A Novel Stacking Ensemble Learning Approach for Predicting PM2.5 Levels in Dense Urban Environments Using Meteorological Variables: A Case Study in Macau

Abstract

Air pollution, particularly particulate matter such as PM2.5 and PM10, has become a focal point of global concern due to its significant impact on air quality and human health. Macau, as one of the most densely populated cities in the world, faces severe air quality challenges. We leveraged daily pollution data from 2015 to 2023 and hourly meteorological pollution monitoring data from 2020 to 2022 in Macau to conduct an in-depth analysis of the temporal trends of and seasonal variations in PM2.5 and PM10, as well as their relationships with meteorological factors. The findings reveal that PM10 concentrations peak during dawn and early morning, whereas PM2.5 distributions are comparatively uniform. PM concentrations significantly increase in winter and decrease in summer, with relative humidity, temperature, and sea-level atmospheric pressure identified as key meteorological determinants. To enhance prediction accuracy, a Stacking-based ensemble learning model was developed, employing LSTM and XGBoost as base learners and LightGBM as the meta-learner for predicting PM2.5 concentrations. This model outperforms traditional methods such as LSTM, CNN, RF, and XGB across multiple performance metrics.

Keywords

• particulate matter
• meteorological factors
• correlation analysis
• ensemble learning
• PM2.5 prediction