孙　正, 曹亚非, 刘　峰, 王德彩, 张甘霖, 吴华勇. 离子型稀土矿原地浸矿场地分类及人体健康风险预测[J]. 土壤通报, 2023, 54(5): 1186 − 1195. DOI: 10.19336/j.cnki.trtb.2023010301
引用本文: 孙　正, 曹亚非, 刘　峰, 王德彩, 张甘霖, 吴华勇. 离子型稀土矿原地浸矿场地分类及人体健康风险预测[J]. 土壤通报, 2023, 54(5): 1186 − 1195. DOI: 10.19336/j.cnki.trtb.2023010301
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SUN Zheng, CAO Ya-fei, LIU Feng, WANG De-cai, ZHANG Gan-lin, WU Hua-yong. Classification and Human Health Risk Prediction of in-situ Leaching Site of Ionic Rare Earth Ore[J]. Chinese Journal of Soil Science, 2023, 54(5): 1186 − 1195. DOI: 10.19336/j.cnki.trtb.2023010301
Citation: SUN Zheng, CAO Ya-fei, LIU Feng, WANG De-cai, ZHANG Gan-lin, WU Hua-yong. Classification and Human Health Risk Prediction of in-situ Leaching Site of Ionic Rare Earth Ore[J]. Chinese Journal of Soil Science, 2023, 54(5): 1186 − 1195. DOI: 10.19336/j.cnki.trtb.2023010301
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离子型稀土矿原地浸矿场地分类及人体健康风险预测

Classification and Human Health Risk Prediction of in-situ Leaching Site of Ionic Rare Earth Ore

    摘要
  • 摘要:
      目的  建立离子型稀土矿原地浸矿场地分类及人体健康风险预测模型，为稀土矿浸矿场地生态恢复和污染治理提供参考。
      方法  以江西省龙南县为研究区，使用人机交互解译方法获取稀土矿原地浸矿场地空间分布信息；建立浸矿场地分类的模糊层次分析模型；结合分类结果，建立浸矿场地人体健康风险预测的随机森林、提升回归树、C5.0决策树及加权集成模型，运用反距离加权插值法对浸矿场地周边人体健康风险概率进行空间制图。
      结果  龙南县稀土矿原地浸矿场地主要集中在县域东北部坡度小于25°的山地丘陵地区；浸矿场地面积是利用模糊层次分析模型开展场地分类的最重要变量；浸矿场地类型在场地集中分布区自东北向西南逐渐由第二类（较低风险）过渡至第四类（高风险）；随机森林模型对浸矿场地人体健康风险预测的精度高于提升回归树模型和C5.0决策树模型，模型的决定系数为0.744，使用简单的加权集成方法，可进一步提升预测的精度；浸矿场地人体健康风险在场地集中分布区的中部风险较高、东西两侧风险较低；浸矿场地周边约3500 m范围内人体健康风险较高，3500 m之外风险较低。
      结论  模糊层次分析模型是离子型稀土矿原地浸矿场地分类的适用模型；随机森林模型是离子型稀土矿原地浸矿场地人体健康风险预测的最优单一模型。

     

    Abstract:
      Objective  Models for classifying and predicting human health risk of in-situ leaching sites of ionic rare earth ore were established for ecological restoration and pollution control of the leaching sites.
      Method  Taking Longnan County, Jiangxi Province as the study area, spatial distribution of in-situ leaching sites of ionic rare earth ore was obtained by using human-computer interactive interpretation. A fuzzy analytic hierarchy process method was established for classifying the leaching sites. Random forest, lifting regression tree, C5.0 decision tree and weighted integration models were used for predicting human health risk probability of the leaching sites. The inverse distance weighted interpolation method was used to map the spatial distribution of human health risk probability around the leaching sites.
      Results  The leaching sites in Longnan County were mainly located in the mountainous and hilly areas with a slope of less than 25° in the northeast of the county. The area of the leaching sites was the most important variable to classify the leaching sites by using the fuzzy analytic hierarchy process. In the concentrated distribution area of the leaching sites, the risk gradually increased from the second type (with a low risk) to the fourth type (with a high risk) from northeast to southwest of the leaching sites. Random forest model (R2 = 0.744) outperformed the lifting regression tree and the C5.0 decision tree models for predicting the human health risk of the leaching sites. A simple weighted integration method can further improve the prediction of human health risk. The risk was high in the middle and low in the east and west of the concentrated distribution area of the leaching sites. The risk was high within about 3 500 m around the leaching sites and low beyond the distance.
      Conclusion  The fuzzy analytic hierarchy process can be used to reasonably classify the leaching sites of ionic rare earth ore. The random forest model was the optimal model for predicting the human health risk of the leaching sites.

     

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