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Record W4402922020 · doi:10.1038/s41598-024-73933-4

Experimental study on bioaerosols behavior and purification measures in a subway compartment

2024· article· en· W4402922020 on OpenAlexaff
Renze Xu, Fan Wu, Lian Shen, Zhiqiang Fan, Jianci Yu, Zhen Huang

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsMinistry of Education and Child Care
FundersTraining Program for Excellent Young Innovators of ChangshaNational Natural Science Foundation of China
KeywordsIndoor bioaerosolCompartment (ship)ChemistryComputer scienceEnvironmental chemistry

Abstract

fetched live from OpenAlex

Bioaerosols in public transportation systems raise critical environmental concerns, seriously threatening passenger health and safety. In this study, we investigate the spread characteristics of bioaerosols in a standard type-B subway compartment using both air sampling and sediment sampling methods. Additionally, without compromising indoor passenger comfort, two self-designed air purification devices, based on intense field dielectric (IFD) and dielectric barrier discharge (DBD) technologies, respectively, are successfully applied for the improvement of the subway air quality. The results show that bioaerosols can propagate rapidly throughout the entire compartment in 5 min via airborne transmission. Under the effect of the symmetric air ducts and compartment structure, the difference in bioaerosol concentration in the air is less than 10% between both ends of the compartment. Concurrent substantial bioaerosol deposition on the ground, seats, and windows underscores the risk of contact transmission. Furthermore, the real-time purification rates of the two devices integrated into the air conditioning system reach 59.40% and 44.98%, respectively. With their demonstrated high efficiency in purifying bioaerosols and modular design featuring low energy consumption, easy cleaning, and reusability, these devices stand out as viable long-term solutions for large traffic vehicles. These research findings provide practical equipment recommendations and installation strategies for optimizing indoor air quality in subways and are applicable to other similar transportation systems.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.268

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 head0.049
GPT teacher head0.342
Teacher spread0.293 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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