Improved prediction of hourly PM_2.5 concentrations with a long short-term memory and spatio-temporal causal...

Accurate prediction of particulate matter with aerodynamic diameter ≤ 2.5 μm (PM_2.5) is important for environmental management and human health protection. In recent years, many efforts have been devoted to develop air quality predictions using the machine learning and deep learning techniques. In this study, we propose a deep learning model for short-term PM_2.5 predictions. The salient feature of the proposed model is that the convolution in the model architecture is causal, where the output of a time step is only convolved with components of the same or earlier time step from the previous layer. The model also weighs the spatial correlation between multiple monitoring stations. Through temporal and spatial correlation analysis, relevant information is screened from the monitoring stations with a strong relationship with the target station. Information from the target and related sites is then taken as input and fed into the model. A case study is conducted in Nanjing, China from January 1, 2020 to December 31, 2020. Using historical air quality and meteorological data from nine monitoring stations, the model predicts PM_2.5 concentrations for the next hour. The experimental results show that the predicted PM_2.5 concentrations are consistent with observation, with correlation coefficient (R²) and Root Mean Squared Error (RMSE) of our model are 0.92 and 6.75 μg/m³. Additionally, to better understand the factors affecting PM_2.5 levels in different seasons, a machine learning algorithm based on Principal Component Analysis (PCA) is used to analyze the correlations between PM_2.5 and its influencing factors. By identifying the main factors affecting PM_2.5 and optimizing the input of the predictive model, the application of PCA in the model further improves the prediction accuracy, with decrease of up to 17.2 % in RMSE and 38.6 % in mean absolute error (MAE). The deep learning model established in this study provide a valuable tool for air quality management and public health protection.