Ensuring Safe Supply to Drinking Water
CEE Associate Professor Ryan Qi Wang, Assistant Professor Kelsey Pieper, and CSSH Professor Daniel Aldrich, in collaboration with Jundong Li from the University of Virginia, are leading a $750k NSF grant for “Dynamical Coupling of Physical and Social Infrastructures: Evaluating the Impacts of Social Capital on Access to Safe Well Water.” This grant is one of the awards NSF is supporting in their Strengthening American Infrastructure program. The program will “support research that utilizes advances in behavioral and social science to improve the value and usefulness of infrastructure in people’s lives, from U.S. roads and highways to state and local power grids.” Wang will serve as Principal Investigator.
Wang and Pieper’s grant will explore the use of cell phone-based mobility data to construct social networks and examine how advantageous positions within these networks may contribute to better water quality in private wells. Private wells serve as a primary water source for many in the US, and are particularly vulnerable to contamination due to severe weather events such as hurricanes or floods. “This project uses data on the mobility of cell phone users to characterize the social assistance that residents call upon,” the project’s abstract states. “Methods are used to account for unequal representation of different groups in such datasets. The analysis considers other variables that may cause variation in water quality, such as demographic and socioeconomic factors. Water quality is evaluated with samples of private wells and surveys with owners. The project places high priority on sharing important findings with stakeholders, including extension services and health departments.” Prof. Li will develop a fairness-AI framework to address possible biases in both the data and the developed deep-learning networks with the aim of improving prediction of well water quality and conditions after hurricanes.
The project builds on and combines the research expertise of Wang and Pieper, who have explored similar themes in past projects, such as Wang’s research using social media data to study mobility between communities during the COVID-19 Pandemic, and Pieper’s work using a citizen science approach to empower well-water reliant communities with the tools to monitor their water quality.
“Physical and social infrastructure are universally and inevitably coupled together for urban dwellers,” says Wang. “It is impossible to improve the physical infrastructure without better understanding, measuring, and improving social capitals, especially when it comes to private wells which are essential for individual households.”
Abstract Source: NSF
Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.
Access to a safe supply of drinking water is essential for the health and welfare of all people. In many places, private wells are the primary source of water for residents. This SAI research project examines the availability of potable drinking water to individuals and households in settings where private wells are the predominant source of water for residents. Maintaining a safe supply of drinking water may be particularly challenging for residents who lack broad access to social support, as reflected in geographic connections to other communities. This support may be especially important in the aftermath of natural disasters and related hazards that disrupt water supplies. This project uses data on the mobility of cell phone users to characterize the social assistance that residents call upon. Methods are used to account for unequal representation of different groups in such datasets. The analysis considers other variables that may cause variation in water quality, such as demographic and socioeconomic factors. Water quality is evaluated with samples of private wells and surveys with owners. The project places high priority on sharing important findings with stakeholders, including extension services and health departments. The project also contributes to middle and high school curricula that will be shared and used in diverse public school settings.
Multiple, complementary datasets are leveraged to examine the ways in which advantageous positions in social networks may contribute to better water quality in private wells, particularly in geographic settings that have been impacted by recent flooding. Social networks are constructed from data on the mobility of cellular phone users, and new algorithmic approaches are developed to address the biases that typify these data. Upon constructing these networks, measures of positions in social networks are used to predict variation in the contamination of private wells. The algorithmic approaches developed for graph neural network analysis will have broader potential applications in similar research that seeks to account for biases in the representativeness of large archival datasets, including biases that disadvantage vulnerable populations. The project involves multiple students, contributing to the training and education of early-career scientists.
This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences and the Directorate for Geosciences.
This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.