Air-pollution prediction in smart city, deep learning approach

Area of Science:

• Environmental Science
• Data Science
• Public Health

Background:

• Air pollution, particularly fine particulate matter (PM2.5), poses a significant global health risk due to industrialization and urbanization.
• PM2.5 exposure is linked to severe respiratory and cardiovascular diseases, necessitating accurate concentration prediction.
• Understanding the complex interplay of meteorological factors and pollutant concentrations is key to effective air quality management.

Purpose of the Study:

• To develop and evaluate a deep learning model for accurate hourly forecasting of PM2.5 concentrations in Beijing.
• To assess the predictive performance of a hybrid CNN-LSTM model against various traditional deep learning algorithms.
• To incorporate spatial-temporal features using historical pollutant and meteorological data for enhanced prediction.

Main Methods:

• Implementation of a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) deep learning model.
• Integration of multivariate data, including historical PM2.5 concentrations, meteorological data, and data from adjacent monitoring stations.
• Comparative analysis of CNN-LSTM performance against LSTM, Bi-LSTM, GRU, Bi-GRU, and CNN models.

Main Results:

• The hybrid CNN-LSTM multivariate model demonstrated superior predictive accuracy compared to all evaluated traditional deep learning models.
• The model effectively captured spatial-temporal dependencies crucial for accurate PM2.5 forecasting.
• Experimental results confirmed the enhanced predictive performance of the proposed hybrid approach.

Conclusions:

• The hybrid CNN-LSTM model offers a robust and accurate solution for hourly PM2.5 concentration forecasting.
• This approach provides a valuable tool for public health protection against the adverse effects of air pollution.
• The findings highlight the potential of deep learning in addressing complex environmental monitoring and prediction challenges.