Analysis and Management of Stormwater Quality: A Case Study of the Town of Jasper in Alberta, Canada
Bibliographic record
Abstract
Stormwater pollutants can have deleterious impacts on the aquatic life of receiving waterbodies. The aim of this thesis is to present the findings from stormwater quality monitoring and modelling performed for the Town of Jasper in Alberta, Canada. This research was conducted in order to understand the stormwater quality issues in the Town and propose Best Management Practices to reduce stormwater impacts to the aquatic life of receiving waterbodies. A monitoring program was conducted over a period of a year, and samples were collected at major outfalls in the Town for various weather events. The objective of the monitoring program was to identify key pollutants of concern and to characterize impacts to sensitive surrounding aquatic ecosystems downstream of the Town. A total of 14 stormwater quality parameters were found to be of high concern to aquatic life, particularly total suspended solids (TSS), metals and phosphorus (TP). TSS concentrations were statistically correlated with metals and TP by using a Spearman rank correlation. It was found that vehicular traffic was a major source of metals and that winter road salting was responsible for excessive chloride concentrations. High hydrocarbon loads were traced to the oil leaks associated with the high volumes of traffic during the tourist season and fecal coliforms were largely present due to the fecal droppings of the resident Elk population. A Stormwater Management Model (SWMM) was developed to assess the performance of street sweeping and infiltration trenches to reduce TSS loads in the stormwater in Jasper. Two catchments models were developed for the Town using long-term meteorological data, as well as watershed and drainage system data provided by the Municipality. The quality parameters were calibrated so that TSS loads at the outfalls were within a 20% error of monitoring data. The performance of proposed Best Management Practices (BMP) was assessed using single-event and continuous data simulations. A combined sweeping/trench control scenario was recommended for implementation as it generated the highest removal efficiency of up to 51%. The modelling results were used in support of a stormwater management plan to protect the sensitive ecosystems and waterbodies that surround the Town of Jasper.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".