang Y, Du C, Song J, Huang J, Gong Y, Jiang H, Tong Y, Yin J, Wang J, Jiang F, Chen Y, Jiang Q, Dong Y, Zhou Y. Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models. Parasit Vectors 2023; 16:377. [PMID: 37872579 PMCID: PMC10591370 DOI: 10.1186/s13071-023-05952-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 08/28/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Oncomelania hupensis is the sole intermediate host of Schistosoma japonicum. Its emergence and recurrence pose a constant challenge to the elimination of schistosomiasis in China. It is important to accurately predict the snail distribution for schistosomiasis prevention and control. METHODS Data describing the distribution of O. hupensis in 2016 was obtained from the Yunnan Institute of Endemic Disease Control and Prevention. Eight machine learning algorithms, including eXtreme Gradient Boosting (XGB), support vector machine (SVM), random forest (RF), generalized boosting model (GBM), neural network (NN), classification and regression trees (CART), k-nearest neighbors (KNN), and generalized additive model (GAM), were employed to explore the impacts of climatic, geographical, and socioeconomic variables on the distribution of suitable areas for O. hupensis. Predictions of the distribution of suitable areas for O. hupensis were made for various periods (2030s, 2050s, and 2070s) under different climate scenarios (SSP126, SSP245, SSP370, and SSP585). RESULTS The RF model exhibited the best performance (AUC: 0.991, sensitivity: 0.982, specificity: 0.995, kappa: 0.942) and the CART model performed the worst (AUC: 0.884, sensitivity: 0.922, specificity: 0.943, kappa: 0.829). Based on the RF model, the top six important variables were as follows: Bio15 (precipitation seasonality) (33.6%), average annual precipitation (25.2%), Bio2 (mean diurnal temperature range) (21.7%), Bio19 (precipitation of the coldest quarter) (14.5%), population density (13.5%), and night light index (11.1%). The results demonstrated that the overall suitable habitats for O. hupensis were predominantly distributed in the schistosomiasis-endemic areas located in northwestern Yunnan Province under the current climate situation and were predicted to expand north- and westward due to climate change. CONCLUSIONS This study showed that the prediction of the current distribution of O. hupensis corresponded well with the actual records. Furthermore, our study provided compelling evidence that the geographical distribution of snails was projected to expand toward the north and west of Yunnan Province in the coming decades, indicating that the distribution of snails is driven by climate factors. Our findings will be of great significance for formulating effective strategies for snail control.
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Affiliation(s)
- Ning Xu
- Fudan University School of Public Health, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Shanghai, 200032, China
| | - Yun Zhang
- Yunnan Institute of Endemic Disease Control and Prevention, Dali, 671000, Yunnan, China
- Yunnan Provincial Key Laboratory of Natural Focal Disease Prevention and Control Technology, Dali, 671000, Yunnan, China
| | - Chunhong Du
- Yunnan Institute of Endemic Disease Control and Prevention, Dali, 671000, Yunnan, China
- Yunnan Provincial Key Laboratory of Natural Focal Disease Prevention and Control Technology, Dali, 671000, Yunnan, China
| | - Jing Song
- Yunnan Institute of Endemic Disease Control and Prevention, Dali, 671000, Yunnan, China
- Yunnan Provincial Key Laboratory of Natural Focal Disease Prevention and Control Technology, Dali, 671000, Yunnan, China
| | - Junhui Huang
- Fudan University School of Public Health, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Shanghai, 200032, China
| | - Yanfeng Gong
- Fudan University School of Public Health, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Shanghai, 200032, China
| | - Honglin Jiang
- Fudan University School of Public Health, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Shanghai, 200032, China
| | - Yixin Tong
- Fudan University School of Public Health, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Shanghai, 200032, China
| | - Jiangfan Yin
- Fudan University School of Public Health, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Shanghai, 200032, China
| | - Jiamin Wang
- Fudan University School of Public Health, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Shanghai, 200032, China
| | - Feng Jiang
- Fudan University School of Public Health, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Shanghai, 200032, China
| | - Yue Chen
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Qingwu Jiang
- Fudan University School of Public Health, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Shanghai, 200032, China
| | - Yi Dong
- Yunnan Institute of Endemic Disease Control and Prevention, Dali, 671000, Yunnan, China.
- Yunnan Provincial Key Laboratory of Natural Focal Disease Prevention and Control Technology, Dali, 671000, Yunnan, China.
| | - Yibiao Zhou
- Fudan University School of Public Health, Shanghai, 200032, China.
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, 200032, China.
- Fudan University Center for Tropical Disease Research, Shanghai, 200032, China.
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Estimation of the Near-Surface Ozone Concentration with Full Spatiotemporal Coverage across the Beijing-Tianjin-Hebei Region Based on Extreme Gradient Boosting Combined with a WRF-Chem Model. ATMOSPHERE 2022. [DOI: 10.3390/atmos13040632] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
With the intensification of global warming and economic development in China, the near-surface ozone (O3) concentration has been increasing recently, especially in the Beijing-Tianjin-Hebei (BTH) region, which is the political and economic center of China. However, O3 has been measured in real time only over the past few years, and the observational records are discontinuous. Therefore, we propose a new method (WRFC-XGB) to establish a near-surface O3 concentration dataset in the BTH region by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) algorithm. Based on this method, the 8-h maximum daily average (MDA8) O3 concentrations are obtained with full spatiotemporal coverage at a spatial resolution of 0.1° × 0.1° across the BTH region in 2018. Two evaluation methods, sample- and station-based 10-fold cross-validation (10-CV), are used to assess our method. The sample-based (station-based) 10-CV evaluation results indicate that WRFC-XGB can achieve excellent accuracy with a high coefficient of determination (R2) of 0.95 (0.91), low root mean square error (RMSE) of 13.50 (17.70) µg m−3, and mean absolute error (MAE) of 9.60 (12.89) µg m−3. In addition, superb spatiotemporal consistencies are confirmed for this model, including the estimation of high O3 concentrations, and our WRFC-XGB model outperforms traditional models and previous studies in data mining. In addition, the proposed model can be applied to estimate the O3 concentration when it has not been measured. Furthermore, the spatial distribution analysis of the MDA8 O3 in 2018 reveals that O3 pollution in the BTH region exhibits significant seasonality. Heavy O3 pollution episodes mainly occur in summer, and the high O3 loading is distributed mainly in the southern BTH areas, which will pose challenges to atmospheric environmental governance for local governments.
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Liu S, Xing J, Westervelt DM, Liu S, Ding D, Fiore AM, Kinney PL, Zhang Y, He MZ, Zhang H, Sahu SK, Zhang F, Zhao B, Wang S. Role of emission controls in reducing the 2050 climate change penalty for PM 2.5 in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 765:144338. [PMID: 33401063 DOI: 10.1016/j.scitotenv.2020.144338] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 05/22/2023]
Abstract
Previous studies demonstrated that global warming can lead to deteriorated air quality even when anthropogenic emissions were kept constant, which has been called a climate change penalty on air quality. It is expected that anthropogenic emissions will decrease significantly in the future considering the aggressive emission control actions in China. However, the dependence of climate change penalty on the choice of emission scenario is still uncertain. To fill this gap, we conducted multiple independent model simulations to investigate the response of PM2.5 to future (2050) climate warming (RCP8.5) in China but with different emission scenarios, including the constant 2015 emissions, the 2050 CLE emissions (based on Current Legislation), and the 2050 MTFR emissions (based on Maximum Technically Feasible Reduction). For each set of emissions, we estimate climate change penalty as the difference in PM2.5 between a pair of simulations with either 2015 or 2050 meteorology. Under 2015 emissions, we find a PM2.5 climate change penalty of 1.43 μg m-3 in Eastern China, leading to an additional 35,000 PM2.5-related premature deaths [95% confidence interval (CI), 21,000-40,000] by 2050. However, the PM2.5 climate change penalty weakens to 0.24 μg m-3 with strict anthropogenic emission controls under the 2050 MTFR emissions, which decreases the associated PM2.5-related deaths to 17,000. The smaller MTFR climate change penalty contributes 14% of the total PM2.5 decrease when both emissions and meteorology are changed from 2015 to 2050, and 24% of total health benefits associated with this PM2.5 decrease in Eastern China. This finding suggests that controlling anthropogenic emissions can effectively reduce the climate change penalty on PM2.5 and its associated premature deaths, even though a climate change penalty still occurs even under MTFR. Strengthened controls on anthropogenic emissions are key to attaining air quality targets and protecting human health in the context of future global climate change.
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Affiliation(s)
- Song Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China.
| | - Daniel M Westervelt
- Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, NY, USA; NASA Goddard Institute for Space Studies, New York, NY, USA
| | - Shuchang Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
| | - Dian Ding
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
| | - Arlene M Fiore
- Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, NY, USA; Department of Earth and Environmental Sciences, Columbia University, Palisades, New York, NY, USA
| | | | - Yuqiang Zhang
- Nicholas School of the Environment, Duke University, Durham, NC, USA
| | - Mike Z He
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Hongliang Zhang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Shovan K Sahu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
| | - Fenfen Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
| | - Bin Zhao
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China.
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Xue W, Zhan Q, Zhang Q, Wu Z. Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:ijerph17010136. [PMID: 31878125 PMCID: PMC6981905 DOI: 10.3390/ijerph17010136] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/08/2019] [Accepted: 12/18/2019] [Indexed: 11/16/2022]
Abstract
High air pollution levels have become a nationwide problem in China, but limited attention has been paid to prefecture-level cities. Furthermore, different time resolutions between air pollutant level data and meteorological parameters used in many previous studies can lead to biased results. Supported by synchronous measurements of air pollutants and meteorological parameters, including PM2.5, PM10, total suspended particles (TSP), CO, NO2, O3, SO2, temperature, relative humidity, wind speed and direction, at 16 urban sites in Xiangyang, China, from 1 March 2018 to 28 February 2019, this paper: (1) analyzes the overall air quality using an air quality index (AQI); (2) captures spatial dynamics of air pollutants with pollution point source data; (3) characterizes pollution variations at seasonal, day-of-week and diurnal timescales; (4) detects weekend effects and holiday (Chinese New Year and National Day holidays) effects from a statistical point of view; (5) establishes relationships between air pollutants and meteorological parameters. The principal results are as follows: (1) PM2.5 and PM10 act as primary pollutants all year round and O3 loses its primary pollutant position after November; (2) automobile manufacture contributes to more particulate pollutants while chemical plants produce more gaseous pollutants. TSP concentration is related to on-going construction and road sprinkler operations help alleviate it; (3) an unclear weekend effect for all air pollutants is confirmed; (4) celebration activities for the Chinese New Year bring distinctly increased concentrations of SO2 and thereby enhance secondary particulate pollutants; (5) relative humidity and wind speed, respectively, have strong negative correlations with coarse particles and fine particles. Temperature positively correlates with O3.
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Affiliation(s)
- Wei Xue
- School of Urban Design, Wuhan University, Wuhan 430072, China
| | - Qingming Zhan
- School of Urban Design, Wuhan University, Wuhan 430072, China
- Correspondence: ; Tel.: +86-139-9566-8639
| | - Qi Zhang
- Bank of Communications, Wuhan 430015, China
| | - Zhonghua Wu
- The Xiangyang Environmental Monitoring Center, Xiangyang 441000, China
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