###### 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. particulate matter; meteorological factors; correlation analysis; ensemble learning; PM2.5 prediction +
Footnote †: journal: Medical  
and concentrations of PM10 and PM2.5 was also found to be weak, with R\({}^{2}\) values from 0.000 to 0.043. At the monitoring sites in Macau, there was almost no correlation between local traffic volumes and PM concentrations at roadside stations, leading to the conclusion that PM concentrations are more likely influenced by regional sources and meteorological conditions. Nevertheless, the complex geographical environment of Macau might also play a role in influencing these findings [7]. Consequently, our study primarily focuses on the impact of meteorological factors on PM in Macau. Machine learning has been extensively applied in the field of air quality prediction, which encompasses pollutants such as PM, sulfur dioxide (SO\({}_{2}\)), nitrogen oxides (NOx), volatile organic compounds (VOCs), ozone (O\({}_{3}\)), and carbon monoxide (CO). To forecast the Air Quality Index (AQI) levels in Taiwan, [PERSON] et al. utilized 11 years of data from the Taiwan Environmental Protection Agency and employed several machine learning algorithms, including Adaptive Boosting (AdaBoost), Artificial Neural Networks (ANNs), Random Forest, and Support Vector Machines (SVMs). The study found that AdaBoost and Random Forest exhibited the best predictive performance. Future work should focus on hyperparameter optimization of these models to further enhance their performance [8]. Research by [PERSON] et al. demonstrated that using hybrid models could improve prediction accuracy. Their study on PM10 levels in the Comodoro Rivadavia area using a hybrid model, which leveraged the nonlinear modeling capabilities of ANN and the time series modeling abilities of ARIMA, showed improved prediction accuracy and the ability to capture extreme events. The potential applications of hybrid models are not limited to Chile but can be extended to other regions for air quality forecasting [9]. In 2019, [PERSON] et al. presented a CNN-LSTM model augmented with a Mutual Information (MI) estimator for predicting daily average PM2.5 concentrations in Beijing. This model employs a Spatiotemporal Feature Vector (STFV) to capture both linear and nonlinear correlations among environmental data collected from 384 monitoring stations across China, spanning from 2015 to 2017. By integrating historical air quality and meteorological data into a deep learning framework, the model demonstrates enhanced accuracy and stability in forecasting PM2.5 levels compared to traditional methods such as MLP and LSTM [10]. In 2021, [PERSON] et al. introduced an Interpolated Convolutional Neural Network (ICNN) for real-time prediction of PM10 and PM2.5 concentrations.