Air pollution is associated with almost 5 million annual deaths world wide as well as harm to countless others through negative health impacts, from asthma and diabetes to cardiovascular disease and cancer.

We focus our attention over three main city for which we have all the data available.

Data comes from three different sources like :

• Satellite-derivedAerosol Optical Depth (AOD) produced from the NASA MODIS instruments
• MeteorologicalData, 365 meteorological data files corresponding to each day of 2018.These data are used to improve the accuracy of the relationship between the satellite-derivedAOD quantity.
• Ground Monitor Data, OpenAQ has collected over 170 millionair quality measurements, and this number grows every10 minutes.

Our aim is to make more clear the datasets available in order to use that without data type problems. After, we try to mix information from several resources in a single file and realize a single index to evaluate the level of pollution in the selected area.

This index is based on the Air Quality Index (AQI).

We have used Google Colaboratory as environment for our code, using python 3.7 programming language. The uses the Folium library with which we are able to create dynamic maps.

The Github repository consist of our presentation and two jupyter notebooks, one with a Demo of our project and the other one contains a set of data cleaning functions.

The link of our Github Repository is presented below:

Github Repo