Pac-Man Ghost Helps With Air Quality Sensing

In the past, building construction methods generally didn’t worry much about air quality. There were enough gaps around windows, doors, siding, and flooring that a house could naturally “breathe” and do a decent enough job of making sure the occupants didn’t suffocate. Modern buildings, on the other hand, are extremely concerned with efficiency and go to great lengths to ensure that no air leaks in or out. This can be a problem for occupants though and generally requires some sort of mechanical ventilation, but to be on the safe side and keep an eye on it a CO2 sensor like this unique Pac-Man-inspired monitor can be helpful.

Although there are some ways to approximate indoor air quality with inexpensive sensors, [Tobias] decided on a dedicated CO2 sensor for accuracy and effectiveness, despite its relatively large cost of around $30. An ESP32 handles the data from the sensor and then outputs the results to an array of LEDs hidden inside a ghost modeled after the ones from the classic arcade game Pac-Man. There are 17 WS2812B LEDs in total installed on a custom PCB, with everything held together in the custom 3D printed ghost-shaped case. The LEDs change from green to red as the air quality gets worse, although a few preserve the ghost’s white eyes even as the colors change.

For anyone looking to recreate this project and keep an eye on their own air quality, [Tobias] has made everything from the code, the PCB, and the 3D printer files open source, and has used accessible hardware in the build as well. Although the CO2 sensors can indeed be pricey, there are a few less expensive ways of keeping an eye on indoor air quality. Some of these methods attempt to approximate CO2 levels indirectly, but current consensus is that there’s no real substitute for taking this measurement directly if that’s the metric targeted for your own air quality.

The sensor hub in all its glory, sensor itself on top, standing on 3D-printed feet, and the PCB on the bottom

Hacker-Friendly And Elegant Air Quality Sensor Hub

Ever wanted an indoor environment sensor that’s dead simple yet a complete package? That’s the anotter-sensor-hub project from [Jana Marie], designed for the Sensirion SEN05x series sensors, with a SEN055 sensor shown in the picture above. Given such a sensor, you can measure VOCs and NOCs (Volatile and Non-Volatile Organic Compounds), as well as PM1, PM2.5, PM4 and PM10 particulate matter indices, with temperature and humidity sensing thrown in for good measure. Fully open and coupled with 3D printable stand files, this alone makes for an air quality hub fit for a hacker’s desk. That’s not all, however — this board’s elegant extensibility is a good match for the sensor’s impressive capabilities!

The PCB itself might look simple, it’s simply an ESP32 and some supporting circuitry required. But you’ll notice there’s also a trove of connector footprints for different interfaces; whatever else you might want to add to your sensor hub, whether it connects through I2C, SPI or PWM, you can! As usual, the sensor itself is the most expensive part of such a project — the boards themselves are around $5 USD apiece fully assembled, but one sensor-included hub will set you back roughly $42 USD. That said, it’s a great value for the price, and the trove of sensing data you can get might just more than pay for itself in quality-of-life improvements you make. Of course, everything is open-source and comes as a complete packages for you to start using. The firmware, KiCad files, 3D holder and even Grafana dashboard files can be found on GitHub.

Such air quality sensor platforms have been getting more and more popular, and hackers have been paying attention. Having a full open-source package like this at our disposal is amazing. If you’re looking for a cheaper “baby’s first air quality sensor”, drop by your local IKEA — there’s a way less featureful but quite cheap sensor that you can equip with an ESP8266, perhaps, even on a custom PCB.

Better Air Quality Sensing With CO2

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.

An acrylic map of the state of Lagos. Each region is lit a different color by LEDs shining on the acrylic panels. The colors coorespond to the air quality index key which is lit in cooresponding colors to the value.

Hackaday Prize 2022: This Interactive Air Quality Map Makes The Invisible Visible

Air quality can have a big impact on your health, but it isn’t always something you can see. [Ahmed Oyenuga] wanted to make air quality something more tangible and developed an Interactive Air Quality Map.

Using addressable LEDs and acrylic panels, [Oyenuga]’s map lights up different regions of his state (Lagos) with colors that correspond to qualitative values of the air quality readings. The color key on the edge of the map becomes a readout when you touch a specific region of the map.

Most of the map’s functionality is handled by an Arduino WiFi 1010, but the capacitive touch is running on a custom board [Oyenuga] designed with an ATSAMD21J17. [Oyenuga] is getting air quality data via a DesignSpark Environmental Sensor Development Kit (ESDK) and then uses reverse geocoding to take the GPS data and turn it into a location the map will understand.

If you’re interested in different options for monitoring air quality that could feed into a map like this, why don’t you check out this LoRa Air Quality Monitor or even a Mobile Air Quality Monitor.

Continue reading “Hackaday Prize 2022: This Interactive Air Quality Map Makes The Invisible Visible”

Weather Station Predicts Air Quality

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.

Anr air quality sensor mounted on a bike's handlebar

Measuring Air Quality Using Mobile Sensors For The Masses

Poor air quality is a major problem for city dwellers the world over. Dust, smoke, particles and noxious gases from vehicles, industry and agriculture makes many megacities downright hazardous to live in. Pinpointing the source of pollution and developing strategies for mitigation requires accurate data on pollutant levels, but obtaining these numbers is not always easy.

Enter CanAirIO, a citizen science project that aims to gather air quality data from around the world by putting sensors into the hands of as many people as possible. Its team has developed two different sensor nodes for this purpose: an indoor one that can measure CO2, and a mobile one that can measure particulate matter (PM) levels. Both versions are powered by an ESP32 microcontroller that reads out the air quality sensors and connects to the Internet using WiFi or BlueTooth. The data can then be shared online to create detailed maps showing local variations in air quality.

The design of the sensor nodes is fully open-source, allowing anyone with basic electronic skills to build them. The sensors are a Sensirion SCD30 for CO2 measurement and an SPS30 for PM levels. The mobile version comes with a neat 3D-printed enclosure that can be mounted on a bike’s handlebar, enabling the user to quickly gather data around their neighbourhood. A mobile app simplifies setting up the sensors and sharing the data.

The project has already been successful in gathering detailed data in the city of Bogotá, Colombia, and will no doubt prove useful in many other pollution hotspots around the world. We’ve seen similar community efforts to monitor air pollution and even radiation in various places, both showing how relatively simple devices can help to make a difference in people’s wellbeing. Continue reading “Measuring Air Quality Using Mobile Sensors For The Masses”

Prepare For Wildfire Season With An Air Quality Monitor

For some reason, wildfire seasons in Australia, North America, and other places around the world seem to happen more and more frequently and with greater and greater fervor. Living in these areas requires special precautions, even for those who live far away from the fires. If you’re not sure if the wildfires are impacting your area or not, one of the tools you can build on your own is an air quality meter like [Costas Vav] shows us in this latest build.

The air quality indicator is based around an Adafruit Feather RP2040 which is in turn based on the 32-bit Cortex M0+ dual core processor. This makes for a quite capable processor in a small package, and helps accomplish one of the design goals of a rapid startup time. Another design goal was to use off-the-shelf components so that anyone could easily build one for themselves, so while the Feather is easily obtained the PMS5003 PM2.5 air quality sensor needed to be as well. From there, all of the components are wrapped up in an easily-printed enclosure and given a small (and also readily-available) OLED screen.

[Costas Vav] has made all of the files needed to build one of these available, from the bill of materials to the software running on the Pi-compatible board to the case designs. It’s a valuable piece of technology to have around even if you don’t live in fire-prone areas. Not only can wildfire smoke travel across entire continents but simple household activities such as cooking (especially with natural gas or propane) can decimate indoor air quality. You can see that for yourself with an army of ESP32-based air quality sensors.