Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 16500, Prague, Czech Republic.
Sci Rep. 2021 Dec 8;11(1):23615. doi: 10.1038/s41598-021-02968-8.
Unhealthy soils in peri-urban and urban areas expose individuals to potentially toxic elements (PTEs), which have a significant influence on the health of children and adults. Hundred and fifteen (n = 115) soil samples were collected from the district of Frydek Mistek at a depth of 0-20 cm and measured for PTEs content using Inductively coupled plasma-optical emission spectroscopy. The Pearson correlation matrix of the eleven relevant cross-correlations suggested that the interaction between the metal(loids) ranged from moderate (0.541) correlation to high correlation (0.91). PTEs sources were calculated using parent receptor model positive matrix factorization (PMF) and hybridized geostatistical based receptor model such as ordinary kriging-positive matrix factorization (OK-PMF) and empirical Bayesian kriging-positive matrix factorization (EBK-PMF). Based on the source apportionment, geogenic, vehicular traffic, phosphate fertilizer, steel industry, atmospheric deposits, metal works, and waste disposal are the primary sources that contribute to soil pollution in peri-urban and urban areas. The receptor models employed in the study complemented each other. Comparatively, OK-PMF identified more PTEs in the factor loadings than EBK-PMF and PMF. The receptor models performance via support vector machine regression (SVMR) and multiple linear regression (MLR) using root mean square error (RMSE), R square (R) and mean square error (MAE) suggested that EBK-PMF was optimal. The hybridized receptor model increased prediction efficiency and reduced error significantly. EBK-PMF is a robust receptor model that can assess environmental risks and controls to mitigate ecological performance.
城市周边和城区的不健康土壤使个人接触到潜在的有毒元素 (PTEs)，这些元素对儿童和成人的健康有重大影响。从弗里德克-米特区采集了 115 个（n=115）土壤样本，深度为 0-20 厘米，使用电感耦合等离子体-光学发射光谱法测量 PTEs 含量。11 个相关交叉相关的 Pearson 相关矩阵表明，金属（类金属）之间的相互作用范围从中度（0.541）相关到高度相关（0.91）。使用母体受体模型正矩阵因子分解（PMF）和基于杂交地质统计学的受体模型（如普通克里金-正矩阵因子分解（OK-PMF）和经验贝叶斯克里金-正矩阵因子分解（EBK-PMF）计算 PTEs 来源。基于源分配，地球成因、车辆交通、磷肥、钢铁工业、大气沉积物、金属工厂和废物处理是导致城市周边和城区土壤污染的主要来源。研究中使用的受体模型相互补充。相对而言，OK-PMF 在因子负荷中比 EBK-PMF 和 PMF 识别出更多的 PTEs。通过支持向量机回归 (SVMR) 和多元线性回归 (MLR) 使用均方根误差 (RMSE)、R 平方 (R) 和均方误差 (MAE) 评估受体模型的性能表明，EBK-PMF 是最佳的。混合受体模型显著提高了预测效率并降低了误差。EBK-PMF 是一种稳健的受体模型，可评估环境风险并控制以减轻生态性能。
