The monthly air quality index (AQI) derived from ground observation stations that obtained daily air pollutants information for 1990- through 2010 was analyzed in this study. AQI was evaluated using the common comparative index method presented by the U.S. Environmental Protection Agency (USEPA), and a statistically based approach was used for predicting the AQI value. With the first method, AQI was predicted using the USEPA subindex formula for different pollutants, such as particulate matter and sulfur dioxide, which contribute the most to air pollution. A combination of the principal component analysis (PCA) and multiple linear regression (MLR) methods were used with the measured values of climate variables obtained from the ground stations for the most effective contributors and a prediction was modelled. The results of these two methods were compared and evaluated for consistency. Two methods were presented for determining the AQI value. According to the findings, the common comparative index method was consistent with the statistical prediction models, and the best results were obtained using PCA models with varimax rotation.