Are there any studies that discuss what meteorological data I should look at if I am creating an air quality model with machine learning? That is, what should I extract from NCEI or ERA5 first if I have limited storage capacity?

Something like wind speed is very obvious, but what else should I be looking for?

  • 2
    $\begingroup$ Are you talking about a dispersion model or gridded chemical transport model? This question will have different answers depending on the type of air quality model you are proposing to develop. Furthermore, each AQM out there already has preprocessors available designed to prepare/extract meteorological data for operation. $\endgroup$
    – f.thorpe
    Sep 28 at 0:47
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    $\begingroup$ @f.thorpe I am experimenting with various machine learning frameworks but have limited time and resources left for thoroughly trying out different features. $\endgroup$
    – Avatrin
    Sep 28 at 7:07

The variables needed for machine learning are different than a standard air quality model. It's actually a much shorter list because you don't have to numerically model atmospheric motion/chemistry/deposition.

Most AQMs have a spatial domain that has horizontal and vertical depth. However, the machine learning forecasts I've seen are only for point locations where meteorological and air quality variables are measured. That is to say, the machine learning model is trained using variables from a ground station, and is only valid for that location. You can train multiple locations using similar variables, but each station would have it's own set of data to train with.

I've seen machine learning forecasts from Washington State University for ozone and PM2.5, and the list of important meteorological variables are basically the same.

For ozone they say:

Hourly data for six meteorological variables (temperature and relative humidity at 2m, wind components u & v, planetary boundary layer height, and sea level pressure) from 4km WRF archives, time information (month, weekday and hour), and the previous day’s observed moving 8-hour averaged O3 mixing ratios are used to train the RF classifier models.

For more info you can see the latest presentation given.


From Seigneur C., Dennis R. (2011) (free access here, probably not in the final published form):

Inputs to air quality models include the emission rates of primary air pollutants and precursors of secondary air pollutants, meteorology (three-dimensional fields of winds, turbulence, temperature, pressure, boundary layer height, relative humidity, clouds and solar radiation), and boundary conditions (baseline or background conditions in the case of source-specific models; see Figure 8.1).

Seigneur C., Dennis R. (2011) Atmospheric Modeling. In: Hidy G., Brook J., Demerjian K., Molina L., Pennell W., Scheffe R. (eds) Technical Challenges of Multipollutant Air Quality Management. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0304-9_9

I am not qualified on the subject in order to distinguish which of the above-mentioned meteorological parameters would be the most important, but it makes sense that parameters related to circulation and temperature would be key factors.


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