An air quality prediction model based on deep learning and wavelet analysis considering the COVID-19 pandemic factors

ABSTRACT

Based on the air pollution data in China from January 1,2014 to December 31,2020, the characteristics of extreme value and period of air quality in different regions on different time scales were studied by using wavelet analysis. Wavelet coherence analysis was used to evaluate the relationship between air quality and meteorological factors in the period of COVID-19. We found that the spatial characteristics of air quality changed significantly in summer. Generally, air pollution is more severe in spring and winter. During the lockdown period, the overall air quality in the study area improved significantly. In general, except for 03, the concentration of all other pollutants has dropped considerably. The improvement in air quality is a direct result of emission reductions due to the implementation of the COVID-19 blockade, which is unsustainable in the long term. Eventually, a prediction model attention_CNN_LSTM based on deep learning method is proposed in this paper. The experimental results show that the attention proposed in this study the model has a good prediction effect in the long-term prediction of air quality, but the attention mechanism's impact is lower. After shortening the prediction period, the attention_CNN_LSTM model has good prediction performance on most data sets, with average MAPE = 2.67% and RMSE = 2.29.