基于机器学习的区域土壤重金属污染风险诊断

钱江波; 陈涤; 王夏晖; 李喜林; 黄国鑫

doi:10.13205/j.hjgc.202312037

基于机器学习的区域土壤重金属污染风险诊断

doi: 10.13205/j.hjgc.202312037

1. 辽宁工程技术大学土木工程学院, 辽宁阜新 123000;
2. 生态环境部环境规划院, 北京 100144

基金项目:

国家重点研发计划项目(2018YFC1800205)

辽宁省教育厅科学研究面上项目(LJKZ0344)

详细信息

作者简介:
钱江波(1999-),男,硕士研究生,主要研究方向为土壤和地下水污染风险管控与修复。2325364514@qq.com

通讯作者:
黄国鑫(1980-),男,研究员,主要研究方向为地下水和土壤污染防治。huanggx@caep.org.cn

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出版历程
- 收稿日期: 2022-10-26
- 网络出版日期: 2024-03-08

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

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

摘要

摘要: 准确描述土壤重金属的空间分布并合理划分土壤污染风险区,是土壤重金属污染风险管控的重要前提。以广东省某工业化区域为研究区,基于随机森林(RF)和模糊c均值(FCM),采集577个采样点的土壤重金属数据和12个环境协变量数据,分析了土壤重金属(Cr、Pb、Cu和Zn)浓度,构建了土壤重金属浓度预测模型,开展了数据驱动的区域土壤重金属污染风险分类,并提出了相应的风险分类管控策略。结果表明:Cr、Pb、Cu和Zn的实测浓度分别为4.00~885.60,9.39~2588.11,2.20~475.00,11.05~8162.42 mg/kg。除Pb外,Cr、Cu和Zn的平均值是当地土壤背景值的1.10~1.29倍。4种重金属变异系数均在86%~319%,属中上强度的变异水平,土壤重金属受人为活动影响较大。当RF节点变量数(m_try)值为5,决策数个数(n_tree)值为800时展现了较好的预测性能,预测性能R²的最大值分别达到0.78(Cr)、0.79(Pb)、0.85(Cu)和0.76(Zn),RMSE的最小值分别达到21.57(Cr)、59.88(Pb)、62.38(Cu)和105.88(Zn)。Cr的高值区多位于研究区的东北部和中北部,而Pb、Cu和Zn的高值区则集中于中北部和中南部。在制定土壤重金属污染风险管控策略时,应优先关注中部地区。研究区被划分为2个风险区,其中A区为高风险区,应严格进行重金属污染风险管控;B区为低风险区,应加强监控预警,采取源头控制措施,防止新增污染。
- 土壤 /
- 重金属 /
- 随机森林 /
- 模糊c聚类 /
- 风险诊断
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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