Aerospace (Aug 2017)
Modelling Airport Pollutants Dispersion at High Resolution
Abstract
Local air quality is a major concern for the population regularly exposed to high levels of air pollution. Due mainly to its aircraft engine activities during taxiing and take-off, the airport is often submitted to heterogeneous but important concentrations of NO x and Particulate Matter (PM). The study suggests an innovative approach to determining the air traffic impact on air quality at the scale of the airport, its runways, and its terminals, to be able to locate the persistent high-concentration spots, for example. The pollutant concentrations at 10 m resolution and 1 s time step are calculated in order to identify the most affected areas of an airport platform and their contributors. A real day of air traffic on a regional airport is simulated, using observations and aircraft trajectories data from radar streams. In order to estimate the aircraft emissions, the Air Transport Systems Evaluation Infrastructure (IESTA) is used. Regarding local air quality, IESTA relies on the non-hydrostatic meso-scale atmospheric model Meso-NH using its grid-nesting capabilities with three domains. The detailed cartography of the airport distinguishes between grassland, parking, and terminals, allowing the computation of exchanges of heat, water, and momentum between the different types of surfaces and the atmosphere as well as the interactions with the building using a drag force. The dynamic parameters like wind, temperature, turbulent kinetic energy, and pollutants concentration are computed at 10 m resolution over the 2 km × 4 km airport domain. The pollutants are considered in this preliminary study as passive tracers, without chemical reactions. This study aims at proving the feasibility of high-scale modelling over an airport with state-of-the-art physical models in order to better understand the repartition of pollutants over an airport, taking into account advection and turbulence in interactions with buildings and regional trends, emissions, Auxiliary Power Units (APU), taxiing, parking, take off. All these processes drive the model at each time step and are not averaged over one hour or more like in Gaussian or Lagrangian ones. This study is investigating the feasibility of high spatio-temporal air quality modelling for research purposes but not for operational forecasting.
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