Advanced Urban Air Quality Control and Management Using XM and RFE Models for Enhanced Predictive Analysis

Urban air pollution, particularly in large cities, has historically been exacerbated by construction activity, resulting in significant surges. Pollutants such as Sulphur dioxide and soot have, however, been significantly reduced in the industrialized Western world as a result of developments in abatement technologies. As a result, the attention has shifted to pollutants related with mobility, such as nitrogen oxides, organic compounds, and fine particulate matter. This work uses a machine learning approach to analyze urban air quality, with a focus on feature selection and data preparation. Data preprocessing, feature selection using Principal Component Analysis (PCA), and model training are all important steps. Normalization of Life Cycle Assessment (LCA) results was also added as a preprocessing step. The model had a maximum accuracy of 91.93% in categorizing and predicting urban air quality. This outcome was greatly influenced by the selection of important features using PCA and model refining by feature deletion. The findings highlight the efficacy of sophisticated machine learning approaches, specifically PCA and normalization, in improving air quality prediction models. The findings show that, while traditional pollutants have been significantly reduced, new concerns in urban air pollution necessitate specific solutions centered on transportation-related particles and other minute pollutants. This paper establishes a paradigm for better urban air quality management and emphasizes the value of adaptive approaches in environmental monitoring.