Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis

Air pollution emerges as a formidable threat to both public health and environmental integrity, especially in regions undergoing rapid development. In this context, Gujarat, situated in Western India, grapples with escalating air quality degradation attributable to industrialization, vehicular emissions, and agricultural practices. The imperative to accurately forecast Air Quality Indices (AQIs) becomes paramount for the prompt implementation of mitigation measures, thereby safeguarding public well-being. This research delves into the utilization of Machine Learning (ML) algorithms, specifically Random Forest (RF) and XGBoost, to predict AQIs in Gujarat. Four pivotal parameters, PM2.5, PM10, SO2, and NOX, are scrutinized due to their substantial impact on air quality and inclusion in publicly available datasets. Remarkably, both RF and XGBoost models exhibit outstanding performance, surpassing 99% accuracy. This exceptional capability underscores the transformative potential of ML in addressing the complex challenges posed by air pollution. Leveraging the precise predictions of AQI values, these models can catalyze the development of robust early warning systems and guide policymaking endeavors directed at enhancing air quality. Notably, this investigation unveils PM2.5 and PM10 as primary culprits influencing AQI levels in Gujarat, underscoring the urgency for stringent emission control measures targeting these pollutants. Furthermore, the study sheds light on the significant impact of meteorological factors, especially maximum temperature, on AQI fluctuations, necessitating adaptive strategies to counteract climate change's repercussions on air quality.