Air Quality PM2.5 Index Prediction Model Based on CNN - LSTM

Abstract

With the intensification of global climate change, accurate prediction of air quality indicators, especially PM2.5 concentration, has
become increasingly important in fields such as environmental protection, public health, and urban management. To address this, we propose
an air quality PM2.5 index prediction model based on a hybrid CNN-LSTM architecture. The model effectively combines Convolutional
Neural Networks (CNN) for local spatial feature extraction and Long Short-Term Memory (LSTM) networks for modeling temporal dependencies in time series data. Using a multivariate dataset collected from an industrial area in Beijing between 2010 and 2015which includes
hourly records of PM2.5 concentration, temperature, dew point, pressure, wind direction, wind speed, and precipitationthe model predicts
the average PM2.5 concentration over 6-hour intervals. Experimental results show that the model achieves a root mean square error (RMSE)
of 5.236, outperforming traditional time series models in both accuracy and generalization. This demonstrates its strong potential in real-world
applications such as air pollution early warning systems. However, due to the complexity of multivariate inputs, the model demands high
computational resources, and its ability to handle diverse atmospheric factors still requires optimization. Future work will focus on enhancing
scalability and expanding support for more complex multivariate weather prediction tasks.