%0 journal article
%@ 1096-2247
%A Matthias, V., Arndt, J.A., Aulinger, A., Bieser, J., Gon, H.D.van der, Kranenburg, R., Kuenen, J., Neumann, D., Pouliot, G., Quante, M.

%D 2018
%J Journal of the Air & Waste Management Association
%N 8
%P 763-800 
%R doi:10.1080/10962247.2018.1424057
%T Modeling emissions for three-dimensional atmospheric chemistry transport models
%U https://doi.org/10.1080/10962247.2018.1424057
8 
%X Poor air quality is still a threat for human health in many parts of the world. In order to assess measures for emission reductions and improved air quality, three-dimensional atmospheric chemistry transport modeling systems are used in numerous research institutions and public authorities. These models need accurate emission data in appropriate spatial and temporal resolution as input. This paper reviews the most widely used emission inventories on global and regional scale and looks into the methods used to make the inventory data model ready. Shortcomings of using standard temporal profiles for each emission sector are discussed and new methods to improve the spatio-temporal distribution of the emissions are presented. These methods are often neither top-down nor bottom-up approaches but can be seen as hybrid methods that use detailed information about the emission process to derive spatially varying temporal emission profiles. These profiles are subsequently used to distribute bulk emissions like national totals on appropriate grids. The wide area of natural emissions is also summarized and the calculation methods are described. Almost all types of natural emissions depend on meteorological information, which is why they are highly variable in time and space and frequently calculated within the chemistry transport models themselves. The paper closes with an outlook for new ways to improve model ready emission data, for example by using external databases about road traffic flow or satellite data to determine actual land use or leaf area. In a world where emission patterns change rapidly, it seems appropriate to use new types of statistical and observational data to create detailed emission data sets and keep emission inventories up-to-date.