Spatial emission modelling for residential wood combustion in Denmark

Residential wood combustion (RWC) is a major contributor to atmospheric pollution especially for particulate matter. Air pollution has significant impact on human health, and it is therefore important to know the human exposure. For this purpose, it is necessary with a detailed high resolution spatial distribution of emissions. In previous studies as well as in the model previously used in Denmark, the spatial resolution is limited, e.g. municipality or county level. Further, in many cases models are mainly relying on population density data as the spatial proxy for distributing the emissions. This paper describes the new Danish model for high resolution spatial distribution of emissions from RWC to air. The new spatial emission model is based on information regarding building type, and primary and supplementary heating installations from the Danish Building and Dwelling Register (BBR), which holds detailed data for all buildings in Denmark. The new model provides a much more accurate distribution of emissions than the previous model used in Denmark, as the resolution has been increased from municipality level to a 1 km x 1 km resolution, and the distribution key has been significantly improved so that it no longer puts an excessive weight on population density. The new model has been verified for the city of Copenhagen, where emissions estimated using both the previous and the new model have been compared to the emissions estimated in a case study. This comparison shows that the new model with the developed weighting factors (76 ton PM2.5)is in good agreement with the case study (95 ton PM2.5), and that the new model has improved the spatial emission distribution significantly compared to the previous model (284 ton PM2.5). Additionally, a sensitivity analysis was done to illustrate the impact of the weighting factors on the result, showing that the new model independently of the weighting factors chosen produce a more accurate result than the old model. (C) 2016 The Authors. Published by Elsevier Ltd.