Nontargeted Analysis with Machine Learning Method Revealing New Chlorinated Byproducts of Tire-Leaching Compounds and Their Formation Pathways During Water Chlorination
Bibliographic record
Abstract
Tire-leaching compounds entering drinking water sources via stormwater runoff can undergo reactions with disinfectants (e.g., Cl 2 ) during water treatment, forming toxic chlorine-containing byproducts. N -(1,3-Dimethylbutyl)- N ’-phenyl-1,4-benzenediamine (6PPD) and its ozonated byproduct, 6PPD-quinone (6PPD-Q), are commonly detected tire-leaching compounds, but their fates during chlorination remain poorly understood. To fill this gap, we investigated the Cl-containing transformation products (Cl-TPs), reaction pathways, and associated toxicity changes of 6PPD and 6PPD -Q during chlorination. Utilizing high-performance liquid chromatography coupled with high-resolution mass spectrometry (HPLC-HRMS) combined with the machine-learning tool ChloroDBPFinder and a suspect screening approach, we identified 24 novel Cl-TPs derived from 6PPD and 6PPD -Q. Our results indicated that these Cl-TPs formed favorably at [Cl 2 ]/[precursor] molar ratios of 5:1 for 6PPD and 10:1 for 6PPD-Q, particularly under alkaline conditions (pH 8.0). Quantitative structure–toxicity relationship (QSTR) analysis revealed that several TPs exhibited higher toxicity than their parent compounds, with TP-180 demonstrating a 101-fold increase in oral rat toxicity compared to 6PPD. These findings enhance our understanding of the tire-derived compounds’ transformation and associated risks during water disinfection processes, providing valuable insights for water treatment facilities, policymakers, and public health officials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".