Factors affecting disinfection by-products from surface source waters on Vancouver Island
Bibliographic record
Abstract
A comparison of disinfection by-product (DBP) results for 12 surface source waters on Vancouver Island, British Columbia determined that there was a distinct spatial difference in DBP production based on biogeoclimatic ecoregions. The main factors affecting the DBP production between ecoregions was source water quality, water body type (stream versus lake) and treatment strategy. Typically water bodies within an ecoregion exhibit similar water quality and produce similar DBPs. Differences in DBPs between water body types were attributed to the source of natural organic matter (NOM), allochthonous or autochthonous origin, and seasonal variability. Additional source water quality parameters such as pH, chlorophyll a, total phosphorus, water temperature and bromide concentrations support the spatial differences observed in DBP production between ecoregions. These results suggest that source water quality and water body type could be used as a preliminary tool to predict the amount of DBPs a community within a specific ecoregion may be exposed to. A review of a long-term data set determined that changes in treatment strategy significantly affected the DBPs produced. The most distinct changes in DBP production were associated with booster station treatment processes in terms of annual versus seasonal re-chlorination and the decommissioning of a booster station. Sooke reservoir water quality, following an inundation project, changed significantly with respect to water temperature and NOM. The increase in TOC levels did not result in any changes to DBP production, however cooler water temperatures reduced the potential for losses of chlorine residual and for the re-growth of bacteria. The results of this research suggest that the management and protection of drinking water sources, along with treatment strategies, is the most promising way to ensure minimal risk to human health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".