Air Pollution Detection from Automobile Emissions Using Machine Learning Model

The research paper aims to elucidate the current state of air pollution in automobile emissions within the contemporary industrial landscape, considering factors such as time-series data on road, maritime, and air traffic, along with meteorological records primarily obtained from Open Data sources. In this study, the researchers introduce a model designed to estimate the contributions to air pollution from automobile emissions using artificial intelligence and modern machine learning techniques. Conventional pollutant dispersion models often face limitations due to incomplete descriptions of atmospheric boundary layer hydrodynamics, partial characterizations of emission inventories, and, in many instances, high computational costs. Additionally, the paper delves into the technologies underpinning the use of modes and mediums which causes less pollution like use of green technology in transportation. Furthermore, there is an attempt to align the development of electric vehicles with machine learning, highlighting their substantial role in diminishing the graph of air pollution.