Classroom CO2, Ventilation, and Illness Risk

ISEF Category: Translational Medical Science

Subcategory: Disease Prevention  ·  Difficulty: Intermediate  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

A crowded classroom can feel fine and still trap stale air. CO2 works like a footprint for how much exhaled air has built up. That makes it a useful clue for ventilation. If you can connect CO2 levels with symptom reports, you can turn a daily annoyance into real public health data.

What Is It?

Indoor CO2 monitoring uses a small sensor to measure how much carbon dioxide builds up in a room. People exhale CO2 every time they breathe out, so higher indoor CO2 often means less fresh air is coming in. Think of a classroom like a fish tank. If the water does not move, waste products build up. In a room, CO2 is the waste signal you can measure.

An NDIR sensor, which stands for non-dispersive infrared, checks how much infrared light the gas absorbs. That gives you a number you can track over time. For your project, you are not measuring viruses directly. You are measuring ventilation quality and comparing it with symptom diaries, such as runny nose, cough, or sore throat reports. That lets you study whether rooms with poorer ventilation tend to line up with more illness-like symptoms.

Why This Is a Good Topic

This is a strong science fair topic because you can measure one clear variable, indoor CO2, and compare it with another clear variable, symptom frequency. You do not need a university lab to collect the data, and you can still ask a real public health question. The topic connects to school building air quality, respiratory illness spread, and decisions about windows, fans, and occupancy. You can also learn sensor calibration, data cleaning, and basic statistics.

Research Questions

• How does average classroom CO2 level relate to the number of student-reported rhinovirus-like symptoms?
• What is the effect of window opening on classroom CO2 trends during the school day?
• Does peak CO2 predict symptom reports better than daily average CO2?
• To what extent do classroom occupancy patterns explain changes in indoor CO2 levels?
• Which classroom features, such as room size or HVAC use, are associated with lower CO2 measurements?
• How does the time spent above a CO2 threshold relate to symptom diary counts?

Basic Materials

• Aranet-class or similar NDIR CO2 sensor with data export.
• Notebook or digital form for symptom diaries.
• Phone or tablet for time stamping observations.
• Clipboard or classroom log sheet.
• Weather app or local outdoor air quality source.
• Spreadsheet software for graphing and basic statistics.
• Room plan or class schedule notes.

Advanced Materials

• Calibrated NDIR CO2 sensor with logging export.
• Portable temperature and relative humidity sensor.
• Occupancy counter or manual headcount log.
• HEPA air cleaner or fan for comparison condition.
• Access to classroom ventilation settings or HVAC records.
• Statistical software for mixed-effects or regression analysis.
• Optional second CO2 sensor for cross-checking drift.

Software & Tools

• Google Sheets: Organizes CO2 readings, symptom counts, and classroom notes in one place.
• Excel: Helps you plot trends, compare rooms, and calculate summary statistics.
• R or Python: Lets you test dose-response models and control for class-to-class differences.
• ImageJ: Helps if you photograph sensor displays and need to read values from images consistently.
• NIH PubMed: Helps you find papers on indoor air quality, CO2, and respiratory symptoms.

Experiment Steps

1. Define the question you will test, and choose one illness-like outcome you can record the same way every day.
2. Select classrooms, time blocks, and comparison conditions so your data covers different ventilation patterns.
3. Plan how you will log CO2, occupancy, and symptoms in a consistent format across all rooms.
4. Build a simple analysis plan that compares average CO2, peak CO2, and time above your chosen threshold.
5. Decide how you will control for confounders such as attendance, weather, and room use type.
6. Pre-plan how you will present the result as graphs, effect sizes, and a clear dose-response claim.

Common Pitfalls

• Changing the sensor location between classrooms, which can make one room look better or worse for the wrong reason.
• Mixing up indoor CO2 with outdoor CO2 baseline, which can hide the true ventilation pattern.
• Using symptom diaries without a fixed definition, which makes mild colds, allergies, and unrelated symptoms blur together.
• Comparing classes with very different occupancy or schedules, which confounds ventilation with crowding.
• Treating one high CO2 day as proof, which ignores day-to-day noise and weakens the dose-response claim.

What Makes This Competitive

A stronger project goes beyond a simple before-and-after graph. You can compare multiple classrooms, adjust for occupancy and weather, and test whether CO2 predicts symptom frequency better than room size alone. You can also separate average exposure from short spikes, which gives your analysis more depth. That kind of careful design turns a basic air-quality project into a real disease-prevention study.

Project Variations

• Compare CO2 and symptom trends in classrooms with windows open versus closed.
• Add humidity and temperature measurements to test whether air comfort changes match symptom reports.
• Compare regular classrooms with spaces that use HEPA filtration or stronger HVAC circulation.

Learn More

• NIH PubMed: Search for review articles on indoor air quality, classroom ventilation, and respiratory infection risk.
• NOAA Climate Data Online: Find local outdoor temperature, humidity, and weather context that can affect ventilation patterns.
• US EPA Indoor Air Quality tools: Read free guidance on CO2, ventilation, and school air quality monitoring.
• CDC Schools and Child Care guidance: Review public health background on respiratory illness spread and prevention in classrooms.
• MIT OpenCourseWare: Search for free course materials on environmental health, data analysis, or statistics for health studies.