Spatial emission modelling for residential wood combustion in Denmark

Abstract 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 × 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.

[1]  J. Christensen,et al.  Spatial and temporal variations in ammonia emissions – a freely accessible model code for Europe , 2011 .

[2]  C. Place,et al.  Modelling the spatial distribution of ammonia emissions in the UK. , 2008, Environmental pollution.

[3]  J. Pacyna,et al.  Mapping the spatial distribution of global anthropogenic mercury atmospheric emission inventories , 2006 .

[4]  Shaodong Xie,et al.  Spatial and temporal variation of historical anthropogenic NMVOCs emission inventories in China , 2008 .

[5]  Omar Masera,et al.  Spatial analysis of residential fuelwood supply and demand patterns in Mexico using the WISDOM approach. , 2007 .

[6]  Yutaka Tonooka,et al.  Development of multiple-species 1km×1km resolution hourly basis emissions inventory for Japan , 2007 .

[7]  Sarath K. Guttikunda,et al.  A GIS based emissions inventory at 1 km × 1 km spatial resolution for air pollution analysis in Delhi, India , 2013 .

[8]  Clemens Mensink,et al.  Spatial surrogates for the disaggregation of CORINAIR emission inventories , 2009 .

[9]  Allan Gross,et al.  Assessment of past, present and future health-cost externalities of air pollution in Europe and the contribution from international ship traffic using the EVA model system , 2013 .

[10]  Tony Bush,et al.  UK Emission Mapping Methodology 2007 , 2010 .

[11]  M. H. Mikkelsen,et al.  Denmark's National Inventory Report 2015 and 2016: Emission Inventories 1990-2014 - Submitted under the United Nations Framework Convention on Climate Change and the Kyoto Protocol , 2016 .

[12]  Henning Sten Hansen,et al.  A Danish decision-support GIS tool for management of urban air quality and human exposures , 2001 .

[13]  Melik Kara,et al.  Development of a GIS-based decision support system for urban air quality management in the city of Istanbul , 2010 .

[14]  Yong Q. Tian,et al.  Model development for spatial variation of PM2.5 emissions from residential wood burning. , 2004 .

[15]  Gufran Beig,et al.  Emissions inventory of anthropogenic PM2.5 and PM10 in Delhi during Commonwealth Games 2010 , 2011 .

[16]  Allan Gross,et al.  Contribution from the ten major emission sectors in Europe and Denmark to the health-cost externalities of air pollution using the EVA model system - an integrated modelling approach , 2013 .

[17]  C. Sharma,et al.  A GIS based methodology for gridding of large-scale emission inventories: Application to carbon-monoxide emissions over Indian region , 2006 .