How Accurate Is the 1 Micron Dylos? Official PM2.5 Comparison Test

Lots of people are buying air quality monitors these days, but how do they compare to the official pollution numbers–are they accurate? When I bought a Dylos DC1100 Pro laser particle counter,  I did a comparison test with the US Embassy. I put my Dylos Pro outside the window of my apartment (at Nanluoguxiang) 70 times and compared the numbers to the US Embassy’s Twitter feed at the same time. I found that the two numbers were very strongly related: r = 0.89 (remember the highest possible correlation is 1).

dylos-pro-vs-us-embassy-en
I did that test with the Pro model, but there’s also a standard model that’s $70 cheaper. At Smart Air, we call it the Standard Dylos.

How does this cheaper machine compare to the government measurements? If it’s similarly accurate, great! That means people can save money and get roughly similar results.

Pro vs. Standard

The Dylos Pro and Standard are almost the same judging from their front looks. So what’s the real difference?

The key is the size of particles they measure. Both of them have 2 size ranges. For the small particle channel, the Dylos Pro measures particles .5 microns and above, while the Standard Dylos measures 1 micron and above. On the large particle channel, the Pro measures 2.5 microns and above, while the Standard measures 5 microns and above. That means the Pro measures an extra range from .5 to 1 micron.

The machine governments use is called the Met One BAM. It measures particles 2.5 microns and below (here’s how government PM2.5 sensors work). If we put the measurement ranges of the BAM, the Dylos Pro, and the Standard Dylos together, here’s what it looks like:

dylos-pro-vs-embassy-vs-dylos-en

The Pro overlaps a bit more the government machine than the Standard. For that reason, I’d expect the Pro correlates better with the government machine. But just how well do they compare empirically?

The Dylos Test

Luckily I’m not the only nerd interested in air pollution problem. An American living in Guangzhou bought the Standard and teamed up with me to test it against the US Consulate.

From November 12th to 17th, she collected data with her Dylos Standard 70 times outside her apartment,which is close to Sun Yat-Sen University in Guangzhou. She also recorded the numbers of PM2.5 concentration released by the US Consulate.

dylos-1-micron-test-map

Results

Remember how well the Pro correlated with the US Embassy? Here’s what I found before (r =.89).

dylos-pro-vs-us-embassy-en

Later I did another test by putting my DC1100 about 100 meters away from the US Embassy (much closer than my apartment). This time it turned out that the two numbers correlated at r = .9 – slightly higher than in the first test.

dylos-pro-vs-us-embassy-closer-en

How does the Standard Dylos compare? Here’s what we found:

standard-1-micron-dylos-vs-us-embassy-en

They correlate at r = .85! Although that’s a bit lower than the correlation between Dylos Pro and US Embassy, the two numbers are still strongly related. I think that result would surprise most people.

Converting the Dylos 1 Micron Readings to PM2.5

Using this data, we can get a rough formula to convert between the Dylos 1 micron and the official numbers. For example, the 24-hour WHO limit is 25 µg/m³, which is approximately 1,250 on the Standard Dylos.

1-micron-dylos-to-us-embassy-en

The simplest formula to convert the Dylos 1 micron to PM2.5 micrograms is:

Dylos 1 micron count × 0.02 = PM2.5 µg/m3

If we use that formula to convert the Dylos numbers to micrograms, here’s how well the numbers compare:

On average, the formula was off from the government numbers by 9.04 micrograms. That’s roughly as much as the average deviation of the Dylos Pro in our separate particle counter tests.

Conclusion

The Standard correlates highly with the government numbers—only slightly less well than the Pro model.

Limitations

The conclusion is based on only one test in Guangzhou. We could draw stronger conclusions if we had more tests done in different locations and in different seasons.

Open Data

As always, I’m making the data and more testing details publicly available for fellow nerds, as well as a new test addressing some other factors like humidity.

P.S. Thanks to Yiwen Wang for help translating and creating figures.


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