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Enregistrement W7055941811

DEVELOPMENT OF A PASSIVE SAMPLING STRATEGY FOR MONITORING OF ORGANIC POLLUTANTS AND THEIR IMPACTS IN AQUATIC SYSTEMS

2023· dissertation· en· W7055941811 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Langueen
DomainePhysics and Astronomy
ThématiqueMagnetic confinement fusion research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAquatic ecosystemPassive samplingWater qualityContaminationSampling (signal processing)PollutantSurface waterDesorptionWater pollution
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Anthropogenic organic compounds are constantly released into the freshwater environment, demanding a better knowledge of the chemical status of our Earth’s surface waters and sediments. Conventional water quality monitoring only provides “snapshots” of information in time and space. Passive sampling has been proposed as an in-situ time integrative sampling technique to offer better monitoring of the chemical status of our environment. In this thesis, the diffusive gradient in thin films (DGT) technique is introduced because the DGT passive sampler allows for assessing time-weighted average concentrations of various organic contaminants with minimal hydrodynamic influence. \nThis thesis first reviewed the available literature on the potential limitations of DGT samplers. This review summarized the current configurations of the DGT samplers for organics, storage stability of analytes in DGT samplers, kinetic desorption of organic contaminants in sediments and at the interface of water and sediment, and combinations of DGT samplers and bioassays. This review identified two critical gaps: (i) there are only limited studies for desorption kinetics of organic contaminants, especially for hydrophilic to moderately hydrophobic compounds, at the interface of water and sediment; and (ii) there are no studies so far for predicting bioavailability in aquatic biota by in situ DGT technique. Based on these gaps, the objectives of this thesis are to (1) develop DGT samplers that can be applied for the monitoring of organic contaminants across the water-sediment interface in the field with an efficient time; (2) describe the kinetic equilibrium of compounds between sediments and overlying water using a dynamic model; and (3) use DGT-derived concentrations to predict bioaccumulation of organic contaminants by invertebrates through in-situ and laboratory-controlled experiments.\n First, this research conducted a 30-day laboratory simulation experiment, where DGT samplers were tested for adsorption performance and then were deployed in sediments spiked with nine model antipsychotic compounds, i.e., amitriptyline, bupropion, carbamazepine, citalopram, clozapine, duloxetine, fluoxetine, lamotrigine, and venlafaxine. A dynamic model, DGT-induced fluxes in soils and sediments (DIFS), was used to reveal the dynamic resupply processes of organic contaminants from the solid phase to the aqueous phase. This experiment showed that antipsychotics could be continuously depleted from the sediment aqueous phase and captured by the DGT binding gel. The highest resupply ability was observed for lamotrigine and carbamazepine. The adsorption process took control of the spiked sediments under laboratory conditions during incubation time.\nSecond, DGT devices were in situ deployed at the sedimentwater interface and in sediments, downstream of the Saskatoon Wastewater Treatment Plant, on the South Saskatchewan River. Apart from the DIFS model, a dynamic fraction transfer model was also developed to consider the real status of organic contaminants in sediments during field deployment. The field experiment revealed that positive fluxes of antipsychotics were found from sediment to overlying water and the desorption process was dominant within a 15 cm depth of sediments. The results from the three-fraction transfer model can be auxiliary to further explain dynamic desorption kinetics calculated by the DIFS model.\nThird, another 30-day laboratory-controlled experiment, where the benthic oligochaete Lumbriculus variegatus was exposed to freshwater sediments spiked with nine antipsychotic compounds, and DGT samplers were synchronously deployed, was conducted to develop a numerical model for passive bioaccumulation using DGT-derived concentrations. Passive uptake of antipsychotic compounds by the benthic oligochaetes could be successfully modeled by inputting the diffusion-induced concentrations measured by DGT samplers in water and sediments. Fast desorption to the labile fraction of analytes in a short response time accounted for the process of uptake by oligochaetes.\nFourth, DGT devices were in situ deployed at a wastewater-impacted site for 20 days to develop a predictive bioaccumulation model by comparison between the modeled concentration using DGT-derived concentrations in water and those in resident benthic invertebrates, specifically crayfish (Faxonius virilis). The results showed that targeted antipsychotics could be constantly resupplied to the interstitial water and absorbed by crayfish. DGT techniques with a steady-state uptake model in the current study for crayfish could provide a close prediction compared to the measured concentrations for some compounds while it still needs further developments to predict different organic compounds. This thesis has the potential to transform the DGT technique to efficiently monitor emerging contaminants and evaluate their bioavailability in the aquatic cycle, and help protect the safety of our water resources for human and environmental health.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,517
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
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 Opus0,020
Tête enseignante GPT0,225
Écart entre enseignants0,205 · 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 tête enseignante, pas un consensus.

Devis d'étudeQualitatif
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

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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