Spatiotemporal Particulate Matter Pollution Prediction Using Cloud-Edge Intelligence

Abstract

This study introduces a novel spatiotemporal method to predict fine dust (or PM$_{2.5}$) concentration levels in the air, a significant environmental and health challenge, particularly in urban and industrial locales. We capitalize on the power of AI-powered Edge Computing and Federated Learning, applying historical data spanning from 2018 to 2022 collected from four strategic sites in Mumbai: Kurla, Bandra-Kurla, Nerul, and Sector-19a-Nerul. These locations are known for high industrial activity and heavy traffic, contributing to increased pollution exposure. Our spatiotemporal model integrates the strengths of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, with the goal to predict PM$_{2.5}$ concentrations 24 h into the future. Other machine learning algorithms, namely Support Vector Regression (SVR), Gated Recurrent Units (GRU), and Bidirectional LSTM (BiLSTM), were evaluated within the Federated Learning framework. Performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and $R^2$. The preliminary findings suggest that our CNN-LSTM model outperforms the alternatives, with a MAE of 0.466, RMSE of 0.522, and $R^2$ of 0.9877.