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Air quality in European cities is still a challenge, with various urban areas frequently exceeding the PM2.5 and NO2 concentration levels allowed by the European Union Air Quality Standards. This is a problem both in terms of legislation compliance, but also in terms of health of citizens, as it has been recently estimated that 400 to 450 thousand people die prematurely every year due to poor air quality. Air quality in cities can be improved with a number of interventions, at different sectoral (industry, traffic, residential, etc …) and geographical (international, European, national, local, etc.) levels. In this paper we explore the potential of city level plans to improve mobility and air quality (excluding electro-mobility options, not considered in this study). We applied the “Sustainable Urban Mobility Plans” (SUMPs) framework to 642 cities in Europe and modelled how the measures they include may impact at first on mobility and emissions at urban level, and then on urban background concentrations of PM2.5 and NO2. Results show that annual averages moderately improve for both pollutants, with reductions of urban background concentrations up to 2% for PM2.5 and close to 4% for NO2. The impact on NO2 at street level (that will be higher than on urban background) is not evaluated in this work. The air quality improvement of the simulated SUMP would only partially alleviate air quality problems in urban areas, but such a reduction in the emissions of air pollutants should still be considered as a positive result of SUMPs, given that they correspond to a set of low-cost measures that can be implemented at local level. Furthermore, the introduction of electro-mobility options (not considered here) would increase the impact on air quality. Other types of benefits, such as reduced fuel consumption, greenhouse gas emissions, higher impact at street level or accident rates reduction further add to the overall positive impact.

[1] The main problem encountered when applying remote sensing and geographic information systems techniques for wildfire risk assessment is the necessity to integrate different data sources. The methods applied so far are usually based on regression techniques or on coefficients relying on experts' knowledge. Hence fire managers are seeking an unbiased statistical model able to highlight the multivariate spatial relationships between the predictor variables, yielding understandable output readily accessible to end users. The present research aims to test the capability of classification and regression trees (CART) analysis to assess long-term fire risk at a local scale. The CART analysis is a nonparametric statistical technique which generates decision rules in the form of a binary tree, for a classification or a regression process. A fire-prone study area was selected in the southeast of Italy. Fire ignition points, relative to a 7 year period (1997)(1998)(1999)(2000)(2001)(2002)(2003), were used to derive a fire occurrence map through a kernel density approach. The resulting map was then used as input response variable for the CART analysis with fire danger variables used as predictors. The rules induced by the regression process allowed the definition of different risk levels, expressed as 30 management units, which is useful for producing a fire risk map. The result of the regression process (r = 0.77), the capability of the CART analysis to highlight the hierarchical relationships among the predictor variables, and the improved interpretability of the regression rules represent a possible tool useful for better approaching the problem of assessing and representing fire risk.Citation: Amatulli, G., M. J. Rodrigues, M. Trombetti, and R. Lovreglio (2006), Assessing long-term fire risk at local scale by means of decision tree technique,