Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,078 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».