Title of article :
Systemic Approach for Health Risk Assessment of Ambient Air Concentrations of Benzene in Petrochemical Environments: In-tegration of Fuzzy Logic, Artificial Neural Network, and IRIS Toxicity Method
Author/Authors :
NOVIN, Vahid Safety and Environment - University of Tehran - Graduate Faculty of Environment, Tehran , GIVEHCHI, Saeed Faculty of Environment - University of Tehran, Tehran , HOVEIDI, Hassan Faculty of Environment - University of Tehran, Tehran
Pages :
11
From page :
1188
To page :
1198
Abstract :
Background: Reliable methods are crucial to cope with uncertainties in the risk analysis process. The aim of this study is to develop an integrated approach to assessing risks of benzene in the petrochemical plant that produces ben-zene. We offer an integrated system to contribute imprecise variables into the health risk calculation. Methods: The project was conducted in Asaluyeh, southern Iran during the years from 2013 to 2014. Integrated me-thod includes fuzzy logic and artificial neural networks. Each technique had specific computational properties. Fuzzy logic was used for estimation of absorption rate. Artificial neural networks can decrease the noise of the data so ap-plied for prediction of benzene concentration. First, the actual exposure was calculated then it combined with Inte-grated Risk Information System (IRIS) toxicity factors to assess real health risks. Results: High correlation between the measured and predicted benzene concentration was achieved (R2= 0.941). As for variable distribution, the best estimation of risk in a population implied 33% of workers exposed less than 1×10-5 and 67% inserted between 1.0×10-5 to 9.8×10-5 risk levels. The average estimated risk of exposure to benzene for en-tire work zones is equal to 2.4×10-5, ranging from 1.5×10-6 to 6.9×10-5. Conclusion: The integrated model is highly flexible as well as the rules possibly will be changed according to the ne-cessities of the user in a different circumstance. The measured exposures can be duplicated well through proposed model and realistic risk assessment data will be produced.
Keywords :
Risk assessment , Exposure estimation , Benzene , Cancer risk , Fuzzy logic , Neural network
Journal title :
Astroparticle Physics
Serial Year :
2016
Record number :
2481075
Link To Document :
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