Automating the Estimation of Noise and Emissions Near Airports With ADS-B Data

Aircraft arrivals and departures significantly affect nearby populations, primarily through noise pollution and the release of pollutants that degrade air quality. Estimating these environmental impacts can be a lengthy process and is typically mandated by legal regulations governing airport operations. This paper proposes a methodology to automate the estimation of environmental impacts for historical scenarios, specifically noise and pollutant emissions in the vicinity of airports, by utilizing open-source data. The automation pipeline developed retrieves the necessary databases and ADS-B data for a specified airport and time frame, and validates, pre-processes and enhances the data before estimating noise and local air quality emissions with it. The developed automation pipeline is applied to the Cologne Bonn Airport for the year of 2019. In addition to the open-source data, confidential datasets were made available containing the airport flight logs and the records from the airport noise measurement stations. This confidential dataset is used to assess the coverage of the ADS-B data and to validate the noise estimates generated with the automated process. The number of flights obtained from the ADS-B network covers ca. 82\% of the flights in the airport flight logs, and the mean noise levels derived from ADS-B data deviate between 0 and 3 dB(A) from the ones recorded by the noise measurement stations, depending on the flight type and location of the noise stations. Possible reasonings for the different discrepancies observed include the assumptions made in the ADS-B data enhancement, as well as the underlying noise model and databases used. As a final step in the Cologne Bonn Airport use case, aircraft emissions reported according to the Landing \& Takeoff cycle are compared with emissions estimates derived from ADS-B data. Significant discrepancies are observed between the two estimation methods which can be attributed to variations in time spent below 3000~ft~AGL, average fuel flow and average EIs for each pollutant. This contribution provides an initial step toward automating the estimation of environmental impacts from arriving and departing aircraft. Further work shall focus on addressing the limitations of the methodology used to enhance the ADS-B tracks obtained and further validation of the environmental impacts estimated.