Abstract
Aim: We investigated the demographic, anthropometric, clinical, biochemical and behavioural factors associated with children and adolescents who missed breakfast. Methods: This 2012 cross-sectional study was carried out in the city of Juiz de Fora, Brazil, with a sample of 684 students: 191 children aged 7-9 and 493 adolescents aged 10-14. Data on demographic, physical activity and breakfast consumption were based on a 24-hour recall record and a three-day dietary record. Weight, height, body fat, waist circumference and blood pressure were also measured. Finally, samples were collected for analysis of blood total cholesterol, low-density and high-density lipoproteins, triglycerides and glucose. The statistics are presented as prevalence ratios (PR) with 95% confidence (95% CI) intervals. Results: Missing breakfast was more common among adolescents than children (30% versus 22%) and among girls of all ages than among boys (33% versus 22%). It was also associated with children, but not adolescents, with increased levels of diastolic blood pressure (PR 5.6, 95% CI 1.8-17.4), total cholesterol (PR 1.5, 95% CI 1.2-1.9) and low-density lipoprotein (PR 2.1, 95% CI 1.5-2.9). Conclusion: Missing breakfast was more common among adolescents and females and associated with increased levels of diastolic blood pressure, total cholesterol and low-density lipoprotein in children.

Abstract
The implementation of efficient strategic decisions such as process design and capacity investment under uncertainty, during the product development process, is critical for the pharmaceutical industry. However, to tackle these problems the widely used multi-stage/scenario-based optimization formulations are still ineffective, especially for the first-stage (here-and-now) solutions where uncertainty has not yet been revealed. This study extends the authors' previous work addressing the stochastic product-launch planning problem, by developing a new Multi-Objective Integer Programming model, embedded in a unified decision-making framework, to obtain the final design strategy that "maximizes" productivity while considering the decision-maker preferences. An approximation of the efficient Pareto-front is determined, and a subsequent Pareto solutions analysis is made to guide the decision process. The developed approach clearly identifies the process designs and production capacities that "maximize" productivity as well as the most promising solutions region for investment. Moreover, a good balance between investment and capacity allocation was achieved.

Abstract
The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a mediumsized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NOx, CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from -15 to 49% for CO, -14 to 31% for VOC, -19 to 46% for NOx and -22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories.

Abstract
The introduction of eco-routing systems has been suggested as a promising strategy to reduce carbon dioxide emissions and criteria pollutants. The objective of this study is to scrutinize the impacts of an eco-routing guidance system on emissions through the use of a case study in a commuting corridor. This research aims at assessing the potential environmental benefits in terms of different pollutant emissions. Simultaneously, it addresses the extent of variations in system travel time (STT) that each eco-routing strategy implies. The methodology consists of three distinct phases. The first phase corresponds to the adjustment of a microsimulation platform of traffic and emissions with empirical data previously collected. Second, to volume-emission-functions (VEF), developed based on the integrated modeling structure. Final, to different scenarios of traffic flow optimization performed at the network level based on a simplified assignment procedure. The results show that if the traffic assignment is performed with the objective to minimize overall impacts, then the total system environmental damage costs can be reduced up to 9% with marginal oscillations in total STT. However, if drivers are advised based on their own emissions minimization, total system emissions may be higher than under the standard user equilibrium flow pattern. Specifically, environmentally friendly navigation algorithms focused on individual goals may tend to divert traffic to roads with less capacity affecting the performance of the remaining traffic. This case study brings new insights about the difficulties and potentials of implementing such systems.

Abstract
Lisbon is one of the European cities where NO2 and PK10 legal limit values are still exceeded, leading to an Air Quality Plan applicable up to 2020. The developed work combined a detailed emission inventory, monitoring data, and modelling in order to assess if the proposed emission abatement scenarios, focused on the road transport sector, were able to tackle exceedances. A maximum decrease of 14% for PM10 concentrations was achieved, and of 21% for NO2, providing compliance. PM10 smallest reduction is related with higher weight of regional background sources, while for NO2 local traffic has more influence on concentrations.

Abstract
Air quality traffic-related measures have been implemented worldwide to control the pollution levels of urban areas. Although some of those measures are claiming environmental improvements, few studies have checked their real impact. In fact, quantitative estimates are often focused on reducing emissions, rather than on evaluating the actual measures' effect on air quality. Even when air quality studies are conducted, results are frequently unclear. In order to properly assess the real impact on air quality of traffic-related measures, a statistical method is proposed. The method compares the pollutant concentration levels observed after the implementation of a measure with the concentration values of the previous year. Short- and long-term impact is assessed considering not only their influence on the average pollutant concentration, but also on its maximum level. To control the effect of the main confounding factors, only the days with similar environmental conditions are analysed. The changeability of the key meteorological variables that affect the transport and dispersion of the pollutant studied are used to identify and group the days categorized as similar. Resemblance of the pollutants' concentration of the previous day is also taken into account. The impact of the road traffic measures on the air pollutants' concentration is then checked for those similar days using specific statistical functions. To evaluate the proposed method, the impact on PM2.5 concentrations of two air quality traffic-related measures (M1 and M2) implemented in the city of Beijing are taken into consideration: M1 was implemented in 2009, restricting the circulation of yellow-labelled vehicles, while M2 was implemented in 2014, restricting the circulation of heavy-duty vehicles. To compare the results of each measure, a time-period when these measures were not applied is used as case-control.