ASSESSING THE IMPACT OF URBAN INTERSECTION TRAFFIC CONGESTION ON AIR QUALITY IN TASHKENT USING ADVANCED MODELING TECHNIQUES

Traffic congestion at urban intersections can significantly degrade air quality, especially through increased vehicle emissions. In Tashkent, a growing vehicle fleet and frequent traffic jams have contributed to worsening pollution levels. For instance, during a peak rush-hour in 2022, Tashkent’s Air Quality Index (AQI) spiked to 262 (considered “very unhealthy”), far exceeding typical levels. Vehicles are identified as the main culprits, accounting for nearly 60% of the 1.3 million tons of pollutants emitted in Uzbekistan during the first nine months of 2022. These emissions include greenhouse gases and harmful pollutants such as carbon dioxide (CO₂), nitrogen dioxide (NO₂), and fine particulate matter (PM₂.₅), all of which pose serious environmental and health risks. Given that most air pollution-related health impacts (e.g., respiratory and cardiovascular diseases) are linked to PM₂.₅, understanding and mitigating traffic-related emissions at intersections is critical. This research focuses on quantifying pollutant concentrations during traffic congestion in Tashkent and applying advanced analytical methods (LSTM neural networks, Kalman filtering, and data segmentation via logistic regression) to model and forecast these pollution levels. The goal is to provide scientifically grounded findings on how congestion elevates pollution and how modern data-driven techniques can improve air quality assessment and management.