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Record W6998945357

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

2022· dissertation· en· W6998945357 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsSnowmeltStormwaterWater qualityHydrology (agriculture)Surface runoffTotal suspended solidsSuspended solidsEffluentSurface waterContamination
DOInot available

Abstract

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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. \n\nThis 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. \n\nSnow 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. \n\nCatchment-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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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.

Opus teacher head0.011
GPT teacher head0.210
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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