CONA - Community Observation Networks for Air

In air quality lingo  we normally refer to small areas of increased pollutant concentrations as hot spots  but since this story is about the temperature  field I needed to use something else ... warm spots  will do. As part of the measurement campaign we deployed in Rangiora we not only used ODINs but also four weather stations (like the one on the picture) scattered around the town to explore small scale wind flows and how uniform or otherwise the temperature field is in the area. Before we dive too deep in the analysis, let's pause and look at the two kind of temperature sensors used here. First is the meteorological sites that have thoroughly calibrated and well housed thermometers, protected from the sunshine while the ODIN thermometers were ... well ... not perfectly housed (see image of the inside of an ODIN) and then put in plain sunlight attached to lamp posts (see image of ODIN attached to a lamp post). Now, don't despair! There is a reason why the ODIN temperature se

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

Pretty typical, really! In terms of understanding air quality we’re usually interested in three things: temperature, wind speed and wind direction. Air temperature might tell us something about how much home heating is going on (or it might not – that’s one of the things we want to confirm in this study). Wind speed tells us how much air pollution is going to be dispersed and diluted. Poor air quality in most New Zealand towns usually occurs on evenings when there are a combination of light winds and colder temperatures. Wind direction is interesting because firstly it is linked to wind speed and temperature. For example in Rangiora in winter south-westerly winds are often stronger and warmer and north-easterly winds are lighter and colder. Secondly, changes in wind direction may also tell us if smoke is being dispersed away from the town or just moved around. To find out what the weather was like we will firstly be looking at data from a permanent weather station on the edge of town r

Hopefully something more substantial than the fox! ... ( that song is still in my head after I let my kids get their hands on the radio!) [EDIT: Added missing link to full analysis script] Let's talk ODIN then ... Now that we have more data from the ODINs we can start to build answers to the questions: How uniform is the air pollution field in Rangiora? Is the plume of emissions recirculated under certain conditions? But there is a big caveat. The ODINs do not give us $PM_{10}$ and it cannot be directly compared to the measurements done by ECan. So ... let's dive in ... carefully. First, let's review where we installed the ODINs. Our original intention was to locate them in two west-to-east lines, one to the north of the town and another one to the south. We were fortunate to be able to use lamp posts for our deployments and Sally and Sam managed to get them pretty close to where we wanted them. This map shows the location of ECan's air quality stati

This initial analysis is going to focus on seven homes from which we were collecting data over 12 days in August. As part of the sign up agreement we promised not to share private data with anyone else. For that reason we will not be identifying which homes are which in this, or any similar analysis. Why are we interested in fire-lighting? To understand air quality in the town it matters a lot to know WHEN smoke is being emitted from people’s chimneys, and from how many. One of the things we know very little about is how much people vary the time they light their fire, or whether we are all creatures of habit and have the same fire-lighting routine each night. Does it depend on the weather, the design of our homes, our lifestyles? We know when our volunteers lit their fires because they had a thermocouple – a device which can measure high temperatures – fixed to the flue of their woodburner. When a fire is lit the temperature recorded by the thermocouple jumps up tens of degrees in jus

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