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Archive for the ‘Water Quality’ Category

While it can be straightforward to distill water to high purity, this is rarely the best method for producing water for useful purposes. Even drinking water typically needs certain minerals in it, plants may need a certain pH, and wastewater systems have a whole host of other qualities that need to be measured. Measuring water quality is a surprisingly complex endeavor as a result and often involves a wide array of sensors, much like this water quality meter from [RowlesGroupResearch].

The water quality meters that they are putting to use are typically set up in remote locations, without power, and are targeting natural bodies of water and also wastewater treatment plants. Temperature and pH are simple enough to measure and grasp, but this device also includes sensors for total dissolved solids (TDS) and turbidity which are both methods for measuring various amounts and types of particles suspended in the water. The build is based around an Arduino so that it is easy for others to replicate, and is housed in a waterproof box with a large battery, and includes data logging to an SD card in order to make it easy to deploy in remote, outdoor settings and to gather the data at a later time.

The build log for this device also goes into detail about all of the steps needed to set this up from scratch, as well as a comprehensive bill of materials. This could be useful in plenty of professional settings such as community wastewater treatment facilities but also in situations where it’s believed that industrial activity may be impacting a natural body of water. For a water quality meter more focused on drinking water, though, we’d recommend this build that is trained on its own neural network.

A blue enclosure with "IoT AI-assisted Deep Algae Bloom Detector w/Blues Wireless" written on the front. Two black cables run over a wooden desk to a cylinder with rocks on the bottom and filled with murky water. A bookshelf lurks in the background.

Harmful Algal Blooms (HABs) can have negative consequences for both marine life and human health, so it can be helpful to have early warning of when they’re on the way. Algal blooms deep below the surface can be especially difficult to detect, which is why [kutluhan_aktar] built an AI-assisted algal bloom detector.

After taking images of deep algal blooms with a boroscope, [kutluhan_aktar] trained a machine learning algorithm on them so a Raspberry Pi 4 could recognize future occurrences. For additional water quality information, the device also has an Arduino Nano connected to pH, TDS (total dissolved solids), and water temperature sensors which then are fed to the Pi via a serial connection. Once a potential bloom is spotted, the user can be notified via WhatsApp and appropriate measures taken.

If you’re looking for more environmental sensing hacks, check out the OpenCTD, this swarm of autonomous boats, or this drone buoy riding the Gulf Stream.

Clean water is one of our most precious resources, but identifying sources of pollution often means expensive equipment. This can also mean taking multiple water quality readings and somehow aggregating them together to be easily usable. As a solution to both problems, Andrei Florian has developed WaterAid — which was recently named a finalist in this year’s Hackaday Prize.

WaterAid consists of a measurement unit that senses water pH, turbidity, and temperature, as well as atmospheric temperature and humidity. Data is relayed to the system’s backend via a cellular connection, using an onboard MKR GSM 1400. Collected information from one or more devices is then displayed on a Soracom Lagoon dashboard for water monitoring from anywhere in the world!

Not only can a fleet of WaterAids be used to continuously track a river, lake, or any other body of water, but individuals looking to get immediate feedback on quality can utilize the portable tool’s NeoPixel ring for color-coded judgement.

More details on the low-cost and scalable project can be found in Florian’s log here.



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