Particle Sniffer For Pollution Point Sources

When measuring air quality, particulate matter is an important metric to watch. The PM2.5 rating refers to particulate matter that has a diameter of less than 2.5 micrometers. While it’s often measured by authorities on a city-wide basis, [rabbitcreek] wanted a way to track down point sources indoors.

The tool [rabbitcreek] built is in a similar form factor to a typical infrared workshop thermometer. Inside, it packs a Honeywell HPMA115S0-TIR laser particle sensor, hooked up to an ESP32 which runs the show. The sensor chosen makes things easy, with the device already set up with a blower and inlet and outlet ports for taking accurate readings.. Results are displayed on an SSD1306 OLED screen. It’s all wrapped up in a 3D printed case with a trigger grip, and a dog nose on the front which hints at the devices true purpose.

In testing, the device proves capable of detecting point sources of atmospheric particulates like flowers and a toaster. It’s something we’re sure would prove handy to those working in HVAC and environmental assessment industries. We’ve seen other rigs for monitoring particulates before, too. Video after the break.

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Measuring Particulate Pollution With The ESP32

Air pollution isn’t just about the unsightly haze in major cities. It can also pose a major health risk, particularly to those with vulnerable respiratory systems. A major part of hazardous pollution is particulate matter, tiny solid particles suspended in the air. Particulate pollution levels are of great interest to health authorities worldwide, and [niriho] decided to build a monitoring rig of their own.

Particulate matter is measured by an SDS011 particulate matter sensor. This device contains a laser, and detects light scattered by airborne particles in order to determine the level of particulate pollution in PM2.5 and PM10 ranges. The build makes use of an ESP32 as the brains of the operation, chosen for its onboard networking hardware. This makes remotely monitoring the system easy. Data is then uploaded to a Cacti instance, which handles logging and graphing of the data.

For those concerned about air quality, or those who are distrustful of official government numbers, this build is a great way to get a clear read on pollution in the local area. You might even consider becoming a part of a wider monitoring network!