Integration of cancer and non-cancer human health risk assessment for Aniline enriched groundwater: a fuzzy inference system-based approach.

This study outlines a methodological approach to evaluate the environmental risk from integrating data of Aniline in groundwater near to coal-based industries using fuzzy logic, and a comprehensive artificial intelligence approach and the results were validated using conventional risk assessment approach. The Aniline is well-known carcinogenic pollutant released from coal-based industries, so to understand the associated cancer and non-cancer risks (CR and NCR), 15 groundwater samples were analyzed for Aniline, whose concentration was found within the range 0.10-0.34 mg/L, which is up to 68 times higher than the permissible limit. The alkaline pH of water samples resulted in reduced attractive forces between the soil particles with Aniline, and thereby increased percolation of Aniline into the groundwater. Women were at least risk in terms of Mamdani cancer risk (MCR) and Mamdani hazard index (MHI) which was observed up to 1.04E-04 and 3.04, respectively, while maximum MCR and MHI were observed in case of children, i.e., 1.21-E04 and 3.26, respectively. The newly proposed fuzzy inference rule-based Mamdani combined index (MCI) depicts the combined effect of both CR and NCR and was found to be highly correlated with each other. The detailed comparison analysis exhibited that the fuzzy inference rule-based MCI has better resolving ability to find out priority risk prediction over conventional methods under efficient parameter uncertainty control. Hence, it can be concluded that the fuzzy analyses can reflect human considerations and expertise in indices, empowering them to manage nonlinear, questionable, uncertain and subjective data. Therefore, this tool can predict the more meaningful risk estimation of any pollutants on human health.