Overlooked Contribution of the Indole Moiety to the Formation of Haloacetonitrile Disinfection Byproducts

Identifying initial transformation products during chlorination of the indole moiety and unveiling their formation mechanisms

To identify toxicity drivers within poorly characterized high-molar-weight disinfection by-products (DBPs), relatively stable high-yield initial transformation products generated from aromatic amino acids and peptides and humic substances have drawn much attention. In this study, initial transformation products in chlorination of the indole moiety in tryptophan (Trp) are proposed and their formation mechanisms were investigated using a quantum chemical computational method. The results indicate that 3-Cl-Trp+ is initially formed after the Cl+ of HOCl attacks the indole moiety, and nucleophilic addition with nucleophilic agents (H2O and OCl-) is thermodynamically preferred over deprotonation to generate 2-X-3-Cl-indoline moiety (X = OH and OCl), which is in contrast to indole. Over 25 types of initial transformation products are proposed from the 2-X-3-Cl-indoline moiety and two ring opening pathways were found at N1-C2 and C2-C3 bonds. Significantly, most structures of initial transformation products proposed based on experimental detection m/z values were confirmed using quantum chemical calculations and some new products are proposed in this work. The results are helpful to expand our understanding of the intrinsic reactivity of aromatic ring towards chlorination by hypochlorous acid.

Widespread Antioxidants during Storm Events Could Serve as Precursors of Regulated, Priority, and New Disinfection Byproducts

Widely used antioxidants can enter the environment via urban stormwater systems and form disinfection byproducts (DBPs) during chlorination in downstream drinking water processes. Herein, we comprehensively investigated the occurrence of 39 antioxidants from stormwater runoff to surface water. After a storm event, the concentrations of the antioxidants in surface water increased by 1.4-fold from 102-110 ng/L to 128-139 ng/L. Widespread antioxidants during the stormwater event could transform into toxic DBPs during disinfection. Moreover, the yields of trihalomethanes, haloacetaldehydes, haloacetonitriles (HANs), and halonitromethanes during the chlorination of widely used antioxidants considerably increased with an increasing chlorine dose and contact time. Specifically, the yields of dichloroacetonitrile during the chlorination of diphenylamine (DPA) and N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) were higher than those of most reported amino acid precursors, indicating that DPA and 6PPD might be important precursors of HANs. Exploring the intermediates using GC × GC-time-of-flight high-resolution mass spectrometry helped reveal potential pathways from DPA to HANs, whose formation could be attributed to the intermediate carbazole and indole moieties detected in this study. This study provides insights into the transport and transformation of commonly used antioxidants in a water environment and during water treatment processes, highlighting the potential risks of anthropogenic pollutants from a DBP perspective.

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).