Published September 29, 2011
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Health Risk Assessment for Sewer Workers using Bayesian Belief Networks
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Description
The sanitary sewerage connection rate becomes an
important indicator of advanced cities. Following the construction of
sanitary sewerages, the maintenance and management systems are
required for keeping pipelines and facilities functioning well. These
maintenance tasks often require sewer workers to enter the manholes
and the pipelines, which are confined spaces short of natural
ventilation and full of hazardous substances. Working in sewers could
be easily exposed to a risk of adverse health effects. This paper
proposes the use of Bayesian belief networks (BBN) as a higher level
of noncarcinogenic health risk assessment of sewer workers. 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 in sewers
and their adverse health effects, and accordingly inferring the
morbidity and mortality of the adverse health effects. The provision of
the morbidity and mortality rates of the related diseases is more
informative and can alleviate the drawbacks of conventional methods.
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References
- Lai CH, Lin CH, Yeh SH, Huang LJ, 2004. Air pollutant exposure and risk assessment for sewer workers, Environmental Informatics Archives 2: 405-412.
- US EPA (US Environmental Protection Agency), 2001. National-Scale Air Toxics Assessment for 1996: Draft for EPA Science Advisory Board Review. Available at http://www.epa.gov/ttn/atw/sab/sabrev.html#A1
- Pearl J, 1988. Probabilistic Reasoning in Intelligent Systems´╝ÜNetworks of Plausible Inference, Morgan Kaufmann, California.
- Lin GX, 2005. Risk assessment for sewer workers in Kaohsiung city, master's thesis, Fooyin University.
- Uusitalo L, 2007. Advantages and challenges of Bayesian networks in environmental modeling. Ecological Modelling 203: 312-318.