## 5 Discussion  
We conducted an in-depth analysis of the spatiotemporal characteristics of PM2.5 and PM10 concentrations in Macau and their associations with meteorological conditions. The results revealed a distinct seasonal pattern in PM concentrations, particularly during autumn and winter seasons. Relative humidity exhibited a significant positive correlation with PM2.5, which may be attributed to the influence of humidity on photochemical reactions and the promotion of secondary pollutant formation, highlighting the need for controlling precursor pollutants and secondary pollutants. Low-temperature and high-humidity conditions could potentially inhibit pollutant dispersion and promote the formation of secondary organic aerosols, exacerbating haze pollution. On the other hand, there is a negative correlation between temperature and PM levels during spring and autumn, mainly due to regional characteristics and atmospheric stability. In terms of regional characteristics, cold air moving south from the continent lowers temperatures and brings pollutants from northern cities to Macau. Conversely, when maritime airflow is dominant, south winds bring clean air from the ocean. Regarding atmospheric stability, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Stacking** & **CNN** & **LSTM** & **RF** & **XGB** \\ \hline MSE & 33.4330 & 57.2014 & 47.8085 & 51.4799 & 45.2650 \\ RMSE & 5.7821 & 7.5631 & 6.9143 & 7.1749 & 6.7279 \\ MAE & 3.9142 & 5.4675 & 5.0109 & 5.1439 & 4.7940 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Stacking, CNN, LSTM, RF, and XGB model performance indicators. when the ground is colder, a temperature inversion occurs with warmer air above and cooler air below, hindering vertical air dispersion. However, when the ground is warmer, convection is more easily triggered, leading to precipitation that helps disperse and settle air pollutants. Hourly data analysis identified daytime and early morning as peak periods for pollutant concentrations in Macau, which is crucial for implementing monitoring, early warning, and emission reduction measures, with particular emphasis on analyzing the composition of secondary pollutants during the day. The research findings provide baseline data for assessing the impact of air pollution on public health, and subsequent analysis of pollution components to identify major harmful substances will be of significant importance, aiding in the development of appropriate public health protection strategies. The integration of multiple classifiers through a Stacking ensemble learning methodology demonstrably enhances performance beyond that achieved by the selection of the best individual classifier [29]. This approach, when juxtaposed with current state-of-the-art techniques, consistently produces lower error rates and attains predictions of higher accuracy [30]. As a result of these findings, the Stacking ensemble learning strategy has achieved broad acclaim within the scientific community. To improve the prediction accuracy of pollutant concentrations, we proposed a model based on Stacking ensemble learning. This model combines LSTM and XGBoost as base learners to capture different patterns in the PM2.5 data, and utilizes LightGBM as a meta-learner to integrate the outputs of the base learners, achieving optimal prediction performance. Empirical results demonstrate that the Stacking model can effectively capture the overall trends and patterns of actual PM2.5 concentrations, exhibiting particularly precise predictions in the tail region of the data. Although there are still some shortcomings in handling peak values and outliers, the model generally exhibits strong robustness and stable predictive ability for PM2.5 fluctuations.