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Record W332183143 · doi:10.2166/wqrjc.2014.018

Evaluations of conventional, ozone and UV/H2O2 for removal of emerging contaminants and THM-FPs

2014· article· en· W332183143 on OpenAlexafffund
Devendra Borikar, Madjid Mohseni, Saad Jasim

Bibliographic record

VenueWater Quality Research Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsOzoneHydrogen peroxideEnvironmental chemistryChemistryWater treatmentEnvironmental impact of pharmaceuticals and personal care productsContaminationWastewaterSewage treatmentEnvironmental scienceEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Although potential risk of pharmaceuticals and personal care products (PPCPs) and endocrine disrupting compounds (EDCs) and trihalomethanes to humans is small or unconfirmed, it is advisable to remove these wastewater-related contaminants to increase public confidence and acceptance as a precautionary principle and consequently their elimination or reduction during drinking water treatment is warranted. Experiments were conducted using the dual train pilot-scale conventional treatment plant with ozone and ultraviolet/hydrogen peroxide (UV/H2O2) with three different raw water sources. Reductions of trihalomethanes-formation potentials (THM-FPs) were 8–52%. Ozone was found to decrease THM-FPs while UV/H2O2 was found to increase THM-FPs in most of the experiments under experimental conditions. Conventional treatment poorly removed the selected PPCPs and EDCs while ozone + conventional treatment provided excellent removal. Conventional + UV/H2O2 treatment also demonstrated effective removal. However, removal of PPCPs and EDCs by conventional + UV/H2O2 treatment provided lower efficacy for Sites B and C, likely due to the presence of scavengers such as organics, bicarbonates, carbonates and particles.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.134
GPT teacher head0.438
Teacher spread0.304 · 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

Citations23
Published2014
Admission routes2
Has abstractyes

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