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Record W4313545344 · doi:10.1039/d2em00405d

Near-source hypochlorous acid emissions from indoor bleach cleaning

2023· article· en· W4313545344 on OpenAlexafffund
Annastacia D. Stubbs, Melodie Lao, Chen Wang, Jonathan P. D. Abbatt, John A. Hoffnagle, Trevor C. VandenBoer, Tara F. Kahan

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsUniversity of TorontoYork UniversityUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAlfred P. Sloan Foundation
KeywordsHypochlorous acidBleachSodium hypochloriteChlorineHypochloriteChemistryChlorine dioxideRespiratorPulp and paper industryEnvironmental chemistryWaste managementEnvironmental scienceInorganic chemistryBiochemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

) analyzer to enable time-resolved (3 s) HOCl quantification. We measured HOCl levels in a residential bathroom while disinfecting a bathtub and sink, with a focus on spatial and temporal trends to improve our understanding of exposure risks during bleach use. Very high (>10 ppmv) HOCl levels were detected near the bathtub, with lower levels detected further away. Hypochlorous acid concentrations plateaued in the room at a level that depended on distance from the bathtub. This steady-state concentration was maintained until the product was removed by rinsing. Mobile experiments with the analyzer inlet secured to the researcher's face were conducted to mimic potential human exposure to bleach emissions. The findings from mobile experiments were consistent with the spatial and temporal trends observed in the experiments with fixed inlet locations. This work provides insight on effective strategies to reduce exposure risk to emissions from bleach and other cleaning products.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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 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

Citations16
Published2023
Admission routes2
Has abstractyes

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