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

集成物联网传感和边缘计算与贝叶斯网络模型相结合，用于暴露评估和蒸汽入侵的针对性修复

• 批准号: 10352963
• 负责人: Yongli Wager
• 金额: $ 22.32万
• 依托单位: WAYNE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-08 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10352963
• 关键词: Address; Adsorption; Aging; Air; Awareness; Bayesian Modeling; Bayesian Network; Benzene; Biological; Carbon; Chemicals; Cities; Collaborations; Collection; Complement; Complex; Coupled; Data; Data Collection; Data Science; Data Sources; Decision Making; Development; Engineering; Environment; Environmental Health; Environmental Monitoring; Environmental Risk Factor; Exhibits; Exposure to; Face; Funding; Future; Goals; Hazardous Substances; Health; Home; Human; Industrialization; Industry Standard; Infrastructure; Internet of Things; Kinetics; Lead; Leadership; Low income; Machine Learning; Mass Fragmentography; Methods; Minority; Mission; Modeling; Modification; Monitor; Online Systems; Outcome; Pathway interactions; Pattern Recognition; Performance; Population; Premature Birth; Property; Public Health; Research; Research Personnel; Research Project Grants; Risk; Sampling; Signal Recognition Particle; Signal Transduction; Site; Soil; Source; Superfund; Surveys; System; Systems Integration; Technology; Testing; Tetrachloroethylene; Time; Toluene; Toxic effect; Trichloroethylene; Variant; Water; Work; Zeolites; air sampling; base; clay; cost; cost efficient; design; environmental justice; exposed human population; ground water; improved; innovation; machine learning algorithm; novel; offspring; particle; practical application; remediation; response; risk prediction model; sensor; stem; tool; urban area; urban setting; vapor intrusion; volatile organic compound; water sampling

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 reduce the amount and toxicity of hazardous substances in the environment (Aim 3). 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, edge computing, smart environmental monitoring, groundwater modeling, machine-learned BN modeling, and sorbent media synthesis, and will benefit from well-established collaborations with partners such as the MI EGLE VI team and Superfund office.

项目总结/摘要 - 项目 E2 集成物联网传感和边缘计算与贝叶斯网络模型相结合 用于暴露评估和蒸汽侵入的针对性修复 负责人：张迪特里希 项目摘要/摘要：项目 E2 支持环境意识和领导力中心 研究 (CLEAR)，重点关注复杂城市环境中与超级基金相关的 VOC 污染物。 E2 项目的目标是开发一个强大的集成平台，结合物联网的力量 具有边缘计算 (IoTEC) 功能的 (IoT) 传感器网络，用于 VOC 暴露评估和有针对性的修复 使用贝叶斯网络 (BN) 模型进行蒸汽入侵 (VI) 我们捕获了 (1) 集成物联网传感器网络。 和边缘计算（IoTEC）相比传统的离线采样，可以提供快速响应、成本低廉的 有效、准确地监测和筛选复杂城市矩阵中 VI 的方法，(2) IoTEC 传感数据 辅之以房屋调查，可以使用区域地下水模型、土壤调查和地理空间工具 开发用于 VI 暴露评估的集成机械-BN 模型，以及 (3) 一种新型 VOC 吸附 及时、有针对性地修复 VI 的方法与 (1) 和 (2) 的产品相结合将起到补充作用 降低 VI 暴露风险的传统工程修复措施将通过三个方面进行检验。 具体研究目标：目标1 - 通过将物联网传感器网络与边缘集成来建立IoTEC工具 用于快速响应、经济有效且准确监测 VI 和 VOC 暴露的计算；目标 2 - 开发 并部署与机械模型集成的动态机器学习 BN 模型以进行暴露评估 优先修复 VI；目标 3 - 开发功能化吸附剂和修复系统 与 IoTEC 监控相集成，以有针对性地修复 VI 风险路径。这项创新工作将发生转变。 VI评估和修复的范式从传统的离线方法到新的数据科学驱动的方法 方法，提供首个具有 VOC 监测和数据功能的平台 此外，还可以收集/分析基于数据的决策并改进补救结果。 微试点治疗系统将通过将 IoTEC 传感器网络与新颖的技术集成来开发 用于快速响应修复挥发性有机化合物的吸附方法，以最大限度地减少空气和土壤水中的暴露风险 吸附材料活性炭、沸石粘土和有机硅颗粒的改性。 将进行调查以解决当前空气净化器的性能问题。该项目解决了三个重要问题。 SRP 指令：SRP 指令 2，评估有害物质对人类健康造成的风险的方法 物质（目标 2）；SRP 指令 3，检测有害物质的方法和技术； 环境（目标 1）；以及 SRP 指令 4，减少污染的基本生物、化学和物理方法 环境中有害物质的数量和毒性（目标 3）与其他 CLEAR 相结合。 旨在降低 VOC 暴露的环境风险并改善公共卫生结果的项目/核心，这 这项工作将为风险描述和优化补救工作提供改进的方法和工具。 这项研究将利用研究人员资助的物联网、边缘计算、智能 环境监测、地下水建模、机器学习的 BN 建模和吸附剂介质合成， 并将受益于与 MI EGLE VI 团队和 Superfund 等合作伙伴的良好合作 办公室。