Forecasting the concentration of sulfur dioxide in Beijing using a novel grey interval model with oscillation sequence. (15th August 2021) - Zeng, Bo; Zhou, Wenhao; Zhou, Meng
Abstract: Beijing is one of the cities with serious sulfur dioxide pollution in China. The frequent occurrence of severe fog and haze brings great challenges to it's economic and social development. It is of great significance to forecast reasonably the trend of sulfur dioxide concentration in Beijing to improve the air quality management and residents' living environment. Traditional grey models use prediction of time points, and it is difficult to reflect the randomness of data in a complex system. When the modeling object is an oscillation sequence with a small sample, they usually perform poorly. The complex atmospheric conditions in Beijing lead to the oscillation of sulfur dioxide concentration, for which the interval prediction method is more appropriate. Based on the idea of linear envelopes, a novel grey interval forecasting model is proposed to predict sulfur dioxide concentration in Beijing. The original modeling data is extended to the area sequence and coordinate sequence with equal information, which is used to deduce the boundary equation of the original sequence. Compared with three other different forecasting models, the proposed model is the best in both simulation and prediction. Based on the novel model, the future sulfur dioxide concentration in Beijing is obtained. The findings can assist the local environmental protection agencies in improving the urban air quality. Highlights: A novel grey interval forecasting model was proposed. The new model was used to predict SO2  concentration in Beijing. The interval prediction of oscillation sequence with small sample was realized. Good experimental results can be a valuable reference in urban environmental management and decisions making.