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     

Improving Risk Assessment: Priorities for Epidemiologic Research

The Epidemiology Work Group at the Workshop on Future Research for Improving Risk Assessment Methods, Of Mice, Men, and Models, held August 16 to 18, 2000, at Snowmass Village, Aspen, Colorado, concluded that in order to improve the utility of epidemiologic studies for risk assessment, methodologic research is needed in the following areas: (1) aspects of epidemiologic study designs that affect doseresponse estimation; (2) alternative methods for estimating dose in human studies; and (3) refined methods for dose-response modeling for epidemiologic data. Needed research in aspects of epidemiologic study design includes recognition and control of study biases, identification of susceptible subpopulations, choice of exposure metrics, and choice of epidemiologic risk parameters. Much of this research can be done with existing data. Research needed to improve determinants of dose in human studies includes additional individual-level data (e.g., diet, co-morbidity), development of more extensive human data for physiologically based pharmacokinetic (PBPK) dose modeling, tissue registries to increase the availability of tissue for studies of exposure/dose and susceptibility biomarkers, and biomarker data to assess exposures in humans and animals. Research needed on dose-response modeling of human studies includes more widespread application of flexible statistical methods (e.g., general additive models), development of methods to compensate for epidemiologic bias in dose-response models, improved biological models using human data, and evaluation of the benchmark dose using human data. There was consensus among the Work Group that, whereas most prior risk assessments have focused on cancer, there is a growing need for applications to other health outcomes. Developmental and reproductive effects, injuries, respiratory disease, and cardiovascular disease were identified as especially high priorities for research. It was also a consensus view that epidemiologists, industrial hygienists, and other scientists focusing on human data need to play a stronger role throughout the risk assessment process. Finally, the group agreed that there was a need to improve risk communication, particularly on uncertainty inherent in risk assessments that use epidemiologic data.     

Assessment of Pollution-Induced Microbial Community Tolerance to Heavy Metals in Soil Using Ammonia-Oxidizing Bacteria and Biolog Assay

This study attempted to investigate if the tolerance of soil bacterial communities in general, and autotrophic ammonia-oxidizing bacteria (AOB) in particular, evolved as a result of prolonged exposure to metals, and could be used as an indigenous bioindicator for soil metal pollution. A soil contaminated with copper, chromium, and arsenic (CCA) was mixed with an uncontaminated garden soil (GS3) to make five test soils with different metal concentrations. A modified potential ammonium oxidation assay was used to determine the metal tolerance of the AOB community. Tolerance to Cr, Cu, and As was tested at the beginning and after up to 13 months of incubation. Compared with the reference GS3 soil, the five CCA soils showed significantly higher tolerance to Cr no matter which form of Cr (Cr³⁺, CrO₄ ^2?, or Cr₂O₇ ^2?) was tested, and the Cr tolerance correlated with the total soil Cr concentration. However, the tolerance to Cu²⁺, As³⁺, and As⁵⁺ did not differ significantly between the GS3 soil and the five CCA soils. Community level physiological profiles using Biolog microtiter plates were also used to examine the chromate tolerance of the bacterial communities extracted after six months of exposure. Our results showed that the bacterial community tolerance was altered and increased as the soil Cr concentration was increased, indicating that the culturable microbial community and the AOB community responded in a similar manner.     

Assessing Toxicity of Mixtures: The Search for Economical Study Designs

Toxicity screening and testing of chemical mixtures for interaction effects is a potentially onerous task due to the sheer volume of combinations that may be of interest. We propose an economical approach for assessing the interaction effects of chemical mixtures that is guided by risk-based considerations. We describe the statistical underpinnings of the approach and use examples from the published literature to illustrate concepts of local versus global mixture assessment. Our approach employs a sequential testing procedure to find the dose combinations that define the dose boundary for a specified acceptable risk level. The first test is conducted for a dose combination consisting of the acceptable doses of each individual chemical in the mixture. The outcome of this first test indicates the dose combination that should be tested next. Continuing in this manner, the boundary of dose combinations for the specified acceptable risk level can be approximated based on measurements for relatively few dose combinations. Dose combinations on one side of the boundary would have responses less than the response associated with the acceptable risk level, and dose combinations on the boundary would be acceptable levels of exposure for the mixture.     

Regional Risk Assessment of a Brazilian Rain Forest Reserve

The objective of this study was to identify subareas inside and near an Atlantic Rain Forest reserve, the Parque Estadual Turístico do Alto Ribeira (PETAR), most likely to be affected by land use in the vicinity of the area. In addition, the study aimed to compare risks per stressor source (agriculture, human settlements and mining) to both epigean (surface) and hypogean (subterranean) aquatic fauna. The methodological approach included the relative vulnerability of endpoints to the stressors (pesticides, metals, nutrients, and particles) and ranking of stressor sources and habitats (epigean and hypogean streams) based on their relative distribution in 14 subareas within the catchment areas of the main rivers that cross PETAR: Pilões, Betari and Iporanga. Four subareas presented high risk for both epigean and hypogean fauna. Three of those areas were located inside the Betari catchment area, where most of the settlements and abandoned lead mines are located. The fourth area was situated in the headwaters of the Pilões River, where agricultural activities are intense. Agriculture and human settlements were the activities most likely to cause impacts on aquatic ecosystems. Uses of risk assessment results include management of the PETAR and communication to stakeholders by the Park Administration.     

Extrapolation for Terrestrial Vertebrates

Risk assessment for terrestrial birds and mammals is most usually conducted for pesticides in standardized systems based on results of limited tests required for regulatory approval. Increasingly, attempts at risk assessment are being made for other chemicals. Typically for pesticides, dietary tests are extrapolated to a few representative species and risk factors derived as ratios against modeled environmental concentrations. There has been criticism of the validity of some of the standard tests, which makes even this simple approach difficult to justify. Attempts have been made to extrapolate from those values considered more reliable using statistical approaches. Relative sensitivity of test species has been determined. However, reliable extrapolation from laboratory to field remains elusive. Statistically derived values from test results probably generate extremely conservative estimates of environmental no-effect levels. Substantial information on the biology, distribution and food preference of species has, thus far, barely been applied to risk assessment. Other promising approaches, such as species differences in metabolic capacity, population dynamics models, and even sublethal effects on reproductive or behavioural endpoints, cannot in themselves provide simple risk factors either. A simple approach to generate approximate or relative risk factors is explored. While the accumulation of a set of circumstantial evidence might not be regarded as risk assessment in the normal sense, it might offer us a means to extrapolate to a reasonable understanding of likely effects in the field and contribute to a weight-of-evidence approach that informs risk management. It also focuses further studies to those areas and species within the environment most likely to be adversely affected     

Salmon and Complexity: Challenges to Assessment

Salmon in the U.S. Pacific Northwest are in widespread decline despite countless environmental assessment studies and billions of dollars spent. Having been involved in environmental assessment for more than three decades, I am forced to conclude that this decline tells us that our established practices of assessment and management are fundamentally deficient. Rather than studying the salmon, we should examine our own practices. These practices presume that, if individual actions are found to be beneficial through analytical assessments, the cumulative outcomes of many actions will also be beneficial. This “linear” presumption is embedded in institutions, analytical methods, and assessment practices. For a whole class of emerging problems, including declining salmon, this presumption is fundamentally wrong. Declining salmon provide a warning that our own analytical habits of thought and notions of progress are leading to outcomes that are both destructive and contrary to our best intentions. This paper is a response to this warning.     

Weight-of-Evidence (WOE): Quantitative Estimation of Probability of Impairment for Individual and Multiple Lines of Evidence

Environmental decision-making is complex and often based on multiple lines of evidence. Integrating the information from these multiple lines of evidence is rarely a simple process. We present a quantitative approach to the combination of multiple lines of evidence through calculation of weight-of-evidence, with reference conditions used to define a not impaired state. The approach is risk-based with measurement of risk computed as the probability of impairment. When data on reference conditions are available, there are a variety of methods for calculating this probability. Statistical theory and the use of odds ratios provide a method for combining the measures of risk from the different lines of evidence. The approach is illustrated using data from the Great Lakes to predict the risk at potentially contaminated sites.     

Uncertainty in the Extrapolation from Individual Effects to Impacts upon Landscapes

Uncertainty is inherent in extrapolating from effects upon the individual to alterations in ecological structure or function. Subtle differences in individuals within a population can give rise to significant evolutionary events. Populations are part of ecological structures, systems that are clearly complex, requiring an understanding of the interacting components, stochastic inputs and spatial scales. A series of patch-dynamic models is used to illustrate the importance of spatial arrangement and initial population size in predicting effects at a landscape level. The importance of understanding the spatial structure of a population in uncertainty reduction is addressed.