Category Archives: Measurement

Watching Propagation with FT8 Spots

Recently I purchased a Hermes Lite 2 SDR receiver (HL2 for short), and I am very impressed with it. One of the very nice features is that it lets you receive several chunks of spectrum (“slices” in SDR parlance) at once.

I also found an excellent piece of software for the digital-mode enthusiast called SparkSDR. SparkSDR can make use of the multiple slices offered by HL2, and thus allows for an infinite number of digital mode receivers to be operated using the HL2 slices. SparkSDR also offers the ability to transmit these modes, but for the purposes of this article, I am only concerned with FT8 reception.

Since FT8 came about, the use of WSPR for making test transmissions and observing receiving locations has pretty much died. However, with the widespread update of FT8 (and similar modes such as FT4, etc.) these transmissions may instead be used as transmitting beacons – when a station calls ‘CQ’ (makes a general call soliciting someone to reply), the software sending the FT8 call encodes the senders location (as a QRA locator) into the sent message. A typical CQ may look something like this:


Similarly, the system of another person responding to my CQ call will also encode the distant station’s location, resulting in a response which may look something like this:


Here “M1GEO” is my callsign, “ZL1A” is a DX (distant) station and “JO02” (and “RF72”) is the first part of the maidenhead locator, covering a 100km square, which looks something like this:

It is possible to use a 6-digit locator, for example JO02HG, which takes the accuracy down to a 10km square (see below). Although there are higher resolution locators than this, they are not often used.

Since we know (at least to within 10km) where a transmitting station is, it becomes possible to plot the stations on a map. You’ve probably seen this done before. Websites like PSKReporter and WSPRnet have been doing this for some time now.

The image above use different coloured markers for different bands:

Marker ColourBand
Blue40m (7074 kHz)
Green30m (10136 kHz)
Yellow20m (14070 kHz)
Brown15m (21074 kHz)

I have been recently enjoying the ability to use the same untuned vertical antenna with the same radio on different bands simultaneously (the HL2 lets you remove any band-pass filtering). This allows you to see, for example, that while 40m was good for working Germany, 30m had good propagation into Australia.

PSKReporter lets you filter by mode, band, and time, so you can see what times a given band is open to a specific location. Excellent for helping to fill those missing DXCC slots you may have.

It was interesting for me to see that much of the more remote stations were received on the lower bands, 40 and 30m, and not 20m as I would have expected:

  • VK7BO at 6:06 UTC on 30m
  • VK3ZH at 6:33 UTC on 30m
  • K6SY at 5:28 UTC on 30m
  • LU1WFU at 1:50 UTC on 40m
  • YE8QR at 14:46 UTC on 40m

Although at the time of writing (August 2020), HF conditions are quite poor, not all of the lack of performance on 20m can be explained due to the band conditions, since 20m is still open to South America and into Asia.

Animating the propagation

After looking at these static images, I wanted to see how things changed with time. Could I, for example, see when the best time to work the west cost of the USA would be? I decided to make a time-lapse animation. The animation runs for approximately 3 days, and includes the following FT8 decodes:

40m (7074 kHz)72,404
30m (10136 kHz)33,871
20m (14070 kHz)156,636
15m (21074 kHz)3,453

It is clear to see sudden bursts of colour when a band opens, and to watch conditions change throughout the day. The dates here were for a Friday to Monday, so, there’s plenty of weekend activity.

I’m keen to further explore the possibilities of this.

Air Particulate Sensor


Around a year ago a colleague mentioned to me the Luftdaten project (en: air data project). It is a sort of citizen science project which measures and records particulate matter in the air. They also have a newer project concerning acoustic noise levels (build notes for this in German here), but I’ll be concentrating on particulate matter here. Often the sensors are combined to collect data on airborne particulate levels, acoustic noise, temperature, humidity and air pressure to name a few. The images below show 24h graphs for some of the measured parameters from various sensors around Europe.

I have long had a fondness for collecting data and the Luftdaten project has plenty! Their website features an interactive map of the world where they plot measurements taken.

The particulates detected include dust, dirt, soot, smoke, and liquid droplets emitted into the air which are small enough to be suspended in the atmosphere. Airborne particulates may be a complex mixture of organic and inorganic substances. The type of sensor used does not differentiate between the particle types, it just gives a measure of how many are present.


I purchased all of the parts cheaply from AliExpress (no affiliation). You’ll find the same parts on eBay, BangGood, etc., so use what you prefer.

The particulate matter sensor is a Nova SDS011 PM sensor (datasheet), which can be purchased for around £14.

You’ll probably also want a DHT22 temperature & humidity sensor (datasheet) which will cost around £2.

Finally a NodeMCU V3 is required. This is a module based on the ESP8622 WiFi chip, which includes a USB-UART interface, some voltage regulators, etc. In a later step, we’ll program this with the Luftdaten code.

A short length of plastic pipe can help to draw air into the SDS011 sensor. The datasheet for the SDS011 suggests an inside diameter of 6mm and outside diameter of 8mm.

The photo below shows my assembled parts! You’ll notice int he bottom right of the image that I have an Influencair V1.2 PCB. Influencair are a spin-off group of Luftdaten based in Belgium. CivicLabsBelgium produced PCBs for project participants to use and made the PCB Gerber Files available here. You can send these off for manufacture using any PCB service you like (for example, in no order JLCPCB, PCBWay, DirtyPCBs, Elecrow, and so on).

Build notes & Programming

The build process is explained thoroughly on the Luftdaten website under the construction manual page. As I won’t be able to do a better job of explaining this, I suggest you go ahead and read their documentation. Below shows the parts mounted on my Influencair V1.2 PCB. I used 3mm screws to hold everything together.

Programming, Configuration & Registering

The default programming proceedure requires setting up the ESP8622 in the Arduino software environment, but Piotr Dobrowolski has created a very useful tool: airrohr-firmware-flasher for the Luftdaten project. I used the Linux variant of the program the NodeMCU in one click, but Windows and macOS versions exist too! Be sure to find the “releases” button on GitHub to download the pre-compiled ready-to-use binaries!

With the NodeMCU connected to the PC with a USB cable, I selected “latest_en.bin” from the version list (en being English), and pressed Upload. As you can see from the figure below, it took just over 8 seconds and it was done!

Power cycling the board resulted in a WiFi device being created by the newly created sensor! I connected my mobile phone to “airRohr-11189671” and was greeted with the following screen.

I tapped on my house WiFi “SmartLAN”, entered my super-secret passkey and we were away! The device power cycled and was then visible from the flasher once again.

From this, I was able to see the sensor was assigned an IP address on my network, and double-clicking on the entry whisked me off to my default web browser viewing the sensor’s internal webpages.

There are many configuration options, but the defaults were suitable for my hardware configuration. There are options for OLED and LCD screens, extra/different sensors and many other configurations which are out of the scope of this article. However you can click “Current Data” to see the sensors latest measurement data. The image below was taken with the sensor indoors.

The final thing to do is to create an account on the Luftdaten project website so your sensor can contribute measurements. There are already a reasonable number of sensors in the UK contributing data to Luftdaten. You can create your account and submit your sensor data here:

I have positioned my sensor in the garden shed with a short section of plastic pipe poking outside of the shed where the wind blows. I live near a busy roundabout and buy a petrol filling station, so it will be interesting to compare my measurements with other local sensor readings.