Scholarlink

<p>Land resources are vital for urban development and construction. Abandoned industrial areas often contain large amounts of heavy metals from past industrial activities. Accurate knowledge of soil pollutant content and spatial distribution is crucial to avoid health risks and achieve sustainable soil use. However, due to the limitation of human, material and financial resources, it is difficult to carry out intensive detection of soil heavy metals in polluted areas. This problem can be solved by using known soil heavy metal content data to predict the heavy metals in unknown regions. This study utilizes a three-dimensional Convolutional Neural Network (3DCNN) model, combined with spatial location and soil physicochemical properties, to predict heavy metal in a typical industrial zone in Qingdao City. The results show that the <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2024_84545_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation> of 3DCNN for predicting cadmium (Cd), lead (Pb), copper (Cu) and nickel (Ni) are 0.59, 0.59, 0.77 and 0.51, respectively. Therefore, 3DCNN can be used as an effective method for spatial prediction of soil heavy metals, which can reduce the cost of sampling and laboratory analysis. The three-dimensional spatial distribution analysis revealed that Cd and Pb were concentrated in the surface soil layer and gradually decreased with the depth, while Cu and Ni contents are mainly concentrated in the range of 3 m, exhibiting downward migration. Therefore, heavy metal enrichment has occurred in this area, and soil heavy metal treatment should be carried out before redevelopment.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prediction of heavy metal spatial distribution in soils of typical industrial zones utilizing 3D convolutional neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月2日

查看全文
  • Chao Liu,
  • Lan Chen,
  • Guoqing Ni,
  • Xiuhe Yuan,
  • Shuai He,
  • Sheng Miao

摘要

Land resources are vital for urban development and construction. Abandoned industrial areas often contain large amounts of heavy metals from past industrial activities. Accurate knowledge of soil pollutant content and spatial distribution is crucial to avoid health risks and achieve sustainable soil use. However, due to the limitation of human, material and financial resources, it is difficult to carry out intensive detection of soil heavy metals in polluted areas. This problem can be solved by using known soil heavy metal content data to predict the heavy metals in unknown regions. This study utilizes a three-dimensional Convolutional Neural Network (3DCNN) model, combined with spatial location and soil physicochemical properties, to predict heavy metal in a typical industrial zone in Qingdao City. The results show that the \(R^2\) of 3DCNN for predicting cadmium (Cd), lead (Pb), copper (Cu) and nickel (Ni) are 0.59, 0.59, 0.77 and 0.51, respectively. Therefore, 3DCNN can be used as an effective method for spatial prediction of soil heavy metals, which can reduce the cost of sampling and laboratory analysis. The three-dimensional spatial distribution analysis revealed that Cd and Pb were concentrated in the surface soil layer and gradually decreased with the depth, while Cu and Ni contents are mainly concentrated in the range of 3 m, exhibiting downward migration. Therefore, heavy metal enrichment has occurred in this area, and soil heavy metal treatment should be carried out before redevelopment.