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Record W4403913041 · doi:10.1016/j.greeac.2024.100169

In-bottle thin film solid phase microextraction coupled to flow modulated comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry for monitoring of organic pollutants in environmental waters

2024· article· en· W4403913041 on OpenAlexafffund
Khaled Murtada, Matthew Edwards, Laura McGregor, Jonathan J. Grandy, Janusz Pawliszyn

Bibliographic record

VenueGreen Analytical Chemistry · 2024
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research Chairs
KeywordsMass spectrometrySolid-phase microextractionPollutantChromatographyEnvironmental chemistryEnvironmental scienceBottleGas chromatography–mass spectrometryAnalytical Chemistry (journal)ChemistryMaterials science

Abstract

fetched live from OpenAlex

• An in-bottle TF-SPME sampler for sampling and extraction has been introduced. • TF-SPME was utilized to detect a wide range of untargeted pollutants in environmental waters. • Organic pollutants were desorbed and analyzed using comprehensive GC × GC-TOFMS. • TF-SPME-GC × GC-TOFMS was employed to screen for the presence of multiclass pollutants. The monitoring of organic compounds in water is critical for assessing environmental health and the effectiveness of treatment processes. In this study, we present a robust and eco-friendly approach to environmental water analysis that integrates thin film solid phase microextraction (TF-SPME) with comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry (GC × GC-TOFMS). This method is designed to detect multiclass organic pollutants in environmental water, thus enabling applications such as detailed analyses of changes post-treatment processes. The supervised PCA results revealed distinct clustering of the three water sample classes - raw wastewater, outflow from WWTF, and river water - each occupying separate regions in the principal component space. Several organohalogen compounds were identified as key differentiators among the water samples, highlighting significant compositional differences across the analyzed classes. Notably, an increased abundance of certain chlorinated compounds was observed in raw wastewater before it underwent purification treatment at the WWTF. Additionally, the greenness evaluation of the in-bottle TF-SPME-GC × GC-TOFMS indicated whiteness scores of 96.7 % for the in-bottle TF-SPME-GC × GC-TOFMS method and 79.0 % for the LLE (US EPA 8270)-GC/MS method. This approach facilitates the identification of a wide array of organic compounds, characterized by diverse physicochemical properties, through their mass spectral profiles. We applied this method to various environmental water samples, including raw wastewater, effluent from wastewater treatment facilities, and river water, to assess the impact of treatment processes on organic pollutant levels. Our developed strategy effectively monitored alterations in several organic compounds within these environmental water samples. Our results demonstrate that TF-SPME coupled with GC × GC-TOFMS proves to be both straightforward and comparable in performance to other methods for analyzing low-level organic pollutants in water samples, making it a valuable tool for environmental monitoring and discovering new emerging contaminates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.015
GPT teacher head0.301
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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