Modelling the Temporal and Spatial Allocation of Emission Data
Atmospheric chemistry transport models (CTMs) need spatially and temporally resolved emission data as input. Atmospheric concentrations of pollutants as well as their deposition depend not only on the emitted amount but also on place and time of the emissions used for the model calculations. Available emission inventories, both regional and global ones, typically provide annual emissions of specific substances on a predefined grid. Often, this grid is of coarser resolution than the model grid and the temporal resolution is not higher than monthly. In addition, many species like volatile organic compounds (VOCs) or particulate matter (PM) are only given as lumped sums and not split into their chemical components. This requires further processing of the emissions in order to produce sufficiently resolved data sets for follow-up CTM runs. As a consequence, emission models were developed for the purpose of creating “model-ready” emissions. They use methods that depend on the emission sector and the additional data available for the disaggregation of the inventory data, e.g. land use and population density data. Recently, new methods for specific sectors like agriculture, residential heating and traffic have been developed. The most commonly used global and regional emission inventories are summarized and an overview of currently applied methods to spatially and temporally disaggregate emission inventory data is given. Particular emphasis is laid on the temporal disaggregation by presenting methods that allow the creation of individual time profiles for each model grid cell.
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