plans for zones where air quality does not comply with the AQD limit values and to
assess possible emission reduction measures to reduce concentration levels. These
emissions reductions then need to be distributed in an optimal and cost effective
way through the territory. Obligations resulting from other EU directives (e.g. the
National Emission Ceiling Directive) and targeting more specific sectors of activity
(e.g. transport, industry, energy, agriculture) must also be considered when
designing and assessing local and regional air quality plans (Syri et al. 2002; Coll
et al. 2009). In order to cope with these various elements MS have in the last decade
developed and applied a wide range of different modelling methods to assess the
effects of local and regional emission abatement policy options on air quality and
human health (e.g. Cuvelier et al. 2007; Thunis et al. 2007; De Ridder et al. 2008;
Carnevale et al. 2011; Lefebvre et al. 2011; Borrego et al. 2012; MediavillaSahagun and ApSimon 2013).
4.2 Available Tools
The following Table 4.1 summarizes the integrated assessment modelling tools
most used in European countries. They can be classified in different ways according
to the blocks of the DPSIR framework they investigate deeper, and are based on
data collected from various public and specific sources.
At the EU level, the state-of-the-art regarding decision-making tools is GAINS
(Greenhouse Gas and Air Pollution Interactions and Synergies), developed at the
International Institute for Applied Systems Analysis, Laxenburg, Austria, by
Amann et al. (2011). The GAINS model considers the co-benefits of simultaneous
reduction of air pollution and greenhouse gas emissions. It has been widely used in
international negotiations (as in the 2012 revision of the Gothenburg Protocol) and
is currently applied to support the EU air policy review. Some national systems
have been developed, starting from the GAINS methodology at EU level. Two
well-known implementations are RAINS/GAINS-Italy (D’Elia et al. 2009) and
RAINS/GAINS-Netherlands (Van Jaarsveld 2004). Another national level implementation is the FRES model (Karvosenoja et al. 2007), developed at the Finnish
Environment Institute (SYKE) to assess, in a consistent framework, the emissions
of air pollutants, their processes and dispersion in the atmosphere, effects on the
environment and potential for their control and related costs. An additional
important initiative at national level is the PAREST project, in which emission
reference scenarios until 2020 were constructed for PM and for aerosol precursors,
for Germany and Europe (Builtjes et al. 2010). The ROSE model (Juda-Rezler
2004) has been developed at Warsaw University of Technology (WUT) for Poland.
ROSE is an effect-based IAM comprised of a suite of models: an Eulerian grid air
pollution model, statistical models for assessing environment sensitivity to the
Sulphur species and an optimization model with modern evolutionary computation
techniques.
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C. Belis et al.
assess possible emission reduction measures to reduce concentration levels. These
emissions reductions then need to be distributed in an optimal and cost effective
way through the territory. Obligations resulting from other EU directives (e.g. the
National Emission Ceiling Directive) and targeting more specific sectors of activity
(e.g. transport, industry, energy, agriculture) must also be considered when
designing and assessing local and regional air quality plans (Syri et al. 2002; Coll
et al. 2009). In order to cope with these various elements MS have in the last decade
developed and applied a wide range of different modelling methods to assess the
effects of local and regional emission abatement policy options on air quality and
human health (e.g. Cuvelier et al. 2007; Thunis et al. 2007; De Ridder et al. 2008;
Carnevale et al. 2011; Lefebvre et al. 2011; Borrego et al. 2012; MediavillaSahagun and ApSimon 2013).
4.2 Available Tools
The following Table 4.1 summarizes the integrated assessment modelling tools
most used in European countries. They can be classified in different ways according
to the blocks of the DPSIR framework they investigate deeper, and are based on
data collected from various public and specific sources.
At the EU level, the state-of-the-art regarding decision-making tools is GAINS
(Greenhouse Gas and Air Pollution Interactions and Synergies), developed at the
International Institute for Applied Systems Analysis, Laxenburg, Austria, by
Amann et al. (2011). The GAINS model considers the co-benefits of simultaneous
reduction of air pollution and greenhouse gas emissions. It has been widely used in
international negotiations (as in the 2012 revision of the Gothenburg Protocol) and
is currently applied to support the EU air policy review. Some national systems
have been developed, starting from the GAINS methodology at EU level. Two
well-known implementations are RAINS/GAINS-Italy (D’Elia et al. 2009) and
RAINS/GAINS-Netherlands (Van Jaarsveld 2004). Another national level implementation is the FRES model (Karvosenoja et al. 2007), developed at the Finnish
Environment Institute (SYKE) to assess, in a consistent framework, the emissions
of air pollutants, their processes and dispersion in the atmosphere, effects on the
environment and potential for their control and related costs. An additional
important initiative at national level is the PAREST project, in which emission
reference scenarios until 2020 were constructed for PM and for aerosol precursors,
for Germany and Europe (Builtjes et al. 2010). The ROSE model (Juda-Rezler
2004) has been developed at Warsaw University of Technology (WUT) for Poland.
ROSE is an effect-based IAM comprised of a suite of models: an Eulerian grid air
pollution model, statistical models for assessing environment sensitivity to the
Sulphur species and an optimization model with modern evolutionary computation
techniques.
70
C. Belis et al.