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AbstractRegulatory impact analyses (RIAs), required for new major federal regulations, are often criticized for not incorporating epistemic uncertainties into their quantitative estimates of benefits and costs. "Integrated uncertainty analysis," which relies on subjective judgments about epistemic uncertainty to quantitatively combine epistemic and statistical uncertainties, is often prescribed. This article identifies an additional source for subjective judgment regarding a key epistemic uncertainty in RIAs for National Ambient Air Quality Standards (NAAQS)—the regulator's degree of confidence in continuation of the relationship between pollutant concentration and health effects at varying concentration levels. An illustrative example is provided based on the 2013 decision on the NAAQS for fine particulate matter (PM2.5). It shows how the regulator's justification for setting that NAAQS was structured around the regulator's subjective confidence in the continuation of health risks at different concentration levels, and it illustrates how such expressions of uncertainty might be directly incorporated into the risk reduction calculations used in the rule's RIA. The resulting confidence‐weighted quantitative risk estimates are found to be substantially different from those in the RIA for that rule. This approach for accounting for an important source of subjective uncertainty also offers the advantage of establishing consistency between the scientific assumptions underlying RIA risk and benefit estimates and the science‐based judgments developed when deciding on the relevant standards for important air pollutants such as PM2.5.

AbstractThe U.S. Clean Air Act (CAA) requires the Administrator of the U.S. Environmental Protection Agency (EPA) to set and periodically review national ambient air quality standards (NAAQS) for criteria pollutants. Because NAAQS must be set without balancing health risks against cost, Administrators look for where health risk tapers off. For some pollutants, however, no evidence exists of such a diminishment. The Administrator must instead evaluate how the strength of evidence for the scientific validity of risk estimates weakens for exposure levels below the central mass of observations indicating a pollutant–health risk relationship. Such an evaluation requires judgments about uncertainties that are inherently subjective. The risk assessments the Agency prepares during NAAQS reviews provide a natural platform for quantitatively characterizing these subjective uncertainty judgments, but the Agency is no longer making use of this opportunity. This article describes EPA's early development of methods to quantitatively characterize subjective uncertainty in NAAQS risk assessments, then traces the progressive elimination of such uncertainty analysis in the risk assessments for the three past NAAQS reviews for fine particulate matter (PM2.5), even while judgments about this uncertainty were becoming increasingly central to Administrators' NAAQS decisions. As a result, the risk assessments now lack relevance to NAAQS decision making. To reestablish a meaningful decision‐support role for NAAQS risk assessments, this article suggests alterations to the process of preparing them. Taking no position on the scientific or legal appropriateness of past NAAQS decisions, the suggested process is intended to better synthesize the scientific evidence to better inform (without constraining) the Administrator's policy decisions.

This article describes inconsistencies between health risk analyses that the U.S. Environmental Protection Agency (EPA) uses to support its decisions on primary National Ambient Air Quality Standards (NAAQS), and in the associated Regulatory Impact Analyses (RIAs) that accompany each NAAQS rulemaking. Quantitative risk estimates are prepared during the NAAQS‐setting deliberations using inputs derived from statistical associations between measured pollutant concentrations and health effects. The resulting risk estimates are not directly used to set a NAAQS, but incorporated into a broader evidence‐based rationale for the standard that is intended to demonstrate conformity with the statutory requirement that primary NAAQS protect the public health with a margin of safety. In a separate process, EPA staff rely on the same risk calculations to prepare estimates of the benefits of the rule that are reported in its RIA for the standard. Although NAAQS rules and their RIAs are released simultaneously, the rationales used to set the NAAQS have become inconsistent with their RIAs' estimates of benefits, with very large fractions of RIAs' risk‐reduction estimates being attributed to populations living in areas that will already be attaining the respective NAAQS. This article explains the source of this inconsistency and provides a quantitative example based on the 2012 revision of the fine particulate matter (PM2.5) primary NAAQS. This article also demonstrates how this inconsistency is amplified when criteria pollutant co‐benefits are calculated in RIAs for non‐NAAQS rules, using quantitative examples from the 2011 Mercury and Air Toxics Standards and the currently proposed Clean Power Plan.

The Environmental Benefits Mapping and Analysis Program (BenMAP) is a software tool developed by the U.S. Environmental Protection Agency (EPA) that is widely used inside and outside of EPA to produce quantitative estimates of public health risks from fine particulate matter (PM2.5). This article discusses the purpose and appropriate role of a risk analysis tool to support risk management deliberations, and evaluates the functions of BenMAP in this context. It highlights the importance in quantitative risk analyses of characterization of epistemic uncertainty, or outright lack of knowledge, about the true risk relationships being quantified. This article describes and quantitatively illustrates sensitivities of PM2.5 risk estimates to several key forms of epistemic uncertainty that pervade those calculations: the risk coefficient, shape of the risk function, and the relative toxicity of individual PM2.5 constituents. It also summarizes findings from a review of U.S.‐based epidemiological evidence regarding the PM2.5 risk coefficient for mortality from long‐term exposure. That review shows that the set of risk coefficients embedded in BenMAP substantially understates the range in the literature. We conclude that BenMAP would more usefully fulfill its role as a risk analysis support tool if its functions were extended to better enable and prompt its users to characterize the epistemic uncertainties in their risk calculations. This requires expanded automatic sensitivity analysis functions and more recognition of the full range of uncertainty in risk coefficients.

AbstractThe U.S. Environmental Protection Agency (EPA) uses health risk assessment to help inform its decisions in setting national ambient air quality standards (NAAQS). EPA's standard approach is to make epidemiologically‐based risk estimates based on a single statistical model selected from the scientific literature, called the "core" model. The uncertainty presented for "core" risk estimates reflects only the statistical uncertainty associated with that one model's concentration‐response function parameter estimate(s). However, epidemiologically‐based risk estimates are also subject to "model uncertainty," which is a lack of knowledge about which of many plausible model specifications and data sets best reflects the true relationship between health and ambient pollutant concentrations. In 2002, a National Academies of Sciences (NAS) committee recommended that model uncertainty be integrated into EPA's standard risk analysis approach. This article discusses how model uncertainty can be taken into account with an integrated uncertainty analysis (IUA) of health risk estimates. It provides an illustrative numerical example based on risk of premature death from respiratory mortality due to long‐term exposures to ambient ozone, which is a health risk considered in the 2015 ozone NAAQS decision. This example demonstrates that use of IUA to quantitatively incorporate key model uncertainties into risk estimates produces a substantially altered understanding of the potential public health gain of a NAAQS policy decision, and that IUA can also produce more helpful insights to guide that decision, such as evidence of decreasing incremental health gains from progressive tightening of a NAAQS.