Using multi-criteria decision analysis to assess the vulnerability of drinking water utilities

Outbreaks of microbiological waterborne disease have increased governmental concern regarding the importance of drinking water safety. Considering the multi-barrier approach to safe drinking water may improve management decisions to reduce contamination risks. However, the application of this approach must consider numerous and diverse kinds of information simultaneously. This makes it difficult for authorities to apply the approach to decision making. For this reason, multi-criteria decision analysis can be helpful in applying the multi-barrier approach to vulnerability assessment. The goal of this study is to propose an approach based on a multi-criteria analysis method in order to rank drinking water systems (DWUs) based on their vulnerability to microbiological contamination. This approach is illustrated with an application carried out on 28 DWUs supplied by groundwater in the Province of Québec, Canada. The multi-criteria analysis method chosen is measuring attractiveness by a categorical based evaluation technique methodology allowing the assessment of a microbiological vulnerability indicator (MVI) for each DWU. Results are presented on a scale ranking DWUs from less vulnerable to most vulnerable to contamination. MVI results are tested using a sensitivity analysis on barrier weights and they are also compared with historical data on contamination at the utilities. The investigation demonstrates that MVI provides a good representation of the vulnerability of DWUs to microbiological contamination.     

A fuzzy multi-stakeholder multi-criteria methodology for water allocation and reuse in metropolitan areas

In this paper, a fuzzy decision making methodology is proposed to find a socially optimal scenario for allocating effluent of wastewater treatment plants and urban and suburban runoffs to agricultural regions and recharging aquifers. The presented methodology named modified fuzzy social choice (MFSC) considers multi-stakeholder multi-criteria problems under uncertainties inherent in a decision making process utilizing a fuzzy ranking method and the fuzzy social choice (FSC) theory. A set of water and wastewater allocation scenarios are proposed for water quantity and quality management of the study area, while six main stakeholders with conflicting utilities and different negotiation powers are involved. The proposed methodology is applied to Tehran metropolitan area, the capital city of Iran with the population of about 8 million people, to examine its applicability and effectiveness. The results shows that using fuzzy multi-stakeholder multi-criteria decision making method considering equal and different negotiation powers can lead to different outcomes. Based on the results, the MFSC method, which considers a number of decision makers having different negotiation powers, degrees of importance of decision making criteria, and some important uncertainties, performs more promising in real water resources management problems.     

A stochastic conflict resolution model for trading pollutant discharge permits in river systems

This paper presents an efficient methodology for developing pollutant discharge permit trading in river systems considering the conflict of interests of involving decision-makers and the stakeholders. In this methodology, a trade-off curve between objectives is developed using a powerful and recently developed multi-objective genetic algorithm technique known as the Nondominated Sorting Genetic Algorithm-II (NSGA-II). The best non-dominated solution on the trade-off curve is defined using the Young conflict resolution theory, which considers the utility functions of decision makers and stakeholders of the system. These utility functions are related to the total treatment cost and a fuzzy risk of violating the water quality standards. The fuzzy risk is evaluated using the Monte Carlo analysis. Finally, an optimization model provides the trading discharge permit policies. The practical utility of the proposed methodology in decision-making is illustrated through a realistic example of the Zarjub River in the northern part of Iran.     

Screening and prioritisation of chemical risks from metal mining operations, identifying exposure media of concern

Metals have been central to the development of human civilisation from the Bronze Age to modern times, although in the past, metal mining and smelting have been the cause of serious environmental pollution with the potential to harm human health. Despite problems from artisanal mining in some developing countries, modern mining to Western standards now uses the best available mining technology combined with environmental monitoring, mitigation and remediation measures to limit emissions to the environment. This paper develops risk screening and prioritisation methods previously used for contaminated land on military and civilian sites and engineering systems for the analysis and prioritisation of chemical risks from modern metal mining operations. It uses hierarchical holographic modelling and multi-criteria decision making to analyse and prioritise the risks from potentially hazardous inorganic chemical substances released by mining operations. A case study of an active platinum group metals mine in South Africa is used to demonstrate the potential of the method. This risk-based methodology for identifying, filtering and ranking mining-related environmental and human health risks can be used to identify exposure media of greatest concern to inform risk management. It also provides a practical decision-making tool for mine acquisition and helps to communicate risk to all members of mining operation teams.     

Sensitive Parameters In Predicting Exposure Contaminants Concentration In A Risk Assessment Process

A sensitivity analysis (SA) was conducted on the analytical models considered in the risk-based corrective-action (RBCA) methodology of risk analysis, as developed by the American Society for Testing of Materials (ASTM), to predict a contaminant‘s concentration in the affected medium at the point of human exposure. These models are of interest because evaluations regarding the best approach to contaminated site remediation are shifting toward increased use of risk-based decision, and the ASTM RBCA methodology represents the most effective and internationally widely used standardized guide for risk assessment process. This paper identifies key physical and chemical parameters that need additional precision and accuracy consideration in order to reduce uncertainty in models prediction, thereby saving time, money and engineering effort in the data collection process. SA was performed applying a variance-based method to organic contaminants migration models with reference to soil-to-groundwater leaching ingestion exposure scenario. Results indicate that model output strongly depends on the organic-carbon partition coefficient, organic-carbon content, net infiltration, Darcy velocity, source-receptor distance, and first-order decay constant.     

A GIS-based approach for the long-term prediction of human health risks at contaminated sites

A Health Index/Risk Evaluation Tool (HIRET) has been developed for the integration of risk assessment and spatial planning using GIS capabilities. The method is meant to assist decision makers and site owners in the evaluation of potential human health risk with respect to land use. Human health risk defined as the potential adverse effects on human life or health is generally accepted as the most important aspect for site assessment and planning of remediation strategies. It concerns polluted sites that endanger human health on one hand and derelict land that does not cause the immediate risk on the other hand. In current state-of-the-art risk-assessment, long-term spatial and temporal changes of risks, in relation to changes in contamination patterns and land use functions, are not taken into account. The aim of this paper is to demonstrate the methodology developed for human health risk assessment in aspect of spatial and temporal domain. HIRET was developed as an extension for ESRI software ArcView 3.2 and allows performing dynamic human health risk assessment in long-term period, which is relevant for land use planning. The paper illustrates how such methodology can assist in environmental decision-making to enhance the efficiency of contaminated land management. A case study of contaminated site is given showing how data can be used within a GIS framework to produce maps indicating areas of potential human health risk.     

Selection of suitable alternatives to reduce the environmental impact of road traffic noise using a fuzzy multi-criteria decision model

Road traffic noise is one of the most significant environmental impacts generated by transport systems. To this regard, the recent implementation of the European Environmental Noise Directive by Public Administrations of the European Union member countries has led to various noise action plans (NAPs) for reducing the noise exposure of EU inhabitants. Every country or administration is responsible for applying criteria based on their own experience or expert knowledge, but there is no regulated process for the prioritization of technical measures within these plans. This paper proposes a multi-criteria decision methodology for the selection of suitable alternatives against traffic noise in each of the road stretches included in the NAPs. The methodology first defines the main criteria and alternatives to be considered. Secondly, it determines the relative weights for the criteria and sub-criteria using the fuzzy extended analytical hierarchy process as applied to the results from an expert panel, thereby allowing expert knowledge to be captured in an automated way. A final step comprises the use of discrete multi-criteria analysis methods such as weighted sum, ELECTRE and TOPSIS, to rank the alternatives by suitability. To illustrate an application of the proposed methodology, this paper describes its implementation in a complex real case study: the selection of optimal technical solutions against traffic noise in the top priority road stretch included in the revision of the NAP of the regional road network in the province of Almeria (Spain).     

风险排序及其在环境风险管控中的应用

从风险排序的概念及应用需求出发,系统阐述了风险排序在当前风险管理特别是环境风险管控领域中的意义。指出风险排序是对风险定义在管理实践层面的应用深化。从风险排序影响因子的类别划分、来源构成、演化趋势以及排序方法的发展规律和优劣特征等角度进行系统综述,指出目前风险排序影响因子的界定及选取已呈现由风险理论限定过渡至突出服务风险管理为导向;风险排序方法学也逐步由单一方法排序过渡至多元方法互补排序的新阶段,逐步实现对风险排序结果的综合考量和准确度量。在此基础上,对风险排序方法在环境领域的实证研究进行归纳分析。总结指出了当前风险排序研究多以结果的量化表征为焦点,而其排序方法及配套排序指标选取的适用性、综合性及代表性检验仍需进一步研究分析,尤其是对排序机理、风险理论及风险管控需求间内在相互关系的系统解析将是未来实现风险排序客观全面提升的重要基础及意义所在。     

Optimal water and waste-load allocations in rivers using a fuzzy transformation technique: a case study

In this paper, a new methodology is developed for integrated allocation of water and waste-loads in river basins utilizing a fuzzy transformation method (FTM). The fuzzy transformation method is used to incorporate the existing uncertainties in model inputs. In the proposed methodology, the FTM, as a simulation model, is utilized in an optimization framework for constructing a fuzzy water and waste-loads allocation model. In addition, economic as well as environmental impacts of water allocation to different water users are considered. For equitable water and waste load allocation, all possible coalition of water users are considered and total benefit of each coalition, which is a fuzzy number, is reallocated to water users who are participating in the coalition. The fuzzy cost savings are reallocated using a fuzzy nucleolus cooperative game and the FTM. As a case study, the Dez River system in south-west of Iran is modeled and analyzed using the methodology developed here. The results show the effectiveness of the methodology in optimal water and waste-loads allocations under uncertainty.     

Developing a framework to assess costs and benefits of Health Impact Assessment

This paper presents some early thinking as to how the costs and benefits of HIA might be assessed. After considering previous work it uses a comprehensive HIA in Dulwich, SE London as a case study to highlight the possibilities and difficulties of collecting necessary data on costs and benefits. It then sets a context for developing a cost–benefit framework for analysis. The framework is viewed alongside the major types of economic evaluation. The paper concludes with a review of outstanding issues and considers how evidence on cost and benefit might make a difference in the application of HIA.     

A risk-based screening approach for prioritizing contaminants and exposure pathways at Superfund sites

Contamination at Superfund sites can involve mixtures of chemicals and radionuclides in a variety of environmental media. Determining priorities for evaluation and remediation of various contaminants is an important part of the initial phases of any site investigation. An effective screening analysis at the beginning of the project can help by identifying both those situations in need of immediate remedial attention and those which require further sampling and evaluation. The screening approach discussed here is made up of two sets of calculations designed to provide upper- and lower-bound estimates of health risk to individuals likely to receive the highest exposures. This approach allows rapid identification of contaminants which pose a negligible risk and can be assigned a low priority for remedial attention or which pose a substantial risk and should be given the highest priority for appropriate remediation efforts. Contaminants designated as neither high- nor low-priority should be investigated in more detail prior to making decisions regarding the need for or method of remediation. The utility of this approach has already been demonstrated in the evaluation of contamination in the Clinch and Tennessee River systems originating from historical operations of atomic weapons and energy research facilities near Oak Ridge, Tennessee.     

Evaluation of a real-time monitoring system for river quality — a trade-off between risk attitudes,costs, and uncertainty

Uncertainty is definitely one of the key topics in environmental assessment and management. Typically, attempts to reduce uncertainty are subject to expenses. But how to compare and trade-off expenses and the reduced uncertainty? They only seldom allow the use of a single unit. Instead, the whole analysis and decision procedure is very subjective. This paper presents one approach to handle such problems, namely the combined use of Bayesian influence diagrams, and probabilistic risk attitude analysis. The approach was used in the evaluation of three alternatives for a real time river water quality forecasting system. A trade-off analysis of risk attitudes, costs and uncertainty indicated the levels of socioeconomic utility required for investments in the respective systems, and accordingly illuminated the impact of the uncertainties involved on inference and decision-making with various risk attitudes and discount rates.     

Fire risk evaluation using multicriteria analysis—a case study

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential “hotspots” of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing “where and when” forest fires will most likely occur.     

Assessment of trace metals pollution in estuarine sediments using SEM-AVS and ERM?CERL predictions

In this paper a PSIAC-based multi-parameter fuzzy pattern recognition (MPFPR) model is proposed and applied for classifying and ranking the potential soil erosion (PSE). In this approach, standard value matrix is used to define the membership degrees of each catchment to each class and the feature values are used for alternative ranking. The characteristic of PSE for each class is expressed by linguistic variables. The proposed method is straightforward, easy to understand, very practical, and its results may easily be interpreted. To assess the performance of the model, the results of PSIAC MPFPR and original PSIAC method are interpreted and compared with the observed data. It is shown that the proposed approach reflects the fuzzy nature of the soil erosion more efficiently and is quite robust for application in real world cases.     

Fuzzy clustering analysis in environmental impact assessment — A complement tool to environmental quality index

In spite of rapid progress achieved in the methodological research underlying environmental impact assessment (EIA), the problem of weighting various parameters has not yet been solved. This paper presents a new approach, fuzzy clustering analysis, which is illustrated with an EIA case study on Baoshan-Wusong District in Shanghai, China. Fuzzy clustering analysis may be used whenever a composite classification of environmental quality/impact incorporates multiple parameters. In such cases the technique may be used as a complement or an alternative to comprehensive assessment. In fuzzy clustering analysis, the classification is determined by a fuzzy relation. After a fuzzy similarity matrix has been established and the fuzzy relation stabilized, a dynamic clustering chart can be developed. Given a suitable threshold, the appropriate classification can be accomplished. The methodology is relatively simple and the results can be interpreted to provide valuable information to support decision making and improve management of the environment.     

IFTCIP: An Integrated Optimization Model for Environmental Management under Uncertainty

In this study, an interval-fuzzy two-stage chance-constrained integer programming (IFTCIP) method is developed for supporting environmental management under uncertainty. The IFTCIP improves upon the existing interval, fuzzy, and two-stage programming approaches by allowing uncertainties expressed as probability distributions, fuzzy sets, and discrete intervals to be directly incorporated within a general mixed integer linear programming framework. It has advantages in uncertainty reflection, policy investigation, risk assessment, and capacity-expansion analysis in comparison to the other optimization methods. Moreover, it can help examine the risk of violating system constraints and the associated consequences. The developed method is applied to the planning for facility expansion and waste-flow allocation within a municipal solid waste management system. Violations of capacity constraints are allowed under a range of significance levels, which reflects tradeoffs between the system cost and the constraint-violation risk. The results indicate that reasonable solutions for both binary and continuous variables have been generated under different risk levels. They are useful for generating desired decision alternatives with minimized system cost and constraint-violation risk under various environmental, economic, and system-reliability conditions. Generally, willingness to take a higher risk of constraint violation will guarantee a lower system cost; a strong desire to acquire a lower risk will run into a higher system cost.     

Optimized contaminated land investigation at minimum overall cost to achieve fitness-for-purpose

A methodology for optimized contaminated land investigation (OCLI) is described that balances the uncertainty of measurements against the cost of taking the measurements and the financial losses that may arise from misclassification of the land. Uncertainty from the sources of both field sampling and chemical analysis is estimated using existing techniques, based on the taking of duplicated samples. The actual costs of sampling and analysis and the expected costs that could arise from either 'false positive' or 'false negative' classification of areas of land were estimated. A loss function was constructed that calculates the expectation of financial loss that will arise for a given uncertainty of measurement. The function shows a clear minimum value of cost at an optimal value of uncertainty. Application of this OCLI technique to two case studies demonstrated this minimum value. Below the optimum value of uncertainty, the costs increased due to higher measurement costs. Above the optimum, the costs increased due to increasing risk of factors such as unnecessary remediation or potential litigation over undetected contamination. Many areas for further development of OCLI are identified, but the technique is demonstrated as a useful new approach to judging fitness-for-purpose of such measurements.     

An Inexact Fuzzy-robust Two-stage Programming Model for Managing Sulfur Dioxide Abatement Under Uncertainty

In this study, an inexact fuzzy-robust two-stage programming (IFRTSP) method is developed for tackling multiple forms of uncertainties that can be expressed as discrete intervals, probabilistic distributions and/or fuzzy membership functions. The model can reflect economic penalties of corrective measures against any infeasibilities arising due to a particular realization of system uncertainties. Moreover, the fuzzy decision space can be delimited into a more robust one with the uncertainties being specified through dimensional enlargement of the original fuzzy constraints. A management problem in terms of regional air pollution control has been studied to illustrate the applicability of the proposed approach. Results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers identify desired pollution-abatement strategy with minimized system cost and maximized environmental efficiency.     

An expert-based approach to forest road network planning by combining Delphi and spatial multi-criteria evaluation

Changes in forest landscapes resulting from road construction have increased remarkably in the last few years. On the other hand, the sustainable management of forest resources can only be achieved through a well-organized road network. In order to minimize the environmental impacts of forest roads, forest road managers must design the road network efficiently and environmentally as well. Efficient planning methodologies can assist forest road managers in considering the technical, economic, and environmental factors that affect forest road planning. This paper describes a three-stage methodology using the Delphi method for selecting the important criteria, the Analytic Hierarchy Process for obtaining the relative importance of the criteria, and finally, a spatial multi-criteria evaluation in a geographic information system (GIS) environment for identifying the lowest-impact road network alternative. Results of the Delphi method revealed that ground slope, lithology, distance from stream network, distance from faults, landslide susceptibility, erosion susceptibility, geology, and soil texture are the most important criteria for forest road planning in the study area. The suitability map for road planning was then obtained by combining the fuzzy map layers of these criteria with respect to their weights. Nine road network alternatives were designed using PEGGER, an ArcView GIS extension, and finally, their values were extracted from the suitability map. Results showed that the methodology was useful for identifying road that met environmental and cost considerations. Based on this work, we suggest future work in forest road planning using multi-criteria evaluation and decision making be considered in other regions and that the road planning criteria identified in this study may be useful.     

Modelling of spatial contaminant probabilities of occurrence of chlorinated hydrocarbons in an urban aquifer

In this study, a 3D urban groundwater model is presented which serves for calculation of multispecies contaminant transport in the subsurface on the regional scale. The total model consists of two submodels, the groundwater flow and reactive transport model, and is validated against field data. The model equations are solved applying finite element method. A sensitivity analysis is carried out to perform parameter identification of flow, transport and reaction processes. Coming from the latter, stochastic variation of flow, transport, and reaction input parameters and Monte Carlo simulation are used in calculating probabilities of pollutant occurrence in the domain. These probabilities could be part of determining future spots of contamination and their measure of damages. Application and validation is exemplarily shown for a contaminated site in Braunschweig (Germany), where a vast plume of chlorinated ethenes pollutes the groundwater. With respect to field application, the methods used for modelling reveal feasible and helpful tools to assess natural attenuation (MNA) and the risk that might be reduced by remediation actions.