Predict
Students choose a place or situation and note what they expect to measure.
Project Particulate Matter
The Particulate Matter Project has students investigate air quality using a particulate sensor, Arduino and their own measurements.
How it works
From sensor to poster session.
Measure
The Arduino sends PM1, PM2.5, PM10 and AQI to the browser. The graph updates live.
Compare
Multiple measurements come together in an overview with averages, maxima and graphs.
Conclude
The report bundles research question, measurement data, graph, conclusion and reflection.
Poster
Present your findings to the class in a public poster session.
The setup
One sensor, one Arduino and a clear measurement.
Students connect the sensor, measure in a real environment and collect their own measurement results.
- The HM330X sensor measures PM1, PM2.5 and PM10.
- The Arduino sends the measurements to the browser.
- The webpage helps compare multiple measurement series fairly.
What will you learn?
Air quality becomes measurable.
Particulate matter
PM1, PM2.5 and PM10 become visible as concentrations in µg/m³.
Data processing
Students compare average and maximum values instead of isolated measurement points.
Research
A measurement location, measurement conditions and possible source are explicitly noted.
Reporting
The PDF turns the measurement into a short research output that can be discussed.
Poster
Students process that output into a manageable poster.
- Sensor
- HM330X with Arduino
- Measurements
- PM1, PM2.5 and PM10
- Presentation
- PDF report and poster session
Place in the learning path
From platforms to projects.
The platforms build computational thinking. The projects use that thinking in a cross-curricular assignment in which students build, measure, test and conclude.
Platforms
STEaM projects
Smart Plants
Arduino, soil moisture, calibration and data.
Robotic hand
Arduino, sensors, servos and movement.
Wind
Micro:bit, energy, voltage and current.
Smart bin
AI, image recognition, data and microcontrollers.
Self-learning games
Getting started with machine learning
Particulate matter
Arduino, air quality, data and poster session.
For teachers
Ready to use in the classroom.
The web version works with real hardware or in demo mode. This lets you prepare the assignment, show it to the class and let students practise without every group immediately needing a working sensor.
- No installation or student account.
- Local libraries for charts and PDF export.
- Suitable for GitHub Pages.
- Report with name, class, graph, summary and reflection.
Privacy and licence
Privacy
This project does not use its own analytics or advertising cookies. Input, progress and measurement data are processed in the browser and may be stored locally on the device. A download or export occurs only when the user initiates it. Connected hardware is accessed only after permission is given in the browser. GitHub Pages logs visitors’ IP addresses for security purposes; external services are governed by their provider’s privacy policy.
License
Original project material by Robbe Wulgaert may be used, copied and adapted free of charge for education, provided that ‘Robbe Wulgaert – AI in de Klas’ is clearly displayed, where reasonably practical with a link to the original project. Commercial sale, paid distribution, sublicensing or repackaging as a commercial product is not permitted without prior written permission. Other uses outside education also require prior written permission. Third-party components remain subject to their own licences.