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Record W4296794270 · doi:10.1021/acs.est.2c02196

Comparison of Simultaneous Measurements of Indoor Nitrous Acid: Implications for the Spatial Distribution of Indoor HONO Emissions

2022· article· en· W4296794270 on OpenAlexaff
Brandon Bottorff, Chen Wang, Emily Reidy, Colleen Marciel F. Rosales, Delphine K. Farmer, Marina E. Vance, Jonathan P. D. Abbatt, P. S. Stevens

Bibliographic record

VenueEnvironmental Science & Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Toronto
FundersAlfred P. Sloan Foundation
KeywordsNitrous acidEnvironmental scienceOzonePollutantEnvironmental chemistryAtmospheric sciencesMixing (physics)ChemistryNitrous oxideMixing ratioVentilation (architecture)MeteorologyPhysics

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Despite its importance as a radical precursor and a hazardous pollutant, the chemistry of nitrous acid (HONO) in the indoor environment is not fully understood. We present results from a comparison of HONO measurements from a time-of-flight chemical ionization mass spectrometer (ToF-CIMS) and a laser photofragmentation/laser-induced fluorescence (LP/LIF) instrument during the House Observations of Microbial and Environmental Chemistry (HOMEChem) campaign. Experiments during HOMEChem simulated typical household activities and provided a dynamic range of HONO mixing ratios. The instruments measured HONO at different locations in a house featuring a typical air change rate (ACR) (0.5 h –1 ) and an enhanced mixing rate (∼8 h –1 ). Despite the distance between the instruments, measurements from the two instruments agreed to within their respective uncertainties (slope = 0.85, R 2 = 0.92), indicating that the lifetime of HONO is long enough for it to be quickly distributed indoors, although spatial gradients occurred during ventilation periods. This suggests that emissions of HONO from any source can mix throughout the house and can contribute to OH radical production in sunlit regions, enhancing the oxidative capacity indoors. Measurement discrepancies were likely due to interferences with the LP/LIF instrument as well as calibration uncertainties associated with both instruments.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.048
GPT teacher head0.336
Teacher spread0.288 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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