How fresh is the air I breathe? To find out, I put my own particulate matter measuring station into operation. Particulate matter (PM) is a mixture of all kinds of organic and inorganic dusts, bacteria that travel through the air, and many other particles that do not exceed 10 micrometers in size.

The above measurement is why we use the term PM10. For comparison, a hair has a diameter of 50 to 75 micrometers. Today, even smaller particles with a size of only 2.5 micrometers (PM2.5) or smaller are considered separately because they penetrate deep into the airways and reach the alveoli.

There are several sensors on the market that can measure both PM classes. I chose the Nova SDS011 PM Sensor by Chinese manufacturer Nova Fitness Co., Ltd. The sensor draws air into a chamber and shoots a laser at it. It uses the dispersion of the reflected light to determine the particle size and quantity. It delivers the data via a serial interface.

A USB adapter is included with the sensor, making it easy to connect to the Raspberry Pi. After connecting, I can see from the syslog that the /dev/ttyUSB0 interface is now available:

[ 2.611448] usb 1-1.5: ch341-uart converter now attached to ttyUSB0

The manufacturer's data sheet [1] reveals that the interface must be set to 9600bps with 8 data bits, no parity, and one stop bit. The stty command handles this:

stty -F /dev/ttyUSB0 9600 raw

The data sheet also says that the sensor operator can expect the values in hexadecimal notation in a fixed, recurring order once per second. The output looks like this:

0000000 aac0 8c00 9400 bcf9 d5ab

This means that values are now available: 8c for the PM2.5 class and 94 for PM10, 140, and 148 decimals.

The data sheet contains the following computation formula: The decimal value of the high byte is multiplied by 256, and the decimal value of the low byte is added to it. The result has to be divided by 10 – this is finally the PM value in micrograms per cubic meter of air. In the example, this would be 14 micrograms/m3 for the PM2.5 class, a low (i.e., good) value. You can then use RRDtool [2] to show your data in graph format (Figure 1).

Figure 1: The dashboard for PM measurement.