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Investigating the effects of particle size and relative humidity on bioaerosol disinfection in an in-duct ultraviolet germicidal irradiation system

2024· article· en· W4399629717 on OpenAlexafffund
Hao Luo, Lexuan Zhong

Bibliographic record

VenueBuilding and Environment · 2024
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioaerosolUltravioletRelative humidityUltraviolet irradiationIrradiationEnvironmental scienceParticle sizeMaterials scienceAerosolHumidityMeteorologyChemical engineeringOptoelectronicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Ultraviolet germicidal irradiation (UVGI) technology has garnered substantial attention in disinfecting airborne microorganisms during and beyond the COVID-19 pandemic. Given that UVGI inactivation performance varies in complex in-duct environments, this study examines the effects of bioaerosol particle size and relative humidity (RH) on the inactivation efficiencies of an in-duct UVGI system. MS2 bioaerosols, with phosphate-buffered saline as suspending media and subjected to a bioaerosol drying process, were used in UVGI tests. At any given RH condition (25 %, 40 %, or 60 % RH), lower UV rate constants were presented for larger bioaerosols (2.1–7 μm) in comparison to smaller ones (0.65–2.1 μm). For humidity, the UV rate constant initially increased and then decreased as RH increased from 25 % to 60 %, peaking at 40 % RH, irrespective of the particle size. Notably, the absence of the bioaerosol dryer altered this trend, where the inactivation efficiency decreased with the increase in RH. In conclusion, our findings suggest that a higher UV dose is required to mitigate hazards from larger bioaerosols in very humid environments. In addition, this work proposes a comprehensive flowchart, a beneficial tool for engineers and designers, which facilitates the effective design and implementation of UVGI technology in controlling bioaerosol hazards.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.197

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)0.0000.000

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 head0.010
GPT teacher head0.245
Teacher spread0.235 · 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.

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

Citations19
Published2024
Admission routes2
Has abstractyes

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