Compositional mapping, uncertainty assessment, and source apportionment via pollution assessment-based receptor models in urban and peri-urban agricultural soils

Purpose Healthy soil and the environment rely on practical risk assessment, controls to improve environmental performance, and the efficient application of receptor models. The primary focus of the study is to evaluate multiple receptor models used to estimate source distribution. Methods This study collected 115 soil samples from the Frydek Mistek district of Czech Republic. Potentially toxic element (PTE) (Pb, As, Cr, Cd, Ni, Mn, Cu, and Zn) concentrations were measured using inductively coupled plasma optical emission spectrometry. Pollution indices like pollution index, ecological risk, geoaccumulation index, and enrichment factor were hybridized with positive matrix factorization (PMF) to create pollution index-PMF (PI-PMF), ecological risk-PMF (ER-PMF), geoaccumulation index-PMF (IGEO-PMF), and enrichment factor-PMF (EF-PMF). ABC composite mapping technique was used to visualize and determine the interaction between PTEs. Results The use of composite mapping multimaps with varying color patterns aided in identifying and establishing the source relationship between PTEs. Pollution assessment-based receptor models (PAB-RMs) revealed that the EF-PMF outperformed the PI-PMF, ER-PMF, IGEO-PMF, and PMF receptor models. EF-PMF outperformed other receptor models in model assessments such as coefficient of determination ( R 2 ), root mean square error (RMSE), and mean absolute error (MAE). The hybridized receptor models performed better in terms of error reduction, as the PAB-RMs’ reduction of DISP (displacement) intervals ratio showed smaller intervals than the parent model. Conclusion The combination of PMF and pollution assessment indices yielded positive results. By optimizing efficiency and reducing error, the current study provides a more reliable receptor model for estimating source distribution.