Exposure estimation probabilistic modelling

As probabilistic exposure and risk assessment methods are developed and become more frequently used for environmental fate and effects assessment, OPP increasingly needs distributions of environmental fate values rather than single point estimates, and quantitation of error and uncertainty in measurements. Probabilistic models currently being developed by the OPP require distributions of environmental fate and effects parameters either by measurement, extrapolation or a combination of the two. The models predictions will allow regulators to base decisions on the likelihood and magnitude of exposure and effects for a range of conditions which vary both spatially and temporally, rather than in a specific environment under static conditions. This increased need for basic data on environmental fate may increase data collection and drive development of less costly and more precise analytical methods. 

It does not contain a probabilistic modeling component that simulates variability therefore, it is not used to predict PbB probability distributions in exposed populations. Accordingly, the current version will not predict the probability that children exposed to lead in environmental media will have PbB concentrations exceeding a health-based level of concern (e.g., 10 pg/dL). Efforts are currently underway to explore applications of stochastic modeling methodologies to investigate variability in both exposure and biokinetic variables that will yield estimates of distributions of lead concentrations in blood, bone, and other tissues. 

More sophisticated probabilistic models are used by EPA to comply with the aggregate and cumulative risk provisions of the FQPA. These models consider rolling windows of exposure, toxicological equivalence factors for pesticides that have common toxicological mechanisms, and include methods to incorporate exposure from drinking water and residential pesticide use into the pesticide exposure estimates. 

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. 

There is a transition away from nsing a deterministic approach in which high end or upper bound point estimates and defanlt valnes are nsed towards nsing a probabilistic approach in distribntional models which incorporate complex data sets to build realistic estimates of exposure. While probabilistic dietary exposure assessments can now be carried out routinely for many pesticides, available occupational and residential exposure data sets are typically insufficiently robust. Work on developing newer exposure databases (e.g. ARTF, ORETF, AHETF and EUROPOEM II) and distributional use pattern data would facilitate this transition. The topic of probabilistic exposure assessment is covered in Chapter 8. 

Two assessments were conducted using the US procedures with the UK food consun tion database and the DEEM-UK m model, in which a total dietary exposure estimate is calculated for all four foods at the same time. When 100% of the crop was assumed to be treated (so that probabilistic sampling was fixim the residue distributions), the resulting exposure estimates resulted in unacceptable estimates of risk. When percent crop treated was included in the assessment, the probabilistic assessment resulted in acceptable risk levels for all four commodities at the same time. 

Stochastic (Probabilistic) Models. One of the most significant advances in exposure estimation in the past 15 to 20 years has been the application of probabilistic statistical methods to many types of data analyses (Duan and Mage 1997 Finley and Paustenbach 1994 Morgan and Henrion 1990 US ERA 1995, 1997, 2000a). Stochastic or probabilistic techniques can help quantify variability and uncertainty in model inputs and outputs, can be used to better characterize the possible range of exposures for a particular scenario when measured data are minimal, and can be employed to better understand the uncertainty inherent in estimates developed from many different types of sources, whether quantitative or qualitative. 

Monte Carlo—A statistical technique commonly used to quantitatively characterize the uncertainty and variability in estimates of exposure or risk. The analysis uses statistical sampling techniques to obtain a probabilistic approximation to the solution of a mathematical equation or model. 

Probabilistic exposure models attempt to provide inputs to exposure models by representing variability or uncertainty via frequency or probability distributions. Probabilistic methods can be used in the exposure assessment because pertinent variables (e.g., concentration, intake rate, exposure duration, and body weight) have been identified, their distributions can be observed, and the formula for combining the variables to estimate the exposure is well defined. 

Exposure Uptake Biokinetic (IEUBK) model stochastic with probabilistic output sensitivity analysis lead exposure for children (6 months to 7 years old) across multiple pathways, routes, and environmental media, estimates blood lead concentrations (2005e)... 

Simulation (SHEDS) model components and probabilistic capabilities sensitivity analyses, uncertainty estimations, and inferences of source or pathway contributions ways and routes of exposure and various environmental media daily exposure through annual absorbed dose is simulated for any aged individual considering time series of exposure (up to 1-min resolution) al. (2001)... 

Using a one-dimensional Monte Carlo analysis to estimate population exposure and dose uncertainty distributions for particulate matter, where model inputs and parameters (e.g. ambient concentrations, indoor particulate matter emission rates from environmental tobacco smoke, indoor air exchange rates, building penetration values, particle deposition rates) are represented probabilistically with distributions statistically fitted to all available relevant data. 

TIERED APPROACHES TO EXPOSURE ASSESSMENT 144 Deterministic (Point-Estimate) Exposure Assessments 145 Probabilistic Exposure Assessments 145 REPORT WRITING 145 Protocol/User s Guide 146 General Description of Exposure Model 146 Detailed Description of Model Inputs and Outputs 146 Exposure Model Validation 146 Quality Assurance Practices 147 Archiving 147... 

Regardless of whether chronic or acute dietary exposure is being estimated, and regardless of whether the model used is deterministic or probabilistic, dietary exposure is a simple function of the amount of food consumed and the residue concentration on the food ... 

Improving the detection limit in many cases is one of the most efficient ways to demonstrate lower exposure. Lor example the exposure to BADGE in canned foodstuffs (food and beverages) was estimated (Oldring et al. 2006) using a stochastic model (probabilistic - Monte-Carlo approach) with two different LODs of 0.3 t,g/dm and 0.5 t,g/dm and the exposure was effectively halved, primarily because many of the foodstuffs consumed were acidic, aqueous or alcoholic where the concentrations of BADGE and its regulated derivatives were non-detectable. 

Probabilistic, where statistical modelling is used to predict those values related to the unknown inputs required in order that a more refined estimate of exposure may be obtained. 

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