Sensors 2009, 9(2), 731-755; doi:10.3390/s90200731
Article

Bayesian Algorithm Implementation in a Real Time Exposure Assessment Model on Benzene with Calculation of Associated Cancer Risks

1,* email, 1email, 1email, 2email, 3email and 4email
Received: 7 November 2008; in revised form: 5 January 2009 / Accepted: 12 January 2009 / Published: 2 February 2009
(This article belongs to the Special Issue Sensor Algorithms)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations.
Keywords: Bayesian algorithm; ANN; PBPK; benzene exposure
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MDPI and ACS Style

Sarigiannis, D.A.; Karakitsios, S.P.; Gotti, A.; Papaloukas, C.L.; Kassomenos, P.A.; Pilidis, G.A. Bayesian Algorithm Implementation in a Real Time Exposure Assessment Model on Benzene with Calculation of Associated Cancer Risks. Sensors 2009, 9, 731-755.

AMA Style

Sarigiannis DA, Karakitsios SP, Gotti A, Papaloukas CL, Kassomenos PA, Pilidis GA. Bayesian Algorithm Implementation in a Real Time Exposure Assessment Model on Benzene with Calculation of Associated Cancer Risks. Sensors. 2009; 9(2):731-755.

Chicago/Turabian Style

Sarigiannis, Dimosthenis A.; Karakitsios, Spyros P.; Gotti, Alberto; Papaloukas, Costas L.; Kassomenos, Pavlos A.; Pilidis, Georgios A. 2009. "Bayesian Algorithm Implementation in a Real Time Exposure Assessment Model on Benzene with Calculation of Associated Cancer Risks." Sensors 9, no. 2: 731-755.

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