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Record W3024203079 · doi:10.1016/j.envint.2020.105781

Exposure to indoor air contaminants in school buildings with and without reported indoor air quality problems

2020· article· en· W3024203079 on OpenAlexaff
Camilla Vornanen-Winqvist, Kati Järvi, Maria A. Andersson, Caroline Duchaine, Valérie Létourneau, Orsolya Kedves, László Kredics, Raimo Mikkola, Jarek Kurnitski, Heidi Salonen

Bibliographic record

VenueEnvironment International · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsUniversité LavalInstitut Universitaire de Cardiologie et de Pneumologie de Québec
FundersTyösuojelurahastoAcademy of Finland
KeywordsIndoor air qualityEnvironmental scienceIndoor bioaerosolEnvironmental healthIndoor airVentilation (architecture)Air quality indexSick building syndromeEnvironmental engineeringWaste managementEngineeringMeteorologyGeographyMedicine

Abstract

fetched live from OpenAlex

Reported indoor air quality (IAQ) complaints are common even in relatively new or renovated school buildings in Finland. However, detecting the causes for complaints with commonly used indoor air measurements is difficult. This study presents data on perceived and measured IAQ in six comprehensive school buildings in Finland. The aim of this study was to discover the possible differences of perceived and measured IAQ between schools with reported IAQ complaints and schools without reported IAQ complaints. The initial categorisation of schools with ('problematic schools') and without ('comparison schools') complaints was ensured via a validated indoor climate survey and a recently developed online questionnaire, which were completed by 186 teachers and 1268 students from the six schools. IAQ measurements of physical parameters, gaseous pollutants, particulate matter and bioaerosols were conducted in four problematic school buildings (26 classrooms) and two comparison school buildings (12 classrooms). Using air sampling as well as exhaust air filters and classroom settled dust to detect the presence of elevated concentrations of airborne cultivable microbes and pathogenic, toxigenic and mycoparasitic Trichoderma strains were the most indicative methods in distinguishing problematic schools from comparison schools. Other IAQ-related measurements did not detect clear differences between problematic and comparison schools, as the concentration levels were very low. The results indicate that the complaints reported by occupants could have been related to excess moisture or mould problems that had not been found or repaired. Ventilation pressure condition investigations and simultaneous exhaust and supply air filter dust culture should be addressed precisely in future studies.

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)0.000
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 score0.064
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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

Citations68
Published2020
Admission routes1
Has abstractyes

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