Parameter uncertainty and sensitivity in a liquid-effluent dose model

Hyman, T. C.; Hamby, D.M.

doi:10.1007/bf00547126

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Many examples of real, and hence more complex, analyses using sampling‐based procedures are available. ( 1–13,70–83 )…”

Section: Discussionmentioning

confidence: 99%

“…Many examples of real, and hence more complex, analyses using samplingbased procedures are available. (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(70)(71)(72)(73)(74)(75)(76)(77)(78)(79)(80)(81)(82)(83) The complexity of many real analysis problems is increased by the presence of both stochastic (i.e., aleatory) uncertainty and subjective (i.e., epistemic) uncertainty. (24)(25)(26)(27)(28)(29)(30)(31) Stochastic uncertainty arises because the system under study can behave in many different ways and thus is a property of the system.…”

Section: Discussionmentioning

confidence: 99%

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Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis

Helton¹,

Davis²

2002

Risk Analysis

253

152

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A sequence of linear, monotonic, and nonmonotonic test problems is used to illustrate sampling-based uncertainty and sensitivity analysis procedures. Uncertainty results obtained with replicated random and Latin hypercube samples are compared, with the Latin hypercube samples tending to produce more stable results than the random samples. Sensitivity results obtained with the following procedures and/or measures are illustrated and compared: correlation coefficients (CCs), rank correlation coefficients (RCCs), common means (CMNs), common locations (CLs), common medians (CMDs), statistical independence (SI), standardized regression coefficients (SRCs), partial correlation coefficients (PCCs), standardized rank regression coefficients (SRRCs), partial rank correlation coefficients (PRCCs), stepwise regression analysis with raw and rank-transformed data, and examination of scatter plots. The effectiveness of a given procedure and/or measure depends on the characteristics of the individual test problems, with (1) linear measures (i.e., CCs, PCCs, SRCs) performing well on the linear test problems, (2) measures based on rank transforms (i.e., RCCs, PRCCs, SRRCs) performing well on the monotonic test problems, and (3) measures predicated on searches for nonrandom patterns (i.e., CMNs, CLs, CMDs, SI) performing well on the nonmonotonic test problems.

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“…Many examples of real, and hence more complex, analyses using sampling‐based procedures are available. ( 1–13,70–83 )…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis

Helton¹,

Davis²

2002

Risk Analysis

253

152

View full text Add to dashboard Cite

show abstract

Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems

Helton

Davis

2003

Reliability Engineering & System Safety

2,018

761

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The following techniques for uncertainty and sensitivity analysis are briefly summarized: Monte Carlo analysis, differential analysis, response surface methodology, Fourier amplitude sensitivity test, Sobol' variance decomposition, and fast probability integration. Desirable features of Monte Carlo analysis in conjunction with Latin hypercube sampling are described in discussions of the following topics: (i) properties of random, stratified and Latin hypercube sampling, (ii) comparisons of random and Latin hypercube sampling, (iii) operations involving Latin hypercube sampling (i.e., correlation control, reweighting of samples to incorporate changed distributions, replicated sampling to test reproducibility of results), (iv) uncertainty analysis (i.e,, cumulative distribution functions, complementary cumulative distribution functions, box plots), (v) sensitivity analysis (i.e., scatterplots, regression analysis, correlation analysis, rank transformations, searches for nonrandom patterns), and (vi) analyses involving stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty.

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Dichloromethane Marine Risk Assessment with Special Reference to the Osparcom Region: North Sea

Rooij

Thompson

Garny³

et al. 2004

Environ Monit Assess

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This risk assessment on dichloromethane was carried out specifically for the marine environment, following methodology given in the EU risk assessment Regulation (1488/94) and Guidance Document of the EU New and Existing Substances Regulation (TGD, 1997). The study consists of collection and evaluation of data on effects and environmental concentrations from analytical monitoring programs in large rivers and estuaries in the North Sea area. The risk is indicated by the ratio of 'predicted environmental concentrations' (PEC) to 'predicted no-effect concentrations' (PNEC) for the marine aquatic environment. In total, 23 studies for fish, 17 studies for invertebrates and 6 studies for algae were evaluated. Both acute and chronic toxicity studies were taken into account and appropriate assessment factors used to define a PNEC value of 830 microg/l. Most of the available monitoring data apply to rivers and estuaries and were used to calculate PECs. The most recent data (1983--1995) support a typical PEC for dichloromethane lower than 0.2 microg/l and a worst case PEC of 13.6 microg/l. Dichloromethane is not a 'toxic, persistent and liable to bioaccumulate' substance sensu the Oslo and Paris Conventions for the Prevention of Marine Pollution (OSPAR-DYNAMEC). The calculated PEC/PNEC ratios give margins of 60 to 4000 between the PNEC and PEC, dilution within the sea would further increase these margins. It can be concluded that the present use of dichloromethane does not present a risk to the marine aquatic environment.

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Parameter uncertainty and sensitivity in a liquid-effluent dose model

Cited by 4 publications

References 12 publications

Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis

Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis

Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems

Dichloromethane Marine Risk Assessment with Special Reference to the Osparcom Region: North Sea

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