Estimation of key pollutant emission during the taxi-out phase using a novel hybrid forecasting algorithm(FoREC-HHO): Application to Istanbul Airport - Ucar, Ukbe Usame (Elsevier Sci Ltd, 2026)
Aircraft emissions during taxi-out operations constitute a significant portion of local air pollution at airports and are rarely modeled in conjunction with operational and meteorological variables. Although numerous studies focus on fuel burn or emission factors based on the ICAO LTO cycle, limited research integrates real-world airport conditions. In this article, CO2, NOx, CO, and HC emissions during the taxi-out phase at Istanbul Airport for the period 2024-2030 were estimated by considering the technical specifications of aircraft, operational delays, and meteorological visibility indices. Detailed analyses were conducted under three categories (Best, Normal, Worst) and 12 scenarios, and the daily intensity of emissions per unit area was evaluated according to the IDLH health risk indicator. In the estimation study, a new hybrid method called the FoREC-HHO algorithm was developed and compared with machine learning, metaheuristic algorithms, and statistical techniques. As a result of the analysis, the FoREC-HHO algorithm showed the highest accuracy rate for all emission types and achieved the lowest MAE values, demonstrating superior prediction performance. According to the analysis findings, in the worst-case scenario, CO2 emissions increased by 80 %, NOx by 76 %, HC by 78 %, and CO by 66 % between 2024 and 2030. In the normal scenario, the emission increases were observed as 57 % for CO2, 52 % for NOx, 53 % for HC, and 46 % for CO. In contrast, under the best-case scenario, these increases were considerably more moderate, measured at 34 % for CO2, 31 % for NOx, 30 % for HC, and 26 % for CO. In addition, by 2030, the risk density for CO2 is projected to reach 2.33 kg/m2/day, while for CO, this value is 0.01293 kg/m2/day. The calculated densities for NOx and HC were determined to be 0.00221 and 0.00030 kg/m2/day, respectively. These values were found to potentially pose high acute toxicity risks for CO, chronic respiratory and nervous system risks for NOx and HC, and climate-related effects and physiological burdens on personnel working in enclosed spaces for CO2. In this study, a comprehensive approach was presented for both temporal and seasonal estimation of emissions associated with the taxi-out process at airports and health-based risk assessment using the newly developed FoREC-HHO algorithm.
Keywords: Istanbul airport; Sustainable aviation; Metaheuristic algorithms; Machine learning; Emission estimation; Climate change