Seasonal Urban Air Quality Characterization in Rome Using Integrated Satellite, Meteorological and Demographic Data

Urban air quality monitoring is essential due to the high concentration of anthropogenic pollution sources in cities. While regulations such as the 2030 EU air quality targets emphasize the need to reduce harmful pollutants, conventional ground-based networks often lack sufficient coverage and spatial detail. Satellite observations offer a powerful complement, providing continuous, high-resolution data to capture urban-scale variability and identify localized pollution hotspots. 

This study focuses on analyzing seasonal air pollution patterns across the municipality of Rome by integrating multi-source datasets, including satellite measurements, ground-based observations, meteorology, land cover, and population distribution. Sentinel-5P TROPOMI data (2018–2024) were used to track the spatiotemporal variability of key trace gases such as NO₂, HCHO, CO, and CH₄. Daily measurements were processed into seasonally aggregated Level-3 products through the Products Algorithm Laboratory (PAL), with quality assurance filtering applied to ensure reliability. These data allowed the identification of emission hotspots and seasonal trends in precursor gases that drive secondary PM₂.₅ formation. 

Aerosol optical depth (AOD) derived from MODIS Terra and Aqua observations using the MAIAC algorithm provided complementary information on aerosol  distribution. Monthly AOD datasets were analyzed after reprojection to a consistent WGS84 grid, enabling direct comparison with TROPOMI-derived trace gas concentrations.  

PM₂.₅ data were collected from the Regional Agency for Environmental Protection (ARPA) ground-based network. Hourly measurements from different ground-based stations were used to analyze the seasonal trend of PM₂.₅ across the city.  This combination allowed for the evaluation of seasonal coupling between gaseous precursors, aerosols, and particulate matter, highlighting periods of increased secondary aerosol formation. 

Meteorological factors were incorporated using ERA5 reanalysis data, providing hourly fields for wind, temperature, precipitation, radiation, and boundary layer dynamics. These variables helped interpret observed seasonal patterns by linking atmospheric transport, mixing, and photochemical activity to pollutant distributions. 

Population dynamics, derived from high-resolution WorldPop datasets, were integrated to assess human exposure and explore how population density interacts with pollution patterns. By combining satellite, ground-based, meteorological, and demographic data, the study delivers a detailed, seasonally resolved understanding of air quality across Rome. This framework supports targeted interventions, prioritization of mitigation measures, and evidence-based planning for urban air quality management.