Probabilistic methods, risk assessment

Bnrmaster DE, Lloyd KJ, Thompson KM. 1995. The need for new methods to backcalculate soil cleannp targets in interval and probabilistic cancer risk assessments. Human Ecol Risk Assess 1 89-100. 

One difference between the US and the EU approach to probabilistic dietary risk assessment is in the method of sampling. In the US approach, probabilistic sampling is done for the residue distribution. However, Exponent s Dietary Exposure Evaluation Model (DEEM [10]) currently used by the EPA for dietary risk assessment uses all of the food consumption data. In the EU, it appears that probability sampling may be performed from both the residue and consumption distributions [11], In the US, the CARES... 

Core damage and containment performance was assessed for accident sequences, component failure, human error, and containment failure modes relative to the design and operational characteristics of the various reactor and containment types. The IPEs were compared to standards for quality probabilistic risk assessment. Methods, data, boundary conditions, and assumptions are considered to understand the differences and similarities observed. 

Probabilistic safety assessment has had its greatest push in relation to the assessment ni risk associated with nuclear power plant operation as documented in the author s previous hook This new book, besides updating and reorganizing the nuclear portions of the previous text, entures into I he salety as.sessment of chemical facilities, another important industry dri ver of probabilistic s.ifety assessment methods and applications. 

Banks, W., Wells, J. E. (1992). A Probabilistic Risk Assessment Using Human Reliability Analysis Methods. In Proceedings of the International Conference on Hazard Identification and Risk Analysis, Human Factors, and Human Reliability in Process Safety. New York American Institute of Chemical Engineers, CCPS. 

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. 

While these objectives for method sensitivity may seem ambitious, experience has shown that data from such studies are much more usable for supporting fate and transport models (development and/or validation efforts) that may have to be used when more precise and geographically detailed probabilistic risk assessments become necessary. 

Table 3 describes the main parts of an environmental risk assessment (ERA) that are based on the two major elements characterisation of exposure and characterisation of effects [27, 51]. ERA uses a combination of exposure and effects data as a basis for assessing the likelihood and severity of adverse effects (risks) and feeds this into the decision-making process for managing risks. The process of assessing risk ranges from the simple calculation of hazard ratios to complex utilisation of probabilistic methods based on models and/or measured data sets. Setting of thresholds such as EQS and quality norms (QN) [27] relies primarily on... 

The determination of the estimated levels of exposure is obviously a critical component of the risk assessment process. Both pesticide residue levels and food consumption estimates must be considered. Methods for determining exposure are frequently classified as deterministic and probabilistic methods (Winter, 2003). 

Probabilistic methods can be applied in dose-response assessment when there is an understanding of the important parameters and their relationships, such as identification of the key determinants of human variation (e.g., metabolic polymorphisms, hormone levels, and cell replication rates), observation of the distributions of these variables, and valid models for combining these variables. With appropriate data and expert judgment, formal approaches to probabilistic risk assessment can be applied to provide insight into the overall extent and dominant sources of human variation and uncertainty. 

Vermeire et al. (1999) have published a discussion paper with focus on assessment factors for human health risk assessment. The status quo with regard to assessment factors is reviewed and the paper discusses the development of a formal, harmonized set of assessment factors. Options are presented for a set of default values and probabilistic distributions for assessment factors based on the state of the art. Methods of combining default values or probabUistic distributions of assessment factors (Section 5.11) are also described. In relation to assessment factors, the authors recommended ... 

The Pellston workshop in February 2002, which produced this book, aimed to develop guidance and increased consensus on the use of uncertainty analysis methods in ecological risk assessment. The workshop focused on pesticides, and used case studies on pesticides, because of the urgent need created by the rapid move to using probabilistic methods in pesticide risk assessment. However, it was anticipated that the conclusions would also be highly relevant to other stressors, especially other contaminants. 

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. 

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. 

Bayesian statistics are applicable to analyzing uncertainty in all phases of a risk assessment. Bayesian or probabilistic induction provides a quantitative way to estimate the plausibility of a proposed causality model (Howson and Urbach 1989), including the causal (conceptual) models central to chemical risk assessment (Newman and Evans 2002). Bayesian inductive methods quantify the plausibility of a conceptual model based on existing data and can accommodate a process of data augmentation (or pooling) until sufficient belief (or disbelief) has been accumulated about the proposed cause-effect model. Once a plausible conceptual model is defined, Bayesian methods can quantify uncertainties in parameter estimation or model predictions (predictive inferences). Relevant methods can be found in numerous textbooks, e.g., Carlin and Louis (2000) and Gelman et al. (1997). 

A probabilistic risk assessment (PRA) deals with many types of uncertainties. In addition to the uncertainties associated with the model itself and model input, there is also the meta-uncertainty about whether the entire PRA process has been performed properly. Employment of sophisticated mathematical and statistical methods may easily convey the false impression of accuracy, especially when numerical results are presented with a high number of significant figures. But those who produce PR As, and those who evaluate them, should exert caution there are many possible pitfalls, traps, and potential swindles that can arise. Because of the potential for generating seemingly correct results that are far from the intended model of reality, it is imperative that the PRA practitioner carefully evaluates not only model input data but also the assumptions used in the PRA, the model itself, and the calculations inherent within the model. This chapter presents information on performing PRA in a manner that will minimize the introduction of errors associated with the PRA process. 

The use of uncertainty analysis and probabilistic methods requires systematic and detailed formulation of the assessment problem. To facilitate this, a) risk assessors and risk managers should be given training in problem formulation, b) tools to assist appropriate problem formulation should be developed, and c) efforts should be made to develop generic problem formulations (including assessment scenarios, conceptual models, and standard datasets), which can be used as a starting point for assessments of particular pesticides. 

EUFRAM (2006) EUFRAM Report, Volume 1. Introducing Probabilistic Methods into the Ecological Risk Assessment of Pesticides. Report No. Dl-4-5, EUFRAM, York http //www.eufram. com/ (last accessed 26 September 2011). 

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. 

Given the tiered system, evidently, any step in the exposure and effects assessment can be considered in a deterministic or probabilistic way. This does affect the outcomes of the risk assessment, but it does not influence the choice of extrapolation methods as guided by the decision tree itself. 

The risk of exposure to individual chemicals as calculated using the SSD method is based on the same mathematical principles used in the derivation of concentration-response curves in single-species toxicity evaluations. As for individual species, both the concentration addition and response addition models can conceptually be applied in ecological risk assessment for species assemblages exposed to mixtures of toxicants, which are now being formulated probabilistically (Traas et al. 2002 Posthuma et al. 2002a De Zwart and Posthuma 2005). 

EUFRAM. 2005. EUFRAM report, volume 1. Introducing probabilistic methods into the ecological risk assessment of pesticides. No. Version 6. York (UK) EUFRAM, 50 p. http //www.eupra.com/report.pdf (accessed December 28, 2007). 

Verdonck FAM, Aldenberg T, Jaworska J, Vanrolleghem VA. 2003. Limitations of current risk characterization methods in probabilistic risk assessment. Environ Toxicol Chem 22 2209-2213. 

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