Variance-based uncertainty estimation methods are the most commonly used
among the global uncertainty assessment approaches. However, local uncertainty
analysis methods (sensitivity methods, OaT) are also significantly represented in the
responses, particularly in the case of RPs (Fig. 3.21).
The following Fig. 3.22 provides information on the AQ modelling elements for
which uncertainty estimation was specifically carried out. As expected, model
formulation was not one of the priority aspects examined in the case of AQPs; it
was however considered in a significant number of RPs. Within AQPs, uncertainties were mostly analysed for meteorology, emissions and boundary conditions.
Regarding RPs, it is interesting to note that uncertainties related to boundary
conditions received less attention. For both AQPs and RPs, emissions related
uncertainties are identified to significantly contribute to the total AQ modelling
uncertainties.
In terms of quality control of model results for planning applications, most of the
studies assumed that the AQ model is adequate when it behaves correctly for
assessment applications (82 %) while in the 18 % of the cases the reliability of the
model is based on model intercomparison and ensemble approaches.
It is interesting to note, that no reference technique is adopted so far to check the
quality of the models used to quantify the impact of emission reduction scenarios in
AQPs.
0%
10%
20%
30%
40%
50%
60%
70%
global (16 answers)
local (8 answers)
Elementary effects
Variance-based methods
Factor mapping and
Metamodelling
OaT
Other
Fig. 3.21 Local and Global analysis methods
56
C. Belis et al.
among the global uncertainty assessment approaches. However, local uncertainty
analysis methods (sensitivity methods, OaT) are also significantly represented in the
responses, particularly in the case of RPs (Fig. 3.21).
The following Fig. 3.22 provides information on the AQ modelling elements for
which uncertainty estimation was specifically carried out. As expected, model
formulation was not one of the priority aspects examined in the case of AQPs; it
was however considered in a significant number of RPs. Within AQPs, uncertainties were mostly analysed for meteorology, emissions and boundary conditions.
Regarding RPs, it is interesting to note that uncertainties related to boundary
conditions received less attention. For both AQPs and RPs, emissions related
uncertainties are identified to significantly contribute to the total AQ modelling
uncertainties.
In terms of quality control of model results for planning applications, most of the
studies assumed that the AQ model is adequate when it behaves correctly for
assessment applications (82 %) while in the 18 % of the cases the reliability of the
model is based on model intercomparison and ensemble approaches.
It is interesting to note, that no reference technique is adopted so far to check the
quality of the models used to quantify the impact of emission reduction scenarios in
AQPs.
0%
10%
20%
30%
40%
50%
60%
70%
global (16 answers)
local (8 answers)
Elementary effects
Variance-based methods
Factor mapping and
Metamodelling
OaT
Other
Fig. 3.21 Local and Global analysis methods
56
C. Belis et al.