Factors Influencing Formation of Trihalomethanes in Drinking Water: Results from Multivariate Statistical Investigation of the Ontario Drinking Water Surveillance Program Database
Bibliographic record
Abstract
Abstract The presence of trihalomethanes (THMs) in drinking water is an important issue in the context of their potential health effects. Numerous studies have developed models in the past three decades relating THMs concentrations to different factors (e.g., dissolved organic carbon [DOC], chlorine dose, pH, etc.). Previous studies characterized the importance of specific factors through controlled studies using synthetic water or source waters from a small number of water treatment plants. Few studies have reported looking for factors related to THMs formation system-wide across many different water supply systems, and in environments where many factors vary simultaneously. This study presents the results of a multivariate statistical analysis for 162 water supply systems in Ontario, Canada for 2000 to 2004. Principal component analysis (PCA) was applied to determine important factors and possible clusters of variation. PCA identified DOC, chlorine dose, pH, temperature, and reaction time as significant factors for THMs formation. Separate clusters were observed for DOC-colour; chlorine dose-total/free residual chlorine; and hardness-alkalinity. Each cluster indicated factors varying together and representing significant variation. Temperature and pH were found significant and uncorrelated throughout the analysis. The multivariate analysis is the first phase of a continuing investigation into THMs formation with the ultimate goal of developing a predictive model, which can be used to perform human health risk-cost balance studies for drinking water quality management.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".