Spatial and Temporal Assessment
To provide appropriate information to the PRESSURE block, it is important to
know not only the quantity but also the physical location and temporal variation of
emission releases. Therefore, in order to be able to resolve the emissions in space
and time, the activities (i.e. the DRIVERS block) must be allocated to certain grid
and temporal patterns. The spatial aspect is particularly important in city or local
level assessments for local emissions may cause considerable impacts on human
populations.
The spatial allocation of point sources simply implies the association of the
geographical location and height of the stack with the corresponding grid cell and
vertical layer of the atmospheric model, respectively. Area emissions, by contrast,
must be spatially allocated using again weighting factors, i.e. surrogates. The choice
of surrogate parameters for different source sectors depends on the availability of
data that would represent the emission distribution in a given sector at the desired
spatial resolution. The temporal variation for different sectors can be based on
internationally, nationally or locally defined default variations or local data (e.g.
questionnaires or observed data). The following provides a proposal for three levels
of complexity in spatial and temporal assessment for different source sectors.
Road traffic network is typically available for spatial allocation. To distinguish
between more or less busy roads and different driving conditions, availability of
data may vary. Non-exhaust emissions vary highly in space and time depending
also on other factors than driving amounts and conditions or vehicle technology
(e.g. road surface type and condition, seasonal and hourly climate conditions).
These factors might be difficult to take into account with a reasonable accuracy
without specific road dust models.
A three-level classification might be:
1. Spatial assessment based on road network data with coarse traffic allocation
scheme (e.g. using road type classification to distinguish more and less trafficked
roads). Temporal variation based on general default variations.
2. Spatial assessment based on road network data with more realistic representation
of traffic flows (e.g. actual traffic counts for each road segment). Temporal
variation based on nationally or locally defined default variations.
3. Spatial assessment based on road network data with representation of district
traffic flows for vehicle categories and/or driving conditions (e.g. based on a city
level traffic model). Traffic demand based modelling approaches are desirable to
assess spatial changes in future projections. Temporal variation should be based
on locally observed data.
Data availability for spatial allocation of off-road traffic and machinery is
variable. For some forms, the locations of activities are relatively well known, e.g.
for sea vessels, trains and airplanes. For many forms of machinery, in contrast, the
basis for spatial allocation can be much more complex.
2 A Framework for Integrated Assessment Modelling
17
To provide appropriate information to the PRESSURE block, it is important to
know not only the quantity but also the physical location and temporal variation of
emission releases. Therefore, in order to be able to resolve the emissions in space
and time, the activities (i.e. the DRIVERS block) must be allocated to certain grid
and temporal patterns. The spatial aspect is particularly important in city or local
level assessments for local emissions may cause considerable impacts on human
populations.
The spatial allocation of point sources simply implies the association of the
geographical location and height of the stack with the corresponding grid cell and
vertical layer of the atmospheric model, respectively. Area emissions, by contrast,
must be spatially allocated using again weighting factors, i.e. surrogates. The choice
of surrogate parameters for different source sectors depends on the availability of
data that would represent the emission distribution in a given sector at the desired
spatial resolution. The temporal variation for different sectors can be based on
internationally, nationally or locally defined default variations or local data (e.g.
questionnaires or observed data). The following provides a proposal for three levels
of complexity in spatial and temporal assessment for different source sectors.
Road traffic network is typically available for spatial allocation. To distinguish
between more or less busy roads and different driving conditions, availability of
data may vary. Non-exhaust emissions vary highly in space and time depending
also on other factors than driving amounts and conditions or vehicle technology
(e.g. road surface type and condition, seasonal and hourly climate conditions).
These factors might be difficult to take into account with a reasonable accuracy
without specific road dust models.
A three-level classification might be:
1. Spatial assessment based on road network data with coarse traffic allocation
scheme (e.g. using road type classification to distinguish more and less trafficked
roads). Temporal variation based on general default variations.
2. Spatial assessment based on road network data with more realistic representation
of traffic flows (e.g. actual traffic counts for each road segment). Temporal
variation based on nationally or locally defined default variations.
3. Spatial assessment based on road network data with representation of district
traffic flows for vehicle categories and/or driving conditions (e.g. based on a city
level traffic model). Traffic demand based modelling approaches are desirable to
assess spatial changes in future projections. Temporal variation should be based
on locally observed data.
Data availability for spatial allocation of off-road traffic and machinery is
variable. For some forms, the locations of activities are relatively well known, e.g.
for sea vessels, trains and airplanes. For many forms of machinery, in contrast, the
basis for spatial allocation can be much more complex.
2 A Framework for Integrated Assessment Modelling
17