The Effect of Dust Storm on the Microbial Quality of Ambient Air in Sanandaj: A City Located in the West of Iran
Bibliographic record
Abstract
BACKGROUND & AIMS: The presence of pathogenic microorganisms in the dust storm can cause diseases such as Asthma, Pneumonia, and respiratory infections. The aim of this study was to determine the relationship between air-borne particles with airborne microorganisms in normal and dusty days in Sanandaj, a city located in the west of Iran. MATERIALS & METHODS: Air sampling was conducted during the normal and dusty days through Andersen single-stage impactor (28.3 L/min) for 2.5 min. Air particles concentration (PM10) was measured daily and microbial sampling was also conducted on every six days and on the dusty days. Finally, the data was analyzed by SPSS-16 (ANOVA and paired T-tests). RESULTS: The concentration of airborne microorganisms (bacteria and fungi) was increased by an increase of the airborne particles. Particles concentration in May, June and July (twice per month) was more than of the standard value. The predominant species of bacteria and fungi during the occurrence of Dust storm was Bacillus spp. (56.2% of total bacteria) and Mycosporium spp. (28.6% of total fungi), respectively. DISCUSSION AND CONCLUSION: The results showed that the number of airborne microorganisms (bacteria and fungi) increased during the dust storm. Therefore, the microorganisms in the dust storm can cause biological harmful effects on human health.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".