Bibliographic record
Abstract
Abstract Trihalomethanes (THMs) are considered to be the major by‐products found in water after the chlorination process. They mainly consist of chloroform (CHCl 3 ), bromodichloromethane (CHBrCl 2 ), dibromochloromethane (CHBr 2 Cl) and bromoform (CHBr 3 ). Their content in drinking water is regulated and the maximum contaminant level (MCL) of 100 µg L −1 established in 1979 by the United States Environmental Protection Agency (USEPA) for total THMs, based on a running annual average, still prevails and has been adopted in many other countries. In this article, we describe the four major analytical techniques routinely used for THM analysis. All of these analytical techniques use gas chromatography (GC) with a halogen‐specific detector such as the electron capture detector (ECD), electrolyte conductivity detector (ELCD) or mass spectrometry (MS). With high‐efficiency capillary columns now commonly available and the high sensitivity of the ECD, all these techniques have detection limits (DLs) at or below 0.1 µg L −1 for each of the four THMs, which is more than adequate for drinking water or beverage applications. The four analytical approaches differ by the way the THMs are introduced into the chromatographic column. Direct aqueous injection (DAI), where the sample is directly injected into the GC column, is fast and simple. It does not require any other piece of equipment than the gas chromatograph itself. However, since everything in the sample is injected in the column, it is limited to relatively clean matrices. It is generally used as a screening technique when a limited number of samples are analyzed. The second method uses liquid–liquid extraction (LLE) of the THMs with an organic solvent, usually pentane, which is then injected into the GC column. The extraction step offers the possibility of concentrating the THMs from a large‐volume sample and isolates them from inorganic salts. However, LLE is by far the most time‐consuming procedure and is not well suited for routine analysis of a large number of samples. The last two methods take advantage of the high volatility of the THMs to isolate and concentrate them in the gas phase. In the headspace (HS) technique, the aqueous sample is heated in a sealed vial and an equilibrium is reached between the THMs present in the water and the HS above, from which an aliquot is injected into the GC column. It is simple to use and easily automated. The sensitivity is limited by the partition coefficient of each species between the liquid and gas phases and the size of the gas aliquot injected into the GC column. The DL has been decreased to 1 ng L −1 by use of a long transfer time to the gas chromatograph and cryogenic trapping in the very first portion of the GC column. Finally, in the purge‐and‐trap (PT) technique, the THMs are purged out of the sample by an inert gas and concentrated on a solid sorbent, thermally desorbed and injected into the GC column. With a purge efficiency close to 100% for these volatile compounds, all the molecules present in a relatively large water sample (typically 5–25 mL) are injected into the GC column. It is the most sensitive of the four analytical methods and a DL below the 1 ng L −1 level has been achieved with cryogenic focusing at the column head.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.078 | 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".