RISK DIAGNOSIS OF HEAVY METAL POLLUTION IN REGIONAL SOIL BASED ON MACHINE LEARNING

QIAN Jiangbo; CHEN Di; WANG Xiahui; LI Xilin; HUANG Guoxin

doi:10.13205/j.hjgc.202312037

Volume 41 Issue 12

Dec. 2023

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QIAN Jiangbo, CHEN Di, WANG Xiahui, LI Xilin, HUANG Guoxin. RISK DIAGNOSIS OF HEAVY METAL POLLUTION IN REGIONAL SOIL BASED ON MACHINE LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(12): 296-303. doi: 10.13205/j.hjgc.202312037

Citation:

QIAN Jiangbo, CHEN Di, WANG Xiahui, LI Xilin, HUANG Guoxin. RISK DIAGNOSIS OF HEAVY METAL POLLUTION IN REGIONAL SOIL BASED ON MACHINE LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(12): 296-303. doi: 10.13205/j.hjgc.202312037

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RISK DIAGNOSIS OF HEAVY METAL POLLUTION IN REGIONAL SOIL BASED ON MACHINE LEARNING

doi: 10.13205/j.hjgc.202312037

1. School of Civil Engineering, Liaoning Technical University, Fuxin 123000, China;
2. Chinese Academy of Environmental Planning, Beijing 100144, China

Received Date: 2022-10-26
Available Online: 2024-03-08

Abstract

Abstract

Accurately describing the spatial distribution of soil heavy metals and reasonably dividing soil pollution risk areas is an important prerequisite for soil heavy metal pollution risk control. In this paper, an industrialized area in Guangdong Province was taken as the study area. Based on random forest (RF) and fuzzy-c-means (FCM), the soil heavy metal data of 577 sampling points and 12 environmental covariate data were used to analyze the soil Cr, Pb, Cu and Zn concentrations. The prediction models of soil heavy metal concentrations were constructed, the data-driven regional soil heavy metal pollution risk classification was carried out, and the corresponding risk classification control strategy was proposed. The results showed that the measured concentrations of Cr, Pb, Cu and Zn were 4.00 to 885.60, 9.39 to 2588.11, 2.20 to 475.00 and 11.05 to 8162.42 mg/kg, respectively. Except for Pb concentration, the average values of Cr, Cu and Zn concentrations were 1.10~1.29 times higher than the local soil background values. The variation coefficients of the four heavy metals were between 86% and 319%, which belonged to the variation level of medium or upper intensity. Soil heavy metals were greatly affected by human activities. The random forest had a good prediction performance when the number of node variables (m_try) was 5 and the number of decision trees (n_tree) was 800. The maximum values of R² were 0.78, 0.79, 0.85 and 0.76, respectively, and the minimum values of RMSE were 21.57, 59.88, 62.38 and 105.88, respectively. The high-value areas of Cr were mostly located in the northeastern and north-central of the study area, while the high-value areas of Pb, Cu and Zn were concentrated in the north-central and south-central parts. In the development of soil heavy metal pollution risk control strategies, priority should be given to the central region. The study area was divided into two risk areas, of which, area A was a high-risk area, and the risk control of heavy metal pollution should be strictly carried out. Area B was a low-risk area. It is also necessary to strengthen monitoring and early warning and take source control measures to prevent new pollution.
- soil,
- heavy metals,
- random forest,
- fuzzy c-means,
- risk diagnosis

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