Sources of Uncertainties

Uncertainties arise in various stages of exposure assessment. These uncertainties should be confronted as an integral part of the process, rather than being limited to the final stage. 

Uncertainty analysis should be an integral part of exposure assessment. 

The level of detail of the assessment may vary greatly, depending on the purpose for which it is carried out (i.e. as a screening-level assessment or as a detailed, probabilistic assessment). The level of detail with which the uncertainty is analysed will vary accordingly and should, as a rule, be consistent with the level of detail of the exposure assessment. 

The objective of an uncertainty analysis is to determine differences in the output of the assessment due to the combined uncertainties in the inputs and to identify and characterize key sources of uncertainty. To this end, a first step in the treatment of the uncertainty in an exposure study consists of the identification of the sources of uncertainty that are relevant for the study. 

There are numerous methods for the subsequent qualitative and quantitative characterization of the relevant uncertainties. These methods are covered in sections 5.1 and 5.2, respectively. 

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Keywords reducing uncertainty, cost-effective information, ranking sources of uncertainty, re-processing seismic, interference tests, aquifer behaviour, % uncertainty, decision tree analysis, value of information, fiscal regime, suspended wells, phased development. 

Appraisal activity should be based upon the information required. The first step is therefore to determine what uncertainties appraisal is trying to reduce, and then what information is required to tie down those uncertainties. For example, if fluid contacts are a major source of uncertainty, drilling wells to penetrate the contacts is an appropriate tool seismic data or well testing may not be. Other examples of appraisal tools are ... 

The choice of the location for well A should be made on the basis of the position which reduces the range of uncertainty by the most. It may be for example, that a location to the north of the existing wells would actually be more effective in reducing uncertainty. Testing the appraisal well proposal using this method will help to identify where the major source of uncertainty lies. 

In nondispersive gas analyzers, interferences by other gases that possibly absorb at the measurement and reference bands should be taken into account. In the measurement of CO, interferences by overlapping in the measurement band can be caused by COS, N2O, CO2, and water vapor. Another source of uncertainty is interference in the reference band. 

After intakes have been estimated, they arc organized by population, as appropriate. Then, tlie sources of uncertainty (e.g., variability in analytical data, modeling results, parameter assumptions) and their effect on tlie exposure estimates are evaluated and sunuiumzed. Tliis information on uncertainty is important to site decision-makers who must evaluate tlie results of the e.xposure... 

Thus, tlie focus of tliis subsection is on qualitative/semiquantitative approaches tliat can yield useful information to decision-makers for a limited resource investment. There are several categories of uncertainties associated with site risk assessments. One is tlie initial selection of substances used to characterize exposures and risk on tlie basis of the sampling data and available toxicity information. Oilier sources of uncertainty are inlierent in tlie toxicity values for each substance used to characterize risk. Additional micertainties are inlierent in tlie exposure assessment for individual substances and individual exposures. These uncertainties are usually driven by uncertainty in tlie chemical monitoring data and tlie models used to estimate exposure concentrations in tlie absence of monitoring data, but can also be driven by population intake parameters. As described earlier, additional micertainties are incorporated in tlie risk assessment when exposures to several substances across multiple patliways are suimned. 

Information or sources of uncertainties and limitations are available in tlie literature. Some of this material is provided below. 

The errors arising in sampling, particularly in the case of heterogeneous solids, may be the most important source of uncertainty in the subsequent analysis of the material. If we represent the standard deviation of the sampling operation (the sampling error) by ss and the standard deviation of the analytical procedures (the analytical error) by sA, then the overall standard deviation sT (the total error) is given by... 

Managing risk—project managers planning research tasks need to identify the most important outstanding sources of uncertainty and work out which most need resolution at any given point in the R D process. 

Unlike non-radiometric methods of analysis, uncertainty modelling in NAA is facilitated by the existence of counting statistics, although in principle an additional source of uncertainty, because this parameter is instantly available from each measurement. If the method is in a state of statistical control, and the counting statistics are small, the major source of variability additional to analytical uncertainty can be attributed to sample inhomogeneity (Becker 1993). In other words, in Equation (2.1) ... 

Reference value A value reported on an SRM Certificate of Analysis and/or a Reference Material (RM) Report of Investigation that represents the best estimate of the true value where all known or suspected sources of bias have not been fully investigated by NIST. The value has an associated uncertainty that may not include all sources of uncertainty and may represent only a measure of the precision of the measurement method(s). 

The results of activation analysis are subject to well known and common analytical sources of uncertainty, as well as method specific uncertainties, e.g. summarized by Greenberg (1997), and also in Section 2.2. In order for INAA experiments to measure differences in induced activity, i.e. differences due to heterogeneity in the amount of analyte in a given test portion, the experimental procedure is designed to allow only the following uncertainties to be part of the result ... 

Many sources of uncertainty must be taken into account in interpreting water quality data collected in the field. Probably the single program that has most prominently... 

The sources of uncertainty in NAA analysis are well understood, and can be derived in advance, modelled and assessed experimentally. There are two main kinds of interferences in the calculation of trace-element concentrations by INAA. The first one is formation of the same radionuclide from two different elements. Another kind of interference is from two radionuclides having very close y lines. Whenever interferences occur, the radionuclide of interest can be carried through a post-irradiation radiochemical separation without the danger of contamination. 

This equation assumes the pressure head at the wetting front to be equal to zero. The value of the pressure head is, however, a source of disagreement and one of the sources of uncertainty in this test. The assumption that the pressure head is zero is a conservative assumption, tending to give a high hydraulic conductivity. 

At first, a clear statement should be made of the measured value y and which relationship exists between y and the parameters piypiy-ypm on which it depends. If possible, that should be done in form of a mathematical equation, y = ffp ypiy ->pm)- From this the sources of uncertainty for each part of the process should be derived and listed. Some of the parameters on their part depend from other variables pij. Also these dependencies have to be considered in form of equations or schemes, where pictograms, spreadsheets, and cause-effect diagrams (as schematically shown in Fig. 4.7) may be applied as useful tools. 

In order to obtain a feeling for the major sources of uncertainty and error in the calculation of reaction rate constants, it is useful to consider the nature of the errors inherent in the measurement of these parameters. 

Due to the various sources of uncertainties associated with both groups of input data (dose and effect) needed for risk assessment, at present it is only possible to give an upper limit for the pure Rn-d related cancer risk. 

In order to determine how many samples we require, it is necessary to consider the sources of uncertainty in the final result. Uncertainty is dealt with in more detail in Chapter 6. In this section, we are mainly concerned with the uncertainty arising from sampling. It is necessary to use a few statistical terms namely, sample standard deviation (s) and variance (.v2). These terms are defined in Chapter 6, Section 6.1.3. 

The major sources of uncertainty relating to the method should be identified. Those contributions not used in the final calculation, because they are considered insignificant, should be mentioned. The overall uncertainty should be listed, together with an explanation of how it was derived. A more detailed treatment may be in a cross-referenced hie. 

As we have seen in previous sections, the result of a measurement is not complete unless an estimate of the uncertainty associated with the result is available. In any measurement procedure, there will be a number of aspects of the procedure that will contribute to the uncertainty. Uncertainty arises due to the presence of both random and systematic errors. To obtain an estimate of the uncertainty in a result, we need to identify the possible sources of uncertainty, obtain an estimate of their magnitude and combine them to obtain a single value which encompasses the effect of all the significant sources of error. This section introduces a systematic approach to evaluating uncertainty. 

Make a list of as many of the potential sources of uncertainty as you can think of that could affect the result of a chemical measurement. 

There are many potential sources of uncertainty. Examples include sampling the nature of the sample matrix sample storage conditions ... 

Storage Conditions If samples are stored for a period prior to analysis, the storage conditions may influence the results. The storage conditions and storage time should therefore be listed as possible sources of uncertainty. 

Random Effects Random effects (see Section 6.3.3) will contribute to the uncertainty in all measurement procedures. Random effects should therefore always appear in the list of sources of uncertainty. 

Your list should have included at least some of the items mentioned above, but you may well have identified other sources of uncertainty. Remember that uncertainty is not about mistakes. The uncertainty estimate is intended to reflect the likely variation in results when a method is carried out correctly and operating under statistical control. Your list of sources of uncertainty should not therefore include any gross errors such as contamination of samples, mistakes in calculations or the analyst failing to follow the standard operating procedure correctly. 

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