Equity and modeling in sustainability science: Examples and opportunities throughout the process.

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.

Modeling prevention behaviors during the COVID-19 pandemic using Bayesian belief networks and protection motivation theory.

Prevention behaviors are important in mitigating the transmission of COVID-19. The protection motivation theory (PMT) links perceptions of risk and coping ability with the act of adopting prevention behaviors. The goal of this research is to test the application of the PMT in predicting adoption of prevention behaviors during the COVID-19 pandemic. Two research objectives are achieved to explore motivating factors for adopting prevention behaviors. (1) The first objective is to identify variables that are strong predictors of prevention behavior adoption. A data-driven approach is used to train Bayesian belief network (BBN) models using results of a survey of N = 7797 $N=7797$ participants reporting risk perceptions and prevention behaviors during the COVID-19 pandemic. A large set of models are generated and analyzed to identify significant variables. (2) The second objective is to develop models based on the PMT to predict prevention behaviors. BBN models that predict prevention behaviors were developed using two approaches. In the first approach, a data-driven methodology trains models using survey data alone. In the second approach, expert knowledge is used to develop the structure of the BBN using PMT constructs. Results demonstrate that trust and experience with COVID-19 were important predictors for prevention measure adoption. Models that were developed using the PMT confirm relationships between coping appraisal, threat appraisal, and protective behaviors. Data-driven and PMT-based models perform similarly well, confirming the use of PMT in this context. Predicting adoption of social distancing behaviors provides insight for developing policies during pandemics.

Can Common Pool Resource Theory Catalyze Stakeholder-Driven Solutions to the Freshwater Salinization Syndrome?

Freshwater salinity is rising across many regions of the United States as well as globally, a phenomenon called the freshwater salinization syndrome (FSS). The FSS mobilizes organic carbon, nutrients, heavy metals, and other contaminants sequestered in soils and freshwater sediments, alters the structures and functions of soils, streams, and riparian ecosystems, threatens drinking water supplies, and undermines progress toward many of the United Nations Sustainable Development Goals. There is an urgent need to leverage the current understanding of salinization's causes and consequences─in partnership with engineers, social scientists, policymakers, and other stakeholders─into locally tailored approaches for balancing our nation's salt budget. In this feature, we propose that the FSS can be understood as a common pool resource problem and explore Nobel Laureate Elinor Ostrom's social-ecological systems framework as an approach for identifying the conditions under which local actors may work collectively to manage the FSS in the absence of top-down regulatory controls. We adopt as a case study rising sodium concentrations in the Occoquan Reservoir, a critical water supply for up to one million residents in Northern Virginia (USA), to illustrate emerging impacts, underlying causes, possible solutions, and critical research needs.

NMR Relaxometry to Characterize the Drug Structural Phase in a Porous Construct.

Nuclear magnetic resonance (NMR) frequency spectra and T2 relaxation time measurements, using a high-power radio frequency probe, are shown to characterize the presence of an amorphous drug in a porous silica construct. The results indicate the ability of non-solid-state NMR methods to characterize crystalline and amorphous solid structural phases in drugs. Two-dimensional T1- T2 magnetic relaxation time correlation experiments are shown to monitor the impact of relative humidity on the drug in a porous silica tablet.

An Empirical Agent-Based Model to Simulate the Adoption of Water Reuse Using the Social Amplification of Risk Framework.

Water reuse can serve as a sustainable alternative water source for urban areas. However, the successful implementation of large-scale water reuse projects depends on community acceptance. Because of the negative perceptions that are traditionally associated with reclaimed water, water reuse is often not considered in the development of urban water management plans. This study develops a simulation model for understanding community opinion dynamics surrounding the issue of water reuse, and how individual perceptions evolve within that context, which can help in the planning and decision-making process. Based on the social amplification of risk framework, our agent-based model simulates consumer perceptions, discussion patterns, and their adoption or rejection of water reuse. The model is based on the "risk publics" model, an empirical approach that uses the concept of belief clusters to explain the adoption of new technology. Each household is represented as an agent, and parameters that define their behavior and attributes are defined from survey data. Community-level parameters-including social groups, relationships, and communication variables, also from survey data-are encoded to simulate the social processes that influence community opinion. The model demonstrates its capabilities to simulate opinion dynamics and consumer adoption of water reuse. In addition, based on empirical data, the model is applied to investigate water reuse behavior in different regions of the United States. Importantly, our results reveal that public opinion dynamics emerge differently based on membership in opinion clusters, frequency of discussion, and the structure of social networks.

Using advanced metering infrastructure data to evaluate consumer compliance with water advisories during a water service interruption.

Water main breaks disrupt services provided by utilities and result in Water Service Interruptions (WSIs). Water utilities can manage WSIs through water advisories, which request that consumers limit their water use. The performance of water advisories depends on consumer compliance and decisions to conserve water. This research explores customer compliance with water advisories using water consumption data collected through Advanced Metering Infrastructure (AMI). AMI provides high temporal and spatial resolution of water consumption data, which is analyzed to identify changes in water use behaviors. This research explores water use changes during a major water main break in Orange County, North Carolina, that caused a significant WSI, limiting water supply for more than 80,000 people. Customers were asked to reduce water use to essential purposes only and to boil water over the course of two days in November 2018. This research analyzes hourly consumption data to evaluate water consumption trends during the WSI and in response to water advisories. Statistical analysis is used to estimate the number of consumers who complied with utility notifications and to evaluate the volume of water saved. Regression analysis is applied to explore compliance across different user segments. Results provide insight about the level and variation of water conservation that can be expected during a WSI.

Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic.

During the coronavirus disease 2019 (COVID-19) pandemic, the daily pattern of activities changed dramatically for people across the globe, as they socially distanced and worked remotely. Changes in daily routines created changes in water consumption patterns. Significant changes in water demands can affect the operation of water distribution systems, resulting in new patterns of flow, with implications for water age, pressure, and energy consumption. This research develops a digital twin to couple Advanced Metering Infrastructure (AMI) data with a hydraulic model to assess impacts on infrastructure due to changes in water demands associated with the COVID-19 pandemic for a case study. Using 2019 and COVID-19 modeling scenarios, the hydraulic model was executed to evaluate changes to water quality based on water age, pressure across nodes in the network, and the energy required by the system to distribute potable water. A water supply interruption event was modeled as a water main break to assess network resiliency for 2019 and COVID-19 demands. A digital twin provides the capabilities to explore and visualize emerging consumption patterns and their effects on the functioning of water systems, providing valuable analyses for water utility managers and insight for optimizing infrastructure operations and planning for long-term impacts.

Meaningful engagement: A crossfunctional framework for digital therapeutics.

Digital mental health interventions, or digital therapeutics, have the potential to transform the field of mental health. They provide the opportunity for increased accessibility, reduced stigma, and daily integration with patient's lives. However, as the burgeoning field continues to expand, there is a growing concern regarding the level and type of engagement users have with these technologies. Unlike many traditional technology products that have optimized their user experience to maximize the amount of time users spend within the product, such engagement within a digital therapeutic is not sufficient if users are not experiencing an improvement in clinical outcomes. In fact, a primary challenge within digital therapeutics is user engagement. Digital therapeutics are only effective if users sufficiently engage with them and, we argue, only if users meaningfully engage with the product. Therefore, we propose a 4-step framework to assess meaningful engagement within digital therapeutics: (1) Define the measure of value (2) Operationalize meaningful engagement for your digital therapeutic (3) Implement solutions to increase meaningful engagement (4) Iteratively evaluate the solution's impact on meaningful engagement and clinical outcomes. We provide recommendations to the common challenges associated with each step. We specifically emphasize a cross-functional approach to assessing meaningful engagement and use an adolescent-focused example throughout to further highlight developmental considerations one should consider depending on their target users.

Developing early warning systems to predict water lead levels in tap water for private systems.

Lead is a chemical contaminant that threatens public health, and high levels of lead have been identified in drinking water at locations across the globe. Under-served populations that use private systems for drinking water supplies may be at an elevated level of risk because utilities and governing agencies are not responsible for ensuring that lead levels meet the Lead and Copper Rule at these systems. Predictive models that can be used by residents to assess water quality threats in their households can create awareness of water lead levels (WLLs). This research explores and compares the use of statistical models (i.e., Bayesian Belief classifiers) and machine learning models (i.e., ensemble of decision trees) for predicting WLLs. Models are developed using a dataset collected by the Virginia Household Water Quality Program (VAHWQP) at approximately 8000 households in Virginia during 2012-2017. The dataset reports laboratory-tested water quality parameters at households, location information, and household and plumbing characteristics, including observations of water odor, taste, discoloration. Some water quality parameters, such as pH, iron, and copper, can be measured at low resolution by residents using at-home water test kits and can be used to predict risk of WLLs. The use of at-home water quality test kits was simulated through the discretization of water quality parameter measurements to match the resolution of at-home water quality test kits and the introduction of error in water quality readings. Using this approach, this research demonstrates that low-resolution data collected by residents can be used as input for models to estimate WLLs. Model predictability was explored for a set of at-home water quality test kits that observe a variety of water quality parameters and report parameters at a range of resolutions. The effects of the timing of water sampling (e.g., first-draw vs. flushed samples) and error in kits on model error were tested through simulations. The prediction models developed through this research provide a set of tools for private well users to assess the risk of lead contamination. Models can be implemented as early warning systems in citizen science and online platforms to improve awareness of drinking water threats.

Developing a framework for classifying water lead levels at private drinking water systems: A Bayesian Belief Network approach.

The presence of lead in drinking water creates a public health crisis, as lead causes neurological damage at low levels of exposure. The objective of this research is to explore modeling approaches to predict the risk of lead at private drinking water systems. This research uses Bayesian Network approaches to explore interactions among household characteristics, geological parameters, observations of tap water, and laboratory tests of water quality parameters. A knowledge discovery framework is developed by integrating methods for data discretization, feature selection, and Bayes classifiers. Forward selection and backward selection are explored for feature selection. Discretization approaches, including domain-knowledge, statistical, and information-based approaches, are tested to discretize continuous features. Bayes classifiers that are tested include General Bayesian Network, Naive Bayes, and Tree-Augmented Naive Bayes, which are applied to identify Directed Acyclic Graphs (DAGs). Bayesian inference is used to fit conditional probability tables for each DAG. The Bayesian framework is applied to fit models for a dataset collected by the Virginia Household Water Quality Program (VAHWQP), which collected water samples and conducted household surveys at 2,146 households that use private water systems, including wells and springs, in Virginia during 2012 and 2013. Relationships among laboratory-tested water quality parameters, observations of tap water, and household characteristics, including plumbing type, source water, household location, and on-site water treatment are explored to develop features for predicting water lead levels. Results demonstrate that Naive Bayes classifiers perform best based on recall and precision, when compared with other classifiers. Copper is the most significant predictor of lead, and other important predictors include county, pH, and on-site water treatment. Feature selection methods have a marginal effect on performance, and discretization methods can greatly affect model performance when paired with classifiers. Owners of private wells remain disadvantaged and may be at an elevated level of risk, because utilities and governing agencies are not responsible for ensuring that lead levels meet the Lead and Copper Rule for private wells. Insight gained from models can be used to identify water quality parameters, plumbing characteristics, and household variables that increase the likelihood of high water lead levels to inform decisions about lead testing and treatment.

Review of Modeling Methodologies for Managing Water Distribution Security.

Water distribution systems are vulnerable to hazards that threaten water delivery, water quality, and physical and cybernetic infrastructure. Water utilities and managers are responsible for assessing and preparing for these hazards, and researchers have developed a range of computational frameworks to explore and identify strategies for what-if scenarios. This manuscript conducts a review of the literature to report on the state of the art in modeling methodologies that have been developed to support the security of water distribution systems. First, the major activities outlined in the emergency management framework are reviewed; the activities include risk assessment, mitigation, emergency preparedness, response, and recovery. Simulation approaches and prototype software tools are reviewed that have been developed by government agencies and researchers for assessing and mitigating four threat modes, including contamination events, physical destruction, interconnected infrastructure cascading failures, and cybernetic attacks. Modeling tools are mapped to emergency management activities, and an analysis of the research is conducted to group studies based on methodologies that are used and developed to support emergency management activities. Recommendations are made for research needs that will contribute to the enhancement of the security of water distribution systems.