About

Dr. Emily Zechman Berglund is an Associate Head for Faculty Development in the Department of Civil, Construction, and Environmental Engineering at North Carolina State University, where she has been a faculty member since July 2011. She holds a B.S. and M.S. in Civil Engineering from the University of Kentucky and earned her Ph.D. in Civil Engineering from North Carolina State University in 2005. Her research focuses on developing computational methodologies to explore feedback mechanisms among social and infrastructure systems, creating socio-technical models that integrate Complex Adaptive Systems modeling with engineering models to simulate adaptive behaviors and feedbacks among consumers, infrastructure, and environmental systems. She specializes in water management, water reclamation and conservation, water security, complex adaptive systems, agent-based modeling, evolutionary computation, human behavior, and social dimensions. Dr. Berglund's work aims to enhance the sustainability, security, and resilience of complex infrastructure and water resources systems, with particular attention to urban water supply and water contamination incidents.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Data Mining
• Ecology
• Computer Security
• Engineering
• Economics
• Database
• Environmental science
• Engineering management
• Environmental economics
• Construction engineering
• Environmental resource management
• Water resource management
• Business
• Environmental engineering

Selected publications

• PipeNetGen: A Methodology for Generating Synthetic Water Distribution Network Models

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-15

  otherOpen accessSenior author

  Abstract: Water distribution networks (WDNs) are centralized systems that deliver water across large geographic areas to diverse user groups. WDN models are critical in developing designs, operations, and management strategies for water infrastructure systems and in the research and development of new methodologies to support decision- making. WDN models, however, are time consuming to create, and under-resourced water utilities may not have the resources to develop a WDN model. This research develops a new method for generating synthetic WDN mod- els using open-source data, graph theory, and Mixed-Integer Linear Programming (MILP). This research intro- duces the Pipe Network Generator methodology (PipeNetGen), which can create a synthetic WDN model for any city using open-source data or private utility information. PipeNetGen includes a core MILP model based on pre- vious research and combines it with a clustering algorithm to run larger systems, a hydraulic simulator to correct MILP assumptions, and a fire flow method to ensure network reliability under high demands. PipeNetGen is ap- plied to Lakewood, California, and the synthetic WDN model is compared with a water utility-provided engi- neered WDN model based on pipe sizes, topology, hydraulics, and demand. PipeNetGen provides a valuable re- source to improve water infrastructure management for utilities and research.

  PublisherDOI

• Models and Codes of USING SMART WATER METER DATA TO ESTIMATE RESIDENTIAL OUTDOOR WATER

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-01

  otherOpen accessSenior author

  Implements five outdoor-use estimation methods from hourly AMI data: MMM-N, MWM-A, MDM-N, MDM-A, and MHM-N. Reproduces figures reported in the manuscript, including MinDay/MinHour, method diurnal patterns, weekday/weekend comparisons, and supplementary day-of-week statistics (Kruskal–Wallis + Dunn with Bonferroni-adjusted p-values). Inputs: hourly demand matrix (time × accounts), list of SFH account IDs, optional list of accounts to exclude. Outputs: figures saved to ./figs/ and intermediate results saved to ./results/.

  PublisherDOI

• Evaluating Exposure to Poor Water Quality by Modeling Premise Plumbing and Personal End Uses

  2026-04-23

  articleSenior authorCorresponding

  Safe drinking water is critical in protecting public health, and premise plumbing plays an essential role in determining the final quality of water consumed by end users. Water quality deteriorates in premise plumbing systems due to intermittent uses, which causes stagnation, low disinfectant residual, and leaching of chemicals from pipes and plumbing material. Water quality deficiencies in premise plumbing expose members of a household to varying levels of contaminants. Exposure of individuals to dangerous chemicals and pathogens emerges due to the intersection of water use behavior at fixtures and the water quality at fixtures. Children may be more susceptible to the effects of exposure based on their body weight. Water use is personal and varies based on existing infrastructure, norms, lifestyles, and attitudes. Personal water use habits, such as the length and temperature of showers, watering lawns and gardens, drinking tap water, and washing hands, affect not only the flows and quality of water in the premise plumbing but also the exposure of an individual to unique water quality signatures. This research develops a modeling framework that couples hydraulic modeling of premise plumbing with agent-based models of household members to assess the exposure of household members to stagnated water. Personalized end-use data were collected for a household of four members over a 1-month period. By surveying the premise plumbing system, a hydraulic model of the hot water and cold water pipelines and fixtures was developed. The modeling system was applied to calculate the exposure to high water age for each household member. Results quantify the exposure to stagnated water based on fixtures where water is used and end-use practices. Further research can model water quality constituents of concern to identify vulnerable household members and assess exposure.

  PublisherDOI

• PipeNetGen: A Methodology for Generating Synthetic Water Distribution Network Models

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-15

  otherOpen accessSenior author

  Abstract: Water distribution networks (WDNs) are centralized systems that deliver water across large geographic areas to diverse user groups. WDN models are critical in developing designs, operations, and management strategies for water infrastructure systems and in the research and development of new methodologies to support decision- making. WDN models, however, are time consuming to create, and under-resourced water utilities may not have the resources to develop a WDN model. This research develops a new method for generating synthetic WDN mod- els using open-source data, graph theory, and Mixed-Integer Linear Programming (MILP). This research intro- duces the Pipe Network Generator methodology (PipeNetGen), which can create a synthetic WDN model for any city using open-source data or private utility information. PipeNetGen includes a core MILP model based on pre- vious research and combines it with a clustering algorithm to run larger systems, a hydraulic simulator to correct MILP assumptions, and a fire flow method to ensure network reliability under high demands. PipeNetGen is ap- plied to Lakewood, California, and the synthetic WDN model is compared with a water utility-provided engi- neered WDN model based on pipe sizes, topology, hydraulics, and demand. PipeNetGen provides a valuable re- source to improve water infrastructure management for utilities and research.

  PublisherDOI

• Water Quality and Stagnation in a Campus Water Distribution System due to Changes in Student Housing Occupancy during Academic Breaks

  2026-04-23

  articleCorresponding

  Intra-system water quality varies across a Water Distribution System (WDS), and large shifts in consumer demands can create spatio-temporal changes in water flows and water quality. Demand shifts exacerbate hotspots of poor water quality by causing changes in flow direction, velocity, and stagnation. Water stagnation allows the decay of residual chlorine, which promotes microbial growth and leads to potential spikes in disinfection by-products and metal elements. Water flow in WDSs does not typically stagnate due to the aggregated water demand in urban areas; however, some significant changes in population location and behaviors can cause water flow to decrease significantly in some areas of a WDS. Phenomena such as working from home, changes in occupancy due to tourism and seasonal work, depopulation of neighborhoods, and adoption of zero water homes can reduce water flows and degrade water quality in a WDS. This research develops a hydraulic model of a college campus to explore the complex interaction between water demand changes and intra-system water quality disparities. A hydraulic model is developed in EPANET and applied to simulate scenarios that represent holidays, such as winter and spring breaks, when students leave campus and water demand drops significantly. Holiday scenarios are simulated to assess changes to water age in the WDS and identify nodes that are vulnerable to poor water quality. This research provides new insight to explore and manage exposure to poor water quality that is caused by large shifts in water demands. Further research can identify management strategies for WDS design, retrofit, and operation to reduce the health impacts of poor water quality.

  PublisherDOI

• Models and Codes of USING SMART WATER METER DATA TO ESTIMATE RESIDENTIAL OUTDOOR WATER

  Open MIND · 2026-01-01

  otherSenior author

  Implements five outdoor-use estimation methods from hourly AMI data: MMM-N, MWM-A, MDM-N, MDM-A, and MHM-N. Reproduces figures reported in the manuscript, including MinDay/MinHour, method diurnal patterns, weekday/weekend comparisons, and supplementary day-of-week statistics (Kruskal–Wallis + Dunn with Bonferroni-adjusted p-values). Inputs: hourly demand matrix (time × accounts), list of SFH account IDs, optional list of accounts to exclude. Outputs: figures saved to ./figs/ and intermediate results saved to ./results/.

  DOI

• PipeNetGen: A methodology for generating synthetic water distribution network models

  Water Research · 2026-03-10

  articleOpen accessSenior author

  Water distribution networks (WDNs) are centralized systems that deliver water across large geographic areas to diverse user groups. WDN models are critical in developing designs, operations, and management strategies for water infrastructure systems and in the research and development of new methodologies to support decision-making. WDN models, however, are time consuming to create, and under-resourced water utilities may not have the resources to develop a WDN model. This research develops a new method for generating synthetic WDN models using open-source data, graph theory, and Mixed-Integer Linear Programming (MILP). This research introduces the Pipe Network Generator methodology (PipeNetGen), which can create a synthetic WDN model for any city using open-source data or private utility information. PipeNetGen includes a core MILP model based on previous research and combines it with a clustering algorithm to run larger systems, a hydraulic simulator to correct MILP assumptions, and a fire flow method to ensure network reliability under high demands. PipeNetGen is applied to Lakewood, California, and the synthetic WDN model is compared with a water utility-provided engineered WDN model based on pipe sizes, topology, hydraulics, and demand. PipeNetGen provides a valuable resource to improve water infrastructure management for utilities and research.

  PublisherDOI

• Adapting to future changes using smart stormwater storage systems to preserve flow regimes

  Journal of Hydrology X · 2025-06-14

  articleOpen accessSenior author

  • Smart control of stormwater storages has great potential to adapt to future changes. • This paper evaluates a proposed control approach for three future scenarios. • These project a 7 %–95 % increase in peak flows and 25 %–57 % increase in volumes. • The control approach is shown to be adaptive by preserving desired target outflow hydrographs. • The control approach does not rely on prediction/calibration to future rainfall events. Worldwide, stormwater systems are increasingly stressed due to increased rainfall and runoff caused by climate change and urbanization. Traditional static strategies for addressing these challenges, including increasing infrastructure capacity, are often inadequate as they are not suited to dealing with large uncertainties. In contrast, adaptive strategies, such as smart real-time control (RTC), are suited to dealing with such uncertainties, as they are able to respond to future changes as they occur. However, existing RTC approaches are not truly adaptive, as they require information on future rainfall. In this paper, we adapt an existing RTC approach that does not require such information so that it is able to match desired outflow hydrographs in the face of changing inflow hydrographs. The utility of the proposed Target Flow Control for Hydrographs (TFC-H) approach is demonstrated by simulating its ability to achieve desired target flow hydrographs for multiple future worlds of a simplified lot-scale system as part of which peak flows increase from 7 % to 95 % and storm volume increases from 25 % to 57 %. The results show that use of the TFC-H approach effectively maintains the desired target outflow hydrograph with less than 5 % error for this wide range of “future worlds”. Importantly, unlike other RTC approaches, the TFC-H approach is able to adapt without any knowledge/predictions of future rainfall/inflow hydrographs. This clearly demonstrates the potential of the TFC-H approach to enable existing stormwater systems to adapt to future changes.

  PublisherDOI

• Equitable Access to Affordable and Clean Water in Pre-Pandemic, Pandemic, and Post-Pandemic Modes of Work

  2025-05-15 · 1 citations

  articleSenior author

  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 bottled 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 wide-spread 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.

  PublisherDOI

• Predicting Drinking Water Quality Using Low-Cost Sensing: Water Aesthetics and Chemistry Strips

  Journal of Water Resources Planning and Management · 2025-11-13

  article1st authorCorresponding

  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.

  PublisherDOI

Recent grants

• An Agent-Based Modeling Approach to Integrate Social Dimensions and Infrastructure Management for Urban Water Reuse

  NSF · $320k · 2012–2015

Frequent coauthors

• Avi Ostfeld

  Technion – Israel Institute of Technology

  23 shared

• Jorge E. Pesantez

  23 shared

• Brent Vizanko

  North Carolina State University

  19 shared

• M. Ehsan Shafiee

  University of Surrey

  18 shared

• Leonid Kadinski

  Technion – Israel Institute of Technology

  16 shared

• Stanley B. Grant

  Virginia Tech

  14 shared

• Payam Aminpour

  14 shared

• Shantanu V. Bhide

  13 shared

Labs

• Civil, Construction and Environmental EngineeringPI

Awards & honors

• Best Research-Oriented Paper Awards in 2010 and 2011 for pub…
• Water Distribution Systems Management Under COVID-X (2021-20…
• Designing Model Protocols to Assess Impacts to Receiving Wat…
• Citizen Science Internship Program to Quantify Racial and Ec…
• Untapping the Crowd: Consumer Detection and Control of Lead…