Identification of influencing factors of regional soil heavy metal pollution based on random forest

CUI Shiwen; CHEN Jian; LIAO Lei; ZHOU Rui; QIAN Jiangbo; HUANG Guoxin

doi:10.13205/j.hjgc.202508018

Volume 43 Issue 8

Aug. 2025

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2025 > 43(8): 195-203

CUI Shiwen, CHEN Jian, LIAO Lei, ZHOU Rui, QIAN Jiangbo, HUANG Guoxin. Identification of influencing factors of regional soil heavy metal pollution based on random forest[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(8): 195-203. doi: 10.13205/j.hjgc.202508018

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CUI Shiwen, CHEN Jian, LIAO Lei, ZHOU Rui, QIAN Jiangbo, HUANG Guoxin. Identification of influencing factors of regional soil heavy metal pollution based on random forest[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(8): 195-203. doi: 10.13205/j.hjgc.202508018

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Identification of influencing factors of regional soil heavy metal pollution based on random forest

doi: 10.13205/j.hjgc.202508018

1. College of New Energy and Environment, Jilin University, Changchun 130021, China;
2. Chinese Academy of Environmental Planning, Beijing 100041, China;
3. Research Institute No. 290, CNNC, Shaoguan 512029, China;
4. Zhejiang Kehuan Environmental Engineering Technology Co., Ltd., Hangzhou 311200, China

Received Date: 2024-12-03
Accepted Date: 2025-01-20
Rev Recd Date: 2025-01-14

Abstract

Abstract

Accurate identification of heavy metal pollution sources is an important prerequisite for soil pollution prevention and control. However， due to the lack of information on the influencing factors of heavy metal pollution， the effectiveness of pollution source tracing analysis is often constrained. In this paper， a typical industrial area was taken as the study area. On the basis of the actual measurements of soil heavy metal concentrations from 577 sampling points and data on the 18 environmental covariates， random forest （RF） and bivariate local spatial autocorrelation methods were applied to identify the influencing factors of the Cd， Pb， and Cr concentrations， determine the quantitative contributions of the 18 influencing factors to these heavy metal concentrations， and further propose targeted strategies for soil environmental management in an industralized study area. The results showed that the optimal performance of the RF prediction models for the three heavy metals was achieved when the coefficient of determination （R²） reached 0.93 with a root mean square error （RMSE） of 0.43 mg/kg for Cd， 0.97 with an RMSE of 48.57 mg/kg for Pb， and 0.93 with an RMSE of 18.57 mg/kg for Cr，namely. There were differences in the spatial distributions of the relative concentrations of the three heavy metals. The regions with relatively high Cd concentration were concentrated in the central and southern parts of the study area， whereas relatively high Pb relative concentration were predominantly found in the central and eastern parts. The regions with relatively high Cr relative concentration were concentrated in the southwestern and northeastern parts. Railway was identified as the most significant factor influencing Cd concentration with a contribution rate of 0.119. Soil pH was identified to be the most significant factor influencing Pb concentrations with a contribution rate of 0.099. The hazardous waste disposal site was identified to be the most significant factor influencing Cr concentrations with a contribution rate of 0.100. Compared to the rest of the study area， the central region exhibited higher concentrations of Cd， Pb， and Cr， more complex human activity， and a greater number of high-high cluster zones， where Cd， Pb， and Cr concentrations and their most significant influencing factors were concentrated. When source prevention and control measures for heavy metal pollution in soil were implemented， particular attention should be prioritized towards the central region of the study area.
- regional scale,
- soil,
- heavy metals,
- random forest,
- influencing factors,
- bivariate local Moran index

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References(34)

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