An innovative approach to visualizing heavy metal pollution risk in chernozem soils: Leveraging the fuzzy logic algorithm

• Agricultural inputs explained 66.3% of soil Cd, versus 33.7% from atmospheric deposition. • FC–PMF mapping captured overlapping Cd source hotspots that conventional mapping misses. • Integrating source-specific health risk with membership maps delineated priority areas. • Targeted fertilizer-input regulation is essential for Cd control in intensive croplands. Accurate control of soil heavy-metal (HM) contamination requires source identification that remains reliable under spatial heterogeneity. This study integrates fuzzy classification with positive matrix factorization (FC–PMF) to visualize continuous membership fields and quantify probabilistic source contributions. Cadmium (Cd) was apportioned primarily to agricultural inputs (66.3%) with a secondary atmospheric component (33.7%); membership surfaces resolved fertilizer-linked Cd hotspots in long-term intensively cultivated fields where Cd exceeded 0.16 mg kg⁻ 1 . The Impact Index of Comprehensive soil Quality (IICQ) classified 75.0%, 19.8%, and 5.2% of units as uncontaminated, slightly contaminated, and moderately contaminated, respectively. For human health risk, a Cd hazard index (HI Cd )—aggregating ingestion, dermal, and inhalation pathways as a comparative indicator—co-located with agricultural memberships, with ingestion contributing most. Joint IICQ–HI Cd overlays on the membership canvas highlighted risk clusters where multiple drivers co-act, particularly fertilizer-linked zones. The framework explicitly mitigates uncertainties arising from overlapping source profiles and localized hotspots, which can blur signals in PMF-only or thresholded maps, and it provides a consistent canvas to overlay ecological and health metrics for risk zoning. By coupling chemically interpretable factors with graded memberships, FC–PMF yields policy-ready maps that sharpen hotspot detection, clarify process co-occurrence, and support targeted remediation in intensively managed chernozem agroecosystems.