K-Means Clustering of Ambient Air Quality Data of Uttarakhand, India during Lockdown Period of Covid-19 Pandemic
Sandeep Kumar Sunori, Pushpa Bhakuni Negi, Sudhanshu Maurya, Pradeep Juneja, Anita Rana, Bhawana Bhawana
Abstract
The analysis of the lockdown effect during covid-19 pandemic on ambient quality of air of Uttarakhand state of India, has been performed. The combination of SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and particulate matter (P.M.10) indicates ambient air quality characteristics. The clustering capability of the K-means clustering technique is investigated with two different approaches of measuring distance using MATLAB. The first approach is termed Euclidean distance and the second one is cosine distance. The data, which is clustered, is the air uualitv data containing three major components of air pollution such as P.M.10, SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> of different major cities of Uttarakhand.