Physical-Statistical Characterization of PM10 and PM2.5 Concentrations and Atmospheric Transport Events in the Azores During 2024

This study presents a comprehensive physical-statistical analysis of atmospheric partic-ulate matter (PM10 and PM2.5) and trace gases (SO₂ and O₃) over Faial Island in the Azores archipelago during 2024. Real-time data were collected at the Espalhafatos rural back-ground station, covering 35,137 observations per pollutant, with 15-minute intervals. De-scriptive statistics, probability distribution fitting (Normal, Lognormal, Weibull, Gamma), and correlation analyses were employed to characterize pollutant dynamics and identify extreme pollution episodes. The results revealed that PM2.5 concentrations are best mod-eled by a Lognormal distribution, while PM10 concentrations fit a Gamma distribution, highlighting the presence of heavy-tailed, positively skewed behavior in both cases. Sea-sonal and episodic variability was significant, with multiple Saharan dust transport events contributing to PM exceedances, particularly during winter and spring months. These events, confirmed by CAMS and SKIRON dust dispersion models, affected not only southern Europe but also the Northeast Atlantic, including the Azores region. Weak to moderate correlations were observed between PM concentrations and meteoro-logical variables, indicating complex interactions influenced by atmospheric stability and long-range transport processes. Linear regression analyses between SO₂ and O₃, and be-tween SO₂ and PM2.5, showed statistically significant but low-explanatory relationships, suggesting that other meteorological and chemical factors play a dominant role. These results highlight the importance of developing air quality policies that address both local emissions and long-range transport phenomena. They support the implementation of early warning systems and health risk assessments based on probabilistic modeling of particulate matter concentrations, even in remote Atlantic locations such as the Azores.