The air we breathe indoors often holds hidden clues about its quality, and a surprisingly accurate indicator is the level of carbon dioxide. In truly fresh air, CO2 hovers around 400 ppm – that’s 400 molecules of CO2 for every million air molecules. Though seemingly a small fraction, this baseline has a significant impact on our well-being.
Inside, however, CO2 levels climb rapidly with every breath we take. It’s a phenomenon easily demonstrated; a crowded movie theater can quickly see CO2 concentrations soar into the thousands of ppm. Elevated CO2 isn’t just a number – it directly affects our cognitive function, leading to sluggishness, difficulty concentrating, and even headaches.
Fortunately, understanding and monitoring CO2 is now accessible to everyone. A dedicated CO2 measuring station provides a simple signal: when levels reach 1500 ppm or higher, it’s time to introduce fresh air. This isn’t about complex systems, but about a proactive approach to a healthier indoor environment.
Building your own CO2 monitor is surprisingly affordable and straightforward. You’ll need a Raspberry Pi – any model will work, running a current version of Raspberry Pi OS. This small computer acts as the brains of the operation.
The heart of the system is a CO2 sensor. The MH-Z19C, produced by Winsen Electronics Technology, is a popular and effective choice, with the MH-Z19B offering similar performance. When purchasing, opt for a version with pre-soldered pins (headers) to simplify the connection process. You’ll also need a set of female-to-female jumper wires.
The entire project typically costs less than $40 – a fraction of the price of commercially available CO2 monitors. Before proceeding, a crucial step is enabling the Raspberry Pi’s serial interface through the ‘raspi-config’ tool, navigating to “Interface Options” and then “Serial Port.”
The MH-Z19C sensor utilizes Non-Dispersive Infrared (NDIR) technology. This method relies on the principle that carbon dioxide absorbs infrared radiation at specific wavelengths. The sensor emits infrared light and measures how much is absorbed, directly correlating to the CO2 concentration.
A light source transmits infrared radiation through the air sample. A filter ensures only the wavelengths absorbed by CO2 reach the sensor. As CO2 levels increase, more infrared light is absorbed, reducing the signal detected by the sensor. This change in light intensity is then precisely converted into a ppm reading.
Connecting the sensor to the Raspberry Pi requires careful attention to the wiring. Begin by powering down and disconnecting the Pi. Locate the underside of the sensor to identify the pin functions. Connect pin 6 (VCC) to pin 4 on the Raspberry Pi for power. Pin 7 (GND) connects to pin 6, establishing a ground connection.
Next, connect pin 2 (Rx) on the sensor to pin 8 on the Raspberry Pi. Finally, connect pin 3 (Tx) on the sensor to pin 10 on the Pi. Double-check all connections before reconnecting the Raspberry Pi to power.
With the hardware connected, you’ll need a Python script to interpret the sensor data. A script developed by a Japanese developer provides a ready-made solution. Open a terminal on the Raspberry Pi and use the command `git clone` to download the necessary files.
Navigate to the newly created “mh-z19” folder and execute the installation script using `./setup.sh`. To retrieve the current CO2 reading, simply run `sudo python -m mh_z19`. The output will display the CO2 concentration in ppm, such as “{“co2″: 3128}”.
Manually checking the CO2 levels is useful, but visualizing the data over time provides deeper insights. By sending the readings to an online service, you can track the impact of ventilation and identify patterns in air quality.
A free online service, monitor3.uedasoft.com, allows you to log in with an email and password. After logging in, note the eight-character “view_id” and identify the corresponding option (e.g., “prgrvpqg”) in the “Elements” menu, activating it and saving the changes.
Return to the “mh-z19” folder on the Raspberry Pi and execute the command `./setid.sh [abcdefgh]`, replacing “[abcdefgh]” with your unique eight-character view ID. Verify the connection with `sudo python -m pondslider`; a “true” output confirms successful communication.
To automate data collection, run `./autostart.sh --on`. The online graph on the Monitor page will now update every five minutes, providing a clear visual representation of CO2 levels. Adjust the number of displayed values using the “Settings” button. This allows you to see, at a glance, the effect of opening a window or running an air purifier.
The data can also be downloaded as a CSV file for further analysis in programs like Excel. The script automatically restarts after a reboot, ensuring continuous monitoring. This system isn’t just about data; it’s about empowering you to create a healthier, more comfortable indoor environment.
The possibilities extend beyond simple monitoring. By adding a red LED, a 330 Ohm resistor, and modifying the Python script, you can create a visual alert that illuminates when CO2 levels exceed a predefined threshold, providing an immediate warning when ventilation is needed.
