Integrated receptor models and multivariate geostatistical simulation for source apportionment of potentially toxic elements in soils

Abstract

Adequately understanding the source characteristic of potentially toxic elements (PTEs) in soils plays an important role in soil environmental protection and regulation. To improve the accuracy of quantifying source contributions, US-EPA positive matrix factorization (EPAPMF), weighted alternating least squares positive matrix factorization (WALSPMF), absolute principal component analysis/multiple linear regression (APCA/MLR), and UNMIX were applied and compared. Spatial distribution maps were delineated by using sequential Gaussian cosimulation (SGCS) with a linear model of coregionalization (LMC) fitting, and can be used to demarcate natural or polluted sources through superposing with auxiliary environmental data. Polluted area of PTEs exceeding a given threshold was determined using uncertainty analysis. The combination of receptor models and SGCS was used to a dataset consisting of As, Cd, Cr, Hg, Ni, Pb, and Zn as a case study. The four receptor models yielded three factors with comparable contributions to seven PTEs, but APCA/MLR produced some negative contributions. The average contributions were calculated based on EPAPMF, WALSPMF, and UNMIX. 85.3% of As, 81.3% of Cr, and 86.7% of Ni originated from natural source, and these three PTEs indicated consistent distributions with parent material map. Cd, Pb, and Zn were contributed by both industrial-traffic emissions and parent materials, with hotspots related to pollution sources and lacustrine deposits. Hg exhibited large-scale high value area around industrial sites, and exclusively derived from atmospheric deposition. A critical probability of 0.95 was adopted to determine polluted areas with PTE content exceeding 1.5 times its background. More than 40.0% of the total area was classified as contaminated for Hg, followed by Cd (3.1%) and Pb (2.9%). The combination of receptor models and SGCS proved to be an effective integrated approach for source apportionment.