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Record W2038640480 · doi:10.1021/es061491b

UV Degradation Kinetics and Modeling of Pharmaceutical Compounds in Laboratory Grade and Surface Water via Direct and Indirect Photolysis at 254 nm

2007· article· en· W2038640480 on OpenAlexfundno aff
Vanessa J. Pereira, Howard S. Weinberg, Karl G. Linden, Philip C. Singer

Bibliographic record

VenueEnvironmental Science & Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
FundersPublic Health Agency of CanadaU.S. Environmental Protection Agency
KeywordsPhotodissociationActinometerQuantum yieldChemistryAbsorbanceReaction rate constantUltravioletDegradation (telecommunications)IrradianceKineticsPhotochemistryAnalytical Chemistry (journal)Yield (engineering)Environmental chemistryChromatographyMaterials scienceOpticsOptoelectronicsFluorescence

Abstract

fetched live from OpenAlex

Direct and indirect photolysis of pharmaceutically active compounds (PhACs) was investigated in laboratory-grade water (LGW) and a local surface water using a low-pressure ultraviolet batch reactor. The PhACs selected in this study belong to different therapeutic classes and are known to occur in environmental samples. Fundamental photolysis and advanced oxidation process parameters obtained in LGW (such as the decadic molar absorption coefficient, quantum yield, and degradation rate constants) are reported and discussed. These parameters, together with the incident photon irradiance, solution depth, and solution absorbance were used to develop UV and UV/ H202 photolysis models that were compared with experimental results obtained in the surface water. The model predicted the experimental UV photolysis removals well but underestimated the UV/H2O2 photolysis results. These models were used to discuss the effects of optical path length and H2O2 concentration on the UV-based rate constant predictions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations324
Published2007
Admission routes1
Has abstractyes

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