Environmental Decisions in the Face of Uncertainty (2013) / Chapter Skim
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2 Risk Assessment and Uncertainty
2 Risk Assessment and Uncertainty
Pages 47-72
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From page 47... ...
decisions. This chapter discusses the uncertainty in the data and the analyses associated with one of those factors, human health risk estimates.
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Box 2-1 offers descriptions of some of the important terms in this area. 1 TheNational Research Council study that led to the Red Book was congressionally mandated and was requested "to strengthen the reliability and objectivity of scientific assessment that forms the basis for federal regulatory policies applicable to carcinogens and other public health hazards" (NRC, 1983, p.
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(Box 2-2 provides BOX 2-2 Development of Estimates of Human Health Risks for Non-Cancer Endpointsa When assessing the risks to human health from a chemical for a non-cancer endpoint, EPA typically develops a reference dose (RfD) .b EPA defines an RfD as an "estimate (with uncertainty spanning perhaps an order of magnitude)
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A hallmark of the modern risk-assessment framework is the expectation not only that the scientific evidence is described, but also that the evaluation of the evidence and any judgments about the quality and relevance of the evidence to the risk assessors are thoroughly and clearly described (OMB and OSTP, 2007)
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. Risk assessments are frequently used by EPA to characterize health risks under existing exposure conditions and also to examine how risks will change if actions are taken to alter exposures (EPA, 2012b)
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However, the scientific uncertainties associated with such predictive efforts include not only the uncertainty associated with the available knowledge but also uncertainty related to the predictive nature of estimates (for example, predicting how much of a decrease in air pollution different control technologies will produce or predicting how many lung cancer cases will be avoided by a given decrease in air pollution)
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Studies in humans that evaluate whether exposure to a substance causes specific adverse effects can provide the most relevant information on hazards and dose response. Clinical trials have a greater chance of yielding unambiguous results regarding causality than do observational studies (Gray-Donald and Kramer, 1988)
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. The issues in the TCE risk assessment illustrate several uncertainties and related choices that risk assessors and decision makers face when evaluating the risk potential of envi ronmental carcinogens, as well as the delays that such uncertainties can lead to.
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be taken into account when considering the duration of exposure for animal studies. It is important to note, however, that despite those limitations enough is known about the similarities and differences between humans and experimental animals to make them relevant to and critical for assessing human health risks (EPA, 2011a)
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. Risk assessments need to account for possible differences in response between the populations that were studied to understand hazards and dose response in the target population, which typically is more diverse than the population studied (Pope, 2000)
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In practice, in reviewing the scientific information available on specific substances or exposures, it becomes clear that there are significant gaps in knowledge or information; agency human health risk assessors adopt the relevant default specified in the guidelines. For example, to account for uncertainties in how to extrapolate from animal data to risks in humans, the default uncertainty factor is 10.
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If that upper bound is itself in the negligible risk range, the uncertainty statement allows the decision maker to assert that any actual risks are likely to be below the negligible range. Fourth, the use of a single point estimate and defaults allows for a simpler risk-communication message.
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If those factors more accurately reflect the differences between animals and humans than default adjustment factors, the use of such dataderived extrapolation factors would decrease the uncertainty in the risk assessment. EPA agrees with the NRC report that specific knowledge should supplant the use of defaults when appropriate and it has adopted that as a general principle (EPA, 2005a)
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, EPA uses a number of different approaches to quantify the uncertainty and variability in different components of a risk assessment. One such method is Monte Carlo analysis, a technique that propagates uncertainty -- including variability and heterogeneity as well as model and parameter uncertainty -- in the various components of the human health risk assessment (for example, in the exposures, toxicokinetics, and the dose response)
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Below, the committee briefly summarizes three risk assessments -- of arsenic in drinking water, the Clean Air Interstate Rule (CAIR) , and of methylmercury -- to highlight how EPA incorporates uncertainty analyses into its estimates of health risks and uses that information in its decisions.
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, concludes that "the current EPA MCL for arsenic in drinking water of 50 μg/L does not achieve EPA's goal for public-health protection and, therefore, requires downward revision as promptly as possible" (p.
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. The example of arsenic in drinking water illustrates the broad spectrum of uncertainty and sensitivity analyses that can be conducted when estimating human health risks.
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Clean Air Interstate Rule In 2005, EPA published its regulatory impact analysis (RIA) for CAIR, a rule developed to implement requirements of the Clean Air Act concerning the transport of air pollution across state boundaries (EPA, 2005b)
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. The uncertainty analyses, however, focus to a large extent on the uncertainties in the health benefits, and not the uncertainties in costs and technological factors.
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all had published risk assessments that used different methods and relied on different studies for their estimates of health risks. The estimates of a "safe" level of exposure from the three different agencies were an RfD of 0.1 microgram/kg/day from EPA, an action level of 0.5 microgram/ kg/day from FDA, and a minimal risk level of 0.3 microgram/kg/day from ATSDR.
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Given that in the regulatory impact analysis much of the monetized benefits come from co-benefits due to decreased PM2.5related premature mortalities, the lack of detailed uncertainty analyses for mercury might be appropriate. The estimated benefits from PM2.5 reductions are presented as a range ($37 billion to $990 billion + B)
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• Regardless of whether agent-specific research-based factors or default adjustment factors are used, communicating the basis of adjustment factors and their impact on human health risk esti mates to decision makers and stakeholders is critical for regulatory decisions. • EPA has made great strides in assessing the uncertainties in risk estimates, for example, by developing and applying probabilistic techniques and Monte Carlo analysis to uncertainty analysis.
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1998b. Study of hazardous air pollutant emissions from electric utility steam generat ing units -- final report to Congress.
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2005b. Regulatory impact analysis for the final Clean Air Interstate Rule.
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2006. Health risks from dioxin and related compounds.
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