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Record W4280531904 · doi:10.1186/s12302-022-00619-x

Assessment of stormwater discharge contamination and toxicity for a cold-climate urban landscape

2022· article· en· W4280531904 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEnvironmental Sciences Europe · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaGlobal Water FuturesCanada First Research Excellence Fund
KeywordsStormwaterEnvironmental scienceWater qualityEnvironmental chemistryOutfallCombined sewerHydrology (agriculture)Environmental engineeringSurface runoffEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Abstract Background Stormwater is water resulting from precipitation events and snowmelt running off the urban landscape, collecting in storm sewers, and typically being released into receiving water bodies through outfalls with minimal to no treatment. Despite a growing body of evidence observing its deleterious pollution impacts, stormwater management and treatment in cold climates remains limited, partly due to a lack of quality and loading data and modeling parameters. This study examines the quality of stormwater discharging during the summer season in a cold-climate, semi-arid Canadian city (Saskatoon, Saskatchewan). Results Seven stormwater outfalls with mixed-land-use urban catchments > 100 km 2 were sampled for four summer (June–August 2019) storm events and analyzed for a suite of quality parameters, including total suspended solids (TSS), chemical oxygen demand (COD), dissolved organic carbon (DOC), metals, and targeted polyaromatic hydrocarbons (PAHs). In addition, assessment of stormwater toxicity was done using the two toxicity assays Raphidocelis subcapitata (algae) and Vibrio fischeri (bacteria). Notable single-event, single-outfall contaminant pulses included of arsenic (420 µg/L), cadmium (16.4 µg/L), zinc (924 µg/L), fluorene (4.95 µg/L), benzo[a]pyrene (0.949 µg/L), pyrene (0.934 µg/L), phenanthrene (1.39 µg/L), and anthracene (1.40 µg/L). The IC 50 in both R. subcapitata and V. fischeri was observed, if at all, above expected toxicity thresholds for individual contaminant species. Principal component analysis (PCA) showed no clear trends for individual sampling sites or sampling dates. In contrast, parameters were correlated with each other in groups including DOC, COD, TSS, and reduced algal toxicity; and total dissolved solids (TDS), sum of metals, and pH. Conclusions In general, stormwater characteristics were similar to those of previous studies, with a bulk of contamination carried by the first volume of runoff, influenced by a combination of rainfall depth, antecedent dry period, land use, and activity within the catchment. Roads, highways, and industrial areas contribute the bulk of estimated contaminant loadings. More intensive sampling strategies are necessary to contextualize stormwater data in the context of contaminant and runoff volume peaks.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.123
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.224
Teacher spread0.213 · 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