Caractérisation de l'impact de cellules de biorétention sur la qualité et la quantité des eaux pluviales à Trois-Rivières, Québec
Bibliographic record
Abstract
RÉSUMÉ: L'urbanisation et les changements climatiques ont tendance à accentuer les débordements d'égouts unitaires. Ces débordements ou surverses peuvent entraîner une diminution de la qualité des eaux de surface et une dégradation de l'environnement. Pour cela, la gestion des eaux pluviales en milieu urbain est devenue une nécessité. Des cellules de biorétention (CBR) en tant que pratique de gestion optimale (PGO) ont été installées sur la rue Saint-Maurice à Trois-Rivières, Québec. L'objectif principal de ce projet est de caractériser l'impact de la mise en place de ces cellules sur la qualité et la quantité des eaux pluviales en milieu urbain. Pour ce faire, la méthodologie adoptée se divise en deux parties. La première phase est de réaliser des campagnes d'échantillonnage en temps sec, en temps de précipitations et en période de fonte des neiges à une échelle réelle sur le terrain, en plus d'effectuer les analyses de qualité nécessaires au laboratoire. La deuxième phase consiste à utiliser le modèle hydraulique SWMM pour la modélisation des CBR en utilisant les données pluviométriques et les données de débits réelles afin d'évaluer leur performance en termes de réduction des volumes de ruissellement et des débits de pointe. ABSTRACT: Urbanization and climate change tend to increase combined sewer overflows. These overflows can lead to surface water contamination and environmental quality degradation. Therefore, the stormwater management in urban areas has become a necessity. Bioretention cells as storm water controls have been implemented on Saint-Maurice Street in Trois-Rivières, Quebec. The main objective of this study is to characterize the impact of these cells on the stormwater quality and quantity in urban areas. For this purpose, the methodology used was divided into two parts. The first was to proceed with sampling and analysis in dry weather, in wet weather and in periods of snowmelt at the field scale. The second part was to use the Storm Water Management Model (SWMM) for modeling bioretention cells using real rainfall and flow data, to assess their performance in terms of reducing runoff volumes and peak flows. Field sampling for experimental monitoring took place in 2019, 2020 and 2021. The results concluded that bioretention cells are overall efficient in terms of flow and pollutant retention. They are able to reduce peak flow by 74.2%, phosphorus from 47.6% to 93.5% and metals from 8.5% to 94.7%. However, the release of nitrogen and organic carbon was observed in most of the tests.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".