Annual and Seasonal Variability of Trichloromethane in Drinking Water of Kunshan City 2016–2022 and Associated Health Risks
Bibliographic record
Abstract
This study aimed to evaluate the annual pollution characteristics of trichloromethane (TCM) in Kunshan City’s tap water from 2016 to 2022. This research analyzed 566 tap water samples from centralized water supply units, utilizing the GB 5749-2006 Sanitary Standard for Drinking Water as the evaluation benchmark. Data analysis employed non-parametric tests and Spearman’s correlation analysis using Excel 2017 and SPSS 26.0. The results indicated a 100% compliance rate with the TCM limit (0.06 mg/L), with median annual concentrations ranging from 0.1 to 6.4 μg/L. Significant inter-annual variations were observed (H = 222.5, p < 0.01), with the lowest levels in 2019 and the highest in 2020. Quarterly analysis revealed significant seasonal differences (H = 94.0, p < 0.01), peaking in the third quarter (8.0 μg/L) and bottoming in the first quarter (3.5 μg/L). TCM concentrations showed significant correlations with annual and quarterly trends, turbidity, and chlorides (|rs| > 0.3, p < 0.01) but not with pH (rs = −0.0025, p = 0.55). While Kunshan City’s drinking water demonstrates satisfactory TCM levels, an increasing annual trend and higher concentrations in the latter half of the year warrant continued monitoring and investigation. In this study, we assessed the health risks for households in Kunshan, China, due to trichloromethane (TCM) in drinking water. The overall carcinogenic risk from multiple exposure pathways was slightly above the ideal level, while the non-carcinogenic risk was within an acceptable range.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".