AI Summary & Key Takeaways
AI InsightSpatial distribution of PM10 concentrations highlights the limitations to characterize urban hot spots based on the measurements from only one traffic station available within the study area and used to validate the urban scale model.
📌 3 Key Takeaways
Özet
The objective of this study is to explore the existing services at a regional scale and study their applicability to the assessment and forecast of air quality at the urban scale. Two approaches were evaluated to characterize urban background pollution levels from the regional operational forecast: (i) Copernicus Atmosphere Monitoring Service (CAMS) and (ii) Chemical Transport Model CHIMERE from the national forecast system. The local contribution from road traffic was analysed using transportation (VISUM) -emission (QTraffic) - dispersion (ADMS-Roads) modelling chain with 10 m grid resolution. The methodology was applied to Coimbra city to estimate PM10 concentrations for the period of one year (2018). In order to validate the model outputs against the daily observations, the FAIRMODE Delta Tool was applied separately for regional and urban scale. The results obtained for Coimbra satisfy the modelling performance criteria. However, both approaches used to characterize background concentrations underestimate PM10 levels. The modelling bias at urban traffic station is about 6 μg m−3 for annual mean concentration. Local highest contribution of road traffic to PM10 annual mean obtained with high grid resolution (10 m) achieves 22 μg m−3 . Spatial distribution of PM10 concentrations highlights the limitations to characterize urban hot spots based on the measurements from only one traffic station available within the study area and used to validate the urban scale model.