[Application of ARIMA Model for Mid- and Long-term Forecasting of Ozone Concentration].

Ozone pollution has recently become a severe air quality issue in the Beijing-Tianjin-Hebei region. Due to the lack of a precursor emission inventory and complexity of physical and chemical mechanism of ozone generation, numerical modeling still exhibits significant deviations in ozone forecasting. Owing to its simplicity and low calculation costs, the time series analysis model can be effectively applied for ozone pollution forecasting. We conducted a time series analysis of ozone concentration at Shangdianzi, Baoding, and Tianjin sites. Both seasonal and dynamic ARIMA models were established to perform mid- and long-term ozone forecasting. The correlation coefficient R between the predicted and observed value can reach 0.951, and the RMSE is only 10.2 µg·m-3 for the monthly average ozone prediction by the seasonal ARIMA model. The correlation coefficient R between the predicted and observed value increased from 0.296-0.455 to 0.670-0.748, and RMSE was effectively reduced for the 8-hour ozone average predicted by the dynamic ARIMA model.

[Using Multiple Linear Regression Method to Evaluate the Impact of Meteorological Conditions and Control Measures on Air Quality in Beijing During APEC 2014].

Meteorological conditions have important impact on the diffusion and transport of air pollutants, thus separating and quantifying the impact of meteorological factors is a prerequisite for evaluation of air pollution control measures. Using observation data on SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 as well as meteorological factors at the Chaoyang site, an urban site in Beijing, we evaluated the impact of meteorological conditions and control measures on air quality in Beijing during APEC 2014 (from 15 October to 30 November, 2014) by the multiple linear regression method. The simulation performance of a multivariate linear regression model based on the parameters of meteorological factors for predicting pollutant concentration assuming constant emission conditions were ideal, produced a range of determination coefficient (R2) of 0.494-0.783. Our results suggested that air pollution control measures reduced the concentration of SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 by 48.3%, 53.5%, 18.7%, 40.6%, 3.6%, 34.8%, 28.8%, and 40.6%, while meteorological conditions reduced the concentration of SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 by 1.7%, -2.8%, 18.7%, 4.5%, 18.6%, 27.5%, 30.6%, and 35.6%. The combination of meteorological factors and control measures has significantly improved the air quality in Beijing during the APEC period. Control measures played a leading role in the reduction of SO2 and nitrogen oxides, and meteorological factors played a leading role in the reduction of CO. Meteorological factors and control measures made roughly equal contributions to the reduction of particulate matter. We also used the relative weight method to study the contribution of meteorological factors to the pollutant concentration. The results showed that the decisive meteorological factors on the concentrations of different pollutants were different.