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

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 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 (R ²= 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.