Effects‐initiated assessments are not risk assessments

True risk assessments address the probability of a future risk occurring given a certain set of circumstances. However, “effects‐initiated assessments”; or “retrospective assessments”; often are improperly included under the broad appellation of “risk assessment”; and are conducted when an apparently adverse effect is seen in some environmental component and the question of cause (i.e., etiology) is raised. Base line risk assessments at Superfund sites or for Natural Resource Damage Assessments are examples of effects‐initiated assessments. We argue here that this type of study is not a risk assessment, either by strict definition of terminology or by logical approach taken in answering the posed question (s), and should more properly be called “diagnostic ecology.”; Diagnostic ecology starts from the premise that ecological effects have occurred and exposure to a Stressor has taken place. The problem then is to pose all possible etiologies and utilize deductive logic to systematically eliminate each agent except for one as the actual cause. A risk assessment, on the other hand, employs inductive reasoning. That is, hypotheses are generated about the possible sources of a stressor and the possible outcome if exposure occurs. Both exercises require an understanding of the ecological relationships of the various components in the ecosystem, both need an understanding of die cause‐and‐effect relationships of agents, and both require a proper framing of the questions being asked. However, risk assessors should not try to fit all environmental impact assessments into a single framework, but rather should recognize that biomedical techniques are better suited for solving diagnostic riddles than are prospective risk assessment approaches.     

The Use of Epidemiology in Risk Assessment: Challenges and Opportunities

The assessment of risk from environmental and occupational exposures incorporates and synthesizes data from a variety of scientific disciplines including toxicology and epidemiology. Epidemiological data have offered valuable contributions to the identification of human health hazards, estimation of human exposures, quantification of the exposure–response relation, and characterization of risks to specific target populations including sensitive populations. As with any scientific discipline, there are some uncertainties inherent in these data; however, the best human health risk assessments utilize all available information, characterizing strengths and limitations as appropriate. Human health risk assessors evaluating environmental and occupational exposures have raised concerns about the validity of using epidemiological data for risk assessment due to actual or perceived study limitations. This article highlights three concerns commonly raised during the development of human health risk assessments of environmental and occupational exposures: (a) error in the measurement of exposure, (b) potential confounding, and (c) the interpretation of non-linear or non-monotonic exposure–response data. These issues are often the content of scientific disagreement and debate among the human health risk assessment community, and we explore how these concerns may be contextualized, addressed, and often ameliorated.     

Extrapolation in Human Health and Ecological Risk Assessments: Proceedings of a Symposium

A symposium was conducted in April 1998 by the U.S. Environmental Protection Agency's National Health and Environmental Effects Research Laboratory (NHEERL) to explore issues of extrapolation in human health and ecological risk assessments. Over the course of three and one half days, leading health and ecology experts presented and discussed research methods and approaches for extrapolating data among taxa and across levels of biological organization, through time, and across spatial scales. The intended result of this symposium was enhanced interaction among a diverse array of scientists, policymakers, and risk assessors to promote identification of approaches for reducing the uncertainties of extrapolation in risk assessment.     

Role of Ecological Modeling in Risk Assessment

Ecological models are useful tools for evaluating the ecological significance of observed or predicted effects of toxic chemicals on individual organisms. Current risk estimation approaches using hazard quotients for individual-level endpoints have limited utility for assessing risks at the population, ecosystem, and landscape levels, which are the most relevant indicators for environmental management. In this paper, we define different types of ecological models, summarize their input and output variables, and present examples of the role of some recommended models in chemical risk assessments. A variety of population and ecosystem models have been applied successfully to evaluate ecological risks, including population viability of endangered species, habitat fragmentation, and toxic chemical issues. In particular, population models are widely available, and their value in predicting dynamics of natural populations has been demonstrated. Although data are often limited on vital rates and doseresponse functions needed for ecological modeling, accurate prediction of ecological effects may not be needed for all assessments. Often, a comparative assessment of risk (e.g., relative to baseline or reference) is of primary interest. Ecological modeling is currently a valuable approach for addressing many chemical risk assessment issues, including screening-level evaluations.     

Scientific data and theories for salmonellosis dose–response assessment

An “expansive” risk assessment approach is illustrated, characterizing dose–response relationships for salmonellosis in light of the full body of evidence for human and murine superorganisms. Risk assessments often require analysis of costs and benefits for supporting public health decisions. Decision-makers and the public need to understand uncertainty in such analyses for two reasons. Uncertainty analyses provide a range of possibilities within a framework of present scientific knowledge, thus helping to avoid undesirable consequences associated with the selected policies. And, it encourages the risk assessors to scrutinize all available data and models, thus helping avoid subjective or systematic errors. Without the full analysis of uncertainty, decisions could be biased by judgments based solely on default assumptions, beliefs, and statistical analyses of selected correlative data. Alternative data and theories that incorporate variability and heterogeneity for the human and murine superorganisms, particularly colonization resistance, are emerging as major influences for microbial risk assessment. Salmonellosis risk assessments are often based on conservative default models derived from selected sets of outbreak data that overestimate illness. Consequently, the full extent of uncertainty of estimates of annual number of illnesses is not incorporated in risk assessments and the presently used models may be incorrect.     

Risk Assessment of Tropospheric Ozone: Human Health,Natural Resources,and Ecology

Ozone is an unusual trace gas in the atmosphere, presenting a challenge for risk assessors and risk managers. The challenge can be traced to the gas’ complex chemistry in the atmosphere (exposure), toxicology in biological systems (response), and the fledgling enterprise of risk assessment for widely distributed, highly reactive pollutants. This paper addresses the (i) co-evolution of the scientific data underlying ozone risk assessment on human health, natural resources (crops and managed forests), and unmanaged ecosystems, (ii) similarities and differences in risk assessment among these receptors, and (iii) utility of indicators in risk assessment. The scientific community has developed a sound database to underpin the ozone risk assessment, although the breadth and depth differ markedly among the three receptors. There are similarities in ozone risk assessment among human health, natural resources, and ecology, including features of exposure (e.g., temporal variation), response of plants and humans (e.g., sensitive cohorts), and integration of exposure and response (e.g., importance of peak and cumulative exposures). Equally important are the notable differences, and the more prominent are scaling of exposure-response relationships, air quality monitoring, economic valuation, and models to complement more traditional experimental approaches. Of the three receptors, the status of indicators for conducting ecological ozone risk assessment is the weakest.     

Hormesis and ecological risk assessment: Fact or fantasy?

Hormesis is a widespread phenomenon across occurring many taxa and chemicals, and, at the single species level, issues regarding the application of hormesis to human health and ecological risk assessment are similar. However, interpreting the significance of hormesis for even a single species in an ecological risk assessment can be complicated by competition with other species, predation effects, etc. In addition, ecological risk assessments may involve communities of hundreds or thousands of species as well as a range of ecological processes. Applying hormetic adjustments to threshold effect levels for chemicals derived from sensitivity distributions for a large number of species is impractical. For ecological risks, chemical stressors are frequently of lessor concern than physical stressors (e.g., habitat alteration) or biological stressors (e.g., introduced species), but the relevance of hormesis to non‐chemical stressors is unclear. Although ecological theories such as the intermediate disturbance hypothesis offer some intriguing similarities between chemical hormesis and hormetic‐like responses resulting from physical disturbances, mechanistic explanations are lacking. While further exploration of the relevance of hormesis to ecological risk assessment is desirable, it is unlikely that hormesis is a critical factor in most ecological risk assessments, given the magnitude of other uncertainties inherent in the process.     

Treatments of Uncertainty and Variability in Ecological Risk Assessment of Single-Species Populations

The selection of the most appropriate model for an ecological risk assessment depends on the application, the data and resources available, the knowledge base of the assessor, the relevant endpoints, and the extent to which the model deals with uncertainty. Since ecological systems are highly variable and our knowledge of model input parameters is uncertain, it is important that models include treatments of uncertainty and variability, and that results are reported in this light. In this paper we discuss treatments of variation and uncertainty in a variety of population models. In ecological risk assessments, the risk relates to the probability of an adverse event in the context of environmental variation. Uncertainty relates to ignorance about parameter values, e.g., measurement error and systematic error. An assessment of the full distribution of risks, under variability and parameter uncertainty, will give the most comprehensive and flexible endpoint. In this paper we present the rationale behind probabilistic risk assessment, identify the sources of uncertainty relevant for risk assessment and provide an overview of a range of population models. While all of the models reviewed have some utility in ecology, some have more comprehensive treatments of uncertainty than others. We identify the models that allow probabilistic assessments and sensitivity analyses, and we offer recommendations for further developments that aim towards more comprehensive and reliable ecological risk assessments for populations.     

Annotated reference compilation: Conducting qualitative and quantitative ecological risk assessments at hazardous waste sites

As the field of ecological risk assessment (ERA) broadens, scientists from various disciplines are called upon to become assessors at hazardous waste sites. Although a United States Environmental Protection Agency (USEPA) Framework for ERAs exists, the guidance is unlike the detailed USEPA guidance available for human risk assessments. Currently, the quality of an ERA is dependent upon the assessor's scientific acumen, professional experience, and recognized reference documents. This annotated reference compilation encompasses published documents which have provided useful and important information for qualitative and quantitative ERAs.     

Challenges for Risk Assessors

At the early part of the 21st century, occupational safety and health risk assessors face a variety of challenges. In addition to technical issues, the challenges for risk assessors include: assessment of risks of mixtures/and synergistic effects; incorporation of biological information into risk assessments; development of different ways of presenting risk information to better inform policy makers and the public; better expressions of uncertainty and assumptions; and harmonization of assessments across agencies and countries. All of these challenges will occur against a background of unfolding understanding of human and other genomes. Risk assessors will be motivated and pressured to use genomic and related technologies, but ethical, social, and technical issues need to be addressed before widespread use.     

Application of the Hazard Quotient Method in Remedial Decisions: A Comparison of Human and Ecological Risk Assessments

This paper evaluates the relative roles of the human health hazard index (HI) and the ecological risk assessment hazard quotient (HQ) in remedial decision-making. Through an analysis of HI outcomes drawn from Superfund Records of Decision, the reduced importance of the HI statistic in human health risk assessments is demonstrated, and the high visibility of the ecological risk assessment (ERA) HQ for terrestrial receptors (birds and mammals) is underscored. Three HQ method limitations common to both HHRA and ERA, deriving either from the mathematical construct of the HQ (a simple binary measure, indicating that an animal's exposure either exceeds its toxicity value or does not) or from dose-response outcomes in animal trials, are reviewed. Two additional HQ limitations unique to ERA (i.e., a propensity for the HQ to easily exceed its threshold value, and a propensity for it to assume values that are unreasonably high), and deriving from the complexities of estimating bird and mammal dietary intakes of contaminants and the availability of toxicological effects information, are also identified. The paper cautions of the potential to err in concluding that terrestrial site receptors are at risk when the HQ threshold is exceeded, and regardless of the toxicological information (NOAELs, LOAELs, etc.) used. It recognizes that because other methods of terrestrial assessment are presently unavailable, HQs are sometimes, out of necessity, used to justify a remedial action. The analysis and discussion are intended to remind ecological risk assessors that the HQ is a measure of a level of concern only and not a measure of risk     

Improving the Use of Toxicity Reference Values in Wildlife Food Chain Modeling and Ecological Risk Assessment

Ecological risk assessments often include mechanistic food chain models based on toxicity reference values (TRVs) and a hazard quotient approach. TRVs intended for screening purposes or as part of a larger weight-of-evidence (WOE) assessment are readily available. However, our experience suggests that food chain models using screening-level TRVs often form the primary basis for risk management at smaller industrial sites being redeveloped for residential or urban parkland uses. Iterative improvement of a food chain model or the incorporation of multiple lines of evidence for these sites are often impractical from a cost-benefit perspective when compared to remedial alternatives. We recommend risk assessors examine the assumptions and factors in the TRV derivation process, and where appropriate, modify the TRVs to improve their ecological relevance. Five areas where uncertainty likely contributes to excessively conservative hazard quotients are identified for consideration.     

The Anna Karenina Principle Applied to Ecological Risk Assessments of Multiple Stressors

Successful ecological risk assessments are all alike; every unsuccessful ecological risk assessment fails in its own way. Tolstoy posited a similar analogy in his novel Anna Karenina: “Happy families are all alike; every unhappy family is unhappy in its own way.” By that, Tolstoy meant that for a marriage to be happy, it had to succeed in several key aspects. Failure on even one of these aspects, and the marriage is doomed. In this paper, I argue that the Anna Karenina principle also applies to ecological risk assessments involving multiple stressors. In particular, I argue that multiple stressors assessments and environmental decision making will not have a happy marriage unless the following can be achieved: (1) there must be societal and political buy-in to the assessment and decision-making process; (2) the assessment must have the latitude to consider a wide range of stressors and potential risk management options; (3) there must be a commitment to following a rigorous focusing of the assessment and to expending resources for model development and data collection; and (4) an adaptive management strategy must be adopted wherein risk management actions are undertaken, system response intensively observed and assessed, and revised management actions taken as appropriate. Failure to meet any of the above criteria for success will doom a multiple stressors assessment and prevent its use in effective decision-making.     

Evaluating Chemical Interaction Studies for Mixture Risk Assessment

We describe a set of criteria to evaluate the quality of data and interpretations in chemical interaction studies. These criteria reflect the consensus of the literature on interaction analysis developed over decades of research in pharmacology, toxicology, and biometry; address common pitfalls in published interaction studies; and can be easily applied to common methods of interaction analysis. The criteria apply broadly to interaction data for drugs, pesticides, industrial chemicals, food additives, and natural products and are intended to assist risk assessors who must evaluate interaction studies for use in component-based mixture risk assessments. The criteria may also assist researchers interested in conducting interaction studies to inform mixture risk assessment. The criteria are also intended to serve larger scientific goals, including increasing the repeatability of results obtained in chemical interaction studies, enhancing the reliability of conclusions drawn from interaction data, providing greater consistency of interpretations among various analysts, and decreasing uncertainty in using interaction data in risk assessments. We describe the basis for each criterion and demonstrate their utility by using them to evaluate interaction studies from the recent toxicological and pharmacological literature, which serve as examples of different types of data sets that the risk assessor may encounter.     

Challenges in Defining Background Levels for Human and Ecological Risk Assessments

A robust approach to defining, understanding, and tracking contaminant levels is crucial to human and ecological risk evaluation and risk management. Whether materials are present in the environment naturally (mercury, radon, nitrogen, phosphorous) and enhanced by human activities or are man-made (DDT and PCBs), the complexity of contaminant distributions and sources requires careful design and implementation of studies to characterize background. Before collection or analyses of data are initiated, an explicit definition of background appropriate to specific risk assessments is needed. Problems associated with determining background levels of contaminants in fish and seafood illustrate some challenges faced by risk assessors. Major differences in estimates of background may result from differences in sampling and analytical methods including selection of sampling locations, approaches to data analysis and synthesis, and tissues selected for analysis. Different approaches may yield background exposure or risk estimates that differ by 50% or more. Methodological transparency is essential in data collection and analysis to establish background and to ensure that data are used appropriately in both human and ecological risk assessments.     

Integrated Risk Assessment — Results from an International Workshop

The World Health Organization's International Programme on Chemical Safety and international partners have developed a framework for integrated assessment of human health and ecological risks and four case studies. An international workshop was convened to consider how ecological and health risk assessments might be integrated, the benefits of and obstacles to integration, and the research and mechanisms needed to facilitate implementation of integrated risk assessment. Using the case studies, workshop participants identified a number of opportunities to integrate the assessment process. Improved assessment quality, efficiency, and predictive capability were considered to be principal benefits of integration. Obstacles to acceptance and implementation of integrated risk assessment included the disciplinary and organizational barriers between ecological and health disciplines. A variety of mechanisms were offered to overcome these obstacles. Research recommendations included harmonization of exposure characterization and surveillance methods and models, development of common risk endpoints across taxa, improved understanding of mechanisms of effect at multiple scales of biological organization, and development of methods to facilitate comparison of risks among endpoints.     

Comparison of Contaminated Site Human Health Risk Assessment Approaches in Canada: Application of Provincial Methods to a Hypothetical Site

Human health risk assessment, whether at the screening level or more complex phase, is not an exact science. A wide variety of advice and direction is offered by international, national, and provincial/state environmental agencies regarding the conduct of risk assessment, and different risk assessors access and rely on the available regulatory advice and direction differently. This may result in wide variability in the estimates of chemical exposure and risk. A comparison of human health risk assessment approaches practiced at the provincial level in Canada was undertaken, wherein each jurisdiction's approach was applied to a hypothetical contaminated site. Approaches were found to vary both in terms of methodological considerations, and in matters of policy. The exercise yielded results in terms of estimated exposures and predicted hazard quotients/indexes and incremental lifetime cancer risks that were in some cases quite consistent (varying by a factor of less than 1.5 times), and in other cases remarkably different (varying by orders of magnitude). This article reviews the various approaches/frameworks applied and discusses the results of the hypothetical risk assessments, in terms of both the observed variation and the source of this variability.     

Axes of Extrapolation in Risk Assessment

Extrapolation in risk assessment involves the use of data and information to estimate or predict something that has not been measured or observed. Reasons for extrapolation include that the number of combinations of environmental stressors and possible receptors is too large to characterize risks comprehensively, that direct characterization is sometimes impossible, and that the power to characterize risk in a particular situation can be enhanced by using information obtained in other similar situations. Three types of extrapolation are common in risk assessments: biological (including between taxa and across levels of biological organization), temporal, and spatial. They can be thought of conceptually as the axes of a 3-dimensional graph defining the state space of biological, temporal, and spatial scales within which extrapolations are made. Each of these types of extrapolation can introduce uncertainties into risk assessments. Such uncertainties may be reduced through synergistic research facilitated by the sharing of methods, models, and data used by human health and ecological scientists