Monthly variations of unregulated brominated disinfection by-products in chlorinated water are correlated with total bromine
Bibliographic record
Abstract
Brominated disinfection by-products (Br-DBPs) can form during chlorination of drinking water in treatment plants (DWTP). Regulations exist for a small subset of Br-DBPs; However, hundreds of unregulated Br-DBPs have been detected and limited information exists on their occurrence, concentrations, and seasonal trends. Here, a data-independent precursor isolation and characteristic fragment (DIPIC-Frag) method was optimized to screen chlorinated waters for Br-DBPs. There were 553 Br-DBPs detected with m/z values ranging from 170.884 to 497.0278 and chromatographic retention times from 2.4 to 26.2 min. With MS2 information, structures for 40 of the 54 most abundant Br-DBPs were predicted. The method was then applied to a year-long study in which raw, clear well, and finished water were analyzed monthly. The 54 most abundant unregulated Br-DBPs were subjected to trend analysis. Br-DBPs with higher oxygen-to-carbon (O/C) and bromine-to-carbon (Br/C) ratios increased as water moved from the clear well to the finished stage, which indicated the dynamic formation of Br-DBPs. Monthly trends of unregulated Br-DBPs were compared to raw water parameters such as natural organic matter, temperature, and total bromine, but no correlations were observed. It was found that total concentrations of bromine (TBr) in finished water (0.04–0.12 mg/L) were consistently and significantly greater than in raw water (0.013–0.038 mg/L, P < 0.001), suggesting the introduction of bromine during the disinfection process. Concentrations of TBr in treatment units, rather than raw water, were significantly correlated to 34 of the Br-DBPs at α = 0.05. This study provides the first evidence that monthly trends of unregulated Br-DBPs can be associated with the concentration of TBr in treated waters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".