CONA - Community Observation Networks for Air

The main reason we asked for some volunteers to accept temperature sensors in the home was to track when people were using their woodburners and how that might change from day to day. But the data from these sensors reveal a whole lot more information if you care to look and we’re just starting to look now. The sensors themselves were thermocouples provided on loan by BRANZ  the building research institute. They consist of a probe which was attached to the top of the firebox and a logger placed nearby. So far I have looked through data from 9 of the 14 homes we surveyed. In a moment, a taste of some of the things we can work out from the indoor temperature data. But first, a quick look at the outdoor temperatures. Below is a 'box plot'. All of the data from every days is combined and sorted into hours. The middle 50 % of all data lies within the box (the short horizontal line is the average). The top and bottom 25% are represented by the dashed lines. This chart shows that, o...

Actually we were ... we got lots of data and that's what we wanted (sorry for the clickbait title ... a scientist has to have some fun right? ) Now in all seriousness, we were very pleased with the data from the first two weeks of PACMAN deployment in Rangiora. They allow us to test the units in a real world  setup outside of the researchers' control and evaluate whether the data we get from them can tell us something useful about the exposure of the people inside to particulate matter. Let's look at data from one unit PACMAN_17 and what it can tell us about the home it was living it. Temperature The PACMAN has an internal temperature sensor within its real time clock to keep it accurate. This is not quite room temperature  but it approximates it pretty well. See a comparison below between the temperature closest to the wood burner stack (BRANZ logger) and the internal PACMAN temperature. Not bad right! However, the same issue that I mentioned before remains ... it is not...

If you thought that we've gone away ... you're partly right, we've been away  but not in a bad way! About a week ago we got our first full set of data from a few of the participants in Rangiora and just to give you an idea ... for every home  we have about one million records with between 5 and 10 parameters each ... that's a lot of numbers! So, yes, we've been away but it has been trying to get our heads around this large dataset ... with various levels of success... as a German general said in the 1800s:  "No plan survives contact with the enemy" . At the beginning of this campaign we thought that the temperature data obtained through the iButtons  and BRANZ's loggers was going to be simple to interpret. High temperature = heater on, low temperature = heater off. The next level in complexity would be ECan's temperature and $PM_{10}$ data as it is standard compliant and we have a long time series for that location that would give us some context ...

Here is PACMAN in its native environment One of the key pieces of hardware we're deploying in this campaign is our PACMAN ( Particles, Activity and Context Measurement Autonomous Node ) ... not this pac-man . This little unit is capable of logging air quality parameters (dust concentrations, $CO_2$, $CO$), activity information (motion, distance to closest object) and context (time, temperature) every second. We expect to use this information to estimate what drives the indoor concentrations of dust in homes. All the participants that want to have instruments in their home will have one of these units installed but before we can do that we need to test if they are working as expected. So, we set them up in a bench facing a wall for a few days and as it is normal with experimental technology, not all of them worked perfectly but most of them did so we are go for PACMAN deployment. If you're curious as to what the data looks like ... here is a link to the test results of one of...