Health Risk Assessment for Sewer Workers using Bayesian Belief Networks

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

Abstract:

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.

Keywords: Bayesian belief networks, sanitary sewerage, healthrisk assessment, hazard quotient, target organ-specific hazard index.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1063234

References:

[1] Lai CH, Lin CH, Yeh SH, Huang LJ, 2004. Air pollutant exposure and risk assessment for sewer workers, Environmental Informatics Archives 2: 405-412.
[2] 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
[3] Pearl J, 1988. Probabilistic Reasoning in Intelligent Systems´╝ÜNetworks of Plausible Inference, Morgan Kaufmann, California.
[4] Lin GX, 2005. Risk assessment for sewer workers in Kaohsiung city, master's thesis, Fooyin University.
[5] Uusitalo L, 2007. Advantages and challenges of Bayesian networks in environmental modeling. Ecological Modelling 203: 312-318.