Abstract
Innovative traffic management measures are needed to reduce transportation-related emissions. While in Europe, road lane management has focused mainly on introduction of bus lanes, the conversion to High Occupancy Vehicles (HOV) and eco-lanes (lanes dedicated to vehicles running on alternative fuels) has not been studied comprehensively. The objectives of this research are to: (1) Develop an integrated microscopic modeling platform calibrated with real world data to assess both traffic and emissions impacts of future Traffic Management Strategies (TMS) in an urban area; (2) Evaluate the introduction of HOV/eco-lanes in three different types of roads, freeway, arterial and urban routes, in an European medium-sized city and its effects in terms of emissions and traffic performance. The methodology consists of three distinct phases: (a) Traffic and road inventory data collection; (b) Traffic and emissions simulation using an integrated platform of microscopic simulation; and (c) Evaluation of scenarios. For the baseline scenario, the statistical analysis shows valid results. The results show that HOV and eco-lanes in a medium European city are feasible, and when the Average Occupancy of Vehicles (AOV) increases, on freeways, the majority of vehicles can reduce their travel time (2%) with a positive impact in terms of total emissions (-38% NOx, -39% HC, -43% CO and -37% CO2). On urban and arterial corridors, the reduction in emissions could be achieved only if the AOV increases from 1.50 to 1.70 passengers/vehicle. Total emissions of the corridor with an AOV of 1.70 passengers/vehicle can be reduced up to 35-36% for the urban route while the values can be reduced by 36-39% for the arterial road. With the introduction of Hybrid Electric Vehicles (HEV) and Electric Vehicles (EV) it is possible to reduce emissions, although the introduction of eco-lanes did not show significant reductions in emissions. When both policies are simulated together, an emissions improvement is observed for the arterial route and for two of the scenarios.

Abstract
Nowadays, several methods to promote a more sustainable distribution of traffic flows are available. In response to rising energy costs and increased environmental concerns, eco-friendly route choices can be provided individually by means of smart navigation tools that allow several vehicle routing options designed to minimize air pollutant emissions and fuel consumption. Simultaneously, the use of intelligent road pricing systems and the use of variable message signs can change the route choice process of drivers (and thus network equilibrium), by varying the perceived attributes of competing routes. However, so far too little attention has been paid to the fact that the ecofriendliness of various routes may change, depending on vehicle characteristics which may cause problems on the efficiency of these systems. This issue has been empirically addressed in this research, using a database of more than 13,330 km of GPS data in six different Origin-Destination (OD) pairs and 9 different routes. Simultaneously, two different approaches for estimating (CO2, HC, CO, NOx) emissions were tested: a) second-by-second vehicle dynamics, using the Vehicle Specific Power (VSP) instantaneous model; and b) route average speed using the EMEP/PEA methodology. The results show that depending on the characteristics of the routes associated with a certain OD pair, the eco-friendly route may differ according to the vehicle model and the emissions estimation method. Innovative approaches to provide accurate emissions and eco-routing information are needed.

Abstract
The accuracy and precision of air quality models are usually associated with the emission inventories. Thus, in order to assess if there are any improvements on air quality regional simulations using detailed methodology of road traffic emission estimation, a regional air quality modelling system was applied. For this purpose, a combination of top-down and bottom-up approaches was used to build an emission inventory. To estimate the road traffic emissions, the bottom-up approach was applied using an instantaneous emission model (Vehicle Specific Power - VSP methodology), and an average emission model (CORINAIR methodology), while for the remaining activity sectors the top-down approach was used. Weather Research and Forecasting (WRF) and Comprehensive Air quality (CAMx) models were selected to assess two emission scenarios: (i) scenario 1, which includes the emissions from the top-down approach; and (ii) scenario 2, which includes the emissions resulting from integration of top-down and bottom-up approaches. The results show higher emission values for PM10, NOx and HC, for scenario 1, and an inverse behaviour to CO. The highest differences between these scenarios were observed for PM10 and HC, about 55% and 75% higher (respectively for each pollutant) than emissions provided by scenario 2. This scenario gives better results for PM10, CO and O-3. For NO2 concentrations better results were obtained with scenario 1. Thus, the results obtained suggest that with the combination of the top-down and bottom-up approaches to emission estimation several improvements in the air quality results can be achieved, mainly for PM10, CO and O-3.

Abstract
Tropospheric ozone is a secondary pollutant having a negative impact on health and environment. To control and minimize such impact the European Community established regulations to promote a clean air all over Europe. However, when an episode is related with natural mechanisms as Stratosphere-Troposphere Exchanges (STE), the benefits of an action plan to minimize precursor emissions are inefficient. Therefore, this work aims to develop a tool to identify the sources of ozone episodes in order to minimize misclassification and thus avoid the implementation of inappropriate air quality plans. For this purpose, an artificial neural network model the Multilayer Perceptron - is used as a binary classifier of the source of an ozone episode. Long data series, between 2001 and 2010, considering the ozone precursors, Be-7 activity and meteorological conditions were used. With this model, 2-7% of a mean error was achieved, which is considered as a good generalization. Accuracy measures for imbalanced data are also discussed. The MCC values show a good performance of the model (0.65-0.92). Precision and F-1-measure indicate that the model specifies a little better the rare class. Thus, the results demonstrate that such a tool can be used to help authorities in the management of ozone, namely when its thresholds are exceeded due natural causes, as the above mentioned STE. Therefore, the resources used to implement an action plan to minimize ozone precursors could be better managed avoiding the implementation of inappropriate measures.

Abstract
Between 1989 and 2011 the aviation traffic has been growing 4.6% per year. The increase on aviation traffic had consequences in terms of greenhouse gases (GHGs) and local pollutant emissions (e.g. carbon monoxide - CO, hydrocarbons - HC, nitrogen oxides - NOx). In order to minimize this problem, the evaluation of sustainable alternatives to current fuel (jet fuel A) has been discussed but their impact on emissions is still unclear. The main research goals of this paper are: (i) to evaluate if the well-to-wake energy consumption in aviation can be reduced with alternative fuels as liquid natural gas (LNG) and/or liquid hydrogen (LH2) and (ii) to assess if alternative fuels can be used in aviation to minimize carbon dioxide emissions and local pollutants. In this analysis two types of flights were evaluated: short flights (<5000 km) and long flights (>5000 km) for six typical aircrafts. We verified that LH2 from SMR can provide between 13% and 21% less environmental and social impacts than jet fuel A, in the same aircraft. Copyright

Abstract
Advanced Traveller Information Systems (ATIS) have been used worldwide to mitigate different road traffic impacts such as traffic congestion. However, the impact of these systems in fuel use and emissions is still an important research topic, due to their non-linear behaviour. Therefore, this paper aims to evaluate the impact of traffic incidents on a regional scale on energy and emissions levels. To achieve these objectives, several scenarios assessing different degrees of information (through the use of ATIS) were analysed. To model both traffic operations and emissions a mesoscopic traffic model (DTALite) and a road emissions methodology (EMEP/EEA) were used. The data related to the characterization of the road network was collected between Oporto and Aveiro, in Portugal. To ensure reliable results, the traffic model was calibrated and validated taking into account statistical methods that compare observed traffic flows and travel times with the values estimated by the model. The simulation platform was then used to assess the impact of traffic incident when it occurs in two different road types (a motorway and a highway), in order to evaluate the impact of usage and availability of information on emissions and fuel consumption. The results show that in a regional road network for each occurred incident, the use of ATIS can allow a maximum reduction of emissions and energy consumption on those routes where they occurred to up 2%. Nonetheless, a global analysis of the network indicates different results depending if the incident occur in a motorway or in a highway. In both cases, drivers tend to choose a route with similar costs to the previously selected. Furthermore, a non-linear trend between the availability of information and the impact on emissions and energy consumption was found. (C) 2014 Published by Elsevier B.V.