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Record W7011174989

Measuring and modelling concentrations of plant protection products and trace metals in the South Saskatchewan River

2023· dissertation· en· W7011174989 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Languageen
FieldMathematics
TopicHistory and Theory of Mathematics
Canadian institutionsnot available
Fundersnot available
KeywordsLindanePesticidePollutantMercury (programming language)SedimentHydrology (agriculture)Aquatic ecosystemContaminationWater pollution
DOInot available

Abstract

fetched live from OpenAlex

Organic chemical pollutants are delivered to riverine habitats via basin land use and hydrology interactions. Aquatic organisms eventually absorb these substances, where they might have negative consequences. However, our capacity to reliably predict potential future changes in pollutant concentrations is now constrained by information gaps relating to the links between hydrological, chemical, and biological processes. In the South Saskatchewan River, Canada, in the years 2020 and 2021, concentrations of three pesticide classes (organochlorines, organophosphates, and herbicides) in the water, sediments, and fish were examined. Organochlorine pesticides have been prohibited in Canada since the 1970s; however, methoxychlor and lindane were occasionally found in samples of sediment and fish that may have been contaminated in the past. Organophosphate pesticides, with the exception of malathion and parathion, were close to detection limit in both sampling years in all matrices, while neonicotinoids were below detection in all samples. On the other hand, for both sampling years, consistent levels of the herbicides 2,4-D and dicamba were found in water samples from all locations. Concentrations were on average three times higher in 2020, when river discharge was two times greater, possibly pointing to contaminated sediments being disturbed by high flows, or run-off from the nearby watershed. Of the trace metals, copper and zinc concentrations at several sampling locations exceeded standards for sediment quality. About 18% of the water and sediment samples that were examined had mercury concentrations that were above recommended levels. These discoveries fill in the gaps in monitoring datasets and show significant connections between hydrology and chemistry that can be further investigated in computational models to forecast pollutant trends in freshwater systems. \nTrace metal concentrations were used to model transport and fate in the South Saskatchewan River using an existing model developed for another freshwater system. The River Analysis System from the Hydrologic Engineering Center was paired with a well-known 1-D modelling technique (HEC-RAS). The stream transport module for the WASP (Water Quality Analysis Simulation Program), TOXI, can calculate the flow of water, sediment, and dissolved constituents through branching and ponded segments and is integrated with flow routing for free-flow streams, ponded segments, and backwater reaches. Two metals with primarily anthropogenic and geogenic origins were chosen: copper and nickel. The South Saskatchewan River was analysed in 2020 and 2021 at 10 distinct locations, both upstream and downstream of the City of Saskatoon. By comparing model predictions with copper and nickel concentrations obtained earlier, model performance was assessed. The model functioned reasonably well for sediment samples and did a good job of estimating the levels of copper and nickel in water samples. In both the water and sediment sample segments, the model overestimated concentrations. Diffuse pollutant loads were increased to enable the model to work more precisely. This work shows the predictive power of merging WASP-TOXI and HEC-RAS models for the prediction of contaminant loading, even though numerous default parameter values had to be employed because primary historical data was unavailable. This proof-of-concept study will be useful for future research, including studies on the effects of climate change on the quality of water in the Canadian prairies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.192
Teacher spread0.149 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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