Air quality prediction and long-term trend analysis: a case study of Beijing

As the availability of air quality data collected at ground-based monitoring stations increases, the researchers use the data in sophisticated models to predict the concentration of different pollutants. This study analyzed the concentration of PM2.5 in Beijing to mine the long-term trend of air quality. The results showed that PM2.5 is in the trend of decreasing year by year but still above the annual maximum limit (35 μg/m3) of WHO with strong seasonality. Besides, this study proposed an attention mechanism (AM)-based prediction method, named MSAQP. Firstly, attention mechanism was introduced into the decoding phase of MSAQP to calculate the context vector. The attention mechanism learned the weight distribution strategy of the original data and integrates all the coding states into the context vector to enhance the representation ability of time characteristics. Secondly, due to the problems of gradient explosion and gradient disappearance in Recurrent Neural Network (RNN), this study adopted long short-term memory network (LSTM). In addition, three different loss functions were applied to the training experiment of the model, respectively. The experimental results showed that the prediction accuracy was improved, among which the MAE was reduced by 3.42, the NMSE was reduced by 0.01, and the R2 was improved by 0.24.