|Title||Uncertainties in emission inventories|
|Author(s)||Aardenne, J.A. van|
|Source||Wageningen University. Promotor(en): L. Hordijk; M.P.J. Pulles; C. Kroeze. - S.l. : S.n. - ISBN 9789058086419 - 143|
Environmental Systems Analysis
|Publication type||Dissertation, internally prepared|
|Keyword(s)||emissie - luchtverontreinigende stoffen - luchtverontreiniging - stikstofoxiden - milieueffect - inventarisaties - emission - air pollutants - air pollution - nitrogen oxides - environmental impact - inventories|
Emission inventories provide information about the amount of a pollutant that is emitted to the atmosphere as a result of a specific anthropogenic or natural process at a given time or place. Emission inventories can be used for either policy or scientific purposes. For policy purposes, emission inventories can be used to monitor the progress of environmental policy or to check compliance with conventions and protocols. For scientific purposes, emission inventories can be used as input into atmospheric dispersion models that are aimed at understanding the chemical and physical processes and the behaviour of air pollutants in the atmosphere. A strict separation between policy and scientific oriented emission inventories is not always possible. The usefulness of emission inventories for policy or science depends on the accuracy and the reliability of the inventories. There is uncertainty about an emission inventory when the accuracy and reliability of the emission estimates are not known. Proper use of emissions inventories requires an assessment of the uncertainties, including identification, qualification and quantification of the uncertainty. Although different methods for the assessment of uncertainty in emission inventories have been proposed, a systematic approach for identification, qualification and quantification of uncertainty does not exist. The objective of this thesis is to develop such a systematic approach for large-scale inventories. In order to meet this objective three research questions have been formulated:
(i) What are the potential sources of uncertainty in emission inventories
(ii) Which methods can be followed for the assessment of uncertainty
(111)To what extent can uncertainty in emission inventories be identified, qualified or quantified.
The methodology of emission inventory compilation typical for large-scale emission inventories has been illustrated by two emission inventories. In chapter 2, time series of past worldwide emission of anthropogenic trace gases for the period 1890 - 1990 are described. Chapter 3 presents projections for NOx emissions in Asia for the period 1990 -2020. The construction of these emission inventories was hampered by the lack of experimental data on the different sources of emission. As a result, the emissions were calculated on another scale than on which the emission processes occur in reality. The activity data and emission factors were based on extrapolation of existing information. Due to these aggregations and extrapolations, the emission inventories are inaccurate representations of the actual emissions.
Chapter 4 describes the theoretical basis for our definitions of uncertainties, followed by a categorisation of uncertainties in emission inventories. It is argued that two types of uncertainty in emission inventories exist. Uncertainty about accuracy is the lack of knowledge about the sources and size of the inaccuracy. Uncertainty about reliability is the lack of knowledge about the degree to which the emission inventory is meeting user-specified quality criteria. These user-specified criteria depend on the purpose of the emission inventory. For scientific purposes the reliability is defined by the accuracy of the inventory. For policy purposes, quality criteria can be related to transparency, application of agreed upon methodologies or sometimes also to the assessment of accuracy. Uncertainty about reliability exists when either the accuracy of the emission inventory is not known or when the documentation of the inventory is inadequate and incomplete. Uncertainty about accuracy exists when the different sources of inaccuracy or the extent to which the inventory is inaccurate is not known. A categorisation of uncertainty about different sources of inaccuracy has been presented. Uncertainty about structural inaccuracy is the lack knowledge about the extent to which the structure of an emission inventory allows for an accurate calculation of the 'real' emission. Three causes for structural inaccuracy have been defined. These are aggregation error, incompleteness and mathematical formulation error. Uncertainty about input value inaccuracy is the lack of knowledge about the values of activity data and emission factors. Four causes for input value inaccuracy have been identified. These are extrapolation error, measurement error, unknown developments and reporting error.
Uncertainty about reliability can be assessed through peer review. For the assessment of inaccuracy, a distinction is made between internal and external assessment of uncertainty. In an internal assessment, the methodology and information to construct an emission inventory form the basis for the assessment of inaccuracy. Based on review of available methodologies six methods for internal assessment are proposed: (i) qualitative discussion, (ii) data quality rating, (iii) calculation cheek and evaluation of mathematical formulation, (iv) expert judgement, (v) error propagation and (vi) importance analysis. In an external assessment, the difference between the emission inventory and external sources of information is used to identify, qualify or quantify inaccuracy in the emission inventory. Four methods can be used:(1)comparison with other emission inventories, (ii) comparison with (in)direct measurements, (iii) forward air quality modelling and (iv) inverse air quality modelling.
Against this background we developed a systematic approach for the assessment of uncertainty in emission inventories. This framework, FRAULEIN (FRamework for the Assessment of Uncertainty in Large-scale Emission INventories) can be used to assess uncertainty about reliability and uncertainty about accuracy. It provides guidance for selection of the methods that can be used to identify, qualify or quantify different sources of uncertainty.
Several methods included in the framework have been analysed in more detail to identify the advantages and disadvantages of these methods in practice. Chapter 5 presents the results of assessment of uncertainties in estimates of 1990 N20 emissions from agriculture in The Netherlands using the methods of error propagation and importance analysis. The results indicate that only a small number (three out of 23) of uncertain inventory parameters have large share in the inaccuracy of the emission inventory. These parameters include emission factors for indirect N20 emissions (EF5), the fraction of N leaching from agricultural soils (Fracleach) and the emission factor for direct soil emissions (EF1). Reducing the inaccuracy in the inventory should therefore focus on improved quantification of indirect emissions (based on EF5 and Fracleach) and direct soil emissions (EF1). From a methodological point of view, the results of the N20 case study show that quantification of input value inaccuracy through error propagation is influenced by the statistical
quantification interpretation of the available information in the IPCC Guidelines (default values, and uncertainty ranges of emission factors in particular). This result provides an indication that the extent to which inaccuracies can be assessed depends not only on the characteristics of the method used for the assessment but also on the available information on inventory parameters. Identification of inventory parameters having the largest share in the inaccuracy, on the other hand, was not influenced by the statistical interpretation of IPCC information.
Chapter 6 describes the results of assessment of uncertainty in a European emission inventory of S02 in 1994 using forward air quality modelling and atmospheric measurements. The problem with this type of assessment is that it is not easy to pinpoint emission inventory inaccuracy as single cause of the deviation between measurements and model results. Inaccuracies exist in both the inventory, model and measurements. In the case study it has been analysed whether wind-direction-dependent differences between calculated and measured concentrations can be used to assess inaccuracies in emission inventories. The results indicate that in three regions within the study domain inaccuracy in the emission inventory is the most likely cause for the discrepancy between modelled and observed S02 concentrations. These regions are Sachsen/Brandenburg (Germany), Central England and the western part of the Russian Federation. In Sachsen/Brandenburg and Central England the spatial distribution of the emissions seems to be inaccurate while in the western part of the Russian Federation the total emission estimate seems to be inaccurate. We developed a relatively simple method to identify inventory inaccuracies based on differences between the air quality model and atmospheric measurements. However, it was also shown that the method is primarily a tool for identifying relatively inaccurate parts of the inventory. The method cannot be used to analyse causes of the inaccuracies, such as inaccurate structure or input values. Furthermore, it was concluded that the method is more a qualitative than a quantitative approach.
There are three ways to use FRAULEIN in practice. First, in situations where the method for uncertainty assessment is prescribed, FRAULEIN clarifies the sources of uncertainty that can be identified, qualified or quantified. Second, if the objective of a study is to assess a specific source of uncertainty, FRAULEIN may serve as a guide for selection of the appropriate methods. Third, if the aim is to perform a full assessment of inaccuracy, FRAULEIN forms the basis of a four-step approach: (1) identification, qualification (2) and quantification (3) of the sources of inaccuracy, followed by evaluation to prioritise further research (4).