Time Series Analysis: Application of LSTM model in predicting PM 2.5 concentration in Beijing

Air pollution forecasting for public health and policy-making has a critical importance, this paper employs a Long Short-Term Memory (LSTM) model to perform in-depth prediction of PM2.5 concentrations measured at the U.S. Embassy in Beijing, outperforming regular forecasting approaches. In the LSTM model, the research examines a very detailed hourly dataset and beats regular forecasting approaches. A key finding is the model’s ability to effectively generalize from historical data to predict future air quality trends, with its adeptness at handling time-dependent relationships. This research emphasizes the importance of LSTM in air pollution prediction and management in environmental science as it provides an effective means for planning and making decisions on air quality management. This research is of great importance in providing a groundwork for further enhancement of prediction modeling. By offering a more reliable and sophisticated picture of air quality variations, this study addresses the current problem about how urban air pollution control could be improved in the city.