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

> **NIH NIH P42** · WAYNE STATE UNIVERSITY · 2022 · $223,177

## Abstract

Project Summary/Abstract - Project E2
Integrated IoT Sensing and Edge Computing Coupled with a Bayesian Network Model
 for Exposure Assessment and Targeted Remediation of Vapor Intrusion
Leads: Zhang, Dittrich
Project Summary/Abstract: Project E2 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 Project E2 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. We 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 will be tested by three
specific research aims: Aim 1 - 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; Aim 2 - develop
and deploy a dynamic, machine-learned BN model integrated with a mechanistic model for exposure assessment
and prioritized remediation of VI; and Aim 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 in both air and soil-water
systems. Modifications to sorption materials including activated carbon, zeolite clay, and organosilica particles
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 (Aim 2); SRP Mandate 3, methods and technologies to detect hazardous substances in the
environment (Aim 1); and SRP Mandate 4, basic biological, chemical, and physical methods to redu...

## Key facts

- **NIH application ID:** 10352963
- **Project number:** 1P42ES030991-01A1
- **Recipient organization:** WAYNE STATE UNIVERSITY
- **Principal Investigator:** Yongli Wager
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $223,177
- **Award type:** 1
- **Project period:** 2022-09-08 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10352963

## Citation

> US National Institutes of Health, RePORTER application 10352963, Integrated IoT Sensing and Edge Computing Coupled with a Bayesian Network Model for Exposure Assessment and Targeted Remediation of Vapor Intrusion (1P42ES030991-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10352963. Licensed CC0.

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