Thank you for commenting on my thoughts, and I must really apologise that I was not specific enough in my response. I should have made it clear that I was only talking about the PM2.5 measurements. I also apologise for a lengthy response.
Your thoughts are totally reasonable, but currently it is still my belief that we should look beyond the need to be absolutely accurate with our measurements and play to the strengths of the counters, which in my opinion is to identify changes rather than give absolute values. Indeed, I think this does tie in with the first paragraph of section 6 of the LUBW document you linked where they discuss being qualitative rather than quantitative. I also ask why the sensor community does not use a correction for humidity. Is it because it is too difficult? Or not worth it?
On the subject of the LUBW document. It is a good preliminary study. However, it only gives results for PM10. Over the time I have owned my SDS011s I have learned to abandon using the PM10 data due to its inaccuracies. In calculating my PM2.5 values I average and normalise the data from more than one low cost system to be more accurate.
I agree with your point that yearly weather patterns might differ. Ideally variation over a number of years should be used to take account of this. I accept that using low cost systems to do this is not really ideal (see last bullet below). I believe that all European Governments are required to do something to reduce the PM2.5 levels (although the UK has not legislated for this yet). I therefore assume that if their measures are effective this will be observed in the low cost sensor data. Equally, if they are not delivering the required outcome, then the low cost systems will also show this. It is our way of keeping a check that the authorities are dealing with this issue and not a means of letting them off the hook.
For point 3 and the most difficult point. There are so many factors that influence the counts at high humidity that there isn’t really a fit all solution. As I said above, if there was an easy solution I think the sensor community would have already implemented a correction factor. These factors include:
- The hygroscopic nature of the particles (variable throughout the year and from location to location).
- The variability of the number of smaller particles in the environment that become more detectable as the humidity increases (effectively this improves the limit of detection and could be telling us something useful).
- How much of the higher count is associated solely with water droplets.
- The accuracy of the BME280 systems. These tend to saturate and read too high, so if this value is used to correct for particle count this could be in error.
- The SDS011s age and give different results with time.
I believe Bernt Laquai discusses all of this in his papers.
Because of all of these variables, rather than use a correction factor, I have been investigating the differences in the PM2.5 concentrations associated with heated and unheated sensors at my location. I have already reported some of my findings (see SDS011 datasets with- & without dryer - #4 by sensorsalnorth12 and Preliminary comparison of heated and unheated systems). There is clearly a difference. At the time of posting, I estimated a difference of around 20% between the heated and unheated systems. However, the heated system doesn’t correct for all circumstances e.g. fog.
My next step is (or was) to determine a way of calibrating the heated low cost system against a calibrated sensor that is already accepted by authorities such as a TEOM or FIDAS (assuming I could get agreement from my local authority). This is when I discovered that there appears to be a similar discrepancy between these systems!
So this is why I am currently thinking that this is far, far more complex than just correcting using a simple formula. The difference in the accepted technologies is a concern. At the moment I am trying to understand which technology was used by the WHO for their guidelines as this should be the one for calibration.
From my perspective the more I find out and think I know about this subject, the more I realise that I do not know.
One final thought is that I believe that some of the above bulleted factors are less significant over 12 months than 24 hours. I recently calculated the annual average PM2.5 concentration for my location and found that it agreed reasonably well with that of the modelled data supplied to UK’s DEFRA.
I hope this explains my reasoning better.