Uncertainty analysis in air quality control systems

Air pollution in the atmosphere derives from complex non-linear relationships, involving anthropogenic and biogenic precursor emissions. Due to this complexity, Integrated Assessment Modelling systems (IAMs) can be used, to help Environmental Authorities to control air quality reducing human and ecosystems pollution exposure effects in a cost efficient way. In this context, the literature suggests control modeling systems solving multi-objective optimization problems. Such approach requires descriptive models linking the control variables to the objectives. As they are assessed thousands and thousands of times by the optimization algorithms, they have to be on one hand no time consuming and on the other hand enough robust. It follows that one of the main aspects to be taken into account assessing the control policies is the impact of uncertainties, in the descriptive models itself and in the optimization control problem results. In this work the application of the general probabilistic framework (GPF) for uncertainty and sensitivity analysis has been applied to assess the sensitivity of the descriptive models in a PM10 exposure control problem over Northern Italy, an area often characterized by high pollution levels.