Air Quality Monitoring and Gas Amalgamation Prediction

Abstract

In the modern era, the environment exposed to various kinds of pollution. All-natural bodies play their role in the environment; the air is one among them. Air quality control and restoration became one of the very important practices in several industrial and residential areas. The air quality is affected by different forms of pollution such as transport, electricity, industrial waste, and harmful gases. Accumulation of harmful gases poses a grave threat in smart cities. Living in a with constant increase in air pollution, there is a need for effective models for monitoring air quality that should obtain intelligence on air pollution levels , predict formation of any harmful gases by amalgamation of harmless gases and also it should give the necessary preventive measures and safe exposure period based on the individual tolerance. Assessment and estimation of the pollution levels has therefore become more valuable. Multidimensional factors, including location, time, and uncertain variables, affect the air quality. The aim of this project is to use machine learning based technique for air quality forecasting