Contamination Of Water Distribution System Research Articles

Detecting and locating chemical intrusion in water distribution systems using 911 calls

ABSTRACT Intentional or unintentional chemical contamination of water distribution systems (WDSs) could have severe health and socio-economic consequences. High potency chemicals constituting, in essence, “super poisons” have the potential to be used in such intrusion scenarios. Some of these contaminants are capable of killing the victim in less than 1 h. Due to their high toxicity levels and short time from exposure to onset of symptoms, 911 call centers are likely the first point of contact for victims or their families with the authorities. Information such as 911 calls could be used to identify the ongoing event and potential intrusion locations. In this way, such emergency calls could function as an intrusion warning system. This study employs network hydraulic modelling to synthesize the 911 call patterns in the aftermath of such events. It then defines the scenarios as a multi-label pattern recognition problem. The synthesized data then was used to train a Convolutional Neural Network (CNN). The trained AI was applied to a real-world WDS with approximately 4000 km of pipe and 26,000 demand nodes. The results indicated that CNN is capable of accurately recognizing the pattern and pinpointing the originating location of the intrusion with an accuracy greater than 93%.

Fungal contamination of the water distribution system of a tertiary hospital water supply system in a resource-limited setting.

Fungal contamination of hospital water distribution systems has been implicated in outbreaks of healthcare-associated infections. To evaluate the prevalence of fungi in the water distribution system of a tertiary hospital in Nigeria. This was a descriptive cross-sectional study. Swabs and water samples were collected from taps and faucets in the hospital categorized into low (Accidents and Emergency Unit, Children Emergency Unit, Acute Stroke Unit and the 24 in-patient hospital wards) and high-risk (Renal Dialysis Unit, Central Sterile Services Department, Theatres and Intensive Care Units (ICUs)) units based on the vulnerability of patients being managed there. The membrane filtration method for water analysis was used. Where possible, isolates cultured were identified to species level. In total, 105 water and 49 swab samples were collected for analysis. All analysed water samples grew fungi. A total of 289 (high-risk; n = 178; low-risk; n = 111) and 76 fungi isolates were recorded from water and swab samples, respectively, with 31 different species identified. Aspergillus was the most predominant genus with five different species: Aspergillus niger (9.9%), terreus (4.4%), flavus (3.3%), fumigatus (8.8%) and versicolor (2.20%) isolated. Twenty-five and 18 species of fungi were identified in the low and high-risk units, respectively. The labour ward (n = 46; 25.8%) and modular theatre (n = 47; 42.3%) were the most contaminated units. Cladosporium spp. and Paecilomyces spp. were the most frequently isolated fungi in the low and high-risk units, respectively. The dialysis centre (n = 9; 8.1%) and renal transplant theatre (n = 7; 6.31%) had the lowest contamination rates in the high-risk units. Aspergillus niger, Cephalosporium curtipes, Penicillium chrysogenum and Penicillium glabrum were each identified in 4/6 units from which swabs were taken. The facility had no documented protocol for its water safety and quality. Our data reveal a high rate of contamination of hospital water sources by fungi, some of which are known to cause life-threatening infections. For better water treatment and water tank cleaning and disinfection, a standard protocol is advised. Ensuring that the water distribution systems in hospital settings are free of fungal contaminants is important to prevent the possibility of waterborne mycosis outbreaks.

Pilot Study on Fire Effluent Condensate from Full Scale Residential Fires

AbstractStudies related to effluent produced by structure and vegetation fires often focus on gas phase or solid condensed phase, with limited treatment of liquid condensate generated as smoke cools to ambient. Recent post-fire human health concerns related to systemic human exposures to fire smoke and contamination of water distribution systems after wildland urban interface fires can be informed by understanding the chemical composition of liquid condensate resulting from large-scale fire experiments. In this pilot study, fire effluent (smoke) samples were continuously drawn from five different full-scale room-and-contents fire experiments, from which condensate was collected as the effluent cooled. Elevated concentrations of several volatile organic compounds (VOCs), including benzene, toluene, xylenes, styrene, naphthalene, and acetone along with several anions were detected in the acidic effluent. Many of these same VOCs have been identified in the air during firefighter safety experiments and in post-fire water distribution systems at levels that raise concern for human health. Benzene and naphthalene concentrations in the condensate were orders of magnitude above typical water quality standards and thus may directly contaminate large volumes of water. Peak benzene concentrations were similar to highest values reported from contaminated water distribution systems after wildfire events, though additional study is needed to understand the mechanisms by which this condensate may contribute to systemic contamination. Improved understanding of liquid condensate from fire effluent may be important to other areas of human and environmental health study, and some considerations are provided for future research.

The Accumulation of Heavy Metals in Shower System Biofilms: Implications for Emissions and Indoor Human Exposure

Biofilms play a crucial role in absorbing various metals from water, including copper, lead, iron, chromium, nickel, zinc, cadmium, and barium. While their presence was revealed in shower system biofilms, the factors affecting metal accumulation in shower system biofilms were poorly explored. This study aimed to investigate the capacity of shower hose biofilms to accumulate heavy metals, in particular in biofilms growing in energy-efficient showerhead systems, and evaluate the potential emission in water and aerosols of metal pollutants during showering. The adsorption efficiency of various metals in biofilms was assessed by ICP/MS and revealed that biofilms accumulate metals as they age and as biofilm biomass increases, indicating a potential influence of heavy metals on biofilm ecology. Furthermore, the study examined the emission of heavy metals during showering and found that it was sporadic and limited primarily to copper and zinc. These findings raise concerns regarding the role of biofilms in both retaining and releasing metal contaminants in water distribution systems, as well as the associated risk of inhalation during showering. By shedding light on the accumulation dynamics of heavy metals in shower hose biofilms and their potential emission patterns, this research highlights the need for further investigation into the impact of biofilms on water quality and human exposure to metal pollutants. The findings underscore the importance of considering biofilm-related processes when addressing the overall management of heavy metal contamination in shower systems and its potential implications for public health.

Placement Strategies for Water Quality Sensors Using Complex Network Theory for Continuous and Intermittent Water Distribution Systems

AbstractWater quality sensors are used to detect contamination in water distribution systems (WDSs) to help supply safe and quality drinking water to society. However, identifying the optimal location to place sensors is still an open challenge. Many approaches have been proposed in literature to solve this problem. Complex network theory‐based approaches (CNW) to sensor placement are simple, easy to implement, take less computational time even for large‐scale networks, and do not require calibrated hydraulic and water‐quality models. However, existing CNW methods perform well for only a subset of common objectives. Optimization‐based approaches offer better placement but are computationally costly and require detailed knowledge of the WDS. We proposed a new method, “EQ‐Water,” to identify the locations to place water quality sensors for continuous and intermittent WDS with variable demand patterns. EQ‐Water is based on complex network theory, but uses minimal additional hydraulic information. We validate the performance of EQ‐Water on four real networks: BWSN 1, BWSN 2, JPN, and D2B networks. We have compared the performance with a number of approaches from literature, including the popular TEVA‐SPOT tool. The comparison is based on the four objective functions, Z1–Z4, which are commonly used, and also on a weighted cumulative objective function. Our results indicate that EQ‐Water is among the top three methods for BWSN 1, JPN and D2B when using diverse weights for the objective functions, and it shows median performance for BWSN 2. We also observe consistently superior performance against other CNW, and it is competitive with simulation‐based approaches while taking lesser time and effort.

The Marshall Fire: Scientific and policy needs for water system disaster response

AbstractThe 2021 Marshall Fire was the costliest fire in Colorado's history and destroyed more than 1,000 homes and businesses. The disaster displaced over 40,000 people and damaged six public drinking water systems. A case study was developed to better understand decisions, resources, expertise, and response limitations during and after the wildfire. The fire caused all water systems to lose power. Power loss was sometimes coupled with structure destruction, distribution depressurization, and the failure of backup power systems. These consequences jeopardized fire‐fighting support and allowed for volatile organic compound and semi‐volatile organic compound contamination of water distribution systems. Water system staff, with help from neighboring systems and external technical experts, stabilized the infrastructure, found and removed the contamination, and restored services. Actions were identified for utilities, governments, and researchers that could help communities minimize wildfire impacts, better protect workers and the population, and enable water systems to more rapidly respond and recover.

Prediction of chlorine concentration of flows exiting pipe in cross junctions: experimental and numerical study

Abstract This paper aims to develop and validate an analytical equation to predict the concentration of residual chlorine exiting a typical pipe junction. In order to investigate the trend of the incoming flow rates before leaving the junction, experiments with inflow rates (North and West) ranging from 0.18 to 2.17 L/s, Reynolds numbers ranging from 14,324 to 110,780, and free chlorine concentrations ranging from 0.5 to 1.80 mg/L were performed. The results showed that flows tend to bifurcate rather than mix completely, and the bifurcation and mixing depend mainly on the relative flow rates entering the cross junction. Based on the experimental results, a dimensionless concentration (R) was estimated, allowing for predicting chlorine at the East outlet. Then, a solute mass balance was also performed within the cross junction to predict chlorine concentration at the South outlet. Finally, an adjustment of the R-value was performed by using 3D computational fluid dynamics (CFD), and the analytical equations were also validated with simulations. The standard k–ε turbulence model with enhanced wall treatment for modeling turbulence and near-wall effects, respectively, were used. More mixing models will improve water quality simulations to ensure proper control of chlorine and possible contaminants in water distribution systems.

Predicting conditional maximum contaminant level exceedance probabilities for drinking water after wildfires with Bayesian regularized network ensembles

The severity and frequency of wildfires have increased throughout the Pacific Northwest in recent decades, costing lives and destroying large amounts of valuable resources and assets. This trend is predicted to persist because of climate change and the associated increased fire risk caused by prolonged droughts in combination with changes in land cover and land use, including rapid increases in wildland urban interface areas. The threat of benzene and other contaminants in drinking water from water distribution systems after wildfires is a relatively recently discovered problem that gained attention because of the significant health hazards that high levels of benzene in drinking water pose for humans. Driving processes leading to post-fire benzene contamination in water distribution systems are largely unknown. Currently, no deterministic process models exist to predict the risk of exceeded benzene levels in water distribution systems after wildfires. To address the lack of predictive models, we developed and tested an approach based on neural network models to spatially predict the conditional probabilities of exceeding maximum contaminant levels for benzene after wildfires. The Bayesian regularized neural networks were trained using high-resolution data layers comprising topography, soil properties, landcover, vegetation, meteorological parameters, fuel load, and infrastructure data for two wildland urban interface areas in northern California. The generalized model ensemble encompassing data from both communities exhibits an accuracy of 83% to 88% in spatially predicting the post-fire exceedance of benzene levels, offering a planning tool for emergency response and future risk mitigation efforts.

Using machine learning classification to detect simulated increases of de facto reuse and urban stormwater surges in surface water

Water quality events such as increases in stormwater or wastewater effluent in drinking water sources pose hazards to drinking water consumers. Stormwater and wastewater effluent enter Lake Mead—an important drinking water source in the southwest USA—via the Las Vegas Wash. Previous studies have applied machine learning and online instruments to detect contamination in water distribution systems. However, alert systems at drinking water intakes would provide more time for corrective action. An array of online instruments measuring pH, conductivity, redox potential, turbidity, temperature, tryptophan-like fluorescence, UV absorbance (UVA254), TOC, and chlorophyll-a was fed raw water directly from Lake Mead. Wastewater effluent, dry weather Las Vegas Wash, and storm-impacted Las Vegas Wash samples were blended into the instrument inlets at known ratios to simulate three types of adverse water quality events. Data preprocessing was conducted to correct for diurnal patterns or instrument drift. Supervised machine learning was conducted using previously published models in R. Ninety-nine models were screened on the raw data. Eight high-performing models were evaluated in-depth and optimized. Weighted k-Nearest Neighbors, Single C5.0 Ruleset, Mixture Discriminant Analysis, and an ensemble of these three models had accuracy over 97% when assigning test set data among three classes (Normal, Event, or Maintenance). The ensemble detected all event types at the earliest timepoint and had one false positive that was not a lag error (i.e., consecutively following a true positive). Omitting Maintenance, the Adaboost model had over 99% test set accuracy and zero false positives that were not lag errors. Data preprocessing was beneficial, but the optimal methods were model-specific. All nine water quality variables were useful for most models, but UVA254 and turbidity were most important.

An Analytical Model for the Decontamination of Water Distribution Systems Using Slug‐Feed Method of Disinfection

AbstractThe increasing risk of intentional, negligent, or accidental intrusion of biological, chemical, or radioactive contaminants in water distribution systems is becoming a major concern that has significant and adverse impacts on public health. As such, it is important to have an effective, robust, and flexible plan that can be readily implemented to minimize the impact of these contamination events. However, limited research has been focused on the strategic planning of the decontamination process of the contaminated infrastructure. This paper proposes an analytical method for modeling the slug‐feed method of disinfection given the drainage and disinfectant dosage profiles. The efficacy of the proposed method has been demonstrated by using it as the evaluation function in a genetic algorithm optimization of two case studies. Results show the proposed method exhibits higher robustness compared to the procedures defined in the current standard and literature. The main contributions of this study are to (1) provide a robust and accurate model to describe the slug‐feed method of disinfection, (2) offer additional resource utilization flexibility for water authorities, and (3) providing additional levels of district metered areas prioritization.

Prediction of Water Distribution System Contamination Based on Wildfire Burn Severity in Wildland Urban Interface Communities

The devastation to infrastructure in wildland urban interface (WUI) communities caused by wildfires has brought attention to the dramatic and unforeseen impacts of high fuel load density within the...

Environmental Surveillance as a Tool for Identifying High-risk Settings for Typhoid Transmission.

Enteric fever remains a major cause of morbidity in developing countries with poor sanitation conditions that enable fecal contamination of water distribution systems. Historical evidence has shown that contamination of water systems used for household consumption or agriculture are key transmission routes for Salmonella Typhi and Salmonella Paratyphi A. The World Health Organization now recommends that typhoid conjugate vaccines (TCV) be used in settings with high typhoid incidence; consequently, governments face a challenge regarding how to prioritize typhoid against other emerging diseases. A key issue is the lack of typhoid burden data in many low- and middle-income countries where TCV could be deployed. Here we present an argument for utilizing environmental sampling for the surveillance of enteric fever organisms to provide data on community-level typhoid risk. Such an approach could complement traditional blood culture-based surveillance or even replace it in settings where population-based clinical surveillance is not feasible. We review historical studies characterizing the transmission of enteric fever organisms through sewage and water, discuss recent advances in the molecular detection of typhoidal Salmonella in the environment, and outline challenges and knowledge gaps that need to be addressed to establish environmental sampling as a tool for generating actionable data that can inform public health responses to enteric fever.

A framework for real-time disinfection plan assembling for a contamination event in water distribution systems

Water distribution system contamination events caused by intentional, negligent, or accidental intrusion of biological, chemical, or radioactive contaminants have significant impacts on the health of the populations that it services. Therefore, it is important to have an effective plan that can be readily implemented to minimize the impact of these contamination events. However, limited research has been focused on strategic planning of the decontamination process of the contaminated infrastructure. This paper proposed a framework for assembling a disinfection plan in real-time by (1) partitioning a WDS into a number of district metered areas (DMAs), (2) generating a solution region for each of the DMAs, and (3) assemble an effective decontamination plan using solution region generated. This framework has been applied to three contamination events. The results show that, when planning for the decontamination stage of a contamination event, the use of the proposed framework can (1) significantly reduce the response time, (2) improve the quality of the decontamination plan, and (3) provide a model for optimizing the resource allocation.

Probabilistic risk assessment of water distribution system in Hyderabad, Pakistan reveals unacceptable health hazards and areas for rehabilitation.

Poor water quality exacerbates multidimensional poverty in developing nations. Often centralized treatment facilities generate acceptable water quality, but the water is contaminated during distribution. Methods to assess sources of contamination in water distribution systems are lacking. A case study of two methods, human risk assessment linked to water distribution system sampling was conducted in Hyderabad, Pakistan to determine areas requiring infrastructure rehabilitation. Water samples from source water (i.e., the Indus River), treatment plant effluent and from taps in the water distribution system were analyzed by atomic adsorption spectroscopy for metals and metalloids (As, Cd, Cr, Hg, and Pb) and water quality parameters (dissolved and suspended solids, pH, conductivity, and total organic carbon). Source water exceeded acceptable drinking water levels for As, Cd, total Cr, and Pb, while the treatment plant effluent concentrations were acceptable. Concentrations of all metals and metalloids, except Hg, increased in the water distribution system post-treatment, exceeding safe drinking limits in at least one location, suggesting contamination of the water during distribution. A deterministic and a probabilistic risk assessment were conducted to evaluate two scenarios: (1) unrestricted use of piped water for all household purposes, including as drinking water and (2) restricted use of the water for purposes other than drinking in the household, including only dermal and inhalation exposure pathways. The water was deemed unsafe for unrestricted use as the sole source of drinking water by both risk assessment methods. Yet when an alternative source of drinking water was assumed and the piped water was used only for bathing and dish washing, the probabilistic risk assessment revealed acceptable health risks to the population, while the overly conservative deterministic risk assessment suggested unacceptable risks. The combined methods of water sampling, risk assessment and correlation analysis suggested areas for rehabilitation of the water distribution system in Hyderabad, Pakistan and these methods can be adopted in other developing nations to target limited funds for infrastructure rehabilitation.

Analysis of building plumbing system flushing practices and communications.

Drinking water distribution system contamination incidents can prompt public agencies and drinking water utilities to issue do-not-drink and do-not-use advisories. After the contaminant is cleared from distribution mains, consumers are often directed to flush their plumbing. However, little validated guidance and few evaluated communications strategies are available on using flushing to decontaminate building water systems. Additionally, limited data support the effectiveness of current practices and recommendations. In this study, expert elicitation was used to assess existing flushing guidance and develop validated flushing guidance and communications for single-family residences. The resulting guidance recommends progressively opening all cold-water taps from the closest to point of entry to the furthest and allowing the water to run for at least 20 minutes. Hot-water taps should be opened progressively and run for at least 75 minutes. The guidance language and format conformed to grade-level and readability scores within recommended health communication ranges. The readability of eight other flushing guidance documents was also evaluated for contamination incidents from 2008-2015. Seven were written at a 10th-12th grade level, above the 6th-7th grade level recommended for health communications.

Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data.

The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling's T2 statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.

Occurrence of Legionella in showers at recreational facilities.

Critical environments, including water systems in recreational settings, represent an important source of Legionella pneumophila infection in humans. In order to assess the potential risk for legionellosis, we analyzed Legionella contamination of water distribution systems in 36 recreational facilities equipped with swimming pools. One hundred and sixty water samples were analyzed from shower heads or taps located in locker rooms or in bathrooms. By culture method and polymerase chain reaction, 41/160 samples were positive for Legionella from 12/36 recreational centers. Hotels (57.1%) and sports centers (41.2%) were the most contaminated. L. pneumophila serotypes 2-14 (25/41) were more frequently found than serotype 1 (10/41). Samples at temperature ≥30 °C were more frequently positive than samples at temperature <30 °C (n = 39 vs n = 2, p < 0.00001). The presence of L. pneumophila was investigated by comparison with heterotrophic plate count (HPC), an indicator of water quality. The presence of L. pneumophila was associated more frequently with high and intermediate HPC load at 37 °C, therefore should be considered a potential source when HPC at 37 °C is >10 CFU/mL. Maintenance, good hygiene practices, interventions on the hydraulic system and regular controls must be implemented to minimize exposure to L. pneumophila infection risk.

Complex Adaptive Systems Framework to Simulate the Performance of Hydrant Flushing Rules and Broadcasts during a Water Distribution System Contamination Event

AbstractIn the event that a contaminant is introduced to a water distribution system, utility managers must respond quickly to protect public health. Mitigation strategies specify response actions,...

Water Quality Correction Within Water Distribution System

Abstract Water suppliers can be treated as production companies whose main product is water delivered to their customers. The article presents problems connected with management of such companies in the conditions of secondary contamination in water distribution systems. This phenomenon exists in water networks all over the world. Its’ presence is particularly visible in countries of former communistic block. In the article particular attention was devoted to the issue of water quality correction in the analysed systems. In the case of water distribution systems, former quality correction methods consisted in special treatment of water pumped into the system, flushing and cleaning of water pipes. In both these cases identification of water quality deficiencies resulted in significant water loss. The situation reflects management processes applied in the manufacturing industry of the 1940s. The authors of this paper put forward the concept of three water quality correction methods which would not entail such considerable water loss. The methods in question are intended for different network types. The implementation of proposed solutions could set new standards in management of distribution systems of water providers.

A dynamic simulation–optimization model for adaptive management of urban water distribution system contamination threats

Urban water distribution systems hold a critical and strategic position in preserving public health and industrial growth. Despite the ubiquity of these urban systems, aging infrastructure, and increased risk of terrorism, decision support models for a timely and adaptive contamination emergency response still remain at an undeveloped stage. Emergency response is characterized as a progressive, interactive, and adaptive process that involves parallel activities of processing streaming information and executing response actions. This study develops a dynamic decision support model that adaptively simulates the time-varying emergency environment and tracks changing best health protection response measures at every stage of an emergency in real-time. Feedback mechanisms between the contaminated network, emergency managers, and consumers are incorporated in a dynamic simulation model to capture time-varying characteristics of an emergency environment. An evolutionary-computation-based dynamic optimization model is developed to adaptively identify time-dependant optimal health protection measures during an emergency. This dynamic simulation–optimization model treats perceived contaminant source attributes as time-varying parameters to account for perceived contamination source updates as more data stream in over time. Performance of the developed dynamic decision support model is analyzed and demonstrated using a mid-size virtual city that resembles the dynamics and complexity of real-world urban systems. This adaptive emergency response optimization model is intended to be a major component of an all-inclusive cyberinfrastructure for efficient contamination threat management, which is currently under development.