AI-Based Emission Inventory Modeling: A Comparative Study for Environmental Monitoring
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Air pollution in metropolitan cities such as in India has significantly increased due to fast urbanization and consistently rising number of vehicles along with its motor activity. Pollutants such as Nitrogen Oxides (NOx), Sulphur Dioxide (SO₂), Particulate Matter (PM₂.₅, PM₁₀), Hydrocarbons (HC), Carbon Monoxide (CO) and others are important components emitted by the vehicle which have negative impact on the environment and human health. Precise emission inventory estimation is crucial for developing management plans and policies for air quality.
In the present study, an Artificial Intelligence (AI) based grid-wise vehicular emission inventory model for two prominent cities of India Nagpur and Mumbai are developed. Combines traditional bottom-up and top-down emission inventory methods with cutting-edge AI technologies to enhance emission estimation and prediction for vehicles. The use of traffic activity data, vehicle registration records, meteorological parameters, road network information and traffic imagery. Vehicle count, vehicle category, traffic density, and traffic speed information are extracted from traffic images and video using deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and vehicles identification algorithm YOLO.
Studying this is done using a spatial grid-based approach to analysing the dispersion of emissions within urban areas and temporal variations over hourly, daily and seasonal scales. Their comparative study with the emissions produced at Mumbai shows that there are significant variations in emissions because of the population density, the traffic composition and the urban infrastructure. Incorporated with AI, this emission inventory model with enhanced accuracy and efficiency offers significant improvement from the typical approach to valuable insights for sustainable transportation planning and air pollution mitigation measures.
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