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Record W4410024661 · doi:10.13031/jash.16127

Fan Exhaust Air Sampling of Livestock Operations as a Proxy for Indoor Bioaerosol Monitoring

2025· article· en· W4410024661 on OpenAlexafffundabout
Joanie Lemieux, Florent Rossi, Asmaâ Khalloufi, Marc Veillette, Valérie Létourneau, Nathalie Turgeon, Marie‐Lou Gaucher, Caroline Duchaine

Bibliographic record

VenueJournal of Agricultural Safety and Health · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsCegep de Saint Hyacinthe
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of Canada
KeywordsIndoor bioaerosolBioaerosolEnvironmental scienceBiosecurityAirborne transmissionVeterinary medicineLivestockIndoor air qualityAmplicon sequencingEnvironmental engineeringEnvironmental healthBiology16S ribosomal RNAEcologyGeographyCoronavirus disease 2019 (COVID-19)MedicineInfectious disease (medical specialty)Bacteria

Abstract

fetched live from OpenAlex

Highlights Fan exhaust air sampling is a reliable monitoring proxy for indoor bioaerosols from livestock operations. Air samples collected indoors and at fan exhaust have highly similar bacterial diversity. At low indoor concentrations, specific microbial markers are still detectable in the air collected at the fan exhaust. Abstract. The incidence of animal and zoonotic diseases is expected to increase in the coming years, imposing the reinforcement of biosecurity measures for livestock operations. Airborne transmission of certain infectious agents underscores the importance of surveilling bioaerosols. However, having access to livestock operations for monitoring purposes is now challenging. Hence, it has become imperative to explore alternative strategies to assess indoor bioaerosols. This study aimed to compare bacterial diversity and quantify microbial markers found in bioaerosols indoors and at the fan exhausts of pig-finishing buildings (PFBs) and broiler chicken barns (BCBs). Bioaerosols were collected using a filter-based, high-flow rate air sampler in 12 facilities (10 PFBs and 2 BCBs) during the warm season in Eastern Canada, corresponding to maximal ventilation rate operations. Four farms—PFB-1, PFB-2, BCB-1, and BCB-2—were visited multiple times, while the other eight PFBs (PFB-3 to PFB-10) were visited once. At each farm, indoor air samples were paired with samples from the corresponding sidewall extraction fans. Amplicon-based sequencing and quantitative PCR (qPCR) were performed to describe bacterial diversity and quantify specific microbial (bacterial and archaeal 16S rRNA genes, Enterococcus spp., and a phage of Aerococcus viridans) and animal (swine and poultry DNA) markers. No significant differences in OTUs abundance and diversity between indoor bioaerosols and their corresponding fan exhaust samples were observed. There were also no significant differences between an indoor and its corresponding fan exhaust air sample when comparing OTUs relative abundance and their presence-absence. Similarly, concentrations of bacterial 16S rRNA genes in indoor samples (10 6 –10 8 ) did not significantly differ from those found in samples collected at the fan exhaust (10 5 –10 8 ) for both PFBs and BCBs. Strong correlations were observed between sampling sites for Archaea, Enterococcus, and A. viridans phage concentrations while poultry and swine DNA concentrations at fan exhausts did not correlate with indoor levels. All investigated markers were detectable at fan exhausts, even at low indoor concentrations (10 2 –10 3 ). Our study suggests that air sampling at the fan exhaust of barns provides a representative picture of the indoor bioaerosols both for bacterial diversity and barn-specific indicators when the fans are in use. This method appears promising for characterizing indoor air quality based on emissions and could be highly valuable in cases where biosecurity measures or outbreaks restrict access to barns. Keywords: Air sampling, Airborne microbiota, Bioaerosols, Broiler, Fan exhaust, Livestock operations, Pig.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.297

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)0.0000.000

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 head0.027
GPT teacher head0.315
Teacher spread0.289 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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