Measuring and modelling concentrations of plant protection products and trace metals in the South Saskatchewan River
Notice bibliographique
Résumé
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.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».