Published Paper Details:

Urbanization and Industrial growth have been identified as the primary sources of air pollution in India's metropolitan cities. One of the most important challenges in urban areas has been highlighted as air pollution. Particulate matter is still one of the most prominent forms of air pollution in cities, with serious health repercussions for both acute and chronic exposures. The presence of particulate matter is mostly determined by meteorological conditions. Citizens and governments around the world have become increasingly concerned about the effects of air pollution on human health, and have proposed sustainable development solutions to address air pollution challenges caused by current industrialization, which include liquid droplets, solid particles, and gas molecules dispersed throughout the atmosphere. The high concentration of particulate matter of sizes PM10 and PM2.5 has a major negative impact on human health. The determination of particulate matter concentration in atmospheric air for the betterment of human being well of primary importance. So, in this paper, a machine learning model were used for predicting particulate matter concentration in atmospheric air is investigated on Vapi Air Quality Monitoring data sets, which were obtained from 2017 to 2021. These models were compared on their performance in particulate matter prediction and the best model was found for predicting particulate matter.