Forecasting air pollution in big cities is of great importance, and there are various types of air pollution indices (APIs). In the present study, the level of air pollution in winter 2011 at four point-locations in Belgrade, Serbia, was measured using wind speed data and non-standard and standard air temperature data, measured at 10 and 2 m, respectively. Using multiple linear regression (MLR) analysis, equations to forecast the API were obtained. This forecast was verified using data from the winter of 2012/2013. The results obtained are well aligned with the monitored API and verified by the root mean square error (RMSE). It is shown that standard meteorological measurements representative of the city can accurately predict the API at individual point-locations as well as using temperature and wind speed measured at each respective location. Three locations, which measured SO 2 , NO 2 , PM 10 , O 3 and CO, showed poor air quality for > 78% of days observed. For the fourth location, the final estimate of prevailing air quality could not be calculated due to the absence of PM 10 measurements. Forecasting the API on a short-term scale can be of great help for long-term air-quality improvements.