Season and city shape urban bioaerosol composition beyond vegetation and socioeconomic gradients
Bibliographic record
Abstract
ABSTRACT Urban vegetation varies with socio-economic gradients, as lower-income neighborhoods often host sparser and less diverse green spaces. This disparity may affect respiratory health by influencing exposure to bioaerosols. Understanding the characteristics of this aerobiome could help anticipate risks related to allergies and other respiratory conditions. Here, we hypothesized that urban vegetation cover and socio-economic status shape urban bioaerosols. We sampled bioaerosols at 65 sites across three Canadian cities of varying population size and density using an active air sampler over four months, and characterized their bacterial, fungal, and pollen composition using amplicon sequencing. Seasonal alpha diversity varied significantly for fungi and pollen. Based on beta diversity, sampling period alone explained up to 40% of pollen, 29% of fungal, and 11% of bacterial community composition variation. In contrast, vegetation cover explained only a minor portion of the variance in bioaerosol composition, and median household income, almost none. These findings provide a critical baseline for understanding the urban aerobiome and highlight the need to study how vegetation identity and diversity, rather than cover alone, may shape bioaerosol dynamics in cities. As cities grow and urban greening initiatives expand, demystifying the aerobiome dynamics becomes an urgent public health priority.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".