The Effects of Urban Form on Ambient Air Pollution and Public Health Risk: A Case Study in Raleigh,North Carolina

Since motor vehicles are a major air pollution source, urban designs that decrease private automobile use could improve air quality and decrease air pollution health risks. Yet, the relationships among urban form, air quality, and health are complex and not fully understood. To explore these relationships, we model the effects of three alternative development scenarios on annual average fine particulate matter (PM_2.5) concentrations in ambient air and associated health risks from PM_2.5 exposure in North Carolina's Raleigh‐Durham‐Chapel Hill area. We integrate transportation demand, land‐use regression, and health risk assessment models to predict air quality and health impacts for three development scenarios: current conditions, compact development, and sprawling development. Compact development slightly decreases (?0.2%) point estimates of regional annual average PM_2.5 concentrations, while sprawling development slightly increases (+1%) concentrations. However, point estimates of health impacts are in opposite directions: compact development increases (+39%) and sprawling development decreases (?33%) PM_2.5‐attributable mortality. Furthermore, compactness increases local variation in PM_2.5 concentrations and increases the severity of local air pollution hotspots. Hence, this research suggests that while compact development may improve air quality from a regional perspective, it may also increase the concentration of PM_2.5 in local hotspots and increase population exposure to PM_2.5. Health effects may be magnified if compact neighborhoods and PM_2.5 hotspots are spatially co‐located. We conclude that compactness alone is an insufficient means of reducing the public health impacts of transportation emissions in automobile‐dependent regions. Rather, additional measures are needed to decrease automobile dependence and the health risks of transportation emissions.     

Estimating the National Public Health Burden Associated with Exposure to Ambient PM2.5 and Ozone

Ground‐level ozone (O₃) and fine particulate matter (PM_2.5) are associated with increased risk of mortality. We quantify the burden of modeled 2005 concentrations of O₃ and PM_2.5 on health in the United States. We use the photochemical Community Multiscale Air Quality (CMAQ) model in conjunction with ambient monitored data to create fused surfaces of summer season average 8‐hour ozone and annual mean PM_2.5 levels at a 12 km grid resolution across the continental United States. Employing spatially resolved demographic and concentration data, we assess the spatial and age distribution of air‐pollution‐related mortality and morbidity. For both PM_2.5 and O₃ we also estimate: the percentage of total deaths due to each pollutant; the reduction in life years and life expectancy; and the deaths avoided according to hypothetical air quality improvements. Using PM_2.5 and O₃ mortality risk coefficients drawn from the long‐term American Cancer Society (ACS) cohort study and National Mortality and Morbidity Air Pollution Study (NMMAPS), respectively, we estimate 130,000 PM_2.5‐related deaths and 4,700 ozone‐related deaths to result from 2005 air quality levels. Among populations aged 65–99, we estimate nearly 1.1 million life years lost from PM_2.5 exposure and approximately 36,000 life years lost from ozone exposure. Among the 10 most populous counties, the percentage of deaths attributable to PM_2.5 and ozone ranges from 3.5% in San Jose to 10% in Los Angeles. These results show that despite significant improvements in air quality in recent decades, recent levels of PM_2.5 and ozone still pose a nontrivial risk to public health.     

Current and Future Particulate‐Matter‐Related Mortality Risks in the United States from Aviation Emissions During Landing and Takeoff

Demand for air travel is projected to increase in the upcoming years, with a corresponding influence on emissions, air quality, and public health. The trajectory of health impacts would be influenced by not just emissions growth, but also changes in nonaviation ambient concentrations that influence secondary fine particulate matter (PM_2.5) formation, population growth and aging, and potential shifts in PM_2.5 concentration‐response functions (CRFs). However, studies to date have not systematically evaluated the individual and joint contributions of these factors to health risk trajectories. In this study, we simulated emissions during landing and takeoff from aircraft at 99 airports across the United States for 2005 and for a 2025 flight activity projection scenario. We applied the Community Multiscale Air Quality (CMAQ) model with the Speciated Modeled Attainment Test (SMAT) to determine the contributions of these emissions to ambient concentrations, including scenarios with 2025 aircraft emissions and 2005 nonaviation air quality. We combined CMAQ outputs with PM_2.5 mortality CRFs and population projections, and evaluated the influence of changing emissions, nonaviation concentrations, and population factors. Given these scenarios, aviation‐related health impacts would increase by a factor of 6.1 from 2005 to 2025, with a factor of 2.1 attributable to emissions, a factor of 1.3 attributable to population factors, and a factor of 2.3 attributable to changing nonaviation concentrations which enhance secondary PM_2.5 formation. Our study emphasizes that the public health burden of aviation emissions would be significantly influenced by the joint effects of flight activity increases, nonaviation concentration changes, and population growth and aging.     

Why income inequality indexes do not apply to health risks

Several recent papers have sought to apply inequality measures from economics, such as the Atkinson Index (AI) for inequality of income distributions, to compare the risk inequality of different mortality risk distributions in an effort to help promote efficiency and environmental justice in pollution-reducing interventions. Closer analysis suggests that such applications are neither logically coherent nor necessarily ethically desirable. Risk inequality comparisons should be based on axioms that apply to probabilistic risks, and should consider the multidimensional and time-varying nature of individual and community risks in order to increase efficiency and justice over time and generations. In light of the limitations of the AI applied to mortality risk distributions, it has not been demonstrated to have ethical or practical value in helping policymakers to identify air pollution management interventions that reduce (or minimize) risk and risk inequity.     

Approximations for Estimating Change in Life Expectancy Attributable to Air Pollution in Relation to Multiple Causes of Death Using a Cause Modified Life Table

There is considerable debate as to the most appropriate metric for characterizing the mortality impacts of air pollution. Life expectancy has been advocated as an informative measure. Although the life‐table calculus is relatively straightforward, it becomes increasingly cumbersome when repeated over large numbers of geographic areas and for multiple causes of death. Two simplifying assumptions were evaluated: linearity of the relation between excess rate ratio and change in life expectancy, and additivity of cause‐specific life‐table calculations. We employed excess rate ratios linking PM_2.5 and mortality from cerebrovascular disease, chronic obstructive pulmonary disease, ischemic heart disease, and lung cancer derived from a meta‐analysis of worldwide cohort studies. As a sensitivity analysis, we employed an integrated exposure response function based on the observed risk of PM_2.5 over a wide range of concentrations from ambient exposure, indoor exposure, second‐hand smoke, and personal smoking. Impacts were estimated in relation to a change in PM_2.5 from 19.5 μg/m³ estimated for Toronto to an estimated natural background concentration of 1.8 μg/m³. Estimated changes in life expectancy varied linearly with excess rate ratios, but at higher values the relationship was more accurately represented as a nonlinear function. Changes in life expectancy attributed to specific causes of death were additive with maximum error of 10%. Results were sensitive to assumptions about the air pollution concentration below which effects on mortality were not quantified. We have demonstrated valid approximations comprising expression of change in life expectancy as a function of excess mortality and summation across multiple causes of death.     

Expert Elicitation of the Value per Statistical Life in an Air Pollution Context

The monetized value of avoided premature mortality typically dominates the calculated benefits of air pollution regulations; therefore, characterization of the uncertainty surrounding these estimates is key to good policymaking. Formal expert judgment elicitation methods are one means of characterizing this uncertainty. They have been applied to characterize uncertainty in the mortality concentration‐response function, but have yet to be used to characterize uncertainty in the economic values placed on avoided mortality. We report the findings of a pilot expert judgment study for Health Canada designed to elicit quantitative probabilistic judgments of uncertainties in Value‐per‐Statistical‐Life (VSL) estimates for use in an air pollution context. The two‐stage elicitation addressed uncertainties in both a base case VSL for a reduction in mortality risk from traumatic accidents and in benefits transfer‐related adjustments to the base case for an air quality application (e.g., adjustments for age, income, and health status). Results for each expert were integrated to develop example quantitative probabilistic uncertainty distributions for VSL that could be incorporated into air quality models.     

Evaluating Efficiency-Equality Tradeoffs for Mobile Source Control Strategies in an Urban Area

In environmental risk management, there are often interests in maximizing public health benefits (efficiency) and addressing inequality in the distribution of health outcomes. However, both dimensions are not generally considered within a single analytical framework. In this study, we estimate both total population health benefits and changes in quantitative indicators of health inequality for a number of alternative spatial distributions of diesel particulate filter retrofits across half of an urban bus fleet in Boston, Massachusetts. We focus on the impact of emissions controls on primary fine particulate matter (PM_2.5) emissions, modeling the effect on PM_2.5 concentrations and premature mortality. Given spatial heterogeneity in baseline mortality rates, we apply the Atkinson index and other inequality indicators to quantify changes in the distribution of mortality risk. Across the different spatial distributions of control strategies, the public health benefits varied by more than a factor of two, related to factors such as mileage driven per day, population density near roadways, and baseline mortality rates in exposed populations. Changes in health inequality indicators varied across control strategies, with the subset of optimal strategies considering both efficiency and equality generally robust across different parametric assumptions and inequality indicators. Our analysis demonstrates the viability of formal analytical approaches to jointly address both efficiency and equality in risk assessment, providing a tool for decisionmakers who wish to consider both issues.     

A New Air Quality Perception Scale for Global Assessment of Air Pollution Health Effects

Despite improvements in air quality in developed countries, air pollution remains a major public health issue. To fully assess the health impact, we must consider that air pollution exposure has both physical and psychological effects; this latter dimension, less documented, is more difficult to measure and subjective indicators constitute an appropriate alternative. In this context, this work presents the methodological development of a new scale to measure the perception of air quality, useful as an exposure or risk appraisal metric in public health contexts. On the basis of the responses from 2,522 subjects in eight French cities, psychometric methods are used to construct the scale from 22 items that assess risk perception (anxiety about health and quality of life) and the extent to which air pollution is a nuisance (sensorial perception and symptoms). The scale is robust, reproducible, and discriminates between subpopulations more susceptible to poor air pollution perception. The individual risk factors of poor air pollution perception are coherent with those findings in the risk perception literature. Perception of air pollution by the general public is a key issue in the development of comprehensive risk assessment studies as well as in air pollution risk management and policy. This study offers a useful new tool to measure such efforts and to help set priorities for air quality improvements in combination with air quality measurements.     

Finely Resolved On‐Road PM2.5 and Estimated Premature Mortality in Central North Carolina

To quantify the on‐road PM_2.5‐related premature mortality at a national scale, previous approaches to estimate concentrations at a 12‐km × 12‐km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on‐road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on‐road PM_2.5‐related premature mortality in central North Carolina with two different concentration estimation approaches—(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36‐km × 36‐km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space–time interpolation technique to provide annual average PM_2.5 concentrations at a Census‐block level (～105,000 Census blocks). The hybrid modeling approach estimated 24% more on‐road PM_2.5‐related premature mortality than CMAQ. The major difference is from the primary on‐road PM_2.5 where the hybrid approach estimated 2.5 times more primary on‐road PM_2.5‐related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on‐road PM_2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near‐road primary PM_2.5 and suggesting that previous studies may have underestimated premature mortality due to PM_2.5 from traffic‐related emissions.     

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks

Decision biases can distort cost‐benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well‐documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk‐cost‐benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk‐reducing regulations. Low‐regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low‐regret learning strategies using regulation of air pollutants with uncertain health effects as an example.     

Enhancing the Characterization of Epistemic Uncertainties in PM2.5 Risk Analyses

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 (PM_2.5). 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 PM_2.5 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 PM_2.5 constituents. It also summarizes findings from a review of U.S.‐based epidemiological evidence regarding the PM_2.5 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.     

Using Uncertainty Analysis to Improve Consistency in Regulatory Assessments of Criteria Pollutant Standards

Regulatory 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 (PM_2.5). 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 PM_2.5.     

Uncertain Benefits Estimates for Reductions in Fine Particle Concentrations

The Environmental Protection Agency's (EPA's) estimates of the benefits of improved air quality, especially from reduced mortality associated with reductions in fine particle concentrations, constitute the largest category of benefits from all federal regulation over the last decade. EPA develops such estimates, however, using an approach little changed since a 2002 report by the National Research Council (NRC), which was critical of EPA's methods and recommended a more comprehensive uncertainty analysis incorporating probability distributions for major sources of uncertainty. Consistent with the NRC's 2002 recommendations, we explore alternative assumptions and probability distributions for the major variables used to calculate the value of mortality benefits. For metropolitan Philadelphia, we show that uncertainty in air quality improvements and in baseline mortality have only modest effects on the distribution of estimated benefits. We analyze the effects of alternative assumptions regarding the value of reducing mortality risk, whether the toxicity is above or below the average for fine particles, and whether there is a threshold in the concentration‐response relationship, and show these assumptions all have large effects on the distribution of benefits.     

Challenges in Development and Implementation of Health‐Risk‐Based Soil Quality Guidelines: Turkey's Experience

Management of contaminated sites is a critical environmental issue around the world due to the human health risk involved for many sites and scarcity of funding. Moreover, clean‐up costs of all contaminated sites to their background levels with existing engineering technologies may be financially infeasible and demand extended periods of operation time. Given these constraints, to achieve optimal utilization of available funds and prioritization of contaminated sites that need immediate attention, health‐risk‐based soil quality guidelines should be preferred over the traditional soil quality standards. For these reasons, traditional soil quality standards are being replaced by health‐risk‐based ones in many countries and in Turkey as well. The need for health‐risk‐based guidelines is clear, but developing these guidelines and implementation of them in contaminated site management is not a straightforward process. The goal of this study is to highlight the problems that are encountered at various stages of the development process of risk‐based soil quality guidelines for Turkey and how they are dealt with. Utilization of different definitions and methodologies at different countries, existence of inconsistent risk assessment tools, difficulties in accessing relevant documents and reports, and lack of specific data required for Turkey are among these problems. We believe that Turkey's experience may help other countries that are planning to develop health‐risk‐based guidelines achieve their goals in a more efficient manner.     

Uncertainty and Variability in Health-Related Damages from Coal-Fired Power Plants in the United States

The health‐related damages associated with emissions from coal‐fired power plants can vary greatly across facilities as a function of plant, site, and population characteristics, but the degree of variability and the contributing factors have not been formally evaluated. In this study, we modeled the monetized damages associated with 407 coal‐fired power plants in the United States, focusing on premature mortality from fine particulate matter (PM_2.5). We applied a reduced‐form chemistry‐transport model accounting for primary PM_2.5 emissions and the influence of sulfur dioxide (SO₂) and nitrogen oxide (NO_x) emissions on secondary particulate formation. Outputs were linked with a concentration‐response function for PM_2.5‐related mortality that incorporated nonlinearities and model uncertainty. We valued mortality with a value of statistical life approach, characterizing and propagating uncertainties in all model elements. At the median of the plant‐specific uncertainty distributions, damages across plants ranged from $30,000 to $500,000 per ton of PM_2.5, $6,000 to $50,000 per ton of SO₂, $500 to $15,000 per ton of NO_x, and $0.02 to $1.57 per kilowatt‐hour of electricity generated. Variability in damages per ton of emissions was almost entirely explained by population exposure per unit emissions (intake fraction), which itself was related to atmospheric conditions and the population size at various distances from the power plant. Variability in damages per kilowatt‐hour was highly correlated with SO₂ emissions, related to fuel and control technology characteristics, but was also correlated with atmospheric conditions and population size at various distances. Our findings emphasize that control strategies that consider variability in damages across facilities would yield more efficient outcomes.     

Acculturation,Ethnicity, and Air Pollution Perceptions

A globalizing world increases immigration between nations, raising the question of how acculturation (or its lack) of immigrants and their descendants to host societies affects risk perceptions. A survey of Paterson, New Jersey, residents tested acculturation's associations with attitudes to air pollution and its management, and knowledge of and self‐reported behaviors concerning air pollution. Linguistic and temporal proxy measures for acculturation were independent variables along with ethnicity, plus controls for gender, age, education, and income in multivariate analyses. About one‐fifth of contrasts between non‐Hispanic whites, non‐Hispanic blacks, English‐interviewed Hispanics, and Spanish‐interviewed Hispanics were statistically significant (Bonferroni‐corrected) and of medium or higher affect size, with most featuring the Spanish‐interviewed Hispanics. Knowledge variables featured the most significant differences. Specifically, Spanish‐interviewed Hispanics reported less concern, familiarity with pollution, recognition of high pollution, and vigorous outdoor activity, and greater belief that government overregulates pollution than English‐interviewed Hispanics (and than the other two groups on most of these variables too). English‐interviewed Hispanics did not differ from non‐Hispanic whites, but did on several variables from non‐Hispanic blacks. Temporal proxies of acculturation among the foreign‐born were far less significant, but concern and familiarity with air pollution increased with time spent in the United States, while belief in overregulation and a positive trend in New Jersey pollution increased with time in the nation of origin. Implications of these acculturation and ethnicity findings for risk perception/communication research and practice are discussed.     

Miscommunicating Risk,Uncertainty, and Causation: Fine Particulate Air Pollution and Mortality Risk as an Example

A recent paper in this journal (Fann et al., 2012) estimated that “about 80,000 premature mortalities would be avoided by lowering PM_2.5 levels to 5 μg/m³ nationwide” and that 2005 levels of PM_2.5 cause about 130,000 premature mortalities per year among people over age 29, with a 95% confidence interval of 51,000 to 200,000 premature mortalities per year.⁽¹⁾ These conclusions depend entirely on misinterpreting statistical coefficients describing the association between PM_2.5 and mortality rates in selected studies and models as if they were known to be valid causal coefficients. But they are not, and both the expert opinions of EPA researchers and analysis of data suggest that a true value of zero for the PM_2.5 mortality causal coefficient is not excluded by available data. Presenting continuous confidence intervals that exclude the discrete possibility of zero misrepresents what is currently known (and not known) about the hypothesized causal relation between changes in PM_2.5 levels and changes in mortality rates, suggesting greater certainty about projected health benefits than is justified.     

A Bayesian Approach to Integrated Ecological and Human Health Risk Assessment for the South River,Virginia Mercury‐Contaminated Site

We conducted a regional‐scale integrated ecological and human health risk assessment by applying the relative risk model with Bayesian networks (BN‐RRM) to a case study of the South River, Virginia mercury‐contaminated site. Risk to four ecological services of the South River (human health, water quality, recreation, and the recreational fishery) was evaluated using a multiple stressor–multiple endpoint approach. These four ecological services were selected as endpoints based on stakeholder feedback and prioritized management goals for the river. The BN‐RRM approach allowed for the calculation of relative risk to 14 biotic, human health, recreation, and water quality endpoints from chemical and ecological stressors in five risk regions of the South River. Results indicated that water quality and the recreational fishery were the ecological services at highest risk in the South River. Human health risk for users of the South River was low relative to the risk to other endpoints. Risk to recreation in the South River was moderate with little spatial variability among the five risk regions. Sensitivity and uncertainty analysis identified stressors and other parameters that influence risk for each endpoint in each risk region. This research demonstrates a probabilistic approach to integrated ecological and human health risk assessment that considers the effects of chemical and ecological stressors across the landscape.     

The Risk Screening for Indoor Air Pollution Chemicals in Japan

In recent years, public health problems caused by indoor air pollution have been drawing strong public concern in Japan. After conducting extensive exposure assessment, governmental agencies have taken effective measures to solve the problem; for instance, "Guidelines for indoor air quality (IAQ)" of 13 chemicals, for example, formaldehyde, toluene, and xylene, has been established. Thousands of chemicals have been identified in the indoor environment. Priority rating of those chemicals, however, was not based on the health risk level. We developed a risk-screening scheme for indoor air pollution chemicals and analyzed the current status of the risk levels of those chemicals in Japan. We researched scientific knowledge of health hazards and exposure surveys of indoor air pollution chemicals in Japan, and classified those chemicals based on the health risk level estimated from the scheme. The risk levels of 93 chemicals were characterized and six chemicals (formaldehyde, acrolein, 1,4-dichlorobenzene, benzene, tetrachloroethylene, and benzo(a)pyrene) were classified in the highest risk category.     

The Application of Genetic Information for Regulatory Standard Setting Under the Clean Air Act: A Decision-Analytic Approach

In 2002, the U.S. Environmental Protection Agency (EPA) released an “Interim Policy on Genomics,” stating a commitment to developing guidance on the inclusion of genetic information in regulatory decision making. This statement was followed in 2004 by a document exploring the potential implications. Genetic information can play a key role in understanding and quantifying human susceptibility, an essential step in many of the risk assessments used to shape policy. For example, the federal Clean Air Act (CAA) requires EPA to set National Ambient Air Quality Standards (NAAQS) for criteria pollutants at levels to protect even sensitive populations from adverse health effects with an adequate margin of safety. Asthmatics are generally regarded as a sensitive population, yet substantial research gaps in understanding genetic susceptibility and disease have hindered quantitative risk analysis. This case study assesses the potential role of genomic information regarding susceptible populations in the NAAQS process for fine particulate matter (PM_2.5) under the CAA. In this initial assessment, we model the contribution of a single polymorphism to asthma risk and mortality risk; however, multiple polymorphisms and interactions (gene‐gene and gene‐environment) are known to play key roles in the disease process. We show that the impact of new information about susceptibility on estimates of population risk or average risk derived from large epidemiological studies depends on the circumstances. We also suggest that analysis of a single polymorphism, or other risk factor such as health status, may or may not change estimates of individual risk enough to alter a particular regulatory decision, but this depends on specific characteristics of the decision and risk information. We also show how new information about susceptibility in the context of the NAAQS for PM_2.5 could have a large impact on the estimated distribution of individual risk. This would occur if a group were consequently identified (based on genetic and/or disease status), that accounted for a disproportionate share of observed effects. Our results highlight certain conditions under which genetic information is likely to have an impact on risk estimates and the balance of costs and benefits within groups, and highlight critical research needs. As future studies explore more fully the relationship between exposure, genetic makeup, and disease status, the opportunity for genetic information and disease status to play pivotal roles in regulation can only increase.