Temporal extrapolation levels

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. 

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. 

The quantity of natural sulfur emitted to the atmosphere is dependent upon the availability of sulfur, the level of natural sulfur-reducing activity, and the environment into which the gases are released. At present, there is a lack of information on specific mechanisms of biological sulfur release. As a result, algorithms designed to predict natural sulfur emissions must be empirically based on analyses of correlations between observed natural sulfur emissions and environmental parameters. In order to extrapolate the available emission rate data, emission functions must be based upon parameters which are measurable and available on an appropriate scale of temporal and spatial resolution. 

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. 

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. 

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