Urban Air Quality Dynamics: A Multifaceted Analysis Of Sources, Dispersion Patterns, And Health Implications

Abstract

This research investigates multifaceted aspects of urban air quality dynamics, incorporating a comprehensive methodology that integrates real-world and synthetic data. Six monitoring stations (S1 to S6) were geographically mapped to establish the spatial context, with latitude and longitude coordinates plotted to visualize distribution. Air quality data from these stations, encompassing pollutants such as PM2.5, PM10, O3, CO, NO2, and SO2, was analyzed using bar plots, following methodologies inspired by prior studies. Synthetic data was generated to simulate air pollution sources, dispersion patterns, and health implications, providing a controlled exploration of various scenarios. Temporal trends of pollutants from industrial, traffic, and residential sources were illustrated, shedding light on the dynamic nature of urban air pollution. Dispersion patterns, examined through a scatter plot correlating wind speed and dispersion index, revealed the pivotal role of meteorological factors in pollutant spread. Health implications were elucidated by correlating the air quality index with the prevalence of respiratory and cardiovascular diseases in a scatter plot. The graphical representations collectively contribute to an intricate understanding of urban air quality, considering spatial, temporal, and health dimensions. This study aims to inform urban planning and policymaking for effective air quality management, emphasizing the interplay between sources, dispersion patterns, and health outcomes in urban environments.