RESUMEN
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.
Asunto(s)
Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Contaminación del Aire/estadística & datos numéricos , Teorema de Bayes , Humedad , Modelos Lineales , Material Particulado/análisis , Reproducibilidad de los Resultados , Estaciones del Año , España , Factores de TiempoRESUMEN
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.