A geostatistical approach to estimating source apportionment in urban and peri-urban soils using the Czech Republic as an example

Abstract

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 (R2) 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.

Spatial distribution and source apportionment of DTPA-extractable metals in soils surrounding the largest Serbian steel production plant

“…Font et al (2022) identified stainless-steel processes as one of the main contributors to Ni concentrations in an industrial area in the United Kingdom [ 71 ]. Although most literature suggests that Fe is of geogenic origin, steel production facilities were also identified as contributors to Fe concentrations mainly due to the blast furnaces' operation [ 72 , 73 ]. An extensive study on topsoil quality assessment near a closed steel smelter at the Capital Iron and Steel Factory, Beijing in China, confirmed that higher concentrations of Cu in soils surrounding the steelmaking site are mainly associated with steel smelting activities [ 4 ].…”

Complexity of hydrology, sewage and industries in distribution and migration pathways of heavy metals at spatial scale of China's brownfields

“…However, to date, there are only a few qualitative and quantitative studies being carried out on heavy metal apportionment in sources when compared to HMC, risk assessment or soil remediation (Jiang et al, 2017). Previously heavy metal source apportionment studies were utilized by geographic information systems‐based geostatistical models (Agyeman et al, 2021; Duan et al, 2020; Lv, 2019), multivariate statistics analyses (Qu et al, 2013) and isotopic signatures (Zhu et al, 2017) mostly as qualitative methods. However, the combination of geographic information system (GIS) with geostatistical and multivariate statistical analysis techniques has increasingly become useful in analysing the regional‐scale arrangement of the HMC within water and soil samples (Kattel et al, 2023; Ma et al, 2018; Zeng et al, 2022; Zhang & Zhang, 2021).…”

Estimating Source Apportionment of Heavy Metals Contamination in Surface Soil Based on a Positive Matrix Factorization (PMF) Model around Cerrito Blanco in San Luis Potosi, Mexico

“…[4,8,10]. To determine the source identifications and the spatial variations of heavy metals in soils, multivariate statistics are implemented in combination with geostatistical techniques [11,12]. There are also many receptor models which are used to identify the sources of heavy metals.…”