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This study investigates the impact of high-resolution spatiotemporal profiles of road transport emissions on urban air quality simulations for Thessaloniki, Greece. Dynamic spatiotemporal emission profiles were developed based on real vehicle speed data and implemented in an integrated air quality modeling system to improve the representation of temporal and spatial traffic activity patterns. The new profiles captured the variability of emissions across hours, days, and months, reflecting differences in congestion intensity and seasonal mobility behavior. Zero-out air quality simulations, in which road transport emissions were entirely removed from the model domain, revealed that road transport is a dominant source of urban air pollution, contributing by up to 47 μg/m3 to daily NO2 and up to 15 μg/m3 to daily PM2.5 concentrations during winter, while remaining significant in summer. The speed-based spatiotemporal profiles affected NO and NO2 concentrations by up to +20 μg/m3 and +3.8 μg/m3, respectively, during the rush hours in winter. The use of dynamic spatiotemporal profiles improved model performance with a maximum daily BIAS reduction of –5 μg/m3 for NO and an increase in the index of agreement of up to 0.13 during the warm period, demonstrating a more accurate representation of traffic-related air pollution dynamics. Improvements for PM2.5 were smaller but consistent across most monitoring sites. Overall, the study demonstrated that incorporating detailed traffic-derived spatiotemporal profiles enhances the accuracy of urban air quality simulations. The proposed approach provides valuable input for municipal action plans, supporting the design of effective traffic management and emission reduction strategies tailored to local conditions.

The average PM10 daily levels over the urban area of Thessaloniki, Greece, usually exceed the air quality limits and therefore the improved PM chemical composition and air quality modeling results that will facilitate the design of the most appropriate mitigation measures (e.g., limitations in wood combustion for heating purposes) are essential. The air quality modeling system WRF-CAMx was applied over a 2 × 2 km2 horizontal resolution grid covering the greater area of Thessaloniki for the year 2015, when Greece was still confronting the consequences of the financial crisis. The output hourly surface concentrations of twelve PM species at three sites of different environmental type characterization in the city of Thessaloniki were temporally and spatially analyzed. Carbonaceous aerosols (organic and elemental) are the major contributor to total PM10 levels during winter representing a 35–40% share. During summer, mineral aerosols (excluding dust) distribute by up to 48% to total PM10 levels, being the major contributor attributed to road traffic. PM species, during winter, increase in the morning and in the afternoon mainly due to road transport and residential heating, respectively, in addition with the unfavorable meteorological conditions. An underestimation of the primary organic carbon aerosol levels during winter is identified. The application of the modeling system using a different speciation profile for the fine particles emissions from residential heating based on observational data instead of the CAMS emissions profile revealed an improvement in the simulated OC/EC values for which a 50% increase was identified compared to the base run.

This study examined the impact of shipping emissions on the atmospheric composition and oxidation capacity in the Eastern Mediterranean, focusing on Greece and the Piraeus port. The analysis used CAMx air quality modeling (simulations for January and July 2019), Positive Matrix Factorization (PMF) receptor modeling, and ship emission inventories from CAMS-REGv6.1 and the “Flexible Emission Inventory for Greece and the Greater Athens Area” (FEI-GREGAA). Zeroing out shipping emissions within Greek maritime waters resulted in substantial reductions in monthly concentrations of pollutants in major Aegean Sea shipping corridors, becoming largest in the Piraeus port. PMF and CAMx estimations for the net shipping contribution to fine particulate matter (PM₂.₅) in the Piraeus passenger port area showed good correlations. However, CAMx average estimates were higher compared to PMF (1.1 µg m − ³ (4.9%) by CAMx and 0.6 µg m − ³ (2.7%) by PMF for January 2019; 2.7 µg m − ³ (20.9%) by CAMx and 1.7 µg m − ³ (11.2%) by PMF for July 2019), partly due to higher CAMx-simulated shipping impact on elemental carbon (EC). More refined ship emission inventories and air quality modeling, optimized chemical speciation of ship emissions, and more experimental data are necessary, while considering also the changing regulations on ship fuels and their future climate implications.

Air quality simulations were performed for Athens (Greece) in ~1 km resolution applying the models WRF-CAMx for July and December 2019 with the secondary organic aerosol processor (SOAP) and volatility basis set (VBS) organic aerosol (OA) schemes. CAMx results were evaluated against particulate matter (PM) and OA concentrations from the regulatory monitoring network and research monitoring sites (including PM2.5 low-cost sensors). The repartition of primary OA (POA) and secondary OA (SOA) by CAMx was compared with positive matrix factorization (PMF)-resolved OA components based on aerosol chemical speciation monitor (ACSM) measurements. In July, OA concentrations underestimation was decreased by up to 24% with VBS. In December, VBS introduced small negative biases or resulted in more pronounced (but moderate) underestimations of OA with respect to SOAP. CAMx performance for POA was much better than for SOA, while VBS decreased the overestimation of POA and the underestimation of SOA in both study periods. Despite the SOA concentrations increases by VBS, CAMx still considerably underestimated SOA (e.g., by 65% in July). Better representation of simulated OA concentrations in Athens could benefit by accounting for the missing cooking emissions, by improvements in the biomass burning emissions, or by detailed integration of processes related to OA chemical aging.