Objective To analyze the characteristics of fine particulate matter(PM2.5)pollution in Urumqi City,Xinjiang Uygur Autonomous Region from 2016 to 2023 and establish a prediction model,so as to provide the reference for air pollution prevention and control.Methods PM2.5 monitoring data of Urumqi City from 2016 to 2023 were collected through the website of Ministry of Ecology and Environment of China.The changing trend of PM2.5 concentration was an-alyzed using temporal chart and seasonal index.PM2.5 monthly average concentrations from 2016 to 2023 were used to establish an autoregressive integrated moving average(ARIMA)model,and the data in 2023 was fitted and compared with the actual values,using mean absolute percentage error(MAPE)to evaluate the effectiveness of the model,and PM2.5 monthly average concentration from 2024 to 2025 was predicted.Results PM2.5 daily average concentration in Urumqi City showed a decreasing trend from 2016 to 2023(rs=-0.239,P<0.001),with high seasonal indexes in Janu-ary,February and December,indicating certain seasonal characteristics.The optional model was ARIMA(1,0,0)(1,1,0)12,with the value of Akaike information criterion,corrected Akaike information criterion,and Bayesian information cri-terion being 727.38,727.88 and 737.10,respectively.PM2.5 monthly average concentration in 2023 was fitted and com-pared with the actual values,with an absolute error range of 0.31-7.45 μg/m3,a relative error range of 0.01-0.53,and MAPE of 14.42%.PM2.5 monthly average concentration in Urumqi City from 2024 to 2025 was predicted to be consis-tent with the trend from 2016 to 2023.Conclusions PM2.5 concentration in Urumqi City showed a tendency towards a decline from 2016 to 2023,and was relatively high in winter.ARIMA(1,0,0)(1,1,0)12 can be used for short-term prediction of PM2.5 pollution in Urumqi City.
关键词
细颗粒物/大气污染/自回归移动平均模型/预测
Key words
fine particulate matter/air pollution/autoregressive integrated moving average model/prediction
维普
万方数据