Scenario-model-parameter: a new method of cumulative risk uncertainty analysis.

The recently developed concepts of aggregate risk and cumulative risk rectify two limitations associated with the classical risk assessment paradigm established in the early 1980s. Aggregate exposure denotes the amount of one pollutant available at the biological exchange boundaries from multiple routes of exposure. Cumulative risk assessment is defined as an assessment of risk from the accumulation of a common toxic effect from all routes of exposure to multiple chemicals sharing a common mechanism of toxicity. Thus, cumulative risk constitutes an improvement over the classical risk paradigm, which treats exposures from multiple routes as independent events associated with each specific route. Risk assessors formulate complex models and identify many realistic scenarios of exposure that enable them to estimate risks from exposures to multiple pollutants and multiple routes. The increase in complexity of the risk assessment process is likely to increase risk uncertainty. Despite evidence that scenario and model uncertainty contribute to the overall uncertainty of cumulative risk estimates, present uncertainty analysis of risk estimates accounts only for parameter uncertainty and excludes model and scenario uncertainties. This paper provides a synopsis of the risk assessment evolution and associated uncertainty analysis methods. This evolution leads to the concept of the scenario-model-parameter (SW) cumulative risk uncertainty analysis method. The SMP uncertainty analysis is a multiple step procedure that assesses uncertainty associated with the use of judiciously selected scenarios and models of exposure and risk. Ultimately, the SMP uncertainty analysis method compares risk uncertainty estimates determined using all three sources of uncertainty with conventional risk uncertainty estimates obtained using only the parameter source. An example of applying the SMP uncertainty analysis to cumulative risk estimates from exposures to two pesticides indicates that inclusion of scenario and model sources.

Exposure apportionment: ranking food items by their contribution to dietary exposure.

This paper identifies and ranks food items by estimating their contribution to the dietary exposure of the US population and 19 subpopulation groups. Contributions to dietary exposures to arsenic, cadmium, chromium, lead, nickel, benzene, chlorpyrifos, and diazinon are estimated using either the Dietary Exposure Potential Model (DEPM) approach, the National Human Exposure Assessment Survey Arizona (NHEXAS-AZ) approach or the combination of the two. The DEPM is a computer model that uses several national databases of food consumption and residue concentrations for estimating dietary. The DEPM approach ranks the contribution of food items to the total dietary exposure using two methods, the direct method that ranks contributions by population exposure magnitude and the weighted method that ranks by subpopulation exposure magnitude. The DEPM approach identifies highly exposed subpopulations and a relatively small number of food items contributing the most to dietary exposure. The NHEXAS-AZ approach uses the NHEXAS-AZ database containing food consumption data for each subject and chemical residues of a composite of food items consumed by each subject in 1 day during the sampling week. These data are then modeled to obtain estimates of dietary exposure to chemical residues. The third approach uses the NHEXAS-AZ consumption data with residue values from the national residue database. This approach also estimates percent contributions to exposure of each ranked food item for the Arizona population. Dietary exposures estimated using the three approaches are compared. The DEPM results indicate groups with highest dietary exposures include Nonnursing Infants, Children 1-6, Hispanic, Non-Hispanic White, Western, Northeast and Poverty 0-130%. The use of the Combined National Residue Database (CNRD) identifies 43 food items as primary contributors to total dietary exposure; they contribute a minimum of 68% of the total dietary exposure to each of the eight chemical residues. The percent contribution of ranked food items estimated using the NHEXAS samples is smaller than those obtained from the western US population via the DEPM. This indicates differences in consumption characteristics of the two groups with respect to the ranked food items. Six of 15 food items consumed by the NHEXAS-AZ subjects per day are ranked food items contributing between 56% and 70% of the estimated NHEXAS-AZ dietary exposure to each of the eight chemical residues. The difference between total dietary exposure estimates from the DEPM and NHEXAS-AZ approaches varies by chemical residue and is attributable to differences in sampling and analytical methods, and geographic areas represented by the data. Most metal exposures estimated using the NHEXAS consumption data with the CNRD have lower values than those estimated via the other approaches, possibly because the NHEXAS-AZ residue values are higher than the CNRD values. In addition, exposure estimates are seemingly affected by the difference in demographic characteristics and factors that affect types and amounts of food consumed. Efficient control strategies for reducing dietary exposure to chemical residues may be designed by focusing on the relatively small number of food items having similar ingredients that contribute substantively to the total ingestion exposure.

On predicting multi-route and multimedia residential exposure to chlorpyrifos and diazinon.

This paper formulates regression models and examines their ability to associate exposures to chlorpyrifos and diazinon in residences with information obtained from questionnaires and environmental sampling of the National Human Exposure Assessment Survey Arizona (NHEXAS-AZ) database. A knowledge-based list of 29 potential exposure determinants was assembled from information obtained from six questionnaires administered in the course of the study. This list was used to select the independent variables of each model statistically and electronically. Depending on the data type of dependent and independent variables, four classes of regression models were developed to determine desired associations. Route-specific exposures were estimated using the indirect method of exposure estimation and measurements from the NHEXAS-AZ field study. The stepwise procedure was used to construct regression models. Significance level at P=0.10 was used for entry and retention of independent variables in a model. Twelve significant regression models were formulated to quantify associations among exposures and other variables in the NHEXAS-AZ database. Route-specific exposures to pesticides associate significantly with questionnaire-based variables such as preparation of pesticides, use of pesticide inside the house, and income level; and with concentration variables in three media: dermal wipe, sill wipe, and indoor air. Models formulated in this study may be used to estimate exposures to each of the pesticides. Yet, the use of these models must incorporate clear statements of the assumptions made in the formulation as well as the coefficient of determination and the confidence and prediction intervals of the dependent variable.