Prediction of PM concentration based on a CNN-LSTM neural network algorithm.

Fine particulate matter (PM) is a major air pollutant affecting human survival, development and health. By predicting the spatial distribution concentration of PM, pollutant sources can be better traced, allowing measures to protect human health to be implemented. Thus, the purpose of this study is to predict and analyze the PM concentration of stations based on the integrated deep learning of a convolutional neural network long short-term memory (CNN-LSTM) model. To solve the complexity and nonlinear characteristics of PM time series data problems, we adopted the CNN-LSTM deep learning model. We collected the PMdata of Qingdao in 2020 as well as meteorological factors such as temperature, wind speed and air pressure for pre-processing and characteristic analysis. Then, the CNN-LSTM deep learning model was integrated to capture the temporal and spatial features and trends in the data. The CNN layer was used to extract spatial features, while the LSTM layer was used to learn time dependencies. Through comparative experiments and model evaluation, we found that the CNN-LSTM model can achieve excellent PM prediction performance. The results show that the coefficient of determination (R) is 0.91, and the root mean square error (RMSE) is 8.216 µg/m. The CNN-LSTM model achieves better prediction accuracy and generalizability compared with those of the CNN and LSTM models (R values of 0.85 and 0.83, respectively, and RMSE values of 11.356 and 14.367, respectively). Finally, we analyzed and explained the predicted results. We also found that some meteorological factors (such as air temperature, pressure, and wind speed) have significant effects on the PM concentration at ground stations in Qingdao. In summary, by using deep learning methods, we obtained better prediction performance and revealed the association between PM concentration and meteorological factors. These findings are of great significance for improving the quality of the atmospheric environment and protecting public health.