王水平, 兴　安, 胡有林, 红　梅, 陈　晨, 徐明明, 孟　畅. 内蒙古某金属冶炼厂周边农田土壤重金属空间分布预测方法研究[J]. 土壤通报, 2024, 55(3): 830 − 839. DOI: 10.19336/j.cnki.trtb.2023060102
引用本文: 王水平, 兴　安, 胡有林, 红　梅, 陈　晨, 徐明明, 孟　畅. 内蒙古某金属冶炼厂周边农田土壤重金属空间分布预测方法研究[J]. 土壤通报, 2024, 55(3): 830 − 839. DOI: 10.19336/j.cnki.trtb.2023060102
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WANG Shui-ping, XING An, HU You-lin, HONG Mei, CHEN Chen, XU Ming-ming, MENG Chang. Prediction Method of Spatial Distribution of Heavy Metals in Agricultural Soil around a Metal Smelter in Inner Mongolia[J]. Chinese Journal of Soil Science, 2024, 55(3): 830 − 839. DOI: 10.19336/j.cnki.trtb.2023060102
Citation: WANG Shui-ping, XING An, HU You-lin, HONG Mei, CHEN Chen, XU Ming-ming, MENG Chang. Prediction Method of Spatial Distribution of Heavy Metals in Agricultural Soil around a Metal Smelter in Inner Mongolia[J]. Chinese Journal of Soil Science, 2024, 55(3): 830 − 839. DOI: 10.19336/j.cnki.trtb.2023060102
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内蒙古某金属冶炼厂周边农田土壤重金属空间分布预测方法研究

Prediction Method of Spatial Distribution of Heavy Metals in Agricultural Soil around a Metal Smelter in Inner Mongolia

    摘要
  • 摘要:
    目的 为准确预测农田土壤重金属含量的空间分布特征，探讨不同预测方法的适用性及不确定性，建立适用于农田土壤重金属空间预测的最优模型。
    方法 以内蒙古西部河套地区某金属冶炼厂周边农田0 ~ 20 cm土壤为研究对象，采用普通克里格（OK）、随机森林（RF）和普通克里格 + 随机森林（RFRK）等预测方法，选取土壤理化性质、地形、气候、人为活动等环境变量为建模协变量，构建农田土壤重金属空间预测的适宜模型，揭示砷（As）、铅（Pb）、铬（Cr）、镉（Cd）等重金属含量的空间分布特征，并探讨不同建模方法的空间预测精度差异。
    结果 ①土壤Cr、Pb和Cd平均值分别为河套地区土壤背景值的3.5、0.66和0.23倍，该地区农田土壤重金属具有一定程度的富集现象，但并未达到国家农用地土壤风险筛选值，研究区北部、中部以及冶炼厂厂区土壤重金属含量高于区内其它部位。②OK、RF和RFRK三模型的土壤As、Pb、Cr、Cd含量预测精度依次为RFRK > RF > OK，即RFRK模型的预测值平均绝对误差（MAE）、均方根误差（RMSE）最小，交叉验证R2值均在0.8以上，实测值和预测值之间相关性最强。
    结论 普通克里格 + 随机森林模型可作为土壤重金属含量空间预测的有效方法，优先应用于区域农田土壤污染调查、评估和防治方面的相关工作中。

     

    Abstract:
    Objective In order to accurately predict the spatial distribution characteristics of heavy metal content in agricultural soils, the applicability and uncertainty of different prediction methods were explored, so that an optimal model applicable to the spatial prediction of heavy metals in agricultural soils could be established.
    Method Taking 0 ~ 20 cm of soil around a metal smelting plant in the Hetao area of western Inner Mongolia as the research object, the prediction methods, such as ordinary kriging (OK), random forest (RF) and ordinary kriging + random forest (RFRK) were adopted, and the environmental variables, such as soil physicochemical properties, topography, climate, anthropogenic activities and so on were selected as modelling covariates to construct a suitable model for the spatial prediction of heavy metals in agricultural soils, to reveal the characteristics of the spatial distribution of the heavy metal contents of As, Pb, Cr and Cd, and to explore the differences in the spatial prediction accuracy of the different modelling methods.
    Result ① The average values of soil Cr, Pb and Cd are 3.5, 0.66 and 0.23 times higher than the background values of the soils in the Loop, and there is a certain degree of enrichment of heavy metals in the agricultural soils in the area, but it does not reach the screening value of soil risk in the agricultural land, and the content of heavy metals in soils in the northern and central parts of the study area, as well as in the area of the smelter, is higher than that of the other parts of the area. ② The prediction accuracies of soil As, Pb, Cr and Cd contents of OK, RF and RFRK models were in the order of RFRK > RF > OK, i.e. the RFRK model had the smallest mean absolute error (MAE) and root mean square error (RMSE) in the predicted values, and the cross-validated R2 values were all over 0.8, which showed the strongest correlation between the measured and predicted values.
    Conclusion The Ordinary Kriging + Random Forest model could be used as an effective method for spatial prediction of heavy metal content in soil, and is priority for application in the investigation, assessment and prevention of soil pollution in regional farmland.

     

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