Characteristics and partitions of traditional and emerging organophosphate esters in soil and groundwater based on machine learning.

ABSTRACT

Organophosphate esters (OPEs) pose hazards to both humans and the environment. This study applied target screening to analyze the concentrations and detection frequencies of OPEs in the soil and groundwater of representative contaminated sites in the Pearl River Delta. The clusters and correlation characteristics of OPEs in soil and groundwater were calculated by self-organizing map (SOM). The risk assessment and partitions of OPEs in industrial park soil and groundwater were conducted. The results revealed that 14 out of 23 types of OPEs were detected. The total concentrations (Σ23OPEs) ranged from 1.931 to 743.571 ng/L in the groundwater, and 0.218 to 79.578 ng/g in the soil, the former showed highly soluble OPEs with high detection frequencies and concentrations, whereas the latter exhibited the opposite trend. SOM analysis revealed that the distribution of OPEs in the soil differed significantly from that in the groundwater. In the industrial park, OPEs posed acceptable risks in both the soil and groundwater. The soil could be categorized into Zone I and II, and the groundwater into Zone I, II, and III, with corresponding management recommendations. Applying SOM to analyze the characteristics and partitions of OPEs may provide references for other new pollutants and contaminated sites.