Geochemical-integrated machine learning approach predicts the distribution of cadmium speciation in European and Chinese topsoils
Abstract Evaluating heavy metals bioavailable is crucial for comprehensive soil contamination assessment but challenging at large scales due to complex and resource-intensive analytical procedures, and the amount of dissolved metal in soils represents the relative solubility and potential mobility o...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-025-02516-6 |
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| _version_ | 1849332214361227264 |
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| author | Naichi Zhang Chen Lv Yan Li Panos Panagos Cristiano Ballabio Jun Man Xueyuan Gu Fang-Jie Zhao Peng Wang Xingmei Liu Yifan Qian Peixin Cui Tongliang Wu Meiying Huang Cun Liu Yujun Wang |
| author_facet | Naichi Zhang Chen Lv Yan Li Panos Panagos Cristiano Ballabio Jun Man Xueyuan Gu Fang-Jie Zhao Peng Wang Xingmei Liu Yifan Qian Peixin Cui Tongliang Wu Meiying Huang Cun Liu Yujun Wang |
| author_sort | Naichi Zhang |
| collection | DOAJ |
| description | Abstract Evaluating heavy metals bioavailable is crucial for comprehensive soil contamination assessment but challenging at large scales due to complex and resource-intensive analytical procedures, and the amount of dissolved metal in soils represents the relative solubility and potential mobility of cadmium, which is a key factor determining bioavailability. Here, we developed a geochemical-integrated machine learning framework using multi-source data to predict cadmium speciation distribution in European and Chinese non-industrial topsoils. Average total cadmium content in Chinese topsoils (0.41 mg kg−1) was ~10.8% higher than the Europe, while average dissolved cadmium content (113.2 μg L−1) was ~16.8% higher. Mechanistic interpretation revealed that lower pH, soil organic matter, and amorphous ferrihydrite contents mainly attributed to the higher bioavailability in China. The framework, coupled with knowledge transfer bridging the knowledge gap between geochemical processes and crop uptake, would facilitate the informed decision-making and targeted remediation measures for sustainable agricultural practices and long-term environmental health. |
| format | Article |
| id | doaj-art-678e697448324ca6866a3916aa3bce5f |
| institution | Kabale University |
| issn | 2662-4435 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Earth & Environment |
| spelling | doaj-art-678e697448324ca6866a3916aa3bce5f2025-08-20T03:46:16ZengNature PortfolioCommunications Earth & Environment2662-44352025-07-01611810.1038/s43247-025-02516-6Geochemical-integrated machine learning approach predicts the distribution of cadmium speciation in European and Chinese topsoilsNaichi Zhang0Chen Lv1Yan Li2Panos Panagos3Cristiano Ballabio4Jun Man5Xueyuan Gu6Fang-Jie Zhao7Peng Wang8Xingmei Liu9Yifan Qian10Peixin Cui11Tongliang Wu12Meiying Huang13Cun Liu14Yujun Wang15State Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of SciencesState Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of SciencesState Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of SciencesEuropean Commission, Joint Research Centre (JRC)European Commission, Joint Research Centre (JRC)State Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of SciencesState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing UniversityCollege of Resources and Environmental Sciences, Nanjing Agricultural UniversityCollege of Resources and Environmental Sciences, Nanjing Agricultural UniversityCollege of Environmental & Resource Sciences, Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang UniversityState Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of SciencesState Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of SciencesState Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of SciencesState Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of SciencesState Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of SciencesState Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of SciencesAbstract Evaluating heavy metals bioavailable is crucial for comprehensive soil contamination assessment but challenging at large scales due to complex and resource-intensive analytical procedures, and the amount of dissolved metal in soils represents the relative solubility and potential mobility of cadmium, which is a key factor determining bioavailability. Here, we developed a geochemical-integrated machine learning framework using multi-source data to predict cadmium speciation distribution in European and Chinese non-industrial topsoils. Average total cadmium content in Chinese topsoils (0.41 mg kg−1) was ~10.8% higher than the Europe, while average dissolved cadmium content (113.2 μg L−1) was ~16.8% higher. Mechanistic interpretation revealed that lower pH, soil organic matter, and amorphous ferrihydrite contents mainly attributed to the higher bioavailability in China. The framework, coupled with knowledge transfer bridging the knowledge gap between geochemical processes and crop uptake, would facilitate the informed decision-making and targeted remediation measures for sustainable agricultural practices and long-term environmental health.https://doi.org/10.1038/s43247-025-02516-6 |
| spellingShingle | Naichi Zhang Chen Lv Yan Li Panos Panagos Cristiano Ballabio Jun Man Xueyuan Gu Fang-Jie Zhao Peng Wang Xingmei Liu Yifan Qian Peixin Cui Tongliang Wu Meiying Huang Cun Liu Yujun Wang Geochemical-integrated machine learning approach predicts the distribution of cadmium speciation in European and Chinese topsoils Communications Earth & Environment |
| title | Geochemical-integrated machine learning approach predicts the distribution of cadmium speciation in European and Chinese topsoils |
| title_full | Geochemical-integrated machine learning approach predicts the distribution of cadmium speciation in European and Chinese topsoils |
| title_fullStr | Geochemical-integrated machine learning approach predicts the distribution of cadmium speciation in European and Chinese topsoils |
| title_full_unstemmed | Geochemical-integrated machine learning approach predicts the distribution of cadmium speciation in European and Chinese topsoils |
| title_short | Geochemical-integrated machine learning approach predicts the distribution of cadmium speciation in European and Chinese topsoils |
| title_sort | geochemical integrated machine learning approach predicts the distribution of cadmium speciation in european and chinese topsoils |
| url | https://doi.org/10.1038/s43247-025-02516-6 |
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