Exposure distribution

Large numbers of samples are required to characterize exposure distributions. Starling food items contained the highest diazinon concentrations and the highest... 

The third step is to select the number of iterations or calculations of dose that are to be performed as a part of each simulation. For the analysis here, a total of 10,000 iterations based on the selection of input variables from each defined distribution were performed as part of each simulation. The large number of iterations performed, as well as the Latin hypercube sampling (non-random sampling) technique employed by the Crystal Ball simulation program, ensured that the input distributions were well characterized, that all portions of the distribution (such as the tails) were included in the analysis, and that the resulting exposure distributions were stable. 

While EPA publicly downplayed the risks documented in the CRA, the results showed that infants aged one to two years old, at the 99.9th percentile of the exposure distribution, face at least twice the level of risk that EPA has traditionally found acceptable. Based on EPA s findings, several thousand children on any given day are likely to be exposed to OPs well in excess of levels consistent with the FQPA s reasonable certainty of no harm standard. 

Currently, the EPA considers acute pesticide exposures to represent a reasonable certainty of no harm when exposure at the 99.9th percentile is below the RfD. When exposure at the 99.9th percentile exceeds the RfD, the EPA will generally conduct a sensitivity analysis to determine whether particular factors that drive the exposure, such as high residue or high consumption levels, are unusual and so may represent artifacts that artificially skew the exposure distribution curve. 

In animal studies, the tissue distribution of " C-1,4-dichlorobenzene in female CFY rats was found to be similar following inhalation, oral, and subcutaneous exposure (Hawkins et al. 1980). The inhalation exposure regimen was 10 consecutive days of exposure to " C-1,4-dichlorobenzene at 1,000 ppm for 3 hours per day, and the highest concentrations of " C were measured in fat (up to 557 g/g via inhalation) and next highest levels in kidneys and liver. Concentrations in kidney and liver were about 5-10% of that found in adipose tissue, irrespective of the route of exposure. Distribution patterns for all routes were also similar to those observed by Kimura et al. (1979) using the oral route, as described below. 

The exposure distribution and species sensitivity distributions were integrated to generate risk curves for chronic effects. From the 504 000 values in the exposure exceedence curve, annual maximum concentrations corresponding to each 0.5th percentile were determined. The percentage of plant or animal species whose chronic NOEC would be exceeded at each of these concentrations was calculated from the log-normal SSD model. The percentage of plant or animal species affected at each exposure exceedence percentile was plotted as shown in Figure 4.5. 

FIGURE 4.5 Risk curves based on exposure distribution for annual maximum atrazine concentrations in Tennessee ponds and chronic species sensitivity distributions for aquatic plants and animals. 

Figure 2 Exposure distributions for all sample locations in Dutch open/estuarine/marine waters and harbours.

The measured exposure coneentrations from the field survey are used as a base for a probabilistic risk assessment. In such an assessment the exposure is presented as an exposure distribution. In this study the exposure distribution presents the spatial variation in the eoneentra-tions measured in e.g. a specific harbour or for all harbours. The exposure distribution ean be eompared to the species sensitivity distribution (SSD) for TBT. The SSD represents the average sensitivity of species and the variation among speeies. As the effect data of TBT, on which the SSD is based, is derived from coneentrations in water it was necessary to reealeulate all exposure... 

C. Describe the basis for the exposure assessment, including any monitoring, modelling or other analyses of exposure distributions, such as Monte Carlo or kriging. 

No studies have been located that specifically follow the distribution of radium in humans or animals following oral exposure. Distribution to the skeleton is assumed due to the findings of osteosarcomas in the dial painter studies as well as the presence of radium in their exhumed skeletal remains. The affinity for bone is assumed to be related to its similarity to calcium (BIER IV 1988). 

At a minimum, one would like to determine the exposure distribution in a population to establish a reference range. 4 A reference range can be used to compare individual or subgroup results. With additional analyses, one can determine whether some members of the population have higher exposures than others. However, a biomonitoring value above the upper bound of the reference range does not necessarily mean that there is a health risk, just as a value below the lower bound of the reference range does not mean that there is no health risk. 

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). 

Tier 2 PRA process involved developing environmental exposure data and chronic toxicity data distributions for individual POPs. The mean concentrations of POPs in local marine water measured at various locations were used as exposure data in the construction of the exposure distribution. The chronic toxicity data distribution was established based on published international acute toxicity data (LC50, EC50) on a variety of aquatic organisms tested in many jurisdictions, drawn primarily from the USEPA ECOTOX database (2002) (available at http //www.epa.gov/ ecotox). If the upper 5th centile of the measured chemical exposure data distribution did not exceed the lower 5th centile of its estimated chronic toxicity distribution, the potential ecological risk posed by the chemical was judged to be tolerable (Hall and Giddings, 2000). 

List and discuss the major routes and sites of exposure, distribution, and elimination of toxicants in the body. 

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. 

Deterministic models use a single value for input variables and provide a point estimate of exposure or dose. Probabilistic models take into account the fact that most input variables will have a distribution of values. These models use probability distributions to develop a range of plausible exposures for the population of concern. Understanding exposure distributions will allow understanding of the range of exposures as well as prediction of risk for the entire population. It will also allow prediction of risk for the most highly exposed individuals. Sophisticated models can be used to develop distributions for different pathways and populations. They can also be used to develop information on interindividual variability and uncertainty in the estimated distributions and to predict the variables that are most important for both exposure and dose. 

Exposure models use available information on concentrations of chemicals in exposure media along with information about when, where, and how individuals might contact the exposure media to estimate exposure. For population assessments, distributional data on exposure factors and environmental concentrations are used to estimate exposure distributions for a population. Examples of various exposure models are summarized in Table 6. 

Hazardous Air Pollutant Exposure Model (HAPEM) Semistochastic, sequential simulation, producing aggregate exposure distributions Used by USEPA to evaluate national exposures to hazardous air pollutants (part of Trim.Expo model) Palma et al. (1996)... 

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