Particulate Matter and Total Volatile Organic Compound Emissions Following Surface Cleaning: Comparison of Cleaning Agents and Locations
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Cleaning activities are essential for maintaining hygiene in indoor environments but can significantly influence indoor air quality (IAQ). We investigated emissions of volatile organic compounds (VOCs) and particulate matter (PM) during cleaning events across various indoor settings including two laboratories, an office, and a residential bathroom, with room volumes ranging from 22 to 206 m 3 and air changes rates (ACR) of 0.85–9.14 h -1 . Four cleaning solutions with different active ingredients were evaluated: quaternary ammonium compounds (quats), hydrogen peroxide (H 2 O 2 ), sodium hypochlorite (bleach), and thymol. Cleaning increased PM 2.5 by 0.7–14.5 μg m –3, depending on location and cleaning solution, with quats generally yielding the greatest increases. Measured total volatile organic compound (TVOC) mixing ratios also increased following cleaning by 10–104 ppbv, with the exception of experiments performed using thymol. We note that sensors such as the photoionization detector (PID) used in this work do not provide quantitative TVOC measurements. In general, greater emissions of PM 2.5 and TVOCs were observed in locations with lower ACR. We also measured PM 2.5 in a lobby, elevator, and public bathroom in a hotel with a number of COVID-positive occupants during routine surface disinfection using a quats-based disinfectant: increases of 5.5–14.2 μg m –3 were observed. This study demonstrates that emissions other than active ingredients can affect IAQ during surface cleaning, and provides information that may help mitigate harmful effects. It also provides insight into the use and limitations of low-cost sensors (LCS) in determining IAQ impacts from cleaning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".