## 3 Temporal Variations in and Correlations between PM and Meteorological Factors
### Hourly Variations in Pollutant Concentrations  
In the exploration of variations in and distributions of pollutant concentrations throughout the day using hourly data, binning for PM2.5 and PM10 concentrations was conducted with a bin width of 2, and their concentration measurements were allocated to the corresponding intervals. By grouping the observation times with the binned pollutant data, the frequency and probability for each concentration interval were calculated. As depicted in Figure 3, filled contours were plotted to visualize the probability of each concentration interval occurring during each hour of the day. Furthermore, the hourly average concentration trend lines were overlaid on top of the contour plot to more clearly display the daily trend of pollutant concentrations, thus enhancing the understanding and interpretation of the diurnal pattern of pollutant concentration changes. The observation results indicated that the concentrations of PM2.5 and PM10 are higher during the day than at night, a pattern that may be associated with increased traffic emissions and human activities during daytime hours. Additionally, an increase in PM10 concentrations was observed during the early morning hours, which could be related to meteorological conditions unfavorable for pollutant dispersion from night until early morning. Overall Correlation and Seasonal Analysis of the Relationship between Pollutants and Meteorological Factors  
Data provided by the Macau Meteorological and Geophysical Bureau were utilized to analyze the correlation between pollutant concentrations and meteorological factors. The dataset includes measurements of pollutants such as PM2.5 and PM10, alongside meteorological data: temperature (TEMP), relative humidity (HUMI), sea-level atmospheric pressure (PSEA), wind direction (WDIR), and wind speed (WSPD), among other parameters. Figure 3: Hourly probability distribution (shading) and hourly average (white solid line) of PM10 (**a**) and PM2.5 (**b**) concentrations (in \(\upmu\)g/m\({}^{3}\) units) from 2020 to 2022. The Pearson correlation coefficients between the pollutant variables and meteorological variables were calculated, and a heatmap of these coefficients was generated to aid in better understanding the relationship between meteorological factors and air quality. As meteorological conditions vary with changing seasons, the impact of seasonal weather patterns on the dispersion and deposition of pollutants was also considered. The Pearson correlation coefficients for each season's data were computed, and corresponding seasonal heatmaps were created. This approach more clearly demonstrates the effect of seasonal variations on the relationship between meteorological conditions and pollutants, which is of significant importance for a deeper understanding of how meteorological conditions impact air quality and of seasonal air quality prediction and management. The overall correlation analysis indicates that a significant correlation exists between PM2.5 and PM10 and meteorological factors. PM is found to be primarily strongly correlated with sea-level atmospheric pressure (PSEA), temperature (TEMP), and relative humidity (HUMI) [20]. PM2.5 is observed to exhibit a negative correlation with temperature (TEMP) and relative humidity (HUMI), and a positive correlation with sea-level atmospheric pressure (PSEA) and the U wind component (the east-west wind vector). PM10 is noted to have a negative correlation with relative humidity (HUMI) and a positive correlation with sea-level atmospheric pressure (PSEA), as shown in Figure 4.  
In the seasonal analysis (Figure 5), the correlations are found to vary across different seasons:  
In spring, PM2.5 and PM10 are shown to have a clear relationship with sea-level atmospheric pressure (PSEA). Additionally, PM10 is also notably correlated with the V wind component (the north-south wind vector) and relative humidity (HUMI). In summer, the correlation of both PM2.5 and PM10 with temperature (TEMP) is found to be significantly stronger.