Short-term forecasting of ozone and NO_2 levels using traffic data in Bilbao (Spain)

摘要

Bilbao is a city with a population of half a million people located in North-Central Spain. In Bilbao, as many other cities in the world, pollution is mainly due to photochemical smog and carbon monoxide. These pollutants are originated by traffic. In this work, models based on Multiple Linear Regression have been built to forecast up to 8 hours ahead ozone and NO_2 levels using current and past values up to 15 hours back of ozone, meteorology and traffic measured in the area. The models were built for four locations in the area of Bilbao. Traffic variables were calculated as the mean values of all the sensors in the central area of Bilbao. First, the models were adjusted with data of year 1993 and then, the obtained coefficients were applied to year 1994 whose data were used to test the goodness of the models. One feature of the models is that future levels of ozone and NO_2 are predicted jointly with a system of two equations with two unknowns.The Multiple Linear Regression models were built using stepwise regression and tolerance filtering to choose the most meaningful variables. In all cases, traffic related variables represented between 5% and 10% of the overall variability in the explanatory models. The results of the models improve persistance of levels and are as good or even better than those obtained with much more sophisticated models. The results are given according to the set of statistical parameters included in the Model Validation Kit (R -correlation coefficient-, NMSE-normalized mean square error-, FA2- factor of two-, FB- fractional bias- and FS -fractional variance) so that they can be compared with future models developed in the area.