A geostatistical approach to estimating source apportionment in urban and peri-urban soils using the Czech Republic as an example 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. Human-related activities such as industry, sewage discharge, mining, atmospheric deposition, and agriculture are primarily characterized by urban and peri-urban soil1. International communities, allied bodies, multinational companies, countries, and humans who are directly affected by potentially toxic elements (PTEs) worldwide have expressed great concern about the threat posed by PTEs. PTEs accumulations in the soil can cause changes in soil fertility and cultivation characteristics of bioavailability, as well as increase the persistence of PTEs toxicity, which can easily be transported and accumulated in a food chain, resulting in food safety hazards and health-related issues in the human body via a variety of pathways (inhalation, ingestion, and dermal uptake)2,3,4. Public quibble about the build-up of PTEs in farmland has been escalating, limiting the soil’s functionality, creating crop and water toxicity, and endanger human health5,6. The impact of PTEs on the soil is a cross-border challenge that is not limited to a particular region but also a worldwide concern, which transcends peri-urban, urban and continental borders. Global integration, trade and movement of goods and services facilitate the impact of PTEs from afar on someone distant from a polluted place. Urban and rural areas, according to Kombe7 and Keshavarzi et al.8, are transitional areas where activities are integrated. This allows for easy accessibility of goods and services and migration of PTEs through torrential rainfall and erosion from urban and peri-urban areas and contrariwise. However, some big cities are expanding in order to incorporate a rising population in peri-urban areas8 closer to urban areas. Though some cities are closer to peri-urban areas, it allows for easy congestion in the towns due to it being a hub for most multinational industries and people having the edge of migrating urban, increases vehicular traffic, creates an avenue for urban expansion and construction activities that contribute to soil pollution in the immediate environment.According to Vázquez Cueva et al.9 and Tume et al.10, in many instances, urban waste, industrial effluents, and even manures and agricultural fertilizers pollute the soils of these locations with PTEs. Anthropogenic pollutants such as leaks and spills, manufacturing and construction activities, agricultural practices, transportation and chemical waste dumping, concomitant with natural pollutants, predominate in urban areas, gradually drifting to the peri-urban area as a result of land acquisition, industrial and urban expansion.The uniqueness and dynamism of each urban and peri-urban area differ from one another geographically. However, the only constant is that PTEs are resident in the soil due to pollution, whether anthropogenic, natural, or both. Fei et al.11 and Huang et al.12 outlined that to minimize the cost and complexity of soil remediation effectively, it is critical to quantify the sources of soil PTEs pollution. The practicality of evidence-based analysis can be relished based on the robustness of the statistical approaches employed either qualitatively or quantitatively. Source apportionment approaches have been applied in multiple disciplines, including soil science, water research, and air quality assessment. Positive matrix factorization (PMF), absolute principal components score-multiple 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. However, authors frequently apply the PMF, and APCS-MLR approaches to quantify source distribution. Lang et al.13; Jain et al.14; Guan et al.15; Salim et al.16; Zhang et al.17; Fei et al.4; Zhang et al.18 and Agyeman et al.19, are some of these authors that fall on the resilience of PMF and APCS-MLR to calculate source apportionment. The healthy academic nemesis between PMF and APCS-MLR has complemented each other in academic space. However, because the terrain (soil science) is so important, most authors sought to apply either one or both in source apportionment. Most comparative analyses, to name a few, Gholizadeh et al.20, Salim et al.16; Jain et al.14 and Guan et al.15 have adjudged PMF or APCS/MLR to be optimal. 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. However, if the applicability of PMF or APCS/MLR or both has an advantage over other receptor models, its excellent performance is hampered by various limitations or constraints. According to Yuanan et al.22, PMF may produce inaccurate estimations if the PTEs identified in topsoil have undergone significant selection enrichment. Furthermore, Wu et al.23 and Guan et al.15 claimed that PMF was unable to effectively determine the nature of the differences in PTEs observed in surface soils across the entire area and create a fitting effect. Zhang et al.17 also added that APCS/MLR could not discharge a lot of sources in each factor loadings.Investigating pollution sources pathways via diverse receptor models aids in controlling pollution hazards in the environment. The use of robust receptor models facilitates in minimizing the risk of pollution and, at the same time, can assist in assuaging occurrences. Essentially, the pathways of pollution sources may be identified using receptor models. The output obtained assists stakeholders in evaluating health and ecological impact and adopting actions to improve sustainability impact. The development of robust receptor models aids in detecting locations that require further attention and assists stakeholders in developing reliable emergency response plans. Wang et al.24 stressed that applying receptor models, which are based on multivariate statistical approaches to identify and quantify pollutants (PTEs) apportionment to their sources, can significantly improve the traditional source apportionment approach. This study intends to use PMF as a base model to build a hybridized receptor model that will enhance efficiency and minimize errors in identifying and estimating source apportionment. PMF will be combined with geostatistical approaches such as ordinary kriging and empirical Bayesian kriging. The study region is an active agricultural area with many industries such as metal works and steel industries. We hypothesized that the dependability of the receptor model is determined by its efficiency and ability to reduce error when applied. 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.The selected study area is in the Czech Republic in the Frydek Mistek district in the Moravian-Silesian area (Fig. 1). The research area’s geomorphology is relatively rugged terrain, mostly part of the Moravian-Silesian Beskydy region, a part of the extracellular matrix mountain range. The study area is positioned at latitude 49° 41′ 0′ North and longitude 18° 20′ 0′ East at an altitude ranging from 225 to 327 m above sea level; however, the Koppen classification system of the area’s climatic condition is classified as Cfb = temperate oceanic climate with a high level of rainfall even in dry months. The temperature fluctuates typically from − 5 to 24 °C throughout the year, with temperatures occasionally falling below − 14 °C or reaching over 30 °C. The maximum average annual rainfall is 83 mm, with a minimum total accumulation of 17 mm25. The district’s area survey is estimated to be 1208 km2, with 39.38% of the land area under cultivation and 49.36% under forest cover. However, the site designated for the study is approximately 889.8 km2 (see Fig. 1). Agriculture, the steel industry, and metal works are all active in and around the Ostrava neighborhood. The soil qualities are easily distinguished from the color, texture, and carbonate concentration of the soil. The soil’s texture is medium to fine, and it is derived from parent materials. They are primarily colluvial, alluvial, or aeolian in nature. Some soil areas have mottles in the top and subsoil, which are usually followed by concrete and bleaching. However, cambisols and stagnosols are the most common soil types in the region26. With elevations ranging from 455.1 to 493.5 m, cambisol soils predominate in the Czech Republic27.Figure 1Soil sampling and soil analysisOne hundred and fifteen topsoil samples were collected from urban and peri-urban areas in the Frydek Mistek district. The sample design used was the regular grid, and the soil sample intervals were 2 × 2 km using a portable GPS unit (Leica Zeno 5 GPS) at a depth of 0 to 20 cm for topsoil. The samples were put in Ziploc bags, labelled correctly, and brought to the laboratory. To obtain a pulverized sample, the samples were air-dried, crushed by a mechanical device (Fritsch disk mill), and sieved (< 2 mm). One gram of the dried, homogenized, and sieved soil sample (sieve size 2 mm) was placed in a labelled Teflon bottle. In each Teflon bottle, 7 ml of 35% HCl and 3 ml of 65% HNO3 were dispensed (using automatic dispensers—one for each acid). The cap was gently closed to allow the sample to remain overnight for reaction (aqua regia procedure). Subsequently, the supernatant was placed on a hot metal plate for 2hrs to boost the digestion process of the sample before being allowed to cool. Then, the supernatant was transferred to a 50 ml volumetric flask and diluted to 50 ml with deionized water. After that, the diluted supernatant was filtered into 50 ml PVC tubes.Furthermore, 1 ml of the diluted solution was diluted with 9 ml of deionized water and filtered into a 12 ml test tube prepared for PTE (Al. Ba, Cd, Pb, Sb, Fe. V) pseudo-concentration. ICP-OES (inductively coupled plasma optical emission spectrometry) (Thermo Fisher Scientific, USA) was used to detect metal concentrations in accordance with conventional methods and protocols. The quality assurance and control (QA/QC) method was ensured by examining each sample's standards reference material (SRM NIST 2711a Montana II soil). The detection limits of the PTEs used in this investigation are as follows: 0.0002 (Cd), 0.0007 (Cr), 0.0060 (Cu), 0.0001 (Mn), 0.0004 (Ni), 0.0015 (Pb), 0.0067 (As), and 0.0060. (Zn). To accomplish QA/QC, we used blank reagents, repeated samples, and standard reference materials. Duplicate analysis was performed to guarantee that the error was minimized (< 5%).Receptor modelsPMF receptor modelPositive matrix factorization (PMF) receptor modelling is often performed with the US-EPA PMF 5.0 software28. The receptor model is one of the multivariate approaches for source analysis used to solve the chemical mass balance, and the original data matrix X is represented in the order m × n, which can be written asG (m × p) represents a factor contribution matrix, F (p × n) also denotes the factor profile matrix, and E (m × n) is a residual error matrix. E is given as$$e_{ij} = mathop sum limits_{k = 1}^{p} g_{ik } f_{ki} – x_{ij}$$where i is the elements 1 to m, j signifies elements 1 to n, and k represents the source from 1 to p. The authors have previously discussed the function of the minimal Q and the uncertainty, and the parameters and implementation techniques involved19.Ordinary kriging – positive matrix factorization (OK-PMF)Ordinary kriging (OK) is an interpolation approach that allowed us to estimate the spatial distribution of PTEs in the site under investigation. Kriging is an interpolation that predicts variable values in areas where data are unavailable based on the spat
https://www.nature.com/articles/s41598-021-02968-8
A geostatistical approach to estimating source apportionment in urban and peri-urban soils using the Czech Republic as an example
