EAGER: Agile Data Integration to Facilitate Scaling of Air Quality Research

EAGER：敏捷数据集成促进空气质量研究规模扩大

• 批准号: 1640749
• 负责人: Kristin Tufte
• 金额: $ 19.99万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640749&HistoricalAwards=false
• 关键词: EAGER; Agile; Data; Integration; Facilitate

Transportation, through vehicle emissions, has a significant impact on air pollution in urban areas - presenting health risks to pedestrians, vehicle occupants and transit users. Air pollutant concentrations near roadways may be up to orders of magnitude higher than average air pollutant levels in urban areas. Further, according to the EPA, transportation accounts for 26% of greenhouse gasses in the United States. Recent research at Portland State University, in collaboration with the City of Portland, Oregon, indicates that a relatively simple technique - modifying the timings of traffic signals - has the potential to reduce vehicle emissions in cities. However, this preliminary research needs to be explored more fully to evaluate its potential. This project would scale the work from a single location to a full transportation corridor, namely the Powell-Division Corridor. In addition, this project will investigate how data management technology can be applied to scale the air quality analysis. The techniques resulting from our exploratory research are expected to inform efforts to advance 'Smart City' approaches in multiple domains. The proposed project capitalizes on unique and time-sensitive resources and opportunities to design an innovative and potentially transformative approach to address a globally relevant problem - reducing traffic-related air emissions. The proposed work has the potential to affect the lives of urban citizens by identifying a relatively easy to implement method for reducing vehicle emissions and thereby reducing greenhouse gas emissions, improving air quality and reducing pedestrian exposure to air pollutants. This project directly contributes to the Portland (Oregon) Global Cities Challenge (GCTC) Action Cluster, recently awarded the $20,000 leadership award at the GCTC 2016 Exposition. The project also aligns with the City of Portland's Ubiquitous Mobility for Portland (UB Mobile PDX) initiative, one of seven finalists in the U.S. Department of Transportation Smart Cities Challenge.This project will investigate and develop Cyber Physical Systems technology, particularly data management technology, to address systematic issues observed in data cleaning and data integration of air quality and transportation data. In practice, data integration and cleaning are still typically automated in an ad-hoc fashion; existing systematic data integration and cleaning technologies do not effectively support scaling of these processes. This project proposes to develop a concept we call Agile Integration, which is designed to address the complex dynamics associated with rapid increases in environmental sensing data, the accelerating pace of change in cities, and mounting pressures on data-intensive decision making. Techniques for semi-automated data cleaning and processing will also be developed to better capture human decisions and judgments that go into data integration. The results will be implemented in a prototype Cyber-Physical System for Data Integration for dense sensor networks. In the long term, the proposed work has the potential to impact the lives of everyday citizens by validating a potential method for reducing vehicle emissions through signal timing changes. Vehicle emissions in urban areas impact greenhouse gas emissions and urban air quality. Since minority and low-socioeconomic status populations disproportionately reside in close proximity to major roadways, the potential impacts of this project directly affect those often underserved populations. Further, the scalability problems described above, while exemplified by the air quality research for this proposal, also appear in the transportation domain and in others such as healthcare, education and environmental sensing. Thus the techniques developed through this project are expected to be extensible to those domains. The work will produce an improved understanding of lower-cost air quality sensors. In terms of educational goals, this project will engage students from PSU?s atmospheric science REU that specifically recruits Native American and rural Oregonians. Results will be disseminated to the computer science, transportation, and air quality professional communities, thus impacting at least three research domains.

通过车辆排放，运输对城市地区的空气污染产生了重大影响 - 向行人，车辆乘员和过境使用者带来健康风险。 道路附近的空气污染物浓度可能高于城市地区的平均空气污染物水平的数量级。此外，根据EPA的说法，运输占美国温室气体的26％。波特兰州立大学与俄勒冈州波特兰市合作的最新研究表明，一种相对简单的技术 - 修改交通信号的时间 - 有可能减少城市中的车辆排放。但是，需要更全面地探索这项初步研究以评估其潜力。该项目将将工作从单个位置扩展到完整的运输走廊，即鲍威尔区走廊。此外，该项目将研究如何应用数据管理技术来扩展空气质量分析。预计由我们的探索性研究产生的技术将为您推进多个领域的“智能城市”方法的努力。 拟议的项目利用了独特的和时间敏感的资源和机会，以设计一种创新的和潜在的变革性方法来解决与全球相关的问题 - 减少与交通相关的空气排放。拟议的工作有可能通过确定一种相对易于实施的减少车辆排放的方法来影响城市公民的生活，从而减少温室气体的排放，改善空气质量并减少行人对空气污染物的接触。该项目直接为波特兰（俄勒冈）全球城市挑战赛（GCTC）行动集群做出了贡献，该集群最近在2016年GCTC 2016年博览会上颁发了20,000美元的领导奖。该项目还与波特兰市对波特兰市无处不在的流动性（UB移动PDX）倡议是一致，这是美国运输部智能城市智能城市挑战赛的七个决赛入围者之一。该项目将调查和开发网络物理系统技术，尤其是数据管理技术，以解决在空气质量和运输数据的数据清洁和数据整合中观察到的系统性问题。实际上，数据集成和清洁通常仍以临时方式自动化；现有的系统数据集成和清洁技术不能有效地支持这些过程的缩放。该项目提出了一个我们称为敏捷整合的概念，该概念旨在解决与环境传感数据快速增加相关的复杂动态，城市变化的加速速度以及对数据密集型决策制定的压力。还将开发用于半自动数据清洁和处理的技术，以更好地捕捉到数据集成的人类决策和判断。结果将在一个原型网络物理系统中实现，以用于密集传感器网络的数据集成。从长远来看，拟议的工作有可能通过验证通过信号时机变化来减少车辆排放的潜在方法来影响日常公民的生活。城市地区的车辆排放会影响温室气体排放和城市空气质量。由于少数群体和低社会经济状况的人群不成比例地居住在主要道路上，因此该项目的潜在影响直接影响那些经常服务不足的人群。此外，上面描述的可伸缩性问题，虽然该提案的空气质量研究证明了这一点，但也出现在运输领域以及医疗保健，教育和环境感应的其他领域。因此，预计通过该项目开发的技术对于这些领域来说是可扩展的。这项工作将提高对低成本空气质量传感器的了解。在教育目标方面，该项目将吸引来自PSU大气科学的学生，该项目专门招募了美国原住民和俄勒冈州农村人。结果将被传播到计算机科学，运输和空气质量专业社区，从而影响至少三个研究领域。