Instituto Politécnico de Castelo Branco, 6001-909 Castelo Branco, Portugal; CERENA/FEUP Research Center, Portugal.
Department of Natural Resources and Environmental Engineering, Univ. of Vigo, Lagoas Marcosende, 36310 Vigo, Spain.
Sci Total Environ. 2017 Dec 15;603-604:167-177. doi: 10.1016/j.scitotenv.2017.06.068. Epub 2017 Jun 15.
Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs.
工农业活动严重制约着土壤质量。潜在有毒元素（PTEs）对公众健康和环境都构成了威胁。在这方面，确定需要修复的区域至关重要。在本研究中，收集了一个包含 14 种元素（Cu、Pb、Zn、Ag、Ni、Mn、Fe、As、Cd、V、Cr、Ti、Al 和 S）的地球化学数据集（230 个样本），这些样本分布在阿斯图里亚斯河口（西班牙北部）的八个不同区域，这些区域主要活动分别为娱乐、农业/畜牧业和重工业。然后，在每个区域的短、中、长距离上使用分层系统抽样方法，以获得所选属性总变异性的代表性图片。然后，根据几种沉积物质量指南（SQGs）的参考值，将信息组合成四个风险类别（低、中、高、修复）。然后，对每个区域进行贝叶斯分析，以确定 PTEs 的相关性特征，结果表明无监督学习网络技术是最合适的。基于所获得的贝叶斯网络结构，选择 Pb、As 和 Mn 作为关键污染参数。对于这 3 个元素，获得的条件概率被分配给每个观测点，并构建了一个简单、直接的指数（贝叶斯风险指数-BRI），作为先前获得的概率加权的预定义风险类别的线性评分。最后，对 BRI 进行了地质统计学建模。计算了一百次序贯高斯模拟（SGS）。获得了均值图像和标准偏差图，允许定义高/低风险聚类（局部 G 聚类）和计算空间不确定性。高风险聚类主要分布在海拔最高的农业/畜牧业区域，显示出较低的空间不确定性，这清楚地表明需要进行修复。主要源自冶金工业的大气排放导致 PTEs 对土壤的污染。
