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Incorporating Personal Monitoring Utilizing Low-cost Sensors into the Undergraduate Curriculum
Citation:
Stratton, J., J. Barone, N. Kaltenhauser, C. Moore, K. Barkjohn, A. Clements, S. Frederick, L. Lim, O. Boyko, J. Munyan, AND R. Leta-Graham. Incorporating Personal Monitoring Utilizing Low-cost Sensors into the Undergraduate Curriculum. Air sensor international conference, Pasadena, California, May 11 - 13, 2022.
Impact/Purpose:
Air sensors have grown in popularity in recent years. They have the potential to provide more spatially dense measurement than conventional monitors. However, technical skills in data analysis and visualization may be needed to successfully interpret air sensor data. In this project undergraduate students at Rider University learned about air monitoring and data analysis during a class where mobile air sensors were used. This training may allow them to serve as community leaders for projects in the future. The project also highlights what resources may be needed from nontechnical users operating air sensor projects. This abstract is for a presentation that will be presented to attendees of the Air Sensor International Conference May 11-13, 2022 in Pasadena, CA.
Description:
Environmentally aware and motivated students are abundant in today’s undergraduate classrooms. While some of these talented young scientists may become professionals in environmental related fields, many of them could serve as leaders for community-led projects in the future. This study aimed to challenge undergraduate students to design, execute, and interpret a personal monitoring project using mobile particulate matter (PM) sensors. This project included experimental design, data collection, data harmonization, manipulation, analysis via a programming language, data visualization, and a final in-class dissemination to their peers. Students were provided with a battery powered Arduino microcontroller, Plantower PMS5003 PM sensor, relative humidity (RH) and temperature (T) sensor (DHT-22), and local data storage to conduct this work. Students used their mobile phone GPS to track their location for comparison with PM concentrations. Students operated these sensors and extracted data from local storage. Although several students reported no previous experience with programing languages, all students were able to organize, merge, analyze, and visualize data using simple R templates provided within the course and tools such as EPA’s Real Time Geospatial Data Viewer (RETIGO). Based on student surveys, students reported a positive experience and were likely to engage in future community-led projects. However, data management and manipulation were the most difficult task. While student feedback recommended help from a technical expert, this study suggests motivated individuals may be capable of minor data management. Disclaimer: Although this abstract was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.