Particulate Matter on Human Health and their Feasibility Study Using Machine Learning Algorithms

Prathik Anandhan, Uma Kaliappa

Abstract

The safety levels of NAAQS pollution standards been set but the measures should be relative to factors such as climate, population and population. Therefore it is essential to reset the levels for the particular location, in order to create the safer environment. This paper aims in determining the pollution level using particulate matters. Naive Bayes and Relevance Vector Machine algorithms are applied to the sample particulate matters studied at various locations across India. In particular Ranipet region. The steps must be taken not only to reduce pollution but also to eradicate it through careful methods of safer waste disposal in all the sectors including vehicular and industrial emissions. This result in the aggravating the pollution. The experimental work conducted on particulate matters such as nitrogen dioxide, sulphur dioxide levels at different test sites indicates that there is an urgency to set modified NAAQS safety limits in order to revert the declining health rate of atmosphere in India.