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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 12
Geosci. Model Dev., 9, 4475–4489, 2016
https://doi.org/10.5194/gmd-9-4475-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 9, 4475–4489, 2016
https://doi.org/10.5194/gmd-9-4475-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 16 Dec 2016

Development and technical paper | 16 Dec 2016

Improving the spatial resolution of air-quality modelling at a European scale – development and evaluation of the Air Quality Re-gridder Model (AQR v1.1)

Mark R. Theobald1,2, David Simpson3,4, and Massimo Vieno5 Mark R. Theobald et al.
  • 1Atmospheric Pollution Division, Research Centre for Energy, Environment and Technology (CIEMAT), Madrid, 28040, Spain
  • 2Dept. Agricultural Chemistry and Analysis, Higher Technical School of Agricultural Engineering, Technical University of Madrid, 28040, Spain
  • 3EMEP MSC-W, Norwegian Meteorological Institute, Oslo, 0313, Norway
  • 4Dept. Earth & Space Sciences, Chalmers University of Technology, Gothenburg, 412 96, Sweden
  • 5Centre for Ecology & Hydrology, Edinburgh Research Station, Penicuik, EH26 0QB, UK

Abstract. Currently, atmospheric chemistry and transport models (ACTMs) used to assess impacts of air quality, applied at a European scale, lack the spatial resolution necessary to simulate fine-scale spatial variability. This spatial variability is especially important for assessing the impacts to human health or ecosystems of short-lived pollutants, such as nitrogen dioxide (NO2) or ammonia (NH3). In order to simulate this spatial variability, the Air Quality Re-gridder (AQR) model has been developed to estimate the spatial distributions (at a spatial resolution of 1  ×  1 km2) of annual mean atmospheric concentrations within the grid squares of an ACTM (in this case with a spatial resolution of 50  ×  50 km2). This is done as a post-processing step by combining the coarse-resolution ACTM concentrations with high-spatial-resolution emission data and simple parameterisations of atmospheric dispersion. The AQR model was tested for two European sub-domains (the Netherlands and central Scotland) and evaluated using NO2 and NH3 concentration data from monitoring networks within each domain. A statistical comparison of the performance of the two models shows that AQR gives a substantial improvement on the predictions of the ACTM, reducing both mean model error (from 61 to 41 % for NO2 and from 42 to 27 % for NH3) and increasing the spatial correlation (r) with the measured concentrations (from 0.0 to 0.39 for NO2 and from 0.74 to 0.84 for NH3). This improvement was greatest for monitoring locations close to pollutant sources. Although the model ideally requires high-spatial-resolution emission data, which are not available for the whole of Europe, the use of a Europe-wide emission dataset with a lower spatial resolution also gave an improvement on the ACTM predictions for the two test domains. The AQR model provides an easy-to-use and robust method to estimate sub-grid variability that can potentially be extended to different timescales and pollutants.

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Impacts of air pollution at a continental scale, estimated using air quality models, can potentially be greatly under- or overestimated due to the low spatial resolution used (grid cells of 10–50 km). We present a method to estimate the spatial variations in air quality within a model grid cell by combining high-resolution emission data with estimates of short range dispersion. This simple but robust technique has the potential to improve estimates of air quality impacts at a continental scale.
Impacts of air pollution at a continental scale, estimated using air quality models, can...
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