Predictive analysis of the air quality indicators in the Yangtze River Delta in China: An application of a novel seasonal grey model

摘要

Foreknowledge of the air quality indicators (i.e. AQI, PM_(2.5). PM_(10), SO_2. CO, NO_2, and O_3) provides decision-makers a possibility for building an early-warning system and tailoring related policies and plans accordingly so as to reduce the negative influences of these pollutants. However, accurate forecasts are hardly obtained because strong seasonal variations in meteorological circumstances can largely give rise to seasonal fluctuations in the time series of these indicators, which are difficult to be described and extracted by traditional forecasting tools. To address such issues, a seasonal nonlinear grey Bernoulli model is developed to provide skillful forecasts, which can effectively grasp the nonlinear and seasonal features. Subsequently, this paper elaborates on the model and method used for parameter estimations. For validation and verification purposes, operational seasonal forecasts of the air quality indicators in the four representative cities (Shanghai, Hangzhou, Nanjing, and Hefei) in the Yangtze River Delta are performed, in comparison with five prevalent forecasting tools including SFGM(1,1), SGM( 1,1), LSSVM, SARIMA, and BPNN. Results show that the proposed model outperforms other competitors in improving the prediction accuracy of the seasonal air quality changes. Thus, the verified model is recommended to produce future estimations of the air quality indicators in the Yangtze River Delta from 2020 to 2021, revealing that Shanghai, Hangzhou, and Hefei will have better air quality than before, while Nanjing will be subjected to a poorer one. Eventually, some suggestions related to the prevention of atmospheric pollution are provided to further improve air quality.