7.1 Exploring the influence of emission patterns and atmospheric modelling
7.1.4 Spatial modelling resolution
Table 7.5 summarises the objectives and related approaches to analyse the influence of spatial modelling resolution, i.e. the resolution of the grid cells for which results are estimated.
Table 7.5: Objectives and research approach to assess the influence of spatial resolution on health damage costs
Objec-tives
1) What is the influence on health damage costs of using a different spatial modelling resolution for a fixed spatial perimeter?
2) What is the influence on health damage costs of using a so-called plume-in-grid modelling approach, i.e. a local Gaussian modelling embedded into the less resolved Eulerian modelling?
Approach for objective 1)
What Assessing the influence of spatial modelling resolution on the health damage costs related to PM2.5, PM10, and O3
Where Île-de-France (IDF): high (~3 km x ~5 km), medium (~17 km x ~25 km) and low (~35 km x ~50 km) modelling resolution, cf. Table 6.4 and Figure 6.3
France (FR): medium (~17 km x ~25 km) and low (~35 km x ~50 km) modelling res-olution, cf. Table 6.4 and Figure 6.3
For Scenario S2, compared to S0 (baseline), cf. Table 6.2
Using Standard modelling framework as described in section 6.1, adapted by using the age group fractions, baseline rates and long-term exposure mortality impact func-tion of the European modelling domain at all domains and thus allowing for a con-sistent comparison
Approach for objective 2)
What Comparing health damage costs related to PM2.5, PM10, and O3 based on a plume-in-grid modelling approach with those based on the standard modelling approach Where Île-de-France (IDF), France (FR), cf. Figure 6.3
For Scenarios S1, S1PIG, S2 and S2PIG, compared to S0 (baseline), cf. Table 6.2 Using Standard modelling framework as described in section 6.1
7.1.4 Spatial modelling resolution
Table 7.5 summarises the objectives and related approaches to analyse the influence of spatial modelling resolution, i.e. the resolution of the grid cells for which results are estimated.
Table 7.5: Objectives and research approach to assess the influence of spatial resolution on health damage costs
Objec-tives
1) What is the influence on health damage costs of using a different spatial modelling resolution for a fixed spatial perimeter?
2) What is the influence on health damage costs of using a so-called plume-in-grid modelling approach, i.e. a local Gaussian modelling embedded into the less resolved Eulerian modelling?
Approach for objective 1)
What Assessing the influence of spatial modelling resolution on the health damage costs related to PM2.5, PM10, and O3
Where Île-de-France (IDF): high (~3 km x ~5 km), medium (~17 km x ~25 km) and low (~35 km x ~50 km) modelling resolution, cf. Table 6.4 and Figure 6.3
France (FR): medium (~17 km x ~25 km) and low (~35 km x ~50 km) modelling res-olution, cf. Table 6.4 and Figure 6.3
For Scenario S2, compared to S0 (baseline), cf. Table 6.2
Using Standard modelling framework as described in section 6.1, adapted by using the age group fractions, baseline rates and long-term exposure mortality impact func-tion of the European modelling domain at all domains and thus allowing for a con-sistent comparison
Approach for objective 2)
What Comparing health damage costs related to PM2.5, PM10, and O3 based on a plume-in-grid modelling approach with those based on the standard modelling approach Where Île-de-France (IDF), France (FR), cf. Figure 6.3
For Scenarios S1, S1PIG, S2 and S2PIG, compared to S0 (baseline), cf. Table 6.2 Using Standard modelling framework as described in section 6.1
Figure 7.6 shows the influence of modelling grid resolution on health damage costs, all other elements being equal. For the modelling domain France, using a higher spatial resolution results in a 9% decrease in health damage costs. For the Île-de-France domain, the direction of change depends on the resolution: moving from a low to medium spatial resolution results in a 2% increase, whereas moving to a high modelling resolution results in a 3% decrease in health damage costs. Using a nesting approach at European level would thus slightly decrease the health damage costs compared to the standard approach without nesting.
Figure 7.6: Annual health damage costs related to emission scenario S2 at different modelling domains and assessed using different spatial modelling resolutions
Regarding the second objective, Figure 7.7 shows the influence of the plume-in-grid (PIG) modelling on the results.
In all analysed cases, the PIG modelling leads to a considerable increase in health damage costs, ranging from a factor of 1.75 (France domain) to a factor of 2.51 (Île-de-France do-main). At the local scale, the effect is more pronounced than at the national scale.
-1
O3 - respiratory hospital admission O3 - minor restricted activity day (MRAD) O3 - cardiovascular hospital admission (excl. stroke) O3 - all-cause natural mortality (acute) PM10 - chronic bronchitis case PM10 - bronchitis prevalence PM10 - asthma symptom day PM10 - all-cause infant mortality PM2.5 - work loss day (WLD) PM2.5 - respiratory hospital admission PM2.5 - net restricted activity day (netRAD) PM2.5 - cardiovascular hospital admission PM2.5 - all-cause natural mortality
x 0.91
x 1.02 x 0.97
Figure 7.7: Health damage costs caused by emission scenarios S1 and S2 using the standard or the plume-in-grid (PIG) modelling approach at the France and Île-de-France modelling domain
To better understand these results, dominated by PM2.5-related health damage costs, the PM2.5 concentration changes at the local (IDF) domain are displayed using either a me-dium or high spatial resolution (Figure 7.8) and for both types of atmospheric modelling (i.e. with or without PIG, Figure 7.9). The colour ranges have been harmonised in order to allow for direct comparisons within each figure, however not across the two figures.
For the IDF modelling domain and the area around the emission source (highlighted by a white dot), a medium spatial modelling resolution leads to a slightly higher level of PM2.5
concentrations than the higher modelling resolution (Figure 7.8), partly explaining the small difference in health damage costs observed above.
Using the plume-in-grid modelling approach leads to a different pattern of concentration changes around the emission source as well as to remarkably higher concentration increases compared to the standard modelling approach (Figure 7.9). This explains the increases in health damage costs for the PIG modelling observed above.
-2 0 2 4 6 8 10 12
S1 S1PIG S2 S2PIG S1 S1PIG S2 S2PIG
FR IDF
€2015/ MWh(el)
PM2.5 PM10 O3
x 1.95
x 2.51 x 1.9
x 1.75
Figure 7.7: Health damage costs caused by emission scenarios S1 and S2 using the standard or the plume-in-grid (PIG) modelling approach at the France and Île-de-France modelling domain
To better understand these results, dominated by PM2.5-related health damage costs, the PM2.5 concentration changes at the local (IDF) domain are displayed using either a me-dium or high spatial resolution (Figure 7.8) and for both types of atmospheric modelling (i.e. with or without PIG, Figure 7.9). The colour ranges have been harmonised in order to allow for direct comparisons within each figure, however not across the two figures.
For the IDF modelling domain and the area around the emission source (highlighted by a white dot), a medium spatial modelling resolution leads to a slightly higher level of PM2.5
concentrations than the higher modelling resolution (Figure 7.8), partly explaining the small difference in health damage costs observed above.
Using the plume-in-grid modelling approach leads to a different pattern of concentration changes around the emission source as well as to remarkably higher concentration increases compared to the standard modelling approach (Figure 7.9). This explains the increases in health damage costs for the PIG modelling observed above.
-2 0 2 4 6 8 10 12
S1 S1PIG S2 S2PIG S1 S1PIG S2 S2PIG
FR IDF
€2015/ MWh(el)
PM2.5 PM10 O3 x 1.95
x 2.51 x 1.9
x 1.75
Figure 7.8: Change in annual ambient mean PM2.5 concentration due to operation scenario S2 at the Île-de-France modelling domain using a medium (left) or high (right) spatial modelling resolution
Figure 7.9: Change in annual ambient mean PM2.5 concentration due to the variable operation scenario S2 using the standard (left) or plume-in-grid (PIG) modelling approach (right) at the Île-de-France modelling domain