Quantitative source apportionment and ecological risk assessment of heavy metals in soil of a grain base in Henan Province, China, using PCA, PMF modeling, and geostatistical techniques.

Abstract

Heavy metals' pollution of agricultural soil is an environmental problem of widespread concern, especially in China, but, how to accurately identify their sources is still a great challenge. Here, we implemented a high-density sampling strategy (2194 samples collected) and combined correlation analysis (CA), principal component analysis (PCA), positive matrix factorization (PMF) modeling, and geostatistical analyses to identify and quantify the source contributions of heavy metals in agricultural soil in a key commodity grain base of China. The results showed that the excess risk rate of Cd was the highest (4.3%), and contributing to 42.7% of the total potential ecological risk in the region. Hence, Cd is the most important ecological risk factor. Heavy metals in the region mainly originated from oil extraction and smelting (7.5%), parent materials and traffic emissions (59.2%), coal-related industrial activities (11.6%), and agricultural sources (21.7%). Cd was mainly derived from oil extraction and smelting (90.1%). As (73.6%), Cr (90.3%), and Pb (67.1%) were closely associated with parent materials and traffic emissions. Hg (87.7%) was mainly originated from coal-related industrial sources. In addition, As (25.6%) and Pb (24.3%) were also from agricultural sources (such as fertilizer, pesticides, and manure). This study confirms that the combination of these methods can accurately identify the sources of heavy metals in agricultural soil. These findings can assist governmental agencies in implementing targeted control strategies to prevent the spread of heavy metals.