30 Modelling the Temporal and Spatial Allocation of Emission Data
197
30.4 Summary and Outlook
Detailed emission data is crucial for 3D atmospheric chemistry transport model
calculations. This concerns not only emission totals but also their temporal and spatial distribution. Because emissions cannot be constantly monitored for all relevant
sources, well suited proxy data can help distributing bulk emissions from certain sectors. Heating emissions from households, emissions from animal husbandry as well
as evaporative emissions from cars all depend on ambient temperatures and partly
also on wind. Therefore, meteorological data often serves as good proxy for their
spatio-temporal allocation. This way, the proxy data is calculated as in a bottom-up
approach and then used to distribute the bulk emissions given on national level.
Large parts of the data that is collected for traffic monitoring could be used to
improve the representation of these emissions in chemistry transport model systems
in the future. This holds not only for cars and trucks but also for ship traffic and
aviation. However, besides the availability of that data for research purposes, new
data science technologies need to be developed for handling such large amounts of
unstructured data.
Questions and Answers
QUESTIONER: Greg Yarwood
QUESTION: Are gridded inventories a limitation for industrial point source emissions in particular?
ANSWER: Yes, they are. The emissions will be taken as an average over the entire
grid cell, which means that they are drastically diluted if the grid is rather coarse.
This will have consequences for the dispersion of the emissions and the chemical
transformations. In addition, if the location of a point source within the grid is not
exactly given, it cannot be attributed to the correct grid cell on a finer grid. This might
cause significant errors in the location of a source when the data is re-gridded.
References
1. A. Aulinger, V. Matthias, M. Quante, An approach to temporally disaggregate Benzo (a) pyrene
emissions and their application to a 3D Eulerian atmospheric chemistry transport model. Water
Air Soil Pollut. 216(1–4), 643–655 (2011)
2. A. Backes, A. Aulinger, J. Bieser, V. Matthias, M. Quante, Ammonia emissions in Europe,
Part I: development of a dynamical ammonia emission inventory. Atmos. Environ. 131, 55–66
(2016)
3. J. Bieser, A. Aulinger, V. Matthias, M. Quante, H.A.C. Denier van der Gon, Vertical emission
profiles for Europe based on plume rise calculations. Environ. Pollut. 159(10), 2935–2946
(2011)
4. H.A.C. Denier van der Gon et al., Particulate emissions from residential wood combustion in
Europe—revised estimates and an evaluation. Atmos. Chem. Phys. 15, 6503–6519 (2015)
5. G. Frost et al., New Directions: GEIA’s 2020 vision for better air emissions information. Atmos.
Environ. 81, 710–712 (2013)
6. C. Hendriks et al., Ammonia emission time profiles based on manure transport data improve
ammonia modelling across north western Europe. Atmos. Environ. 131, 83–96 (2016)
197
30.4 Summary and Outlook
Detailed emission data is crucial for 3D atmospheric chemistry transport model
calculations. This concerns not only emission totals but also their temporal and spatial distribution. Because emissions cannot be constantly monitored for all relevant
sources, well suited proxy data can help distributing bulk emissions from certain sectors. Heating emissions from households, emissions from animal husbandry as well
as evaporative emissions from cars all depend on ambient temperatures and partly
also on wind. Therefore, meteorological data often serves as good proxy for their
spatio-temporal allocation. This way, the proxy data is calculated as in a bottom-up
approach and then used to distribute the bulk emissions given on national level.
Large parts of the data that is collected for traffic monitoring could be used to
improve the representation of these emissions in chemistry transport model systems
in the future. This holds not only for cars and trucks but also for ship traffic and
aviation. However, besides the availability of that data for research purposes, new
data science technologies need to be developed for handling such large amounts of
unstructured data.
Questions and Answers
QUESTIONER: Greg Yarwood
QUESTION: Are gridded inventories a limitation for industrial point source emissions in particular?
ANSWER: Yes, they are. The emissions will be taken as an average over the entire
grid cell, which means that they are drastically diluted if the grid is rather coarse.
This will have consequences for the dispersion of the emissions and the chemical
transformations. In addition, if the location of a point source within the grid is not
exactly given, it cannot be attributed to the correct grid cell on a finer grid. This might
cause significant errors in the location of a source when the data is re-gridded.
References
1. A. Aulinger, V. Matthias, M. Quante, An approach to temporally disaggregate Benzo (a) pyrene
emissions and their application to a 3D Eulerian atmospheric chemistry transport model. Water
Air Soil Pollut. 216(1–4), 643–655 (2011)
2. A. Backes, A. Aulinger, J. Bieser, V. Matthias, M. Quante, Ammonia emissions in Europe,
Part I: development of a dynamical ammonia emission inventory. Atmos. Environ. 131, 55–66
(2016)
3. J. Bieser, A. Aulinger, V. Matthias, M. Quante, H.A.C. Denier van der Gon, Vertical emission
profiles for Europe based on plume rise calculations. Environ. Pollut. 159(10), 2935–2946
(2011)
4. H.A.C. Denier van der Gon et al., Particulate emissions from residential wood combustion in
Europe—revised estimates and an evaluation. Atmos. Chem. Phys. 15, 6503–6519 (2015)
5. G. Frost et al., New Directions: GEIA’s 2020 vision for better air emissions information. Atmos.
Environ. 81, 710–712 (2013)
6. C. Hendriks et al., Ammonia emission time profiles based on manure transport data improve
ammonia modelling across north western Europe. Atmos. Environ. 131, 83–96 (2016)