Laser particle detectors are a high-tech way for quantifying whats floating around in the air. With a fan, a laser, and a sensitive photodetector, they can measure smoke and other particulates in real-time. Surprisingly, they are also fairly cheap, going for less than $20 USD on some import sites. They just need a bit of encouragement to do our bidding.
The ZH03B has PWM and UART output modes, but [Dave] focused his attention on UART. With the addition of a CP2102 USB-UART adapter, he was able to connect it to his Pi and Mac, but still needed to figure out what it was saying. He eventually came up with some Python code that lets you use the sensor both as part of a larger network or service like Mycodo and as a stand-alone device.
His basic Python script (currently only tested on Linux and OS X), loops continuously and gives a running output of the PM1, PM2.5, and PM10 measurements. These correspond to particles with a diameter of 1, 2.5, and 10 micrometers respectively. If you want to plug the sensor into another service, the Python library is a bit more mature and lets you do things like turn off the ZH03B’s fan to save power.
Since the industrial age, air pollution has increasingly become a problem on society’s radar. Outside of concerns about global warming and greenhouse gases, particulate emissions can be highly hazardous to human health. Over time, various organizations have set up measuring systems to check and report the particulate pollution level in cities around the world – but what if you could get an immediate idea on the pollution in your immediate vicinity? Enter less-smog.org.
In an integration sense, it’s a straightforward project. An ESP-12F is used as the brains behind the operation. This then talks to a combination of sensors to measure the local air quality. The system is set up to use a variety of temperature or humidity sensors depending on what the builder has to hand. As for particulate concentration measurements, those are achieved with the use of a PMS7003 sensor. This device shines a laser into a cavity containing an air sample from the surrounding environment and measures the scattered light to determine the concentration of particles in the PM2.5 range. This is the range most commonly used to make judgments on air quality regarding human health.
Data is collected and then output to a series of bright RGB LEDs. By turning the numerical PM2.5 reading into a color output, it becomes much simpler to get an instant idea of the pollution conditions in the immediate area. This has the benefit of being readable by even very young children, or those with poor eyesight, at the cost of leaving the colorblind and otherwise vision impaired at a loss.
The project presents a tidy way to create a series of indicators in a modern public environment that can give the average person an at-a-glance reading of whether its advisable to stay out or to head inside until conditions improve. We’d love to see this project deployed in cities to both collect data and help people gain a better understanding of the air quality around them.
When [James] moved to Lima, Peru, he brought his jogging habit with him. His morning jaunts to the coast involve crossing a few busy streets that are often occupied by old, smoke-belching diesel trucks. [James] noticed that his throat would tickle a bit when he got back home. A recent study linking air pollution to dementia risk made him wonder how cities could monitor air quality on a street-by-street basis, rather than relying on a few scattered stations. Lima has a lot of taxis, so why wire them up with sensors and monitor the air quality in real-time?
This taxi data logger’s chief purpose is collect airborne particulate counts and illustrate the pollution level with a Google Maps overlay. [James] used a light-scattering particle sensor and a Raspi 3 to send the data to the cloud via Android Things. Since the Pi only has one native UART, [James] used it for the particle sensor and connected the data-heavy GPS module through an FTDI serial adapter. There’s also a GPS to locate the cab and a temperature/humidity/pressure sensor to get a fuller environmental picture.
Take a ride past the break to go on the walk through, and stick around for the testing video if you want to drive around Lima for a bit. Interested in monitoring your own personal air quality? Here’s a DIY version that uses a dust sensor.
Water is kind of like information: both are a vital part of life and are found all around us. But not all water or information is healthy. Much of it may look harmless, but is actually polluted. A staggering number of people in the world have no access to fresh, clean water. ROVs can collect samples and detect pollution, but commercial types are way too expensive for the legions of people who need them.
[allai5] wants to be the catalyst for change. She’s the president of Rogue Robotics, a group of high school students throughout central New Jersey who have pooled their talents to design and build a simple, open-source ROV that’s affordable, repeatable, and environmentally friendly. The team uses Volturnus ROV to collect water samples and UV light to determine the presence of a general type of pollutant known as optical brightening agents (OBAs). This is the stuff they add to laundry detergents and copy paper to whiten the fibers’ appearance. By design, OBAs fluoresce brightly under UV light. After soaking a cotton pad in water sample, it’s easy to see if OBAs are present.
At 12″ x 12″ x 18″, Volturnus ROV is compact enough to explore most of the nooks and crannies of any body of water. It moves under the power of three thrusters—500 GPH bilge pump motors driven by a pair of L298N controllers—and is controlled by an Arduino Mega using a wireless joystick. The driver of the ROV navigates the drink through the eyes of a waterproof car back-up camera whose feed is flipped with a Python script.
Volturnus ROV is not a one-stop solution for dealing with marine pollution. The team would like to add filtration in the future and move the electronics to the bottom so it can go faster. Rogue Robotics’ aim has always been to make an ROV that does a few things well. Right now, it’s an excellent jumping-off point for awareness and blueprint for action. Find your inspiration after the break.
There’s a big to-do going on right now in Germany over particulate-matter air pollution. Stuttgart, Germany’s “motor city” and one of Dante’s seven circles of Hell during rush hour, had the nation’s first-ever air pollution alert last year. Cities are considering banning older diesel cars outright. So far, Stuttgart’s no-driving days have been voluntary, and the change of the seasons has helped a lot as well. But that doesn’t mean there’s not a problem.
But how big is the issue? And where is it localized? Or is particulate pollution localized at all? These questions would benefit from a distributed network of particulate sensors, and the OK Lab in Stuttgart has put together a simple project(translated here) to get a lot of networked sensors out into the wild, on the cheap.
The basic build is an ESP8266 with an SDS011 particulate sensor attached, with a temperature and humidity sensor if you’re feeling fancy. The suggested housing is very clever: two 90° PVC pipe segments to keep the rain out but let the dust in through a small pipe. The firmware that they supply takes care of getting the device online through your home WiFi. Once you have it running, shoot them an e-mail and you’re online. If you want help, swing by the shackspace.
We love these sort of aggregated, citizen-science monitoring projects — especially when they’re designed so that the buy-in is low, both in terms of money spent and difficulty of getting your sensor online. This effort reminds us of Blitzortung, this radiation-monitoring network, or of the 2014 Hackaday-Prize-Winning SATNOGS. While we understand the need for expensive and calibrated equipment, it’s also interesting to see how far one can get with many many more cheap devices.
If you’d like to risk blowing your fingers off for a good cause this week, look no further than [M. Bindhammer]’s search for an eco-friendly rocket fuel. [M. Bindhammer] predicts the increasing use of solid rocket boosters in the future. We’re into that. For now, rocket launches are so few and far between that the pollution doesn’t add up, but when we’re shipping consumer electronics to the moon and back twice a day, we might have a problem.
The most common solid rocket fuel emits chlorine gas into the atmosphere when burned. [Bindhammer] is exploring safe ways to manufacture a eutectically balanced and stabilized fuel compromised of sugar or sugar-alcohol, and potassium nitrate. If you watch home chemistry videos for fun on the weekend like us, [Bindhammer] goes through all his thinking, and even spells out the process for duplicating his fuel safely in a lab.
He’s done a lot of work. The resulting fuel is stable, can be liquid or solid. It has a high ignition temperature, but as you can see in the video after the break. Once ignited. It goes off like rocket fuel.
Air quality is becoming a major issue these days, and not just for cities like Beijing and Los Angeles. It’s important for health, our environment, and our economy no matter where we live. To that end, [Radu] has been working on air quality monitors that will be widely deployed in order to give a high-resolution air quality picture, and he’s starting in his home city of Timisoara, Romania.
[Radu] built a similar device to measure background radiation (a 2014 Hackaday Prize Semifinalist), and another to measure air quality in several ways (a 2015 Hackaday Prize Finalist and a Best Product Finalist; winners will be announced next weekend). He is using the platforms as models for his new meter. The device will use a VOC air sensor and an optical dust sensor in a mobile unit connected to a car to gather data, and from that a heat map of air quality will be generated. There are also sensors for temperature, pressure, humidity, and background radiation. The backbone of the project is a smart phone which will upload the data to a server.
We’ve seen other air quality meters before as well, and even ones based around the Raspberry Pi, but this one has a much broader range of data that it is acquiring. Its ability to be implemented as an array of sensors to gather data for an entire city is impressive as well. We can envision sensor networks installed on public transportation but to get to all parts of every neighborhood it would be interesting to team up with the Google Streetview Cars, Uber, or UPS.
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