Spatial extrapolation assessment

Spatial Extrapolation in Ecological Effect Assessment of Chemicals... 

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. 

Extrapolations between different media are common in risk assessment. Such extrapolations are based on physical and chemical interactions between components of the matrix and the toxic substance that may enhance or reduce the biological availability of the substance, thus affecting its apparent toxicity because of the change in exposure. In this chapter, the word medium is reserved to indicate the major environmental compartments air, water, sediment, and soil. The word matrix is associated with the physicochemical properties of the media. The problems associated with extrapolating between one medium or type of matrix to another are intricate and are generally due to the varying chemical, physical, biological, and spatial characteristics associated with the different media. 

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. 

Spatial variability and ecotoxicological data extrapolation This section describes the current knowledge and available extrapolation tools with respect to the effect assessment of the same type of stressor in test systems of different sizes, in different types of aquatic ecosystem within a region, and in comparable ecosystems in different geographical regions. 

Landscape ecotoxicology This section describes current developments and extrapolation tools used in landscape ecotoxicology. Because ecological effect assessment of chemicals at the landscape level requires the integration of both spatial and temporal aspects, this section in particular builds further on the data presented in Chapter 6 on temporal extrapolation in ecological effect assessment of chemicals. 

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). 

It is often the case that, when using the system, various extrapolation techniques must be applied in sequence, and it is proposed that (in general) the extrapolation should follow the pathway from cause to effect. When applicable, and generally this will be in higher tiers of risk assessment, specific attention can be paid to spatial and temporal modifications of risk. 

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. 

In addition to these extrapolations, an evaluation of indirect effects, other levels of organization, other temporal and spatial scales, and recovery potential may be necessary. Whether these analyses are required in a particular risk assessment will depend on the assessment endpoints identified during problem formulation. 

A central issue for pesticide risk assessment is extrapolation from individual- to population-level effects and from small temporal and spatial scales to larger ones. Empirical methods to tackle these issues are limited. Models are thus the only way to explore the full range of ecological complexities that may be of relevance for ecological risk assessment. However, EMs are not a silver bullet. Transparency is key, and certain challenges exist, for example, translating model output to useful risk measures. To make full use of models and get them established for risk assessment, we need case studies that clearly demonstrate the added value of this approach (Chapter 10). 

Such simple models need validation and for this reason ETAD is conducting in a field study to investigate some representative dyes (at manufacturing sites and dyehouses) under a project termed Pathways of Colorants to the Environment. The environmental risk posed by a colorant is a function of both its inherent ecotoxicity and the concentrations attained in the environmental compartments. Unlike other substances eg, household detergents) which are emitted continuously, dyes releases result mainly from batch processes and result in spatial and temporal peak emissions. Obviously, short-time concentrations should be compared with acute data on ecotoxicity, whereas long-tom residual concentrations need to be cranpared with chronic effect levels. Because, data on chronic effects are not often available, empirical information serves as a basis for the effects assessment, ie, the extrapolation to a Predicted No Effect Concentration (PNEC). This PNEC value is to be compared with the so-called Predicted Environmental Concentration (PEC) in order to estimate safe levels of residual dye in the environment. Since it is the dissolved state in which a dyes may become biologically available, it is the aquatic environmental compartment which is primarily addressed here. Nonetheless, some consideration of the impact of dyes on sewage and soil is also included. 

It would be extremely useful if laboratory measurements at a variety of spatial scales, including the nanometer scale, could be accurately extrapolated to the field scale so that the consequences of a field-scale pollution event could be assessed and predicted in quantitative detail as a function of time and location. Conversely, it would be useful if... 

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