aceMachine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty
Potentially toxic elements (PTEs) in soils pose persistent risks to ecosystems, groundwater, and food systems, creating a need for reliable spatial assessment tools. Machine learning (ML) is increasingly used to map PTE concentrations from environmental covariates, but many studies still rely on spatially naive validation, limited interpretation, and incomplete uncertainty reporting. This review synthesizes recent advances (2020-2025) in ML-based PTE mapping with emphasis on four requirements for monitoring-grad…
Machine learning can map potentially toxic element concentrations from environmental covariates and produce exceedance-probability maps aligned with regulatory thresholds for soil-contamination management.
Many ML studies of soil potentially toxic elements rely on spatially naive validation, and random cross-validation often overestimates predictive performance when spatial dependence is ignored, with incomplete uncertainty reporting.
Evidence
- Peer-reviewedEnvironmental Monitoring and Assessment2026-08-29
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Truvace Impact Record TRV-2026-0932, v1: “Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty.” Truvace, 2026-08-31. /record/TRV-2026-0932 (accessed at citation time). sha256 bf2afb63323afd2e…
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