Efficient Identification of Unknown Groundwater Pollution Sources Using Linked Simulation-Optimization Incorporating Monitoring Location Impact Factor and Frequency Factor

This study aims to improve the accuracy of groundwater pollution source identification using concentration measurements from a heuristically designed optimal monitoring network. The designed network is constrained by the maximum number of permissible monitoring locations. The designed monitoring network improves the results of source identification by choosing monitoring locations that reduces the possibility of missing a pollution source, at the same time decreasing the degree of non uniqueness in the set of possible aquifer responses to subjected geo-chemical stresses. The proposed methodology combines the capability of Genetic Programming (GP), and linked simulation-optimization for recreating the flux history of the unknown conservative pollutant sources with limited number of spatiotemporal pollution concentration measurements. The GP models are trained using large number of simulated realizations of the pollutant plumes for varying input flux scenarios. A selected subset of GP models are used to compute the impact factor and frequency factor of pollutant source fluxes, at candidate monitoring locations, which in turn is used to find the best monitoring locations. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show the efficiency in source identification when concentration measurements from the designed monitoring network are utilized.     

Dynamic Optimal Monitoring Network Design for Transient Transport of Pollutants in Groundwater Aquifers

Optimal groundwater pollution monitoring network design models are developed to prescribe optimal and efficient sampling locations for detecting pollution in groundwater aquifers. The developed methodology incorporates a two dimensional flow and transport simulation model to simulate the pollutant concentrations in the study area. Different realizations of the pollutant plume are randomly generated by incorporating the uncertainty in both source and aquifer parameters. These concentration realizations are incorporated in the optimal monitoring network design models. Two different objectives are considered separately. The first objective function minimizes the summation of unmonitored concentrations at different potential monitoring locations. This objective function in effect minimizes the probability of not monitoring the pollutant concentrations at those locations where the probable concentration value is large. Although this probability is not explicitly incorporated in the model, a surrogate form of this objective is included as the objective function. The second objective function considered is the minimization of estimation variances of pollutant concentrations at various unmonitored locations. This objective results in a design that chooses optimal monitoring locations where the uncertainties in simulated concentrations are large. The developed optimization models are solved using Genetic Algorithm. The variances of estimated concentrations at potential monitoring locations are computed using the geostatistical tool, kriging. The designed monitoring network is dynamic in nature, as it provides time varying network designs for different management periods, to account for the transient pollutant plumes. Such a design can eliminate temporal redundancy and is therefore, economically more efficient. The optimal design incorporates budgetary constraints in the form of limits on the number of monitoring wells installed in any particular management period. The solution results are evaluated for an illustrative study area comprising of a hypothetical aquifer. The performance evaluation results establish the potential applicability of the proposed methodology for optimal design of the dynamic monitoring network for detection and monitoring of pollutant plumes in contaminated aquifers.     

Artificial neural network modeling for identification of unknown pollution sources in groundwater with partially missing concentration observation data

Groundwater pollution sources are characterized by spatially and temporally varying source locations, injection rates, and duration of activity. Concentration measurement data at specified observation locations are generally utilized to identify these sources characteristics. Identification of unknown groundwater pollution sources in terms of these source characteristics becomes more difficult in the absence of complete breakthrough curves of concentration history at all the time steps. If concentration observations are missing over a length of time after an unknown source has become active, it is even more difficult to correctly identify the unknown sources. An artificial neural network (ANN) based methodology is developed to identify these source characteristics for such a missing data scenario, when concentration measurement data over an initial length of time is not available. The source characteristics and the corresponding concentration measurements at time steps for which it is not missing, constitute a pattern for training the ANN. A groundwater flow and transport numerical simulation model is utilized to generate the necessary patterns for training the ANN. Performance evaluation results show that the back-propagation based ANN model is essentially capable of extracting hidden relationship between patterns of available concentration measurement values, and the corresponding sources characteristics, resulting in identification of unknown groundwater pollution sources. The performance of the methodology is also evaluated for different levels of noise (or measurement errors) in concentration measurement data at available time steps.     

Identification of Pollution Sources in Transient Groundwater Systems

A methodology using a nonlinear optimization model is presentedfor estimating unknown magnitude, location and duration ofgroundwater pollution sources under transient flow and transportconditions. The proposed optimization model incorporates thegoverning equations of flow and solute transport as binding equalityconstraints, and thus essentially simulates the physical processes oftransient flow and transient transport in the groundwater systems.The proposed inverse model identifies unknown sources of pollution byusing measured values of pollutant concentration at selectedlocations. Performance of the proposed model for the identificationof unknown groundwater pollution sources is evaluated for anillustrative study area in a hypothetical confined aquifer underdifferent cases of data availability. The effect of observation welllocation vis-à-vis pollution source location on identificationaccuracy is also investigated. Performance of the developedidentification model is also evaluated for a condition whenconcentration measurements are missing during few initial timeperiods after the pollution sources become active. The effect ofspecified initial guesses of the variable values on the optimalsolutions are also investigated. These performance evaluation resultsdemonstrate the limitations and potential applicability of theproposed optimization model for identifying the sources of pollutionin transient groundwater systems.     

跨国界流域重金属污染溯源体系框架初步构建

通过调研国内外水环境污染溯源的主要研究方法,结合我国跨国界流域重金属污染和监管的现状,提出构建以空间溯源为主线、行业溯源和成分溯源作为重要补充的跨国界流域重金属污染溯源技术体系,综合运用基于最优搜索理论的优化监测排查法、主成分分析和因子分析法,对跨国界流域内的重金属污染源的空间位置、行业类型及工艺环节等多个方面进行分析定位,旨在为我跨国界流域污染控制和外事协调管理提供重要依据。     

Linked Simulation-Optimization based Dedicated Monitoring Network Design for Unknown Pollutant Source Identification using Dynamic Time Warping Distance

Implementation of monitoring strategy for increasing the efficiency of groundwater pollutant source characterization is often necessary, especially when only inadequate and arbitrary concentration measurement data are initially available. Two main parameters that need to be estimated for efficient and accurate characterization of groundwater pollution sources are: location of the source and the time when the source became active. Complexities involved with the explicit estimation of the time of start and source activity have not been addressed so far in previous studies. The main complexity arises due to the fact that the spatial location and time of activity are inter-related. Therefore, specifying one and solving for the other simplifies the source characterization problem. Hence, in this study, both the source location and time of initiation are treated as unknowns. The developed methodology uses dynamic time warping distance in the linked simulation-optimization model to address some complex issues in designing a monitoring network to efficiently estimate source characteristics including the time of first activity of unknown groundwater source. Performance of the developed methodology is evaluated on illustrative contaminated aquifer. These evaluation results demonstrate the potential use of the developed methodology.     

Multiobjective Design of Groundwater Monitoring Network Under Epistemic Uncertainty

A methodology is proposed for optimal design of groundwater quality monitoring networks under epistemic uncertainty. The proposed methodology considers spatiotemporal pollutant concentrations as fuzzy numbers. It incorporates fuzzy ordinary kriging (FOK) within the decision model formulation for spatial estimation of contaminant concentration values. A multiobjective monitoring network design model incorporating the objectives of fuzzy mass estimation error and spatial coverage of the designed network is developed. Nondominated Sorting Genetic Algorithm-II (NSGA-II) is used for solving the monitoring network design model. Performances of the proposed model are evaluated for hypothetical illustrative system. Evaluation results indicate that the proposed methodology perform satisfactorily under uncertain system conditions. These performance evaluation results demonstrate the potential applicability of the proposed methodology for optimal groundwater contaminant monitoring network design under epistemic uncertainty.     

Application of Information Theory to Groundwater Quality Monitoring Networks

Using the criteria of maximizing information and minimizing cost,a methodology is developed for design of an optimal groundwater-monitoring network for water resources management. A monitoring system is essentially an information collection system. Therefore, its technical design requires a quantifiablemeasure of information which can be achieved through applicationof the information (or entropy) theory. The theory also providesinformation-based statistical measures to evaluate the efficiencyof the monitoring network. The methodology is applied to groundwater monitoring wells in a portion of Gaza Strip in Palestine.     

基于和声搜索算法的地下水污染源与未知含水层参数的同步反演研究

地下水污染源反演问题和含水层参数反演问题都是典型的地下水逆问题。在未知含水层参数(渗透系数、弥散度等)等先决信息的情况下进行地下水污染源反演计算时,需要根据已有的监测数据(水位和浓度等)对地下水污染源和未知含水层参数进行同步反演。在同步反演优化问题中,决策变量包括污染源位置、强度以及待求的含水层参数。论文首先介绍同步反演模型的框架组成(包括污染物迁移模型和反演优化模型),然后在对已有的各种和声搜索改进算法进行研究的基础上结合同步反演模型提出一种改进的和声搜索算法,最后将同步反演模型和改进的和声搜索算法应用于具体的算例研究。研究表明,改进的和声搜索算法具有算法稳定高效、求解精度高等特点,能够广泛应用于复杂的地下水污染源和含水层参数反演问题。     

基于流域要素空间关系的水污染溯源研究

为快速准确地实现水质监测断面水质超标时的河流水污染溯源,提出整合现有流域要素,构建水污染溯源的流域要素空间关系模型。该模型以划分为4个等级的河段和汇水单元为基础,建立汇水单元层级与上下游河段的编码关系;再将全流域要素与汇水单元建立空间关联关系,构建5个流域要素空间关系子系统模型;最后各子系统模型集聚组成一个相互协作的流域要素空间关系网络。以敖江流域实时水质监测断面为污染溯源触发点,将各个流域要素空间关系子系统模型直接应用于水污染溯源过程,结果表明,建立的关系模型在实际应用中能最大限度地提供污染源空间分布和结构信息,有效地识别和筛选研究区域的水污染引发源,快速缩小未知污染源搜查范围,提高了水污染溯源搜寻过程的效率和精度。