Simultaneous Determination of Disinfection By-products in Water Samples from Advanced Membrane Treatments by Headspace Solid Phase Microextraction and Gas Chromatography-Mass Spectrometry
Bibliographic record
Abstract
A headspace-solid phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS) method has been developed for the simultaneously determination of 20 disinfection by-products (DBPs) in water samples from reverse osmosis (RO) membranes. Selected compounds belong to different families including: trihalomethanes (THMs), halogenated acetonitriles (HANs), halogenated nitromethanes (HNMs), halogenated ketones (HKs) and other halogenated DBPs. Four commercial fibres with different polarities were tested for the extraction of the compounds and the main variables affecting HS-SPME such as extraction time, extraction temperature and pH of the samples were optimized by applying a central composite design. The method showed good detection limits in the range of 0.003 g l-1 up to 0.010 g l-1 for most of the compounds with reasonable linearity with r2 higher than 0.991. Moreover, the repeatability of the method, expressed as relative standard deviation (RSD) was lower than 13% (n = 5, 1 g l-1 ) in brackish and wastewater samples. The validated method has been applied for the determination of the target DBPs in RO water samples from application research units, which treated water from various origins (wastewater, brackish water and sea water), showing good performance in the different types of studied samples. The analysis revealed the presence of several DBPs regarding different families, such as trichloromethane (with concentrations up to 0.36 μg l -1 ), chloroiodomethane (0.5-1.44 μg l -1 ), dibromochloromethane (found at concentrations up to 0.76 μg l -1 ) and tribromoacetaldehyde (at concentrations up to 11 μg l -1 in the influent samples). The tendency of most of them indicated a trend of removal by reverse osmosis treatments, especially the total concentration of THMs which decreased below the limit of detection.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".