Probabilistic assessment

The Society of Petroleum Engineers (SPE) and the World Petroleum Congress (WPC) developed a probabilistic hydrocarbon-resource classification scheme, that takes into account the probability with which a reserve can be produced (SPE, 2007) 4 but such a probabilistic assessment is also subject to a potential level of misinterpretation.5 Finally, as for resources, very few estimates exist, and those estimates that do exist are also subject to considerable uncertainty and the speculative character is even more pronounced than for reserves.6 BGR (2003) refers to resources as those quantities that are geologically demonstrated, but at present... 

Deterministic and Probabilistic Assessment Factors Suggested for Use in Human Health Risk Assessment... 

Vermeire, T., M. Pieters, M. Rennen, and P. Bos. 2001. Probabilistic assessment factors for human health risk assessment A practical guide. RIVM report 601516 005, TNO report V3489. Bilthoven RIVM. http // www.rivm.nl /bibliotheek/rapporten/601516005.pdf... 

As described in detail in this book, the use of assessment factors is an established practice in chemical risk assessment to account for uncertainties inherent in the hazard (effects) assessment and consequently, inherent in the risk assessment. The use of assessment factors to address this uncertainty is part of the conventional approach that has developed over the years. According to the current risk assessment paradigm, the usual approach is simply to multiply these individual assessment factors in order to establish an overall composite numerical assessment factor (Section 5.10). An alternative to the traditional assessment factor approach is to combine estimates of the ranges that these factors may encompass through a probabilistic assessment this is essentially a variation of the standard paradigm. 

The US Environmental Protection Agency (USEPA 1998) describes problem formulation as an iterative process with 4 main components integration of available information, definition of assessment endpoints, definition of conceptual model, and development of an analysis plan. These 4 components apply also to probabilistic assessments. In addition, it is useful to emphasize the importance of a 5th component dehnition of the assessment scenarios. The relationships between all 5 components are depicted in Figure 2.1. Note that the bidirectional arrows represent the interdependency of the different components and imply that they may need to be revised iteratively as the formulation of the problem is rehned. 

The following sections discuss each of the components of problem formulation in turn, with particular attention to the needs of probabilistic assessments. 

Integration of available information is an iterative process that normally occurs throughout problem formulation (USEPA 1998). In general, for probabilistic assessments there will be a greater emphasis on obtaining information in quantitative rather than qualitative forms. In particnlar, probabilistic assessments require increased attention to obtaining information on... 

For example, existing databases and risk assessment publications often omit statistical measnres of variability or nncertainty and sample sizes and rarely report the underlying data. These types of information are rarely used in deterministic assessments but are a fundamental reqnirement for probabilistic assessments. 

Various approaches and graphical conventions have been used in drawing conceptual model diagrams. Consideration could be given to recommending a standardized approach for use in probabilistic assessments. 

Two common failings of probabilistic assessments (Warren-Hicks and Moore 1998 US SAP 1999) are... 

When planning probabilistic assessments, the following issues require special attention. 

A critical extra phase to be included when planning probabilistic assessments is the selection and parameterization of distributions, to represent the sources of variability and uncertainty that have been identified in the conceptual model. The issues and approaches involved are discussed elsewhere in this book. 

A 2nd critical addition when planning a probabilistic assessment is the choice of methods for propagating variability and nncertainty. The workshop reviewed a range of contrasting methods of analyzing uncertainty in risk assessments ... 

From the standpoint of practical regulatory assessment, it would be desirable to reach a consensus on the selection of methods for routine use for pesticide risk assessments while recognizing that there may be scientific reasons for preferring alternative methods in particnlar cases. Such a consensus does not yet exist. Further case studies are required, covering a range of contrasting pesticides and scenarios, to evaluate the available methods more fully. While a consensus is lacking, it is important that reports on probabilistic assessments clearly explain how their methods work and why they were selected. 

An important question when planning a probabilistic assessment is whether to separate variability and uncertainty in the analysis and results. This is one of the key issues that were given special consideration at the Pellston workshop that developed this book. While there was not a consensus, the majority view was that there are potential advantages to separating variability and uncertainty, but further case studies are needed to evalnate the benehts and practicality of this for routine pesticide assessment. 

Another important qnestion when planning a probabilistic assessment is how to deal with dependencies. This also is one of the key issues that were identified for the Pellston workshop. Fnrther work is needed to evaluate these options. Some additional points are made here. 

The analysis plan should specify not only how the analysis will be conducted, but also how the results will be presented. Indeed, the way results will be communicated will usually influence the choice of both model structure and analysis method and is ultimately driven by the information needs of risk managers and other stakeholders and their management goals (see Figure 2.2). Careful advance planning for the communication of results is especially important for probabilistic assessments because they are more complex than deterministic assessments and less familiar to most audiences. It may be beneficial to present probabilistic and deterministic assessments together, to facilitate familiarization with the newer approaches. 

It will be apparent from this chapter that problem formulation for a probabilistic assessment can be a substantial undertaking, and perhaps the most difficult and... 

USEPA] US Environmental E otection Agency. 1998. Report of the Workshop on Selecting Input Distributions for Probabilistic Assessments. EPA/630/R-98/004. Available from www.epa.gov/ncea/pdfs/input/input.pdf Vose D. 2000. Risk analysis. A quantitative guide. New York Wiley. 

Probability theory is, of conrse, designed precisely to estimate these chances. Becanse of this, probabilistic assessment is regarded by many as the heir apparent to worst case analysis. However, traditional applications of probability theory also have some severe limitations. As it is used in risk assessments today, probability theory... 

There are also some technical problems with current probabilistic assessments. Notably, there is no satisfactory way to handle uncertainty abont the proper mathematical model to use. (Is this the correct expression to compute in the first place Are these assumptions appropriate ) Just as important is the lack of effective strategies... 

The insectivorous bird assessment can be compared to a more traditional probabilistic assessment based on precise distribution functions and particular dependence assumptions. For comparison purposes, we conducted such a simulation. The variable BW was modeled by the same normal distribution with mean 14.5 g and standard deviation 3 g. The variable FIR, on the other hand, was modeled by a log-normal distribution with mean 5.23 and variance 2.3 g per day. The choice of... 

Tucker WT, Person S. 2003. Setting cleanup targets in a probabilistic assessment. In Mishra S, editor. Groundwater quality modeling and management under uncertainty. Reston (VA) American Society of Civil Engineers. 

Most probabilistic assessments have tended to combine variability and parameter uncertainty, and not consider model or decision rule uncertainty. Recent guidance from the US National Academy of Sciences (NRC 1994), USEPA (1997), US DOE (Bechtel Jacobs Company 1998), and others (Hattis and Burmaster 1994 Hoffman and Hammonds 1994) has emphasized the importance of tracking variability and parameter uncertainty separately. Indeed, the USEPA (2000) states that the risk assessor should strive to distinguish between variability and uncertainty. Two major advantages of tracking variability and parameter uncertainty separately in an uncertainty analysis are... 

Jaworska, J.S., Dimitrov, S., Nikolova, N. and Mekenyan, O. (2002) Probabilistic assessment of biodegradability based on metabolic pathways CATABOL System. SAR OSAR Environ. Res., 13 (2),... 

It is most appropriate for the exposure estimates determined from acute probabilistic assessment techniques to be compared with acute single-day RfDs to determine the acceptability of such exposures. Unfortunately, since accurate acute RfDs for most pesticides have not been determined, the exposure estimates are often compared with RfDs derived from longer-term (28 to 90 days) or chronic toxicology studies. In most cases, the acute RfDs may be much higher than those obtained from longer-term studies. This is particularly important in cases where pharmacokinetic factors such as absorption, distribution, biotransformation, and excretion of a pesticide have been established and demonstrate that repeated exposure to the pesticide could cause an increase in... 

Probabilistic Assessment of Laboratory-Derived Acute Toxicity Data for the Triazine Herbicides to Aquatic Organisms... 

The methodology in the case study for chronic exposure, as well as several advances in probabilistic assessment methodology for acute exposure (e.g., a person s exposure on a single day), are being incorporated into the Cumulative and Aggregate Risk Evaluation System (CARES) begun in 2000 and being further developed with the International Life Sciences Institute (ILSI) in 2004. 

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