A partition computing-based positive matrix factorization (PC-PMF) approach for the source apportionment of agricultural soil heavy metal contents and associated health risks.

Apportion soil heavy metal sources across large-scale regions is a challenging task. The present study developed a modified receptor model to estimate the contributions of various sources to soil heavy metals and the associated health risks at a large scale. A positive matrix factorization model based on a partition computing approach was employed; the entire study area was divided into several zones for the source apportionment and then calculated together, termed partition computing-PMF (PC-PMF). The agricultural soil in Tianjin, China, was chosen for the case study. The PC-PMF results showed that irrigation, atmospheric deposition and sludge application were the main anthropogenic sources, with contributions of 26.60 %, 19.56 % and 2.86 %, respectively. We subsequently combined PC-PMF with a human health risk assessment model (HHRA) to obtain the human health risk of every source category. The natural background was regarded as a major factor influencing human health in the study area, with contributions of 38.03 % for the noncarcinogenic risk and 28.68 % for the carcinogenic risk. The results indicated that PC-PMF performed better at the source apportionment of soil heavy metals than PMF. This study provides a good example of how the spatial variability can be utilized to reduce the uncertainty in source apportionment.