Chapter 30
Modelling the Temporal and Spatial
Allocation of Emission Data
Volker Matthias, Jan Arndt, Armin Aulinger, Johannes Bieser
and Markus Quante
Abstract 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.
30.1 Introduction
Atmospheric chemistry transport models are applied for investigating the interactions between emissions from different sources and their influence on the spatial
and temporal distribution of pollutant concentrations. These models need accurate
V. Matthias (B) · J. Arndt · A. Aulinger · J. Bieser · M. Quante
Helmholtz-Zentrum Geesthacht, Institute of Coastal Research, Max-Planck-Strasse 1, 21502
Geesthacht, Germany
e-mail: volker.matthias@hzg.de
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_30
193
Modelling the Temporal and Spatial
Allocation of Emission Data
Volker Matthias, Jan Arndt, Armin Aulinger, Johannes Bieser
and Markus Quante
Abstract 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.
30.1 Introduction
Atmospheric chemistry transport models are applied for investigating the interactions between emissions from different sources and their influence on the spatial
and temporal distribution of pollutant concentrations. These models need accurate
V. Matthias (B) · J. Arndt · A. Aulinger · J. Bieser · M. Quante
Helmholtz-Zentrum Geesthacht, Institute of Coastal Research, Max-Planck-Strasse 1, 21502
Geesthacht, Germany
e-mail: volker.matthias@hzg.de
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_30
193