Aerosols play a crucial role in air quality, climate regulation, and public health; their timely monitoring is hence fundamental. The aerosol optical depth (AOD) is the parameter used to investigate the spatial–temporal distribution of aerosols from space. Specifically, the AOD retrieved from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm applied to a Moderate Resolution Imaging Spectroradiometer (MODIS) is suitable for aerosol investigation at a local scale by exploiting its high spatial resolution (1 km × 1 km). In this study, the MAIAC AOD retrieval over Rome (Italy) was validated for the first time, using ground-based data provided by an AERONET station operating in a semi-rural environment close to the city, over a time series from January 2001 to December 2022. Moreover, AOD trends were evaluated in a study area encompassing Rome and its surroundings, characterized by a transition zone between urban and rural environments. The results show a general underestimation of the MAIAC AOD; specifically, the validation process highlighted the less accurate performance of the algorithm under higher aerosol loading and with predominantly coarse mode aerosol. Interesting results were obtained concerning the influence of the geometrical configuration of satellite acquisition on the accuracy of the MAIAC product. In particular, the solar zenith angle, the relative azimuth and the scattering angle between the principal plane of the sun and satellite synergistically influence retrievals. Finally, the spatial distribution of the AOD shows a decreasing trend over the 2001–2022 period and a strong influence of the city of Rome over the whole study area.

First continuous measurements of atmospheric CH₄ were carried out for one year (June 2023–May 2024) at Liberti Observatory of CNR-IIA, in a semi-rural site near Rome. Seasonal and diurnal variations were analyzed. CH₄ monthly mean concentrations showed maximum and minimum values in winter and summer, respectively, which agree with the other European trends. Minimum CH₄ values during summer could likely be due to a combination of favorable atmospheric mixing properties and increased atmospheric CH₄ oxidation. The correlation analysis showed that temperature, global radiation, and wind speed revealed significant negative correlations with this greenhouse gas, indicating the influence of local sources. However, poor correlations during different seasonal periods also suggested the role of air mass transport sources. The CH₄ concentrations exhibited clear diurnal cycles with daytime low and night-time high values, mainly driven by atmospheric stability conditions and photochemistry. A cluster analysis of air mass trajectories showed that CH₄ concentrations were influenced all year by anthropogenic emissions. Elevated concentrations arrived from NE Europe, except in winter when the influence of NW European and local contributions became more significant. Furthermore, level-3 XCH₄ data from the satellite TROPOMI showed a methane columnar concentration increase from 2018 to 2024 in agreement with the global annual increase from the NOAA network during the same period.

Ground-based monitoring networks often provide uneven spatial coverage, limiting the characterization of air-quality in heterogeneous regions. This study proposes a data-driven framework that identifies spatially coherent atmospheric regimes from multi-gas satellite observations. The framework is applied to investigate the seasonal distribution of PM_2.5-related trace-gas indicators and aerosol loading over the Lazio region, central Italy, using TROPOMI-derived vertical column densities of NO₂, HCHO, and CO products together with MODIS-MAIAC Aerosol Optical Depth, and integrating satellite observations with land-cover, topographic, and ground-based NO₂ data. In this approach, TROPOMI provides spatially continuous information on atmospheric composition, unsupervised clustering identifies multi-gas patterns, and land information supports the interpretation of drivers of emission. The seasonal analysis showed winter NO₂ maxima associated with combustion-related emissions, urban areas, and transport corridors, while HCHO peaked during summer, reflecting enhanced VOC-related photochemical activity. CO displayed a more diffuse spatial distribution, consistent with its longer atmospheric lifetime and broader combustion-related sources. The multivariate classification highlighted the Sacco Valley as a multi-pollutant area, the Tiber Valley as a peri-urban agricultural corridor, and the Lepini Mountains as a cleaner sector. The proposed framework provides spatially explicit information useful for interpreting regional air-quality variability, identifying under-monitored sectors, and supporting monitoring and mitigation strategies.