Atmospheric dispersion modelling of gaseous emissions from Beirutinternational airport activities
Bibliographic record
Abstract
The projected increase of civil aviation activity, the degradation of air quality and the location of Beirut Airport embedded in a very urbanized area, in addition to the special geography and topography surrounding the airport which plays a significant role in drawing emissions to larger distances, demanded anassessment of the spatial impact of the airport activities on the air quality of Beirut and its suburbs. This is the first study in the Middle East region that model pollutant concentrations resulting from an international airport's activities using an advanced atmospheric dispersion modelling system in a country with no data. This followed validation campaigns showing very strong correlations (r = 0.85) at validation sites as close as possible to emission sources. The modelling results showed extremely high NO2 concentrations within the airport vicinity, i.e., up to 110 μg∙m-3 (which is greater than the World Health Organization annual guidelines) posing a health hazard to the workers in the ramp. The major contribution of Beirut–Rafic Hariri International Airport to the degradation of air quality was in the airport vicinity; however, it extended to Beirut and its suburbs in addition to affecting the seashore area due to emissions along the aircraft trajectory; this isan aspect rarely considered in previous studies. On the other hand, elevated volatile organic compound levels were observed near the fuel tanks and at the aerodrome center. This study provides (ⅰ) a methodology to assess pollutant concentrations resulting from airport emissions through the use of an advanced dispersion model in a country with no data; and (ⅱ) a tool for policy makers to better understand the contribution of the airport's operations to national pollutant emissions, which is vital for mitigation strategies and health impact assessments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".