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I am trying to learn some Spatial Data Analysis before applying for a PhD in Environmental Epidemiology and Exposomics (I am a Computational Biologist). Therefore I want to work on a small personal project: I want to predict PM2.5 or PM10 concentrations starting from satellite data using Deep Learning.

For this I need a dataset of PM2.5/10 measurements. I read some papers on this topic and most seem to use daily averages from just a few stations (for instance, in NY (USA) there a just a bunch of stations measuring PM concentrations): I would like to have a more fine dataset (e.g., more stations per state). Obviously, I need to have access to historical data, but it does not matter the country or even the continent. As an example, the data from the World's Air Pollution: Real-time Air Quality Index are excellent in terms of coverage but limited in time. On the other hand, the datasets from the US EPA are excellent in terms of time-series, but limited in coverage. Are you aware of any open resource providing time-series data with good coverage that I could use?

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You can get PM10 and other air pollution measurement data from multiple locations in the city of Zürich, Switzerland

https://data.stadt-zuerich.ch/dataset?q=luftqualit%C3%A4t

The data comes either as

  • hourly, for the last 24 hours, updated every hour

  • daily, for the last 30 days, updated every day

  • daily, going back to 2012 (and also 1983-2011), updated every month

More details here, including some live charts.

enter image description here

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