The present research aims at developing an efficient and reliable module, for operational concentration levels of particulate matter with aerodynamic diameter up to 10@mm (PM"1"0) for the city of Thessaloniki. The Thessaloniki urban area is very densely built, with a high degree of motorisation and industrial activities concentration. The increase of emissions mainly from traffic and industry are responsible for the increase in atmospheric pollution levels during the last years. The air quality data sets examined in the current study are collected by a network of monitoring stations operated by the Municipality of Thessaloniki and correspond to PM"1"0 concentrations for the years 1994-2000. In order to provide with an operational air quality forecasting module for PM"1"0, statistical methods are investigated and applied. The presented results demonstrate that CART and Neural Network (NN) methods are capable of capturing PM"1"0 concentration trends, while CART may have a better performance concerning the index of agreement. Methods studied (including linear regression and principal component analysis) demonstrate promising operational forecasting capabilities.