Application of Deep Learning Techniques for Air Quality Prediction: A Case Study in Macau

To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI requires first determining the sub-indices for several pollutants, including respirable suspended particulates (PM10), fine suspended particulates (PM2.5), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon monoxide (CO). Accurate prediction of AQI is crucial in providing early warnings to the public before pollution episodes occur. To improve AQI prediction accuracy, deep learning methods such as artificial neural networks (ANNs) and long short-term memory (LSTM) models were applied to forecast the six pollutants commonly found in the AQI. The data for this study was accessed from the Macau High-Density Residential Air Quality Monitoring Station (AQMS), which is located in an area with high traffic and high population density near a 24 h land border-crossing facility connecting Zhuhai and Macau. The novelty of this work lies in its potential to enhance operational AQI forecasting for Macau. The ANN and LSTM models were run five times, with average pollutant forecasts obtained for each model. Results demonstrated that both models accurately predicted pollutant concentrations of the upcoming 24 h, with PM10 and CO showing the highest predictive accuracy, reflected in high Pearson Correlation Coefficient (PCC) between 0.84 and 0.87 and Kendall’s Tau Coefficient (KTC) between 0.66 and 0.70 values and low Mean Bias (MB) between 0.06 and 0.10, Mean Fractional Bias (MFB) between 0.09 and 0.11, Root Mean Square Error (RMSE) between 0.14 and 0.21, and Mean Absolute Error (MAE) between 0.11 and 0.17. Overall, the LSTM model consistently delivered the highest PCC (0.87) and KTC (0.70) values and the lowest MB (0.06), MFB (0.09), RMSE (0.14), and MAE (0.11) across all six pollutants, with the lowest SD (0.01), indicating greater precision and reliability. As a result, the study concludes that the LSTM model outperforms the ANN model in forecasting air pollutants in Macau, offering a more accurate and consistent prediction tool for local air quality management.