Individuals probabilistic risk assessments

EPRI NP-5664 is a study based on interviews of personnel at 10 utilities and 15 NRC personnel regarding the usefulness of PSA (they use the term PRA - probabilistic risk assessment). The general utility motivation for using PSA is to demonstrate an acceptably low level of risk to the NRC. Some utilities applied PSA to individual systems, functions, or issues. These smaller [ir(>grams served to train a PSA cadre and introduce PSA to other utility personnel and management. 

This chapter presents a quantitative probabilistic risk assessment for atrazine and simazine conducted for Syngenta Crop Protection, Inc. The risk of an effect is the likelihood that an individual will develop the effect as a result of that individual s exposure to atrazine and/or simazine. The risk assessment is quantitative because it characterizes the likelihood in numerical terms. The risk assessment does include some qualitative discussion of the uncertainties associated with the quantitative characterization of the likelihood. It also assumes relevance of an animal effect in humans even when a lack of human relevance has been established, as is the case with atrazine and simazine [US Environmental Protection Agency (USEPA), 2006]. 

Different safety factors may have been used in the derivation of the reference values of the individual substances (RfDA deterministic HI thus sums risk ratios that may reflect different percentile values of a risk probability distribution. Assessment and interpretation of the uncertainty in the HI may be severely hampered by this summation of dissimilar distribution parameters. In a probabilistic risk assessment, the uncertainty in the exposure and reference values is often characterized by lognormal distributions. The ratio of 2 lognormal distributions also is a lognormal distribution. The variance in a quotient of 2 random variables can be approximated as follows (Mood et al. 1974, p 181) ... 

Probabilistic risk assessment methods are used to incorporate uncertainty and variability into both aggregate and cumulative risk assessments. Herein, uncertainty refers to lack of knowledge or the limitations in the current state of knowledge. For example, the dermal permeability of a pesticide may not be known with certainty. Variability, on the other hand, refers to a value that differs from one individual to another individual in a population or from one instance to another. For example, the number of apphcations of a residential pesticide in a year may vary from one individual to another. Probabilistic methods use probability distributions to incorporate uncertainty and variability into both aggregate and cumulative risk assessments. 

Probabilistic risk assessment methods are described herein for determining a popnlation s distribution of the dose from exposure and the combination of that exposnre characterization with appropriate toxicological information to form aggregate and cumulative risk assessments. An individual s dose from exposure is characterized as a set of chemical- and route-specific dose profiles over time. Toxic equivalence factors (TEFs) that reflect the toxic endpoint and exposure duration of concern are used to scale chemical- and route-specific doses to toxic equivalent doses (TEDs). The latter are combined in a temporally consistent manner to form a profile over time of the Total TED. For each individual, a Total MOE is calculated by dividing a toxicologically relevant benchmark dose (e.g. an EDio) by the individual s Total TED. The distribution of the Total MOE in a popnlation provides important information for risk management decisions. 

This report summarises results of the Individual Plant Examinations for 74 nuclear power plant sites in the USA. The Individual Plant Examinations all involved Level 1 probabilistic risk assessments. Some also included level 2 analyses. The report describes the ranges of core damage frequencies in terms of the plant and containment types. The report provides a good indication of the t3q)es of frequency dominant accidents that might have to be addressed in the development of accident management strategies. 

The total consequence-weighted risk is the sum of the consequence weighted risks of the individual accidents. The process of calculating consequence-weighted risk is illustrated in Table 2.6-1 for a hypothetical plant that has only four possible accidents. Consequence-weighted risk is so widely used in probabilistic risk assessments that the modifier consequence-weighted (or actuarial) is usually dropped, and the total consequence-weighted risk is simply called the plant risk. 

This book, for the most part, is a stand-alone text. It addresses not only the fundamentals of PSA as a science, but insights on the regulatory framework affecting its development and apidication. In particular, it provides the basic methods of analysis that can be employed, available databases, an excellent set of examples, software resources, chapter summaries that tacilitate comprehension, and problem sets that are very well connected to the theory. While much has been written about probabilistic safety assessment over the last three decades, this is the most comprehensive attempt so far to provide a much needed college level textbook for the education of risk and safety professionals. It also provides a valuable reference for any individual curious enough about the risk and safety sciences to want to become much more informed. 

As described in detail in this book, the use of assessment factors is an established practice in chemical risk assessment to account for uncertainties inherent in the hazard (effects) assessment and consequently, inherent in the risk assessment. The use of assessment factors to address this uncertainty is part of the conventional approach that has developed over the years. According to the current risk assessment paradigm, the usual approach is simply to multiply these individual assessment factors in order to establish an overall composite numerical assessment factor (Section 5.10). An alternative to the traditional assessment factor approach is to combine estimates of the ranges that these factors may encompass through a probabilistic assessment this is essentially a variation of the standard paradigm. 

If default constants are used for each of several different parameters in the risk assessment, then the conservative aspect of the individual components is compounded when they are combined in the risk characterization. Furthermore, the extent of the overestimation cannot be readily quantified, and so the magnitude of the overestimation of the risk is not identified. However, distributional techniques make it possible to combine exposures more realistically - whether from multiple years, subpopulations, exposure pathways, or chemicals - without having to assume the worst case for each component. By carrying all the information for each component of the risk assessment through to the end of the entire risk characterization, instead of requiring interim single-number characterizations, probabilistic techniques help avoid the compounding of the conservative aspect of multiple parameters. 

The risk of exposure to individual chemicals as calculated using the SSD method is based on the same mathematical principles used in the derivation of concentration-response curves in single-species toxicity evaluations. As for individual species, both the concentration addition and response addition models can conceptually be applied in ecological risk assessment for species assemblages exposed to mixtures of toxicants, which are now being formulated probabilistically (Traas et al. 2002 Posthuma et al. 2002a De Zwart and Posthuma 2005). 

We have reviewed current conceptual and modeling approaches in mixture eco-toxicology as well as current experimental evidence to derive practical risk assessment protocols for species and species assemblages. From the review of conceptual approaches in mixture ecotoxicology, it appears that there is a difference between a mechanistic view of joint action from a compound mixture and a probabilistic perspective on combined toxicity and mixture risk. A mechanistic view leads to emphasis on the distinction of modes of action and physicochemical properties first, then on the choice of the appropriate joint toxicity model, followed by a comparison of the models prediction with experimental observations. A probabilistic orientation leads to the observation that concentration addition often yields a relatively satisfactory quantitative prediction of observations for the integral level of effects as observed in individual organisms or populations. In these applications, concentration addition is frequently connected with a slight bias to conservatism, especially for compounds with different modes of action (Backhaus et al. 2000,2004 Faust et al. 2003). 

One problem encountered when assessing exposure of human populations to contaminated land is spatial heterogeneity of pollution. To overcome this problem, Gay and Korre (2006) propose the combinations of spatial statistical methods for mapping soil concentrations, and probabilistic human health risk assessment methods. They applied geostatistical methods to map As concentrations in soil. Subsequently, an age-stratified human population was mapped across the contaminated area, and the intake of As by individuals was calculated using a modified version of the Contaminated Land Exposure Assessment (CLEA) model. This approach allowed a... 

First, individuals interpreting data must decide on whether to use a deterministic or a probabilistic approach to generate an exposure estimate for the analysis. The deterministic approach (point-estimate) is widespread and beginning with this approach is consistent with the tiered approach to exposure and risk assessment. 

Hope BK, Generating probabilistic spatially-explicit individual and population exposure estimates for ecological risk assessments, Risk Anal., 20, 573, 2000. 

If quantitative probabilistic safety criteria have been used in the development of the plant design, a comparison of the main PSA results with these criteria should be provided to demonstrate compliance. These criteria may relate to both individual and societal risk measures to ensure that all aspects of assessing the risks to the public due to the plant have been adequately considered. 

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