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)
Journal article, 2016
© Author(s) 2016.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-spatialresolution 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.