(539i) Engaging Middle and High School Students in Hypothesis Generation Using a Citizen-Scientist Network of Air Quality Sensors

Authors: 
Kelly, K., University of Utah
Moore, J., University of Utah
Xing, W., University of Utah
Dailey, M., University of Utah
Le, K., University of Utah
Becnel, T., University of Utah
Goffin, P., University of Utah
Meyer, M., University of Utah
Gaillardon, P. E., University of Utah
Whitaker, R., University of Utah
Butterfield, A., University of Utah
Wiese, J., University of Utah
Poor air quality affects 90% of the world's population and contributes to 7 million premature deaths annually. Typically, government organizations measure air quality at sparsely distributed measurement stations, equipped with expensive, high-quality instrumentation, and the resulting measurements become available after an hour or two delay. Salt Lake City, Utah periodically experiences some of the worst air quality in the nation, and it has complex meteorology and topography. Consequently, the limited number of stations cannot capture community-level variations in air quality. Our team has deployed a network of approximately 100 low-cost particulate matter (PM) sensors and an accompanying website (www.aqandu.org) for improving the community’s understanding of air quality and supporting citizen scientists interested in air quality. This network has generated a rich set of PM measurements, capturing how PM levels evolve over time and space, particularly during pollution events. Building on prior educational outreach and citizen science exercises, we developed an interactive, team-based teaching module using local real-world data. This teaching module's goal is to engage students in generating and testing hypotheses while also encouraging citizen use of real-time air quality data for their own interests, such as exploration, science fair projects, or environmental oversight. We have piloted this module with over 300 students across 6 local high schools in various chemistry, engineering, environmental science, and physics classrooms. Structured around a data analysis exercise with local air quality data, the module helps guide students through creating and testing hypotheses about air quality under various conditions. The module also incorporates fundamental analysis tasks, such as loading and plotting data in a spreadsheet program to build students' familiarity with basic data analysis techniques. Using pre- and post-survey responses, this work seeks to evaluate how a guided, team-based outreach module impacts students' perceptions of outdoor air quality, sense of engagement with a data analysis exercise, ability to generate and test hypotheses, and overall success with incorporating publicly available local data from distributed sensor networks into their curriculum. Finally, this module introduces traditionally underrepresented minorities in STEM to distributed data collection, interpolation, modeling, and visualization concepts used for generating community-scale air quality models. Over half of the visited schools had a minority enrollment exceeding 50% of the student body, and all but two surpassing the state average of 25%.

Preliminary results show this module to be highly engaging, and effective for improving students' awareness of air quality's geospatial and temporal variations during a variety of pollution episodes. Survey results also show this module is effective at introducing hypothesis generation and testing techniques. All classrooms reported wanting to host the module again and having plans to incorporate their local air quality data for future activities and assignments.

Drs. K. Kelly and P.-E. Gaillardon have an interest in the company Tetrad: Sensor Network Solutions, LCC, which commercializes solutions for environmental monitoring.