AI Summary & Key Takeaways
AI InsightResults show that theUBM model successfully predicts concentrations of CO, NO2, NOx, O3 and PM2.5 while the predicted SO2 concentrations are unsatisfactory.
📌 3 Key Takeaways
Özet
This study estimates air pollution at urban background level for Quito, Ecuador, using the Urban Background Model(UBM) developed at Aarhus University, Denmark. Hourly concentrations of CO, NO2, NOx, O3, PM2.5 and SO2 werecalculated for the year 2009. UBM performance is evaluated at six monitoring locations. The air pollution emissioninventory was scaled, using calibration factors, until modeled concentrations were in line with observations.Predicted values were graphically and statistically evaluated by comparison to measurements. The statistical assessment is conducted for: Fraction of predictions within a factor of two of the observations (FAC2), Fractional meanbias (FB), Normalized mean-square error (NMSE) and Normalized absolute difference (NAD). Results show that theUBM model successfully predicts concentrations of CO, NO2, NOx, O3 and PM2.5 while the predicted SO2 concentrations are unsatisfactory. PM2.5 modeling meets the criteria of acceptance, but their results depend largely onthe regional levels, so the quality of this information is extremely relevant. The UBM model was applied for the years2008 and 2010 using meteorological data retrieved from the modeling sites with emissions and calibration factorsderived for the year 2009, showing a performance similar to that of 2009. The findings confirm the applicability ofUBM to predict air pollution at the urban background level in Quito. Satisfactory results are obtained by applyingmeteorological data derived from any of the available monitoring stations. The unsatisfactory results for SO2 suggestthat emission data should be reviewed and that this cannot be obtained simply by scaling.