To support the analyses for the eighth meeting of CAEP in February 2010, a thorough evaluation of the proposed models and databases was carried out. The goal of this evaluation was to advise CAEP as to which tools are sufficiently robust, rigorous, transparent, and appropriate for which analysis (e.g., stringency, CNS/ATM, market-based measure), and to understand any potential differences in modelling results.
Evaluation teams were established for each of the modelling areas: noise, local air quality, greenhouse gas emissions, and economics. A common methodology was developed to ensure consistency in the model evaluation process across the four modelling areas, which included a review of the key characteristics of a robust model or database.
The models were then used to assess two sample problems: the effects of reduced thrust takeoff, and the effects of hypothetical NOx stringency. One of the goals of the sample problems was to advance candidate model evaluation and development by practicing on a set of problems that are similar to those that were considered as part of the CAEP/8 work programme. The practice analyses were accompanied by a rigorous assessment process, so that the strengths and deficiencies in the models could be identified, and appropriate refinements and improvements implemented. This ensured that the models were sufficiently well understood and robust to support a broad range of CAEP/8 analyses.
Models approved for use by CAEP/8
Figure 1: Characteristics of a robust model or database.
Each model and database has its strengths and weaknesses, and the use of multiple models provided CAEP insight into sensitivities of the results. Going forward, the model evaluation process developed for CAEP/8 has established a framework for the future evaluation of new models and updates to the existing tools.
Of key importance is the fact that the input databases were common to all of the models. This allowed, for the first time, exploration of the interrelationships between noise, local air quality, and greenhouse gas emissions. As experience is gained investigating these interdependencies, and as the models mature further, more advanced decision making on aviation environmental protection will become possible.