Quantitative assessment of meteorological and air quality variables using airport monitoring data: Istanbul International Airports

The relationship between air pollutants and meteorological variables may vary depending on the temporal length of the dataset and regional characteristics. This study includes meteorological data from three international airports in Istanbul between 2021 and 2022: Istanbul International Airport, Istanbul Atatürk International Airport, and Istanbul Sabiha Gökçen International Airport. Airport Routine Meteorological Reports (METAR) and Selected Special Weather Reports (SPECI) data were collected from these airports, while pollutant data were obtained from six nearby air quality monitoring stations (Arnavutköy, Avcılar, Yenibosna, Şirinevler, Kartal, and Tuzla). A comprehensive correlation analysis was performed using both Pearson and Spearman rank correlation coefficients to account for linear and monotonic relationships, respectively. The study was further categorized by season (summer and winter), time of day (daytime and nighttime), and weekdays versus weekends to evaluate temporal changes in the meteorological influence on air pollution levels. Results indicated that DBT and WBT had the strongest and most consistent positive correlations with SO2 concentrations during the summer months, especially at suburban stations such as Tuzla and Sabiha Gökçen Airport (e.g., r ≈ 0.42–0.44). PM10 and PM2.5 levels were significantly lower in summer, likely due to increased atmospheric dispersion and the absence of residential heating, with the highest concentrations observed in winter under stable conditions. Conversely, WS and TCC showed moderate to strong negative correlations with nitrogen-based pollutants (NO, NO2, NOx), particularly during winter and at nighttime. Although daytime correlations were generally stronger across all pollutants, local meteorological and topographical factors caused site-specific differences. Overall, the findings highlight the importance of considering meteorological variability in air quality management and modeling, particularly when developing localized mitigation strategies for airport surroundings and nearby urban areas.