An introductory guide to uncertainty analysis in environmental and health risk assessment

Hoffman, F.O.; Hammonds, J.S.

doi:10.2172/7048843

Cited by 20 publications

(5 citation statements)

References 27 publications

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“…The distribution of X will tend to be approximately lognormal even when the parameters p; q; and r are assigned distribution shapes other than lognormal (HOFFMAN and HAMMONDS, 1992). The median value (or geometric mean, X g ) of the lognormal distribution can be obtained by adding the means of the logarithmic terms on the RHS of Eq.…”

Section: Methodsmentioning

confidence: 99%

“…Uniform, triangular, and normal distributions are among those widely used, which are shown in Figure 2. As long as the mean and variance are held constant, the exact shape of the distribution of a parameter has minimal effect on the distribution of the model prediction (HOFFMAN and HAMMONDS, 1992). When the range of a parameter extends over one or more orders of magnitude, logarithmic form of the distribution is used.…”

Section: Uncertainty Analysismentioning

confidence: 99%

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Uncertainty Analysis in Atmospheric Dispersion Modeling

Rao

2005

Pure appl. geophys.

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The concentration of a pollutant in the atmosphere is a random variable that cannot be predicted accurately, but can be described using quantities such as ensemble mean, variance, and probability distribution. There is growing recognition that the modeled concentrations of hazardous contaminants in the atmosphere should be described in a probabilistic framework. This paper discusses the various types of uncertainties in atmospheric dispersion models, and reviews sensitivity/uncertainty analysis methods to characterize and/or reduce them. Evaluation and quantification of the range of uncertainties in predictions yield a deeper insight into the capabilities and limitations of atmospheric dispersion models, and increase our confidence in decision-making based on models.

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Section: Methodsmentioning

confidence: 99%

Section: Uncertainty Analysismentioning

confidence: 99%

Uncertainty Analysis in Atmospheric Dispersion Modeling

Rao

2005

Pure appl. geophys.

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“…The primary objective of sensitivity analysis is to assess how the input variables of a model influence its output or response. Thus, the sensitivity analysis can evaluate the relative significance of different variables and their effects on the overall outcomes [31,32]. Sensitivity analysis facilitates a deeper comprehension of the system's behavior, aids in identifying critical components or parameters, and enables the optimization of decision-making processes.…”

Section: Identifying Critical Componentsmentioning

confidence: 99%

The Impact of Climate Change and Urbanization on Groundwater Levels: A System Dynamics Model Analysis

Asadollahi,

Sohrabifar,

Ghimire

et al. 2024

Environmental Protection Research

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Climate change and population growth have placed increasing stress on groundwater resources. Effective management of groundwater resources is crucial for promoting sustainable development, especially in areas heavily reliant on groundwater supply. However, sustainable groundwater management is more complex due to various factors affecting key drivers and their feedback interactions. In this regard, this study aims to examine the key driving forces and their interplay concerning fluctuations in groundwater levels within a study area in Illinois, United States, which heavily relies on groundwater for water supply. To achieve this objective, a system dynamics (SD) simulation model was developed, utilizing hydrology, climate, and urbanization data spanning from 2010 to 2020. Through calibration and validation of the SD model with historical data, the model demonstrated reliable simulation capabilities for groundwater levels. Additionally, sensitivity analysis and exploration of water resource scenarios were conducted to evaluate the critical factors influencing groundwater levels. Results revealed that population dynamics, agricultural land, and groundwater recharge were analyzed as key drivers influencing the change in groundwater level for the study area, among the climate and urbanization factors that had different contributions to the groundwater dynamics. A 10% increase in population, agricultural land, and groundwater recharge led to a decrease of approximately 5.5% and increases of 4.5% and 4.27% in groundwater levels, respectively. The results of the four-scenario analysis indicated that both climate change and urbanization exerted significant and adverse impacts on the groundwater level in the study area. These scenarios represented prominent and worst-case situations, highlighting the potential challenges and risks associated with the combination of these factors, which resulted in about a 77 mm decrease in groundwater levels by 2030. Overall, given limited data availability for SD model parameterization, the study emphasizes the need to consider the effects of both urbanization and climate change on groundwater supplies in decision-making for sustainable groundwater management, as well as the need for adequate management measures to maintain the long-term usage of groundwater resources.

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“…However, after taking into account the uncertainty in the input parameters and running a Monte Carlo analysis, Hoffman and Hammonds (1992) came to exactly the opposite conclusion. The 95% upper confidence limit on the HI for the soil ingestion pathway was 0.7.…”

Section: Example 1: Does the Uncertaintymentioning

confidence: 99%

“…Hoffman and Hammonds (1992) describe a lake contaminated with methyl mercury and the surrounding sediments contaminated with inorganic mercury. The upper 95% confidence limit of the concentration of methyl mercury in lake fish was 0.3 mg/kg.…”

Section: Example 1: Does the Uncertaintymentioning

confidence: 99%

Use of Monte Carlo methods in environmental risk assessments at the INEL: Applications and issues

Harris¹,

Horn²

1996

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Under DOE Idaho Operations Office Contract DE-AC07-941Di3223. I DISCLAIMERPortions of this document may be illegible in electronic image products. Images are produced from the best available original document. Use of Monte Carlo Methods in Environmental Risk Assessment at the INEL:Applications and Issues EXECUTIVE SUMMARYMonte Carlo Analysis (MCA) is a powerful tool for evaluating risks in the presence of uncertainty. The technique could be useful in future environmental risk assessment work done at the Idaho National Engineering Laboratory (INEL). This document provides the background information useful for risk assessors and risk managers to understand MCA. It presents the general methodology of MCA and identifies issues that will need to be resolved before MCA can be effectively implemented at the INEL.MCA is a conceptually simple method of probabilistic risk assessment. For the input parameters in the risk model, a probability distribution is assigned based on available information about the parameter, these distributions are each randomly sampled n times. The n random realizations from each input parameter are then entered into the model to produce n risk estimates. The input distributions take into account uncertainties in those parameters; the resulting risk values provide an estimate of the risk distribution. Hence, the Monte Carlo analysis essentially propagates the input uncertainties through the risk model to the resulting risk estimates.In this report, the motivation for using MCA as an improvement over the baseline risk assessment (BRA) point estimates is given. Two major shortcomings of the point estimate approach is that it suffers from creeping conservatism so that the user does not know how conservative the estimate is, and it provides limited information to the risk manager and public. The use of MCA helps alleviate both of these problems.The use of MCA helps focus attention on the uncertainty in the risk assessment. Further, it provides a quantitative description of the uncertainty. In this way, the manager and stakeholders are better able to understand the risks and are better able to make informed decisions. A key theme of the document is that uncertainty matters and MCA provides a practical description of the uncertainty.V

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An introductory guide to uncertainty analysis in environmental and health risk assessment

Cited by 20 publications

References 27 publications

Uncertainty Analysis in Atmospheric Dispersion Modeling

Uncertainty Analysis in Atmospheric Dispersion Modeling

The Impact of Climate Change and Urbanization on Groundwater Levels: A System Dynamics Model Analysis

Use of Monte Carlo methods in environmental risk assessments at the INEL: Applications and issues

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