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Enregistrement W6998945357

Assessment of Stormwater and Snowmelt Quality and Quantity Discharging from a Cold-Climate City to a Freshwater River

2022· dissertation· en· W6998945357 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueUrban Stormwater Management Solutions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSnowmeltStormwaterWater qualityHydrology (agriculture)Surface runoffTotal suspended solidsSuspended solidsEffluentSurface waterContamination
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Stormwater (SW) and snowmelt (SM) are waters which originate from precipitation and thaw events, run off the urban landscape, collect surface contamination, and discharge from storm sewers to receiving water bodies. Effluents are typically untreated and the impacts of these contaminants on various water quality parameters and on aquatic organisms is well-documented. Regulatory bodies are seeking management strategies for these effluents in response to the toxic risk posed by SW and SM contaminants to receiving environments and human health. Moreover, SM runoff in cold climates differs in flow and contaminants from ice-free SW. Local data is necessary for identifying sources of contamination and selecting locally appropriate management strategies. This study examined the quality and estimated loading of SW and SM discharging to a river from a semi-arid urban landscape. Four ice-free storm events were sampled across seven SW outfalls draining major catchments (>1 km2) over June-August 2019. The spring melt season comprised sampling four snow storage facilities between April 2019 and March-May 2020. Aggregate samples from the surfaces of snow piles (SPs) were collected in 2019 while on-site SM puddles were collected in 2020. Analyses included pH, electrical conductivity (EC), total dissolved solids (TDS), chemical oxygen demand (COD), dissolved organic carbon (DOC), total suspended solids (TSS), metals, polycyclic aromatic hydrocarbons (PAHs), and bioassays on Rapidocelis subcapitata and Vibrio fischeri. Coliforms were additionally analyzed in SW and select samples were analyzed for chloride and/or rubber tire vulcanizers. Using existing GIS land-use data, catchment loading was estimated for SW. Traffic-related loading was estimated for SM using UAS-LIDAR data to estimate the volume of the SP present at a designated snow storage facility and to extrapolate city-wide loading data based on site-specific contaminant data. Snow sample pH was comparable to SW sample pH, with higher pH in SP relative to SM. The TDS and EC of SM was 1-3 orders of magnitude higher than in SP, though average TDS concentration was similar to SW. Conversely, TSS was approximately an order of magnitude greater in SP relative to both SW and SM (which ranged comparably in this study). The range of COD was comparable across samples except for those proceeding a road surfacing event. Limited DOC was found in SM and values in SW were attributed to the same road surfacing event as elevated COD. Average dissolved metals were highest in SW and lowest in SP. Concentrations of aluminum, copper, and zinc exceeded the Canadian Council of Ministers of the Environment (CCME) Guidelines for the Protection of Aquatic Life in almost all samples, with regular lead and selenium exceedances in SP, arsenic and cadmium exceedances in SW, and chromium exceedances in SM. Average dissolved targeted PAHs were twice as high in SW as in SM and 60-fold greater in SP as in SW. Pyrene, anthracene, and benzo[a]pyrene concentrations exceeded the aforementioned CCME guidelines in almost all samples and benz[a]anthracene exceeded thresholds in 51% of SP and SW samples. Threshold-exceeding concentrations of fluorene and phenanthrene were also observed in almost all SP samples. Of the five targeted vulcanizer compounds, N,N’diphenylguanidine (DPG) was the most abundant, with peak SW concentrations representing some of the highest reported concentrations of this compound in urban runoff globally. 6PPD-quinone was measured in 57% of SW samples with >20% exceeding the acute LC50 for coho salmon. The compound was also present in >80% of SM samples. Aquatic sample toxicity, however, was not pronounced in R. subcapitata nor V. fischeri with little to no observable growth inhibition in diluted samples. Toxic contamination may be particle-bound, indicating a potential impact to benthic organisms. Catchment-scale evaluation of SW contaminant loading was performed using previous GIS analysis mapping land use distribution in study catchments. Land use-based SMCs obtained from literature were used due to lack of local SMC data and compared to experimentally obtained seasonal averages. The strongest correlations for overall SW loading derived from industrial followed by residential land use. Contaminant loading from SM was considered with respect to city-wide winter traffic loading. Basic seasonal loading estimates of TDS, TSS, COD, DOC, copper, chromium, manganese, nickel, lead, selenium, zinc, and ΣPAHs were highest at the paved facility, though TDS and DOC values indicative of poor on-site infiltration were observed at one of three non-paved sites. In addition, TSS values in SM were comparable across three differently characterized sites, indicating SM may uptake TSS by eroding transport pathways versus transporting SP-deposited particles. Highly concentrated pulses of contaminants in both SW and SM may misrepresent site-specific loads and further analysis is limited without flow measurement values.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,975
Score d'incertitude au seuil0,049

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,210
Écart entre enseignants0,199 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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