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1.
Stochastic environmental risk assessment considers the effects of numerous biological, chemical, physical, behavioral and physiological processes that involve elements of uncertainty and variability. A methodology for predicting health risks to individuals from contaminated groundwater is presented that incorporates the elements of uncertainty and variability in geological heterogeneity, physiological exposure parameters, and in cancer potency. An idealized groundwater basin is used to perform a parametric sensitivity study to assess the relative impact of (a) geologic uncertainty, (b) behavioral and physiological variability in human exposure and (c) uncertainty in cancer potency on the prediction of increased cancer risk to individuals in a population exposed to contaminants in household water supplied from groundwater. A two-dimensional distribution (or surface) of human health risk was generated as a result of the simulations. Cuts in this surface (fractiles of variability and percentiles of uncertainty) are then used as a measure of relative importance of various model components on total uncertainty and variability. A case study for perchloroethylene or PCE, shows that uncertainty and variability in hydraulic conductivity play an important role in predicting human health risk that is on the same order of influence as uncertainty of cancer potency.  相似文献   

2.
 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.  相似文献   

3.
 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.  相似文献   

4.
Generic indoor air:subslab soil gas attenuation factors (SSAFs) are important for rapid screening of potential vapor intrusion risks in buildings that overlie soil and groundwater contaminated with volatile chemicals. Insufficiently conservative SSAFs can allow high‐risk sites to be prematurely excluded from further investigation. Excessively conservative SSAFs can lead to costly, time‐consuming, and often inconclusive actions at an inordinate number of low‐risk sites. This paper reviews two of the most commonly used approaches to develop SSAFs: (1) comparison of paired, indoor air and subslab soil gas data in empirical databases and (2) comparison of estimated subslab vapor entry rates and indoor air exchange rates (IAERs). Potential error associated with databases includes interference from indoor and outdoor sources, reliance on data from basements, and seasonal variability. Heterogeneity in subsurface vapor plumes combined with uncertainty regarding vapor entry points calls into question the representativeness of limited subslab data and diminishes the technical defensibility of SSAFs extracted from databases. The use of reasonably conservative vapor entry rates and IAERs offers a more technically defensible approach for the development of generic SSAF values for screening. Consideration of seasonal variability in building leakage rates, air exchange rates, and interpolated vapor entry rates allows for the development of generic SSAFs at both local and regional scales. Limitations include applicability of the default IAERs and vapor entry rates to site‐specific vapor intrusion investigations and uncertainty regarding applicability of generic SSAFs to assess potential short‐term (e.g., intraday) variability of impacts to indoor air.  相似文献   

5.
Bisphenol A (BPA) is an endocrine disruptor widely used in the production of polycarbonate plastics and epoxy resins. Exposures to BPA have been associated with reproductive, developmental, and cardiovascular effects. In this study, the CalTOX model was used to assess the aggregate health risks on BPA by integrating the currently available BPA data in various environmental media in Taiwan. Local parameters such as chemical properties, local landscape data, and exposure factors were used as model inputs under the continuous source mode. A reference dose (RfD) of 50 μg/kg-day was adopted in this assessment. Monte Carlo simulation was used to simulate great variability of the environmental data. Our results show that an upper limit of 95 % confidence interval of aggregate exposures for the adults (19–64 years old) was 1.05 μg/kg-day, corresponding to a hazard index (HI) of 0.021. The chemical properties (BPA half-life in surface water), intake rates (fruit, vegetable, and fluid intake), and landscape data (average depth of surface waters and leaf wet density) are critical parameters. Finally, HI value would approach to 1 as BPA concentrations in ambient air, surface water, and sediment was greater than 20 ng/m3, 100 μg/L, and 3.3 mg/kg. The quality of the risk assessment on BPA can be further improved by reduction of uncertainty of the abovementioned critical parameters as well as considering additional BPA exposures from canned and packaged goods.  相似文献   

6.
Jew Das 《水文科学杂志》2018,63(7):1020-1046
In this study, classification- and regression-based statistical downscaling is used to project the monthly monsoon streamflow over the Wainganga basin, India, using 40 global climate model (GCM) outputs and four representative concentration pathways (RCP) scenarios. Support vector machine (SVM) and relevance vector machine (RVM) are considered to perform downscaling. The RVM outperforms SVM and is used to simulate future projections of monsoon flows for different periods. In addition, variability in water availability with uncertainty and change point (CP) detection are accomplished by flow–duration curve and Bayesian analysis, respectively. It is observed from the results that the upper extremes of monsoon flows are highly sensitive to increases in temperature and show a continuous decreasing trend. Medium and low flows are increasing in future projections for all the scenarios, and high uncertainty is noticed in the case of low flows. An early CP is detected in the case of high emissions scenarios.  相似文献   

7.
Probabilistic-fuzzy health risk modeling   总被引：3，自引：2，他引：1  
Health risk analysis of multi-pathway exposure to contaminated water involves the use of mechanistic models that include many uncertain and highly variable parameters. Currently, the uncertainties in these models are treated using statistical approaches. However, not all uncertainties in data or model parameters are due to randomness. Other sources of imprecision that may lead to uncertainty include scarce or incomplete data, measurement error, data obtained from expert judgment, or subjective interpretation of available information. These kinds of uncertainties and also the non-random uncertainty cannot be treated solely by statistical methods. In this paper we propose the use of fuzzy set theory together with probability theory to incorporate uncertainties into the health risk analysis. We identify this approach as probabilistic-fuzzy risk assessment (PFRA). Based on the form of available information, fuzzy set theory, probability theory, or a combination of both can be used to incorporate parameter uncertainty and variability into mechanistic risk assessment models. In this study, tap water concentration is used as the source of contamination in the human exposure model. Ingestion, inhalation and dermal contact are considered as multiple exposure pathways. The tap water concentration of the contaminant and cancer potency factors for ingestion, inhalation and dermal contact are treated as fuzzy variables while the remaining model parameters are treated using probability density functions. Combined utilization of fuzzy and random variables produces membership functions of risk to individuals at different fractiles of risk as well as probability distributions of risk for various alpha-cut levels of the membership function. The proposed method provides a robust approach in evaluating human health risk to exposure when there is both uncertainty and variability in model parameters. PFRA allows utilization of certain types of information which have not been used directly in existing risk assessment methods.  相似文献   

8.
Reliance on motor vehicle travel and the internal combustion engine has provided mobility, but the public health costs are substantial: Road deaths, adverse deleterious health effects from air pollution and noise, reduction in physical exercise, and toxic hazards associated with the refining, transport, use and disposal of petrochemicals. For traumatic road injury, kinetic energy is the pathogen. Risks for injury and death rise with the second and fourth power of increases in velocity upon impact; emissions of many air pollutants also increase exponentially with speed. Models derived from vector transmission in infectious diseases have proven useful for defining risks and designing interventive strategies. These models predict the number of lives saved and injuries prevented from a package of low-cost, effective measures, which can be quickly implemented. Eradication of road deaths and elimination of air pollution emissions are achievable public health goals. Speed camera systems produce sustainable levels of detection deterring speeding, and thereby reducing human injury and environmental damage. “Education” and building more roads, part of the scenario “predict and provide,” have not been shown to reduce injury risks. Building more roads, which in the long run, promotes urban sprawl and congestion, does not reduce travel time. High speed toll roads and circular beltways, which involve trade-offs among time-saving, risk of injury, and diversion of traffic from population centers, need to be re-evaluated and compared to alternative strategies based on modal shifts. We suggest that revenues resulting from massive use of speed cameras can serve as the first step for funding the first steps of sustainable transportation policies based on developing alternatives to private vehicle use and trucking. Such alternatives could lead to even further reductions in injury and death and adverse effects of air pollution. More involvement by epidemiologists in overseeing and evaluating strategies can expedite progress towards the goal of eradication of deaths from road injury, and at the same time, reduce emissions of air pollutants. This revised version was published online in July 2006 with corrections to the Cover Date.  相似文献   

9.
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.  相似文献   

10.
At a utility service center, gasoline from an underground storage tank had leaked into subsurface vadose zone soils for several years. To remediate the site, a soil vapor extraction (SVE) system was installed and operated. At the completion of the SVE operation, gasoline-containing residues in several confirmation soil borings exceeded agency-mandated cleanup levels. Rather than continue with SVE, a risk-based approach was developed to evaluate what levels of gasoline-containing residues could be left in the soil and still protect human health. The risk-based approach consisted of simulating the fate of chemical residues through the vadose zone and then into both the ground water and atmosphere. Receptor point concentrations were predicted, and health risks were assessed. The risk assessment concluded that ingestion of contaminated ground water and inhalation of air while showering were the largest potential contributors to risk, and that risks associated with inhalation of vapor-containing ambient air are small. However, all predicted risks are below the acceptable risk levels of 10−6 individual cancer risk probability and 1.0 hazard index. Therefore, the lead agency accepted the recommendation that the site requires no further remediation. The service center continues normal operations today.  相似文献   

11.
This work examines future flood risk within the context of integrated climate and hydrologic modelling uncertainty. The research questions investigated are (1) whether hydrologic uncertainties are a significant source of uncertainty relative to other sources such as climate variability and change and (2) whether a statistical characterization of uncertainty from a lumped, conceptual hydrologic model is sufficient to account for hydrologic uncertainties in the modelling process. To investigate these questions, an ensemble of climate simulations are propagated through hydrologic models and then through a reservoir simulation model to delimit the range of flood protection under a wide array of climate conditions. Uncertainty in mean climate changes and internal climate variability are framed using a risk‐based methodology and are explored using a stochastic weather generator. To account for hydrologic uncertainty, two hydrologic models are considered, a conceptual, lumped parameter model and a distributed, physically based model. In the conceptual model, parameter and residual error uncertainties are quantified and propagated through the analysis using a Bayesian modelling framework. The approach is demonstrated in a case study for the Coralville Dam on the Iowa River, where recent, intense flooding has raised questions about potential impacts of climate change on flood protection adequacy. Results indicate that the uncertainty surrounding future flood risk from hydrologic modelling and internal climate variability can be of the same order of magnitude as climate change. Furthermore, statistical uncertainty in the conceptual hydrological model can capture the primary structural differences that emerge in flood damage estimates between the two hydrologic models. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

12.
In this study we present results of uncertainty analysis in eight regional climate model (RCM) outputs over the area of the Czech Republic. The RCM simulations come from the EU 5th Framework program project PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects). Using the analysis of variance we have found that the main source of uncertainty in projected changes of mean seasonal air temperature is the driving global climate model. In case of precipitation changes, the RCM is the largest source of uncertainty in all seasons except for the spring. With the second method, the Reliability Averaging method, we have focused on the uncertainty coming from the RCM itself. The results of both methods showed that the relative contribution of the regional climate model to the uncertainty of simulated mean seasonal air temperature and precipitation changes is largest in summer and smallest in winter.  相似文献   

13.
Although temperature is an important determinant of many biogeochemical processes in groundwater, very few studies have attempted to forecast the response of groundwater temperature to future climate warming. Using a composite linear regression model based on the lagged relationship between historical groundwater and regional air temperature data, empirical forecasts were made of groundwater temperature in several aquifers in Switzerland up to the end of the current century. The model was fed with regional air temperature projections calculated for greenhouse‐gas emissions scenarios A2, A1B, and RCP3PD. Model evaluation revealed that the approach taken is adequate only when the data used to calibrate the models are sufficiently long and contain sufficient variability. These conditions were satisfied for three aquifers, all fed by riverbank infiltration. The forecasts suggest that with respect to the reference period 1980 to 2009, groundwater temperature in these aquifers will most likely increase by 1.1 to 3.8 K by the end of the current century, depending on the greenhouse‐gas emissions scenario employed.  相似文献   

14.
 Models of dose–response for environmental pollutants generally do not include explicit consideration of the stochastic nature of the spatial pattern of dose delivered to an organ or tissue, or the correlation between events leading to a final health endpoint (such as cancer). The result can be significant errors in risk calculations when these stochastic properties contribute as strongly to the dose–response relationship as do the dose–response relationships for individual cells. The present paper considers the issue of stochasticity of dose and events (initiation, promotion and inactivation) for the case of carcinogenicity following exposure to environmental pollutants, using the case of irradiation by high LET emitters such as radon and progeny from water or air. The model is based on the concepts of hit probabilities and effect-specific track length probabilities (probability of damage per unit track length), and is applied first to in vitro data and then to predictions in vivo. It is shown that inhomogeneity of dose throughout an irradiated tissue or organ volume, and correlation between initiation, promotion and inactivation, can lead to significant differences in predicted risk.  相似文献   

15.
Empirical fragility curves, constructed from databases of thousands of building-damage observations, are commonly used for earthquake risk assessments, particularly in Europe and Japan, where building stocks are often difficult to model analytically (e.g. old masonry structures or timber dwellings). Curves from different studies, however, display considerable differences, which lead to high uncertainty in the assessed seismic risk. One potential reason for this dispersion is the almost universal neglect of the spatial variability in ground motions and the epistemic uncertainty in ground-motion prediction. In this paper, databases of building damage are simulated using ground-motion fields that take account of spatial variability and a known fragility curve. These databases are then inverted, applying a standard approach for the derivation of empirical fragility curves, and the difference with the known curve is studied. A parametric analysis is conducted to investigate the impact of various assumptions on the results. By this approach, it is concluded that ground-motion variability leads to flatter fragility curves and that the epistemic uncertainty in the ground-motion prediction equation used can have a dramatic impact on the derived curves. Without dense ground-motion recording networks in the epicentral area empirical curves will remain highly uncertain. Moreover, the use of aggregated damage observations appears to substantially increase uncertainty in the empirical fragility assessment. In contrast, the use of limited randomly-chosen un-aggregated samples in the affected area can result in good predictions of fragility.  相似文献   

16.
A model is presented for estimating vapor concentrations in buildings because of volatilization from soil contaminated by non- aqueous phase liquids (NAPL) or from dissolved contaminants in ground water. The model considers source depletion, diffusive- dispersive transport of the contaminant of concern (COC) and of oxygen and oxygen-limited COC biodecay. Diffusive-advective transport through foundations and vapor losses caused by foundation cross-flow are considered. Competitive oxygen use by various species is assumed to be proportional to the product of the average dissolved-phase species concentration and a biopreference factor. Laboratory and field data indicate the biopreference factor to be proportional to the organic carbon partition coefficient for the fuel hydrocarbons studied. Predicted indoor air concentrations were sensitive to soil type and subbase permeability. Lower concentrations were predicted for buildings with shallow foundations caused by flushing of contaminants by cross-flow. NAPL source depletion had a large impact on average exposure concentration. Barometric pumping had a minor effect on indoor air emissions for the conditions studied. Risk-based soil cleanup levels were much lower when biodecay was considered because of the existence of a threshold source concentration below which no emissions occur. Computed cleanup levels at NAPL-contaminated sites were strongly dependent on total petroleum hydrocarbon (TPH) content and COC soil concentration. The model was applied to two field sites with gasoline-contaminated ground water. Confidence limits of predicted indoor air concentrations spanned approximately two orders of magnitude considering uncertainty in model parameters. Measured contaminant concentrations in indoor air were within model-predicted confidence limits.  相似文献   

17.
Robert L. Wilby 《水文研究》2005,19(16):3201-3219
Despite their acknowledged limitations, lumped conceptual models continue to be used widely for climate‐change impact assessments. Therefore, it is important to understand the relative magnitude of uncertainties in water resource projections arising from the choice of model calibration period, model structure, and non‐uniqueness of model parameter sets. In addition, external sources of uncertainty linked to choice of emission scenario, climate model ensemble member, downscaling technique(s), and so on, should be acknowledged. To this end, the CATCHMOD conceptual water balance model was used to project changes in daily flows for the River Thames at Kingston using parameter sets derived from different subsets of training data, including the full record. Monte Carlo sampling was also used to explore parameter stability and identifiability in the context of historic climate variability. Parameters reflecting rainfall acceptance at the soil surface in simpler model structures were found to be highly sensitive to the training period, implying that climatic variability does lead to variability in the hydrologic behaviour of the Thames basin. Non‐uniqueness of parameters for more complex model structures results in relatively small variations in projected annual mean flow quantiles for different training periods compared with the choice of emission scenario. However, this was not the case for subannual flow statistics, where uncertainty in flow changes due to equifinality was higher in winter than summer, and comparable in magnitude to the uncertainty of the emission scenario. Therefore, it is recommended that climate‐change impact assessments using conceptual water balance models should routinely undertake sensitivity analyses to quantify uncertainties due to parameter instability, identifiability and non‐uniqueness. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

18.
An inexact fuzzy-random-chance-constrained programming model (IFRCCMM) was developed for supporting regional air quality management under uncertainty. IFRCCMM was formulated through integrating interval linear programming within fuzzy-random-chance-constrained programming framework. It could deal with parameter uncertainties expressed as not only fuzzy random variables but also discrete intervals. Based on the stochastic and fuzzy chance-constrained programming algorithms, IFRCCMM was solved when constraints was satisfied under different satisfaction and violation levels of constraints, leading to interval solutions with different risk and cost implications. The proposed model was applied to a regional air quality management problem for demonstration. The obtained results indicated that the proposed model could effectively reflect uncertain components within air quality management system through employing multiple uncertainty-characterization techniques (in random, fuzzy and interval forms), and help decision makers analyze trade-offs between system economy and reliability. In fact, many types of solutions (i.e. conservative solutions with lower risks and optimistic solutions with higher risks) provided by IFRCCMM were suitable for local decision makers to make more applicable decision schemes according to their understanding and preference about the risk and economy. In addition, the modeling philosophy is general and applicable to many other environmental problems that may be complicated with multiple forms of uncertainties.  相似文献   

19.
Decision-making in traffic regulations is challenging with uncertainty in the environment. In this research we extend probabilistic engineering design concepts to policy decision-making for urban traffic with variability from field data. City traffic is simulated using user equilibrium and cellular automata. A cellular automata (CA) model is developed by combing existing CA models with tailored rules for local traffic behaviors in Tainan, Taiwan. Both passenger sedans and motorcycles are considered with the possibility of passing between different types of vehicles. The tailpipe emissions from all mobile sources are modeled as Gaussian dispersion with finite line sources. Speed limits of all roads are selected as independent policy design variables, resulting in a problem with 50 dimensions. We first study the impacts of a particular policy-setting on traffic behaviors and on the environment under various sources of uncertainties. The genetic algorithm, combined with probabilistic analysis, is then used to obtain the optimal regulations with the minimal cost to the environment in compliance to the current ambient air quality standards.  相似文献   

20.
Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making.  相似文献   

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