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I have a dataset which contains daily NO2 and ozone levels of the 50 largest cities in Spain for one year (so 50 x 365 observations). The data are retrieved from http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-7. When regressing ozone on NO2, I find a significantly negative coefficient for contemporaneous NO2 (-0.35, standard error 0.027), the first lag (-0.05, standard error 0.014), insignificant coefficients on NO2-levels on the second and third lag (-0.0014 and 0.014 respectively), and a positive coefficient on the level of NO2 five days ago (0.50, standard error 0.013). I am also controlling for time-invariant local characteristics (fixed effects) and (lagged) weather conditions (temperature, sunshine, cloud cover, precipitation, wind).

I'm an economist so I don't know much about these things. I guess this could be explained by the complex mechanics of ozone formation? It would be great if you have some reference or idea why such a relation can be observed.

Edit: Added some more hopefully useful details.

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  • $\begingroup$ Could you specify 'different location': which distance? Which emission sources do you have? $\endgroup$ – daniel.neumann Jun 18 '16 at 12:07
  • $\begingroup$ Also, please indicate the correlation coefficients you derived, and the sample number, so that we can assess the statistical significance of the data. $\endgroup$ – Gordon Stanger Jun 18 '16 at 13:48
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I am unsure of the details of your dataset (length, temporal resolution, location, etc.)

For he one day time lag, it is easily explained. NO2 is consumed to produce ozone, so as NO2 decreases, O3 increases. However, for that reaction to occur, you need UV radiation. So as NO2 decreases, ozone increases.

For the fourth day lag, I would be a bit suspicious to take meaning out of this, due to my lack of knowledge on details of the dataset. For example, it could've been cloudy preventing the production of NO2 and ozone, but letting NO build up, such that when the sky cleared, the NO was allowed to photlyze, but that is just a guess.

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