Probabilistic risk assessment exposure characterization

This chapter presents a quantitative probabilistic risk assessment for atrazine and simazine conducted for Syngenta Crop Protection, Inc. The risk of an effect is the likelihood that an individual will develop the effect as a result of that individual s exposure to atrazine and/or simazine. The risk assessment is quantitative because it characterizes the likelihood in numerical terms. The risk assessment does include some qualitative discussion of the uncertainties associated with the quantitative characterization of the likelihood. It also assumes relevance of an animal effect in humans even when a lack of human relevance has been established, as is the case with atrazine and simazine [US Environmental Protection Agency (USEPA), 2006]. 

Different safety factors may have been used in the derivation of the reference values of the individual substances (RfDA deterministic HI thus sums risk ratios that may reflect different percentile values of a risk probability distribution. Assessment and interpretation of the uncertainty in the HI may be severely hampered by this summation of dissimilar distribution parameters. In a probabilistic risk assessment, the uncertainty in the exposure and reference values is often characterized by lognormal distributions. The ratio of 2 lognormal distributions also is a lognormal distribution. The variance in a quotient of 2 random variables can be approximated as follows (Mood et al. 1974, p 181) ... 

Probabilistic risk assessment methods are described herein for determining a popnlation s distribution of the dose from exposure and the combination of that exposnre characterization with appropriate toxicological information to form aggregate and cumulative risk assessments. An individual s dose from exposure is characterized as a set of chemical- and route-specific dose profiles over time. Toxic equivalence factors (TEFs) that reflect the toxic endpoint and exposure duration of concern are used to scale chemical- and route-specific doses to toxic equivalent doses (TEDs). The latter are combined in a temporally consistent manner to form a profile over time of the Total TED. For each individual, a Total MOE is calculated by dividing a toxicologically relevant benchmark dose (e.g. an EDio) by the individual s Total TED. The distribution of the Total MOE in a popnlation provides important information for risk management decisions. 

Travis, K.Z. and P. Hendley (2001). Probabilistic aquatic risk assessment of pyrethroids IV. Landscape-level exposure characterization. Environ. Toxicol. Chem., 20 679-686. 

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. 

If default constants are used for each of several different parameters in the risk assessment, then the conservative aspect of the individual components is compounded when they are combined in the risk characterization. Furthermore, the extent of the overestimation cannot be readily quantified, and so the magnitude of the overestimation of the risk is not identified. However, distributional techniques make it possible to combine exposures more realistically - whether from multiple years, subpopulations, exposure pathways, or chemicals - without having to assume the worst case for each component. By carrying all the information for each component of the risk assessment through to the end of the entire risk characterization, instead of requiring interim single-number characterizations, probabilistic techniques help avoid the compounding of the conservative aspect of multiple parameters. 

ASSESSMENT OF RISKS TO HUMANS EXPOSED TO PESTICIDES 2 The Four Steps in Risk Assessment 2 Hazard Identification 2 Dose-Response Assessment 3 Margin of Safety Approach 3 Quantitative Risk Assessment 3 Exposure Assessment 4 Risk Characterization 4 RISK MANAGEMENT 5 ADVANCES IN DATA INTERPRETATION 5 Probabilistic Approaches 5 Recognition of the Tier Approach 5 Aggregate Exposure 6 Cumulative Exposure 6 Impact of New Scientific Advances 7 Post-Registration Monitoring 7 HARMONIZATION OF REGULATORY APPROACHES SUMMARY 9... 

Residential exposure should be estimated by taking into account distributions of exposure factors. Methods to assess distributions are through the deterministic or probabilistic approach (Figure 6.6). The former is often taken in preventive risk assessment in which each default value is determined from each distribution as a reasonable worst-case . The estimated exposures for the deterministic approach are expected to occur in the upper range. For actual risk assessments, the probabilistic approach directly uses the parameter distributions instead of single values to calculate distributions of exposure. To characterize exposure, an... 

Monte Carlo analysis is a specific probabilistic assessment method that can be used to characterize health risks and their likelihood of occurrence based on a wide range of parameters (Shade and Jayjock 1997). The U.S. EPA s Stochastic Human Exposure and Dose Simulation (SHEDS) model allows for the quantification of exposures based on a probabilistic assessment of multiple exposure pathways and multiple routes of exposure (Mokhtari et al. 2006 US EPA 2003b). Additional applications of probabilistic techniques wiU be discussed in the section below on conducting an uncertainty analysis of reconstructed exposure values. 

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