Combination of enrichment factor and positive matrix factorization in the estimation of potentially toxic element source distribution in agricultural soil.

Abstract

The study intended to assess the level of pollution of potential toxic elements (PTEs) at different soil depths and to evaluate the source contribution in agricultural soil. One hundred and two soil samples were collected for both topsoil (51), and the subsoil (51) and the content of PTEs (Cr, Cu, Cd, Mn, Ni, Pb, As and Zn) were determined using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The concentrations of Zn and Cd in both soil horizons indicated that the current study levels were higher than the upper continental crust (UCC), world average value (WAV), and European average values (EAV). Nonetheless, the concentration values of PTEs such as Mn and Cu for EAV, As, Cu, Mn, and Pb for UCC, and Pb for WAV were lower than the average values of the corresponding PTEs in this study. The single pollution index, enrichment factor, and ecological risk revealed that the pollution level ranged from low to high. The pollution load index, Nemerow pollution index, and risk index all revealed that pollution levels ranged from low to high. The spatial distribution confirmed that pollution levels varied between the horizons; that is, the subsoil was considered slightly more enriched than the topsoil. Principal component analysis identified the PTE source as geogenic (i.e. for Mn, Cu, Ni, Cr) and anthropogenic (i.e. for Pb, Zn, Cd, and As). PTEs were attributed to various sources using enrichment factor-positive matrix factorization (EF-PMF) and positive matrix factorization (PMF), including geogenic (e.g. rock weathering), fertilizer application, steel industry, industrial sewage irrigation, agrochemicals, and metal works. Both receptor models allotted consistent sources for the PTEs. Multiple linear regression analysis was applied to the receptor models (EF-PMF and PMF), and their efficiency was tested and assessed using root-mean-square error (RMSE), mean absolute error (MAE), and R2 accuracy indicators. The validation and accuracy assessment of the receptor models revealed that the EF-PMF receptor model output significantly reduces errors compared with the parent model PMF. Based on the marginal error levels in RMSE and MAE, 7 of the 8 PTEs (As, Cd, Cr, Cu, Ni, Mn, Pb, and Zn) analysed performed better under the EF-PMF receptor model. The EF-PMF receptor model optimizes the efficiency level in source apportionment, reducing errors in determining the proportion contribution of PTEs in each factor. The purpose of building a model is to maximize efficiency while minimizing inaccuracy. The marginal error limitation encountered in the parent model PMF was circumvented by EF-PMF. As a result, EF-PMF is feasible and useful for apparently polluted environments, whether farmland, urban land, or peri-urban land.