Sustainable land use requires precise monitoring of soil pollution, yet accurately predicting the spatial distribution of heavy metals often relies on post hoc accuracy comparisons with limited a priori diagnosis. To address the challenge of cost effective environmental monitoring, we conducted a PRISMA guided systematic review (2000–2024) and synthesized 135 studies to develop a mechanism-informed, context aware method selection framework. Evidence revealed three regularities: (i) element–driver coupling is structured (Pb/Cd/Zn predominantly anthropogenic; Cr/Ni geogenic; As/Hg mixed), with dominant influence scales from local to regional; (ii) model performance hinges on alignment between algorithmic assumptions, and context hybrid machine learning models integrating multi-source covariates tend to excel under strong, non-stationary anthropogenic heterogeneity, whereas kriging variants are more robust when geogenic continuity holds; and (iii) applicability is jointly constrained by environmental context, data foundations, and management objectives. Building on these insights, we propose a three-step decision workflow—goal definition, contextual diagnosis, and method matching. This framework serves as a decision support tool that shifts selection from trial and error to a priori alignment, optimizing resource allocation and enhancing the reliability of pollution assessments for sustainable soil remediation and policymaking.
Yang et al. (Thu,) studied this question.