Role of location, season, occupant activity, and chemistry in indoor ozone and nitrogen oxide mixing ratios
Bibliographic record
Abstract
Understanding the oxidizing environment indoors is important for predicting indoor air quality and its impact on human health. We made continuous time-resolved measurements (30 s) of several oxidants and oxidant precursors (collectively referred to as oxidant*): ozone (O3), nitric oxide (NO), and NO2* - the sum of nitrogen dioxide (NO2) and nitrous acid (HONO). These species were measured in three indoor environments - an occupied residence, a chemistry laboratory, and an academic office - in Syracuse, New York, during two seasons in 2017 and 2018. Oxidant* levels differed greatly between the residence, the lab and the office. Indoor-to-outdoor ratios (I/O) of O3 were 0.03 and 0.67 in the residence and office; I/ONO (I/ONO2*) were 11.70 (1.26) in the residence and 0.13 (1.70) in the office. Little seasonal variability was observed in the lab and office, but O3 and NO2* levels in the residence were greater in spring than in winter, while NO levels were lower. Human activities such as cooking and opening patio doors resulted in large changes in oxidant* mixing ratios in the residence. In situ chamber experiments demonstrated that the increase in O3 and NO2* levels during door-open periods was due to a combination of physical mixing between indoor and outdoor air, gas-phase production of NO2 from O3-NO chemistry, and heterogeneous formation of HONO on indoor surfaces. Our results also highlight the importance of chemistry (with NO, alkenes, and surfaces) in O3 mixing ratios in the residence, especially during door-open periods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".