THE ASSUMPTION EFFECT OF USING BIODIESEL AS AN AIRCRAFT FUEL ON AIR QUALITY IN SOME EGYPTIAN AIRPORTS
Bibliographic record
Abstract
Total Landing and Takeoff Cycle (LTO) emissions from aircrafts at SSH for year 2013 were estimated as 442.047 t /y for HC, 37660.742 t /y for CO, 69340.331 t /y for NOX, and 9.674 t /y for PM. The predicted total LTO emissions from aircrafts at Sharm El-Sheikh International airport(SSH) for the year 2050 were calculated as 4928.8241 t /y for HC, 419917.27 t /y for CO, 773144.69 t /y for NOX, and 107.8651 t /y for PM. The aircrafts at SSH are the major sources of NOx emissions (99.95%), Boeing 777 (large aircraft) has the biggest portions in NOx total emissions, in which contributing 6.836t /LTO for NOX. Flight numbers are expected to reach 483822 by 2050. The emissions concentrations at SSH are below the air quality limit values given in Law No. (4/1994) of Egypt and its amendment. The assumption of using biodiesel (Soy biodiesel B20) for aircraft engines at SSH for year 2013 leads to the substantial reduction in PM, HC and CO emissions 0.987 t/ year, 93.272 t/ year, and 4142.682 t/ year, respectively accompanying with the increase in NOx emission 1386.806 t/ year. Moreover, the prediction of reduction in emissions for year 2050 are estimated as 11.00505 t/ year for PM, 1039.9828 t/ year for HC, and 46190.9043 t/ year for CO, accompanying with the increase in NOx emission 15462.8869 t/ year. There are very little effect on emissions reduction when using biodiesel (Soy biodiesel B20) for APU & aircraft handling comparing with aircraft main engines. The measurement of average concentration of the regulated air emissions (HC, NOx, CO) at distance away 8 km from Runway were estimated for using Diesel (Jet A1) as 0.8281 µg/m3 for HC, and 4.617 µg/m3 for CO, and 343.7607 µg/m3 for NOx, while for The assumption of using biodiesel (Soy biodiesel B20) for aircraft engines as 0.6534 µg/m3 for HC, and 4.1091 µg/m3 for CO, and 350.6359 µg/m3 for NOx. Keywords: Biodiesel; Aircraft; Air quality: air pollution; Airport
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".