The Green Airport project is pursuing the goal of integrating environmental aspects in the management of airport processes. After a broad initial examination of the field, the focus was set on the reduction of noise and emissions, as these have the greatest impact. Other areas, such as energy, waste, water, and space usage, have been identified but not yet analysed in detail.
Models are being created to facilitate the measurement and evaluation of environmental aspects. Bearing in mind the goal "Airport 2030" (third flagship project of the Leading-Edge Cluster strategy), there is a conscious awareness that the calculation of such models is very slow today and that necessary parameters may not yet be available. More powerful computers and the availability of further environmentally relevant parameters will resolve these issues in the next 10 to 15 years.
The findings from these models will be presented graphically and/or as normalised KPIs (Key Performance Indicators), thus providing users with an indication as to the areas in which environmentally relevant limits are being exceeded. For example, delayed arrivals combined with a specific wind direction can cause the CO2 emissions in a residential area to exceed the permissible limits. In this case, there are various options to improve the situation, from different approach procedures to changing the approach heading. As the context for a decision is very complex, assistant systems will generate suggestions and guide the necessary approval and coordination processes. In the case of a changed approach procedure, this has to be agreed with air traffic services and the airline before it can be implemented and have any effect.
Thomas Fluegel, email@example.com, 0531/226-2690
Infrastructure & Cities Sector / Mobility and Logistics Division / Logistics and Airport Solutions
Flughafen Hamburg GmbH, Environmental Protection Centre
German Aerospace Center, Institute of Air Transportation Systems
Federal Ministry of Education and Research within the framework of the Leading-Edge Cluster strategy