The Ability of Airborne Microalgae and Cyanobacteria to Survive and Transfer the Carcinogenic Benzo(a)pyrene in Coastal Regions
Bibliographic record
Abstract
Air pollution has been a significant problem threatening human health for years. One commonly reported air pollutant is benzo(a)pyrene, a dangerous compound with carcinogenic properties. Values which exceed normative values for benzo(a)pyrene concentration in the air are often noted in many regions of the world. Studies on the worldwide spread of COVID-19 since 2020, as well as avian flu, measles, and SARS, have proven that viruses and bacteria are more dangerous to human health when they occur in polluted air. Regarding cyanobacteria and microalgae, little is known about their relationship with benzo(a)pyrene. The question is whether these microorganisms can pose a threat when present in poor quality air. We initially assessed whether cyanobacteria and microalgae isolated from the atmosphere are sensitive to changes in PAH concentrations and whether they can accumulate or degrade PAHs. The presence of B(a)P has significantly affected both the quantity of cyanobacteria and microalgae cells as well as their chlorophyll a (chl a) content and their ability to fluorescence. For many cyanobacteria and microalgae, an increase in cell numbers was observed after the addition of B(a)P. Therefore, even slight air pollution with benzo(a)pyrene is likely to facilitate the growth of airborne cyanobacteria and microalgae. The results provided an assessment of the organisms that are most susceptible to cellular stress following exposure to benzo(a)pyrene, as well as the potential consequences for the environment. Additionally, the results indicated that green algae have the greatest potential for degrading PAHs, making their use a promising bioremediation approach. Kirchneriella sp. demonstrated the highest average degradation of B(a)P, with the above-mentioned research indicating it can even degrade up to 80% of B(a)P. The other studied green algae exhibited a lower, yet still significant, B(a)P degradation rate exceeding 50% when compared to cyanobacteria and diatoms.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".