5.5 Conclusions
From the experiences of application of a comprehensive IAM system (RIAT+) to
the test cases of Brussels Capital Region and the Great Porto area a number of
conclusions can be drawn.
The list of options for abatement measures is restricted not only by what is
technically and economically feasible but possibly even more by political and social
acceptance. IAM tools should therefore be further extended to take into account the
implications of political and social acceptance at an early stage of the decision
process (see also Laniak et al. 2013).
Existing tools can be practically applied in an integrated assessment of air
quality not only to consider compliance to the concentration limits but also to
efficiently take into account internal and external costs (e.g. health impact) of
different available abatement options.
The biggest task when implementing such a comprehensive IAM is—as is also
the case in regular air quality modelling applications—to obtain high quality input
data on local emissions and the cost and effectiveness of possible abatement
measures. When such data is lacking, one can still rely on existing European
inventories and databases with data on abatement measures such as EMEP and
GAINS well keeping in mind the assumed validity of such data for the region of
interest and the implications for the results obtained using the IAM.
If an IAM system uses S/R relationships (artificial neural networks, linear
regression, …) to relate emission changes to air quality changes, such relationships
should be carefully tested to ensure that they not only correctly replicate the concentration values obtained through more complex modelling tools (e.g. CTMs) but
also capture the dynamics i.e. the concentration changes calculated by the model for
which they are a surrogate.
In the Brussels case, a lot of effort was put into defining and evaluating specific
measures while the impact on air quality of these measures is rather limited due to
the dimension of the area selected. A first screening step such as a simple scenario
to check the importance of the impacts should be done before using a complex
methodology, as the latter has limited added value in such cases.
In the Porto case, a list of available technologies from an existing database was
used and the main sectors were selected and identified. Nevertheless, a more local
list of measures needs to be decided and discussed with stakeholders and policy
makers. With the optimization approach, it was possible to quickly identify the
sectors and the entity of optimal investment costs to achieve a given air quality
objective and the corresponding benefits.
Acknowledgments This chapter is partly taken from APPRAISAL Deliverable D4.3 (downloadable from the project website http://www.appraisal-fp7.eu/site/documentation/deliverables.
html).
102
C. Carnevale et al.
From the experiences of application of a comprehensive IAM system (RIAT+) to
the test cases of Brussels Capital Region and the Great Porto area a number of
conclusions can be drawn.
The list of options for abatement measures is restricted not only by what is
technically and economically feasible but possibly even more by political and social
acceptance. IAM tools should therefore be further extended to take into account the
implications of political and social acceptance at an early stage of the decision
process (see also Laniak et al. 2013).
Existing tools can be practically applied in an integrated assessment of air
quality not only to consider compliance to the concentration limits but also to
efficiently take into account internal and external costs (e.g. health impact) of
different available abatement options.
The biggest task when implementing such a comprehensive IAM is—as is also
the case in regular air quality modelling applications—to obtain high quality input
data on local emissions and the cost and effectiveness of possible abatement
measures. When such data is lacking, one can still rely on existing European
inventories and databases with data on abatement measures such as EMEP and
GAINS well keeping in mind the assumed validity of such data for the region of
interest and the implications for the results obtained using the IAM.
If an IAM system uses S/R relationships (artificial neural networks, linear
regression, …) to relate emission changes to air quality changes, such relationships
should be carefully tested to ensure that they not only correctly replicate the concentration values obtained through more complex modelling tools (e.g. CTMs) but
also capture the dynamics i.e. the concentration changes calculated by the model for
which they are a surrogate.
In the Brussels case, a lot of effort was put into defining and evaluating specific
measures while the impact on air quality of these measures is rather limited due to
the dimension of the area selected. A first screening step such as a simple scenario
to check the importance of the impacts should be done before using a complex
methodology, as the latter has limited added value in such cases.
In the Porto case, a list of available technologies from an existing database was
used and the main sectors were selected and identified. Nevertheless, a more local
list of measures needs to be decided and discussed with stakeholders and policy
makers. With the optimization approach, it was possible to quickly identify the
sectors and the entity of optimal investment costs to achieve a given air quality
objective and the corresponding benefits.
Acknowledgments This chapter is partly taken from APPRAISAL Deliverable D4.3 (downloadable from the project website http://www.appraisal-fp7.eu/site/documentation/deliverables.
html).
102
C. Carnevale et al.