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

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 better understanding of exposure variables and uncertainty and variability associated with them. Probabilistic risk assessment (PRA) studies use probability distributions for one or more variables of the risk equation in order to quantitatively characterize variability and uncertainty. Two-dimensional Monte Carlo Analysis (2D MCA) is one of the advanced modeling approaches that may be used to conduct PRA studies. In this analysis the variables of the risk equation along with the parameters of these variables (for example mean and standard deviation for a normal distribution) are described in terms of probability density functions (PDFs). A variable described in this way is called a second order random variable. Significant data or considerable insight to uncertainty associated with these variables is necessary to develop the appropriate PDFs for these random parameters. Typically, available data and accuracy and reliability of such data are not sufficient for conducting a reliable 2D MCA. Thus, other theories and computational methods that propagate uncertainty and variability in exposure and health risk assessment are needed. One such theory is possibility analysis based on fuzzy set theory, which allows the utilization of incomplete information (incomplete information includes vague and imprecise information that is not sufficient to generate probability distributions for the parameters of the random variables of the risk equation) together with expert judgment. In this paper, as an alternative to 2D MCA, we are proposing a 2D Fuzzy Monte Carlo Analysis (2D FMCA) to overcome this difficulty. In this approach, instead of describing the parameters of PDFs used in defining the variables of the risk equation as random variables, we describe them as fuzzy numbers. This approach introduces new concepts and risk characterization methods. In this paper we provide a comparison of these two approaches relative to their computational requirements, data requirements and availability. For a hypothetical case, we also provide a comperative interpretation of the results generated.     

An inexact fuzzy-chance-constrained two-stage mixed-integer linear programming approach for flood diversion planning under multiple uncertainties

In this study, an inexact fuzzy-chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is developed for flood diversion planning under multiple uncertainties. A concept of the distribution with fuzzy boundary interval probability is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets and probability distributions. IFCTIP integrates the inexact programming, two-stage stochastic programming, integer programming and fuzzy-stochastic programming within a general optimization framework. IFCTIP incorporates the pre-regulated water-diversion policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised targets are violated. More importantly, it can facilitate dynamic programming for decisions of capacity-expansion planning under fuzzy-stochastic conditions. IFCTIP is applied to a flood management system. Solutions from IFCTIP provide desired flood diversion plans with a minimized system cost and a maximized safety level. The results indicate that reasonable solutions are generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of flood flows.     

Biomarkers and environmental risk assessment: guiding principles from the human health field

Although the potential use of biomarkers within environmental risk assessment (ERA) has long been recognised their routine use is less advanced compared with clinical human health risk assessment, where a number of familiar biomarkers (such as blood pressure and serum cholesterol) are in common usage. We have examined how biomarkers are incorporated into human health risk assessment and have identified several 'required elements'. These include identification of the (clinical) assessment endpoint at the outset, rational selection of the biomarker(s) (the measurement endpoint), biomarker 'validation' (e.g. QA/QC) and biomarker 'qualification' (evidence linking the measurement and assessment endpoints). We discuss these elements in detail and propose that their adoption will facilitate the routine use of biomarkers in environmental risk assessment. Furthermore, our analysis highlights the need for cooperation between those working with biomarkers within human and environmental risk assessment to exchange best practice between common disciplines for mutual advantage.     

An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution

High concentrations of air pollutants in the ambient environment can result in breathing problems with human communities. Effective assessment of health-impact risk from air pollution is important for supporting decisions of the related detection, prevention, and correction efforts. However, the quality of information available for environmental/health risk assessment is often not satisfactory enough to be presented as deterministic numbers. Stochastic method is one of the methods for tackling those uncertainties, by which uncertain information can be presented as probability distributions. However, if the uncertainties can not be presented as probabilities, they can then be handled through fuzzy membership functions. In this study, an integrated fuzzy-stochastic modeling (IFSM) approach is developed for assessing air pollution impacts towards asthma susceptibility. This development is based on Monte Carlo simulation for the fate of SO₂ in the ambient environment, examination of SO₂ concentrations based on the simulation results, quantification of evaluation criteria using fuzzy membership functions, and risk assessment based on the combined fuzzy-stochastic information. The IFSM entails (a) simulation for the fate of pollutants in ambient environment, with the consideration of source/medium uncertainties, (b) formulation of fuzzy air quality management criteria under uncertain human-exposure pathways, exposure dynamics, and SPG-response variations, and (c) integrated risk assessment under complexities of the combined fuzzy/stochastic inputs of contamination level and health effect (i.e., asthma susceptibility). The developed IFSM is applied to a study of regional air quality management. Reasonable results have been generated, which are useful for evaluating health risks from air pollution. They also provide support for regional environmental management and urban planning.     

Probabilistic risk and uncertainty analysis for bioremediation of four chlorinated ethenes in groundwater

Groundwater contamination risk assessment for health-threatening compounds should benefit from a stochastic environmental risk assessment which considers the effects of biological, chemical, human behavioral, and physiological processes that involve elements of biotic and abiotic aquifer uncertainty, and human population variability. This paper couples a complex model of chemical degradation and transformation with movement in an aquifer undergoing bioremediation to generate a health risk analysis for different population cohorts in the community. A two-stage Monte Carlo simulation has separate stages for population variability and aquifer uncertainty yielding a computationally efficient and conceptually attractive algorithm. A hypothetical example illustrates how risk variance analysis can be conducted to determine the distribution of risk, and the relative impact of uncertainty and variability in different sets of parameters upon the variation of risk values for adults, adolescents, and children. The groundwater example considers a community water supply contaminated with chlorinated ethenes. Biodegradation pathways are enhanced by addition of butyrate. The results showed that the contribution of uncertainty to the risk variance is comparable to that of variability. Among the uncertain parameters considered, transmissivity accounted for the major part of the output variance. Children were the most susceptible and vulnerable population cohort.     

A naive uncertainty model for measuring operational risks faced by financial institutions

When insufficient data are available for measuring operational risk faced by a financial institute, most of the models depending on the probability theory are failure. Differing from that we use a probability distribution to depict random uncertainty, in this paper we use a number to represent the naive uncertainty in a phase serving for operational risk identification. The simplest form of the naive uncertainty model for measuring operational risk with multiple phases is the weighted mean with the uncertainties. It is also valid when we have a rough judgment for the uncertainties with intervals or fuzzy values. In this paper, we give a calculation case in lending operational risk to demonstrate the model validity.     

Partitioned multiobjective risk modeling of carcinogenic compounds in groundwater

Quantifying human cancer risk arising from exposure to contaminated groundwater is complicated by the many hydrogeological, environmental, and toxicological uncertainties involved. In this study, we used Monte Carlo simulation to estimate cancer risk associated with tetrachloroethene (PCE) dissolved in groundwater by linking three separate models for: (1) reactive contaminant transport; (2) human exposure pathways; and (3) the PCE cancer potency factor. The hydrogeologic model incorporates an analytical solution for a one-dimensional advective–dispersive–reactive transport equation to determine the PCE concentration in a water supply well located at a fixed distance from a continuous source. The pathway model incorporates PCE exposure through ingestion, inhalation, and dermal contact. The toxicological model combines epidemiological data from eight rodent bioassays of PCE exposure in the form of a composite cumulative distribution frequency curve for the human PCE cancer potency factor. We assessed the relative importance of individual model variables through their correlation with expected cancer risk calculated in an ensemble of Monte Carlo simulations with 20,000 trials. For the scenarios evaluated, three factors were most highly correlated with cancer risk: (1) the microbiological decay constant for PCE in groundwater, (2) the linear groundwater pore velocity, and (3) the cancer potency factor. We then extended our analysis beyond conventional expected value risk assessment using the partitioned multiobjective risk method (PMRM) to generate expected-value functions conditional to a 1 in 100,000 increased cancer risk threshold. This approach accounts for low probability/high impact outcomes separately from the conventional unconditional expected values. Thus, information on potential worst-case outcomes can be quantified for decision makers. Using PMRM, we evaluated the cost-benefit relationship of implementing several postulated risk management alternatives intended to mitigate the expected and conditional cancer risk. Our results emphasize the importance of hydrogeologic models in risk assessment, but also illustrate the importance of integrating environmental and toxicological uncertainty. When coupled with the PMRM, models integrating uncertainty in transport, exposure, and potency constitute an effective risk assessment tool for use within a risk-based corrective action (RBCA) framework.     

模糊逻辑理论在矿区勘探与解释中的应用

本文介绍了模糊逻辑理论及其在矿区勘探与解释中的应用,并给出了相应的计算公式及应用模糊逻辑理论预测某金矿远景区的计算实例。     

Organotin contamination in seafood and its implication for human health risk in Hong Kong

Organotins (OTs) have caused widespread adverse effects on marine organisms, while they can also induce health problems to humans via consumption of contaminated seafood. This study aimed to quantify the tissue concentrations of OTs in 11 seafood species in Hong Kong, and assess the human health risk for consuming these species. The tongue sole Paraplagusia blochii had the highest concentration of total OTs. Triphenyltin (TPT) accounted for 56–97% of total OTs. The highest hazard quotient (HQ) for TPT was 1.41 in P. blochii, while the HQs for butyltins were much less than 1. The results indicated that it is likely to have certain health risks for consuming P. blochii due to its high TPT contamination. Therefore, TPT should be a priority pollutant of concern. Appropriate management actions should be taken to control its use and release in the region in order to safeguard the marine ecosystem and human health.     

The incorporation of stochasticity in risk analysis and management: a case study

 The selection of optimal management strategies for environmental contaminants requires detailed information on the risks imposed on populations. These risks are characterized by both inter-subject variability (different individuals having different levels of risk) and by uncertainty (there is uncertainty about the risk associated with the Yth percentile of the variability distribution). In addition, there is uncertainty introduced by the inability to agree fully on the appropriate decision criteria. This paper presents a methodology for incorporating uncertainty and variability into a multi-medium, multi-pathway, multi-contaminant risk assessment, and for placing this assessment into an optimization framework to identify optimal management strategies. The framework is applied to a case study of a sludge management system proposed for North Carolina and the impact of stochasticity on selection of an optimal strategy considered. Different sets of decision criteria reflecting different ways of treating stochasticity are shown to lead to different selections of optimal management strategies.     

Perceptual models of uncertainty for socio-hydrological systems: a flood risk change example

Characterizing, understanding and better estimating uncertainties are key concerns for drawing robust conclusions when analyzing changing socio-hydrological systems. Here we suggest developing a perceptual model of uncertainty that is complementary to the perceptual model of the socio-hydrological system and we provide an example application to flood risk change analysis. Such a perceptual model aims to make all relevant uncertainty sources – and different perceptions thereof – explicit in a structured way. It is a first step to assessing uncertainty in system outcomes that can help to prioritize research efforts and to structure dialogue and communication about uncertainty in interdisciplinary work.     

Perceptual models of uncertainty for socio-hydrological systems: a flood risk change example

ABSTRACT

Characterizing, understanding and better estimating uncertainties are key concerns for drawing robust conclusions when analyzing changing socio-hydrological systems. Here we suggest developing a perceptual model of uncertainty that is complementary to the perceptual model of the socio-hydrological system and we provide an example application to flood risk change analysis. Such a perceptual model aims to make all relevant uncertainty sources – and different perceptions thereof – explicit in a structured way. It is a first step to assessing uncertainty in system outcomes that can help to prioritize research efforts and to structure dialogue and communication about uncertainty in interdisciplinary work.     

与特征断层结合的地震活动空间光滑模型在衡水市活断层地震危险性评价中的应用

在衡水市活断层地震危险性评价中,应用研究区域的地震活动性数据建立恰当的分布式地震活动性空间光滑模型,并结合断层对周边地震危险性的影响,计算前磨头断裂和衡水断裂在不同超越概率下上限震级分别为6.5、6.0级的区域基岩峰值加速度。研究发现,研究区域内50年超越概率为63%的基岩峰值加速度略有增加;50年超越概率为10%、5%的基岩峰值加速度在断层附近有所增加。研究表明在较低超越概率的情况下,断层周围如深州市、冀州市和衡水市辖区的地震危险性大于其他地区;利用仪器记录资料得到的地震活动性模型并结合断层资料计算出的地震危险性结果能够反映衡水地区现今的地震活动水平和危险性水平。     

高原富营养化湖泊杞麓湖微囊藻毒素分布特征、相关因子及其健康风险分析

杞麓湖作为典型的富营养化高原湖泊,其藻华暴发引发的微囊藻毒素(microcystins, MCs)污染问题会危害水生态安全并造成人体健康风险。为了评估杞麓湖流域MCs的秋冬季节污染现状以及健康风险情况,对湖内和7条主要入湖河流入湖口的表层水进行采样检测,分析MCs主要异构体的时空分布特征,研究MCs与水质和浮游植物等环境因子的关系,并通过人体非致癌健康风险评价MCs的风险等级。结果发现,MCs浓度自秋季到冬季呈下降趋势,秋季湖内胞内MCs(IMCs)占比超过99%,冬季IMCs与胞外MCs(EMCs)浓度接近。MC-RR和MC-LR是主要的MCs异构体类型,其中MC-RR在秋季浓度占比高于MC-LR和MC-YR,而冬季MC-LR最高。MCs与微囊藻密度等生物因子呈极显著正相关关系,与TN、TP和NH₃-N营养盐呈显著负相关关系。杞麓湖EMCs污染的风险指数范围为0.004~0.110,处于极低或低风险,冬季风险略高于秋季,因南岸入湖EMCs污染以及湖内IMCs释放带来的风险需要进一步关注。     

Exploring the role of risk perception in influencing flood losses over time

ABSTRACT

What implications do societies’ risk perceptions have for flood losses? This study uses a stylized, socio-hydrological model to simulate the mutual feedbacks between human societies and flood events. It integrates hydrological modelling with cultural theory and proposes four ideal types of society that reflect existing dominant risk perception and management: risk neglecting, risk monitoring, risk downplaying and risk controlling societies. We explore the consequent trajectories of flood risk generated by the interactions between floods and people for these ideal types of society over time. The results suggest that flood losses are substantially reduced when awareness-raising attitudes are promoted through inclusive, participatory approaches in the community. In contrast, societies that rely on top-down hierarchies and structural measures to protect settlements on floodplains may still suffer significant losses during extreme events. This study illustrates how predictions formed through social science theories can be applied and tested in hydrological modelling.     

Exploring the role of risk perception in influencing flood losses over time

ABSTRACT

What implications do societies’ risk perceptions have for flood losses? This study uses a stylized, socio-hydrological model to simulate the mutual feedbacks between human societies and flood events. It integrates hydrological modelling with cultural theory and proposes four ideal types of society that reflect existing dominant risk perception and management: risk neglecting, risk monitoring, risk downplaying and risk controlling societies. We explore the consequent trajectories of flood risk generated by the interactions between floods and people for these ideal types of society over time. The results suggest that flood losses are substantially reduced when awareness-raising attitudes are promoted through inclusive, participatory approaches in the community. In contrast, societies that rely on top-down hierarchies and structural measures to protect settlements on floodplains may still suffer significant losses during extreme events. This study illustrates how predictions formed through social science theories can be applied and tested in hydrological modelling.     

Grey fuzzy optimization model for water quality management of a river system

A grey fuzzy optimization model is developed for water quality management of river system to address uncertainty involved in fixing the membership functions for different goals of Pollution Control Agency (PCA) and dischargers. The present model, Grey Fuzzy Waste Load Allocation Model (GFWLAM), has the capability to incorporate the conflicting goals of PCA and dischargers in a deterministic framework. The imprecision associated with specifying the water quality criteria and fractional removal levels are modeled in a fuzzy mathematical framework. To address the imprecision in fixing the lower and upper bounds of membership functions, the membership functions themselves are treated as fuzzy in the model and the membership parameters are expressed as interval grey numbers, a closed and bounded interval with known lower and upper bounds but unknown distribution information. The model provides flexibility for PCA and dischargers to specify their aspirations independently, as the membership parameters for different membership functions, specified for different imprecise goals are interval grey numbers in place of a deterministic real number. In the final solution optimal fractional removal levels of the pollutants are obtained in the form of interval grey numbers. This enhances the flexibility and applicability in decision-making, as the decision-maker gets a range of optimal solutions for fixing the final decision scheme considering technical and economic feasibility of the pollutant treatment levels. Application of the GFWLAM is illustrated with case study of the Tunga–Bhadra river system in India.     

Biomarkers in environmental and human health risk assessment

Marine pollution is a major threat to human and environmental health. Given the complexity of function of marine and coastal ecosystems, it is unlikely that a balanced view of the nature and extent of risk will easily be achieved if human and environmental risk assessments continue to be conducted in isolation. Here, the integration of assessment protocols is advocated as a holistic means of improving risk management. Biomarkers can provide the common conceptual framework and measurable endpoints necessary for successful integration. Examples are given of the ways in which suites of biomarkers encompassing molecular change, cellular pathology and physiological impairment can be developed and adapted for human and ecological scenarios. By placing a greater emphasis on the health status of impacted biota, it is more likely that risk assessment will develop the efficiency, reliability and predictive power to adapt to the unforeseen environmental threats that are an inevitable consequence of human development and global change.     

A review of ecological risk assessment and associated health risks with heavy metals in sediment from India

Heavy metal (HM) pollution in sediment is a serious concern particularly in developing nations, warranting an extensive survey to understand the current situation and propose possible remedial measures. This paper compiles the data of HMs cadmium (Cd), iron (Fe), cobalt (Co), manganese (Mn), arsenic (As), lead (Pb), chromium (Cr), copper (Cu), zinc (Zn) and nickel (Ni) in aquatic sediment from India from 1979 to 2017. It was found that mean values of Cu, Co, Zn, Pb, As, and Cr in Indian sediment were high in comparison to the Australian Interim Sediment Quality Guidelines, World Surface Rock Average, and the Threshold Effect Level for freshwater ecosystems. Anthropogenic activities, lithogenic factors, and sand intrusion are the main factors associated with HM pollution as observed using cluster analysis and principal component analysis. The results of contamination indices indicate that HM contamination ranged from average to high, in the sediment. The ecological risk assessment results showed that 11% HMs present very high risk. The cancer risk, due to the high contents of Cd, As, and Cr the ingestion pathway, showed high risk of cancer through food/water contaminated with sediment. At source reduction of HMs in industrial effluents by effluent treatment plants, and plantation of phytoremediating rooted macrophytes in sediment may help in HM mitigation of the sediment.     

Stochastic multimedia risk assessment for a site with contaminated groundwater

 There exist many sites with contaminated groundwater because of inappropriate handling or disposal of hazardous materials or wastes. Health risk assessment is an important tool to evaluate the potential environmental and health impacts of these contaminated sites. It is also becoming an important basis for determining whether risk reduction is needed and what actions should be initiated. However, in research related to groundwater risk assessment and management, consideration of multimedia risk assessment and the separation of the uncertainty due to lack of knowledge and the variability due to natural heterogeneity are rare. This study presents a multimedia risk assessment framework with the integration of multimedia transfer and multi-pathway exposure of groundwater contaminants, and investigates whether multimedia risk assessment and the separation of uncertainty and variability can provide a better basis for risk management decisions. The results of the case study show that a decision based on multimedia risk assessment may differ from one based on risk resulting from groundwater only. In particular, the transfer from groundwater to air imposes a health threat to some degree. By using a methodology that combines Monte Carlo simulation, a rank correlation coefficient, and an explicit decision criterion to identify information important to the decision, the results obtained when uncertainty and variability are separate differ from the ones without such separation. In particular, when higher percentiles of uncertainty and variability distributions are considered, the method separating uncertainty and variability identifies TCE concentration as the single most important input parameter, while the method that does not distinguish the two identifies four input parameters as the important information that would influence a decision on risk reduction.