Risk assessment uncertainty

Risk assessment starts with risk identification, a systematic use of available information to identify hazards (i.e., events or other conditions that have the potential to cause harm). Information can be from a variety of sources including stakeholders, historical data, information from the literature, and mathematical or scientific analyses. Risk analysis is then conducted to estimate the degree of risk associated with the identified hazards. This is estimated based on the likelihood of occurrence and resultant severity of harm. In some risk management tools, the ability to detect the hazard may also be considered. If the hazard is readily detectable, this may be considered a factor in the overall risk assessment. Risk evaluation determines if the risk is acceptable based on specified criteria. In a quality system environment, criteria would include impact on the overall performance of the quality system and the quality attributes of the finished product. The value of the risk assessment depends on how robust the data used in the assessment process is judged to be. The risk assessment process should take into account assumptions and reasonable sources of uncertainty. Risk assessment activities should be documented. 

Research to reduce uncertainty Risk assessment Risk reduction... 

The severe accident research program improved public risk assessment, reduced uncertainties, and the reliance on subjective expert opinion. To close two severe accident issues in NRC s Severe Accident Research Plan (NUREG-1365) Mark I Liner Attack and Direct Containment Heating (DCH) were addressed with a new approach using the Risk Oriented Accident Analysis Method (ROAAM) (Theofanous, 1994, 1989). The resolution of the Mark-I Liner Attack issue constitutes the first full demonstration of ROAAM. It emphasizes the determinism and provides a basis for synergistic collaboration among experts through a common communication frame. 

There are many definitions of the word risk. It is a combination of uncertainty and damage a ratio of Itazards to safeguards a triplet combination of event, probability, and consequences or even a measure of economic loss or human injury in terms of both the incident likelihood and tlie magnitude of the loss or injuiy (AICliE, 1989). People face all kinds of risks eveiyday, some voluntarily and otliers involuntarily. Tlierefore, risk plays a very important role in today s world. Studies on cancer caused a turning point in tlie world of risk because it opened tlie eyes of risk scientists and healtli professionals to tlie world of risk assessments. 

Uncertainty on tlie other hand, represents lack of knowledge about factors such as adverse effects or contaminant levels which may be reduced with additional study. Generally, risk assessments carry several categories of uncertainly, and each merits consideration. Measurement micertainty refers to tlie usual eiTor tliat accompanies scientific measurements—standard statistical teclmiques can often be used to express measurement micertainty. A substantial aniomit of uncertainty is often inlierent in enviromiiental sampling, and assessments should address tliese micertainties. There are likewise uncertainties associated with tlie use of scientific models, e.g., dose-response models, and models of environmental fate and transport. Evaluation of model uncertainty would consider tlie scientific basis for the model and available empirical validation. 

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. 

The risk assessment steps and the risk characterization are influenced by uncertainty and variability. Variability arise from heterogeneity such as dose-response differences within a population, or differences in contaminant levels in tlie environment. Uncertainty on tlie other lumd, represents lack of knowledge about factors such as adverse effects or contaminant levels. 

The reader should note tliat since many risk assessments have been conducted on the basis of fatal effects, there are also uncertainties on precisely what constitutes a fatal dose of thennal radiation, blast effect, or a toxic chemical. Where it is desired to estimate injuries as well as fatalities, tlie consequence calculation can be repeated using lower intensities of exposure leading to injury rather titan dcatli. In addition, if the adverse healtli effect (e.g. associated with a chemical release) is delayed, the cause may not be obvious. Tliis applies to both chronic and acute emissions and exposures. 

Tliis part of tlie book reviews and develops quantitative metliods for tlie analysis of liazard conditions in terms of the frequency of occurrence of unfavorable consequences. Uncertainty characterizes not only Uie transformation of a liazard into an accident, disaster, or catastrophe, but also tlie effects of such a transformation. Measurement of uncertainty falls witliin tlie purview of matliematical probability. Accordingly, Chapter 19 presents fundamental concepts and Uieorems of probability used in risk assessment. Chapter 20 discusses special probability distributions and teclmiques pertinent to risk assessment, and Chapter 21 presents actual case studies illustrating teclmiques in liazard risk assessment tliat use probability concepts, tlieorems, and special distributions. 

The structure and mathematical expressions used in PBPK models significantly simplify the true complexities of biological systems. If the uptake and disposition of the chemical substance(s) is adequately described, however, this simplification is desirable because data are often unavailable for many biological processes. A simplified scheme reduces the magnitude of cumulative uncertainty. The adequacy of the model is, therefore, of great importance, and model validation is essential to the use of PBPK models in risk assessment. 

The reality of risk assessment in investment for new processes is somewhat more complex than this. The specific innovations are often not discrete and the confidence of success of each item is a probability distribution rather than a single value. Techniques to handle the mathematical aspects have been available for many years [61] and computational tools are now readily available. A detailed coverage of managing uncertainty is beyond the scope of the current text and this simplistic approach suffices to address the key question of how to effectively manage the N-and C-values. 

Cronin WJ, Oswald EJ, Shelley ML, et al. 1995. A trichloroethylene risk assessment using a Monte Carlo analysis of parameter uncertainty in conjunction with physiologically-based pharmacokinetic modeling. Risk Anal 15 555-565. 

Risk characterization is the last step in the risk assessment procedure. It is the quantitative or semi-quantitative estimation, including uncertainties, of frequency and severity of known or potential adverse health effects in a given population based on the previous steps. Risk characterization is the step that integrates information on hazard and exposure to estimate the magnitude of a risk. Comparison of the numerical output of hazard characterization with the estimated intake will give an indication of whether the estimated intake is a health concern. ... 

The degree of confidence in the final estimation of risk depends on variability, uncertainty, and assumptions identified in all previous steps. The nature of the information available for risk characterization and the associated uncertainties can vary widely, and no single approach is suitable for all hazard and exposure scenarios. In cases in which risk characterization is concluded before human exposure occurs, for example, with food additives that require prior approval, both hazard identification and hazard characterization are largely dependent on animal experiments. And exposure is a theoretical estimate based on predicted uses or residue levels. In contrast, in cases of prior human exposure, hazard identification and hazard characterization may be based on studies in humans and exposure assessment can be based on real-life, actual intake measurements. The influence of estimates and assumptions can be evaluated by using sensitivity and uncertainty analyses. - Risk assessment procedures differ in a range of possible options from relatively unso-... 

As probabilistic exposure and risk assessment methods are developed and become more frequently used for environmental fate and effects assessment, OPP increasingly needs distributions of environmental fate values rather than single point estimates, and quantitation of error and uncertainty in measurements. Probabilistic models currently being developed by the OPP require distributions of environmental fate and effects parameters either by measurement, extrapolation or a combination of the two. The models predictions will allow regulators to base decisions on the likelihood and magnitude of exposure and effects for a range of conditions which vary both spatially and temporally, rather than in a specific environment under static conditions. This increased need for basic data on environmental fate may increase data collection and drive development of less costly and more precise analytical methods. 

---