Abstract
The fast economic growth of China along the last two decades has created a strong impact on the environment. The occurrence of heavy haze pollution days is the most visible effect. Although many researchers have studied such problem, a high number of spatio-temporal limitations in the recent studies were identified. From our best knowledge the long trends of PM2.5 concentrations were not fully investigated in China, in particular the year-to-year trends and the seasonal and daily cycles. Therefore, in this work the PM2.5 concentrations collected from automatic monitors from five urban sites located in megacities with different climatic zones in China were analysed: Beijing (40 degrees N), Chengdu (31 degrees N), Guangzhou (23 degrees N), Shanghai (31 degrees N) and Shenyang (43 degrees N). For an inter-comparison a meta-analysis was carried out. An evaluation conducted since 1999 demonstrates that PM2.5 concentrations have been reduced until 2008, period which match with the occurrence of the Olympic Games. However, a seasonal analysis highlight that such decrease occurs mostly during warmer seasons than cold seasons. During winter PM2.5 concentrations are typically 1.3 to 2.7 higher than in summer. The average daily cycle shows that the lowest and highest PM2.5 concentrations often occurs in the afternoon and evening hours respectively. Such daily variations are mostly driven by the daily variation of the boundary layer depth and emissions. Although the PM2.5 levels have showing signs of improvement, even during the warming season the values are still too high in comparison with the annual environmental standards of China (35 mu g m(-3)). Moreover, during cold seasons the north regions have values twice higher than this limit. Thus, to fulfil these standards the governmental mitigation measures need to be strongly reinforced in order to optimize the daily living energy consumption, primarily in the north regions of China and during the winter periods.

Abstract
Eco-routing has been shown as a promising strategy to reduce emissions. However, during peak periods, with limited additional capacity, the eco-friendliness of various routes may change. We have explored this issue empirically by covering about 13,300 km, in three different areas, using GPS-equipped vehicles to record second-by-second vehicle dynamics. This study has confirmed the importance of the eco-routing concept given that the selection of eco-friendly routes can lead to significant emissions savings. Furthermore, these savings are expected to be practically unchanged during the peak period. However, some potential negative externalities may arise from purely dedicated eco-friendly navigation systems.

Abstract
In the last decades, traffic microsimulation platforms have a growing complexity allowing a detailed description of vehicle traffic dynamics in a second-by-second basis. However, to project spatially their outputs, some precautions must be followed. Therefore, we analyze some variables used in the microscopic traffic models which have a high impact on further applications, especially when a spatial projection is required. To assess these objectives, a microsimulation framework which includes traffic and emission models was defined to characterize traffic flows and to evaluate vehicular emissions. This general methodology was then applied in a European medium sized city using two scenarios: (i) considering a Lagrangian approach and (ii) using an Eulerian approach of the simulation road traffic platform. The Lagrangian approach shows that if we have long links (some hundred meters, e.g. >500 m), we lose the spatial detail on emissions. On the other hand, using the Eulerian approach to define very small links (some few meters, e.g. <30 m), a significant statistic representation of traffic dynamics, in that link, was not obtained, particularly in areas with low traffic flow. The latter situation can occur because the vehicle speed can be high enough that did not allow recording any information in that link, even considering a high time resolution analysis (second-by-second). Thus, a non-linear trend of the error is identified when such data are analyzed geographically. Accordingly, depending on the use of those microsimulation tools, we identify some best practices related with the traffic model design that must be followed to minimize those errors.