Jacqueline Gibson

The long-term objective of this study is to develop theoretical frameworks for how water and health systems adapt to and learn from risks associated with water system-based disruptions to enhance resiliency. The first objective in support of this long-term objective is to identify the range of risks and disruptions in water and public health systems in urban areas and assess the extent to which the systems possess characteristics of resilience. The second objective is to evaluate how the public engages with drinking water and public health systems. The third objective is to model how drinking water and public health systems respond to water system disruptions. To achieve these objectives, we first will conduct case studies of a range of recent disruptions (e.g., routine water main breaks, large-scale disasters) in cities (Detroit, Flint, MI; Toledo, OH; Raleigh, NC) and tribal communities (Robeson County, NC). Results from these cases will be used to build on existing resilience frameworks with a coupled model of these two interdependent systems of how these systems jointly function and adapt to risks and hazards. Bayesian network modeling and machine-learning approaches will be used to predict system characteristics associated with disruptions and the interdependent system responses. Next, the case study results will inform a national survey of drinking water and public health systems to both test and refine the coupled model to support each aim including the long-term objective. North Carolina State University���s main role in this project is to build and test the machine-learned, Bayesian network early warning system.

Project E2 relates to the Center for Leadership in Environmental Awareness and Research (CLEAR) with a focus on the Superfund-relevant VOC contaminants in complex urban environments. The goal of Project E2 is to develop a robust platform that integrates an Internet of Things (IoT) sensing network and edge computing (IoTEC) with a Bayesian network model for exposure assessment and targeted remediation of VOC vapor intrusion (VI). We hypothesize that (1) integrated IoT sensing and edge computing (IoTEC), compared to conventional off-line sampling,��provides a rapid-response and cost-efficient approach to monitor and screen for VI in complex urban matrices, (2) IoTEC sensing data supplemented with house survey, regional groundwater modeling, soil survey, and geospatial tools can be used to develop a Bayesian-based tool for exposure assessment of VI, and (3) a novel VOC adsorption approach for timely and targeted remediation of VI coupled with the products of (1) and (2) will complement conventional engineering remediation to reduce exposure risk of VI. This hypothesis will be tested by three specific research aims: (1) establish the IoTEC tool by integrating the IoT sensing technology with edge computing for cost-efficient and rapid screening and monitoring of VI and VOC exposure; (2) deploy a dynamic, machine-learned Bayesian network model integrated with a mechanistic model for exposure assessment and prioritized remediation; and (3) develop functionalized sorbents and remediation systems for integration with IoTEC monitoring for targeted remediation of VI risk pathways. This innovative work will transform the paradigm of VI assessment and remediation from conventional off-line methods to a new data-science driven approach, providing a first-of-its-kind platform with functionality ranging from VOC monitoring and data collection / analysis to data-based decision making and improved remediation outcomes. In addition, labscale micropilot treatment systems will be developed by integrating the IoTEC sensor network with the novel adsorption approach for rapid-response remediation of VOC to minimize exposure risks. Modifications to sorption materials including activated carbon, zeolite clay, and organosilica particles and foams will be investigated to address current air purifier performance concerns. This project addresses three important SRP mandates: SRP Mandate 2, methods to assess the risks to human health presented by hazardous substances; SRP Mandate 3, methods and technologies to detect hazardous substances in the environment; and SRP Mandate 4, basic biological, chemical, and physical methods to reduce the amount and toxicity of hazardous substances in the environment. In combination with other CLEAR projects / cores to reduce environmental risk to VOC exposure as well as improve public health outcomes, this work will provide improved methods and tools for risk characterization and optimization of remediation efforts. This research will leverage the investigators��� funded research projects in IoT sensing, edge computing, smart environmental monitoring, groundwater modeling, machine-learned Bayesian network modeling, and VOC adsorption as well as will benefit from well-established collaborations with partners such as the EGLE/MI Superfund office.

Project E2 relates to the Center for Leadership in Environmental Awareness and Research (CLEAR) with a focus on the Superfund-relevant VOC contaminants in complex urban environments. The goal of Project E2 is to develop a robust platform that integrates an Internet of Things (IoT) sensing network and edge computing (IoTEC) with a Bayesian network model for exposure assessment and targeted remediation of VOC vapor intrusion (VI). We hypothesize that (1) integrated IoT sensing and edge computing (IoTEC), compared to conventional off-line sampling,��provides a rapid-response and cost-efficient approach to monitor and screen for VI in complex urban matrices, (2) IoTEC sensing data supplemented with house survey, regional groundwater modeling, soil survey, and geospatial tools can be used to develop a Bayesian-based tool for exposure assessment of VI, and (3) a novel VOC adsorption approach for timely and targeted remediation of VI coupled with the products of (1) and (2) will complement conventional engineering remediation to reduce exposure risk of VI. This hypothesis will be tested by three specific research aims: (1) establish the IoTEC tool by integrating the IoT sensing technology with edge computing for cost-efficient and rapid screening and monitoring of VI and VOC exposure; (2) deploy a dynamic, machine-learned Bayesian network model integrated with a mechanistic model for exposure assessment and prioritized remediation; and (3) develop functionalized sorbents and remediation systems for integration with IoTEC monitoring for targeted remediation of VI risk pathways. This innovative work will transform the paradigm of VI assessment and remediation from conventional off-line methods to a new data-science driven approach, providing a first-of-its-kind platform with functionality ranging from VOC monitoring and data collection / analysis to data-based decision making and improved remediation outcomes. In addition, labscale micropilot treatment systems will be developed by integrating the IoTEC sensor network with the novel adsorption approach for rapid-response remediation of VOC to minimize exposure risks. Modifications to sorption materials including activated carbon, zeolite clay, and organosilica particles and foams will be investigated to address current air purifier performance concerns. This project addresses three important SRP mandates: SRP Mandate 2, methods to assess the risks to human health presented by hazardous substances; SRP Mandate 3, methods and technologies to detect hazardous substances in the environment; and SRP Mandate 4, basic biological, chemical, and physical methods to reduce the amount and toxicity of hazardous substances in the environment. In combination with other CLEAR projects / cores to reduce environmental risk to VOC exposure as well as improve public health outcomes, this work will provide improved methods and tools for risk characterization and optimization of remediation efforts. This research will leverage the investigators��� funded research projects in IoT sensing, edge computing, smart environmental monitoring, groundwater modeling, machine-learned Bayesian network modeling, and VOC adsorption as well as will benefit from well-established collaborations with partners such as the EGLE/MI Superfund office.