Risk characterization description

When risk characterization is complete, a description of the risk assessment is communicated to the risk manager (Figure 28.1) to support a risk management decision. This communication usually is a report and might include ... 

Risk characterization is either the quantitative or qualitative description of risk. For example, a quantitative risk characterization could be either a point estimate of risk (a single value for the risk as opposed to a range of values), an upper bound on risk (which implies a range of values for the risk), or a distribution of risk (which implies a range of values for the risk and the relative likelihood of each value in that range). 

The uncertainties and variability of the database, along with the judgements and assumptions that were made during the assessment, should be clear. The description should include the major strengths and weaknesses of the database and the limits of understanding of particular mechanisms of toxicity that may be involved in the effect(s). Whenever alternative views can be supported by the database, these should be addressed in the risk characterization. If the assessment favours one view over others, the rationale for choosing that view should be stated. 

Risk characterization provides for both qualitative and quantitative descriptions of risk. The step involves integrating the results of the hazard identification, dose-response assessment, and exposure assessment to characterize risk. Often, a direct comparison between exposure criteria developed in the first two steps and the results of the exposure assessment (concentration in the environmental media or the estimated dose, as appropriate) provide a basis for determining whether risks are acceptable. Typically, if criteria are exceeded, the risk is not acceptable. What is defined as acceptable, as well as the way risk is expressed, is often a... 

Thus, while the results of a risk characterization may be expressed as a single value for example, X additional cancers per million people exposed, a full description of the risk must include a discussion of the uncertainties in the risk estimate. Unfortunately, many of these uncertainties are hidden in the design of the dose-response studies and, in addition, there are many implicit value judgments that are part of... 

Risk characterization. This compartment is comprised of the risk estimation and risk description boxes. The integration of the exposure and effects data from the analysis compartment is reconciled in the risk estimation process. 

It is important to clearly describe and quantitatively estimate the assumptions and uncertainties involved in the evaluation, where possible. Examples include natural variability in ecological characteristics and responses and uncertainties in the test system and extrapolations. The description and analysis of uncertainty in characterization of ecological effects are combined with uncertainty analyses for the other ecological risk assessment elements during risk characterization. 

The uncertainty analysis identifies and, to the extent possible, quantifies the uncertainty in problem formulation, analysis, and risk characterization. The uncertainties from each of these phases of the process are carried through as part of the total uncertainty in the risk assessment. The output from the uncertainty analysis is an evaluation of the impact of the uncertainties on the overall assessment and, when feasible, a description of the ways in which uncertainty could be reduced. 

Risk characterization concludes with a risk description that ... 

Risk Characterization - The process of estimating the probable incidence of an adverse health effect to humans under various conditions of exposure, including a description of the uncertainties involved. 

Main Analyses. The main analyses section of the software system includes those program modules that directly assist the user in either generating or reviewing exposure and risk assessments for hazardous waste sites. They include modules for Case Description, Exposure Assessment, and Risk Characterization, as well as a series of Superfund Checklist Modules. 

The NRC document calls for hazard identification, dose-response assessment, exposure assessment, and risk characterization. In an effort to place descriptive experimental toxicity results in a clearer perspective and place more emphasis on evaluation, this outline deviates slightly from the NRC document and calls for hazard evaluation, hazard extrapolation, exposure assessment and risk characterization. In addition, a few comments on risk acceptability are given. Exposure assessments have been adequately discussed elsewhere in this symposium and will be discussed here only as they relate to hazard identification, evaluation, extrapolation and risk characterization. 

The risk characterization should also contain a description and estimate of the uncertainty of the assessment. 

Risk characterization is the final step in the risk assessment process. It comprises quantitative or semiquantitative estimations, including uncertainties, of the probability of adverse health effects in people associated with exposure to the toxic agents. Risk characterization is based on the information gathered through the first three steps in the risk assessment procedure. It is important that the weight of evidence leading to the conclusions be openly discussed. Risk characterization should include a description of the primary causes of uncertainties. 

Each of these steps combines numerous assumptions that allow the risk assessor to extrapolate from the available data to potential real-world scenarios. A detailed description of the art and science of risk characterization is beyond the scope of this book the reader should simply be aware that the process can reflect both scientific thinking and subjective judgment [78], and that in consequence risk assessments can produce a variety of estimates of risk from the same data. 

The preceding description of LCIA alludes to risk characterization steps however, LCIA differs from risk assessment in several important respects [1]. LCIA seeks to link a system, as defined in the LCA, with potential effects in order to allow for a relative comparison of the potential impacts from alternative products or processes. LCIA is not designed to quantify accurately the risk of actual harm to an exposed population, ecosystem, or resource. 

Part 2 and its chapters presented the topic of human lead exposure in global and categorical terms, addressing the technical areas of lead intakes, uptakes (absorption), toxicokinetics, integration of toxicokinetics into in vivo disposition in a manner allowing quantitative assessments of lead exposure, etc. In contrast to these broadly descriptive aspects of human Pb exposme, the applied health discipline of quantitative risk assessment requires prescriptive approaches for site-specific, case-specific, and environmental scenario-specific lead exposure characterizations. Data from such specific exposure characterizations are combined with available data for lead dose—response relationships to arrive at some quantitative risk characterization indexed as some endpoint for human health risk. 

The risk characterization step involves two components risk estimation and risk description. The risk estimation component is similar to the hnman health risk characterization conducted for non-cancer effects of chemicals in that it qnantifies potential effects from chemical exposure. Depending on the methods nsed to estimate exposure and toxicity, the methods nsed in risk estimation for ecological receptors may differ from those used for humans. One method that can be used, which is similar to the method used for humans, is the toxicity quotient method. In this method, the estimated exposure is divided by a safe level of exposure developed in the characterization of effects component. The resulting value is compared to a threshold level of one. Below this level, no effects are expected (regardless of what the impact might be). Above this level, there may be effects. 

Another added feature in ecological risk assessments is the risk description component of risk characterization. In this component, the nature and intensity of effects, their spatial and temporal scale, and the potential for ecosystem recovery are all addressed. This component partially serves to identify ways to remedy effects at a site. 

Risk assessment pertains to characterization of the probability of adverse health effects occurring as a result of human exposure. Recent trends in risk assessment have encouraged the use of realistic exposure scenarios, the totality of available data, and the uncertainty in the data, as well as their quality, in arriving at a best estimate of the risk to exposed populations. The use of "worst case" and even other single point values is an extremely conservative approach and does not offer realistic characterization of risk. Even the use of arithmetic mean values obtained under maximum use conditions may be considered to be conservative and not descriptive of the range of exposures experienced by workers. Use of the entirety of data is more scientific and statistically defensible and would provide a distribution of plausible values. 

Typically extrapolations of many kinds are necessary to complete a risk assessment. The number and type of extrapolations will depend, as we have said, on the differences between condition A and condition B, and on how well these differences are understood. Once we have characterized these differences as well as we can, it becomes necessary to identify, if at all possible, a firm scientific basis for conducting each of the required extrapolations. Some, as just mentioned, might be susceptible to relatively simple statistical analysis, but in most cases we will find that statistical methods are inadequate. Often, we may find that all we can do is to apply an assumption of some sort, and then hope that most rational souls find the assumption likely to be close to the truth. Scientists like to be able to claim that the extrapolation can be described by some type of model. A model is usually a mathematical or verbal description of a natural process, which is developed through research, tested for accuracy with new and more refined research, adjusted as necessary to ensure agreement with the new research results, and then used to predict the behavior of future instances of the natural process. Models are refined as new knowledge is acquired. 

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