Microorganisms in the air through the lenses of atmospheric chemistry and microphysics 
Bibliographic record
Abstract
Microorganisms in the atmosphere comprise a tiny fraction (~10-8%) of the Earth’s microbiome. A significant portion of this ‘aeromicrobiome’ consists of bacteria that typically remain airborne for a few days before being returned to the ground through wet or dry deposition. Unlike bacteria in the other Earth surface spheres (e.g., litho-, hydro-, phyllo-, cryospheres), atmospheric bacteria are aerosolized, residing in individual particles and separated by considerable distances (a few centimeters) from each other. Within these small isolated microcosms, bacteria are exposed to particular chemical and physical conditions that potentially affect their stress levels, survival and general functioning. Using fundamental chemical and microphysical concepts of atmospheric aerosol particles and cloud droplets, we examine these specific environmental conditions. In particular, we challenge the concept of clouds as microbial oases by illustrating the water amounts and time scales inside clouds. In addition, we suggest that the small volumes of cloud droplets may cause greater nutrient limitations but simultaneously reduce oxidative stress compared to other aquatic environments. Various chemical and microphysical factors may act as microbial stressors (e.g., oxidative, osmotic, and UV-induced) in the atmosphere, which may either enhance or diminish the survival and diversity of atmospheric bacteria. Based on established atmospheric chemical and microphysical principles, we discuss that observed trends of bacterial community properties and pollutant concentrations may lead to incorrect interpretations due to confounding factors. In summary, our presentation aims to motivate future experimental and modeling studies to disentangle the complex interplay of chemical and microphysical factors with the atmospheric microbiome. Such studies are important to eventually allow for a comprehensive understanding of the atmosphere’s role in affecting airborne microorganisms, a small yet rapidly evolving component of the Earth’s microbiome. Ervens, B., Amato, P., Aregahegn, K., Joly, M., Khaled, A., Labed-Veydert, T., Mathonat, F., Nuñez López, L., Péguilhan, R., and Zhang, M.: Ideas and perspectives: Microorganisms in the air through the lenses of atmospheric chemistry and microphysics, Biogeosciences, 22, 243–256, https://doi.org/10.5194/bg-22-243-2025, 2025.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".