T ravelling and Source Point Identification of Some Transboundary Air Pollutants by Trajectory Analysis in Sathkhira, Bangladesh
Bibliographic record
Abstract
Trajectory of transboundary air pollutants are studied in Satkhira district through HYSPLIT Model4. Atmospheric pollutants data and meteorological data collected from Department of Environment (DoE) and Bangladesh Meteorological Department (BMD) are used in this study. The pollutants are collected with passive sampler and analyzed through suitable analytical methods. Atmospheric air pollutants data were studied from December 2005 to April 2007. It is found that the level of SOx is much higher in the dry (December to February) season than wet season. The highest concentration of SOx and NOx observed during the month of February 2006 and December 2006 (13 µg/m 3 ), and January and February 2007 (7 µg/m 3 ). During November, December of the year 2006 and January, February, March of the year 2007 pollutants concentration is estimated at increased level. The application of SOx to NOx ratio depicts that during dry season power plant and coal burning is the major source of these pollutants, but in wet season they are mainly from vehicular emission of the Bay of Bengal. Backward air mass trajectory showed that the level of SOx and NOx increase when there is an air mass movement over India (North and North West) and fall when the trajectories spend most of their 5day time over Bay of Bengal. It is evident from the study that transboundary traveling has a significant effect on air quality and the pollutants traveled firmly beyond the boundary line of Bangladesh.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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".