Based on the daily average concentration monitoring data of important pollutants and the meteorological data at ground national control stations from January 1, 2019 to December 31, 2020, a comprehensive assessment of urban air quality in Hefei City, China, was conducted using the principal component analysis method. The pollutant data includes daily values of important atmospheric pollutants such as fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃), and the corresponding daily mean meteorological parameters include temperature, wind speed, sunshine duration, precipitation, atmospheric pressure, and relative humidity. Firstly, the indicators that have a significant impact onthe air quality were selected through stepwise regression, and then these significant indicators were reduced to a lower dimension through the principal component analysis. According to the theory of principal component analysis, two principal components were extracted from five significant impact indicators, and the cumulative variance contribution rate of the two extracted principal components reached 82.9%. The first principal component was characterized by high loadings of PM_2.5, CO, and PM₁₀ concentrations, implying the significant impact of these parameters on the air quality of Hefei. The second principal component was characterized by high loadings of O₃ concentration and sunshine duration, demonstrating their important role on the air quality of Hefei. Overall, good correlation was observed between the results from principal component analysis and the air quality index with a correlation coefficient of 0.78. In addition, it is found that the proposed method is more suitable to assess the air quality in winter and under good air quality conditions. These findings highlight the good performance of principal component analysis method on the assessment of urban air quality.