Due to the increasing diversity of organic contaminants discharged into the environment, reactivity prediction is necessary for chemical persistence evaluation, for water treatment and risk assessment purposes. Iron is the fourth most abundant element in the earth’s crust and the redox reactivity of various Fe(II) forms plays important roles in contaminant degradation. There are four major types of Fe(II) species: aqueous Fe(II), Fe(II) complexed with ligands, minerals bearing structural Fe(II), and sorbed Fe(II) on mineral oxide surfaces. However, almost all quantitative structure activity relationships (QSARs) that describe rates of contaminant transformation apply only to narrowly-defined, relatively homogenous families of reactants (e.g., dechlorination of alkyl halides) toward a small number of Fe(II) reductants. In this work, we first developed predictive models for abiotic reduction of 60 organic compounds with diverse reducible functional groups by a complexed Fe(II). We then employed both conventional chemical descriptor-based and machine learning (ML)-based approaches to develop QSARs; the ML-based model covered reduction rates for all classes of compounds and all of the conditions studied with the prediction accuracy similar to those of the conventional QSARs for individual classes. Our current work expands the reductants to all four types of Fe(II) species, and develops comprehensive ML models based on a large dataset of 1691 kinetic data points for 116 organic contaminants. Overall, this work offers novel modelling approaches to comprehensive abiotic reactivity prediction for persistence evaluation of organic compounds in anoxic water environments.

Abiotic reduction kinetics of organic compounds by Fe(II)-associated reductants

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