Temporal extrapolation exposure

Extrapolation used to infer toxicity from one type of exposure regimen to another is often termed temporal extrapolation. The most common of these extrapolations is that from acute to chronic exposures, but the issue of pulsed versus continuous exposure is also important in assessing possible effects in real-world environmental settings. These extrapolations may involve the use of modified tests with standard species or whole-model ecosystems to simulate realistic exposures such as those of variable duration or those of pulsed exposure for compounds that rapidly dissipate in the environment. In many cases, these involve alterations in exposure route and intensity, both of which can have significant impacts on the toxic responses. Extrapolation from acute responses to NOECs or chronic responses is particularly important as chronic tests are more costly and time-consuming than acute tests. Methods for accurate and precise acute-to-chronic extrapolations have been developed and are available as computer programs such as ACE (Mayer et al. 1999, 2001 De Zwart 2002 Ellersieck et al. 2003) and are discussed in Chapter 6. 

Temporal extrapolation is important in terms of the duration of the exposure, the number of exposures, and the nature of the response to these in the organism. Chapter 6 reviews relationships between temporal exposure in relation to acute-to-chronic extrapolation, reversibility, and latency in terms of the interaction between substances and individual organisms. Other temporal extrapolation approaches are needed when considering temporal processes in organisms themselves. These relate to seasonal variability in sensitivity, recovery at the population level, and adaptation to stressors. 

Derivation of AEGL-1 (key study, critical effect, dose-exposure concentration, uncertainty factor application and justification, temporal extrapolation, assumptions, confidence, consistency with human data if... 

As probabilistic exposure and risk assessment methods are developed and become more frequently used for environmental fate and effects assessment, OPP increasingly needs distributions of environmental fate values rather than single point estimates, and quantitation of error and uncertainty in measurements. Probabilistic models currently being developed by the OPP require distributions of environmental fate and effects parameters either by measurement, extrapolation or a combination of the two. The models predictions will allow regulators to base decisions on the likelihood and magnitude of exposure and effects for a range of conditions which vary both spatially and temporally, rather than in a specific environment under static conditions. This increased need for basic data on environmental fate may increase data collection and drive development of less costly and more precise analytical methods. 

Besides meeting its assumptions, other problems in the application of SSD in risk assessment to extrapolate from the population level to the community level also exist. First, when use is made of databases (such as ECOTOX USEPA 2001) from which it is difficult to check the validity of the data, one does not know what is modeled. In practice, a combination of differences between laboratories, between endpoints, between test durations, between test conditions, between genotypes, between phenotypes, and eventually between species is modeled. Another issue is the ambiguous integration of SSD with exposure distribution to calculate risk (Verdonck et al. 2003). They showed that, in order to be able to set threshold levels using probabilistic risk assessment and interpret the risk associated with a given exposure concentration distribution and SSD, the spatial and temporal interpretations of the exposure concentration distribution must be known. 

Recently, metapopulation models have been successfully applied to assess the risks of contaminants to aquatic populations. A metapopulation model to extrapolate responses of the aquatic isopod Asellus aquaticus as observed in insecticide-stressed mesocosms to assess its recovery potential in drainage ditches, streams, and ponds is provided by van den Brink et al. (2007). They estimated realistic pyrethroid concentrations in these different types of aquatic ecosystems by means of exposure models used in the European legislation procedure for pesticides. It appeared that the rate of recovery of Asellus in pyrethroid-stressed drainage ditches was faster in the field than in the isolated mesocosms. However, the rate of recovery in drainage ditches was calculated to be lower than that in streams and ponds (van den Brink et al. 2007). In another study, the effects of flounder foraging behavior and habitat preferences on exposure to polychlorinated biphenyls in sediments were assessed by Linkov et al. (2002) using a tractable individual-based metapopulation model. In this study, the use of a spatially and temporally explicit model reduced the estimate of risk by an order of magnitude as compared with a nonspatial model (Linkov et al. 2002). 

In keeping with the need to characterize and understand exposures in risk assessment, the second chapter, on matrix and media extrapolation, deals with the very important physical and chemical interactions between the exposure matrix and the biological availability of the substance. This process is key to extrapolation in both the spatial and the temporal contexts, where there are differences in the environments where organisms may be exposed. This chapter reviews the methods of extrapolation that may be used and provides guidance as to the tools to use for this purpose. 

The implications of adverse effects at spatial scales beyond the immediate area of concern may be evaluated by considering ecological characteristics such as community structure and energy and nutrient dynamics. In addition, information from the characterization of exposure on the stressor s spatial distribution may be useful. Extrapolations between different temporal scales (e.g., from short-term impacts to long-term effects) may consider the stressors distribution through time (intensity, duration, and frequency) relative to ecological dynamics (e.g., seasonal cycles, life cycle patterns). 

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