A machine learning and counterfactual reasoning-integrated decision framework for cadmium remediation: Predictive optimization and cost-benefit analysis driven by iron plaque regulation.
Uložené v:
| Názov: | A machine learning and counterfactual reasoning-integrated decision framework for cadmium remediation: Predictive optimization and cost-benefit analysis driven by iron plaque regulation. |
|---|---|
| Autori: | Li Y; School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China., Hou J; Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China. Electronic address: houjie@caas.cn., Liu M; Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China., Li R; Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China., Du Z; Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China., Yao Y; Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China., Sun P; School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China., Zhao L; School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China., An Y; Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China. Electronic address: anyi@caas.cn. |
| Zdroj: | Journal of hazardous materials [J Hazard Mater] 2025 Oct 05; Vol. 497, pp. 139584. Date of Electronic Publication: 2025 Aug 16. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 9422688 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3336 (Electronic) Linking ISSN: 03043894 NLM ISO Abbreviation: J Hazard Mater Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam : Elsevier, |
| Výrazy zo slovníka MeSH: | Cadmium*/chemistry , Cadmium*/analysis , Machine Learning* , Soil Pollutants*/chemistry , Soil Pollutants*/analysis , Environmental Restoration and Remediation*/methods , Environmental Restoration and Remediation*/economics , Iron*/chemistry, Cost-Benefit Analysis ; Oryza/chemistry ; Soil/chemistry ; Hydrogen-Ion Concentration |
| Abstrakt: | Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Environmental remediation strategies for cadmium (Cd)-contaminated rice paddies often face challenges due to reliance on time-consuming field trials and limited pre-assessment of intervention efficacy. Here, we propose a machine learning and causal inference-integrated framework to enable proactive decision-making, using iron plaque-mediated Cd immobilization as a model system. By analyzing 76 paired soil-rice samples, extreme gradient boosting (XGBoost) and SHapley Additive exPlanations (SHAP) identified six critical drivers of grain Cd accumulation from 31 physicochemical and microbial indicators. Structural equation modeling revealed iron plaque reduced root Cd accumulation by 38.6 %, primarily regulated by available Fe (41.9 %) and pH (37.7 %). Counterfactual scenario simulations further quantified threshold combinations of Fe availability (300-400 mg·kg⁻¹) and pH (>5.5) to maintain grain Cd below 0.2 mg·kg⁻¹. Economic analysis in five towns of Xiangtan country demonstrated region-specific optimization: FeSO₄ amendment generated 1.37 × 10 6 CNY·km -2 net profit in soils with high native Fe content, while pH adjustment via Na₂SiO₃ achieved 2.68 × 10 6 CNY·km -2 in acidic regions. This framework bridges predictive analytics with cost-benefit evaluation, providing a paradigm shift from post hoc to preemptive remediation planning. (Copyright © 2025. Published by Elsevier B.V.) |
| Contributed Indexing: | Keywords: Counterfactual inference; Environmental remediation; Machine learning; Rice cadmium pollution; Scenario simulation |
| Substance Nomenclature: | 00BH33GNGH (Cadmium) 0 (Soil Pollutants) E1UOL152H7 (Iron) 0 (Soil) |
| Entry Date(s): | Date Created: 20250830 Date Completed: 20251007 Latest Revision: 20251007 |
| Update Code: | 20251007 |
| DOI: | 10.1016/j.jhazmat.2025.139584 |
| PMID: | 40885063 |
| Databáza: | MEDLINE |
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstrakt: | Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Environmental remediation strategies for cadmium (Cd)-contaminated rice paddies often face challenges due to reliance on time-consuming field trials and limited pre-assessment of intervention efficacy. Here, we propose a machine learning and causal inference-integrated framework to enable proactive decision-making, using iron plaque-mediated Cd immobilization as a model system. By analyzing 76 paired soil-rice samples, extreme gradient boosting (XGBoost) and SHapley Additive exPlanations (SHAP) identified six critical drivers of grain Cd accumulation from 31 physicochemical and microbial indicators. Structural equation modeling revealed iron plaque reduced root Cd accumulation by 38.6 %, primarily regulated by available Fe (41.9 %) and pH (37.7 %). Counterfactual scenario simulations further quantified threshold combinations of Fe availability (300-400 mg·kg⁻¹) and pH (>5.5) to maintain grain Cd below 0.2 mg·kg⁻¹. Economic analysis in five towns of Xiangtan country demonstrated region-specific optimization: FeSO₄ amendment generated 1.37 × 10 <sup>6</sup> CNY·km <sup>-2</sup> net profit in soils with high native Fe content, while pH adjustment via Na₂SiO₃ achieved 2.68 × 10 <sup>6</sup> CNY·km <sup>-2</sup> in acidic regions. This framework bridges predictive analytics with cost-benefit evaluation, providing a paradigm shift from post hoc to preemptive remediation planning.<br /> (Copyright © 2025. Published by Elsevier B.V.) |
|---|---|
| ISSN: | 1873-3336 |
| DOI: | 10.1016/j.jhazmat.2025.139584 |