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.
Highlights
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
As summarized by Lee et al.[21], the preference for Positive matrix factorization (PMF) or APCS/multiple linear regression (MLR) or both over the other receptor models based on the competitive advantage such as (i) the use of efficient monitoring processes, the establishment of a sizeable database which has become a general practice;(ii) these receptor models do not require pre-measured source profiles in discrepancy with chemical mass balance (CMB); and (iii) the receptor model’s capability permits it to cope with significant amounts of monitoring data
This study addresses the following research question: How reliable are the hybridized receptor models compared to the base model (PMF)? What is the performance of the receptor models in terms of efficiency and error reduction? The specific objectives of this paper revolve around the following: determining the concentration of PTEs in urban and peri-urban soil, comparing diverse receptor models for source apportionment, and proposing and validating receptor model technique that is efficient and more practical for source apportionment estimation
Summary
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. 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. Positive matrix factorization (PMF), absolute principal components scoremultiple linear regression (APCS-MLR), UNMIX, and chemical mass balance (CMB) are some of the multivariate statistics utilized in the quantification of source apportionment of pollutants. As summarized by Lee et al.[21], the preference for PMF or APCS/MLR or both over the other receptor models based on the competitive advantage such as (i) the use of efficient monitoring processes, the establishment of a sizeable database which has become a general practice;(ii) these receptor models do not require pre-measured source profiles (i.e., backward tracking) in discrepancy with chemical mass balance (CMB); and (iii) the receptor model’s capability permits it to cope with significant amounts of monitoring data. Zhang et al.[17] added that APCS/MLR could not discharge a lot of sources in each factor loadings
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