Liu Chao, Chen Lan, Ni Guoqing, Yuan Xiuhe, He Shuai, Miao Sheng
School of Environment and Municipal Engineering, Qingdao University of Technology, Qingdao, 266520, China.
School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520, China.
Sci Rep. 2025 Jan 2;15(1):396. doi: 10.1038/s41598-024-84545-3.
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 [Formula: see text] 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.
土地资源对城市发展和建设至关重要。废弃工业区往往含有过去工业活动产生的大量重金属。准确了解土壤污染物含量和空间分布对于避免健康风险和实现土壤可持续利用至关重要。然而，由于人力、物力和财力的限制，在污染区域对土壤重金属进行密集检测较为困难。利用已知的土壤重金属含量数据来预测未知区域的重金属含量可以解决这一问题。本研究利用三维卷积神经网络（3DCNN）模型，结合空间位置和土壤理化性质，对青岛市某典型工业区的重金属进行预测。结果表明，3DCNN对镉（Cd）、铅（Pb）、铜（Cu）和镍（Ni）的预测[公式：见正文]分别为0.59、0.59、0.77和0.51。因此，3DCNN可作为土壤重金属空间预测的有效方法，可降低采样和实验室分析成本。三维空间分布分析表明，Cd和Pb集中在表层土壤层，并随深度逐渐降低，而Cu和Ni含量主要集中在3 m范围内，呈现向下迁移趋势。因此，该区域已发生重金属富集，在重新开发前应进行土壤重金属治理。
