Measuring air quality, as anyone who has tried to tackle this problem can attest, is not as straightforward as it might seem. Even once the nebulous term “quality” is defined, most sensors use something as a proxy for overall air health. One common method is to use volatile organic compounds (VOCs) as this proxy but as [Larry Bank] found out, using these inside a home with a functional kitchen leads to a lot of inaccurate readings. In the search for a more reliable sensor, he built this project which uses CO2 to help gauge air quality.

Most of the reason that CO2 sensors aren’t used as air quality sensors is cost. They are much more expensive than VOC sensors, but [Larry] recently found one that was more affordable and decided to build this project around it. The prototype used an Arduino communicating over I2C to the sensor and an OLED screen, which he eventually put in a 3D printed case to carry around to sample CO2 concentration in various real-world locations. The final project uses a clever way of interfacing with the e-paper display that we featured earlier.

While CO2 concentration doesn’t tell the full story of air quality in a specific place, it does play a major role. [Larry] found concentrations as high as 3000 ppm in his home, which can cause a drop in cognitive function. He’s made some lifestyle changes as a result which he reports has had a beneficial impact. For human-occupied indoor spaces, CO2 can easily be the main contributor to poor air quality, and we’ve seen at least one other project to address this concern directly.

Measuring air quality at any particular location isn’t too complicated. Just a sensor or two and a small microcontroller is generally all that’s needed. Predicting the upcoming air quality is a little more complicated, though, since so many factors determine how safe it will be to breathe the air outside. Luckily, though, we don’t need to know all of these factors and their complex interactions in order to predict air quality. We can train a computer to do that for us as [kutluhan_aktar] demonstrates with a machine learning-capable air quality meter.

The build is based around an Arduino Nano 33 BLE which is connected to a small weather station outside. It specifically monitors ozone concentration as a benchmark for overall air quality but also uses an anemometer and a BMP180 precision pressure and temperature sensor to assist in training the algorithm. The weather data is sent over Bluetooth to a Raspberry Pi which is running TensorFlow. Once the neural network was trained, the model was sent back to the Arduino which is now capable of using it to make much more accurate predictions of future air quality.

The build goes into quite a bit of detail on setting up the models, training them, and then using them on the Arduino. It’s an impressive build capped off with a fun 3D-printed case that resembles an old windmill. Using machine learning to help predict the weather is starting to become more commonplace as well, as we have seen before with this weather station that can predict rainfall intensity.

According to [Dr. Tom Lehrer’s] song Pollution, “Wear a gas mask and a veil. Then you can breathe, long as you don’t inhale!” While the air quality in most of the world hasn’t gotten that bad, there is a lot of concern about long-term exposure to particulates in the air causing health problems. [Ashish Choudhary] married an Arduino with a display and a pollution sensor to give readings of the PM2.5 and PM10 levels in the air.

The sensor uses a laser diode and a photodiode to detect and count particles, while a fan moves air through the system. If you aren’t up on pollution metrics, PM2.5 is a count of very fine particles (under 2.5 microns) and PM10 is a count of particles for 10 microns. You can find a datasheet for the device online.

One thing to note is that the sensor has a finite lifespan. The datasheet claims “up to” 8,000 hours. If you ran the sensor continuously that’s not quite a year, so you might want to be judicious about how often you light up the device.

This isn’t the first time we’ve seen this particular sensor. If you want to find the exact source of a pollutant, consider this build.

Handheld measuring devices make great DIY projects. One can learn a lot about a sensor or sensor technology by just strapping it onto a spare development board together with an LCD for displaying the sensor output. [Richard’s] DIY air quality meter and emissions tester is such a project, except with the custom laser-cut enclosure and the large graphic LCD, his meter appears already quite professional.

For his build, [Richard] used a Sharp GP2Y1010AU0F dust sensor. This $11 device has a little hole, through which airborne dust particles can pass. On the inside, an infrared LED and a photodiode are arranged in a way that allows no direct light, but only light reflected by the passing by dust particles, to reach the photodiode. An accurately trimmed amplifier within the sensor package translates the diode’s photocurrent into an analog output voltage proportional to the dust density. With a bit of software wizardry, it’s even possible to differentiate between house dust and smoke by analyzing the pulse pattern of the output voltage.

The development board used in this project, a PDI-1 (which stands for Programmable Device Interface) is [Richard’s] own design. Manufactured in the UK, it isn’t the cheapest, but it’s the part that makes this build a breeze. It’s basically an Arduino Nano with a lot of onboard peripherals, including a large graphic LCD, some buttons, a speaker, plenty of H-bridges, and a few more.

After bodge-wiring the dust sensor to the board and taking the enclosure out of the laser cutter, the hardware side of this project was almost done. A little fan was added to ensure airflow through the sensor. Eventually [Richard] wrote a basic firmware to display a graph of sensor readings on the LCD. A first test in the exhaust stream of his car, cycling through idling and revving as shown in the title image, suggests that the meter works as intended. Of course, air quality and emission testing depend on more parameters than just dust density, but if you want to replicate and extend this build, [Richard] provides you with all the Arduino compatible source files.

“Under the Dome – PMgami” is an installation created by designer Jiayu  Lui using Arduino Nano. Inspired by paper origami techniques, the digitally fabricated flowers move and change color according to the quality of air measured locally. The main aim of the installation is to obtain a more intuitive way to communicate pollution data and  the relationship between technology and nature.

In the gallery you can explore some other pictures and the schematic. Check the video below to see the installation in action!

PLOTS guys propose an interesting way to measure the quality of the air for indoor environments, by hacking a second-hand Roomba robot (an autonomous vacuum cleaner).

These robots are programmed to randomly move inside rooms to clean up the floor, so by adding a simple air quality sensor on top of one of them, it is possible to easily implement a sort of “random walker” that will sense for us the presence of gases (volatile organic chemicals, VOCs), such as NH3, alcohol, CO2 and so forth.

To keep track of the air quality measurements, the authors equipped the so hacked Roomba with an RGB led, whose color can be changed according to the air sample. By taking a long exposure picture of the room where the robot was roaming in, they could determine the areas where a high concentration of VOCs was present.

The complete description of the project can be found on the PLOTS’ website, while here you may find a short video about it:

PLOTS guys are also working on a different approach to air sensing, which does not make use of a Roomba robot but uses a hamster ball, instead. Further details can be found here.

[Via: Public Laboratory for Open Technology and Science]