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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".