A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models
Heavy metal contamination in paddies poses a serious threat to ecological and human health. Current researches about heavy metal pollution mainly focus on source apportionment, while robust and accurate predictions on its spatial distribution and driving mechanisms is still lacking. Herein, we devel...
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Elsevier
2025-02-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025009995 |
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| author | Unurnyam Jugnee Le Jiao Sainbayar Dalantai Lili Huo Yi An Bayartungalag Batsaikhan Undrakhtsetseg Tsogtbaatar Munguntuul Ulziibaatar Boldbaatar Natsagdorj |
| author_facet | Unurnyam Jugnee Le Jiao Sainbayar Dalantai Lili Huo Yi An Bayartungalag Batsaikhan Undrakhtsetseg Tsogtbaatar Munguntuul Ulziibaatar Boldbaatar Natsagdorj |
| author_sort | Unurnyam Jugnee |
| collection | DOAJ |
| description | Heavy metal contamination in paddies poses a serious threat to ecological and human health. Current researches about heavy metal pollution mainly focus on source apportionment, while robust and accurate predictions on its spatial distribution and driving mechanisms is still lacking. Herein, we developed a general methodological framework to predict and assess heavy metal pollution of paddies in Hunan province, China, by employing Random Forest (RF), Extra Trees Regressor (ETR), Extreme Gradient Boost Regression (XGBR), and Gradient Boosting Regression Tree (GBRT). Results demonstrated that RF performed superiorly in predicting As (R2 = 0.706), Cr (R2 = 0.746), Cu (R2 = 0.705), and Hg (R2 = 0.73), while the ETR showed good performance in predicting Cd (R2 = 0.521), Zn (R2 = 0.404), and Pb (R2 = 0.625). GBRT performed well in predicting Ni (R2 = 0.61). The Shapley additive explanations suggested significant differences in the driving factors and their contributions to prediction models for each heavy metal. Climate variables were potentially valuable predictors of heavy metal content. The visualized spatial distribution of Pollution Load Index showed that 79.8 % of the study area was moderately polluted and the remaining 20.2 % was in a severe polluted state. The soil pollution state worsened from the west to the east of the study area. These findings provide valuable information on effective soil pollution control and soil conservation efforts. |
| format | Article |
| id | doaj-art-b81353d549dc4ca58cc1944ffca6fd37 |
| institution | DOAJ |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-b81353d549dc4ca58cc1944ffca6fd372025-08-20T02:45:38ZengElsevierHeliyon2405-84402025-02-01114e4261910.1016/j.heliyon.2025.e42619A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning modelsUnurnyam Jugnee0Le Jiao1Sainbayar Dalantai2Lili Huo3Yi An4Bayartungalag Batsaikhan5Undrakhtsetseg Tsogtbaatar6Munguntuul Ulziibaatar7Boldbaatar Natsagdorj8Division of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia; School of Natural Sciences, National University of Mongolia, Ulaanbaatar, 210646, MongoliaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300191, China; China-Mongolia Joint Laboratory for Multi-source Monitoring and Spatiotemporal Succession of Agricultural Environment, Tianjin, 300191, China; Xiangtan Experimental Station of Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan, 411100, ChinaDivision of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, MongoliaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300191, China; China-Mongolia Joint Laboratory for Multi-source Monitoring and Spatiotemporal Succession of Agricultural Environment, Tianjin, 300191, China; Xiangtan Experimental Station of Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan, 411100, ChinaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300191, China; China-Mongolia Joint Laboratory for Multi-source Monitoring and Spatiotemporal Succession of Agricultural Environment, Tianjin, 300191, China; Xiangtan Experimental Station of Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan, 411100, China; Corresponding author. Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300191, China.Division of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia; China-Mongolia Joint Laboratory for Multi-source Monitoring and Spatiotemporal Succession of Agricultural Environment, Tianjin, 300191, China; Corresponding author. Division of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, MongoliaDivision of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, MongoliaDivision of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, MongoliaDivision of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, MongoliaHeavy metal contamination in paddies poses a serious threat to ecological and human health. Current researches about heavy metal pollution mainly focus on source apportionment, while robust and accurate predictions on its spatial distribution and driving mechanisms is still lacking. Herein, we developed a general methodological framework to predict and assess heavy metal pollution of paddies in Hunan province, China, by employing Random Forest (RF), Extra Trees Regressor (ETR), Extreme Gradient Boost Regression (XGBR), and Gradient Boosting Regression Tree (GBRT). Results demonstrated that RF performed superiorly in predicting As (R2 = 0.706), Cr (R2 = 0.746), Cu (R2 = 0.705), and Hg (R2 = 0.73), while the ETR showed good performance in predicting Cd (R2 = 0.521), Zn (R2 = 0.404), and Pb (R2 = 0.625). GBRT performed well in predicting Ni (R2 = 0.61). The Shapley additive explanations suggested significant differences in the driving factors and their contributions to prediction models for each heavy metal. Climate variables were potentially valuable predictors of heavy metal content. The visualized spatial distribution of Pollution Load Index showed that 79.8 % of the study area was moderately polluted and the remaining 20.2 % was in a severe polluted state. The soil pollution state worsened from the west to the east of the study area. These findings provide valuable information on effective soil pollution control and soil conservation efforts.http://www.sciencedirect.com/science/article/pii/S2405844025009995Heavy metalHunan provinceMachine learningPaddy soilPollution prediction |
| spellingShingle | Unurnyam Jugnee Le Jiao Sainbayar Dalantai Lili Huo Yi An Bayartungalag Batsaikhan Undrakhtsetseg Tsogtbaatar Munguntuul Ulziibaatar Boldbaatar Natsagdorj A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models Heliyon Heavy metal Hunan province Machine learning Paddy soil Pollution prediction |
| title | A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models |
| title_full | A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models |
| title_fullStr | A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models |
| title_full_unstemmed | A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models |
| title_short | A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models |
| title_sort | general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models |
| topic | Heavy metal Hunan province Machine learning Paddy soil Pollution prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025009995 |
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