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.