High resolution maps of nitrogen dioxide for the Province of Styria, Austria

Abstract

Knowledge about the spatial distribution of NO 2 concentrations and its main contributors is beneficial for setting up air quality measurement plans, assessing exposure, or in licensing procedures, where background concentration levels are required. To produce air quality maps with reasonable spatial resolution, a suitable dispersion model is needed. The province of Styria with 16.400 km² and a population of 1.2 million is the second largest in Austria. While the northern part is dominated by the Alps (peak elevation: 2.995 m), the Southeast is much more flat with gentle hills and some larger plains. In order to take topographical effects on the pollutant dispersion properly into account, wind field libraries have been computed using the prognostic non-hydrostatic mesoscale model GRAMM (Graz Mesoscale Model). The chosen horizontal grid resolution of 300 m did not allow to model wind fields for the whole of Styria in one single run; instead the province has been divided into more than 20 overlapping sub-regions. Local observations of wind speed,-direction and estimated stability classes have been used as meteorological input. Subsequently quasi steady state wind fields have been computed and stored for later use in dispersion modelling utilizing the Lagrangian particle model GRAL (Graz Lagrangian Model). In order to capture strong NO 2 concentration gradients in the vicinity of roads, horizontal grid spacing was set 10 m in the dispersion calculations. Building effects on dispersion have been taken into account by applying a simple mass-conservative diagnostic flow field model implemented in GRAL. Modelled annual mean NO 2 concentrations compare well with observations, although in some areas unexpected patterns appear. In some cases these could be reasonably explained by wrong emissions. Apart from correctly assigning some emission sources in space (e.g. tractors, domestic heating), wind field simulations should be further improved. High resolution maps for PM 10 and B(a)P are foreseen in future, too.

Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions

“…Pollutants are treated as chemically inert, and the formation of secondary particles is not considered. In order to estimate the concentration of the monitored pollutant NO 2 , the calculated NO X concentration is typically modified according to empirical relationships derived from observations at different sites and over several years [63].…”

Evaluation of high-resolution GRAMM–GRAL (v15.12/v14.8) NO<sub><i>x</i></sub> simulations over the city of Zürich, Switzerland

“…We found only minor differences between the simulations with the two modes and resolutions and thus discuss only the results for the diagnostic mode in the following. Lagrangian dispersion simulations are computed with virtual particles released from prescribed emission sources (Oettl and Hausberger, 2006;Oettl, 2014) and transported according to the pre-computed GRAL wind fields. Turbulent diffusion is represented by specific Langevin equations applicable for the full range of wind speeds, in particular for low wind speeds (Anfossi et al, 2006).…”

Including the Urban Canopy Layer in a Lagrangian Particle Dispersion Model

“…Consequently, many operational and experimental air pollution dispersion models are used in urban areas to forecast air pollution concentrations. Examples include models like AER-MOD (Cimorelli et al 2005), QUIC-PLUME (Williams et al 2002), MicroSpray (Tinarelli et al 2012, and references therein), GRAMM/GRAL (Oettl 2014(Oettl , 2015, and SIRANE (Soulhac et al 2011). All these models have in common that they do-based on various approaches-include the mean flow through the region between buildings, i.e., the urban canopy layer (UCL, see Fig.…”