NDLI: 2D Monte Carlo versus 2D Fuzzy Monte Carlo health risk assessment

Content Provider	Springer Nature Link
Author	Kentel, E. Aral, M. M.
Copyright Year	2005
Abstract	Risk estimates can be calculated using crisp estimates of the exposure variables (i.e., contaminant concentration, contact rate, exposure frequency and duration, body weight, and averaging time). However, aggregate and cumulative exposure studies require a better understanding of exposure variables and uncertainty and variability associated with them. Probabilistic risk assessment (PRA) studies use probability distributions for one or more variables of the risk equation in order to quantitatively characterize variability and uncertainty. Two-dimensional Monte Carlo Analysis (2D MCA) is one of the advanced modeling approaches that may be used to conduct PRA studies. In this analysis the variables of the risk equation along with the parameters of these variables (for example mean and standard deviation for a normal distribution) are described in terms of probability density functions (PDFs). A variable described in this way is called a “second order random variable.” Significant data or considerable insight to uncertainty associated with these variables is necessary to develop the appropriate PDFs for these random parameters. Typically, available data and accuracy and reliability of such data are not sufficient for conducting a reliable 2D MCA. Thus, other theories and computational methods that propagate uncertainty and variability in exposure and health risk assessment are needed. One such theory is possibility analysis based on fuzzy set theory, which allows the utilization of incomplete information (incomplete information includes vague and imprecise information that is not sufficient to generate probability distributions for the parameters of the random variables of the risk equation) together with expert judgment. In this paper, as an alternative to 2D MCA, we are proposing a 2D Fuzzy Monte Carlo Analysis (2D FMCA) to overcome this difficulty. In this approach, instead of describing the parameters of PDFs used in defining the variables of the risk equation as random variables, we describe them as fuzzy numbers. This approach introduces new concepts and risk characterization methods. In this paper we provide a comparison of these two approaches relative to their computational requirements, data requirements and availability. For a hypothetical case, we also provide a comperative interpretation of the results generated.
Starting Page	86
Ending Page	96
Page Count	11
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	19
Issue Number	1
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-01-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Health risk assessment Variability Uncertainty Two-dimensional Monte Carlo Analysis Fuzzy numbers Math. Applications in Geosciences Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences Numerical and Computational Methods in Engineering Math. Application in Environmental Science Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Probabilistic risk and uncertainty analysis for bioremediation of four chlorinated ethenes in groundwater

An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution

Assessing environmental risks through fuzzy parameterized probabilistic analysis

Sampling-based flood risk analysis for fluvial dike systems

Assessing traffic noise impact based on probabilistic and fuzzy approaches under uncertainty

Joint Monte Carlo and possibilistic simulation for flood damage assessment

Stochastic generation of hourly rainstorm events

Vadose zone modeling of dispersed waste sites in the framework of an integrated stochastic environmental transport and impacts assessment code for the Hanford Site

Cancer risk analysis and assessment of trihalomethanes in drinking water

2D Monte Carlo versus 2D Fuzzy Monte Carlo health risk assessment

Similar Documents

Probabilistic risk and uncertainty analysis for bioremediation of four chlorinated ethenes in groundwater

An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution

Assessing environmental risks through fuzzy parameterized probabilistic analysis

Sampling-based flood risk analysis for fluvial dike systems

Assessing traffic noise impact based on probabilistic and fuzzy approaches under uncertainty

Joint Monte Carlo and possibilistic simulation for flood damage assessment

Stochastic generation of hourly rainstorm events

Vadose zone modeling of dispersed waste sites in the framework of an integrated stochastic environmental transport and impacts assessment code for the Hanford Site

Cancer risk analysis and assessment of trihalomethanes in drinking water

2D Monte Carlo versus 2D Fuzzy Monte Carlo health risk assessment