From national emission totals to regional ambient air quality information for Austria

摘要

This paper describes a method to calculate the spatial variability of the expected annual average ambient air concentration of NO_x using standard NO_x emission inventories. As the first step, countrywide NO_x emission inventories were spatially disaggregated into emission-relevant areas. Pollutant dispersion from emission grids into the neighboring receptor grids was calculated using source-specific dispersion profiles. Comparison of model results with data from the air monitoring network has shown that the model reflects well the overall NO_x air quality situation in Austria. there are no major contradictions between model results and observations and the uncertainties are in an acceptable range. Deviations between model results and monitoring data are within ±15 μg/m~3, which is low given the many assumptions needed to establish the model. In many cases, deviations between model results and monitoring data could be explained by the fact that monitoring sites do not represent the overall air quality situation in the wider neighborhood. However, model results do usually better reflect the average situation in its model grid area. These results confirm that emission inventories can indeed be used to estimate the small-scale regional long-term average air quality. This work explores the possibility of using existing standard emission inventory data to estimate the regional ambient air pollution situation with the help of GIS tools. Such modeling is useful to supplement ambient air monitoring networks. While monitoring networks give accurate short-term information about the particular air quality situation at a fixed location, modeling results allow an overall assessment of the long-term average regional variability in larger areas. This method does not take into account meteorological influences and stochastic situations, therefore, it cannot predict the actual extent and the locations of short-term high pollutant concentrations. However, the model is useful to identify regional long-term air pollution 'hotspots'. It can also be used to improve the siting of air pollution monitoring networks in regions of particular importance. If ambient air monitoring data are available, the model results can be used to verify emission inventory data. Finally - as the number of air pollutants covered in emission inventories is much larger than the number of air pollutants measured in monitoring networks - the model can also be used to estimate the regional ambient air pollution for substances for which no monitoring data are available.