<p>Thank you all for your suggestions, I greatly appreciate it. I was able to get a solution to this problem which essentially obtains correlated errors (using ARMA correlation) in a linear (or non-linear model) and the resulting residual values are subtracted from the original values to provide some adjustment for effects.</p>

<p>Please see this post for more details: <a href="https://stats.stackexchange.com/questions/314338/how-can-i-adjust-values-in-a-timeseries-to-account-for-effects-from-other-variab/314359?noredirect=1#comment597434_314359">https://stats.stackexchange.com/questions/314338/how-can-i-adjust-values-in-a-timeseries-to-account-for-effects-from-other-variab/314359?noredirect=1#comment597434_314359</a> </p>

CC BY-SA 3.0
2017-11-21T14:44:11.217Z
2017-11-21T14:44:11.217Z
toferkey
<p>Sounds like you want a dataset that is unadjusted for rainfall.  I'm not sure what you have to gain from that, but there are ways to do it.  One way to do this would be to find the inverse correlation between rain and black carbon concentrations, and extrapolate to zero rainfall.  Or, if you have any months that have zero rainfall, you could use that for other months where temperature is similar (thus the mixing layer should be similar).  This would all need to be done for each location separately, so that source contributions weren't mixed up.</p>

CC BY-SA 3.0
2017-11-17T03:37:31.497Z
2017-11-17T03:37:31.497Z
f.thorpe
<p><strong>How can I adjust black carbon surface mass air pollution concentrations (ug/m3) for rainfall effects?</strong></p>

<p>Specifically, I have monthly and daily averaged black carbon pollution data (i.e. 2.0ug/m3 Jan 2016, Feb 2016, etc) obtained from MERRA-2 reanalysis dataset. I also have averaged monthly rainfall data (i.e. cm per month).</p>

<p><strong>Generally, the air pollution decreases in the higher rainfall months and increases during dry months. I'm interested to remove the rainfall effect from the air pollution values.</strong> The end goal is to compare the different months to explore biomass burning effects.</p>

<p>Any suggestions would be greatly appreciated.</p>

CC BY-SA 3.0
2017-11-19T02:16:06.277Z
Some chemical components should have a wet deposition coefficient. Maybe this parameter can shed some insight for your analysis.
Han Zhengzu
<meteorology><air-pollution><statistics><air-quality><reanalysis>
How can I account for rainfall effects on air pollution?
toferkey
# How can I account for rainfall effects on air pollution?

(Asked by: username_0 on 2017-11-16)

**How can I adjust black carbon surface mass air pollution concentrations (ug/m3) for rainfall effects?**

Specifically, I have monthly and daily averaged black carbon pollution data (i.e. 2.0ug/m3 Jan 2016, Feb 2016, etc) obtained from MERRA-2 reanalysis dataset. I also have averaged monthly rainfall data (i.e. cm per month).

**Generally, the air pollution decreases in the higher rainfall months and increases during dry months. I'm interested to remove the rainfall effect from the air pollution values.** The end goal is to compare the different months to explore biomass burning effects.

Any suggestions would be greatly appreciated.

**Tags:** meteorology, air-pollution, statistics, air-quality, reanalysis

> **Comments:**
> **username_1**: Some chemical components should have a wet deposition coefficient. Maybe this parameter can shed some insight for your analysis.  

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## 2 answers

### Answer by username_0 on 2017-11-21. Score: 1 | Accepted answer

Thank you all for your suggestions, I greatly appreciate it. I was able to get a solution to this problem which essentially obtains correlated errors (using ARMA correlation) in a linear (or non-linear model) and the resulting residual values are subtracted from the original values to provide some adjustment for effects.

Please see this post for more details: <https://stats.stackexchange.com/questions/314338/how-can-i-adjust-values-in-a-timeseries-to-account-for-effects-from-other-variab/314359?noredirect=1#comment597434_314359>


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### Answer by username_2 on 2017-11-17. Score: 2

Sounds like you want a dataset that is unadjusted for rainfall. I'm not sure what you have to gain from that, but there are ways to do it. One way to do this would be to find the inverse correlation between rain and black carbon concentrations, and extrapolate to zero rainfall. Or, if you have any months that have zero rainfall, you could use that for other months where temperature is similar (thus the mixing layer should be similar). This would all need to be done for each location separately, so that source contributions weren't mixed up.


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