确定潜在有毒元素(PTE)的来源和空间分布是土壤污染调查的关键问题。然而,对源贡献的不确定性估计仍然缺乏,准确的空间预测仍然具有挑战性。将稳健的贝叶斯多元受体模型(RBMRM)应用于青州市土壤数据集(429个样本中有8个PTE),计算具有不确定性的源贡献。提出了多任务卷积神经网络(MTCNN)来预测土壤PTE的空间分布。RBMRM提供了三个来源,与US-EPA正矩阵分解一致。以天然来源为主的As,Cr,Cu,和镍含量(78.5%~86.1%),贡献了37.1%,61.0%,和65.9%的Cd,Pb,Zn,不确定度指数(UI)<26.7%,不确定度低。工业,交通,农业来源对Cd有显著影响,Pb,和锌(30.2%~61.9%),UI<39.3%。汞主要来自大气沉积(99.1%),不确定性相对较高(UI=87.7%)。MTCNN获得了令人满意的准确性,R2为0.357-0.896,nRMSE为0.092-0.366。As的空间分布,Cd,Cr,Cu,Ni,Pb,和Zn受母体材料的影响。Cd,Hg,Pb,锌在城市地区表现出明显的热点。这项工作进行了新的方法探索,并对土壤污染治理提出了实际意义。
Determining sources and spatial distributions of potentially toxic elements (PTEs) is a crucial issue of soil pollution survey. However, uncertainty estimation for source contributions remains lack, and accurate spatial prediction is still challenging. Robust Bayesian multivariate receptor model (RBMRM) was applied to the soil dataset of Qingzhou City (8 PTEs in 429 samples), to calculate source contributions with uncertainties. Multi-task convolutional neural network (MTCNN) was proposed to predict spatial distributions of soil PTEs. RBMRM afforded three sources, consistent with US-EPA positive matrix factorization. Natural source dominated As, Cr, Cu, and Ni contents (78.5 %∼86.1 %), and contributed 37.1 %, 61.0 %, and 65.9 % of Cd, Pb, and Zn, exhibiting low uncertainties with uncertainty index (UI) < 26.7 %. Industrial, traffic, and agricultural sources had significant influences on Cd, Pb, and Zn (30.2 %∼61.9 %), with UI < 39.3 %. Hg originated dominantly from atmosphere deposition (99.1 %), with relatively high uncertainties (UI=87.7 %). MTCNN acquired satisfactory accuracies, with R2 of 0.357-0.896 and nRMSE of 0.092-0.366. Spatial distributions of As, Cd, Cr, Cu, Ni, Pb, and Zn were influenced by parent materials. Cd, Hg, Pb, and Zn showed significant hotspot in urban area. This work conducted a new approach exploration, and practical implications for soil pollution regulation were proposed.