the socio-economic impacts of the policy decisions. So, as models are becoming more and more important in the policy arena, the complexity of the relationship between their output and the consequent decisions together with the limited evaluation of their robustness can cause a lack of confidence, and a possible growing mistrust on the capability of systems to support important strategic choices. A riorous robustness analysis approach may be the answer to overcome this problem. The US Environmental Protection Agency indeed recommends that both model developers and users perform such an analysis to help determining when a model can be used to take reliable decisions (EPA, 2009). Similarly, in Europe, the FAIRMODE (Forum for Air Quality Modelling) initiative is dealing with guidelines on “fitness for purpose” to assess if an air quality model reaches the minimum level of quality to be used in the science-to-policy interface (Thunis et al., 2015). In the case of Integrated Assessment Models (IAMs), the analysis becomes even more complex, since it does not consider a single model, but a set of interlinked specific models. It means that the robustness analysis should assess the uncertainty and sensitivity not only for each single component of the system, but also for the system in its entirety (Amann et al. 2011). To be more specific, let’s consider the case of Integrated Assessment Modelling (IAM) in the air quality field. In this context, the analyst should answer questions such as: Does the preferable decision change, if it is assessed that the results of the model of the (physical, economic, social) system are inaccurate? How much risk affects the implementation of a certain decision, given that there is no certainty about the model result? The results of an air quality IAM may be inaccurate for a number of different reasons that are common to any modelling exercise, going from the too simplistic formulation to the impossibility of measuring the necessary inputs. In addition, uncertainty might derive from various sources, that are peculiar to this context. The space of feasible actions (options/alternatives/decisions) is normally very limited and, more
Table 1 presents, for each single DPSIR block (), the available literature. In any case, for both uncertainty and sensitivity analysis, the first step of the procedure is the identification of the uncertainty sources. For this reason, the next section presents the main uncertainty cause of the different DPSIR blocks. Uncertainty Methodology Kouridis et al., 2010 (N) Kioutsioukis et al., 2004 (N) Crosetto and Tarantola, 2001 (N) Kouridis et al., 2010 (N) IPCC, 2000 (N) Freeman et al., 1986 (A) Zabkar et al., 2013(N) Boynard et al., 2011 (N) Cheng and Sandu, 2009 (N) Hanna et al., 2001 (N) Isukapall et al., 1998 (N) Tatang et al., 1997 (N) Uliasz, 1988 (N) Gao et al., 1996 (N) Irwin et al., 1987 (N) Fuentes, 2009 (N) Chart-asa and Mac Donald Gibson, 2015 (N) Baroni et al., 2013 (N) Pisoni et al., 2010 (N) \ Sensitivity Methodology Kouridis et al., 2010 (N) Kioutsioukis et al., 2004 (N) Crosetto and Tarantola, 2001 (N) Kouridis et al., 2010 (N) Vlachokostas et al., 2009 (A) Martien et al, 2006 (N) Hakami 2004(N) Hakami 2003 (N) Menut et al., 2000 (N) Carmichael et al., 1997 Seigneur et al., 1981 Cukier et al., 1973 Vlachokostas et al., 2009 (A) Chart-asa and Gibson, 2015 (N) Vlachokostas et al., 2009 (A) Butler et al., 2014 (N) Baroni et al. 2013 (N) Pisoni et al., 2010. (N) Ravalico et al., 2009 (N) Rios Insua and French, 1991 BLOCK Drivers Pressures States Impacts Responses
PRESSURES The functionality of the PRESSURES block aims at estimating emission data or/and reliable emission projections. The main uncertainties related to this are indirect, due to the output of the DRIVERS block (activity levels) and direct, related to the data on emission factors, to the spatial and temporal distributions (gridding) procedures and the proxies used (Ponche, 2002, François et al., 2005). STATES The functionality of the STATES block is to produce air quality (AQ) state, i.e. pollutant concentrations or deposition, starting from emissions. Generally, AQ state is simulated through the use of a numerical model, but AQ state can also be described through measurements together with a geo-statistical interpolation method and source-receptor models to link emissions and concentrations at a specific location. When the AQ is evaluated through measurements only, uncertainties are related to the measurements themselves (generally well known), to the geo-statistical methods used to interpolate point measurements and to uncertainties related to source apportionment technique used to link emissions to the AQ state. In Hanna (2001) the total uncertainty related to a model simulation can be computed as the sum of both linguistic (model formulation and inherent variability due to the stochastic nature of atmospheric processes) and epistemic (input data) uncertainty. IMPACTS Health Impact Assessment (HIA) aims to the quantification of the expected health burden induced by atmospheric pollution in the target population, most commonly expressed in of years of life lost attributable to the exposure to the atmospheric pollutant(s) under study (Krzyzanowski et al., 2002). Beside the indirect effects from the STATE block output, the IMPACT block is strongly affected by indirect uncertainty sources related to the computation of exposure levels. Moreover, there is uncertainty related