# Derive PPB/PPM of tropospheric NO2

Hi there,

I’m trying to get the parts-per-billion/million of tropospheric NO2.

So far Harp is able to show how it can convert from ‘tropospheric column number density’ to NO2 column density (kg/m2) by using the HARPDUMP utility.

NO2_column_density {time} [kg/m2] (double) from
NO2_column_number_density {time} [molec/m2] (double)

I’m not clear on this output as it seems like it would average over the total column rather than the tropospheric column that I’m interested in.

This question has been looked at for another harp product here.

And the documentation shows that derivations are possible, though it’s not easy to understand how to make this work with the derive function.

Some advice on this would be much appreciated. And if I could make a small observation, the documentation around the derive function could be made simpler to understand. Ie. how does the information in the documentation about algorithms relate to how one would use the derive function?

Sorry if this is basic knowledge for some but I don’t quite get it yet.

D

Good morning D,

There is no basic knowledge unless you have mastered it first! So don’t worry at all about asking questions, this forum is the best place for it, no judgements and Sander is excellent in replying to all bizarre questions we pose him.

I think there is a slight confusion with the units in your question. You mention the question I posed here, S5P CH4 | Column ppb to column number density mol/m2 conversion, which however relates to how the CH4 product is being retrieved from the data. One can translate volume mixing ratio to number density, and vice versa, but it is not trivial to translate column density to ppm/ppb.

The product you require I think is the tropospheric_NO2_column_number_density check here: S5P_L2_NO2 — HARP 1.12 documentation (stcorp.github.io)

However this product is provided in mol/m2 and to translate this into ppm [i.e. a surface concentration] you require input from a chemical transport model.

Convert the unit from molecules/m^2 to ppm - Atmospheric Toolbox

and

Map Sentinel 5p CH4, SO2 and CO Data (Visan) - HARP - Atmospheric Toolbox

Good luck!
MariLiza

Thanks for your answer MariLiza. And indeed Sander has been very helpful with some of my other questions, so thanks to both of you.

I have been looking at the tropospheric column number density. Having read through some of the links you have shared and looking further into it, I can see that the ppbv number would distort the data. That being said, is there a way to do a similar averaging of NO2 over the total column or tropospheric column as is done with CH4, for instance? Would that achieve a ppb or ppm number? I understand that it would lose some accuracy but at the moment the NO2 number gives a 2D reading in a 3D space. Whereas the CH4 data is sensed under the same conditions, yet it is presented in density. I began working with CH4 data which is already in ppb so I, perhaps naively, expected the conversion to be possible. I would not think of the data as representing the surface NO2 levels as the satellite data doesn’t sense at that altitude, yet the sources of the pollution are absolutely on the ground, as far as my project is concerned.

For anyone else looking at this problem here are some relevant discussions:

All the best.

D

Good afternoon D.,

One of our MSc. students just attempted such an endeavour, http://ikee.lib.auth.gr/record/322595/files/GRI-2020-28846.pdf, and showed that the original comparisons between CTM model surface concentrations and in situ concentrations of pollutants does improve when including information from S5P/TROPOMI, via a simple methodology originally by Lamsal et al., 2008, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007JD009235.

However you do require both the surface concentration from a CTM as well as the columnar CTM amount. In your case, I would suggest that you download the Copernicus CAMS air quality CTM dataset, https://atmosphere.copernicus.eu/data and try it out.

Since the lifetime of NO2 and CH4, as well as their spatial distribution, is so different, I would not assume that an assumption that holds for one, also holds for the other.

Best wishes,
MariLiza