Seasonal variation of benzo(a)pyrene in the Spanish airborne PM10. Multivariate linear regression model applied to estimate BaP concentrations

Multivariate linear regression models (MLRM) were used to predict BaP air concentrations in two sampling places, taking PM10 and meteorological variables as possible predictors. The model obtained with data from two sampling sites (all sites model) (R2 = 0.817, PRESS/SSY = 0.183) included the significant variables like PM10, temperature, solar radiation and wind speed and was internally and externally validated. The first validation was performed by cross validation and the last one by BaP concentrations from previous campaigns carried out in Zaragoza from 2001-2004. The proposed model constitutes a first approximation to estimate BaP concentrations in urban atmospheres with very good internal prediction (QCV2=0.813, PRESS/SSY = 0.187) and with the maximal external prediction for the 2001-2002 campaign (Qext2=0.679 and PRESS/SSY = 0.321) versus the 2001-2004 campaign (Qext2=0.551, PRESS/SSY = 0.449).