摘要:

Probabilistic risk assessment (PRA) represents an important step in the evolution of risk assessment methodology to assist decision-making at hazardous waste sites. Despite considerable progress in the development of PRA techniques, regulatory acceptance of PRA has been limited, in part because a number of practical issues in its use must yet be resolved. A recent workshop on PRA identified several areas to be addressed, including the need for: (1) better demonstration of the value of PRA in risk management; (2) PRA training and education opportunities; (3) the development of technical criteria for acceptability of a PRA; (4) policy decisions on acceptable risk distributions; (5) ways to deal with risk communication issues; and (6) a variety of technical issues, including ways to include estimates of variability and uncertainty associated with toxicity values. Solutions to many of these issues will require better dialog between risk assessors and risk managers than has existed in the past.

ISSN:1080-7039    

DOI:10.1080/10807039.1999.9657737

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

Application of probabilistic risk analysis to human health and ecological risk assessment is a young science. Probabilistic risk assessment (PRA), as exemplified by Monte Carlo Analysis (MCA), is more suitable to quantify the confidence or level of uncertainty in risk estimates compared with the traditional point estimate approach. Within the United States Environmental protection Agency (USEPA) the Office of Emergency and Remedial Response (OERR) is implementing PRA as part of the Superfund administrative reform activities. The OERR is completing a guidance document accompanied by a workbook. OERR is continuing its outreach effort to present PRA to the public and USEPA staff, and is organizing a training course. This paper presents an overview of the OERR's PRA implementation effort to date.

ISSN:1080-7039    

DOI:10.1080/10807039.1999.9657738

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

The importance of fitting distributions to data for risk analysis continues to grow as regulatory agencies, like the Environmental Protection Agency (EPA), continue to shift from deterministic to probabilistic risk assessment techniques. The use of Monte Carlo simulation as a tool for propagating variability and uncertainty in risk requires specification of the risk model's inputs in the form of distributions or tables of data. Several software tools exist to support risk assessors in their efforts to develop distributions. However, users must keep in mind that these tools do not replace clear thought about judgments that must be made in characterizing the information from data. This overview introduces risk assessors to the statistical concepts and physical reasons that support important judgments about appropriate types of parametric distributions and goodness-of-fit. In the context of using data to improve risk assessment and ultimately risk management, this paper discusses issues related to the nature of the data (representativeness, quantity, and quality, correlation with space and time, and distinguishing between variability and uncertainty for a set of data), and matching data and distributions appropriately. All data analysis (whether “Frequentist” or “Bayesian” or oblivious to the distinction) requires the use of subjective judgment. The paper offers an iterative process for developing distributions using data to characterize variability and uncertainty for inputs to risk models that provides incentives for collecting better information when the value of information exceeds its cost. Risk analysts need to focus attention on characterizing the information appropriately for purposes of the risk assessment (and risk management questions at hand), not on characterization for its own sake.

ISSN:1080-7039    

DOI:10.1080/10807039.1999.9657739

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

In 1995, Oregon enacted amendments to its state Cleanup Law that emphasize risk-based remedial action decisions and allow a responsible party to conduct probabilistic human health risk assessments. This change required selection and/or development of probability density functions for exposure factors frequently used in human health risk assessments. Methods used to obtain distributions for body weight, soil, water, vegetable/fruit, fish, and animal product ingestion, soil adherence, daily inhalation rate, various event and exposure frequencies, and exposure duration are described. Primary data sources were U.S. Environmental Protection Agency guidance and peer-reviewed scientific literature. These distributions of exposure factors may be used, in conjunction with a probabilistic age- and gender-based model, to calculate prospective exposures and risks. A brief overview of this model, which handles temporal parameters (age, exposure frequency, exposure duration) in a manner substantially different from that typically used in deterministic assessments, is also provided. Oregon's development of an age/ gender-based exposure model, and its selection of exposure factor value distributions for that model, represents one of the first attempts to develop practical approaches to using probabilistic techniques in a hazardous waste regulatory program.

ISSN:1080-7039    

DOI:10.1080/10807039.1999.9657740

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

This communication examines the role of back-calculation in developing a cleanup goal from a probabilistic risk assessment. Although back-calculation is not always appropriate with a Monte Carlo analysis, if the target risk level is specified as a single value (e.g., 95% of the population must have a cancer risk below 10−5), then back-calculation can be used to solve for a cleanup goal that represents an average concentration for an exposure area consistent with the stated target risk. This rule applies in developing screening levels and in probabilistic risk assessments that examine the influence of uncertainty in the average concentration. Back-calculation is not used to develop cleanup goals when risks arising from variable concentrations are assessed, for example, when exposure areas are very small such as for some ecological receptors, or when exposure frequency is very low such as for tourist fishermen. In this case, the cleanup goal is derived from an iterative risk calculation considering various possible truncation values of the concentration distribution. A cleanup goal derived in this manner does not correspond to a required average, but rather represents the maximum concentration that should be left in the field. Finally, although single value target risk specifications are common today, there may be advantages to setting target risks for multiple percentiles of the population, complicating the effort to calculate a cleanup goal.

ISSN:1080-7039    

DOI:10.1080/10807039.1999.9657741

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

Four different probabilistic risk assessment methods were compared using the data from the Sangamo Weston/Lake Hartwell Superfund site. These were one-dimensional Monte Carlo, two-dimensional Monte Carlo considering uncertainty in the concentration term, two-dimensional Monte Carlo considering uncertainty in ingestion rate, and microexposure event analysis. Estimated high-end risks ranged from 2.0×10−4to 3.3×10−3. Microexposure event analysis produced a lower risk estimate than any of the other methods due to incorporation of time-dependent changes in the concentration term.

ISSN:1080-7039    

DOI:10.1080/10807039.1999.9657762

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

Exposure of children to lead in the environment was assessed at the Murray Smelter Superfund site using both a deterministic risk assessment approach, the Integrated Exposure Uptake Biokinetic (IEUBK) model, and a probabilistic approach, the Integrated Stochastic Exposure (ISE) model. When site-specific data on lead in environmental media were input as point estimates into the IEUBK model, unacceptable risks were predicted for children living within five of eight study zones. The predicted soil cleanup goal was 550 ppm. Concentration and exposure data were then input into the ISE model as probability distribution functions and a one-dimensional Monte Carlo analysis (ID MCA) was run to predict the expected distribution of exposures and blood lead values. Uncertainty surrounding these predictions was examined in a two-dimensional Monte Carlo analysis (2-D MCA). The ISE model predicted risks that were in the same rank order as those predicted by the IEUBK model, although the probability estimates of exceeding a blood lead level of 10 µg/dl (referred to as the P10) from the ISE model were uniformly lower than those predicted by the IEUBK model. The 2-D MCA allowed evaluation of the confidence around each P10 level, and identified the main sources of both uncertainty and variability in exposure estimates. The ISE model suggested cleanup goals ranging from 1300 to 1500 ppm might be protective at this site.

ISSN:1080-7039    

DOI:10.1080/10807039.1999.9657763

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor