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Assessment of Variability and Uncertainty Distributions for Practical Risk Analyses
Author(s) -
Hattis Dale,
Burmaster David E.
Publication year - 1994
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.1994.tb00282.x
Subject(s) - superfund , baseline (sea) , uncertainty analysis , statistics , econometrics , risk assessment , agency (philosophy) , point (geometry) , variation (astronomy) , contrast (vision) , probability distribution , environmental science , computer science , risk analysis (engineering) , mathematics , engineering , hazardous waste , artificial intelligence , business , philosophy , oceanography , geometry , computer security , physics , epistemology , astrophysics , geology , waste management
In recent years the U.S. Environmental Protection Agency has been challenged both externally and internally to move beyond its traditional conservative single‐point treatment of various input parameters in risk assessments. In the first section, we assess when more involved distribution‐based analyses might be indicated for such common types of risk assessment applications as baseline assessments of Superfund sites. Then in two subsequent sections, we give an overview with some case studies of technical analyses of (A) variability/heterogeneity and (B) uncertainty. By “inter‐individual variability” is meant the real variation among individuals in exposure‐producing behavior, in exposures, or some other parameter (such as differences among individual municipal solid waste incinerators in emissions). In contrast, “uncertainty” is a description of the imperfection in knowledge of the true value of a particular parameter or its real variability in an individual or a group. In general uncertainty is reducible by additional information‐gathering or analysis activities (better data, better models), whereas real variability will not change (although it may be more accurately known) as a result of better or more extensive measurements. The purpose of the rather long‐winded exposition of these two final sections is to show the differences between analyses of these two different things, both of which are described using the language of probability distributions.