Diversity of Iodinated Water Disinfection Byproducts and Their Formation Dynamics Discovered Using Exposome-Scale Nontargeted Analysis and Machine Learning.

Water disinfection is critical to minimizing microbial risk, but unintentionally produces disinfection byproducts (DBPs). Exposure to the currently regulated chloro- and bromo-DBPs in water cannot fully account for the health effects observed from epidemiological studies. Iodinated disinfection byproducts (I-DBPs) are among the most toxic DBPs, but they have not been fully characterized because of significant technical challenges. We report here the first exposome-scale study of iodinated disinfection byproducts formed under environmentally relevant conditions. Here, "exposome-scale" refers to broad-scope, nontargeted profiling of exposure-relevant environmental contaminants. The machine-learning-assisted nontargeted analysis of chloramine-treated water enabled detection and characterization of 8551 and 1541 high-confidence iodinated molecular features, generated under the negative and positive ionization modes, respectively. Sixty-two new iodinated disinfection byproducts were identified, with 11 I-DBPs confirmed using authentic standards. Across four Environmental Protection Agency toxicity prediction end points (fathead minnow, Daphnia magna, Tetrahymena pyriformis, and rat), the 11 I-DBPs consistently showed higher or comparable toxicities relative to both the regulated DBPs and the four known I-DBPs, with predicted toxicities reaching up to 2-3 orders of magnitude greater than benchmark compounds such as chloroform and iodoacetic acid. This study demonstrates improved understanding of the diversity and formation dynamics of highly toxic I-DBPs, enabled by advances in integrating exposomics with machine learning techniques.