Identification of Chemical Feature Profiles in Agricultural and Surface Waters
Notice bibliographique
Résumé
DOM (dissolved organic matter) is a complex mixture of natural organic materials made up from multiple sources. The main anthropogenic sources of DOM are agricultural runoff, urban runoff, and/or water treatment activities. DOM sources and character, interactions, and degradation, on spatial and temporal scales, show a complex makeup that is ever changing due to various biotic processes and fluctuating from external and internal sources. Technological advances in mass spectrometry instruments have allowed high mass resolution and mass accuracy analysis of complex mixtures with novel techniques, such as direct infusion of water samples with minimal sample preparation. This thesis investigates the analysis, the identification and potential quantification, of every chemical entity within a specific water sample. Irrigation water in Alberta is important for agriculture, livestock, rural communities, wildlife, and also various recreational activities. However, few studies have monitored these waters over long periods of time and most used different methods and objectives for analysis. Samples between 2016 and 2019, during the month of July, were randomly chosen from the full dataset provided by the Irrigation District Water Quality (IDWQ) website (Alberta’s Irrigation Districts et al, 2023). This dataset included the samples analyzed by mass spectrometry at the Toxicology Centre. Irrigation water samples were successfully analyzed, by direct infusion, with the Thermo Orbitrap instrument. Several hundred peaks were identified as being consistent across samples. The results from both the IDWQ data and the MS data suggested that Secondary and Return locations shared similar characteristics to each other while being somewhat distinct from Primary location samples. Analysis of data presented by the IDWQ, between 2016 and 2019, in July showed that WQI values were consistently lower in irrigation water that was being returned to the natural river source than in the water initially taken from the river. Additionally, median WQI values in Return locations were shown to improve after 2016, highlighting the importance of monitoring water sources and involvement from the local government in improving water quality as various mitigation strategies are applied. The artificial reservoirs Lake Diefenbaker (LD) and Buffalo Pound Lake (BPL) are essential resources for the province of Saskatchewan, Canada and provide water to Saskatchewan residents, agricultural lands, and livestock. A total of 240 individual analyses (including the locations, chosen at random, that were analyzed in triplicate) were conducted on samples, in 2019 and 2021, from Lake Diefenbaker and Buffalo Pound Lake. Features in the mass spectra were identified with the use of an R script. Both BPL and LD PCA analysis of water samples showed that water samples taken in months closest in time tend to cluster together. The months of Summer that overlap the Autumn samples are the latest months of summer. Therefore, there is a consistent and gradual seasonal change of the chemical composition of DOM in the water samples in both lakes. Algorithms written the R statistical software environment were successful in generating chemical formulae for organic compounds and detecting isotopes for identification. Additional adducts and elements are required for more accurate formula generation as well as additional steps to decrease the number of chemical formulae generated for higher masses (>400 m/z).
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».