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Record W2769730548 · doi:10.1016/j.watres.2017.11.055

The influence of precursors and treatment process on the formation of Iodo-THMs in Canadian drinking water

2017· article· en· W2769730548 on OpenAlexaffabout
Anca-Maria Tugulea, Rocio Aranda‐Rodriguez, Denis Bérubé, M. Giddings, France Lemieux, Joan Hnatiw, Luda Dabeka, François Breton

Bibliographic record

VenueWater Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsHealth Canada
Fundersnot available
KeywordsIodoformWater treatmentEnvironmental chemistryBromideWater qualityIodineChemistryTotal organic carbonIodideTrihalomethaneEnvironmental scienceContaminationEnvironmental engineering

Abstract

fetched live from OpenAlex

The National Survey of Disinfection By-Products and Selected Emerging Contaminants investigated the formation of various disinfection by-products and contaminants in 65 water treatment systems (WTSs) across Canada. Results for six iodo-trihalomethanes (iodo-THMs) are reported in this paper. The participating water treatment systems included large, medium and small systems using water sources and treatment processes which were representative of Canadian drinking water. Five water samples (source water, treated water and three water samples along the distribution system) were collected from each treatment system, both under winter and summer conditions. Samples were stabilized, shipped cold and analysed for six iodo-THMs (dichloroiodomethane-DCIM; dibromoiodomethane-DBIM; bromochloroiodomethane-BCIM; chlorodiiodomethane-CDIM; bromodiiodomethane-BDIM and triiodomethane or iodoform-TIM), using a SPME-GC-ECD method developed in our laboratory (MDLs from 0.02 μg/L for iodoform to 0.06 μg/L for bromodiiodomethane). Concentrations of relevant precursors like dissolved organic carbon (DOC), bromide, iodide and total iodine, as well as other water quality parameters, were also determined. Detailed information about the treatment process used at each location was recorded using a questionnaire. The survey showed that one or more iodo-THMs were detected at 31 out of 64 water treatment systems (WTSs) under winter conditions and in 46 out of 64 WTSs under summer conditions (analytical results from one site were excluded due to sampling challenges). Total iodo-THM concentrations measured during this survey ranged from 0.02 μg/L to 21.66 μg/L. The highest total iodo-THM concentration was measured in WTS 63 where all six iodo-THMs were detected and iodoform was present in the highest concentration. The highest iodo-THM formation was found to occur in treatment systems where water sources had naturally occurring ammonium as well as high bromide, high iodide and/or total iodine concentrations. In two such water systems the total concentration of iodo-THMs exceeded the concentration of regulated THMs.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.319
Teacher spread0.274 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations32
Published2017
Admission routes2
Has abstractyes

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