Health Risk Assessment in Lead Battery Smelter Factory: A Bayesian Belief Network Method
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32799
Health Risk Assessment in Lead Battery Smelter Factory: A Bayesian Belief Network Method

Authors: Kevin Fong-Rey Liu, Ken Yeh, Cheng-Wu Chen, Han-Hsi Liang

Abstract:

This paper proposes the use of Bayesian belief networks (BBN) as a higher level of health risk assessment for a dumping site of lead battery smelter factory. On the basis of the epidemiological studies, the actual hospital attendance records and expert experiences, the BBN is capable of capturing the probabilistic relationships between the hazardous substances and their adverse health effects, and accordingly inferring the morbidity of the adverse health effects. The provision of the morbidity rates of the related diseases is more informative and can alleviate the drawbacks of conventional methods.

Keywords: Bayesian belief networks, lead battery smelter factory, health risk assessment.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334213

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1678

References:


[1] Pearl, J., 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, California.
[2] Liao, Y., Wang, J., Guo, Y., Zheng, X., 2010. Risk assessment of human neural tube defects using a Bayesian belief network. Stochastic Environmental Research and Risk Assessment 24(1): 93-100.
[3] Ticehurst, J.L., Curtis, A., Merritt, W.S., 2010. Using Bayesian Networks to complement conventional analyses to explore landholder management of native vegetation. Environmental Modelling & Software 26(1): 52-65.
[4] Dawsey, W. J., Minsker, B. S., VanBlaricum, V. L., 2006. Bayesian belief networks to Integrate Monitoring Evidence of Water Distribution System Contamination. Water Resources Planning and Management 132(4): 234-241.
[5] Newton, A.C., 2010. Use of a Bayesian network for Red Listing under uncertainty. Environmental Modelling & Software 25: 15-23.