Abstract
The ability to describe variables in a health risk model through probability theory enables us to estimate human health risk. These types of risk assessment are interpreted as probabilistic risk assessment (PRA). Generally, PRA requires specific estimate of the parameters of the probability density of the input variables. In all circumstances, such estimates of the parameters may not be available due to the lack of knowledge or information. Such types of variables are treated as uncertain variables. These types of information are often termed uncertainty which are interpreted through fuzzy theory. The ability to describe uncertainty through fuzzy set theory enables us to process both random variable and fuzzy variable in a single framework. The method of processing aleatory and epistemic uncertainties into a same framework is coined as hybrid method. In this paper, we are going to talk about such type of hybrid methodology for human health risk assessment. Risk assessment on human health through different pathways of exposure has been attempted many a times combining Monte Carlo analysis and extension principle of fuzzy set theory. The emergence of credibility theory enables transforming fuzzy variable into credibility distribution function which can be used in those hybrid analyses. Hence, an attempt, for the first time, has been made to combine probability theory and credibility theory to estimate risk in human health exposure. This method of risk assessment in the presence of credibility theory and probability theory is identified as probabilistic-credibility method (PCM). The results obtained are then interpreted through probability theory, unlike the other hybrid methodology where the results are interpreted in terms of possibility theory. The results obtained are then compared with probability-fuzzy risk assessment (PFRA) method. Generally, decision under hybrid methodology is made on the index of optimism. An optimistic decision maker estimates from the \(\alpha\)-cut at 1, whereas a pessimistic decision maker estimates from the \(\alpha\)-cut at 0. The PCM is an optimistic approach as the decision is always made at \(\alpha\)=1.
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References
Adomian G (1980) Applied stochastic processes. Academic press, New York
Arunraj NS, Mandal S, Maiti J (2013) Modeling uncertainty in risk assessment: an integrated approach with fuzzy set theory and monte carlo simulation. Accid Anal Prev 55:242–255
Baudrit C, Guyonnet D, Dubois D (2007) Joint propagation of variability and imprecision in assessing the risk of groundwater contamination. J Contam Hydrol 93(1):72–84
CalEPA (2000) Air toxics hot spots program risk assessment guidelines, exposure assessment and stochastic analysis (part 4). Technical report
Chen Z, Huang GH, Chakma A (2003) Hybrid fuzzy-stochastic modeling approach for assessing environmental risks at contaminated groundwater systems. J Environ Eng 129(1):79–88
Chen Z, Zhao L, Lee K (2010) Environmental risk assessment of offshore produced water discharges using a hybrid fuzzy-stochastic modeling approach. Environ Model Softw 25(6):782–792
Chutia R, Mahanta S, Datta D (2011) Arithmmetic of triangular fuzzy variable from credibility theory. Int J Energy Inf Commun 2(3):9–20
Chutia R, Mahanta S, Datta D (2013) Uncertainty modelling of atmospheric dispersion model using fuzzy set and imprecise probability. J Intell Fuzzy Syst 25(3):737–746
Chutia R, Mahanta S, Datta D (2013) Non-probabilistic sensitivity and uncertainty analysis of atmospheric dispersion. Ann Fuzzy Math Inf 5(1):213–228
Chutia R (2013) Environmental risk modelling under probability-normal interval-valued fuzzy number. Fuzzy Inf Eng 5(3):359–371
Chutia R, Mahanta S, Datta D (2014) Uncertainty modelling of atmospheric dispersion by stochastic response surface method under aleatory and epistemic uncertainties. Sadhana 39(2):467–485
EPA (1989) Risk assessment guidance for superfund. volume 1: Human health evaluation manual (part a). Technical report, EPA/540/1-89/002
EPA (2001) Risk assessment guidance for superfund. volume 3: Process for conducting probabilistic risk assessment. Technical report, EPA/540/R/02/2002
EPA (2004) Risk assessment guidance for superfund. vol 1: Human health evaluation manual, supplemental guidance, dermal risk assessment (part e). Technical report, EPA/540/R/99/005
Ershow AG, Brown LM, Cantor KP (1991) Intake of tapwater and total water by pregnant and lactating women. Am J Public Health 81(3):328–334
Finley B, Paustenbach D (1994) The benefits of probabilistic exposure assessment: Three case studies involving contaminated air, water, and soil. Risk Anal 14(1):53–73
Frey HC (1992) Quantitative analysis of uncertainty and variability in environmental policy making. Technical report, Fellowship Program for Environmental Science and Engineering, American Association for the Advancement of Science, Washington DC
Frey HC, Bammi S (2003) Probabilistic nonroad mobile source emission factors. J Environ Eng 129(2):162–168
Frey HC, Li S (2003) Methods for quantifying variability and uncertainty in ap-42 emission factors: case studies for natural gas-fueled engines. J Air Waste Manag Assoc 53(12):1436–1447
Guyonnet D, Come B, Perrochet P, Parriaux A (1999) Comparing two methods for addressing uncertainty in risk assessments. J Environ Eng 125(7):660–666
Guyonnet D, B Bourgine, D Dubois, H Fargier, B Côme, JP Chilès (2003) Hybrid approach for addressing uncertainty in risk assessments. J Environ Eng 129(1):68–78
Ibrahim RA (1987) Structural dynamics with parameter uncertainties. Appl Mech Rev 40(3):309–328
Isukapalli SS (1999) Uncertainty analysis of transport-transformation models. PhD thesis, Rutgers, The State University of New Jersey
Isukapalli SS, Roy A, Georgopoulos PG (1998) Stochastic response surface methods (srsms) for uncertainty propagation: application to environmental and biological systems. Risk Anal 18(3):351–363
Kentel E (2006) Uncertainty modeling in health risk assessment and groundwater resources management. PhD thesis, Georgia Institute of Technology
Kentel E, Aral MM (2004) Probabilistic-fuzzy health risk modeling. Stoch Environ Res Risk Assess 18(5):324–338
Kentel E, Aral MM (2005) 2D monte carlo versus 2D fuzzy monte carlo health risk assessment. Stoch Environ Res Risk Assess 19(1):86–96
Kentel E, Aral MM (2007) Risk tolerance measure for decision-making in fuzzy analysis: a health risk assessment perspective. Stoch Environ Res Risk Assess 21(4):405–417
Lee Yong W, Dahab Mohamed F, Istvan Bogardi (1995) Nitrate-risk assessment using fuzzy-set approach. J Environ Eng 121(3):245–256
Lee YW, Dahab MF, Bogardi I (1994) Fuzzy decision making in ground water nitrate risk management. JAWRA 30(1):135–147
Lee SC, Guo H, Lam SMJ, Lau SLA (2004) Multipathway risk assessment on disinfection by-products of drinking water in Hong Kong. Environ Res 94(1):47–56
Leung HW, Jin L, Wei S, Tsui MM, Zhou B, Jiao L, Cheung PC, Chun YK, Murphy MB, Lam PK (2013) Pharmaceuticals in tap water: human health risk assessment and proposed monitoring framework in China. Environ Health Perspect 121(7):839
Li JB, Chakma A, Zeng GM, Liu L (2003) Integrated fuzzy-stochastic modeling of petroleum contamination in subsurface. Energy Sources 25(6):547–563
Li J, Huang GH, Zeng G, Maqsood I, Huang Y (2007) An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination. J Environ Manag 82(2):173–188
Li X, Liu B (2006) A sufficient and necessary condition for credibility measures. Int J Uncertain Fuzziness Knowl Based Syst 14(5):527–535
Liu L, Hao RX, Cheng SY (2003) A possibilistic analysis approach for assessing environmental risks from drinking groundwater at petroleum-contaminated sites. J Environ Inform 2(1):31–37
Liu L, Cheng SY, Guo HC (2004) A simulation-assessment modeling approach for analyzing environmental risks of groundwater contamination at waste landfill sites. Hum Ecol Risk Assess 10(2):373–388
Liu B (2004) Uncertainty theory: an introduction to its axiomatic foundations. Springer, Berlin
Liu B (2006) A survey of credibility theory. Fuzzy Optim Decis Mak 5(4):387–408
Liu B (2007) Uncertainty theory. Springer, Berlin
Liu B, Liu YK (2002) Expected value of fuzzy variable and fuzzy expected value models. IEEE Trans Fuzzy Syst 10(4):445–450
Ma H-W (2002) Stochastic multimedia risk assessment for a site with contaminated groundwater. Stoch Environ ResRisk Assess 16(6):464–478
Ma H, Hung ML, Chen PC (2007) A systemic health risk assessment for the chromium cycle in Taiwan. Environ Int 33(2):206–218
Mahadevan S, Raghothamachar P (2000) Adaptive simulation for system reliability analysis of large structures. Comput Struct 77(6):725–734
Maxwell NI, Burmaster DE, Ozonoff D (1991) Trihalomehanes and maximum contaminant levels: the significance of inhalation and dermal exposures to chloroform in household water. Regul Toxicol Pharmacol 14(3):297–312
Maxwell RM, Pelmulder SD, Tompson AF, Kastenberg WE (1998) On the development of a new methodology for groundwater-driven health risk assessment. Water Resour Res 34(4):833–847
Maxwell MR, Kastenberg EW (1999) Stochastic environmental risk analysis: an integrated methodology for predicting cancer risk from contaminated groundwater. Stoch Environ Res Risk Assess 13(1):27–47
McKone TE, Bogen KT (1991) Predicting the uncertainties in risk assessment. Environ Sci Technol 25(10):1674–1681
Mofarrah A, Husain T (2011) Fuzzy based health risk assessment of heavy metals introduced into the marine environment. Water Qual Expo Health 3(1):25–36
Naz A, Mishra BK, Gupta SK (2016) Human health risk assessment of chromium in drinking water: a case study of sukinda chromite mine, Odisha, India. Expo Health 8(2):253–264
NRC (1993) Pesticides in the diets of infants and children. Technical report, National Academic Science Research Council, Washington DC (1993)
Qin XS (2012) Assessing environmental risks through fuzzy parameterized probabilistic analysis. Stoch Environ Res Risk Assess 26(1):43–58
Roseberry AM, Burmaster DE (1992) Lognormal distributions for water intake by children and adults. Risk Anal 12(1):99–104
Saeedi M, Fakhraee H, Sadrabadi MR (2008) A fuzzy modified gaussian air pollution. Res J Environ Sci 2(3):156–169
Schwarzenbach RP, Escher BI, Fenner K, Hofstetter TB, Johnson CA, Von Gunten U, Wehrli B (2006) The challenge of micropollutants in aquatic systems. Science 313(5790):1072–1077
Slovic P (1999) Trust, emotion, sex, politics, and science: surveying the risk-assessment battlefield. Risk Anal 19(4):689–701
Wu B, Zhang Y, Zhang X, Cheng S (2009) Health risk from exposure of organic pollutants through drinking water consumption in nanjing, china. Bull Environ Contam Toxicol 84(1):46–50
Yang AL, Huang GH, Qin XS (2010) An integrated simulation-assessment approach for evaluating health risks of groundwater contamination under multiple uncertainties. Water Resour Manag 24(13):3349–3369
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
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Chutia, R., Datta, D. Probability-credibility health risk assessment under uncertain environment. Stoch Environ Res Risk Assess 31, 449–460 (2017). https://doi.org/10.1007/s00477-016-1335-2
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DOI: https://doi.org/10.1007/s00477-016-1335-2