Sun Yongqiao, Lei Shaogang, Zhao Yibo, Wei Cheng, Yang Xingchen, Han Xiaotong, Li Yuanyuan, Xia Jianan, Cai Zhen
University of Mining and Technology, Xuzhou 221116, Jiangsu Province, China; School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China.
University of Mining and Technology, Xuzhou 221116, Jiangsu Province, China; School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China.
J Hazard Mater. 2024 Mar 5;465:133114. doi: 10.1016/j.jhazmat.2023.133114. Epub 2023 Nov 29.
Predicting the precise spatial distribution of heavy metals in soil is crucial, especially in the fields of environmental management and remediation. However, achieving accurate spatial predictions of soil heavy metals becomes quite challenging when the number of soil sampling points is relatively limited. To address this challenge, this study proposes a hybrid approach, namely, Light Gradient Boosting Machine plus Ordinary Kriging (LGBK), for predicting the spatial distribution of soil heavy metals. A total of 137 soil samples were collected from the Shengli Coal-mine Base in Inner Mongolia, China, and their heavy metal concentrations were measured. Leveraging environmental covariates and soil heavy metal data, we constructed the predictive model. Experimental results demonstrate that, in comparison to traditional models, LGBK exhibits superior predictive performance. For copper (Cu), zinc (Zn), chromium (Cr), and arsenic (As), the coefficients of determination (R²) from the cross-validation results are 0.65, 0.52, 0.57, and 0.63, respectively. Moreover, the LGBK model excels in capturing intricate spatial features in heavy metal distribution. It accurately forecasts trends in heavy metal distribution that closely align with actual measurements. ENVIRONMENTAL IMPLICATION: This study introduces a novel method, LGBK, for predicting the spatial distribution of soil heavy metals. This method yields higher-precision predictions even with a limited number of sampling points. Furthermore, the study analyzes the spatial distribution characteristics of Cu, Zn, Cr, and As in the grassland coal-mine base, along with the key environmental factors influencing their spatial distribution. This research holds significant importance for the environmental regulation and remediation of heavy metal pollution.
预测土壤中重金属的精确空间分布至关重要，尤其是在环境管理和修复领域。然而，当土壤采样点数量相对有限时，实现土壤重金属的准确空间预测极具挑战性。为应对这一挑战，本研究提出一种混合方法，即轻梯度提升机加普通克里金法（LGBK），用于预测土壤重金属的空间分布。在中国内蒙古胜利煤矿基地共采集了137个土壤样本，并测量了它们的重金属浓度。利用环境协变量和土壤重金属数据，我们构建了预测模型。实验结果表明，与传统模型相比，LGBK具有卓越的预测性能。对于铜（Cu）、锌（Zn）、铬（Cr）和砷（As），交叉验证结果的决定系数（R²）分别为0.65、0.52、0.57和0.63。此外，LGBK模型在捕捉重金属分布的复杂空间特征方面表现出色。它能准确预测与实际测量结果高度吻合的重金属分布趋势。环境意义：本研究引入了一种预测土壤重金属空间分布的新方法LGBK。即使在采样点数量有限的情况下，该方法也能产生更高精度的预测。此外，该研究分析了草原煤矿基地中Cu、Zn、Cr和As的空间分布特征，以及影响其空间分布的关键环境因素。本研究对重金属污染的环境监管和修复具有重要意义。
