provide the number of PM10 daily exceedances on a cell, the annual mean of NO 2
aggregated over a domain, etc.
In the following, we focus on concentrations only as a state indicator, but the
content would be basically the same for deposition.
It can be noticed that sometimes the PRESSURES block may be seen as acting
directly on the IMPACT block, if simplifying the scheme and assuming a direct
relationship between emissions and effects, with no evaluation of the STATE
conditions.
Input
In IAM, the AQ state is described as the joint responses to pressures, constituting
driving forces on which society can act at the spatial scale of the study, and external
conditions, such as meteorology and pollution coming from the larger scale.
Depending on the method chosen to perform an IAM, these forcing can be treated
explicitly (this is the case when using a numerical model including meteorological
and boundary conditions data), or act implicitly on other data. In certain cases,
when AQ models are used for state evaluation, AQ observations can also be
considered as input data, when these are used for model validation, data assimilation, or as initial or boundary conditions for models.
Functionality
The different methods that can be used to evaluate the AQ state, i.e. pollutant
concentrations, are summarized in Fig. 2.3 and will be described in the following
paragraphs. In parallel to the method used to define pollutant concentrations,
methods are also often defined to estimate the contribution of the different emissions to the concentration (source apportionment).
The STATE block three-level classification is as follows:
LEVEL 1: The simplest way to characterize AQ state is to use measurements taken
routinely, or during a measurement campaign (together with a geostatistic interpolation method if the aim is to obtain a map of concentrations over a studied area).
Some studies also use the strong and highly uncertain hypothesis that local concentrations are proportional to local emissions to estimate source contributions.
LEVEL 2: It is based on a characterization of the AQ state using one model,
adapted to the studied spatial scale. This model should be validated over the studied
area and should use emissions input data also adapted to this scale. Concentrations
used as boundary conditions of the model can be either extrapolated from measurements or extracted from a larger scale model. Observed concentrations can be
used to correct the model (data assimilation) at least for the reference year, often
used as a starting point for IAM applications. If the IAM is a prospective study,
aiming to evaluate future policy scenarios, a method could be used to correct the
model. A possibility in this context is to estimate, through data assimilation
(if observations are available), map of increments/bias (related to the base case) to
be used to “correct” the concentrations of future alternative emission reduction
scenarios. Another input to the model are meteorological data, which can be
obtained from observations or from a meteorological model. Spatial and temporal
26
N. Blond et al.
aggregated over a domain, etc.
In the following, we focus on concentrations only as a state indicator, but the
content would be basically the same for deposition.
It can be noticed that sometimes the PRESSURES block may be seen as acting
directly on the IMPACT block, if simplifying the scheme and assuming a direct
relationship between emissions and effects, with no evaluation of the STATE
conditions.
Input
In IAM, the AQ state is described as the joint responses to pressures, constituting
driving forces on which society can act at the spatial scale of the study, and external
conditions, such as meteorology and pollution coming from the larger scale.
Depending on the method chosen to perform an IAM, these forcing can be treated
explicitly (this is the case when using a numerical model including meteorological
and boundary conditions data), or act implicitly on other data. In certain cases,
when AQ models are used for state evaluation, AQ observations can also be
considered as input data, when these are used for model validation, data assimilation, or as initial or boundary conditions for models.
Functionality
The different methods that can be used to evaluate the AQ state, i.e. pollutant
concentrations, are summarized in Fig. 2.3 and will be described in the following
paragraphs. In parallel to the method used to define pollutant concentrations,
methods are also often defined to estimate the contribution of the different emissions to the concentration (source apportionment).
The STATE block three-level classification is as follows:
LEVEL 1: The simplest way to characterize AQ state is to use measurements taken
routinely, or during a measurement campaign (together with a geostatistic interpolation method if the aim is to obtain a map of concentrations over a studied area).
Some studies also use the strong and highly uncertain hypothesis that local concentrations are proportional to local emissions to estimate source contributions.
LEVEL 2: It is based on a characterization of the AQ state using one model,
adapted to the studied spatial scale. This model should be validated over the studied
area and should use emissions input data also adapted to this scale. Concentrations
used as boundary conditions of the model can be either extrapolated from measurements or extracted from a larger scale model. Observed concentrations can be
used to correct the model (data assimilation) at least for the reference year, often
used as a starting point for IAM applications. If the IAM is a prospective study,
aiming to evaluate future policy scenarios, a method could be used to correct the
model. A possibility in this context is to estimate, through data assimilation
(if observations are available), map of increments/bias (related to the base case) to
be used to “correct” the concentrations of future alternative emission reduction
scenarios. Another input to the model are meteorological data, which can be
obtained from observations or from a meteorological model. Spatial and temporal
26
N. Blond et al.