Health risk assessment of heavy metal(loid)s in the farmland of megalopolis in China by using APCS-MLR and PMF receptor models: Taking Huairou District of Beijing as an example

Abstract

The quality of agricultural soils is important for agricultural production and food safety. The contamination of agricultural soils by heavy metal(loid)s (HMs) has aroused global attention. Fifty-two topsoil samples with 8 HMs were gathered to assess the health risks of farmland soil in Huairou District, Beijing. As a significantly enriched pollutant, the results revealed that Hg had greater ecological risks relative to other HMs. We found that the positive matrix factorization (PMF) model appears to be more physically plausible in identifying complex pollution sources compared to the absolute principal components score-multiple linear regression (APCS-MLR) model, which had a higher fit coefficient (r2 = 0.69–0.99). Five HMs from pollution sources, including agricultural activities, traffic source, natural source, fuel burning, and industrial production, were identified by integrating the PMF model with Pearson's correlation analysis, revealing corresponding contribution rates of 29.40%, 22.54%, 20.16%, 15.20%, and 12.70%, respectively. The probabilistic health risk evaluation results showed an absence of non-carcinogenic risks in all populations, but the carcinogenic risk could not be ignored, especially in children. In addition, the source-oriented health risks showed that agricultural activities made the largest contribution to the health risks of all populations. This research provides scientific evidence for preventing HMs contamination and control of farmland.