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
Bibliographic record
Abstract
• 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".