Machine Learning-Enhanced DGT Passive Sampling Coupled with Non-Targeted Analysis for High-Throughput Monitoring of Chemical Pollutants in Aquatic Systems

High-throughput monitoring of organic pollutants in aquatic environments remains a formidable challenge due to heterogeneous emission sources, irregular emission patterns, and the scarcity of analytical standards. The diffusive gradients in thin-films (DGT) technique provides time-integrated and representative sampling. High-resolution mass spectrometry-based nontargeted analysis (NTA) enables broad-spectrum chemical identification. However, the application of DGT-based NTA (DGT-NTA) is hindered by the lack of pollutant-specific parameters required for concentration calculations, including diffusion coefficients ( D ) and ionization efficiencies ( IE ). This study proposes a machine learning (ML)-assisted framework that enabled semiquantitative analysis in DGT-NTA workflows by predicting D and IE, thereby reducing the dependence on compound-specific standards in the routine quantification step. When applied to municipal wastewater effluents, the framework enabled semiquantitative estimation of 85 identified pollutants. Targeted validation demonstrated that the mean prediction error was 1.78-fold for compounds within the applicability domains of the constructed ML models for D and IE . An additional ecological risk assessment was conducted using predicted concentrations and aquatic toxicity data, thereby prioritizing 8 potentially high-risk pollutants. This framework provides a promising avenue for high-throughput monitoring of emerging pollutants in aquatic environments.