An integrated method for source apportionment of heavy metal(loid)s in agricultural soils and model uncertainty analysis.

Elevated concentrations of heavy metals in agricultural soils threatening ecological security and the quality of agricultural products, and apportion their sources accurately is still a challenging task. Multivariate statistical analysis, GIS mapping, Pb isotopic ratio analysis (IRA), and positive matrix factorization (PMF) were integrated to apportion the potential sources of heavy metal(loid)s of orchard soil in Karst-regions. Study region soils were moderately contaminated by Cd. Obvious enrichment and moderate contamination level of Cd were found in study region surface soils, followed by As, Zn, and Pb. Correlation analysis (CA) and principal component analysis (PCA) indicated Ba, Co, Cr, Ni, V were mainly from natural sources, while As, Cd, Cu, Pb, Zn were derived from two kinds of anthropogenic sources. Based on Pb isotope composition, atmospheric deposition and livestock manure were the main sources of soil Pb accumulation. Further source identification and quantification results with PMF model and GIS mapping revealed that soil parent materials (46.44%) accounted for largest contribution to the soil heavy metal(loid)s, followed by fertilizer application (31.37%) and mixed source (industrial activity and manure, 22.19%). Uncertainty analysis indicated that the three-factors solution of PMF model was an optimal explanation and the heavy metal(loid) with lower percentage contributions had higher uncertainty. This study results can help to illustrate the sources of heavy metals more accurately in orchard agricultural soils with a clear expected future for further applications.