This post gives some further examples of the data gathered from our volunteers. The previous post showed some NO2 data captured by the Air Quality Egg sensors.
This post will share some example PM 2.5 data captured using the AirBeam sensors – more info on these sensors is available in previous posts.
The map below shows pm 2.5 readings for 26 Feb 2017 pm. Key roads are highlighted. The readings are generally green and good.
Below is the time-series equivalent of this data showing the data was captured between 16:28 and 17:09. The rise in the levels at the end of the chart may or may not be the result of higher traffic levels as peak hour begins. As always with this sort of data a lot of caution should be exercised before drawing conclusions or insights too quickly.
The map below is also for 26 February but this time during the morning. This time from 11:52 to 13:30. Again the readings are generally green.
There are a batch of readings coloured yellow which appear to be actually located in a large retail store. This can reflect internal local effects, such as a cafe or restaurant with gas burners for cooking. Again, interpretation of this data on its own should always be treated with caution.
The final mapping shared here is from 27 February 2017. All of these readings (more than 5,000) are green and show low levels of PM2.5.
This project is about trying to understand how this sort of data and technology can help. We will post further here on the lessons learnt from discussion with our friends at Breathe Easy and elsewhere.