SOURCES OF UNCERTAINTIES IN RISK ASSESSMENT

The hazard index only provides an indication of the probable presence or absence of effects from exposure to noncarcinogens. 

Carcinogenic risk is a function of the chronic daily intake (calculated using Equation (1) and the slope factor (SF))  

The value for risk is the quantitative end point determined in risk assessment calculations it is commonly used in regulatory and management decisions regarding hazardous wastes. As with noncarcinogens, the risk term is calculated for each contaminant, each route of exposure, and for all sets of receptor populations each element of risk is then summed to provide the value of cumulative risk. 

Suter (1990) emphasized that at our current level of knowledge, the detrimental effects of hazardous chemicals on ecosystems cannot be adequately predicted. The current methods can only assess risks in a simplified manner by providing a relative ranking of risk—from chemical-to-chemical or site-to-site. Nonetheless, such relative-risk ranking provides a useful basis for prioritizing environmental hazards, particularly if data are analyzed by qualified risk assessors. 

Most risk assessments use data based on assumptions and extrapolations. These generate uncertainties due to the lack of knowledge or data. A degree of caution is used by risk assessors in assigning absolute numbers to risk values and hazard indices because a significant degree of uncertainty is inherent in each of the four steps of risk assessment, which is then compounded into the final risk value. 

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Sources of Uncertainties in Risk Assessment Table 4 Slope factors of some common hazardous compounds. 13... 

Lack of data is often a signihcant source of uncertainty in risk assessments. Unavailable data may include source concentrations or source contaminants such as those not quantihed by standard analyses such as EPA methods 624 and 625. RfDs and SFs are currently only available for fewer than two hundred chemicals however, since thousands of chemicals are potentially present at contaminated sites and hazardous waste facilities, the paucity of available data may be responsible for a significant amount of uncertainty. 

Another typical source of uncertainty in mixture assessment is the potential interaction between substances. Interactions may occur in the environment (e.g., precipitation after emission in water), during absorption, transportation, and transformation in the organism, or at the site of toxic action. Interactions can be either direct, for example, a chemical reaction between 2 or more mixture components, or indirect, for example, if 1 mixture component blocks an enzyme that metabolizes another mixture component (see Chapters 1 and 2). Direct interactions between mixture components are relatively easy to predict based on physical-chemical data, but prediction of indirect interactions is much more difficult because it requires detailed information about the processes involved in the toxic mechanisms of action. One of the main challenges in mixture risk assessment is the development of a method to predict mixture interactions. A first step toward such a method could be the setup of a database, which contains the results of mixture toxicity tests. Provided such a database would contain sufficient data, it could be used to predict the likelihood and magnitude of potential interaction effects, that is, deviations for CA and RA. This information could subsequently be used to decide whether application of an extra safety factor for potential interaction effects is warranted, and to determine the size of such a factor. The mixture toxicity database could also support the search for predictive parameters of interaction effects, for example, determine which modes of action are involved in typical interactions. 

The objective of this monograph is to provide an overview on the nature and characterization of uncertainty in exposure assessments, including guidance on the identification of sources of uncertainty, its expression and application, not only in risk assessment, but also in risk management decisions, delineation of critical data gaps and communication to decisionmakers and the public. 

Such threshold values are often estimated using no-observed-effect concentrations or levels (NOECs or NOELs). It might be tempting to substitute the individual ECx values in the CA equation (Equation 4.2) with NOELs in order to calculate a mixture NOEL. But this would imply that all NOELs provoke the same, statistically insignificant effect that is, all of them must have been determined in an identical experimental setup (in terms of number of replicates, spacing of test concentrations, variance structure), which is hardly ever the case. Nevertheless, a range of methods, such as TEFs or TEQs (see Chapters 1 and 5), makes use of a CA-like approach and sums up NOEL-based hazard quotients. This introduces an additional source of uncertainty in the risk assessment, which is fundamentally different from the question of whether CA is an appropriate concept for the mixture of interest. 

Stayner L, Bailer AJ, Smith R, et al. 1999. Sources of uncertainty in dose-response modeling of epidemiological data for cancer risk assessment. Arm N Y Acad Sci 895 212-222. 

Our ability to accurately assess risks is affected by the uncertainties inherent in the risk assessment process at each step. Some of the sources of uncertainties in the toxicity assessment include inadequate human or animal data, inappropriate dose-response models, lack of biological basis for the adverse effects, and so on. The impact of these uncertainties is that the risk assessment tends to be conservative. For example, as described above, the U.S. EPA and others generally apply uncertainty factors to adjust the safe dose downwards when data are lacking as a matter of policy, despite the fact that some of these factors might actually adjust the safe dose upwards if sufficient data were available to characterize the uncertainties. 

A complete discussion of uncertainty is beyond the scope of this report, and the reader is referred to the works of Finkel (1990), Holling (1978), and Suter (1990b). However, a brief discussion of the major sources of uncertainty in ecological risk assessment is appropriate. For illustrative purposes, four major areas of uncertainty are presented below. These are not discrete categories, and overlap does exist among them. Any specific risk assessment may have uncertainties in one or all of these categories. 

What are the main sources of uncertainties in CL calculation Discuss these uncertainties from the position of Environmental Risk Assessment approach. 

Table 3. Main factors influencing the radiation dose received by an individual following exposure to radon and daughters. Clearly, different individuals exposed to the same air would receive different doses. Broad models, based on the above parameters have been devised to assess average doses to average individuals for dosimetry purposes. Results from such models vary by factors of about 2 or 3, but this represents only one part of the sources of uncertainty in estimating risks from Radon exposure.

In the previous section, we investigated main sources of uncertainty in the probabilistic fire load procedure. There are many parameters to be used for the risk assessment. Each parameter is uncertain and can be expressed by probability distributions. For instance, when leak size distributions are represented by uniform distribution with specific ranges. Representative values are randomly selected in accordance with their distributions. These sets of values compose the sets of representative scenarios and are also used as input for simulating time-dependent leak rate profile and radiation intensity of fire. 

Describe three sources of uncertainty in carcinogenic risk assessment. 

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. 

Variability and uncertainty affect every element of every risk assessment. For example, participants in the European Workshop on Probabilistic Risk Assessment for the Environmental Impacts of Plant Protection Products (EUPR A) were asked to list sources of uncertainty affecting current procedures for assessing pesticide risks to aquatic... 

Some key sources of uncertainty affecting current risk assessments for pesticides in Europe, as listed by the EUPRA workshop (Hart 2001)... 

Some sources of uncertainty and variability may have so little influence on risk that they can be held constant and not treated probabilistically in the assessment. The analysis plan should state the rationale for deciding which variables and hypotheses this applies to (USEPA 1998). 

For assessing the risks of chemicals, the approach is similar to that used with radiation in those cases where human data are available, but the data are rarely as complete as with radiation. Furthermore, estimation of the dose is usually more difficult with chemicals because of the lack of good monitoring data and other sources of uncertainty (see Section 5). For example, the dose is not usually well quantified even at levels of exposure where carcinogenic effects are conspicuous. 

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. 

Diagrams can be used to illustrate the relationships described by the conceptual model and risk hypotheses. Conceptual model diagrams are useful tools for communicating important pathways and for identifying major sources of uncertainty. These diagrams and risk hypotheses can be used to identify the most important pathways and relationships to consider in the analysis phase. The hypotheses considered most likely to contribute to risk are identified for subsequent evaluation in the risk assessment. 

Sensitivity analysis can be used to identify and prioritize key sources of uncertainty or variability. Knowledge of key sources of uncertainty and their relative importance to the assessment end-point is useful in determining whether additional data collection or research would be useful in an attempt to reduce uncertainty. If uncertainty can be reduced in an important model input, then the corresponding uncertainty in the model output would also be reduced. Knowledge of key sources of controllable variability, their relative importance and critical limits is useful in developing risk management options. 

Through the review of approaches to explicit qualitative consideration of contributing sources, this section offers a framework to facilitate and promote a qualitative consideration of the impact of uncertainties on exposure assessment where data are very limited and/or as a prelude to more quantitative characterization of uncertainties. Transparency to address uncertainties and specification of those uncertainties that impact most on outcome are essential to effective decision-making in risk management. 

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