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Transport activities contribute significantly to the air pollution and its impact on emissions is a key element in the evaluation of any transport policy or plan. Calculation of emissions has therefore gained institutional importance in the European Community. To obtain emission factors several methods make use of only vehicle mean velocity, which can be easily obtained by vehicle flow and density in the road. Recently in ARTEMIS project by Rapone et al. (2005–2007) a meso scale emission model, named KEM (Kinematic Emission Model), able to calculate emission factor has been developed. This model is based on a new statistical methodology, capable to consider more attributes than the simple mean speed to characterize driving behaviour. An interesting approach to determine the exact mix of driving cycles is represented by the use of microscopic traffic simulation models that could be used to avoid the very expensive costs of experimental campaigns needed to obtain real driving cycle.

One of the objectives of the Atena project was the definition of methods for the predictive evaluation of the environmental impact of different types of vehicles used in an urban scenario. The target is to obtain a methodology that allows the decision maker to verify in simulation the effects of possible measures like the law enforcement to the access restrictions or vehicle fleet composition. The main obstacle is the realization and managing of real driving cycles in order to overtake the limits derived from the utilization of typical cycles (i.e., ECE + EUDC) or the simple consideration of average speed. The starting point is a digital representation of the urban network where all the roads are represented with one or more arcs and for all these arcs are available an estimation of the traffic variables like the vehicle flow (vehicles per hour) or the average speed (kph). Every arc is described in terms of traffic parameters like the type of road (i.e., highway, district road).

A statistical model for the evaluation of exhaust emissions of catalyst gasoline car operating under real conditions has been developed and proposed in this paper. Emission data, relative to tests performed in the laboratory simulating real driving conditions on the dynamometer chassis, are considered in the analysis. In the paper, the building and estimation of parameters of the statistical model developed according to the PLS approach is presented, as well as its application to the case of a small size gasoline catalyst car. Data and models of CO, HC, NOx and CO2 are compared with the results obtained by the application of the COPERT emission model to the specific car type.

The aim of this study is to develop an experimental approach and statistical methodology to assess the effectiveness of technological measures to improve vehicle emissions in real world use. The ultimate purpose is to provide a tool to support decisions made by public administrators and transit authorities, and assess the costs/benefits of environment-oriented investments. In this paper we report some results from the application of the proposed approach within a research project carried out by the Istituto Motori of the National Research Council of Italy, funded by the Ministry of the Environment, in cooperation with urban and extra-urban public transit companies and other public agencies. Our research focused on measuring particulate emissions in real use with and without an after-treatment device. Tests were performed in three Italian cities (Naples, Palermo and Lecce) using seven buses (homologation class EURO 0, 1 and 2) from three public local transit companies.

Transport activities contribute significantly to air pollution. For this reason any policy or plan, carried out by administration or institution, requires the assessment of its impact on the emissions. To assess the overall pollutant production from transport, it is necessary to calculate emission factors. For this aim several methods exist which only use the average speed of the traffic stream, which can be theoretically obtained by vehicles flow and density on the road. Recently, a new statistical approach has been developed capable to consider more attributes than the simple mean speed to characterize driving behaviour, not only in the determination of driving cycles but also in the emission modelling. In this context, a meso scale emission model, named KEM, Kinematic Emission Model, able to calculate emission factor was developed. However, it is necessary to consider that the input to this model is, in any case, the driving cycle.

A novel model has been developed for the analysis and the evaluation of average vehicle emissions in a real driving cycle (emission factors) from data in an emission database. The model assumes that emission variation can be explained by parameters determined from dynamic vehicle equation and by the frequency of acceleration events at different speeds. Because the number of resulting X-variables is large, and variables are correlated, a regression method based on principal components, the Partial Least Squares (PLS) method actually, has been adopted. In this paper, model potentiality is illustrated by an application to a case study taken from the database built within the UE V Framework Project ARTEMIS. Data are relative to tests performed under hot conditions with a sample of EURO III 1.4-2.0 l gasoline passenger cars. A set of real driving cycles was utilized as representative of urban, rural and motorway operating conditions detected in different European countries.