When it comes to measuring Air Quality, the golden standard nowdays is to measure PM_2.5, also known as small respirable particle matter. That was however not the case few years ago, when the golden standard was still about measuring PM₁₀, also known as standard particle matter, as well as other Volatile Oganic Compounds (aka VOC, which includes Ozone, NO₂, SO₂ and CO).

The reason for upgrading from PM₁₀ to PM_2.5, explained by Dr Sarath Guttikunda in this explantion about PM₁₀ vs PM2.5 is that there are now new studies presenting evidence that PM_2.5 is more harmful than PM₁₀. As Dr Dr Sarath Guttikunda sates in the article, “physically, this makes sense since smaller than particle have a higher probability to go deeper into the lungs and harm people”.

Many countries, like China have done tremendous efforts in modernizing their monitoring equipement to the PM_2.5 standard. Howerver, there are still quite a lot of countries not yet providing PM_2.5 data, but only PM₁₀. And among those countries with only PM₁₀ data, the most surprising one in South Korea (with the exception of Seoul).

It is surprising since the Air Quality in South Korea is very similar to it's neighboors countries, namely Japan and China - where PM_2.5 AQI is quite often peaking above 200, while PM₁₀ AQI is acceptable and below 100. From the picture on the right (taken from tenki.jp), which represent the PM_2.5 data flux, there is no doubt that South Korea is suffering from the same PM_2.5 high densities as China and Japan.

Correlating PM_2.5 and PM₁₀

So, the question that we would like to answer is: Can we deduct the PM_2.5 AQI when only PM₁₀ data readings are avaialble? The answer is yes, and the main reason is that there is a very strong correlation between the PM₁₀ and PM_2.5 mass concentration. The South Korea governement has recently released, for few stations, both PM₁₀ and PM_2.5 hourly concentration data, and this will be the basis for this experiment. The graph below shows the last 30 days concentration for the station 경기 탄벌동 (gyeong-gi tanbeoldong) for both PM₁₀ and PM2.5:

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Note that the ordinate axis scale is different for PM₁₀ and PM2.5, so although the graphs overlap, the values are actually different. From the correlation graph, there is just no doubt that there is a very strong linear correlation for the PM₁₀ and PM_2.5 concentration. The pearsons cooeficient for the last 30 days is . The resulting linear regression is defined with this formula:

Also, it is worth noticing that there are three periods where PM₁₀ is significantly devaiting from PM2.5, two between October 27th and November 3rd, and one arround November 18th. This usually occurs during sand storms.

Deducting PM_2.5 from PM₁₀

So, all we need to convert the PM₁₀ AQI into the PM_2.5 AQI, is to stretch the mass scale from PM₁₀ to PM_2.5 to determince the corresponding PM_2.5 mass value from the PM10 reading, and finally use the PM_2.5 AQI scale to deduct the final AQI.

The graph on the right shows the mass to AQI convertion formula. The absissa (x axis) is the mass (in milligrams) and the ordinate (y axis) is the AQI. The two graphs correspond to the PM10 and PM2.5 deducted from PM₁₀ readings. Note that it empirically confirms the hypothesis that PM_2.5 AQI is always higher than PM10 when the PM₁₀ mass is not too hight (in this case, lower than 480 milligrams).

PM_2.5 deducted map

From the two maps below, the one titled "Interpolated PM_2.5 AQI" is using the above formula to show what the "actual" Air Quality could be, if PM_2.5 stations would be used. The result is indeed quite scarying, but on the other hand, that's not a surprise: The PM_2.5 AQI is much more strict than the PM₁₀ AQI, as explained in the begiging of this article (see explantion about PM₁₀ vs PM2.5).

Important notes

Before concluding, there are few very important points to mention:

• First, this convertion only applies to the PM₁₀ readings, and in summer, the Ozone tends to be very often the major pollutant. So, if you do the convertion yourself, please, make sure that you are converting the PM₁₀ AQI and not the overall AQI, which is defined as the maximum of all individual AQIs.
  
  
• Second, this is an experiment, and despite having a very good coorelation cooeficient (pearsons factor is 0.96) between PM₁₀ and PM2.5, there are some days where PM₁₀ is much higher than PM_2.5 (for instance, check this experiment about PM₁₀ vs PM2.5). Those days, the convertion formula should not be used.
  
  
• Last, the linear regression equation is very specific to the place where you measure the data. This is also known as the "dust type" property, which says that the "typical" dust in South Korea is different from the one in US or Malaysia. This means that this formula should be updated if used outside of South Korea. Duing this summer, we will start a new reasearch where will will study the PM₁₀ / PM_2.5 correlation factors accros different countries.
  
  

Conclusion

As a conclusion, provided you take all the precautions related to the 3 remarks above, you could use the following convertion table to calculate the actual AQI:

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Note that the data provided is this article follows the Korea Open Government License (kogl.or.kr)