Air pollution Dynamics: The role of meteorological factors in PM10 concentration patterns across urban areas

• Published in: City and environment interactions 2025-01, Vol.25, p.100184, Article 100184
• Main Authors: Girotti, Carolina, Fernando Kowalski, Luiz, Silva, Tiago, Correia, Ezequiel, R. Prata Shimomura, Alessandra, Akira Kurokawa, Fernando, Lopes, António
• Format: Article
• Language: English
• Subjects: Air quality; Atmospheric Stability Conditions; Big data; Urban Canyons; Urban environment
• ISSN: 2590-2520; 2590-2520
• DOI: 10.1016/j.cacint.2024.100184

•Big data analysis showed that traffic volume alone doesn’t determine PM10 levels.•Atmospheric stability conditions and boundary layer height strongly affected seasonal and diurnal PM10 concentration variations.•Wind direction is a critical factor in pollutant dispersion.•Dense street tree cover lowers PM10 levels in winter, contrasting with areas lacking vegetation.•Areas with increased atmospheric stability experienced higher concentrations of PM10. Air pollution is a major health problem in urban areas, influenced by traffic and atmospheric conditions. This study investigates the relationship between meteorological factors—wind direction, wind speed, boundary layer height, and atmospheric stabilityconditions —street trees, and PM10 concentration in three urban canyons: Avenida da Liberdade and Estrada de Benfica in Lisbon, and Marginal Tietê in São Paulo. Five years of hourly meteorological data and PM10 concentrations were analysed. Despite differences in scale and traffic volume, the results show that PM10 concentration patterns were similar in both Lisbon study areas. These areas also indicated a significant influence of atmospheric variables such as wind speed, boundary layer height, and atmospheric stabilityconditions. Tietê, with a higher vehicle density and different atmospheric conditions (lower wind speeds and greater atmospheric stability), presents higher PM10 peaks. Seasonal analysis revealed distinct patterns influenced by atmospheric instability, wind speed, and direction. In winter, areas with dense street tree cover had reduced PM10 levels, while those without showed higher concentrations due to increased stability. Wind direction played a crucial role, favouring the pollutant dispersal in canyons with parallel winds. The Factorial Analysis of Mixed Data method identified qualitative variables linked to the seasons, wind direction, and presence of trees. PM10 levels below the were associated with the summer and autumn period, parallel winds, and street trees, while levels above the limit were linked to winter period and areas without street trees. By integrating big data analytics with environmental monitoring, this research underscores the importance of considering the local atmospheric conditions and environmental variables in the urban air quality management. Thus, it demonstrates that the traffic volume alone does not determine PM10 concentrations; instead, the interplay of multiple factors, including meteorological conditions and ur