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.

项目概要/摘要-项目E2 集成物联网传感和边缘计算，结合贝叶斯网络模型 用于蒸汽侵入的暴露评估和有针对性的补救 负责人：Zhang、迪特里希 项目概要/摘要：项目E2支持环境意识领导中心， 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。 (IoT)具有边缘计算的传感器网络（IoTEC），用于VOC的暴露评估和有针对性的补救 使用贝叶斯网络（BN）模型的蒸汽侵入（VI）。我们假设（1）集成物联网传感器网络 与传统的离线采样相比，边缘计算（IoTEC）可以提供快速响应， 在复杂的城市矩阵中监测和筛选VI的有效，准确的方法，（2）IoTEC传感数据 辅以房屋调查，可使用区域地下水建模、土壤调查和地理空间工具 开发了用于VI暴露评估的集成机械BN模型;（3）一种新的VOC吸附 一种及时和有针对性的VI补救方法，加上（1）和（2）的产品，将补充 传统的工程补救措施，以减少暴露风险的VI。这一假设将由三个 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，建立IoTEC工具 计算快速响应，成本效益高，准确监测VI和VOC暴露;目标2 -开发 并部署一个动态的、机器学习的BN模型，该模型与用于暴露评估的机械模型相集成 目标3 -开发功能化吸附剂和修复系统， 与IoTEC监控集成，针对VI风险途径进行有针对性的补救。这项创新工作将改变 VI评估和补救的范式从传统的离线方法到新的数据科学驱动 方法，提供了一个首个具有VOC监测和数据等功能的平台 收集/分析数据为基础的决策和改善补救成果。此外，LabScale 微型治疗系统将通过将IoTEC传感器网络与新型 用于挥发性有机化合物快速反应补救的吸附方法，以最大限度地减少空气和土壤-水中的暴露风险 系统.吸附材料的改性，包括活性炭、沸石粘土和有机硅颗粒 将进行调查，以解决目前的空气净化器性能问题。该项目涉及三个重要的 SRP任务：SRP任务2，评估危险化学品对人类健康构成的风险的方法 （目标2）;《简化报告程序》任务3， 环境（目标1）;和SRP任务4，减少温室气体排放的基本生物、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 项目/核心，以减少挥发性有机化合物暴露的环境风险，并改善公共卫生成果，这 将开展工作，为风险定性和优化补救工作提供更好的方法和工具。 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BN建模和吸附剂介质合成， 并将受益于与MI EGLE VI团队和超级基金等合作伙伴的良好合作 办公室