Your browser doesn't support javascript.
loading
A novel four-dimensional prediction model of soil heavy metal pollution: Geographical explanations beyond artificial intelligence "black box".
Wang, Qi; Li, Cangbai; Hao, Dongmei; Xu, Yafei; Shi, Xuewen; Liu, Tongxu; Sun, Weimin; Zheng, Zelong; Liu, Jianfeng; Li, Wanqi; Liu, Wengang; Zheng, Jiaxue; Li, Fangbai.
Afiliación
  • Wang Q; National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Science, Guangzhou 510650,
  • Li C; Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China.
  • Hao D; School of Management, Lanzhou University, Lanzhou 730099, China.
  • Xu Y; School of Management, Lanzhou University, Lanzhou 730099, China.
  • Shi X; School of Management, Lanzhou University, Lanzhou 730099, China.
  • Liu T; National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Science, Guangzhou 510650,
  • Sun W; National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Science, Guangzhou 510650,
  • Zheng Z; Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China.
  • Liu J; Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China.
  • Li W; School of Management, Lanzhou University, Lanzhou 730099, China.
  • Liu W; School of Management, Lanzhou University, Lanzhou 730099, China.
  • Zheng J; School of Data Science and Artificial Intelligence, Dongbei University of Finance & Economics, Dalian 116025, China.
  • Li F; National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Science, Guangzhou 510650,
J Hazard Mater ; 458: 131900, 2023 Sep 15.
Article en En | MEDLINE | ID: mdl-37385097
The current artificial intelligence (AI)-based prediction approaches of soil pollutants are inadequate in estimating the geospatial source-sink processes and striking a balance between the interpretability and accuracy, resulting in poor spatial extrapolation and generalization. In this study, we developed and tested a geographically interpretable four-dimensional AI prediction model for soil heavy metal (Cd) contents (4DGISHM) in Shaoguan city of China from 2016 to 2030. The 4DGISHM approach characterized spatio-temporal changes in source-sink processes of soil Cd by estimating spatio-temporal patterns and the effects of drivers and their interactions of soil Cd at local to regional scales using TreeExplainer-based SHAP and parallel ensemble AI algorithms. The results demonstrate that the prediction model achieved MSE and R2 values of 0.012 and 0.938, respectively, at a spatial resolution of 1 km. The predicted areas exceeding the risk control values for soil Cd across Shaoguan from 2022 to 2030 increased by 22.92% at the baseline scenario. By 2030, enterprise and transportation emissions (SHAP values 0.23 and 0.12 mg/kg, respectively) were the major drivers. The influence of driver interactions on soil Cd was marginal. Our approach surpasses the limitations of the AI "black box" by integrating spatio-temporal source-sink explanation and accuracy. This advancement enables geographically precise prediction and control of soil pollutants.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Hazard Mater Asunto de la revista: SAUDE AMBIENTAL Año: 2023 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Hazard Mater Asunto de la revista: SAUDE AMBIENTAL Año: 2023 Tipo del documento: Article Pais de publicación: Países Bajos