<p>No, you shouldn't really do that.  A vertical column density (VCD) retrieval provides the user with a representation of the total number of molecules over some vertical slice (e.g. the entire troposphere).  A mixing ratio, often defined in parts per million, is meant to express the average number of molecules in a parcel of air.  The problem with converting VCD to ppm is that the troposphere is a whole lot more than "a parcel of air".  The density of air and mixing ratio of NO2 changes as you move vertically through the atmosphere. So, saying that NO2 has any one mixing ratio through the troposphere is really not appropriate.  </p>

<p>There are methods you could use to estimate ppm NO2 from VCD (such as inferring mixing ratio from the shape of the <em>a priori</em> profile), but they would be inherently uncertain for many reasons:</p>

<ul>
<li>The retrieval of tropospheric NO2 from OMI is not representative of the surface, but rather a well-mixed free troposphere.  </li>
<li>The NO2 product is dependent on a global model to subtract the stratospheric contribution.  </li>
<li>There are many quality control steps needed for quality assurance, such as accounting for clouds, terrain, surface albedo, detector issues (e.g. striping or row anomalies), etc. </li>
</ul>

<p>In my experience with OMI tropospheric NO2 data, any expected temporal correlation to daily surface measurements is futile.  You will sometimes find temporal correlation at the seasonal or coarser scale, for what that is worth. </p>

<p>Spatial correlation with models and surface monitor networks should be expected, but your results will be highly dependent on your 3D spatial comparison methodology. When comparing to a model, you will want to apply the "averaging kernels" which help to remove the dependence on the <em>a priori</em> profile of the satellite retrieval.  </p>

<p>In general, OMI tropospheric NO2 data is useful for identifying large emissions sources that are missing from air quality models.   It is also useful for identifying inconsistencies across political borders that appear in air quality models due to old emission inventories (e.g. mixing circa 2006 Canadian emissions with circa 2011 USA emissions).  </p>

CC BY-SA 3.0
2016-10-10T23:05:11.773Z
2016-10-10T23:10:44.380Z
2016-10-10T23:10:44.380Z
f.thorpe
<p>I want to convert OMI AURA data on NO2 column amounts in molecules/cm2 into ppm for comparison purposes with other literature. Can it be done? Is there a standard way of doing it? </p>

<p>I looked into the <a href="http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/documents/v003/OMNO2_readme_v003.pdf" rel="nofollow">readme file</a> but could not find an answer.</p>

CC BY-SA 3.0
2016-10-10T12:30:49.243Z
In my opinion, it will be similar with tackling AOD data to PM2.5. The NO2 column amount is the sum up of multi-vertical layer. With introducing Air Mass Fraction(AMF), the column amount can be divided into several layers. Then, the ground level NO2 concentration or the certain level of retrieved concentration can compare with the NO2 measurement.
Han Zhengzu
CC BY-SA 3.0
2016-10-10T13:06:47.067Z
Thanks for your answer! I'm not sure I follow, I'm not familiar with AOD nor AMF. The value included in the OMNO2d product is only one for the whole column so I don't see how I can divide it into several layers?
Francisco
CC BY-SA 3.0
2016-10-10T19:29:46.413Z
You could use the "a priori" profiles to infer ppm at any given altitude... but in my experience it is not really comparable to surface measurements.
f.thorpe
<remote-sensing><air-pollution>
Convert OMI NO2 Vertical Column Density (molecules/cm^2) into Mixing Ratio (ppm)
Francisco
# Convert OMI NO2 Vertical Column Density (molecules/cm^2) into Mixing Ratio (ppm)

(Asked by: username_0 on 2016-10-10)

I want to convert OMI AURA data on NO2 column amounts in molecules/cm2 into ppm for comparison purposes with other literature. Can it be done? Is there a standard way of doing it?

I looked into the [readme file](http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/documents/v003/OMNO2_readme_v003.pdf) but could not find an answer.

**Tags:** remote-sensing, air-pollution

> **Comments:**
> **username_1**: In my opinion, it will be similar with tackling AOD data to PM2.5. The NO2 column amount is the sum up of multi-vertical layer. With introducing Air Mass Fraction(AMF), the column amount can be divided into several layers. Then, the ground level NO2 concentration or the certain level of retrieved concentration can compare with the NO2 measurement.  
> **username_0**: Thanks for your answer! I'm not sure I follow, I'm not familiar with AOD nor AMF. The value included in the OMNO2d product is only one for the whole column so I don't see how I can divide it into several layers?  
> **username_2**: You could use the "a priori" profiles to infer ppm at any given altitude... but in my experience it is not really comparable to surface measurements.  

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## 1 answers

### Answer by username_2 on 2016-10-10. Score: 5 | Accepted answer

No, you shouldn't really do that. A vertical column density (VCD) retrieval provides the user with a representation of the total number of molecules over some vertical slice (e.g. the entire troposphere). A mixing ratio, often defined in parts per million, is meant to express the average number of molecules in a parcel of air. The problem with converting VCD to ppm is that the troposphere is a whole lot more than "a parcel of air". The density of air and mixing ratio of NO2 changes as you move vertically through the atmosphere. So, saying that NO2 has any one mixing ratio through the troposphere is really not appropriate.

There are methods you could use to estimate ppm NO2 from VCD (such as inferring mixing ratio from the shape of the *a priori* profile), but they would be inherently uncertain for many reasons:

* The retrieval of tropospheric NO2 from OMI is not representative of the surface, but rather a well-mixed free troposphere.
* The NO2 product is dependent on a global model to subtract the stratospheric contribution.
* There are many quality control steps needed for quality assurance, such as accounting for clouds, terrain, surface albedo, detector issues (e.g. striping or row anomalies), etc.

In my experience with OMI tropospheric NO2 data, any expected temporal correlation to daily surface measurements is futile. You will sometimes find temporal correlation at the seasonal or coarser scale, for what that is worth.

Spatial correlation with models and surface monitor networks should be expected, but your results will be highly dependent on your 3D spatial comparison methodology. When comparing to a model, you will want to apply the "averaging kernels" which help to remove the dependence on the *a priori* profile of the satellite retrieval.

In general, OMI tropospheric NO2 data is useful for identifying large emissions sources that are missing from air quality models. It is also useful for identifying inconsistencies across political borders that appear in air quality models due to old emission inventories (e.g. mixing circa 2006 Canadian emissions with circa 2011 USA emissions).


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