Assessing regulatory violations of disinfection by-products in water distribution networks using a non-compliance potential index

Inactivating pathogens is essential to eradicate waterborne diseases. However, disinfection forms undesirable disinfection by-products (DBPs) in the presence of natural organic matter. Many regulations and guidelines exist to limit DBP exposure for eliminating possible health impacts such as bladder cancer, reproductive effects, and child development effects. In this paper, an index named non-compliance potential (NCP) index is proposed to evaluate regulatory violations by DBPs. The index can serve to evaluate water quality in distribution networks using the Bayesian Belief Network (BBN). BBN is a graphical model to represent contributing variables and their probabilistic relationships. Total trihalomethanes (TTHM), haloacetic acids (HAA5), and free residual chlorine (FRC) are selected as the variables to predict the NCP index. A methodology has been proposed to implement the index using either monitored data, empirical model results (e.g., multiple linear regression), and disinfectant kinetics through EPANET simulations. The index’s usefulness is demonstrated through two case studies on municipal distribution systems using both full-scale monitoring and modeled data. The proposed approach can be implemented for data-sparse conditions, making it especially useful for smaller municipal drinking water systems.     

Using penalty functions to evaluate aggregation models for environmental indices

The purpose of indices is to summarize a large volume of information into a single number that is easy to understand and interpret. Environmental indices provide a composite picture of an environmental condition derived from a series of observed measurements and parameters. They are used as communication tools by regulatory agencies to characterize the state of a specific environmental system (air, water, and sediments) and to study the impact of regulatory policies on various environmental management practices. In the development of environmental indices, a few issues and problems have been encountered arising as a result of the abstraction of information and data. These problems are referred to as characteristic properties that include ambiguity, eclipsing, compensation and rigidity. These characteristic properties have long been identified and interpreted in Boolean (e.g., Yes/No) or qualitative (e.g., low, medium, high) terms. In this paper, we propose a new approach to describe the above stated characteristic properties on a continuous scale to evaluate and compare the behavior of various aggregation models. Our approach is based on developing penalty functions for each characteristic property. A water quality index example by Swamee and Tyagi (2000) is used to explain our approach. A detailed case study for a developing microbial risk index is also provided to show how the proposed approach can be extended to complex hierarchical systems. Results show that it is possible to improve aggregation models for index development. Future research directions to improve index development are also discussed.     

Handling data uncertainties in event tree analysis

Event tree analysis (ETA) is an established risk analysis technique to assess likelihood (in a probabilistic context) of an accident. The objective data available to estimate the likelihood is often missing (or sparse), and even if available, is subject to incompleteness (partial ignorance) and imprecision (vagueness). Without addressing incompleteness and imprecision in the available data, ETA and subsequent risk analysis give a false impression of precision and correctness that undermines the overall credibility of the process. This paper explores two approaches to address data uncertainties, namely, fuzzy sets and evidence theory, and compares the results with Monte Carlo simulations. A fuzzy-based approach is used for handling imprecision and subjectivity, whereas evidence theory is used for handling inconsistent, incomplete and conflicting data. Application of these approaches in ETA is demonstrated using the example of an LPG release near a processing facility.     

Water distribution system failure: a framework for forensic analysis

The main purpose of a water distribution system (WDS) is to deliver safe water of desirable quality, quantity and continuity to consumers. However, in many cases, a WDS fails to fulfill its goal owing to structural and associated hydraulic failures and/or water quality failures. The impact of these failures can be reduced significantly if preventive actions are taken based on their potential of occurrences or if a failure occurs and is detected within a minimum period of time after its occurrence. The aim of this research was to develop a forensic system for WDS failures. As part of the proposed forensic analysis, a framework has been developed, which investigates structural and associated hydraulic failures as well as water quality failures and integrates all failure investigation under a single platform. Under this framework, four different models have been developed to evaluate and identify structural and associated hydraulic failures and water quality failures. If a failure is detected in the system, the framework is capable of identifying the most probable location of the failure. To investigate the effectiveness of the proposed framework, the developed models have been tested and implemented in different WDSs.     

Metal contamination of drinking water from corrosion of distribution pipes

The objectives of this study were to evaluate metal contamination of drinking water resulting from the corrosion of distribution pipes and its significance to human health. A community in Dhahran, which is served from its own desalination facilities, was chosen for this study. About 150 drinking water samples were collected and analyzed for metal concentrations using an inductively coupled argon plasma analyzer. It was found that copper, iron and zinc in the drinking water increased during its transportation from the desalination plant to the consumers. This increase was related to the length and material of distribution pipes. Concentrations of copper and zinc were increased during overnight storage of water in the appliances. Metal concentrations found in this study are discussed with reference to human health.     

Risk-Based Prioritization of Air Pollution Monitoring Using Fuzzy Synthetic Evaluation Technique

Air pollution monitoring programs aim to monitor pollutants and their probable adverse effects at various locations over concerned area. Either sensitivity of receptors/location or concentration of pollutants is used for prioritizing the monitoring locations. The exposure-based approach prioritizes the monitoring locations based on population density and/or location sensitivity. The hazard-based approach prioritizes the monitoring locations using intensity (concentrations) of air pollutants at various locations. Exposure and hazard-based approaches focus on frequency (probability of occurrence) and potential hazard (consequence of damage), respectively. Adverse effects should be measured only if receptors are exposed to these air pollutants. The existing methods of monitoring location prioritization do not consider both factors (hazard and exposure) at a time. Towards this, a risk-based approach has been proposed which combines both factors: exposure frequency (probability of occurrence/exposure) and potential hazard (consequence).This paper discusses the use of fuzzy synthetic evaluation technique in risk computation and prioritization of air pollution monitoring locations. To demonstrate the application, common air pollutants like CO, NO_x, PM₁₀ and SO_x are used as hazard parameters. Fuzzy evaluation matrices for hazard parameters are established for different locations in the area. Similarly, fuzzy evaluation matrices for exposure parameters: population density, location and population sensitivity are also developed. Subsequently, fuzzy risk is determined at these locations using fuzzy compositional rules. Finally, these locations are prioritized based on defuzzified risk (crisp value of risk, defined as risk score) and the five most important monitoring locations are identified (out of 35 potential locations). These locations differ from the existing monitoring locations.     

Interpreting drinking water quality in the distribution system using Dempster-Shafer theory of evidence

Interpreting water quality data routinely generated for control and monitoring purposes in water distribution systems is a complicated task for utility managers. In fact, data for diverse water quality indicators (physico-chemical and microbiological) are generated at different times and at different locations in the distribution system. To simplify and improve the understanding and the interpretation of water quality, methodologies for aggregation and fusion of data must be developed. In this paper, the Dempster-Shafer theory also called theory of evidence is introduced as a potential methodology for interpreting water quality data. The conceptual basis of this methodology and the process for its implementation are presented by two applications. The first application deals with the interpretation of spatial water quality data fusion, while the second application deals with the development of water quality index based on key monitored indicators. Based on the obtained results, the authors discuss the potential contribution of theory of evidence as a decision-making tool for water quality management.     

Analyzing system safety and risks under uncertainty using a bow-tie diagram: An innovative approach

A bow-tie diagram combines a fault tree and an event tree to represent the risk control parameters on a common platform for mitigating an accident. Quantitative analysis of a bow-tie is still a major challenge since it follows the traditional assumptions of fault and event tree analyses. The assumptions consider the crisp probabilities and “independent” relationships for the input events. The crisp probabilities for the input events are often missing or hard to come by, which introduces data uncertainty. The assumption of “independence” introduces model uncertainty. Elicitation of expert's knowledge for the missing data may provide an alternative; however, such knowledge incorporates uncertainties and may undermine the credibility of risk analysis.This paper attempts to accommodate the expert's knowledge to overcome missing data and incorporate fuzzy set and evidence theory to assess the uncertainties. Further, dependency coefficient-based fuzzy and evidence theory approaches have been developed to address the model uncertainty for bow-tie analysis. In addition, a method of sensitivity analysis is proposed to predict the most contributing input events in the bow-tie analysis. To demonstrate the utility of the approaches in industrial application, a bow-tie diagram of the BP Texas City accident is developed and analyzed.     

Treatment,residual chlorine and season as factors affecting variability of trihalomethanes in small drinking water systems

Seasonal variability in source water can lead to challenges for drinking water providers related to operational optimization and process control in treatment facilities. The objective of this study is to investigate seasonal variability of water quality in municipal small water systems (<3000 residents) supplied by surface waters. Residual chlorine and trihalomethanes (THM) were measured over seven years (2003–2009). Comparisons are made within each system over time, as well as between systems according to the type of their treatment technologies. THM concentrations are generally higher in the summer and autumn. The seasonal variability was generally more pronounced in systems using chlorination plus additional treatment. Chloroform, total THM (TTHM) and residual chlorine concentrations were generally lower in systems using chlorination plus additional treatment. Conversely, brominated THM concentrations were higher in systems using additional treatment. Residual chlorine was highest in the winter and lowest in the spring and summer. Seasonal variations were most pronounced for residual chlorine in systems with additional treatment. There was generally poor correlation between THM concentrations and concentrations of residual chlorine. Further study with these data will be beneficial in finding determinants and indicators for both quantity and variability of disinfection byproducts and other water quality parameters.     

Bioaccumulation of nickel and vanadium by clams (Meretrix meretrix) living in different salinities along the Saudi coast of the Arabian Gulf

The objective of this study was to investigate bioaccumulation of nickel (Ni) and vanadium (V) in clams living in different salinity regimes along the Saudi coast of the Arabian Gulf. Several hundred clam (Meretrix meretrix), sediment, and seawater samples were collected from 12 locations. Concentrations of Ni and V were determined in these samples using an inductively coupled argon plasma analyzer. Concentrations of Ni and V in the clams varied between 0.35 and 2.61 mg kg(-1) and between 0.13 and 0.35 mg kg(-1) wet tissue, respectively. Analysis of variance of the data revealed significant (P<0.01) inter- and intra-station variations in Ni concentrations in clams. In contrast, the mean concentration of V in clams from all the stations were statistically similar. Correlation between the biometric characteristics of clams and Ni and V concentrations were not statistically significant. Significant (P<0.05) geographical variations in Ni and V concentrations in the sediment samples were found, with relatively higher concentrations in the northern part of the Gulf where there are many oil fields. Ni and V in the sediments were significantly (P<0.05) correlated, suggesting a common contamination source for these elements. Interactions between Ni and V in clams and sediment were poor.