Exposure to Occupational Air Pollution in the Manufacturing Industries of Bhutan
Bibliographic record
Abstract
Airborne pollution effects human health, mainly the respiratory and cardiovascular system. Industrial workers are highly susceptible to those air pollution and particulate matter emitted from their workplaces. The objective of this study is to determine the occupational exposure level of air pollution affecting respirable dust of various parameters in the manufacturing industries of Bhutan. The method used in this study include convenience sampling method and grab sampling method. All the 20 major manufacturing industries located in Phuntsholing, Samtse and Gomtu were selected for this study. However, the working area where possibilities of emitting higher air pollution during the work process within the companies was selected for grab air sampling. The level of respirable dust in the cement plants was found to be 13.1 times higher than Bhutan’s permissible level. Similarly, the respirable dust level in the ferroalloy plants was 2.34 times higher than OSHA and Bhutan’s permissible exposure level and 3.9 times higher than the ACGIH TLV. The level of total particulate matter in ferroalloy, cement and dolomite industries exceeds the Bhutan and OSHA permissible exposure level and ACGIH TLV. This study has found the prevalence of higher concentration level of particulate matter in the working environment of manufacturing industries in Bhutan. Therefore, it is also suggested for study on occupational health effect to build the relationship between exposure level and health effect.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".