Intra-system water quality varies across a water distribution system, and large, unexpected shifts in consumer demands can create changes in the spatio-temporal dynamics of water flows and water quality. Exposure to poor water quality leads to negative health outcomes, and households that seek to avoid tap water and use bottled water as an alternative source of water can bear a substantial cost. This research applies a sociotechnical agent-based modeling framework, the COVID-19 social distancing and tap water avoidance (TWA) agent-based model (COST-ABM) to explore how intra-system changes in water quality lead to poor water quality and issues with affordability for households and neighborhoods. COST-ABM simulates the movement of individual agents to and from home, work, and leisure locations under decisions to social-distance and the purchase of bottled water under decisions to avoid tap water. For scenarios of changing demands associated with social distancing, high water age is exacerbated, which leads to TWA. COST-ABM assesses equity based on the total cost of tap and bottled water as a percentage of income for low-income households. COST-ABM is applied for a synthetic case study that was developed to represent Clinton, North Carolina. A synthetic hydraulic model is created using street maps to place pipes and nodes, well locations to place water sources, and building types to determine demands. Household demographics are distributed to represent the population of Clinton based on census data. Results demonstrate spatial changes in water quality that lead to TWA and economic inequities. Emergent results demonstrate that inequities among demographic groups do not emerge, but that inequities emerge across income groups and water quality at nodes. The sociotechnical modeling approach that is developed in this research applies COST-ABM to identify and quantify inequities in drinking water quality and water affordability in piped distribution systems. This work focuses on demand changes associated with pandemic scenarios and can be applied to assess equity in water for other demand shifting scenarios, such as extreme weather events, adoption of decentralized water systems, and working from home.

Time-of-use (TOU) tariffs charge higher prices for water used during designated peak periods, encouraging users to shift their water use to off-peak periods. Research in electricity markets has shown that TOU tariffs can have adverse effects on households with low incomes or who lack access to technology, raising equity concerns. This research assesses the differential impact of TOU tariffs on heterogeneous water users using an agent-based modeling (ABM) approach. The ABM is applied to a case study to simulate the response of a community of water users to TOU tariffs and assess the equity impacts of the household cost of water under various scenarios. Findings suggest households with characteristics consistent with disadvantaged groups, including lower incomes, high density, and limited access to technology, may experience an increase in water costs under TOU rates.

At-home water quality test kits and aesthetic observations can report the quality of drinking water at the tap through low-cost and convenient approaches. These data are collected at low resolution and report high rates of error, however, and are typically used only anecdotally to indicate problems with the quality of drinking water. This research tests the statistical rigor of using low-resolution and low-quality data and explores the use of these data in regression modeling to indicate water quality problems. This research uses a participatory science (citizen science) approach to develop data and uses statistical analysis to test the relationships of building and occupant characteristics, aesthetics, and chemistry (reagent) strips with chemicals present in drinking water. Through an online portal, participants report household and demographic characteristics and the results of chemistry strips. Participants also mail tap water samples to a laboratory for analysis. This research tests the associations of predictor variables, including building and occupant characteristics, aesthetics, and chemistry strips with 20 chemicals present in tap water. Results demonstrated that chemistry strip parameters and aesthetics report significant associations with chemical contaminants. This research also tests the relationships of predictor variables with response variables through backward stepwise linear regression. Results identified that chemistry strip parameters, including alkalinity, hardness, lead, and fluoride, are significantly related to chemicals in drinking water. Other significant predictor variables include well water and metallic taste. The combined use of aesthetic variables, chemistry strip variables, and building characteristics led to models with r2 values in the range of 0.6-0.7 for some chemical constituents. The performance of regression models are compared with the accuracy of chemistry strips for four chemicals. Using low-resolution low-quality data within a regression model substantially improves the mean absolute error and the root-mean square error reported by chemistry strips. Although chemistry strips are subject to high error, chemistry strip data are significantly associated with chemical concentrations in water and may be useful in early warning systems and prioritizing households for further water quality testing.

Centralized infrastructure meets urban demands through large-scale treatment and distribution but has resulted in unanticipated social, economic, and environmental costs. Decentralized technologies, including rainwater harvesting, backyard wells, and greywater, can replace centralized systems and reduce consumption of freshwater. Decentralized technologies can be deployed within hybrid water systems, in which water consumers access water supply from both decentralized and centralized sources, providing a transition from centralized to decentralized water supply regimes. The performance of hybrid water systems is challenging to predict due to the complexity of human decision-making around end uses, the adoption of decentralized systems, and the interplay of centralized and decentralized systems. This research explores the performance of decentralized technologies within a hybrid system to reduce consumption of centrally supplied water through the development of an agent-based model. Agents represent households that apply water from decentralized and centralized sources for outdoor and indoor purposes. Agents are assigned rainwater harvesting tanks, bore water (backyard wells), and greywater resources, and exert outdoor demands based on recent precipitation. The agent-based model is coupled with a groundwater model to assess changes in groundwater tables under varying levels of adoption of decentralized systems. This research applies a unique water use dataset from Perth, Australia, which was collected at 36 households that use hybrid water systems. The dataset describes 30-minute water demand data for rainwater, bore water, greywater, and mains water and is used to simulate realistic water demands and uses in a hybrid system. Hybrid water systems provide the opportunity to implement novel economic strategies to further improve the efficiency of water use, and this research applies the agent-based modeling framework to explore how the performance of water micro-trading programs affect the performance of hybrid water systems. Results demonstrate that hybrid water systems reduce mains water use and improve groundwater tables, and implementing micro-trading further improves the impact of hybrid water systems by allowing more efficient use of rainwater.

Intermittent water supply systems provide water supply service that is available to consumers less than 24 h per day. Intermittent water supply systems do not maintain a continuously pressurized network, resulting in degraded water quality, exacerbation of pipe deterioration, and an undesirable level of service. Methods that support the design and operation of intermittent water distribution networks are needed to help water utilities maximize the quantity and quality of water provided and avoid unnecessary loss of water and revenue. This research implements and develops the genetic algorithm for rehabilitation and analysis of intermittent networks (GA-RAIN). GA-RAIN is developed using Python scripts and the water network tool for resilience (WNTR) to couple a genetic algorithm approach with hydraulic simulation. A rehabilitation problem is formulated to identify design decisions, including repairing leaks and resizing pipes, tanks, pumps, and valves to reduce overall system leakage, maximize the hours of water availability per consumer, and maximize the minimum network pressure, subject to constraints imposed by a limited budget over a planning horizon. A sequential approach is developed that reduces the number of decisions, decomposes the problem into two subproblems (an initial condition problem and a planning horizon problem), and applies a genetic algorithm to solve two problems in serial. A genetic algorithm is applied to the initial condition problem to develop a set of alternative solutions that specify initial conditions for the infrastructure configuration. In the second stage, the genetic algorithm is applied to design rehabilitation efforts over a five-year time horizon for a selected set of alternative initial condition solutions. The placement and upgrading of new infrastructure and control settings for pumps, tanks, and valves are used as decision variables. Flow rate at the maximum demand time step is evaluated to identify critical pipes and reduce the number of decision variables. A rehabilitation strategy is identified for an illustrative case study to improve infrastructure performance over a five-year planning period. GA-RAIN performed similarly to other methods and relies on relatively low human interaction or engineering judgment, which improves the practical application of this method for rehabilitating underresourced utilities.

Increasing water use efficiency through demand side management techniques, such as water pricing and conservation, can alleviate water supply shortages and reduce the financial burden of expanding water supply sources. Demand side management programs rely on estimates of the average water user to design incentives and educational information. Using estimates of the average water use, however, neglects nuances in individual water uses, and personalized end use datasets are needed to understand differences between water users. Personalized end use datasets are typically collected using written water diaries or living laboratories, but these approaches are unreliable, intrusive, and expensive. Further, these methods are not typically applied to delineate the differences in water use among individual users and collect water data for one user or the household as a unit. This research develops a system for collecting personalized end use datasets to specify the volume of water used per person at each fixture in a house. The electronic device for residential indoor personal water sensing ( EDRIPS) is a deployable button array that is placed at each fixture and allows individual residents to quickly and conveniently log water end uses. EDRIPS records water fixture usage through a wireless local area network. The EDRIPS button arrays are paired with smart meters that record trace water flows at the meter. EDRIPS was deployed at four households for a period of 4 weeks to collect personalized end use datasets. Datasets report the volume of water that is consumed at each fixture, and analysis explores differences in water use across genders and age of water consumers. The EDRIPS system can be used to develop insights about nuances in water use among individual consumers to better inform water pricing and conservation programs.

Water distribution systems (WDSs) should deliver safe and affordable water for communities, yet consumers are regularly exposed to tap water that violates federal guidelines for pathogens and chemicals. In response to reduced water quality, consumers may shift demands away from tap water to bottled water, increasing household water spending. Intra-system water quality fluctuates with changes in demands, a consequence observed during the COVID-19 pandemic. This research develops the COVID-19 social-distancing and tap water avoidance agent-based model (COST-ABM), which simulates water quality in a water distribution network and decisions to avoid tap water and purchase bottled water. COST-ABM is developed to assess equitable access to affordable water. Agents represent water consumers that decide to avoid tap water by purchasing bottled water for cooking, cleaning, and hygienic end uses, reducing demand from the system. The agent-based model is tightly coupled with a water distribution system model that calculates the spatiotemporal dynamics of water quality in a pipe network, which is used in agent decision-making. Equity is evaluated in a bottom-up approach using the cost of tap and bottled water as a percentage of household income, calculated at each household. The framework is applied for a virtual water distribution system, and results demonstrate economic inequities in water affordability. This research presents a framework to assess equity in a WDS based on tap water avoidance and water affordability and can be used to facilitate infrastructure management that provides equitable access to safe and affordable water.

The distribution of clean water through public systems can be inequitable, as variations in water quality are common drivers for negative health outcomes and can lead households to spend more on water treatment or alternative sources of water, such as bott led water. The COVID-19 pandemic caused complex, location dependent changes to demands due to social distancing that led to changes in water quality. The first wave of social distancing was characterized by widespread adoption of work- from-home practices and social distancing, causing water demands to shift from places of employment and recreation to residences. Subsequent changes have ushered in a new post-pandemic regime that is characterized by a hybrid work force. The hybrid work force includes many individuals who have adopted a personal schedule of working from home and working from the office that increases the uncertainty in modeling daily population movement changes and demands. The changes in water quality that followed the change from pre-pandemic, business-as-usual scenarios to COVID-19 social distancing scenarios were modeled using an agent-based modeling framework coupled with a virtual WDS network, but further research is needed to explore how changes to hybrid work generate changes in demands and water quality. Water quality changes caused by the transition from pandemic to post pandemic regimes have not been quantified, and this research develops a tool to test and compare water quality, equitable access to clean water, and the cost of water in pre-pandemic, pandemic, and post-pandemic scenarios. An agent-based model (ABM) is developed to simulate the movement of individual agents to and from home, work, and leisure locations. The cost of buying water is calculated using the demand specified in the hydraulic network. Equity is assessed using the cost of water as a percentage of income for households in the lower 20% of incomes. In this work, an ABM is applied to a hydraulic system synthesized for Clinton, North Carolina. The hydraulic model is created using street maps to place pipes and nodes, well locations to place water sources, and census data to determine the demand required. Household incomes are distributed to represent Clinton, North Carolina, using census data. The ABM is applied for three scenarios, pre-pandemic, pandemic, and post-pandemic, and results demonstrate changes in water quality that lead to economic inequities. The modeling framework that is developed in this research can be applied to assess equity impacts of water quality changes such as those associated with pandemic scenarios.

Current regulatory tools are not well suited to address freshwater salinization in urban areas, and the conditions under which bottom-up management is likely to emerge remain unclear. We hypothesize that Elinor Ostrom’s social-ecological systems (SESs) framework can be used to explore how current understanding of salinization might foster or impede its collective management. We focus on the Occoquan Reservoir, a critical urban water supply in Northern Virginia, USA, and use fuzzy cognitive maps (FCMs) to characterize stakeholder understanding of the SES that underpins salinization in the region. Hierarchical clustering of FCMs reveals four stakeholder groups with distinct views on the causes and consequences of salinization, and actions that could be taken to mitigate salinization, including technological, policy, and governance interventions and innovations. Similarities and differences across these four groups, and their degree of concordance with measured or modeled SES components, point to actions that could be taken to catalyze collective management of salinization in the region.

Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation. Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.

Microbially induced carbonate precipitation (MICP) is a biogeochemical process that uses microbes to create favorable conditions in a porous medium for bio-cementation. The hydrolysis of urea and the resulting variation in pore-scale chemistry creates variations in MICP. Research is needed to characterize the spatial and temporal development of precipitation at the particle level that influences soil properties and mechanical behavior due to varying granular characteristics. Several researchers have used reactive transport modeling to predict precipitation distribution patterns over meter-length scales; however, this continuum approach is less applicable for assessing varying granular characteristics at the pore-scale. This study investigates agent-based modeling (ABM) as a new framework to simulate complex interactions at the micro-level to generate phenomena observed at the macro-scale. In this study, an ABM was developed to simulate urea hydrolysis by simplifying the complex biogeochemical interactions between the bacteria Sporosarcina pasteurii and an aqueous MICP environment, including the pH-dependent ammonium speciation. Results show that the ABM captures similar urea degradation trends compared to existing reactive transport models and experimental data. Additionally, insights from the ABM suggest that nutrient depletion zones emerge during ureolysis and are influenced by super-agent size and distribution. These findings indicate that ABM can provide additional insights into the MICP process that cannot be captured by reactive transport modeling.

Water distribution systems (WDSs) are designed to deliver potable water across urban areas. Unpredicted changes in water demands and hydraulics can increase the residence time in pipes, leading to the growth of microbes and decreased water quality at some locations in a network. During the COVID-19 pandemic, large-scale reductions in demands, especially in industrial and commercial areas as individuals worked from home, led to hot-spots of increased water age. In response to reduced water quality, consumers may avoid using tap water for end uses including drinking, cooking, and cleaning. The lack of access to clean water can create high costs for some households due to the cost of buying bottled water. Inequitable access to safe, affordable water is explored in this research in the context of the COVID-19 pandemic through a coupled framework. This research extends an existing agent-based modeling (ABM) framework that simulated COVID-19 transmission, social distancing decision-making, reductions in water demands, and flows in a water distribution system. The ABM is extended in this work to simulate households that perceive water quality problems with tap water and choose to buy bottled water for cooking, cleaning, and hygienic purposes. Agents choose tap water avoidance behaviors based on water age, a surrogate for water quality. Equity is evaluated using the cost of water, both tap and bottled, as a percentage of income. An optimization approach is coupled with the ABM framework and applied to design operational strategies that improve equitable access to safe affordable water. A graph theory approach identifies valves that should be opened and closed to improve water quality at nodes and maximize equity. The results demonstrate an increase in water age due to social distancing behaviors, and water of high age is observed to be disproportionately located near industrial areas. Adjusted income demonstrates inequities in access to safe and affordable water. Operational strategies are developed to improve equity for a community through valve operations that improve the equitable delivery of safe water. This research develops an approach to assess equity of the quality of delivered water and can be used to facilitate WDS management that provides equitable access to safe water.

The COVID-19 pandemic changed daily routines for people around the globe due to the adoption of social distancing measures, such as working from home and restricted travel. Changes in daily routines created new water demand patterns, and the spatial redistribution of water demands in urban water distribution systems affected water quality. A range of factors can influence individual decisions to social distance, including demographics, risk perceptions, and prior experience with infectious disease. This research develops an agent-based modeling framework to simulate decisions to social distance, the effect of social distancing on water demands, and effects on the performance of water infrastructure and the quality of delivered drinking water. This framework couples a hydraulic model, a COVID-19 transmission model, and Bayesian belief network (BBN) driven decision-making models within an agent-based modeling framework. The model is applied for a virtual city, Micropolis, to explore the effects of social distancing decisions on water age. Results demonstrate an increase in average water age and changes to the expected flow directions in pipes under scenarios of increasing social distancing. Nodes near industrial areas experience higher degradation of water quality. This research provides a new framework to develop and evaluate water infrastructure management strategies during pandemics.

Centralized urban water supply systems affect high social, economic, and environmental costs through large-scale treatment and distribution of potable water for all end uses. This technological regime is being challenged by climate change, population growth, and aging infrastructure. One sustainable practice is the reuse of wastewater for non-potable end uses through a dual water reticulation system. There are few large-scale implementations of dual systems in the US, and proactive adoption of dual systems is rare. This is because water infrastructure, as a sociotechnical system, exists in a three-dimensional landscape of technology, society, and institutions. The transition from a centralized water management paradigm to a decentralized, fit-for-purpose regime is a major transformation which involves not only technological changes, but also changes in the momentum of behaviors, regulations, infrastructure, and symbolic meaning. This research applies the multi-level perspective (MLP), which is a leading theory in the field of sustainable transitions, to characterize the transition from a centralized water system to a dual water system. The MLP is applied to characterize landscape pressures by calculating changes in four standardized exposure metrics: water stress, public attention to water supply issues, financial stress, and policy developments. Time series of the four exposure metrics are synthesized to explore periods of accelerated change (PoACs) when data show increasing public attention, higher water stress, and multiple new water-related policies. In 2001, the Town of Cary became the first water utility in North Carolina to utilize a secondary water reticulation network for providing reclaimed water to households and businesses. This research applies the MLP to characterize landscape pressures in the context of water stress and public attention to the Town of Cary dual system. The MLP provides a description and explanation of the effects of landscape changes on consumer support for reclaimed water reuse.

Water demand management is crucial for ensuring the efficient use of water resources. Smart water meters have emerged as a valuable tool for managing water usage in residential areas. Smart water meter data analysis can provide valuable insights for informing water resource management strategies and policy decisions. Using hourly AMI data for water demand management provides more accurate and frequent estimates of water use compared to using billing data, which can lead to more effective and targeted policies for demand management. In this study, we used hourly demand data from smart water meters in 18,000 residential accounts in Lakewood, CA, over two years to estimate the volume and timing of outdoor water use. We used the minimum day method-network level (MDM-N), minimum day method-account level (MDM-A), and minimum hour method network level (MHM-N) to develop estimates of indoor water use, which we used to calculate outdoor water use patterns. Our findings indicate that, on average, outdoor water use ranges from 31% to 41% of total demand using the MDM-N, MDMA, and MHMN. Our study highlights the potential of smart water meter data analysis as a valuable tool for water demand management. The ability to estimate and understand outdoor water use patterns can help policymakers develop more effective water demand policies and inform water resource management strategies to ensure the efficient use of water resources. Smart water meters and new modeling methods can help optimize water demand management strategies by providing a more accurate and detailed picture of outdoor water usage.

Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak hourly demands generate significant costs due to the energy requirements of producing and pumping large volumes of potable water. High peak demands also require large investments in infrastructure expansion to support urban growth and economic development. This research develops an agent-based model to assess a demand-side management strategy that shifts high hourly demands. This research envisions a gamification system that rewards players through a leaderboard for shifting water end uses for laundry, irrigation, and bathing from on-peak to off-peak periods. Gamification of demand shifting behaviors is enabled through advanced metering infrastructure, which measures hourly or sub-hourly demands at the account level, and can be analyzed to assess water use behaviors. This research develops an Agent-based Modeling approach based on the Opinion Dynamics theory to calculate the changing opinion of household agents toward shifting water demands, based on utility broadcasts, observations of neighbor water use, social media, and participation in a leaderboard. The model is applied for eight months of hourly demand data collected through smart meters at a participating utility. Results show that a high level of participation in the leaderboard increases the adoption rate of customers shifting the time of specific end uses and may account for up to a 37 % reduction of the peak volume. The diurnal curve is flattened as consumers shift water use from on-peak to off-peak hours. The Agent-Based Model - Opinion Dynamics approach can assist water utilities in designing and evaluating gamification approaches that mitigate peak demands to support water infrastructure management.

Water distribution networks are critical infrastructure systems that deliver water at expected levels of quality, quantity, and pressure across a wide geography to diverse water users. Systems analysis tools, including modeling and optimization, are vital in developing designs and management strategies for building and operating pipe networks. Water utilities need to protect data to manage infrastructure security, resulting in limited accessibility of water distribution network models that can be used to develop insight for cities. Existing methods generate pipe network models that can be used in research to test simulation and optimization approaches for managing infrastructure. Model generating methods use open-source data as input and apply mixed integer linear programming (MILP) to size pipes. This research extends existing model generating methods by including additional open-source tile map datasets and data describing water infrastructure components, such as water towers. The MILP method is linked with the EPANET hydraulic solver to validate hydraulic assumptions and assess the accuracy of the MILP approach to calculate pressures. The method is demonstrated in this research to generate a representative network for cities using readily available open-source data. This tool can aid researchers in developing and testing water management strategies.