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Abstract: Many cities in Europe have difficulties to meet the air quality standards set by the European legislation, most particularly the annual mean Limit Value for NO2. Road transport is often the main source of air pollution in urban areas and therefore, there is an increasing need to estimate current and future traffic emissions as accurately as possible. As a consequence, a number of specific emission models and emission factors databases have been developed recently. They present important methodological differences and may result in largely diverging emission figures and thus may lead to alternative policy recommendations. This study compares two approaches to estimate road traffic emissions in Madrid (Spain): the COmputer Programme to calculate Emissions from Road Transport (COPERT4 v.8.1) and the Handbook Emission Factors for Road Transport (HBEFA v.3.1), representative of the ‘average-speed’ and ‘traffic situation’ model types respectively. The input information (e.g. fleet composition, vehicle kilometres travelled, traffic intensity, road type, etc.) was provided by the traffic model developed by the Madrid City Council along with observations from field campaigns. Hourly emissions were computed for nearly 15 000 road segments distributed in 9 management areas covering the Madrid city and surroundings. Total annual NOX emissions predicted by HBEFA were a 21% higher than those of COPERT. The discrepancies for NO2 were lower (13%) since resulting average NO2/NOX ratios are lower for HBEFA. The larger differences are related to diesel vehicle emissions under “stop & go” traffic conditions, very common in distributor/secondary roads of the Madrid metropolitan area. In order to understand the representativeness of these results, the resulting emissions were integrated in an urban scale inventory used to drive mesoscale air quality simulations with the Community Multiscale Air Quality (CMAQ) modelling system (1 km2 resolution). Modelled NO2 concentrations were compared with observations through a series of statistics. Although there are no remarkable differences between both model runs, the results suggest that HBEFA may overestimate traffic emissions. However, the results are strongly influenced by methodological issues and limitations of the traffic model. This study was useful to provide a first alternative estimate to the official emission inventory in Madrid and to identify the main features of the traffic model that should be improved to support the application of an emission system based on “real world” emission factors. [Copyright &y& Elsevier]

Greenhouse gas emissions caused by urban road mobility have reached unsustainable levels, being partly responsible for both, the current global warming alarming situation and the pollutant emissions risk to the health of citizens. Transport electrification represents an encouraging solution to this problem. The development and improvement of electric vehicle technology combined with transportation management strategies have become relevant research topics, not only in the area of mobility of people, but also to large companies whose supply chain may involve hundreds of commercial vehicles. The costs from running these vehicles represent a significant impact on their finance and controlling fuel usage is often a key contributor to keep running costs under control across their fleet and supply chain. In this context, traffic regulation elements evaluation, considering its uncertainty, could represent a significant influence on the vehicle consumption and, consequently, on its indirect greenhouse gas emissions. For this reason, traffic regulation system analysis may represent a step forward for developing solutions that collaborate with both, pollution and global warming, abatement. The present study proposes a predictive methodology for determining battery electric vehicle consumption variations when modifying traffic regulation elements. It uses stochastic speed profiles for neutralizing human intervention in consumption and multiple linear regressions to predict the energy consumed by the electric vehicle as a function of a set of factors that represent the traffic regulations. The research does not aim to provide particular energy consumption data, but to expose the variations in consumption and emissions caused by urban planning modifications. Model accuracy and achieved conclusions are illustrated through the development of a case study. The methodology could be a help for urban planners, fleet managers, logistics and supply chain decision makers environmentally concerned. Image 1 • Traffic regulations influence in electric vehicle energy consumption is assessed. • Route length, stop events number and road speeds are the most influential factors. • A linear regression model for vehicle consumption prediction is performed. • Intrinsic uncertainty of traffic control elements is considered and evaluated. • A case study in the city of Madrid is performed, illustrating model effectiveness. [ABSTRACT FROM AUTHOR]