It seems that there are frequent questions about why a particular PurpleAir sensor or group of sensors don’t seem to agree with an EPA sensor. Besides the technical differences in the sensors, in these discussion I’m interested that I’ve never seen (and certainly may have missed…) a discussion about local environmental effects.
I live in the mountains where such effects can be quite exaggerated and consequently easier to notice. Within a short walking distance there can be temperature variations of 5 or 10 degrees with similar changes in humidity (and using my lungs as sensors, particulate pollution as well). These topoclimatic effects can be seen on even smaller scales around here and I imagine even within a very small location at a particular dwelling (open air vs sheltered in the back under an eave or whatever)
There aren’t many PurpleAir sensors in our area, but I frequently notice a very significant difference between our sensor and those few which are in the valley, which is about 6 miles down the mountain and from the local EPA sensors.
So, it doesn’t seem to me in general, recognizing the technical differences, that there should be much surprise that any given PurpleAir sensor might be different, sometimes significantly from the local EPA reports.
I would love to hear from or be pointed to someone who has a real understanding of these effects (in contrast to my own which are uneducated observations).
A very astute observation. The best way to look for local effects would be to locate the nearest EPA sensor (either FRM or FEM monitor), 10-20 of the nearest outdoor PurpleAir monitors, and collect data for (best case) a year or so to allow for seasonal effects.
This was actually carried out over a 5-year period (2017-2021) for 10,000 outdoor and 4400 indoor PurpleAir monitors in the West Coast states… However, the purpose was not to focus on local effects but to calibrate the PurpleAir monitors to the mean EPA sensors. This resulted in the pm2.5 alt (aka ALT CF-3) algorithm now available for downloading. However, the extensive data could be analyzed to look at variations around this mean, but that has not been carried out.
The density of monitors in our area is pretty low so tracking local monitors here wouldn’t be very helpful. On a very quick count there are about 15 senors in the immediate 1200 sq miles which includes one EPA sensor.
In the study you reference, there’s certainly an impressive amount of data. It would have been interesting if the data/evaluation had included included those local effects. During the data collection and evaluation process in that study was some of the other data available and evaluated in these monitors such as humidity, operating temperature, etc.?
I think one could have an interesting conversation about the attempts at correction of the sensor data. Though not a very solid data point, I find that clinically, how i feel, i.e. how the air quality affects my breathing, correlates nicely with the raw PM2.5 and less well with the ALT CF-3 correction.
something you can consider is making sure you’re looking at the correct averaging intervals. for example, most air agencies (& EPA AirNow maps) report out 1-hour averaged data, and there is a slight delay in updating each hour of new data on the map. Also, AirNow is displaying something called the NowCast, which is a rolling calculation that takes into account previous hours and trend analysis. It’s typically not a straight value reported from a concentration.
If you’re looking at real time or even 10-minute data from PurpleAir sensors, you’re bound to get very different results than EPA monitors that are reporting NowCast hourly values. Also, PurpleAir sensors report higher concentrations than regulatory monitors, but applying the USEPA correction factor can help address the over-estimation bias and get you closer readings.
I am not sure if you’re referring to ‘EPA Sensors’ as the regulatory equipment, or PurpleAir sensors owned & operated by EPA.
Thanks - I think what I was originally getting at in my original post and what I am more focused on is the idea that variability in correlation between sensors shouldn’t be surprising. It seems to be related to some degree, large or small, to local effects and that this idea isn’t commonly discussed. In my own very limited experience and from an even smaller (maybe microscopic) knowledge base, these topoclimatec effects can be pretty large, depending upon geography, vegetation or even density and type of building construction. A great example for me of local variability this time of year is in watching a high wind gust blow snow across our field where the actual heterogeneity of air movement can be easily visualized in the swirling snow patterns in that concentrated space with areas of high density and with areas of apparent voids.
We recently had a fire 2 miles from my house and the purple air AQI was elevated the whole time and went up to 2,500, but the airnow.gov in the same vicinity said it was green/under 50 the whole time
