The incorporation of an air quality sub-model in integrated assessment models (IAMs) has been an increasingly active research topic in the last decades. However, due to the high computational resources that air quality models require, the integrated assessment approaches frequently use model reduction techniques.

The Air Quality IAMs provide technical support to the debates on the precautionary actions against the risk on an air quality degradation and help to efficiently allocate the efforts of reduction of pollutant emissions. We present a particular example of air quality integrated assessment model, the Luxembourg Energy Air Quality (LEAQ) model. More specifically, the LEAQ model couples a techno-economic model (ETEM Luxembourg) and an air quality model including a transport calculator and a fast photochemical module (AUSTAL2000 + AYLTP). The integrated assessment model performs air quality cost-effectiveness analyses, by finding optimal emissions with respect to air quality standards and also the minimal cost energy infrastructures solutions. The model sampling uncertainty has been included in the decision process, as in decision making, uncertainties play an important role. The coupling is carried by an optimization problem, solved using an advanced convex optimization method and the oracle-base optimization engine.

We propose two ozone control planning exercises, one based on the national emissions, and the other the sectoral emissions. The results from a study case for Luxembourg demonstrate the capabilities and limitations of the optimization framework, and the problem resolution is achieved in a reasonable time, e.g. few hours.


This seminar has been jointly organized by FEEM and IEFE, Bocconi University.