Bounding Analysis of Drinking Water Health Risks from a Spill of Hydraulic Fracturing Flowback Water

A bounding risk assessment is presented that evaluates possible human health risk from a hypothetical scenario involving a 10,000‐gallon release of flowback water from horizontal fracturing of Marcellus Shale. The water is assumed to be spilled on the ground, infiltrates into groundwater that is a source of drinking water, and an adult and child located downgradient drink the groundwater. Key uncertainties in estimating risk are given explicit quantitative treatment using Monte Carlo analysis. Chemicals that contribute significantly to estimated health risks are identified, as are key uncertainties and variables to which risk estimates are sensitive. The results show that hypothetical exposure via drinking water impacted by chemicals in Marcellus Shale flowback water, assumed to be spilled onto the ground surface, results in predicted bounds between 10⁻¹⁰ and 10⁻⁶ (for both adult and child receptors) for excess lifetime cancer risk. Cumulative hazard indices (HI_CUMULATIVE) resulting from these hypothetical exposures have predicted bounds (5th to 95th percentile) between 0.02 and 35 for assumed adult receptors and 0.1 and 146 for assumed child receptors. Predicted health risks are dominated by noncancer endpoints related to ingestion of barium and lithium in impacted groundwater. Hazard indices above unity are largely related to exposure to lithium. Salinity taste thresholds are likely to be exceeded before drinking water exposures result in adverse health effects. The findings provide focus for policy discussions concerning flowback water risk management. They also indicate ways to improve the ability to estimate health risks from drinking water impacted by a flowback water spill (i.e., reducing uncertainty).     

Environmental Concerns in the Marcellus Shale

Hydraulic fracturing used to remove natural gas from the Marcellus Shale has raised environmental concerns in the region both in terms of air and water pollution. This article will examine those concerns and how the natural gas industry has responded to them. After discussing the issues related to groundwater contamination and air quality. I discuss industry responses and how the costs and harm associated with fracking could be reduced, with the knowledge that despite opposition from environmental groups, fracking will continue. The hope is that more drillers will begin to operate in a socially responsible manner that will allow companies to be profitable while limiting harm to the environment and to individuals living near drilling sites.     

On the Need for Restricting the Probabilistic Analysis in Risk Assessments to Variability

It is common perspective in risk analysis that there are two kinds of uncertainties: i) variability as resulting from heterogeneity and stochasticity (aleatory uncertainty) and ii) partial ignorance or epistemic uncertainties resulting from systematic measurement error and lack of knowledge. Probability theory is recognized as the proper tool for treating the aleatory uncertainties, but there are different views on what is the best approach for describing partial ignorance and epistemic uncertainties. Subjective probabilities are often used for representing this type of ignorance and uncertainties, but several alternative approaches have been suggested, including interval analysis, probability bound analysis, and bounds based on evidence theory. It is argued that probability theory generates too precise results when the background knowledge of the probabilities is poor. In this article, we look more closely into this issue. We argue that this critique of probability theory is based on a conception of risk assessment being a tool to objectively report on the true risk and variabilities. If risk assessment is seen instead as a method for describing the analysts’ (and possibly other stakeholders’) uncertainties about unknown quantities, the alternative approaches (such as the interval analysis) often fail in providing the necessary decision support.     

Lung Cancer Risk from Radon in Marcellus Shale Gas in Northeast U.S. Homes

The amount of radon in natural gas varies with its source. Little has been published about the radon from shale gas to date, making estimates of its impact on radon‐induced lung cancer speculative. We measured radon in natural gas pipelines carrying gas from the Marcellus Shale in Pennsylvania and West Virginia. Radon concentrations ranged from 1,520 to 2,750 Bq/m³ (41–74 pCi/L), and the throughput‐weighted average was 1,983 Bq/m³ (54 pCi/L). Potential radon exposure due to the use of Marcellus Shale gas for cooking and space heating using vent‐free heaters or gas ranges in northeastern U.S. homes and apartments was assessed. Though the measured radon concentrations are higher than what has been previously reported, it is unlikely that exposure from natural gas cooking would exceed 1.2 Bq/m³ (<1% of the U.S. Environmental Protection Agency's action level). Using worst‐case assumptions, we estimate the excess lifetime (70 years) lung cancer risk associated with cooking to be 1.8×10^?4 (interval spanning 95% of simulation results: 8.5×10^?5, 3.4×10^?4). The risk profile for supplemental heating with unvented gas appliances is similar. Individuals using unvented gas appliances to provide primary heating may face lifetime risks as high as 3.9×10^?3. Under current housing stock and gas consumption assumptions, expected levels of residential radon exposure due to unvented combustion of Marcellus Shale natural gas in the Northeast United States do not result in a detectable change in the lung cancer death rates.     

Importance of Uncertainty and Variability to Predicted Risks from Trophic Transfer of PCBs in Dredged Sediments

Biomagnification of organochlorine and other persistent organic contaminants by higher trophic level organisms represents one of the most significant sources of uncertainty and variability in evaluating potential risks associated with disposal of dredged materials. While it is important to distinguish between population variability (e.g., true population heterogeneity in fish weight, and lipid content) and uncertainty (e.g., measurement error), they can be operationally difficult to define separately in probabilistic estimates of human health and ecological risk. We propose a disaggregation of uncertain and variable parameters based on: (1) availability of supporting data; (2) the specific management and regulatory context (in this case, of the U.S. Army Corps of Engineers/U.S. Environmental Protection Agency tiered approach to dredged material management); and (3) professional judgment and experience in conducting probabilistic risk assessments. We describe and quantitatively evaluate several sources of uncertainty and variability in estimating risk to human health from trophic transfer of polychlorinated biphenyls (PCBs) using a case study of sediments obtained from the New York-New Jersey Harbor and being evaluated for disposal at an open water off-shore disposal site within the northeast region. The estimates of PCB concentrations in fish and dietary doses of PCBs to humans ingesting fish are expressed as distributions of values, of which the arithmetic mean or mode represents a particular fractile. The distribution of risk values is obtained using a food chain biomagnification model developed by Gobas by specifying distributions for input parameters disaggregated to represent either uncertainty or variability. Only those sources of uncertainty that could be quantified were included in the analysis. Results for several different two-dimensional Latin Hypercube analyses are provided to evaluate the influence of the uncertain versus variable disaggregation of model parameters. The analysis suggests that variability in human exposure parameters is greater than the uncertainty bounds on any particular fractile, given the described assumptions.     

Competing Failure Risk Analysis Using Evidence Theory

Safety systems are important components of high-consequence systems that are intended to prevent the unintended operation of the system and thus the potentially significant negative consequences that could result from such an operation. This presentation investigates and illustrates formal procedures for assessing the uncertainty in the probability that a safety system will fail to operate as intended in an accident environment. Probability theory and evidence theory are introduced as possible mathematical structures for the representation of the epistemic uncertainty associated with the performance of safety systems, and a representation of this type is illustrated with a hypothetical safety system involving one weak link and one strong link that is exposed to a high temperature fire environment. Topics considered include (1) the nature of diffuse uncertainty information involving a system and its environment, (2) the conversion of diffuse uncertainty information into the mathematical structures associated with probability theory and evidence theory, and (3) the propagation of these uncertainty structures through a model for a safety system to obtain representations in the context of probability theory and evidence theory of the uncertainty in the probability that the safety system will fail to operate as intended. The results suggest that evidence theory provides a potentially valuable representational tool for the display of the implications of significant epistemic uncertainty in inputs to complex analyses.     

Probability and Possibility‐Based Representations of Uncertainty in Fault Tree Analysis

Expert knowledge is an important source of input to risk analysis. In practice, experts might be reluctant to characterize their knowledge and the related (epistemic) uncertainty using precise probabilities. The theory of possibility allows for imprecision in probability assignments. The associated possibilistic representation of epistemic uncertainty can be combined with, and transformed into, a probabilistic representation; in this article, we show this with reference to a simple fault tree analysis. We apply an integrated (hybrid) probabilistic‐possibilistic computational framework for the joint propagation of the epistemic uncertainty on the values of the (limiting relative frequency) probabilities of the basic events of the fault tree, and we use possibility‐probability (probability‐possibility) transformations for propagating the epistemic uncertainty within purely probabilistic and possibilistic settings. The results of the different approaches (hybrid, probabilistic, and possibilistic) are compared with respect to the representation of uncertainty about the top event (limiting relative frequency) probability. Both the rationale underpinning the approaches and the computational efforts they require are critically examined. We conclude that the approaches relevant in a given setting depend on the purpose of the risk analysis, and that further research is required to make the possibilistic approaches operational in a risk analysis context.     

Risk Perceptions Toward Drinking Water Quality Among Private Well Owners in Ireland: The Illusion of Control

In rural areas where no public or group water schemes exist, groundwater is often the only source of drinking water and is extracted by drilling private wells. Typically, private well owners are responsible for the quality and testing of their own drinking water. Previous studies indicate that well owners tend to underestimate the risks of their well water being contaminated, yet little is known about why this is the case. We conducted a qualitative study by interviewing private well owners in Ireland to investigate their beliefs surrounding their water quality, which, in turn, inform their risk perceptions and their willingness to regularly test their water. Based on our findings we designed a theoretical model arguing that perceived control is central in the perceived contamination risks of well water. More specifically, we argue that well owners have the illusion of being in control over their water quality, which implies that people often perceive themselves to be more in control of a situation than they actually are. As a result, they tend to underestimate contamination risks, which subsequently impact negatively on water testing behaviors. Theoretical and practical implications are highlighted.     

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.     

The Effect of Geographic Proximity to Unconventional Oil and Gas Development on Public Support for Hydraulic Fracturing

With the rapid growth of unconventional oil and natural gas development transforming the U.S. economic and physical landscape, social scientists have increasingly explored the spatial dynamics of public support for this issue—that is, whether people closer to unconventional oil and gas development are more supportive or more opposed. While theoretical frameworks like construal‐level theory and the “Not in My Backyard” (or NIMBY) moniker provide insight into these spatial dynamics, case studies in specific locations experiencing energy development reveal substantial variation in community responses. Larger‐scale studies exploring the link between proximity and support have been hampered by data quality and availability. We draw on a unique data set that includes geo‐coded data from national surveys (nine waves; n  = 19,098) and high‐resolution well location data to explore the relationship between proximity and both familiarity with and support for hydraulic fracturing. We use two different measures of proximity—respondent distance to the nearest well and the density of wells within a certain radius of the respondent's location. We find that both types of proximity to new development are linked to more familiarity with hydraulic fracturing, even after controlling for various individual and contextual factors, but only distance‐based proximity is linked to more support for the practice. When significant, these relationships are similar to or exceed the effects of race, income, gender, and age. We discuss the implications of these findings for effective risk communication as well as the importance of incorporating spatial analysis into public opinion research on perceptions of energy development.     

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.     

Effects of Acknowledging Uncertainty about Earthquake Risk Estimates on San Francisco Bay Area Residents’ Beliefs,Attitudes, and Intentions

We test here the risk communication proposition that explicit expert acknowledgment of uncertainty in risk estimates can enhance trust and other reactions. We manipulated such a scientific uncertainty message, accompanied by probabilities (20%, 70%, implicit [“will occur”] 100%) and time periods (10 or 30 years) in major (≥magnitude 8) earthquake risk estimates to test potential effects on residents potentially affected by seismic activity on the San Andreas fault in the San Francisco Bay Area (n = 750). The uncertainty acknowledgment increased belief that these specific experts were more honest and open, and led to statistically (but not substantively) significant increases in trust in seismic experts generally only for the 20% probability (vs. certainty) and shorter versus longer time period. The acknowledgment did not change judged risk, preparedness intentions, or mitigation policy support. Probability effects independent of the explicit admission of expert uncertainty were also insignificant except for judged risk, which rose or fell slightly depending upon the measure of judged risk used. Overall, both qualitative expressions of uncertainty and quantitative probabilities had limited effects on public reaction. These results imply that both theoretical arguments for positive effects, and practitioners’ potential concerns for negative effects, of uncertainty expression may have been overblown. There may be good reasons to still acknowledge experts’ uncertainties, but those merit separate justification and their own empirical tests.     

A Framework for Estimating the Adverse Health Effects of Contamination Events in Water Distribution Systems and its Application

Intentional or accidental releases of contaminants into a water distribution system (WDS) have the potential to cause significant adverse health effects among individuals consuming water from the system. A flexible analysis framework is presented here for estimating the magnitude of such potential effects and is applied using network models for 12 actual WDSs of varying sizes. Upper bounds are developed for the magnitude of adverse effects of contamination events in WDSs and evaluated using results from the 12 systems. These bounds can be applied in cases in which little system‐specific information is available. The combination of a detailed, network‐specific approach and a bounding approach allows consequence assessments to be performed for systems for which varying amounts of information are available and addresses important needs of individual utilities as well as regional or national assessments. The approach used in the analysis framework allows contaminant injections at any or all network nodes and uses models that (1) account for contaminant transport in the systems, including contaminant decay, and (2) provide estimates of ingested contaminant doses for the exposed population. The approach can be easily modified as better transport or exposure models become available. The methods presented here provide the ability to quantify or bound potential adverse effects of contamination events for a wide variety of possible contaminants and WDSs, including systems without a network model.     

Waterborne Disease‐Related Risk Perceptions in the Sonora River Basin,Mexico

Waterborne disease is estimated to cause about 10% of all diseases worldwide. However, related risk perceptions are not well understood, particularly in the developing world where waterborne disease is an enormous problem. We focus on understanding risk perceptions related to these issues in a region within northern Mexico. Our findings show how waterborne disease problems and solutions are understood in eight small communities along a highly contaminated river system. We found major differences in risk perceptions between health professionals, government officials, and lay citizens. Health professionals believed that a high level of human‐waste‐related risk existed within the region. Few officials and lay citizens shared this belief. In addition, few officials and lay citizens were aware of poor wastewater‐management‐related disease outbreaks and water contamination. Finally, aside from health professionals, a few interviewees understood the importance of basic hygiene and water treatment measures that could help to prevent disease. Our results add to the literature on environmentally‐related risk perceptions in the developing world. We discuss recommendations for improving future human‐wastewater‐related risk communication within the region.     

Performance Assessment in Support of the 1996 Compliance Certification Application for the Waste Isolation Pilot Plant

The conceptual and computational structure of a performance assessment (PA) for the Waste Isolation Pilot Plant (WIPP) is described. Important parts of this structure are (1) maintenance of a separation between stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty, with stochastic uncertainty arising from the many possible disruptions that could occur over the 10,000-year regulatory period that applies to the WIPP, and subjective uncertainty arising from the imprecision with which many of the quantities required in the analysis are known, (2) use of Latin hypercube sampling to incorporate the effects of subjective uncertainty, (3) use of Monte Carlo (i.e., random) sampling to incorporate the effects of stochastic uncertainty, and (4) efficient use of the necessarily limited number of mechanistic calculations that can be performed to support the analysis. The WIPP is under development by the U.S. Department of Energy (DOE) for the geologic (i.e., deep underground) disposal of transuranic (TRU) waste, with the indicated PA supporting a Compliance Certification Application (CCA) by the DOE to the U.S. Environmental Protection Agency (EPA) in October 1996 for the necessary certifications for the WIPP to begin operation. The EPA certified the WIPP for the disposal of TRU waste in May 1998, with the result that the WIPP will be the first operational facility in the United States for the geologic disposal of radioactive waste.     

A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis

In risk analysis, the treatment of the epistemic uncertainty associated to the probability of occurrence of an event is fundamental. Traditionally, probabilistic distributions have been used to characterize the epistemic uncertainty due to imprecise knowledge of the parameters in risk models. On the other hand, it has been argued that in certain instances such uncertainty may be best accounted for by fuzzy or possibilistic distributions. This seems the case in particular for parameters for which the information available is scarce and of qualitative nature. In practice, it is to be expected that a risk model contains some parameters affected by uncertainties that may be best represented by probability distributions and some other parameters that may be more properly described in terms of fuzzy or possibilistic distributions. In this article, a hybrid method that jointly propagates probabilistic and possibilistic uncertainties is considered and compared with pure probabilistic and pure fuzzy methods for uncertainty propagation. The analyses are carried out on a case study concerning the uncertainties in the probabilities of occurrence of accident sequences in an event tree analysis of a nuclear power plant.     

On the Treatment of Uncertainty and Variability in Making Decisions About Risk

Much attention has been paid to the treatment of dependence and to the characterization of uncertainty and variability (including the issue of dependence among inputs) in performing risk assessments to avoid misleading results. However, with relatively little progress in communicating about the effects and implications of dependence, the effort involved in performing relatively sophisticated risk analyses (e.g., two‐dimensional Monte Carlo analyses that separate variability from uncertainty) may be largely wasted, if the implications of those analyses are not clearly understood by decisionmakers. This article emphasizes that epistemic uncertainty can introduce dependence among related risks (e.g., risks to different individuals, or at different facilities), and illustrates the potential importance of such dependence in the context of two important types of decisions—evaluations of risk acceptability for a single technology, and comparisons of the risks for two or more technologies. We also present some preliminary ideas on how to communicate the effects of dependence to decisionmakers in a clear and easily comprehensible manner, and suggest future research directions in this area.     

Reducing Drinking Water Supply Chemical Contamination: Risks from Underground Storage Tanks

Drinking water supplies are at risk of contamination from a variety of physical, chemical, and biological sources. Ranked among these threats are hazardous material releases from leaking or improperly managed underground storage tanks located at municipal, commercial, and industrial facilities. To reduce human health and environmental risks associated with the subsurface storage of hazardous materials, government agencies have taken a variety of legislative and regulatory actions—which date back more than 25 years and include the establishment of rigorous equipment/technology/operational requirements and facility‐by‐facility inspection and enforcement programs. Given a history of more than 470,000 underground storage tank releases nationwide, the U.S. Environmental Protection Agency continues to report that 7,300 new leaks were found in federal fiscal year 2008, while nearly 103,000 old leaks remain to be cleaned up. In this article, we report on an alternate evidence‐based intervention approach for reducing potential releases from the storage of petroleum products (gasoline, diesel, kerosene, heating/fuel oil, and waste oil) in underground tanks at commercial facilities located in Rhode Island. The objective of this study was to evaluate whether a new regulatory model can be used as a cost‐effective alternative to traditional facility‐by‐facility inspection and enforcement programs for underground storage tanks. We conclude that the alternative model, using an emphasis on technical assistance tools, can produce measurable improvements in compliance performance, is a cost‐effective adjunct to traditional facility‐by‐facility inspection and enforcement programs, and has the potential to allow regulatory agencies to decrease their frequency of inspections among low risk facilities without sacrificing compliance performance or increasing public health risks.     

A Risk Assessment Framework for Assessing Metallic Nanomaterials of Environmental Concern: Aquatic Exposure and Behavior

Nanomaterials are finding application in many different environmentally relevant products and processes due to enhanced catalytic, antimicrobial, and oxidative properties of materials at this scale. As the market share of nano‐functionalized products increases, so too does the potential for environmental exposure and contamination. This study presents some exposure ranking methods that consider potential metallic nanomaterial surface water exposure and fate, due to nano‐functionalized products, through a number of exposure pathways. These methods take into account the limited and disparate data currently available for metallic nanomaterials and apply variability and uncertainty principles, together with qualitative risk assessment principles, to develop a scientific ranking. Three exposure scenarios with three different nanomaterials were considered to demonstrate these assessment methods: photo‐catalytic exterior paint (nano‐scale TiO₂), antimicrobial food packaging (nano‐scale Ag), and particulate‐reducing diesel fuel additives (nano‐scale CeO₂). Data and hypotheses from literature relating to metallic nanomaterial aquatic behavior (including the behavior of materials that may relate to nanomaterials in aquatic environments, e.g., metals, pesticides, surfactants) were used together with commercial nanomaterial characteristics and Irish natural aquatic environment characteristics to rank the potential concentrations, transport, and persistence behaviors within subjective categories. These methods, and the applied scenarios, reveal where data critical to estimating exposure and risk are lacking. As research into the behavior of metallic nanomaterials in different environments emerges, the influence of material and environmental characteristics on nanomaterial behavior within these exposure‐ and risk‐ranking methods may be redefined on a quantitative basis.     

Functional Approximation and Optimal Specification of the Mechanical Risk Index

The mechanical risk index (MRI) is a numerical measure that quantifies the complexity of drilling a well. The purpose of this article is to examine the role of the component factors of the MRI and its structural and parametric assumptions. A meta-modeling methodology is applied to derive functional expressions of the MRI, and it is shown that the MRI can be approximated in terms of a linear functional. The variation between the MRI measure and its functional specification is determined empirically, and for a reasonable design space, the functional specification is shown to a good approximating representation. A drilling risk index is introduced to quantify the uncertainty in the time and cost associated with drilling a well. A general methodology is outlined to create an optimal MRI specification.