model depends on the objectives of the IAM, on the nature of the pollutant (nonlinearities, chemical reactivity, etc.) and, above all, on the output necessary for the
subsequent IMPACT block (Carnevale et al. 2012b).
LEVEL 3: is based on a characterization of the AQ state using a downscaling models
chain, both in term of AQ and meteorological models, from large scale (Europe, for
example) to regional (country or regions) and local scale (city or street level). Using a
downscaling model chain allows to take into consideration interactions between the
various scales, such as transport of pollutant from large scale or interactions between
mesoscale wind flows and local dynamics. Nesting between models can be one-way
or two-ways, allowing local information to be passed to the larger scale model run.
Sub-grid modelling approaches can also be used to combine different scales. The
same model could be used for different parts of the chain, running the model itself at
different resolutions; or different models could be applied at different scales, as local
models (Gaussian models, for example) may use boundary conditions from a larger
scale Eulerian model. Data assimilation and meteorological data representativeness
issues are similar to those described for Level 2.
Output
The output of the STATE block may go from spatially and temporally-resolved
concentrations of the targeted pollutants, i.e. hourly/daily concentrations on receptor
sites or in each grid of the studied domain, to aggregated AQ indexes calculated
through spatial/temporal aggregations. Typical aggregated indexes are, for instance,
the number of PM10 daily exceedances, or annual mean of NO 2 in few or all domain
cells. Other variable describing the STATE could be related to pollution depositions
and climate change indicators (CO 2 emissions, global warming potential, etc.). In
general, the choice of the correct output is based, on one side, on those adopted by
the EEA, on the other, on their use for the calculation of IMPACT.
Synergies among scales
Using a downscaling model chain allows to take into consideration the interactions
between different scales, both in terms of pollutant transport from large scale and in
term of interactions between dynamic flows at various scale.
There is a close connection between climate change and air quality. Pollutant
concentrations in the air are strongly influenced by changes in the weather (e.g.,
heat waves or droughts). At the same time, concentrations of pollutants such as O 3
and particles impact the climate through direct and indirect forcing. The first
relation can be taken into account by using meteorological conditions from a climate model. However the relevance of using future climate meteorological conditions for short term studies (e.g., five years as in some cases in AQ plans) has not
been demonstrated yet, as future meteorological conditions may not vary enough in
5 to 10 years. On the other way, estimating the impact of local changes in O 3 and
particles on climate would require the use of meteorology-atmospheric chemistry
coupled models at the regional scale. In this case, the STATE would not be the
pollutant concentrations, but rather climate change related metrics, such as global
warming potential or radiative forcing.
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N. Blond et al.
subsequent IMPACT block (Carnevale et al. 2012b).
LEVEL 3: is based on a characterization of the AQ state using a downscaling models
chain, both in term of AQ and meteorological models, from large scale (Europe, for
example) to regional (country or regions) and local scale (city or street level). Using a
downscaling model chain allows to take into consideration interactions between the
various scales, such as transport of pollutant from large scale or interactions between
mesoscale wind flows and local dynamics. Nesting between models can be one-way
or two-ways, allowing local information to be passed to the larger scale model run.
Sub-grid modelling approaches can also be used to combine different scales. The
same model could be used for different parts of the chain, running the model itself at
different resolutions; or different models could be applied at different scales, as local
models (Gaussian models, for example) may use boundary conditions from a larger
scale Eulerian model. Data assimilation and meteorological data representativeness
issues are similar to those described for Level 2.
Output
The output of the STATE block may go from spatially and temporally-resolved
concentrations of the targeted pollutants, i.e. hourly/daily concentrations on receptor
sites or in each grid of the studied domain, to aggregated AQ indexes calculated
through spatial/temporal aggregations. Typical aggregated indexes are, for instance,
the number of PM10 daily exceedances, or annual mean of NO 2 in few or all domain
cells. Other variable describing the STATE could be related to pollution depositions
and climate change indicators (CO 2 emissions, global warming potential, etc.). In
general, the choice of the correct output is based, on one side, on those adopted by
the EEA, on the other, on their use for the calculation of IMPACT.
Synergies among scales
Using a downscaling model chain allows to take into consideration the interactions
between different scales, both in terms of pollutant transport from large scale and in
term of interactions between dynamic flows at various scale.
There is a close connection between climate change and air quality. Pollutant
concentrations in the air are strongly influenced by changes in the weather (e.g.,
heat waves or droughts). At the same time, concentrations of pollutants such as O 3
and particles impact the climate through direct and indirect forcing. The first
relation can be taken into account by using meteorological conditions from a climate model. However the relevance of using future climate meteorological conditions for short term studies (e.g., five years as in some cases in AQ plans) has not
been demonstrated yet, as future meteorological conditions may not vary enough in
5 to 10 years. On the other way, estimating the impact of local changes in O 3 and
particles on climate would require the use of meteorology-atmospheric chemistry
coupled models at the regional scale. In this case, the STATE would not be the
pollutant concentrations, but rather climate change related metrics, such as global
warming potential or radiative forcing.
28
N. Blond et al.