HazeL: A Low-Cost Learning Platform for Aerosol Measurements

被引:4
|
作者
de Vera, Glen Andrew [1 ]
Brown, Benjamin Y. [1 ]
Cortesa, Steven [1 ]
Dai, Mona [1 ]
Bruno, Jack [1 ]
LaPier, Jonas [1 ]
Sule, Nishant [1 ]
Hancock, Melissa [1 ]
Yoon, Bryan [1 ]
Chalah, Anas [1 ]
Sunderland, Elsie M. [1 ,2 ]
Wofsy, Steven C. [1 ,3 ]
机构
[1] Harvard John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 USA
[2] Harvard T H Chan Sch Publ Hlth, Boston, MA 02215 USA
[3] Harvard Univ, Dept Earth & Planetary Sci, Cambridge, MA 02138 USA
关键词
Atmospheric Chemistry; Environmental Chemistry; Distance Learning; Hands-on Learning; Instrumental Methods; Laboratory Equipment/Apparatus; First-Year Undergraduate; General; Upper-Division Undergraduate; CHEMISTRY; STUDENTS;
D O I
10.1021/acs.jchemed.2c00535
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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 mu m 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.
引用
收藏
页码:3203 / 3210
页数:8
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