Abstract
To investigate the characteristics and health risks of heavy metal (HM) contamination in soils of typical industrial sites during urban renewal, this study selected Pudong New District, Shanghai, as a case. Seven HMs (Cd, Pb, Cu, Zn, Ni, Hg, and As) were analyzed for their concentrations, ecological risks, spatial patterns, and potential sources. Inverse Distance Weighted (IDW) interpolation was used to assess spatial distribution, Random Forest (RF) regression to predict HM concentrations, and a two-dimensional Monte Carlo simulation to evaluate human health risks. The results showed that all HMs except As exceeded Shanghai background values in surface soils, with varying levels observed in subsoil and saturated layers. The Index of Geoaccumulation (Igeo) and Risk Index (RI) indicated low contamination and moderate ecological risk. Pearson correlation combined with Positive Matrix Factorization (PMF) identified four major sources: traffic emissions dominated by Cd and Zn, combustion-related sources dominated by Pb and Hg, industry-related inputs dominated by Cu and Ni, and a natural source dominated by As. The RF model demonstrated strong predictive accuracy for Cd, Pb, Hg, and As (R2 = 0.80–0.94), and predicted values were consistent with observations. Monte Carlo results showed that non-carcinogenic risks for children and adults were within acceptable limits, while carcinogenic risks reached “notable” levels with probabilities of 62.06%, 55.65%, and 22.49% for children, adult females, and adult males, respectively. Cd and As were identified as key contributors. This work provides scientific support for soil pollution prevention and remediation during urban renewal.
| Original language | English |
|---|---|
| Article number | 315 |
| Journal | Toxics |
| Volume | 14 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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SDG 11 Sustainable Cities and Communities
Keywords
- health risk assessment
- random forest prediction
- soil heavy metals
- source apportionment
- spatial distribution
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