Urban air pollution is a matter of growing concern for both public administrations and citizens. Road traffic is one of the main sources of air pollutants, though topography characteristics and meteorological conditions can make pollution levels increase or diminish dramatically. In this context an upsurge of research has been conducted towards functionally linking variables of such domains to measured pollution data, with studies dealing with up to one-hour resolution meteorological data. However, the majority of such reported contributions do not deal with traffic data or, at most, simulate traffic conditions jointly with the consideration of different topographical features. The aim of this study is to further explore this relationship by using highresolution real traffic data. This paper describes a methodology based on the construction of regression models to predict levels of different pollutants (i.e. CO, NO, NO 2 , O 3 and PM 10 ) based on traffic data and meteorological conditions, from which an estimation of the predictive relevance (importance) of each utilized feature can be estimated by virtue of their particular training procedure. The study was made with one hour resolution meteorological,

The analysis of local climate conditions to test artificial urban boundaries and related climate hazards through modelling tools should become a common practice to inform public authorities about the benefits of planning alternatives. Different finishing materials and sheltering objects within urban canyons (UCs) can be tested, predicted and compared through quantitative and qualitative understanding of the relationships between the microclimatic environment and subjective thermal assessment. This process can work as support planning instrument in the early design phases as has been done in this study that aims to analyze the thermal stress within typical UCs of Bilbao (Spain) in summertime through the evaluation of Physiologically Equivalent Temperature using ENVI-met. The UCs are characterized by different orientations, height-to-width aspect ratios, pavement materials, trees’ dimensions and planting pattern. Firstly, the current situation was analyzed; secondly, the effects of asphalt and red brick stones as streets’ pavement materials were compared; thirdly, the benefits of vegetation elements were tested. The analysis demonstrated that orientation and aspect ratio strongly affect the magnitude and duration of the thermal peaks at pedestrian level; while the vegetation elements improve the thermal comfort up to two thermophysiological assessment classes. The outcomes of this study, were transferred and visualized into green planning recommendations for new and consolidated urban areas in Bilbao.

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative-log [NO2], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity-and qualitative-working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa's Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R(2) = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75-0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.

Traffic is the major air pollution source in most urban areas. Nowadays, most of the strategies carried out to improve urban air quality are focused on reducing traffic emissions. Nevertheless, acting locally on urban design can also reduce levels of air pollutants. In this paper, both strategies are studied in several scenarios for a medium-sized town of the Basque Country (Spain). Two main actions are analysed in order to reduce traffic emissions: (1) minor extension ofa pre-existing low emission zone (LEZ); (2) substitution of 10% of passenger cars that are older than 5 years by hybrid and electric vehicles. Regarding local urban design, three alternatives for the development of one side of a street canyon are considered: (1) a park with trees; (2) an open space without obstacles; (3) a building. Two different urban traffic dispersion models are used to calculate the air quality scenarios: PROKAS (Gaussian&box) to analyse the reduction of traffic emissions in the whole urban area and WinMISKAM (CFD) to evaluate specific urban designs. The results show the effectiveness of the analysed actions. On one hand, the definition of a small LEZ, as well as the introduction in 2015 of vehicles with new technology (hybrid and electric), results in minor impacts on PM10 and NO2 ambient concentrations. On the other hand, local urban design can cause significant variation in spatial distribution ofpollutant concentrations emitted inside street canyons. Consequently, urban planners should consider all these aspects when dealing with urban air pollution control.