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TRV-2026-0926Certified recordPeer-reviewed

Quantitative decoupling of source-pathway-receptor driving mechanisms for integrated soil risk via interpretable machine learning

Most existing concentration-based risk assessments of potentially toxic elements (PTEs) in farmland soils tend to overlook source-specific transport pathways and receptor-related risks. In this study, an integrated risk index-machine learning framework based on the source-pathway-receptor concept is developed to comprehensively evaluate PTE risks in a typical mining city. The framework integrates the improved Nemerow index (INI), potential ecological risk index, Monte Carlo simulation-based health risk assessmen…

Climate · G Space — documented gain · certified 2026-08-30 · v1 · article view · machine-readable

Current reading — gain

An integrated source-pathway-receptor framework using XGBoost and SHAP improved differentiation of industrial versus agricultural PTE risks in farmland soils and supported targeted zoned control to avoid excessive remediation.

What this doesn’t fix

Predictive performance was evaluated only under internal 8:2 training-testing validation without reported external validation in other regions or time periods.

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Truvace Impact Record TRV-2026-0926, v1: “Quantitative decoupling of source-pathway-receptor driving mechanisms for integrated soil risk via interpretable machine learning.” Truvace, 2026-08-30. /record/TRV-2026-0926 (accessed at citation time). sha256 b41aadfdebf2687a…

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