*4.2.3. Interpretation of the results*

This analysis shows that including meteorological factors as model inputs improves the prediction accuracy of PM2.5 concentrations (r = 0.58). The performance is slightly improved by applying a model tree, which is composed of four linear regressions (r = 0.63).

Thus, the results suggest that the use of a quite affordable meteorological station enables us to significantly improve the prediction of the concentration of fine particulate matter (The correlation coefficient is twice higher than with the traffic monitoring only.) All the meteorological factors are relevant for the prediction, except the precipitation accumulation. Rain seems to be excluded from the model, because it is a very rare event.

Next, it is studied if a multiple model approach, based on three models a day, could improve the prediction accuracy.
