Occurrence and in vitro toxicity of unregulated disinfection by-products in two Saskatchewan drinking water treatment plants
Notice bibliographique
Résumé
Halogenated disinfection by-products (DBPs) are a diverse class of compounds formed during the treatment of drinking water through reactions between natural organic matter (NOM), inorganic precursors such as bromide, and applied disinfectants. Health Canada regulates a handful of DBPs, but there are over 700 unregulated DBPs that have been described and many of these are more toxic than the regulated DBPs. Here, a data-independent precursor isolation and characteristic fragment (DIPC-Frag) method operated on a Q ExactiveTM Hybrid Quadrupole-OrbitrapTM Mass Spectrometer equipped with a UHPLC system was adapted for the detection of brominated and iodinated DBPs (Br-DBPs and I-DBPs) in chlorinated water. Extraction and analytical conditions were optimized, chemometric strategies were applied, and a library of 553 Br-DBPs and 112 I-DBPs was established with structures predicted for the most abundant compounds. As the method exhibited good precision (~15% RSD), it was then used to study trends of formation and temporal trends of unregulated Br-DBPs in a year-long study that sampled raw, clearwell, and finished waters. While most Br-DBPs increased through the treatment process, cluster I Br-DBPs decreased between the clearwell and finished stages, a pattern significantly related to their chemical properties of low O/C and Br/C ratios. Correlation matrices were used to determine if quality parameters of the source waters (e.g. NOM, turbidity, river level, temperature, bromine (Br)) could explain monthly variations of Br-DBPs, but few significant relationships were found. Unexpectedly, total Br increased from 0.013-0.038 mg/L in raw water to 0.04-0.12 mg/L in finished water, which indicated introduction of Br during disinfection. Concentrations of Br in clearwell and finished water were significantly correlated to detection of 34/54 Br-DBPs at α=0.05 and 14/54 Br-DBPs at α=0.001. As few studies have evaluated toxicity of DBPs in mixtures, the next goal of this thesis was to explore temporal changes in whole mixture toxicity and to determine if raw water parameters could predict toxicity of finished water. By use of a 72 h CHO-K1 cytotoxicity assay and an Nrf2/ARE oxidative stress assay, results indicated cytotoxicity was greatest in finished water collected in November and March while oxidative stress was greatest in June and November, both of which could be related to seasonal trends in unregulated Br-DBPs. These toxic endpoints were correlated (R2 = 0.53, p = 7.4x10-3) and three classes of Br-DBPs (Br2, BrCl, S-DBPs) demonstrated significant correlations to both. The greatest predictors of mixture toxicity were concentration of Br and applied doses of chlorine at related stages. These were equally correlated to both cytotoxicity (R2 = 0.43, p = 0.002) and oxidative stress (R2 = 0.67, p = 0.001). This study is the first to explore temporal trends in whole mixture toxicity of DBPs. It is also the first to suggest that the concentration of Br may be a predictor of the occurrence of unregulated Br-DBPs as well as whole mixture toxicity.
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 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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».