2D Monte Carlo versus 2D Fuzzy Monte Carlo health risk assessment

Abstract

Risk estimates can be calculated using crisp estimates of the exposure variables (i.e., contaminant concentration, contact rate, exposure frequency and duration, body weight, and averaging time). However, aggregate and cumulative exposure studies require a bet-

HYRISK: An R package for hybrid uncertainty analysis using probability, imprecise probability and possibility distributions

“…• When imprecision on the parameters of the probability distributions are also represented by probability distributions (even though it would mix randomness with imprecision, see discussion by Baudrit, Dubois, and Perrot (2008), the propagation is conducted within a 2D Monte-Carlo scheme (e.g., (Kentel and Aral 2005)). …”

Empirical Comparison of Methods for the Hierarchical Propagation of Hybrid Uncertainty in Risk Assessment, in Presence of Dependences

“…In the MC-FIA approach, the propagation of the hybrid uncertainty is performed by combining the MC technique 34,35 with the extension principle of fuzzy set theory [36][37][38][39][40][41][42][43][44][45] within a "level-2" setting by means of the following main steps: 24,[46][47][48][49][50][51] (1) select one possibility value 1 ∈ (0, 1] and the corresponding cuts propagation method clearly assumes independence between the group of probabilistic (i.e., aleatory or random) variables and the group of the possibilistic (i.e., epistemicallyuncertain) parameters of the aleatory probability distributions.…”

“…The main steps of the procedure are: 24,[34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] (1) set 1 = 0 (outer loop processing epistemic uncertainty by fuzzy interval analysis); …”

“…[23][24][25][26] When both aleatory and epistemic uncertainties are represented by probability distributions, their propagation is carried out by a two-dimensional (or double) Monte Carlo (MC) simulation approach. 5,32,33 Instead, when a hybrid (probabilistic and possibilistic) uncertainty representation is considered, two approaches are here undertaken: (i) the hybrid MC and Fuzzy Interval Analysis (FIA) approach, b where the MC technique 34,35 is combined with the extension principle of fuzzy set theory, [36][37][38][39][40][41][42][43][44][45] within a "level-2" hierarchical setting; 24,[46][47][48][49][50][51] (ii) the Monte Carlo (MC)-based Dempster-Shafer (DS) approach employing Independent Random Sets (IRSs), c where the possibility distributions describing the epistemically-uncertain parameters are discretized into focal sets that are randomly and independently sampled by MC.…”

How to measure uncertainties in environmental risk assessment

“…They developed a decision-analysis-based model to evaluate the risks that polluted sites might pose to human health. Quantitative estimates of risks were calculated for both Identification of optimal sludge-management strategies [22] Groundwater A rule-based fuzzy-set approach Risk analysis of nitrate-contaminated water to assess risk to human health [44] Groundwater Decision support system based on fuzzy logic Evaluation of the groundwater-pollution risk [4] River-water quality Fuzzy logic and arithmetic Risk assessment of water quality in the Ganges River, India [30] Polluted site Fuzzy logic A model to assess the risk from a contaminated site to the environment, in particular, human health [45] Polluted site Fuzzy logic Evaluation of the risks that polluted sites might pose to human health [33] Soil Fuzzy logic Realistic approach to decision making for riskbased soil interpretations [46] Agriculture Fuzzy expert system Development of ''Ipest'' index to assess potential risk of pesticides for the environment [32] Hazardous waste facilities Fuzzy reasoning Geographic expression of the concept ''safe distance'' applied to hazardous-facility siting [3] Ecotoxic substances releases Fuzzy logic Methodology to assess risk of release of ecotoxic substances in chemical plants [47] Aquatic organisms Fuzzy logic Risk assessment for aquatic organisms exposed to brominated flame retardants (BFRs) [48] Groundwater Hybrid-fuzzy stochastic model Risk assessment of a petroleum-contaminated groundwater system in Canada [39] Groundwater Integrated fuzzy-stochastic approach Risk assessment of groundwater contaminated by xylene [19] River-water quality Monte-Carlo and fuzzy approaches combined Risk assessment for river-water-quality management in Bhadra River (Southern India) [49] Water quality Probabilistic-fuzzy method Health-risk analysis due to exposure to contaminated waters [24,50] Soil Monte-Carlo approach combined with fuzzy calculus Risk assessment for human exposure to cadmium present in surface soils in the north of France [16] Trends Trends in Analytical Chemistry, Vol. 27, No.…”