Comprehensive screening of persistent organic pollutants \nin industrial wastewater using GC and LC \ncyclic ion mobility-high resolution mass spectrometry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".