基于随机模拟的地下水铬污染健康风险动态评价

在考虑污染场地水文地质参数的空间变异性以及浓度随时间变化的基础上,构建了污染场地健康风险动态评价模型。通过灵敏度分析选取空间变异对数值模拟影响较大的随机参数,运用地下水模拟系统(Groundwater Modeling System,GMS)中的蒙特卡罗随机模拟得到敏感点污染物任意时刻的概率浓度分布特征,进而选取50%和95%分位点分别代表正常情况和最不利情况,预测敏感点健康风险历时变化特征。以石家庄市藁城区某铬化工污染场地为案例进行分析,结果表明,敏感点初始时健康风险值为0.81,在可以接受的风险范围内,污染羽运移1a后,在正常情况和最不利情况下,风险值分别为1.28、1.52,均大于1,即存在非致癌风险,随后逐渐增大,正常情况下,直到第13年才达到风险可接受水平,说明敏感点未来一段时间都存在较大的健康风险,因此需要对污染场地地下水进行修复。     

A comparison of various uncertainty models: An example of subsurface contaminant transport

Groundwater resources are under increasing threat of contamination and wasteful use in many parts of the world. Groundwater flow and integrated contaminant transport models are commonly used to predict the fate of contaminants in the subsurface environment. However, the lack of reliable data and complexity of the natural environmental systems, the predictions are subjected to large uncertainties. For reliable decision-making, these contaminant transport models are required to explicitly consider associated uncertainties in their parameters. This paper aims to compare the results of four common uncertainty models using an example of contaminant transport in groundwater. The research employed an advection–dispersion equation (ADE) to describe the transport of a contaminant in groundwater. For simplicity, two parameters – dispersion coefficient and velocity – were considered in the uncertainty analysis. Fuzzy set theory, one- and two-dimensional (1-D and 2-D) Monte Carlo simulations, and Probability Box (P-Box) methods were investigated. The cumulative distribution functions generated from these analyses were compared to evaluate the capabilities of these methods. The comparison showed that P-Box method provides a more comprehensive analysis with lesser assumptions as compared to other methods, and also found to be more pragmatic way to describe and propagate uncertainties in complex environmental systems. Furthermore, execution time required to perform uncertainty analysis using P-Box method is comparatively much less than 2-D Monte Carlo simulations.     

Identifying Optimal Water Resources Allocation Strategies through an Interactive Multi-Stage Stochastic Fuzzy Programming Approach

In this study, an interactive multi-stage stochastic fuzzy programming (IMSFP) approach has been developed through incorporating an interactive fuzzy resolution (IFR) method within an inexact multi-stage stochastic programming framework. IMSFP can deal with dual uncertainties expressed as fuzzy boundary intervals that exist in the objective function and the left- and right-hand sides of constraints. Moreover, IMSFP is capable of reflecting dynamics of uncertainties and the related decision processes through constructing a set of representative scenarios within a multi-stage context. A management problem in terms of water resources allocation has been studied to illustrate applicability of the proposed approach. The results indicate that a set of solutions under different feasibility degrees (i.e., risk of constraint violation) has been generated for planning the water resources allocation. They can not only help quantify the relationship between the objective-function value and the risk of violating the constraints, but also enable decision makers (DMs) to identify, in an interactive way, a desired compromise between two factors in conflict: satisfaction degree of the goal and feasibility degree of constraints. Besides, a number of decision alternatives have been generated under different policies for water resources management, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic penalties when the promised water-allocation targets are violated, and thus help DMs to identify desired water-allocation schemes under uncertainty.     

IFTSQP: An Inexact Optimization Model for Water Resources Management under Uncertainty

Abstract

An interval-fuzzy two-stage quadratic programming (IFTSQP) method is developed for water resources management under uncertainty. The methodincorporates techniques of interval-parameter programming, two-stage stochastic programming, and fuzzy quadratic programming within a general optimization framework to tackle multiple uncertainties presented as intervals, fuzzy sets and probability distributions. In the model formulation, multiple control variables are adopted to handle independent uncertainties in the model's right-hand sides; fuzzy quadratic terms are used in the objective function to minimize the variation in satisfaction degrees among the constraints. Moreover, the method can support the analysis of policy scenarios that are associated with economic penalties when the promised targets are violated. The developed method is then applied to a case study of water resources management planning. The results indicate that reasonable solutions have been obtained. They can help provide bases for identifying desired water-allocation plans with a maximized system benefit and a minimized constraint-violation risk.     

Experimental design of diffusion and desorption of contaminant in heterogeneous media

Storage of contaminants in low permeability media (LPM) presents a great challenge for prediction of remediation effectiveness and efficiency. The reason lies in the contaminants' complex behaviors within heterogeneous media. Both interparticle and intraparticle diffusion contribute to the difficulty of precise site assessment. Sorption of contaminants--especially within LPM--may sequester the contaminants from active treatment, while desorption over a long period of time leads to contaminant release from storage and consequent re-contamination. Research has been conducted toward better understanding of contaminant diffusion and sorption/desorption processes to better predict contaminant response to site treatment. However, most of the research has been carried out within homogeneous media, while real scenarios in environmental problems feature media whose permeability and other characteristics vary significantly over the treatment volume. Further, few efforts have combined the interparticle/intraparticle diffusion and sorption/desorption processes together. This research aims at a feasible experimental design of diffusion and desorption of contaminant in heterogeneous media to address the gaps in previous research. A 2-D experimental system was designed to evaluate interparticle/intraparticle diffusion processes of trichloroethylene (TCE) in heterogeneous media. The 2-D system was modified to include organic matter in media for simulation of sorption/desorption processes. Results of the research will improve the understanding of how these different transport processes act together within heterogeneous media. Results will also allow for the evaluation of the impact of contaminant mass transport from within low permeability media at a potential treatment site and can support the development of mathematical tools/models combining interparticle/intraparticle and sorption/desorption processes. Such a model will promote more accurate site assessment and provide more confidence in the choice of an effective, economically optimized remediation strategy.     

参数随机模拟在地下水溶质运移数值模拟中的应用

从水文地质参数的随机性出发,以我国南方某大型铀尾矿库为例,采用GMS软件(地下水模拟系统)建立了参数随机模拟的数值模型,分析了渗透系数的空间变异性对地下水溶质运移的影响.结果表明:渗透系数的空间变异性对地下水溶质运移结果有较大的影响.在其它条件都相同的情况下,随着渗透系数标准差的增大,污染物运移的范围扩大,污染物浓度标准差等值线图变得越来越不规则,污染物质心位置偏离面源中心.     

防洪预报调度方式综合特性风险率计算方法研究

水库防洪预报调度中的多种不确定性因素是风险产生的根本原因。分析水库防洪预报调度中不确定性因素的随机性、模糊性及灰色性,并在已有的防洪预报调度方式随机风险率计算方法的基础上,提出了随机-模糊、随机-灰色及综合特性风险率的计算方法,研究了水库实施防洪预报调度方式相对于常规调度方式水库风险率的变化。桓仁水库的应用研究表明,防洪预报调度方式综合特性风险率计算方法是可行的,相对于常规调度方式并未增加水库的防洪风险。     

水环境风险评价的随机模拟与三角模糊数耦合模型

探讨了将三角模糊数理论用于描述和处理水环境风险评价系统中的随机性、模糊性,以及资料信息的不完整、不精确性等不确定性特征的具体实现途径。采用Monte Carlo方法模拟三角模糊数,把三角模糊数及其函数之间的运算转化为普通的实数之间的运算,建立了水环境风险评价的随机模拟与三角模糊数耦合模型(SS-TFN)。应用结果表明:SS-TFN通过Monte Carlo方法可得到水环境系统的质量状态变量的大量模拟序列,其计算结果比三角模糊数的截集技术的计算结果更简便、全面和合理;用SS-TFN通过调整水环境系统的质量状态变量的控制参数,可方便地获得水环境风险管理的对策方案。     

基于特定污染场地的蒸汽入侵健康风险评估案例

对某汽车零部件厂搬迁后的场地环境调查结果显示, 该场地浅层地下水中存在氯乙烷、 1, 1 2二氯乙烯和 1, 12 二氯乙烷三种氯代烃污染, 且可能存在通过挥发进入室内对人体造成健康危害的风险。该场地浅层地下水平均水 位 0182 m, 地下室埋深3166 m, 地下水水位在地下室底板以上, 现有导则中基于地下室在地下水水位以上的蒸汽入 侵模型不适用于该场地。构建了适用于实际场地的地下室渗水蒸汽入侵模型, 并利用该模型和现有蒸汽入侵模型 分别进行了致癌健康风险评价, 完成了地下水污染物风险控制值计算。对比不同模型计算结果发现, 现有模型计算 所得的健康风险水平均比地下室渗水模型小 250 倍以上, 远远低估了健康风险。     

Simulation of a subsurface vertical flow constructed wetland for CSO treatment.

Constructed wetlands (CWs) have proved to be a highly effective measure to reduce the ecological impact of combined sewer overflows (CSOs) on receiving waters. Due to the stochastic nature of the loading regime and the multitude of environmental influences, assessment of the performance of such plants requires detailed mathematical modelling. A multi-component reactive transport module (CW2D) was applied to simulate the flow, transport and degradation processes occurring in a CW for CSO treatment. CW2D was originally developed to simulate the treatment of municipal wastewater in subsurface flow CWs. Loading and operational conditions in CSO treatment differ fundamentally from the conditions occurring for wastewater treatment. Despite these differences, first results from the simulation of lab-scale experiments show, that the model is generally applicable to this type of plant. Modelling of adsorption, degradation processes, and influent fractionation, however, require further research.     

Streamlining contaminated sediment management: The use of advanced sediment characterization — preliminary results

Simply knowing the geographic extent of sediment contamination within a bay or estuary does not provide sufficient insight to help a site manager or stakeholder begin to assess how to manage sediments deemed either an ecological risk or regulatory violation. A better approach is to assess contaminant distribution and behavior at a micro-scale in parallel with macro-scale site assessments. In such an approach, a site investigation includes an assessment of contaminant/sediment biogeochemistry, as it will impact bioavailability, risk, and potential management options. This paper reviews some approaches for assessing contaminant bioavailability, distribution and degradability, and presents preliminary results from some biogeochemical sediment assays. Some of the potential uses and limitations of such assays are discussed. Selective leaches, using alcohol or digestive juices to determine bioavailable contaminant fractions still require some validation. Assessments of contaminant distribution as a function of grain size can lend insight into both contaminant mobility and management strategies. Biodegradation studies in real sediments are complicated by analytical and heterogeneity difficulties, but can, if carried out carefully, provide useful information.     

COMPOSITE MODELING APPROACH AND EXPERIMENTAL STUDY FOR SUBSURFACE TRANSPORT OF CONTAMINANTS FROM LAND-DISPOSAL SITES

1 .　INTRODUCTIONThecontaminationofsoiland groundwatersystemsandtheadverseeffectsofthecontamina tionoftheseresourceshavebecomeamatterofconsiderableconcern .Thetransportofcontami nantsinsoilandgroundwaterisacompositionofcomplexchemicalandhydraulicphenomena .Dur ingthepastdecadesmuchattentionhasbeengiventothetheoryandmodelingofcontaminanttrans portinsaturatedsoil (Davidetal.1996 ;Bouchard1988;Brusseauetal .1989) .Thisdevelopmenthasbeenacceleratedandincreasedduetothedifficultyandhighcostsof…     

大坝防洪安全的评估和校核

洪水漫坝风险失事，是影响大坝安全的主要原因之一。本文寻求一种既能反映大坝防洪系统随机性和模糊性，又能合理描述大坝防洪能力的风险模型，以实现对大坝防洪能力的定量化，进而为已建大坝和待建大坝的防洪安全评估和校核创造条件。     

三角模糊数随机模拟的防洪工程联系数风险评价模型

针对受多种不确定性因素综合作用的流域防洪工程体系风险评价问题，提出用防洪工程体系风险评价指标样本与评价标准等级之间的多元联系数定量描述样本与标准等级之间隶属关系的层次性和模糊性，用三角模糊数和多元联系数理论相结合的方法处理风险评价中以区间数形式表示的评价等级标准的模糊性，用三角模糊数定量表示联系数的差异度系数的连续变化过程和各指标权重的不确定性，再通过随机模拟三角模糊数的方法建立了三角模糊数随机模拟的防洪工程联系数风险评价模型（CN-SSTFN）。研究结果表明：CN-SSTFN综合利用了流域防洪工程体系风险评价过程的各种主客观不确定性评价信息，以置信区间形式表示的评价结果比现有方法提供了评价结果可靠性方面的更多重要信息，能反映受多种不确定性因素综合影响的流域防洪工程体系风险评价的客观实际情况；CN-SSTFN的计算方法简便、通用，在各种系统风险等级评价问题中具有推广应用价值，有利于促进集对分析和可变模糊集理论的进一步发展。     

Planning of Groundwater Supply Systems Subject to Uncertainty Using Stochastic Flow Reduced Models and Multi-Objective Evolutionary Optimization

The typical modeling approach to groundwater management relies on the combination of optimization algorithms and subsurface simulation models. In the case of groundwater supply systems, the management problem may be structured into an optimization problem to identify the pumping scheme that minimizes the total cost of the system while complying with a series of technical, economical, and hydrological constraints. Since lack of data on the subsurface system most often reflects upon the development of groundwater flow models that are inherently uncertain, the solution to the groundwater management problem should explicitly consider the tradeoff between cost optimality and the risk of not meeting the management constraints. This work addresses parameter uncertainty following a stochastic simulation (or Monte Carlo) approach, in which a sufficiently large ensemble of parameter scenarios is used to determine representative values selected from the statistical distribution of the management objectives, that is, minimizing cost while minimizing risk. In particular, the cost of the system is estimated as the expected value of the cost distribution sampled through stochastic simulation, while the risk of not meeting the management constraints is quantified as the expected value of the intensity of constraint violation. The solution to the multi-objective optimization problem is addressed by combining a multi-objective evolutionary algorithm with a stochastic model simulating groundwater flow in confined aquifers. Evolutionary algorithms are particularly appropriate in optimization problems characterized by non-linear and discontinuous objective functions and constraints, although they are also computationally demanding and require intensive analyses to tune input parameters that guarantee optimality to the solutions. In order to drastically reduce the otherwise overwhelming computational cost, a novel stochastic flow reduced model is thus developed, which practically allows for averting the direct inclusion of the full simulation model in the optimization loop. The computational efficiency of the proposed framework is such that it can be applied to problems characterized by large numbers of decision variables.     

New Approach: Integrated Risk-Stochastic Dynamic Model for Dam and Reservoir Optimization

Multiple studies have developed management models to identify optimal operating policies for reservoirs in the last four decades. In an uncertain environment, in which climatic factors such as stream flow are stochastic, the economic returns from reservoir releases that are based on policy are uncertain. Furthermore, the consequences of reservoir release are not fully realized until it occurs. Rather than explicitly recognizing the full spectrum of consequences that are possible within an uncertain environment, the existing optimization models have focused on addressing these uncertainties by identifying the release policies that optimize the summative metric of the risks that are associated with release decisions. This technique has limitations for representing risks that are associated with release policy decisions. In fact, the approach of these techniques may conflict with the actual attitudes of the decision-makers regarding the risk aspects of release policies. The risk aspects of these decisions affect the design and operation of multi-purpose reservoirs. A method is needed to completely represent and evaluate potential consequences that are associated with release decisions. In this study, these techniques were reviewed from the stochastic model and risk analysis perspectives. Therefore, previously developed optimization models for operating dams and reservoirs were reviewed based on their advantages and disadvantages. Specifically, optimal release decisions that use the stochastic variable impacts and the levels of risk that are associated with decisions were evaluated regarding model performance. In addition, a new approach was introduced to develop an optimization model that is capable of replicating the manner in which reservoir release decision risks are perceived and interpreted. This model is based on the Neural Network (NN) theory and enables a more complete representation of the risk function that occurs from particular reservoir release decisions.     

水库洪水调度方式模糊风险分析

为了计算水库洪水调度方式综合风险,建立水库洪水调度方式的模糊风险模型。该模型通过水平截集三角模糊数,以风险识别指标值的区间变化表示多种风险源对调度方式风险的影响。结合随机模拟蒙特卡洛方法,计算出水库洪水调度方式模糊风险的区间值。实例分析表明,模糊风险能够表示不同程度的风险,较确定的风险指标值更符合实际,可为决策者提供更多的决策信息。     

Risk-Based Decision Analysis in the Design of Water Supply Projects

The application of a decision analysis methodology to a small-scale water-supply/aquifer-contamination problem is presented. The main characteristic of the study is the development of a series of alternative strategies to ensure a continuous water supply to a village under variable risk conditions. It is assumed that the risks involved in the analysis reflect the uncertainty as to the hydraulic conductivity. Thus, a stochastic simulation model for groundwater flow and contaminant transport is employed in order to evaluate the implications of uncertainty in the system's behaviour upon the overall design decisions. Alternative decision strategies are formulated by considering both actual and probabilistic costs, and they are finally compared using a risk–cost–benefit objective function.     

Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties

Global change in climate and consequent large impacts on regional hydrologic systems have, in recent years, motivated significant research efforts in water resources modeling under climate change. In an integrated future hydrologic scenario, it is likely that water availability and demands will change significantly due to modifications in hydro-climatic variables such as rainfall, reservoir inflows, temperature, net radiation, wind speed and humidity. An integrated regional water resources management model should capture the likely impacts of climate change on water demands and water availability along with uncertainties associated with climate change impacts and with management goals and objectives under non-stationary conditions. Uncertainties in an integrated regional water resources management model, accumulating from various stages of decision making include climate model and scenario uncertainty in the hydro-climatic impact assessment, uncertainty due to conflicting interests of the water users and uncertainty due to inherent variability of the reservoir inflows. This paper presents an integrated regional water resources management modeling approach considering uncertainties at various stages of decision making by an integration of a hydro-climatic variable projection model, a water demand quantification model, a water quantity management model and a water quality control model. Modeling tools of canonical correlation analysis, stochastic dynamic programming and fuzzy optimization are used in an integrated framework, in the approach presented here. The proposed modeling approach is demonstrated with the case study of the Bhadra Reservoir system in Karnataka, India.     

Meshless Local Strong Form-based Method for Transport Simulation of Nonlinearly Adsorbing Solutes in a Highly Heterogeneous Confined Aquifer

A meshless simulation model based on the radial point collocation method (RPCM) is developed in this study to simulate contaminant transport in heterogeneous aquifers with nonlinear adsorption. The nonlinear adsorption is represented using popularly used Langmuir’s isotherm. The nonlinear governing equation of the contaminant transport is solved utilizing linearization through timesteps. The directional derivatives of hydraulic conductivity field through vectorization are used to simulate flow in highly heterogeneous aquifers. Multiquadrics radial basis functions (MQ-RBF) are used for the approximation of heads and concentrations and their gradients. The relevant equations are solved locally using the domain decomposition technique, which avoids the ill-conditioning issue associated with globally supported domains. The developed models are named RPCM-NA (for only transport) and RPCM-CNA (for coupled flow and transport). The RPCM-NA model is validated against semi-analytical solutions, and its advantages over the Finite Difference Method (FDM) are demonstrated. Two case studies are considered. The performance of the RPCM-CNA model is compared to that of FDM-based MODFLOW and MT3DMS models, with the maximum percentage error for head estimation being 0.25%. For contaminant transport simulation in the mobile and adsorbed phases, the maximum values of Normalized Mean Absolute Error obtained are 0.02 and 0.0385 respectively, showing the effectiveness of the proposed model.