Comprehensive screening of persistent organic pollutants \nin industrial wastewater using GC and LC \ncyclic ion mobility-high resolution mass spectrometry
Notice bibliographique
Résumé
Industrial chemicals play an important role in all facets of modern society; from flame retardants in electronics and furniture to non-stick coatings in cookware and food packaging. However, despite their extensive applications and many desired benefits, chemicals are sometimes released during their lifecycle resulting in deleterious ecological and human health effects. Industrial wastewater effluents are rich in chemical pollutants, both known and unknown as well as legacy and emerging. In this study, a combination of screening strategies was used to analyze industrial wastewater samples from over 10 sectors in Ontario for halogenated persistent organic pollutants (POPs). Samples were characterized with both gas chromatographic and liquid chromatographic cyclic ion mobility mass spectrometry (GC/LC-cIM-MS) methods. \nA novel non-target screening (NTS) technique utilizing GC-cIM-MS, capable of isolating unknown per- and polyfluoroalkyl substances (PFAS) and other halogenated compounds based on the ratio of their mass and collision cross section (CCS) values, was recently developed in our group. When the combined dataset from GC-cIM-MS analysis of the wastewater samples was subjected to this novel filtering strategy, 344 potentially brominated, chlorinated or fluorinated chemical species were identified from the ~27,000 initially present. Following the application of a previously developed script tool (R code) and manual investigation, 44% of these ions were confirmed to be halogenated. Five compounds belonging to frequently detected classes were identified by suspect screening (e.g., polybrominated diphenyl ethers; PBDEs, polychlorinated biphenyls; PCBs, organophosphate flame retardants; OPFRs and perfluorosulfonamides; PFSMs). \nConfirmed suspects represented a mere 14% of the halogenated ions (9% intensity) indicating that 86-91% of the halogenated content is truly “unknown”. A more in-depth look at these unknown ions revealed 19 suspected PFAS including 2 classes that were detected in the environment for the first time. Targeted analyses showed that legacy pollutants such as PBDEs, PCBs, polychlorinated naphthalenes (PCNs) and organochlorine pesticides (OCPs) were either not detected or present at low levels. \nFor characterization via LC-cIM-MS, wastewater samples were extracted using a tandem solid phase extraction (SPE) technique with weak anion exchange (WAX) and weak cation exchange (WCX) cartridges. LC-cIM-MS experiments revealed the presence of ~50,000 chemical species across all samples and filtering based on CCS and m/z yielded 937 likely brominated, chlorinated or fluorinated compounds. Further data reduction and mass defect analysis led to the discovery of roughly 300 potential PFAS by NTS. Only half of them were matched to a suspect screening database implying that the chemical identities of several PFAS in the Ontario environment are unknown. Multiply charged ions formed during electrospray ionization were found to be non-problematic when filtering data using CCS and m/z. As such, this novel way of data prioritization is a promising approach for PFAS discovery in complex samples when analyzed by LC-ESI-IM-MS. GC-APCI-IM-MS was also found to be a complementary technique for PFAS discovery since comparable numbers were identified using the same workflow.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».