mathematical formulation and calculation errors (the mathematical formulation
used is generally highly simplified, and assumes, for example, that the relation
between emission and activity is linear).
The uncertainties on the input data are mainly due to the lack of information on
the different parameters used to estimate the emissions of an inventory. These
emissions result mainly in the combination of input data like activity values and
emission factors. The uncertainty on the values of input data can be due to simplification hypotheses, for example in the case of a large number of similar sources,
supposed to have an average behaviour. They can be divided into four categories:
extrapolation errors (when lacking emission factors or specific data related to some
emissions sources, the corresponding values are extrapolated from other available
data); measurement errors (they can lead to inaccurate activity data or emission
factors); errors of copy (errors made during the reporting of values); errors in case
of unknown evolution (future emission scenarios are associated to probability
factors which can be seen as uncertainty or indefiniteness).
It is obvious that some relations exist between these different types of uncertainties and it is sometimes difficult to distinguish them.
The uncertainties of an emission inventory can be evaluated in a qualitative or
quantitative way. The qualitative evaluation is mainly performed by experts (IPCC
2000; EPA 1996), while the quantitative one is based on error propagation methods
and Monte Carlo methods. There is also a semi-quantitative method that can be
used to evaluate the uncertainties, which consists in the rating of the data quality.
Some numerical or alphabetical scores are attributed by experts to emission factors
and activity data to describe the uncertainties of these data. There are two main
classifications for these methods (see: EPA 1996): (1) the DARS method (Data
Attribute Rating System) that attributes to each dataset a score ranging between 1
and 10 (the most accurate); (2) the AP-42 emission factor rate system that is the
main reference in the USA but only for emission factors evaluation. The scores
range from A (most accurate) to E. Both methods attribute scores, which are general
indications on the reliability and the robustness of the data.
2.3.3 State
In the DPSIR approach, STATE is defined as the “environmental conditions of a
natural system”. In the case of air quality, it describes the ambient concentrations of
targeted pollutant (in specific applications also pollutant’s deposition). AQ state can
be described as gridded concentrations/depositions over the studied area, or as local
concentrations/depositions on receptor sites, depending on the objectives of the
IAM and on the available tools. In addition to the spatial dimension, the AQ state
also has a temporal dimension, considering that a pollutant can be monitored/
modelled with a temporal resolution of hours/days, etc. Once concentrations/
depositions are evaluated in space and time with the different available approaches,
AQ indicators are usually calculated, such as aggregation of the initial AQ data to
2 A Framework for Integrated Assessment Modelling
25
used is generally highly simplified, and assumes, for example, that the relation
between emission and activity is linear).
The uncertainties on the input data are mainly due to the lack of information on
the different parameters used to estimate the emissions of an inventory. These
emissions result mainly in the combination of input data like activity values and
emission factors. The uncertainty on the values of input data can be due to simplification hypotheses, for example in the case of a large number of similar sources,
supposed to have an average behaviour. They can be divided into four categories:
extrapolation errors (when lacking emission factors or specific data related to some
emissions sources, the corresponding values are extrapolated from other available
data); measurement errors (they can lead to inaccurate activity data or emission
factors); errors of copy (errors made during the reporting of values); errors in case
of unknown evolution (future emission scenarios are associated to probability
factors which can be seen as uncertainty or indefiniteness).
It is obvious that some relations exist between these different types of uncertainties and it is sometimes difficult to distinguish them.
The uncertainties of an emission inventory can be evaluated in a qualitative or
quantitative way. The qualitative evaluation is mainly performed by experts (IPCC
2000; EPA 1996), while the quantitative one is based on error propagation methods
and Monte Carlo methods. There is also a semi-quantitative method that can be
used to evaluate the uncertainties, which consists in the rating of the data quality.
Some numerical or alphabetical scores are attributed by experts to emission factors
and activity data to describe the uncertainties of these data. There are two main
classifications for these methods (see: EPA 1996): (1) the DARS method (Data
Attribute Rating System) that attributes to each dataset a score ranging between 1
and 10 (the most accurate); (2) the AP-42 emission factor rate system that is the
main reference in the USA but only for emission factors evaluation. The scores
range from A (most accurate) to E. Both methods attribute scores, which are general
indications on the reliability and the robustness of the data.
2.3.3 State
In the DPSIR approach, STATE is defined as the “environmental conditions of a
natural system”. In the case of air quality, it describes the ambient concentrations of
targeted pollutant (in specific applications also pollutant’s deposition). AQ state can
be described as gridded concentrations/depositions over the studied area, or as local
concentrations/depositions on receptor sites, depending on the objectives of the
IAM and on the available tools. In addition to the spatial dimension, the AQ state
also has a temporal dimension, considering that a pollutant can be monitored/
modelled with a temporal resolution of hours/days, etc. Once concentrations/
depositions are evaluated in space and time with the different available approaches,
AQ indicators are usually calculated, such as aggregation of the initial AQ data to
2 A Framework for Integrated Assessment Modelling
25