During the recent lockdown due to Coronovirus around the world and the recent forest fires in california and Australia have significantly affected the air quality for hundreds of miles. We sought to analyse the various information about the air quality in cities around the world. Our data was obtained from WAQI, an API for extracing air quality information based on various parameters. One of the reasons why we selected WAQI is due to its standardized air quality index. An air quality index (AQI) is a number used to communicate how polluted the air currently is. Today, there is no globally standardized approach to providing air quality information, meaning AQIs differ from country to country. The WAQI values ranges between 0 to 300+ where lesser numbers indicate Excellent air quality and the higher indicates Poor air quality, and is determined based on concentrations of predefined pollutants.
We obtained a list of the 500 most populous cities from the worldCities.csv. Using city in the file, we were able to obtain air quality data for 200 cities using the Python requests library. This allowed us to create custom URLs, based on each city, to request data from WAQI, and receive it in a JSON format. We requested data about air quality, concentration of pollutants. The tools we used in this project include:
Extracting and Sorting data from WAQI Build Plots using Plotly Build HTML pages to display the visualization Build Geomapping Get Live Data from WAQI
Our findings indicate the air quality is densely populated cities tend to be worse on the Indian sub-continent and along east Asia. It also indicated that the western Hemisphere had slightly better air quality. Also noticed that the air quality did not correlate with city population, as Tokyo, the most populous in our database had "good" air quality.
