relative contributions of the major components of fine PM, especially organic
carbon and metals/dust. In particular, substantial uncertainties in gas-phase and
aqueous-phase chemistry mechanisms remain, including key inorganic reactions,
aromatic and biogenic reactions and aqueous-phase chemistry. Future research
might also include stratospheric chemistry as the spatial domain for air quality
models increases when climate applications are considered. The exchange processes with the surface should be further improved considering for example surface
bidirectional exchange (ammonia, mercury or polyaromatic hydrocarbons) or the
interaction with vegetation, and models have to better couple physics (meteorology)
and chemistry processes. This is not only relevant for connecting air quality and
climate change modelling, but it is also important when moving to smaller scales
(<1 km) where the meteorological models start to resolve turbulent eddies.
Measurements contain valuable information, which can be used as complementary
input to modelling results. It is striking that in 40 % of the APPRAISAL reported
studies, measurement data were not used at all, not even for model evaluation. This is
clearly a point where air quality assessment reports and more specifically air quality
plans could be improved. Even if affected by an intrinsic imprecision, monitoring data
have the clear advantage that field concentration levels are evaluated with much more
accuracy than model results. The main question, which arises in IA applications, is:
“how these measurement data can be used most appropriately?” Most of the model
results in IA studies are dealing with future projections under certain policy options.
By definition, no measurement data are available for this kind of future estimates.
A key approach to this problem is to use measurement data in combination with
model results at least for the reference case of a recent year. This reference case is
most often used as a starting point in the IA exercise. This procedure is referred to as
“model calibration” or “data assimilation”.
Discussion arises when this combined information has to be used for the simulation of policy scenarios. The use of data assimilation corrections (or calibration
factors) as “relevant” information for scenario runs is generally considered appropriate. However, specific and well-defined methodologies to do so are not at hand.
One possible approach is to assess the simulated concentration changes of a set of
specific policy options in relation to the reference case/year. The resulting concentration changes (so called “deltas”) can then be applied on top of the calibrated
or data assimilated concentration fields of the reference year (see for example
Kiesewetter et al. 2013). However, more research is required to pin down appropriate methodologies to combine reference year measurements with modelling
results for future policy scenarios.
Model evaluation is inherent to all these developments and also to common
modelling practice. There are already several reported and applied procedures to
evaluate models (including model intercomparison exercises), but with different
purposes and focusing on particular types of models and/or applications. There is
enough information to provide a standardized evaluation protocol organized
according to different modelling needs and characteristics. This protocol would be
particularly important for stakeholders who need to understand model results in
76
C. Belis et al.
carbon and metals/dust. In particular, substantial uncertainties in gas-phase and
aqueous-phase chemistry mechanisms remain, including key inorganic reactions,
aromatic and biogenic reactions and aqueous-phase chemistry. Future research
might also include stratospheric chemistry as the spatial domain for air quality
models increases when climate applications are considered. The exchange processes with the surface should be further improved considering for example surface
bidirectional exchange (ammonia, mercury or polyaromatic hydrocarbons) or the
interaction with vegetation, and models have to better couple physics (meteorology)
and chemistry processes. This is not only relevant for connecting air quality and
climate change modelling, but it is also important when moving to smaller scales
(<1 km) where the meteorological models start to resolve turbulent eddies.
Measurements contain valuable information, which can be used as complementary
input to modelling results. It is striking that in 40 % of the APPRAISAL reported
studies, measurement data were not used at all, not even for model evaluation. This is
clearly a point where air quality assessment reports and more specifically air quality
plans could be improved. Even if affected by an intrinsic imprecision, monitoring data
have the clear advantage that field concentration levels are evaluated with much more
accuracy than model results. The main question, which arises in IA applications, is:
“how these measurement data can be used most appropriately?” Most of the model
results in IA studies are dealing with future projections under certain policy options.
By definition, no measurement data are available for this kind of future estimates.
A key approach to this problem is to use measurement data in combination with
model results at least for the reference case of a recent year. This reference case is
most often used as a starting point in the IA exercise. This procedure is referred to as
“model calibration” or “data assimilation”.
Discussion arises when this combined information has to be used for the simulation of policy scenarios. The use of data assimilation corrections (or calibration
factors) as “relevant” information for scenario runs is generally considered appropriate. However, specific and well-defined methodologies to do so are not at hand.
One possible approach is to assess the simulated concentration changes of a set of
specific policy options in relation to the reference case/year. The resulting concentration changes (so called “deltas”) can then be applied on top of the calibrated
or data assimilated concentration fields of the reference year (see for example
Kiesewetter et al. 2013). However, more research is required to pin down appropriate methodologies to combine reference year measurements with modelling
results for future policy scenarios.
Model evaluation is inherent to all these developments and also to common
modelling practice. There are already several reported and applied procedures to
evaluate models (including model intercomparison exercises), but with different
purposes and focusing on particular types of models and/or applications. There is
enough information to provide a standardized evaluation protocol organized
according to different modelling needs and characteristics. This protocol would be
particularly important for stakeholders who need to understand model results in
76
C. Belis et al.