近年来城市臭氧（O₃）污染问题较为突出，预测臭氧浓度进行早期防控尤为重要。合肥市以O₃为首要污染物的天数呈上升趋势，目前O₃已替代PM_2.5成为影响空气质量的首要污染物。基于此，以合肥市作为研究对象，选取2022年1月至2023年6月臭氧日均浓度构成时间序列，通过建立季节性差分自回归滑动平均（SARIMA）模型，对2023年夏季臭氧浓度进行短期预测并检验模型效果。结果表明，构建的模型拟合值曲线与实测值曲线相差不大，3天短期预测的相对误差均在20%以内；经评估发现，模型的均方根误差RMSE为21.18 μg·m^-3，拟合优度R²可达0.904 3，拟合及预测效果较好。通过近地面臭氧预测，为环保部门提供科学依据，为城市规划提供决策支持，同时也能对公众健康产生积极影响。

In recent years, urban ozone pollution has become increasingly prominent, making the prediction of ozone concentrations crucial for early prevention and control measures. In Hefei, the number of days with ozone as the primary pollutant is increasing, and ozone has now surpassed PM_2.5 as the leading contributor to air quality degradation. In response, this study focuses on Hefei and uses the daily average ozone concentrations from January 2022 to June 2023 to create a time series. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model is then developed to forecast the ozone levels for the summer of 2023 and the model's performance is evaluated. The results indicate that the model's fitted curves basically align with the actual measurements, with the relative errors of the three-day short-term forecasts remaining within 20%. The model's root-mean-square-error (RMSE) is 21.18 μg·m^-3, and the goodness-of-fit R² reaches 0.904 3, demonstrating robust fitting and predictive capabilities. These ozone forecasts provide a scientific basis for environmental protection efforts, support decision-making in urban planning, and positively impact public health.