Assessing the Impact of Aviation Emissions on Air Quality at a Regional Greek Airport Using Machine Learning.

Abstract

Aviation emissions significantly impact air quality, contributing to environmental degradation and public health risks. This study aims to assess the impact of aviation-related emissions on air quality at Alexandroupolis Regional Airport, Greece, and evaluate the role of meteorological factors in pollution dispersion. Using machine learning models, we analyzed emissions data, including CO₂, NOx, CO, HC, SOx, PM_2.5, fuel consumption, and meteorological parameters from 2019-2020. Results indicate that NOx and CO₂ emissions showed the highest correlation with air traffic volume and fuel consumption (R = 0.63 and 0.67, respectively). Bayesian Linear Regression and Linear Regression emerged as the most accurate models, achieving an R² value of 0.96 and 0.97, respectively, for predicting PM_2.5 concentrations. Meteorological factors had a moderate influence, with precipitation negatively correlated with PM_2.5 (-0.03), while temperature and wind speed showed limited effects on emissions. A significant decline in aviation emissions was observed in 2020, with CO₂ emissions decreasing by 28.1%, NOx by 26.5%, and PM_2.5 by 35.4% compared to 2019, reflecting the impact of COVID-19 travel restrictions. Carbon dioxide had the most extensive percentage distribution, accounting for 75.5% of total emissions, followed by fuels, which accounted for 24%, and the remaining pollutants, such as NOx, CO, HC, SOx, and PM_2.5, had more minor impacts. These findings highlight the need for optimized air quality management at regional airports, integrating machine learning for predictive monitoring and supporting policy interventions to mitigate aviation-related pollution.