Effect of spring runoff on 2,6-dichloro-1,4-benzoquinone formation during water treatment
Bibliographic record
Abstract
This study investigated the impacts of spring runoff on the formation of halobenzoquinones (HBQs) and their correlation with common water quality parameters (WQPs) and aromatic amino acids (AAs) in source water. Source water and treated water samples were collected at two drinking water treatment plants in 2021, 2022, and 2023. HBQs and aromatic AAs were analyzed using solid phase extraction with high performance liquid chromatography-tandem mass spectrometry methods. The only HBQs detected in treated water were 2,6-dichloro-1,4-benzoquinone (DCBQ) and hydroxy‑DCBQ (OH-DCBQ). The concentration of DCBQ was 3-4 times higher during spring runoff events than during non-spring-runoff periods, suggesting the impact of spring runoff on the formation of DCBQ. The DCBQ concentrations in finished water positively correlated with the color, dissolved organic carbon, total organic nitrogen, and specific ultraviolet absorbance WQPs of source water in 2021 and 2022. The temporal trend of the total aromatic AAs determined in source water was strongly and positively correlated to DCBQ in finished water. Finally, there was a significant positive correlation between the concentration of DCBQ determined immediately following the addition of chlorine and the presence of its transformation product, OH-DCBQ, in finished water. The results also showed that powdered activated carbon can remove some of the HBQ precursors in the source water to reduce DCBQ formation. This study demonstrated that WQPs and aromatic AAs are useful indicators for the removal of precursors to reduce HBQ formation during drinking water treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".