We have located links that may give you full text access.
Systemic Approach for Health Risk Assessment of Ambient Air Concentrations of Benzene in Petrochemical Environments: Integration of Fuzzy Logic, Artificial Neural Network, and IRIS Toxicity Method.
Iranian Journal of Public Health 2016 September
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 benzene. 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 method 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 applied for prediction of benzene concentration. First, the actual exposure was calculated then it combined with Integrated 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 entire 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 necessities 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.
METHODS: The project was conducted in Asaluyeh, southern Iran during the years from 2013 to 2014. Integrated method 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 applied for prediction of benzene concentration. First, the actual exposure was calculated then it combined with Integrated 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 entire 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 necessities 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.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app