Fuzzy Based Health Risk Assessment of Heavy Metals Introduced into the Marine Environment

Abstract

There are concerns among scientists about the significant amount of heavy metals introduced into the marine environment by the petroleum industry during exploration and production phases. The toxicity of heavy metals such as arsenic (As), cadmium (Cd), chromium (Cr), and mercury (Hg) are of particular concern, because they may pose major human health risks through consumption of contaminated food. This study conducts a conservative human health risk assessment study for the selected heavy metals discharged into the marine environment through petroleum operations. Probabilistic risk assessment technique, together with fuzzy set theory, is used to incorporate uncertainties into the risk assessment model. Random and fuzzy variables were integrated to develop the membership functions to individuals' risk at different fractiles, and corresponding cumulative distribution functions (CDF) of risks were developed. The α-cut concept was used to handle fuzzy arithmetic and Monte Carlo simulation (MCS) was used to carry out the statistical calculations. Using human ingestion pathway, the 90th percentile membership function of cumulative cancer risk due to various heavy metals was calculated, and the support of this fuzzy cancer risk is from 1.0E-08 to 2.50E-05. Non-cancer risk was evaluated as well and found to be within the acceptable limits.

Qualitative zoning of groundwater to assessment suitable drinking water using fuzzy logic spatial modelling via GIS

“…Fuzzy modelling allows flexible standardization and aggregation by partial membership. Numerous studies attempted to use fuzzy modelling in development of water quality index and water quality assessment (Dahiya et al 2007;Icaga 2007;S¸en 2009;Mofarrah & Husain 2011;Sahu et al 2011;Gharibi et al 2012;Liu & Zou 2012;Scannapieco et al 2012;Kord & Asghari Moghaddam 2014) or in the other proposes that are near to aim of our study (Chen & Paydar 2012;Lewis et al 2014;Akumu et al 2015;Caniani et al 2016;Araya-Muñoz et al 2017;Hellwig et al 2017;Zhang et al 2017). All of these researches are emphasized on the very high accuracy of the results due to use of the fuzzy logic method.…”

“…Numerous studies attempted to use fuzzy modelling in development of water quality index and water quality assessment (Dahiya et al . ; Icaga ; Şen ; Mofarrah & Husain ; Sahu et al . ; Gharibi et al .…”

Uncertainty Evaluation of Water Inrush in Karst Tunnels Based on Epistemic Uncertainty with Possibility Theory

“…In such a case, the propagation of uncertainty can be defined by fuzzy interval analysis (FIA) in possibility theory [30]. In summary, it can be seen that convolutions defining fuzzy arithmetic essentially reduce to interval arithmetic repeated many times, once for each level of possibility; however, unlike interval analysis, fuzzy arithmetic [31] provides a possibility distribution rather than a simple range.…”