Treffer: HazeL: A Low-Cost Learning Platform for Aerosol Measurements

Title:
HazeL: A Low-Cost Learning Platform for Aerosol Measurements
Language:
English
Source:
Journal of Chemical Education. 2022 99(9):3203-3210.
Availability:
Division of Chemical Education, Inc. and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
Peer Reviewed:
Y
Page Count:
8
Publication Date:
2022
Document Type:
Fachzeitschrift Journal Articles<br />Reports - Research
Education Level:
Higher Education
Postsecondary Education
DOI:
10.1021/acs.jchemed.2c00535
ISSN:
0021-9584
1938-1328
Entry Date:
2024
Accession Number:
EJ1442328
Database:
ERIC

Weitere Informationen

The switch to online instruction during the COVID-19 pandemic forced educators to adapt hands-on environmental engineering experiments to a remote curriculum previously conducted in a laboratory using expensive analytical instruments (>$2000 per device). Here, we describe how we developed a low-cost (<$200) aerosol sensor platform as a successful solution for supporting remote laboratories on air quality for environmental engineering courses in Spring 2021, and continued for in-person classes in Spring 2022. This sensor platform, called HazeL (Haze Laser Sensor), consists of an externally mounted aerosol sensor, a GPS receiver, and temperature and pressure sensors coupled to an Arduino MKR WiFi 1010 microcontroller connected via a Grove system. Using a project-based learning approach and implementing the scientific method, students worked asynchronously to design experiments, collect aerosol measurements, and analyze and visualize data using the R programming language. Students generated hypotheses regarding factors affecting air pollution, measured =0.3 µm particles in different locations, tested differences between samples, and rejected the null hypothesis if appropriate. HazeL was also used for projects on data processing and statistical inference in an upper-level computational course. We present an instructional guide on manufacturing the HazeL platform and using it as a teaching tool for enhancing student experiential learning, participation, and engagement.

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