彭雨璇, 陈 剑, 韩春媚, 张家铭, 李书鹏, 赵永存. 融合多源信息的某农药车间地块土壤氯仿三维空间预测研究[J]. 土壤通报, 2024, 55(5): 1229 − 1238. DOI: 10.19336/j.cnki.trtb.2023122601
引用本文: 彭雨璇, 陈 剑, 韩春媚, 张家铭, 李书鹏, 赵永存. 融合多源信息的某农药车间地块土壤氯仿三维空间预测研究[J]. 土壤通报, 2024, 55(5): 1229 − 1238. DOI: 10.19336/j.cnki.trtb.2023122601
PENG Yu-xuan, CHEN Jian, HAN Chun-mei, ZHANG Jiaming, LI Shu-peng, ZHAO Yong-cun. Three-dimensional Mapping of Soil Chloroform at a Pesticide Workshop Region by Integrating Multi-source Auxiliary Information[J]. Chinese Journal of Soil Science, 2024, 55(5): 1229 − 1238. DOI: 10.19336/j.cnki.trtb.2023122601
Citation: PENG Yu-xuan, CHEN Jian, HAN Chun-mei, ZHANG Jiaming, LI Shu-peng, ZHAO Yong-cun. Three-dimensional Mapping of Soil Chloroform at a Pesticide Workshop Region by Integrating Multi-source Auxiliary Information[J]. Chinese Journal of Soil Science, 2024, 55(5): 1229 − 1238. DOI: 10.19336/j.cnki.trtb.2023122601

融合多源信息的某农药车间地块土壤氯仿三维空间预测研究

Three-dimensional Mapping of Soil Chloroform at a Pesticide Workshop Region by Integrating Multi-source Auxiliary Information

  • 摘要:
    目的 准确刻画场地土壤污染物的三维(3D)空间分布,克服稀疏钻孔条件下传统空间插值的平滑效应。
    方法 在某农药厂农药车间地块,采用场地多源辅助信息和钻孔数据构建了土壤-景观推理-反距离加权(SoLIM-IDW)3D混合模型,预测场地土壤氯仿浓度的3D空间分布,并与传统空间插值方法(IDW和OK)进行比较。通过地理探测器评估多源辅助数据对土壤氯仿空间预测的影响。
    结果 该地块氯仿污染热点主要分布在中部的农药车间表层至深层土壤以及东南部废水处理区的上层土壤中。SoLIM-IDW方法比IDW和OK方法空间预测精度更高,融合各辅助变量集的SoLIM-IDW模型空间预测R2为0.37 ~ 0.39,RMSE为83.15 ~ 84.48 mg kg–1,通过空间预测结果计算的超标土方量为7024 ~ 7980 m3。多源辅助信息存在交互作用,整合多源场地辅助信息比单一辅助信息获得了更高的精度。
    结论 在成本有限、稀疏钻孔的条件下,充分利用易于获取、成本低廉的多源污染辅助信息有利于提高场地污染物3D空间预测、风险评估准确性。

     

    Abstract:
    Objective The aim was to accurately map the three-dimensional (3D) spatial distribution of soil pollutants and overcome the smoothing effects of traditional spatial interpolation and achieve precise characterization of the 3D spatial distribution of soil pollutants under sparse borehole data conditions.
    Method A Soil-Landscape Inference-Mixed Inverse Distance Weighting (SoLIM-IDW) 3D hybrid model was constructed using multi-source auxiliary information and soil borehole data. This model was employed to predict the 3D distribution of chloroform concentrations in the soil within a pesticide workshop region. The performance was compared with traditional spatial interpolation methods such as IDW and OK. The impact of multi-source auxiliary data on the spatial prediction of soil chloroform was evaluated using a geographic detector.
    Result The results indicated that chloroform contamination hotspots were mainly distributed in the surface to deep layers of soil in the central pesticide workshop, and the upper layer of the southeast wastewater treatment area. The SoLIM-IDW method exhibited higher spatial prediction accuracy, with R2 ranging from 0.37 to 0.39, and RMSE ranging from 83.15 to 84.48 mg kg–1. The estimated volume of soil exceeding the standard through spatial prediction ranged from 7024 to 7980 m3. Multi-source auxiliary information interacted, and the integration of the multi-source auxiliary data yielded higher accuracy compared to relying on a single auxiliary data source.
    Conclusion This study demonstrated that making full use of easily accessible and low-cost multi-source pollution auxiliary information under the conditions of limited cost and sparse boreholes contributes to improve the accuracy of 3D spatial prediction and risk assessment of site pollutants.

     

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