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.

Fine particulate matter with an aerodynamic diameter below 2.5µm (PM2.5) is widely recognized as one of the most harmful air pollutants due to the impact on human health, ecosystems, and climate. The importance of controlling PM2.5 concentrations has been reinforced by the European Air Quality Directive 2024, which aligns particulate matter standards more closely with World Health Organization guidelines. PM2.5 originates from both direct emissions and secondary formation processes involving gaseous precursors such as nitrogen dioxide (NO2) and volatile organic compounds (VOCs). Secondary aerosols often dominate PM2.5 mass in urban and regional environments, making the characterization of the spatial and seasonal variability of these precursors essential for understanding formation pathways and supporting season-specific air quality management strategies.Tropospheric vertical column densities (VCDs) of NO2 and HCHO (used as a proxy for VOCs) were retrieved from the TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P. The results reveal pronounced seasonal variations in precursor concentrations. NO2 spatial distributions closely follow urban centers and major road networks, with higher winter concentrations driven by stable atmospheric conditions and increased emissions from heating and traffic, and lower summer levels reflecting enhanced photochemical processing and atmospheric mixing. In contrast, HCHO shows a more widespread seasonal pattern, with higher summer concentrations largely driven by intensified photochemical activity and biogenic emissions, with isoprene acting as a key local precursor. These seasonal dynamics are consistent with established atmospheric chemistry and emission patterns.The application of k-means clustering enabled the identification of regions of interest, distinguishing highly polluted areas from cleaner backgrounds and highlighting urban and agricultural hotspots such as Rome, the Sacco Valley, and the Tiber Valley. Comparison with land cover data indicates that elevated pollution levels are associated with urban, industrial, and transportation-related emissions, while areas with natural vegetation exhibit greater mitigation capacity.Aerosol optical depth (AOD) derived from the MAIAC algorithm applied to MODIS data from the AQUA and TERRA satellites was employed to investigate the relationship between gaseous precursors and particulate matter formation. The results indicate a distinct seasonal coupling between precursor gases and AOD. During winter, NO2 shows stronger associations with AOD, highlighting the dominant role of inorganic secondary aerosol formation. During summer, HCHO exhibits a closer relationship with AOD, pointing to the increased importance of photochemically driven secondary organic aerosol production.Overall, satellite-based Earth observation provides a powerful complement to ground-based monitoring for investigating PM2.5 precursors and demonstrates strong potential to support the implementation of the 2024 European Air Quality Directive. By identifying spatial hotspots and seasonal drivers of precursor gases, this analysis supports the development of effective, season-specific emission reduction strategies and improves understanding of the atmospheric processes controlling air quality in the Lazio region.

The tendency of SVOCs to partition between the vapor and particulate phases influences the reaction kinetics of these pollutants, pushing for the development of selective sampling systems. Traditional methods like filters and adsorbents have limited accuracy in describing the distribution of SVOCs due to sampling artifacts, such as analyte stripping during active air sampling. This distinction is crucial for designing pollutant abatement systems in industrial settings and developing collective and individual protection devices against airborne chemical agents. This study presents the development of a multi-channel diffusion denuder for the sampling and the selective enrichment of SVOCs in the vapor phase for subsequent direct determination through thermal desorption coupled with GC/MS. The analysis of compounds sampled using the denuder was compared with that of a common granular carbon trap. The multi-channel denuder has been evaluated for its ability to selectively sample the vapor phase of an aerosol without retaining the particulate phase. It was demonstrated that, on average, this type of denuder retains only 13% of the total particulate matter, with Aed between 14 and 800 nm. Total transmission is achieved for particles with Aed greater than 800 nm. An ozone scrubber prevents degradation and oxidation product formation, showing effective preservation of compounds in the presence of ozone. Results show that denuder sampling significantly improves chromatographic resolution and efficiency. The multi-channel denuder developed in this study proved to be an efficient tool for selectively sampling SVOCs in the vapor phase, making it particularly suitable for indoor air monitoring and workplace exposure assessment. Theoretical calculations showed efficiencies higher than 95% for all analytes.

Chlorophyll-a (Chl-a) is commonly used as an indicator of phytoplankton biomass and eutrophication in inland waters, as it reflects changes in primary productivity and nutrient availability. Turbidity describes the optical effect of suspended particles in the water column and, in oligotrophic lakes, is typically low but highly responsive to external factors such as wind-induced mixing and precipitation. Analyzing Chl-a and turbidity together in relation to atmospheric conditions is therefore crucial for evaluating water quality and identifying potential pressures on aquatic ecosystems.In this study, Lake Bolsena was investigated as a representative oligotrophic system to evaluate how atmospheric conditions influence Chl-a concentration and turbidity. The analysis was conducted over the lake surface and an additional surrounding land buffer of approximately 15 km, selected to account for meteorological and atmospheric processes that are not confined to the water body itself but can indirectly affect its optical and biological properties.Chl-a and turbidity were derived from the data set (version 2.1) of the ESA Lakes_cci project based on the processing of OLCI images for the period 2016-2022. Meteorological variables considered include wind speed at 10m, 2-m air temperature, surface pressure, boundary layer height, precipitation, and solar radiation, all derived from the ERA5 reanalysis dataset (Hersbach et al., 2020). In oligotrophic lakes, wind speed regulates water column mixing and sediment resuspension, while air temperature and solar radiation influence thermal stratification and the energy available for phytoplankton growth; precipitation contributes to suspended material modifying surface optical properties. Boundary layer height and surface pressure provide additional information on atmospheric stability and mixing conditions that modulate air–water exchanges.Aerosol Optical Depth (AOD) retrieved using the MAIAC algorithm was also included, although it is not available directly over the lake surface but only in the surrounding area (Lyapustin et al., 2018). AOD was used as a proxy for regional aerosol loading to investigate its potential indirect effects on the lake through dry and wet deposition of particulate matter and nutrients, which may alter water transparency and, over time, phytoplankton dynamics even under oligotrophic conditions.Correlation analysis revealed significant seasonal variability throughout the studied period. Chl-a is particularly influenced by multiple atmospheric forces in autumn, while turbidity is primarily driven by meteorological factors in summer. Both water quality parameters exhibit variable but significant dependencies in spring; on the other hand, atmospheric influence is less relevant in winter.ReferencesHersbach, H., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018.