Advancing Fluorine Mass Balance Using Machine Learning-Based Quantitative Nontarget Screening
Year: 2026
Type: preprint
Abstract: Per- and polyfluoroalkyl substances (PFAS) often account for only a small fraction of the extractable organic fluorine (EOF) in fluorine mass balance (FMB) studies. Closing this fluorine gap requires identification and quantification of a broader range of fluorinated compounds, including low-fluorinated substances that are difficult to prioritize by nontarget screening (NTS). Here, a workflow combining fluorine-specific NTS with machine learning-based quantification (qNTS) to improve FMB closure was developed. Extreme Gradient Boosting (XGBoost)-based qNTS models for electrospray ionization (ESI) + and ESI − were trained with 597 reference compounds (185 fluorinated) reaching mean absolute error of ×2.8 and ×2.5 on held-out folds, respectively. An independent validation set of 57 compounds previously unseen by the model reached a mean error of ×2.1. The workflow was applied to urban runoff extracts and AFFF-impacted soils. In sewage-associated runoff, fluorinated pharmaceuticals and their transformation products dominated the identified fluorine, whereas conventional PFAS (i.e. those containing a perfluoroalkyl chain) contributed <3%. Non-PFAS organofluorines (including many pharmaceuticals) contributed up to 48% of the EOF. Identified compounds explained 54–107% of measured EOF in sewage-associated samples but only 1–24% in street runoff, indicating a large fraction of unidentified fluorine in the latter. Application to aqueous film forming foam (AFFF)-impacted soils showed that qNTS performed well for anionic PFAS but was less reliable for cationic and zwitterionic PFAS, which were poorly represented in the training data. Overall, qNTS provided concentration estimates that enabled rapid identification of the compounds contributing most to EOF, thereby directing subsequent target confirmation and FMB closure.
Source: ChemRxiv
Language: English
Subfield: Environmental Chemistry
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W7211878788