Probabilistic Environmental Risk Assessment for Dibutylphthalate (DBP)

In the risk assessment methods for new and existing chemicals in the European Union (EU), environmental “risk” is characterized by the deterministic quotient of exposure and effects (PEC/PNEC). From a scientific viewpoint, the uncertainty in the risk quotient should be accounted for explicitly in the decision making, which can be done in a probabilistic risk framework. To demonstrate the feasibility and benefits of such a framework, a sample risk assessment for an existing chemical (dibutylphthalate, DBP) is presented in this paper. The example shows a probabilistic framework to be feasible with relatively little extra effort; such a framework also provides more relevant information. The deterministic risk quotients turned out to be worst cases at generally higher than the 95th percentile of the probability distributions. Sensitivity analysis proves to be a powerful tool in identifying the main sources of uncertainty and thus will be effective for efficient further testing. The distributions assigned to the assess ment factors (derivation of the PNEC) dominate the total uncertainty in the risk assessment; uncertainties in the release estimates come second. Large uncertainties are an inherent part of risk assessment that we have to deal with quantitatively. However, the most appropriate way to characterise effects and risks requires further attention. Recommendations for further study are identified.     

Ecological Risk Assessment Approaches Within the Regulatory Framework

Ecological risk assessment has a short history but a framework similar to human health risk assessment. The Toxic Substances Control Act (TSCA) and the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) played a significant role in the development of the ecological risk process. Data developed and risk procedures used within TSCA and FIFRA have become generally standardized. Fundamental components of the risk process require data on the effects of chemicals in the form of concentration (or dose) — response profiles for species and an exposure profile to quantify the magnitude, spatial and temporal patterns of exposure relevant to significant biological endpoints being studied. Risk characterization generally involves comparing exposure and effects using point estimates (e.g., quotient method) but risk estimation is moving toward a probabilistic approach by comparing distributions of values with more consideration of the sources of uncertainty. Ecological testing guidelines in TSCA and FIFRA are discussed along with the risk characterization process used in each statute.     

The 3MRA Risk Assessment Framework—A Flexible Approach for Performing Multimedia,Multipathway, and Multireceptor Risk Assessments Under Uncertainty

A flexible framework for conducting nationwide multimedia, multipathway and multireceptor risk assessments (3MRA) under uncertainty was developed to estimate protective chemical concentration limits in a source area. The framework consists of two components: risk assessment and uncertainty analysis. The risk component utilizes linked source, fate/transport, exposure and risk assessment models to estimate the risk exposures for the receptors of concern. Both human and ecological receptors are included in the risk assessment framework. The flexibility of the framework is based on its ability to address problems varying in spatial scales from site-specific to regional and even national levels; and its ability to accommodate varying types of source, fate/transport, exposure and risk assessment models. The uncertainty component of the 3MRA framework is based on a two-stage Monte Carlo methodology. It allows the calculation of uncertainty in risk estimates, and the incorporation of the effects of uncertainty on the determination of regulatory concentration limits as a function of variability and uncertainty in input data, as well as potential errors in fate and transport and risk and exposure models. The framework can be adapted to handle a wide range of multimedia risk assessment problems. Two examples are presented to illustrate its use, and to demonstrate how regulatory decisions can be structured to incorporate the uncertainty in risk estimates.     

The Use of Probabilistic Risk Assessment in Establishing Drinking Water Quality Objectives

There has been a trend in recent years toward the use of probabilistic methods for the analysis of uncertainty and variability in risk assessment. By developing a plausible distribution of risk, it is possible to obtain a more complete characterization of risk than is provided by either best estimates or upper limits. We describe in this paper a general framework for evaluating uncertainty and variability in risk estimation and outline how this framework can be used in the establishment of drinking water quality objectives. In addition to characterizing uncertainty and variability in risk, this framework also facilitates the identification of specific factors that contribute most to uncertainty and variability. The application of these probabilistic risk assessment methods is illustrated using tetrachloroethylene and trihalomethanes as examples.     

Equine Risk Assessment for Insecticides Used in Adult Mosquito Management

Since West Nile virus (WNV) was introduced to New York City in 1999, it has subsequently spread through the Americas, creating human and animal health risks. Our equine risk assessment focused on three pyrethroid insecticides (phenothrin, resmethrin, and permethrin), pyrethrins, and two organophosphate insecticides (malathion and naled). Piperonyl butoxide, a synergist commonly used in pyrethroids, was also assessed. The objective was to use deterministic and probabilistic risk assessment methodologies to evaluate health risks to horses from vector management tactics used for control of adult mosquitoes. Our exposure estimates were derived from the Kenaga nomogram for food deposition, AgDRIFT® for deposition onto soil and hair, AERMOD for ambient air concentrations, and PRZM-EXAMS for water concentrations. We used the risk quotient (RQ) method for our assessment with the RQ level of concern (LOC) set at 1.0. RQs were determined by comparing the exposure to no-observable-effect-levels. Acute deterministic RQs ranged from 0.0004 for phenothrin to 0.2 for naled. Subchronic deterministic RQs ranged from 0.001 for phenothrin to 0.6 for naled. The probabilistic assessment revealed estimates of deterministic acute and subchronic RQs were highly conservative. Our assessment revealed that risks to horses from adult mosquito insecticides are low and not likely to exceed the LOC.     

Comparison of Approaches for Developing Distributions for Carcinogenic Slope Factors

In recent years, risk assessors have increasingly been moving away from deterministic risk assessment approaches and are applying probabilistic approaches that incorporate distributions of possible values for each input parameter. This paper reviews several approaches that are being used or that could potentially be used to develop distributions for carcinogenic slope factors (CSFs). Based on the primary tool or framework that is applied, these approaches have been divided into the following three categories: the statistical framework, the decision analysis framework, and the biological framework. Work that has been done on each approach is summarized, and the aspects of variability and uncertainty that are incorporated into each approach are examined. The implications of the resulting distributional information for calculating risk estimates or risk-based concentrations is explored. The approaches differ in their stage of development, the degree to which they diverge from the U.S. Environmental Protection Agency's (EPA) current practice in establishing CSF values, their flexibility to accommodate varying data sets or theories of carcinogenicity, and their complexity of application. In some cases, wide ranges of potential potency estimates are indicated by these approaches. Such findings suggest widely divergent risk assessment implications and the need for additional evaluation of the goals of developing CSF distributions for use in risk assessment applications and the types of information that should be reflected in such distributions. Some combination of the features offered by these approaches may best support risk assessment and risk management decisions.     

Importance of dose‐response model form in probabilistic risk assessment: A case study of health effects from methylmercury in fish

We compared the effect of uncertainty in dose‐response model form on health risk estimates to the effect of uncertainty and variability in exposure. We used three different dose‐response models to characterize neurological effects in children exposed in utero to methylmercury, and applied these models to calculate risks to a native population exposed to potentially contaminated fish from a reservoir in British Columbia. Uncertainty in model form was explicitly incorporated into the risk estimates. The selection of dose‐response model strongly influenced both mean risk estimates and distributions of risk, and had a much greater impact than altering exposure distributions. We conclude that incorporating uncertainty in dose‐response model form is at least as important as accounting for variability and uncertainty in exposure parameters in probabilistic risk assessment.     

Review of Information Resources to Support Human Exposure Assessment Models

Efforts to model human exposures to chemicals are growing more sophisticated and encompass increasingly complex exposure scenarios. The scope of such analyses has increased, growing from assessments of single exposure pathways to complex evaluations of aggregate or cumulative chemical exposures occurring within a variety of settings and scenarios. In addition, quantitative modeling techniques have evolved from simple deterministic analyses using single point estimates for each necessary input parameter to more detailed probabilistic analyses that can accommodate distributions of input parameters and assessment results. As part of an overall effort to guide development of a comprehensive framework for modeling human exposures to chemicals, available information resources needed to derive input parameters for human exposure assessment models were compiled and critically reviewed. Ongoing research in the area of exposure assessment parameters was also identified. The results of these efforts are summarized and other relevant information that will be needed to apply the available data in a comprehensive exposure model is discussed. Critical data gaps in the available information are also identified. Exposure assessment modeling and associated research would benefit from the collection of additional data as well as by enhancing the accessibility of existing and evolving information resources.     

区域生态风险评价的关键问题与展望

区域生态风险评价具有多风险因子、多风险受体、多评价终点、强调不确定性因素以及空间异质性的特点,它与传统的生态风险评价在风险源、胁迫因子和评价尺度上具有明显区别。尝试建立了一个基于陆地生态系统的区域生态风险评价框架,同时针对目前区域生态风险评价的研究现状,指出不确定性分析、尺度外推难、评价指标不统一、评价标准不统一、风险因子筛选及优先排序、区域内污染物复合、水生过渡到陆生生态系统风险评价、特殊的人为因素等是目前区域生态风险评价存在的关键问题及难点所在,并提出解决这些问题可能所需的工具、手段和理论方法突破。最后指出区域生态风险观测与数据采集加工、区域生态风险指标体系的统一与整合、区域生态风险评价方法论、区域生态风险的空间分布特征与表达以及区域生态风险评价反馈与管理机制5个方面是区域生态风险评价未来的研究重点。     

Impact of Random Variables Probability Distribution on Public Health Risk Assessment from Contaminated Soil

Probabilistic methods are now being applied increasingly to public health risk assessment instead of the deterministic, conservative, point estimates. An essential part of the probabilistic methods is the selection of probability distribution functions to represent the uncertainty of the random variables considered. We study the effect of selection of different probability distribution functions on the probabilistic outcome using the first-order reliability method (FORM). An example of cancer risk resulting from dermal contact with benzo(a)pyrene (BaP)-contaminated soil is given. Cancer potency factor, soil concentration, and fraction of skin area exposed were assigned normal, lognormal, and uniform probability distribution functions, and the effect of probability of exceeding a target risk level (termed the probability of failure) and sensitivity measures were studied. We investigated the question: what happens when one assumes different distribution shapes with the same mean and standard deviation? The results indicate that the selection of a probability distribution function for the random variables had a moderate impact on the probability of failure when the target risk is at the 50th percentile level, while the impact was much larger for a 95th target risk percentile. We conclude that the probability distribution will have a large impact because in most cases the regulatory threshold risk is at the tail end of the risk distribution. The impact of the distributions on probabilistic sensitivity, however, showed a reversed trend, where the impact was slightly more appreciable for the 50th percentile than for the 95th percentile. The selection of distribution shape did not, however, alter the order of probabilistic sensitivity of the basic random variables.     

Distributions Selected for Use in Probabilistic Human Health Risk Assessments in Oregon

In 1995, Oregon enacted amendments to its state Cleanup Law that emphasize risk-based remedial action decisions and allow a responsible party to conduct probabilistic human health risk assessments. This change required selection and/or development of probability density functions for exposure factors frequently used in human health risk assessments. Methods used to obtain distributions for body weight, soil, water, vegetable/fruit, fish, and animal product ingestion, soil adherence, daily inhalation rate, various event and exposure frequencies, and exposure duration are described. Primary data sources were U.S. Environmental Protection Agency guidance and peer-reviewed scientific literature. These distributions of exposure factors may be used, in conjunction with a probabilistic age- and gender-based model, to calculate prospective exposures and risks. A brief overview of this model, which handles temporal parameters (age, exposure frequency, exposure duration) in a manner substantially different from that typically used in deterministic assessments, is also provided. Oregon's development of an age/ gender-based exposure model, and its selection of exposure factor value distributions for that model, represents one of the first attempts to develop practical approaches to using probabilistic techniques in a hazardous waste regulatory program.     

Extrapolation for Exposure Duration in Oral Toxicity: A Quantitative Analysis of Historical Toxicity Data

For human risk assessment, experimental data often have to be extrapolated for exposure duration, which is generally done by means of default values. The purpose of the present study was twofold. First, to derive a statistical distribution for differences in exposure duration that can be used in a probabilistic concept for combining assessment factors in risk characterization. Second, to obtain insight in the magnitude of the change in No-Observed-Adverse-Effect-Level (NOAEL) with exposure duration, which will lead to more science-based assessment factors for exposure duration. A large historical database, including 198 substances, was consulted. Ratios were calculated for pairs of NOAELs for systemic toxicity from oral toxicity studies with the same species (rats or mice, various strains) and different exposure duration categories. The Geometric Mean (GM), Geometric Standard Deviation (GSD), and the 90th and 95th percentile values were determined. The traditionally applied default factors for subacute to semichronic (10), for semichronic to chronic (10), and for subacute to chronic exposure (100) corresponded with the 93, 87, and 99-percentiles of the respective distributions. Options are presented for a set of default values and probabilistic distributions for assessment factors for exposure duration based on data from the consulted historical database.     

The Use of Probabilistic Analysis to Improve Decision-Making in Environmental Regulation in a Developing Context: The Case of Arsenic Regulation in Chile

Using probabilistic analysis may be very useful for risk management in developing countries, where information, resources, and technical expertise are often scarce. Currently, most regulatory agencies recommend using deterministic approaches for the analysis of problems relating to decision-making. However, this approach does not incorporate uncertainty in the variables, nor the propagation of uncertainty through the different processes in which they are involved. The complexity of the problem is therefore arbitrarily reduced, and valuable information that could be useful for proposing realistic policies is not considered. This article compares the results of a deterministic analysis with those of a probabilistic one for regulating arsenic in Chile, and differences are established for public policy as a result of building uncertainty into the analysis. It is concluded that the use of a deterministic approach can lead to higher risks than necessary and that probabilistic results can help the regulator negotiate stricter standards. Alternatively, the regulator may end up imposing much higher costs to sources than originally expected as these will be forced to use expensive technology to comply consistently with a given standard.     

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.     

Comparative Risk Assessment Methods and Their Applicability to Dredged Material Management Decision-Making

This article reviews the status of comparative risk assessment within the context of environmental decision-making; evaluates its potential application as a decision-making framework for selecting alternative technologies for dredged material management; and makes recommendations for implementing such a framework. One of the most important points from this review for decision-making is that comparative risk assessment, however conducted, is an inherently subjective, value-laden process. There is some objection to this lack of total scientific objectivity (“hard version” of comparative risk assessment). However, the “hard versions” provide little help in suggesting a method that surmounts the psychology of choice in decision-making schemes. The application of comparative risk assessment in the decision-making process at dredged material management facilities will have an element of value and professional judgment in the process. The literature suggests that the best way to incorporate this subjectivity and still maintain a defensible comparative framework is to develop a method that is logically consistent and allows for uncertainty by comparing risks on the basis of more than one set of criteria, more than one set of categories, and more than one set of experts. It should incorporate a probabilistic approach where necessary and possible, based on management goals.     

Bayesian Updating of Model-Based Risk Estimates Using Imperfect Public Health Surveillance Data

Model-based estimation of the human health risks resulting from exposure to environmental contaminants can be an important tool for structuring public health policy. Due to uncertainties in the modeling process, the outcomes of these assessments are usually probabilistic representations of a range of possible risks. In some cases, health surveillance data are available for the assessment population over all or a subset of the risk projection period and this additional information can be used to augment the model-based estimates. We use a Bayesian approach to update model-based estimates of health risks based on available health outcome data. Updated uncertainty distributions for risk estimates are derived using Monte Carlo sampling, which allows flexibility to model realistic situations including measurement error in the observable outcomes. We illustrate the approach by using imperfect public health surveillance data on lung cancer deaths to update model-based lung cancer mortality risk estimates in a population exposed to ionizing radiation from a uranium processing facility.     

The assessment of spray drift damage for ten major crops in Belgium

According to the Council Directive 91/414/EC pesticide damage should be assessed by considering the risk for persons arising from occupational, non-dietary exposure and risk to the environment. In this research an assessment for the pesticide damage by droplet spray drift was set up. The percentages of spray drift were estimated with the Ganzelmeier drift curves and the IMAG drift calculator. Knowing the percentages of drift and the applied doses of pesticide formulations in a given crop, the human and environmental exposures (water and bottom) were predicted. Thereupon risk indices were calculated for water organisms, soil organisms and bystanders. A risk index is the ratio of a predicted exposure to a toxicological reference value and gives an indication of the incidence and the severity of the adverse effects likely to occur. Considering the risk index it is possible to define the minimal width of an unsprayed field margin or "buffer zone" to reduce this risk at an acceptable level.     

What Can Be Done to Ensure the Usefulness of Epidemiologic Data for Risk Assessment Purposes?

Epidemiologic studies can play a central role in risk assessments. They are used in all risk assessment phases: hazard identification, dose-response, and exposure assessment. Epidemiologic studies have often been the first to show that a particular environmental exposure is a hazard to health. They have numerous advantages with respect to other sources of data which are used in risk assessments, the most important being that they do not require the assumption that they are generalizable to humans. For this reason, fewer and lower uncertainty factors may be appropriate in risk characterization based on epidemiologic studies. Unfortunately, epidemiologic studies have numerous problems, the most important being that the exposures are often not precisely measured. This article presents in detail the advantages of and problems with epidemiologic studies. It discusses two approaches to ensure their usefulness, biomarkers and an ordinance which requires baseline and subsequent surveillance of possible exposures and health effects from newly sited potentially polluting facilities. Biomarkers are biochemical measures of exposure, susceptibility factors, or preclinical pathological changes. Biomarkers are a way of dealing with the problems of poor measures, differential susceptibility and lack of early measures of disease occurrence that inherent in many environmental epidemiologic studies. The advantages of biomarkers is they can provide objective information on exposure days, months or even years later and evidence of pathology perhaps years earlier. The ordinance makes possible the use of a powerful epidemiologic study design, the prospective cohort study, where confounder(s) are best measured, and exposures, pathological changes, and health effects can be detected as soon as possible.