MétaCan
Menu
Back to cohort
Record W2082138961 · doi:10.1080/02786826.2011.576281

How Particle Resuspension from Inner Surfaces of Ventilation Ducts Affects Indoor Air Quality—A Modeling Analysis

2011· article· en· W2082138961 on OpenAlexaff
Bin Zhou, Bin Zhao, Zhongchao Tan

Bibliographic record

VenueAerosol Science and Technology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDuct (anatomy)Indoor air qualityAirflowVentilation (architecture)Environmental scienceParticle (ecology)Air quality indexAerosolMechanicsAtmospheric sciencesMeteorologyEnvironmental engineeringEngineeringPhysicsGeologyMechanical engineering

Abstract

fetched live from OpenAlex

Dust particles deposited on the inner surfaces of the ventilation ducts can be resuspended by passing airflow. A physical-science-based model is developed to understand how particle resuspension affects the indoor air quality. This integrated model takes into consideration particle mass balance models for straight ventilation duct, duct bend, ventilation room, and air filter. The straight duct and room models have been validated using experimental data. With the integrated model, we find that in-duct resuspension of particles could lead to significant increase in exposure to airborne particles for indoor occupants. It is also found that indoor particle exposure is a linear function of dust mass loading. Greater ventilation rate, which means higher air speed above the dust particles, would lead to greater exposure ratio, while fresh air ratio has little influence. Possible control methods are discussed as well.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.065
GPT teacher head0.298
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations49
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueAerosol Science and TechnologySame topicAir Quality and Health ImpactsFrench-language works237,207