for each pollutant is simply added. On the one hand, this resulting SEI can directly
be used by an air pollution model. But, on the other hand, some information
concerning the distribution of source categories as well as the accuracy of the
source locations may be lost.
Synergies among scales
In theory, it is possible to use the spatial characteristics and locations of the
emission sources in order to project the data on any kind of grid domain.
In practice, it is very difficult to manage, or even to find, a detailed and complete
description of all the sources over large areas (scale of a continent or large countries). It follows that the first output of the large scale SEIs is based more on area
than point and line sources in comparison to small scale SEIs. The sources of large
scale SEIs are calculated using more top-down than bottom-up approaches.
Consequently, the locations of the sources in large scale SEIs are not accurate and
the projections of such SEIs on fine resolution grid lead to an overestimation of the
sources dilution. It becomes then necessary to “re-concentrate” the sources using
different earth surface characteristics defined at smaller scale. For example, the
emission can be redistributed according to the land use (emissions release over the
ground only and no emissions over water surfaces), the density of population (more
emissions over dense population areas), the road network (road transport emissions
only in cells crossed by roads), etc. Apart from simple redistribution proportional to
these supplementary characteristics, which is typically done using linear regression,
also more advanced approaches can be applied, e.g. using geostatistical methods,
like kriging (Singh et al. 2011).
When using AQ models, it often happens that an accurate detailed emission
inventory is available only on a part of the grid domain on which the study has to be
performed. It is therefore necessary to combine data provided by different scale
SEIs. In this situation, the best procedure is, first, to project all the SEI outputs on
the same grid (using “re-concentration” when necessary) and then, to keep on each
cell the data provided by the most accurate SEI. Even if there is a risk of inconsistency between the different SEIs because they have been produced using different
methodologies (top-down or bottom-up for example) this procedure is a good
compromise between consistency and accuracy.
Uncertainty
The uncertainties associated to emissions inventories (Werner 2009) are directly
related to accuracy. This accuracy can be split into two main contributions:
– Structural inaccuracy, which is due to the structure of the inventory;
– Inaccuracy on the input data (i.e. activity data, emission factors).
The structural accuracy estimates the inventory structure ability to calculate as
precisely as possible the real emissions. This uncertainty can be split into three
contributions: inaccuracy due to aggregations (the emissions are calculated on
defined spatial and time scales that may lack the information on the emission
processes or on the variability of the real emissions); incompleteness (an emission
inventory may be inaccurate due to the absence of emission sources); inaccurate
24
N. Blond et al.
be used by an air pollution model. But, on the other hand, some information
concerning the distribution of source categories as well as the accuracy of the
source locations may be lost.
Synergies among scales
In theory, it is possible to use the spatial characteristics and locations of the
emission sources in order to project the data on any kind of grid domain.
In practice, it is very difficult to manage, or even to find, a detailed and complete
description of all the sources over large areas (scale of a continent or large countries). It follows that the first output of the large scale SEIs is based more on area
than point and line sources in comparison to small scale SEIs. The sources of large
scale SEIs are calculated using more top-down than bottom-up approaches.
Consequently, the locations of the sources in large scale SEIs are not accurate and
the projections of such SEIs on fine resolution grid lead to an overestimation of the
sources dilution. It becomes then necessary to “re-concentrate” the sources using
different earth surface characteristics defined at smaller scale. For example, the
emission can be redistributed according to the land use (emissions release over the
ground only and no emissions over water surfaces), the density of population (more
emissions over dense population areas), the road network (road transport emissions
only in cells crossed by roads), etc. Apart from simple redistribution proportional to
these supplementary characteristics, which is typically done using linear regression,
also more advanced approaches can be applied, e.g. using geostatistical methods,
like kriging (Singh et al. 2011).
When using AQ models, it often happens that an accurate detailed emission
inventory is available only on a part of the grid domain on which the study has to be
performed. It is therefore necessary to combine data provided by different scale
SEIs. In this situation, the best procedure is, first, to project all the SEI outputs on
the same grid (using “re-concentration” when necessary) and then, to keep on each
cell the data provided by the most accurate SEI. Even if there is a risk of inconsistency between the different SEIs because they have been produced using different
methodologies (top-down or bottom-up for example) this procedure is a good
compromise between consistency and accuracy.
Uncertainty
The uncertainties associated to emissions inventories (Werner 2009) are directly
related to accuracy. This accuracy can be split into two main contributions:
– Structural inaccuracy, which is due to the structure of the inventory;
– Inaccuracy on the input data (i.e. activity data, emission factors).
The structural accuracy estimates the inventory structure ability to calculate as
precisely as possible the real emissions. This uncertainty can be split into three
contributions: inaccuracy due to aggregations (the emissions are calculated on
defined spatial and time scales that may lack the information on the emission
processes or on the variability of the real emissions); incompleteness (an emission
inventory may be inaccurate due to the absence of emission sources); inaccurate
24
N. Blond et al.