DiY Sensor Dataset for Air Pollution Monitoring
Authors/Creators
Description
The research engaged students from a private university in Bilbao in designing and implementing a project that involved young students in collecting and analyzing air quality data using air meters they assembled. An interesting aspect of the project was the development of an image processing technique for analyzing dust captured on petroleum jelly, enhancing the quantification of particulate matter and providing insights into air quality at various school locations. Additionally, the study introduced a synthetic data generation algorithm designed to simulate air quality data for educational purposes, which allowed students to engage with data analysis and interpretation, thereby enriching their learning experience and understanding of air pollution dynamics.
Files
DIY_Sensor_Dataset_And_Guidelines.zip
Files
(26.5 MB)
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Additional details
References
- A Low-Cost Image Sensor for Particulate Matter Detection to Streamline Citizen Science Campaigns on Air Quality Monitoring