Exposure Assumption Distributions

Exposure Assumption Distribution Assumed Relevant Values Single Point Value  

Exposure duration Custom 7 years (average), 30 years or more (top 11 percent) 30 years 

Exposure frequency Triangular 330 (most likely), 365 (maximum) 350 days/year 

Exposure time Custom 16 (50 percent), 24 (50 percent) 24 hours 

Soil ingestion rate Uniform 10 (minimum), 200 (maximum) 100 grams/day 

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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. 

Clearness and transparency with respect to the choice of models, methods, assumptions, distributions and parameters are two prerequisites for trust and confidence openness about uncertainties is another. Exposure assessment as an applied science should follow the main scientific desiderata empirical testing, documentation and reproducibility of results, explicit reporting of uncertainty, peer review and an open debate about underlying theories and models. This way, the main attributes for characterizing uncertainty discussed in the last chapter, the appraisal of the knowledge base and the subjectivity of choices, are clarified. 

Ultimately, transparency about the choice of models, methods, assumptions, distributions and parameters is a prerequisite for trust and confidence in the quality of data used in exposure assessments and for credibility of the outcomes of the assessments themselves. 

Figure 10.4. Sample Distributions of Exposure Assumptions in Human Populations...

In the holding section of a continuous sterilizer, correct exposure time and temperature must be maintained. Because of the distribution of residence times, the actual reduction of microbial contaminants in the holding section is significantly lower than that predicted from plug flow assumption. The difference between actual and predicted reduction in viable microorganisms can be several orders of magnitude therefore, a design based on ideal flow conditions may fail. 

Although the ecological consequences of enhanced UVB exposure to algal species are still largely unexplored, some data exist and some assumptions can be made. Based on the differential adaptation and acclimation capabilities in different algal species, UVB may, even under non-depleted ozone conditions, substantially affect the structure of communities, as well as modulate productivity, reproduction, vertical distribution, biodiversity and succession, competition, and alga-herbivore interactions (Bischof et al. 2006a). 

Another important reason for using multiple scenarios is to represent major sources of variability, or what-if scenarios to examine alternative assumptions about major uncertainties. This can be less unwieldy than including them in the model. Also, the distribution of outputs for each separate scenario will be narrower than when they are combined, which may aid interpretation and credibility. A special case of this occurs when it is desired to model the consequences of extreme or rare events or situations, for example, earthquakes. An example relevant to pesticides might be exposure of endangered species on migration. This use of multiple scenarios in ecological risk assessment has been termed scenario analysis, and is described in more detail in Ferenc and Foran (2000). 

The expected number of fatalities is hypothetical and is based on the assumption that exposure levels are independent and lognor-mally distributed over the working month, i.e., P (fatality) =... 

In conclusion, pharmacokinetics is a study of the time course of absorption, distribution, and elimination of a chemical. We use pharmacokinetics as a tool to analyze plasma concentration time profiles after chemical exposure, and it is the derived rates and other parameters that reflect the underlying physiological processes that determine the fate of the chemical. There are numerous software packages available today to accomplish these analyses. The user should, however, be aware of the experimental conditions, the time frame over which the data were collected, and many of the assumptions embedded in the analyses. For example, many of the transport processes described in this chapter may not obey first-order kinetics, and thus may be nonlinear especially at toxicological doses. The reader is advised to consult other texts for more detailed descriptions of these nonlinear interactions and data analyses. 

The last step in the problem formulation phase is the development of an analysis plan or proposal that identifies measures to evaluate each risk hypothesis and that describes the assessment design, data needs, assumptions, extrapolations, and specific methods for conducting the analysis. There are three categories of measures that can be selected. Measures of effect (also called measurement end points) are measures used to evaluate the response of the assessment end point when exposed to a stressor. Measures of exposure are measures of how exposure may be occurring, including how a stressor moves through the environment and how it may co-occur with the assessment end point. Measures of ecosystem and receptor characteristics include ecosystem characteristics that influence the behavior and location of assessment end points, the distribution of a stressor, and life history characteristics of the assessment end point that may affect exposure or response to the stressor. These diverse measures increase in importance as the complexity of the assessment increases. 

In section 2.3 of this chapter the present approach to characterisation of dose-response relationships was described. In most cases it is necessary to extrapolate from animal species that are used in testing to humans. It may also be necessary to extrapolate from experimental conditions to real human exposures. At the present time default assumptions (which are assumed to be conservative) are applied to convert experimental data into predictive human risk assessments. However, the rates at which a particular substance is adsorbed, distributed, metabolised and excreted can vary considerably between animal species and this can introduce considerable uncertainties into the risk assessment process. The aim of PB-PK models is to quantify these differences as far as possible and so to be able to make more reliable extrapolations. 

Human health risk assessment has often been dominated by the use of default assumptions and worst case analyses, based on the use of upper bounds on the dose from exposure instead of distributional characterizations of that dose. There are severe limitations associated with the use of default assumptions and upper bounds instead of distributions when detailed exposure and/or dose-response data are available. The US National Academy of Sciences, the USEPA, and many others have recognized the need for new risk assessment methodology (NRC, 1983, 1993, 1994 USEPA, 1992 CRARM, 1997). This need has promoted the development of new quantitative risk assessment methods that use probabilistic techniques, especially Monte Carlo simulation and distributional characterizations of dose-response, exposure, and risk. For these reasons, this paper uses a probabilistic approach. An indication of some of these new methods and the type of results they produce are given below. 

The outcome of the exposure equation is a dose. This dose varies because of the variability of the components in the equation. The probability distribution of the dose is generally quite difficult to calculate analytically, but can be fairly readily approximated using a Monte Carlo simulation. The simulation consists of numerous iterations. In an iteration, a single value for each component in the exposure equation is randomly sampled from its corresponding distribution. These component values are then substituted into the exposure equation, and the outcome (exposure) is explicitly calculated. The frequency distribution of the calculated values from numerous iterations is the simulated exposure distribution. The exposure equations and the probability distributions of the components are treated as known in the distributional results presented in this chapter. Thus, the simulated exposure distributions reflect exposure variability - but not uncertainty about these equations, the distributions of the components, and related assumptions. This uncertainty and its quantitative impact on the simulated exposure distribution are presented in Sielken et al. (1996). 

After the series of metabolic pathways had been elucidated for the three model compounds 1-3, these data were implemented into the mathematical model PharmBiosim. The nonlinear system s response to varying ketone exposure was studied. The predicted vanishing of oscillatory behavior for increasing ketone concentration can be used to experimentally test the model assumptions in the reduction of the xenobiotic ketone. To generate such predictions, we employed as a convenient tool the continuation of the nonlinear system s behavior in the control parameters. This strategy is applicable to large systems of coupled, nonlinear, ordinary differential equations and shall together with direct numerical simulations be used to further extend PharmBiosim than was sketched here. This model already allows more detailed predictions of stereoisomer distribution in the products. 

Textual descriptions of the exposure assessment results might be useful if statements about the mean, the central tendency estimate (median) or a selected quantile of the exposure distribution are given without a description of uncertainty. However, each of the point estimates mentioned will have a different level of uncertainty with respect to model assumptions, database and calculation method. A typical wording to describe results might be, for example ... 

The subjectivity of the qualitative assessment (see section 5.1.2.2) also opens the possibility of conscious or unconscious bias by the assessor. For this reason, it is desirable to report the steps from (1) to (5) in a transparent way so that others can review and evaluate the judgements that have been made. This has the advantage that it is always possible to do and that it is sufficient if the result is clearly conservative (protective) overall. However, it has disadvantages with regard to subjectivity when the outcome is not clearly conservative and when using separate uncertainty factors for many parameters that can lead to compounding conservatism. If an exposure/risk assessment contains a number of conservative assumptions, then the above table is likely to end up with an overall assessment that the true risk is probably lower than the quantitative estimate. However, if the assessment attempts to use realistic estimates/distributions for most inputs, then a table of unquantified uncertainties is the likely result. This undoubtedly is a difficulty for decision-makers unless the assessor can evaluate the combined uncertainty relative to the decision-makers decision threshold. 

All of these numbers are the result of a long series of assumptions and, for instance, do not account for the distribution of exposures or individual sensitivities among the population, intake fractions among substances or emission factors per produced volume among substances. 

Using an HPLC procedure with coulometiic detection, chlorpromazine was assayed in hair samples of 23 subjects who had been taking the drug in fixed daily doses. Chlorpromazine concentrations ranged from 1.6 to 27.5 ng/mg and was significantly correlated with the daily dose (r = 0.788, p < lO" ). With the assumption of a hair growth rate of 1 cm per month, the individual history of chlopromazine doses in all patients could be deduced from the distribution of chlorpromazine along the hair shaft. The authors concluded that these results indicate that hair could serve as an indicator of individual exposure to neuroleptics and could yield retrospective information. 

Fluorescent tracer techniques hold the promise of improved accuracy in assessing dermal exposures, as they require no assumptions regarding the distribution of exposure across skin surfaces. However, this approach also has several limitations. First, it requires introduction of the tracer compound into the agricultural spray mix. Secondly, there must be demonstration of a correspondence between pesticide deposition and deposition of the fluorescent compound for the production, such that the fluorescence can indeed be considered a tracer of chemical deposition. Thirdly, range-finding and quality assurance studies may be needed to ensure the accuracy of tracer measurements. Fourthly, when protective clothing is worn by workers, the relative penetration of the pesticide and tracer needs to be characterized. All of these limitations make fluorescent tracer methods technically challenging. 

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