Assessing uncertainties

Deque M, Rowell DP, Liithi D, Giorgi E, Christensen JH, Rockel B, Jacob D, Kjellstrom E, de Castro M, van den Hurk B (2007) An intercomparison of regional climate simulations for Europe assessing uncertainties in model projections. Clim Change 81 53-70 doi 10.1007/ S10584-006-9228-X... 

Selgrade, M.K., Use of immunotoxicity data in health risk assessments Uncertainties and research to improve the process, Toxicology, 133, 59, 1999. 

Final cover, in landfill design, 25 879 Final product specification, 5 779-780 Final Report on Post Consumer PET Container Recycling, 20 55 Financial conflicts of interest, 24 370 Financial risk assessment, uncertainty analysis in, 26 1001... 

Source Modified from KEMl, Human health risk assessment. Proposals for the use of assessment (uncertainty) factors. 

FIGURE S.6 Schematic illustration of the traditional setting of an acceptable level of exposure (ADI) by dividing the NOAEL from an animal study by an assessment factor (AF). The two dose-response relationships have identical NOAEL. If a uniform assessment factor is applied, there will be an adequate MOS at the ADI for effect b but not for effect a. (Modified from KEMI, Human health risk assessment. Proposals for the use of assessment (uncertainty) factors. Application to risk assessment for plant protection products, industrial chemicals and biocidal products within the European Union. Report No. 1/03, Solna, Sweden, 2003. 

Bayesian fundamentals are reviewed here because several chapters in this volume apply these methods in complex ways to assessing uncertainty. The goal is to create enough understanding so that methods described in later chapters can be fully appreciated. 

Henrion M, Fishoff B. 1986. Assessing uncertainty in physical constants. Am J Phys 54 791-797. 

In exposure assessment, uncertainty arises from insufficient knowledge about relevant exposure scenarios, exposure models and model inputs. Each of these sources of uncertainty has factors that determine the magnitude of uncertainty and variation. For example, Mosbach-Schulz (1999) identified three factors related to the uncertainty and variability of input data ... 

Uncertainty is the lack of knowledge of vital parts that are needed to perform an exposure assessment. Uncertainty can, at least in principle, be reduced by research to obtain necessary or applicable information or data. 

Sensitivity analysis can be either qualitative or quantitative. In a qualitative analysis, the assessor identifies the uncertainties affecting the assessment and forms a judgement of their relative importance. Such an evaluation is implicit in the qualitative approaches to assessing uncertainty, described in section 5.1. This is subjective and therefore less reliable than a quantitative sensitivity analysis. However, it is a practical first step for identifying the most important uncertainties as a focus for quantitative analysis. 

Communication of the results of exposure assessment uncertainties to the different stakeholders should reflect the different needs of the audiences in a transparent and understandable manner. 

In essence, the level of detail and quantification of assessment uncertainties should be related to the quality of the underlying data, the degree of refinement called for in the underlying exposure analysis and decision criteria specified by risk management strategies. 

The main question when assessing uncertainties in the cloud chemistry simulation results is What system parameters have the greatest influence on the observed chemistry Uncertainties in previously observed transformation rates of SO2 to sulfate with this facility are as large as a factor of ten (5). If we assume the first order rate of transformation of SO2 to SO4, Rs02 t0 be... 

This subsection discusses practical approaches to assessing uncertainty in Superfiind site risk assessments and describes ways to present key information bearing on tlie level of confidence in quantitative risk estimates for a site. The risk measmes used in Superfund site risk assessments usually are not fully probabilistic estimates of risk, but conditional estimates given a considerable number of assumptions about exposure and toxicity (e.g., risk given a particular future land use). Thus, it is important to fully specify the assumptions and uncertainties inlierent in tlie risk assessment to place tlie risk estimates in proper perspective. Another use of uncertainty characteriztition can be to identify areas where a moderate amount of additional data collection might significantly improve the basis for selection of a remedial alternative. 

To assess uncertainty, a type B estimation of uncertainty is performed. After analysis of the samples the type B estimation can be verified by a type A estima-... 

So, uncertainty has traditionally been treated as the range of values about the final result within which the true value of the measured quantity is believed to lie [17], However, there was no agreement on the best method for assessing uncertainty. Consistent with the traditional subdivision, the random uncertainty and the systematic uncertainty each arising from corresponding sources should be kept separate in the evaluation of a measurement, and the question of how to combine them was an issue of debate for decades. 

In view of the increasing complexity of water problems, the use of models in policy formulation and the need to understand and assess uncertainties associated with these models will become a fundamental issue in the future. 

K, the formation constant was recalculated to be (173 64). However, if the value for log,/f°(A.16) was changed from 1.98 to 1.96, well within the assessed uncertainty bound, the value became (140 72). The uncertainties here are merely the 2ct uncertainties in the set of seven values recalculated from [59NA1/NAN], and do not include uncertainties in the auxiliary data. Even though the solute concentrations are low (/ < 0.05), and the overall changes to the activity coefficients from the SIT interaction terms are small, the calculated value of log, AT (A.15) increases from (173 64) to (201 35) if all the e values are set to zero (but the maximum change in from the interaction terms is 0.004). 

Tolerance verification it defines inspection planning and metrological procedures for functional requirements, functional specifications, and manufacturing specifications. It is very important to consider tolerance verification early in the design activities to be able to assess uncertainties. Tolerance verification permits to close the process loop, to check the product conformity, and to verify assumptions made by the designer. 

Aven, T. (2008). Risk analysis. Assessing uncertainties beyond expected values and probabilities. Chichester, Wiley. 

A comment needs to be made on the form of the uncertainty assessment suggested here. The uncertainty assessment in Table 1 is semi-quantitative, in the sense that qualitatively described conditions are established for the classification of uncertainty factors. However, the classification itself involves quantitative analysis, for example in classifying the sensitivity of an uncertainty factor. The alternative would be a completely quantitative uncertainty assessment, say a standard Bayesian analysis. As an example, uncertainty factor 8 in Table 1 relates to the duration of annual planned/scheduled maintenance. In the traditional analysis this duration is assumed to be a fixed quantity. Alternatively, we could introduce it as an unknown quantity and assess uncertainty about it quantitatively. Two points can be made here. Firstly, such an uncertainty assessment would also be based on a number of assumptions. Secondly, there is a balance to be made between the effort put into such an assessment and its usefulness, which is questionable if the backgroimd knowledge is poor. 

Aven, T. (2008b) Risk Analysis - Assessing Uncertainties beyond Probabilities and Expected Values. New York Wiley. 

Aven, T. (2008a) Risk analysis Assessing uncertainties beyond probabilities. John Wiley Sons Ltd, Chichester, England. 

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