Fuzzy assessment of human-health risks due to air pollution

Reshetin, V. P.

doi:10.1504/ijram.2008.019319

Cited by 2 publications

(12 citation statements)

References 4 publications

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“…Population risk is considered acceptable when R is less than 10 −6 (Reshetin 2008). When the slope factor is not known, HQ should not be greater than 1.…”

Section: Health Risk Modelsmentioning

confidence: 99%

“…Classifications of risk levels could depend on many factors, such as monetary value, social impacts, and remedial priorities (Reshetin 2008). Assessors and decisionmakers should jointly define what is high risk, medium risk, and acceptable risk.…”

Section: Health Risk Modelsmentioning

confidence: 99%

“…Assessors and decisionmakers should jointly define what is high risk, medium risk, and acceptable risk. In Table 1, Reshetin (2008) define risk based on social responses to risk values. For example, when the annual risk value is 1:1000, people are expected to be active to reduce it.…”

Section: Health Risk Modelsmentioning

confidence: 99%

“…For example, when the annual risk value is 1:1000, people are expected to be active to reduce it. However, when risk is lower such that annual risk is 1 in a million, there may be no reaction as this is considered socially acceptable risk (Reshetin 2008).…”

Section: Health Risk Modelsmentioning

confidence: 99%

“…The article highlighted reasons for the application of fuzzy logic rather than stochastic methods. Reshetin (2008) explained that input parameters (e.g., volume of emissions and exposure levels) for environmental risk assessment are fuzzy in nature and their uncertainty lacks the structure required for stochastic modeling. Moreover, the probability distributions require homogenous randomization of events taking place under the same conditions, which is not possible in the natural environment.…”

Section: Application Of Fuzzy Logic In Human Health Risk Assessmentmentioning

confidence: 99%

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A Fuzzy Logic Approach to Assess, Manage, and Communicate Carcinogenic Risk

Matbouli

Hipel

Kilgour

et al. 2014

Human and Ecological Risk Assessment: An International Journal

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A prospective approach to addressing carcinogen risk assessment is presented. Fuzzy reasoning is used to assess carcinogenic risk, characterize it, and control it. The approach is inspired by fuzzy control inference that deploys linguistic intelligence as input to a system described numerically through membership functions. Fuzzy-based reasoning to estimate carcinogenic risk provides several advantages as discussed here. The fuzzy reasoning approach has more capabilities than traditional models in dealing with risk agents that are probably carcinogens, possibly carcinogens, not classifiable as carcinogens, and probably not carcinogens. Input-output surfaces are presented for each hazard group to enable fast inferencing. Then, a hypothetical example is given to compare the results of traditional methods and the fuzzy-based approach to estimating the risk of a carcinogen to a human population. Results show similarity in risk characterization with less input information to the fuzzybased approach. Fuzzy reasoning characterizes risk in more explicit and easy to grasp terms. Two outputs of the inferencing system are risk characterization and risk control or remediation.

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“…Population risk is considered acceptable when R is less than 10 −6 (Reshetin 2008). When the slope factor is not known, HQ should not be greater than 1.…”

Section: Health Risk Modelsmentioning

confidence: 99%

Section: Health Risk Modelsmentioning

confidence: 99%

Section: Health Risk Modelsmentioning

confidence: 99%

Section: Health Risk Modelsmentioning

confidence: 99%

Section: Application Of Fuzzy Logic In Human Health Risk Assessmentmentioning

confidence: 99%

See 3 more Smart Citations