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Record W4417190129 · doi:10.1007/s44274-025-00479-1

Assessment of indoor air quality in a primary school

2025· article· en· W4417190129 on OpenAlexfundno aff
Adekunle A. Dosumu, I. Colbeck

Bibliographic record

VenueDiscover Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent University
KeywordsIndoor air qualityParticulatesRelative humidityAir quality indexNoonAir pollution

Abstract

fetched live from OpenAlex

Abstract Indoor air quality (IAQ) in schools is important as it directly affects the health, comfort, and learning ability of students and staff. This study assessed the IAQ in ten rooms at St. Peters Catholic Primary School, Sittingbourne, Kent, between February and March 2024. A portable uHoo business air quality monitor was used to assess ambient temperature, relative humidity, total volatile organic compounds (TVOC), formaldehyde, carbon monoxide (CO), carbon dioxide (CO 2 ), particulate matter (PM 2.5 and PM 10 ). The air quality index results revealed means of: temperature (18.45 ± 2.23 °C), relative humidity (57.59 ± 4.32%), CO (0.14 ± 0.11 ppm), CO 2 (737.20 ± 346.87 ppm), TVOC (2148.62 ± 5456.78 ppb), formaldehyde (18.68 ± 67.67 ppb), PM 2.5 (5.82 ± 2.31 µg/m 3 ), and PM 10 (11.23 ± 2.71 µg/m 3 ). The results revealed variations in the diurnal variations in particulate matter (PM 2.5 and PM 10 ) during school hours and significant differences at 10.00 am break time (7.9 µg/m 3 and 13.3 µg/m 3 ), noon lunch time (7.8 µg/m 3 and 12.9 µg/m 3 ) and 3.00 pm end of school (7.5 µg/m 3 , 12.5 µg/m 3 ). This study concludes that PM in this school is, primarily, driven by PM in the local area, not the school. All parameters were within permissible limits, except for TVOC. The school's management should adopt proactive measures, including routine IAQ assessments. This will allow the school to cultivate healthier environments that foster academic excellence.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.008
GPT teacher head0.267
Teacher spread0.259 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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