Proposal: Analysis of air pollution levels before and after lockdowns

[Mukulikaa]

Hi all,
I am planning to write a tutorial on the analysis of air pollution levels before and after lockdowns.

Aim:

Users would hopefully learn about analyzing time-series data with NumPy. It will also increase awareness about pollutants in the air we breathe and show us whether the complete shutdown of human activities in a region has a large enough impact on its air quality.

I am interested to know if this is a suitable topic to showcase and teach NumPy's functionalities!

cc: @melissawm @rossbar

[melissawm]

I love this idea! Do you have a list of topics you want to explain, or are you still considering data sources?

[Mukulikaa]

I have found a good source for the data- https://openaq.org/. It collects publicly available data from air monitoring centres around the world; I will most probably use New Delhi, India data. I found a paper: Air Quality Before and After COVID-19 Lockdown Phases Around New Delhi, India; they have used statistical analysis methods here and I'll use it as a guide so that my results aren't too weird.

I haven't yet made a concrete list of topics as such... If you already had some in mind please do suggest them!

[rossbar]

I like this idea as well!

An important question to answer IMO is whether you want to use the tutorial to highlight a specific NumPy feature, or whether the tutorial will be more application-focused without explaining any one feature specifically. For instance, the masked array is an example of the former: it uses COVID-19 data to illustrate the use of masked arrays specifically. OTOH, there are tutorials like the plotting fractals tutorial, which doesn't focus on a specific NumPy feature per se, but rather shows how NumPy can be used for an interesting application.

IMO either avenue is completely fine and valuable, but it might help to think a bit beforehand whether you want the tutorial to primarily explain a numpy feature, or demonstrate an application.