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.

项目摘要/摘要 - 项目E2 集成的物联网传感和边缘计算与贝叶斯网络模型相结合 用于暴露评估和针对蒸气侵入的有针对性补救 线索：张，迪特里奇 项目摘要/摘要：E2项目支持环境意识和 研究（Clear）着重于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结合物联网力量的强大集成平台 （IoT）具有边缘计算（IOTEC）的传感器网络用于暴露评估和对VOC的有针对性修复 使用贝叶斯网络（BN）模型的蒸气入侵（VI）。我们假设（1）集成的物联网传感器网络 与传统的离线抽样相比，边缘计算（IOTEC）可以提供快速响应，成本 - 在复杂的城市物质中监视和筛选VI的有效且准确的方法，（2）IOTEC感应数据 可以使用房屋调查，区域地下水建模，土壤调查和地理空间工具 开发综合机械BN模型以进行VI的暴露评估，以及（3）一种新颖的VOC吸附 及时和有针对性修复VI的方法与（1）和（2）的产品结合使用 常规的工程修复以降低VI的接触风险。该假设将通过三个 特定的研究目的：目标1-通过将IoT传感器网络与Edge集成来建立IOTEC工具 计算快速响应，成本效益和对VI和VOC暴露的准确监控；目标2-发展 并部署与机械模型集成的动态，机器学习的BN模型，以进行暴露评估 并确定对VI的修复；目标3-开发功能化吸附剂和补救系统 与IOTEC监控的集成，以实现对VI风险途径的靶向修复。这项创新的工作将改变 VI评估的范式和从常规离线方法到新的数据科学驱动器的修复 方法，提供一个首先的平台，其功能范围从VOC监视和数据不等 收集/分析基于数据的决策和改进的补救结果。另外，LabScale Micropilot治疗系统将通过将IOTEC传感器网络与新颖 快速响应修复VOC的吸附方法，以最大程度地降低空气和土壤水中的风险 系统。修改焊接材料，包括活性炭，沸石粘土和有机硅颗粒 将研究以解决当前的空气净化器性能问题。该项目解决了三个重要的 SRP授权：SRP授权2，评估危险带来的人类健康风险的方法 物质（目标2）； SRP授权3，方法和技术检测危险物质 环境（目标1）；和SRP授权4，基本的生物学，化学和物理方法，以减少 环境中有害物质的数量和毒性（目标3）。与其他清晰 降低VOC暴露环境风险并改善公共卫生结果的项目 /核心，这 工作将提供改进的方法和工具，用于对补救工作的风险表征和优化。 这项研究将利用研究人员在物联网，边缘计算，智能的研究项目中利用 环境监测，地下水建模，机器学习的BN建模和吸附的媒体合成， 并将受益于与Mi Egle VI团队和SuperFund等合作伙伴的合作。 办公室。