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Wayne State University

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Superfund Research Program

Integrated IoT Sensing and Edge Computing Coupled with a Bayesian Network Model for Exposure Assessment and Targeted Remediation of Vapor Intrusion

Project Leader: Yongli Wager
Co-Investigator: Timothy Dittrich
Grant Number: P42ES030991
Funding Period: 2022-2027
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Project Summary (2022-2027)

This project supports 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 this project is to develop a robust integrative platform that combines the power of an Internet of Things (IoT) sensor network with edge computing (IoTEC) for exposure assessment and targeted remediation of VOC vapor intrusion (VI) using a Bayesian network (BN) model. The researchers hypothesize that (1) integrated IoT sensor network and edge computing (IoTEC), compared to conventional off-line sampling, can provide a rapid-response, cost- efficient, and accurate 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 integrated mechanistic-BN models 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 is tested by three specific research aims:

  • Establish the IoTEC tool by integrating the IoT sensor network with edge computing for rapid-response, cost-efficient, and accurate monitoring of VI and VOC exposure.
  • Develop and deploy a dynamic, machine-learned BN model integrated with a mechanistic model for exposure assessment and prioritized remediation of VI.
  • Develop functionalized sorbents and remediation systems for integration with IoTEC monitoring for targeted remediation of VI risk pathways.

This innovative work transforms 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 are developed by integrating the IoTEC sensor network with the novel adsorption approach for rapid-response remediation of VOC to minimize exposure risks in both air and soil-water systems. Modifications to sorption materials including activated carbon, zeolite clay, and organosilica particles are investigated to address current air purifier performance concerns. In combination with other CLEAR projects / cores to reduce environmental risk to VOC exposure as well as improve public health outcomes, this provides improved methods and tools for risk characterization and optimization of remediation efforts. This research leverages the investigators’ funded research projects in IoT, edge computing, smart environmental monitoring, groundwater modeling, machine-learned BN modeling, and sorbent media synthesis, and benefits from well-established collaborations with partners such as the MI EGLE VI team and Superfund office.

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