基于SARIMA模型的合肥市近地面臭氧浓度预测  

Prediction of Near-surface Ozone Concentration in Hefei Based on the SARIMA Model

作　　者：许成丽 郑朝阳 项衍 吕立慧 刘人龙 XU Cheng-li;ZHENG Chao-yang;XIANG Yan;LV Li-hui;LIU Ren-long(School of Artificial Intelligence and Big Data,Hefei University,Hefei Anhui 230601;Institute of Material Science and Information Technology,Anhui University,Hefei Anhui 230601)

机构地区：[1]合肥大学人工智能与大数据学院,安徽合肥230601 [2]安徽大学物质科学与信息技术研究院,安徽合肥230601

出　　处：《巢湖学院学报》2024年第6期20-25,共6页Journal of Chaohu University

基　　金：安徽高校自然科学研究项目(项目编号:KJ2021A0998);合肥学院人才科研基金项目(项目编号:20RC23);合肥学院基础教研室示范项目(项目编号:2020hfujyssf02)。

摘　　要：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PM2.5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21.18μg·m-3,拟合优度R2可达0.9043,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 PM2.5 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 21.18μg·m-3,and the goodness-of-fit R2 reaches 0.9043,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.

关 键 词：臭氧浓度 时间序列 SARIMA模型 季节性 短期预测 

分 类 号：X830.3[环境科学与工程—环境工程]