Using big data searching and machine learning to predict human health risk probability from pesticide site soils in China
文献类型：期刊论文
| 作者 | Wang, Xin1,3; Yu, Dongsheng1,3; Ma, Lixia1; Lu, Xiaosong1,3; Song, Jie1,3; Lei, Mei2,3 |
| 刊名 | JOURNAL OF ENVIRONMENTAL MANAGEMENT
 |
| 出版日期 | 2022-10-15 |
| 卷号 | 320页码:9 |
| ISSN号 | 0301-4797 |
| 关键词 | Pesticide site Human health risk Risk probability prediction Semi -supervised learning model Label propagation algorithm |
| DOI | 10.1016/j.jenvman.2022.115798 |
| 通讯作者 | Yu, Dongsheng(dshyu@issas.ac.cn) |
| 英文摘要 | Soil pollutants emitted from pesticide sites seriously threaten human health, and predicting the human health risk (HHR) is necessary to the control and management of these sites. A database of 1933 pesticide sites in China, including 32 surveyed site samples, was created through big data searching and cleaning. First, six risk predic-tion indicators were screened through a correlation analysis, ANOVA, and cardinality test. The six indicators included soil pollution proba-bility, proportions of the population over 60 and under 15 years old, soil sand at the 30-100 cm layer, elevation, and proportion of arable land area within 1 km of the sites. Second, the surveyed samples were divided into training and testing sets at a ratio of 8:2, and the synthetic minority oversampling technique was used to achieve sample size rebalancing in the training set between positive and counter examples. Two semi -supervised learning models based on the self-training method and label propagation algorithm (SSL-LPA) were trained and tested. SSL-LPA was screened for the prediction of HHR probability, and it had the highest prediction accuracy with a cross-validation accuracy of 93% and recall rate of 100%. Lastly, about 3.7% of the un-surveyed sites were predicted by SSL-LPA as risky sites with an HHR probability of over 0.94, and they were mainly distributed in northern and eastern China. These sites were predicted as risky due to the effects of soil pollution proba-bility and the spatial distribution of pesticide sites, especially in the surveyed risky sites. This study provides technical support for HHR control and management of pesticide sites in China. |
| WOS关键词 | HEAVY-METAL POLLUTION ; ORGANOCHLORINE PESTICIDES ; URBAN SOILS ; CLASSIFIERS ; INDUSTRIAL ; SEDIMENTS ; RIVER ; CITY |
| 资助项目 | National Key Research and Development Program of China[2018YFC1800104] ; National Key Research and Development Program of China[2021YFC1809104] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:000883780700006 |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187091]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yu, Dongsheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Xin,Yu, Dongsheng,Ma, Lixia,et al. Using big data searching and machine learning to predict human health risk probability from pesticide site soils in China[J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT,2022,320:9. |
| APA | Wang, Xin,Yu, Dongsheng,Ma, Lixia,Lu, Xiaosong,Song, Jie,&Lei, Mei.(2022).Using big data searching and machine learning to predict human health risk probability from pesticide site soils in China.JOURNAL OF ENVIRONMENTAL MANAGEMENT,320,9. |
| MLA | Wang, Xin,et al."Using big data searching and machine learning to predict human health risk probability from pesticide site soils in China".JOURNAL OF ENVIRONMENTAL MANAGEMENT 320(2022):9. |
入库方式： OAI收割
来源：地理科学与资源研究所
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