AI Data Analytics for Air Quality Modeling

Air quality is a critical concern globally, with implications for public health and environmental sustainability. While improvements have been made since the implementation of the Clean Air Act of 1970, the adverse effects of air pollution persist, leading to significant health burdens and mortality rates worldwide. In this context, this study focuses on analyzing air quality in Leeds, UK, during the year 2020, leveraging data from monitoring stations administered by the Department for Environment, Food, and Rural Affairs (DEFRA). Given the unique circumstances of 2020, which included lockdowns due to the COVID-19 pandemic, this period offers valuable insights into the interplay between human activity and air pollution. The research employs a multi-phase approach, encompassing descriptive, inferential, machine learning, and deep learning analyses to comprehensively understand air quality patterns and predict pollutant concentrations. While existing literature predominantly focuses on other regions, particularly China and India, this study addresses the scarcity of research specific to the UK context. Findings reveal variations in pollutant levels, with particulate matter (PM10 and PM2.5), nitrogen oxides (NO2 and NO) and carbon monoxide (CO) being the primary pollutants of concern. Inferential analyses highlight significant differences in pollutant levels and correlations, potentially influenced by lock-down measures. Machine learning algorithms, notably random forests, demonstrate efficacy in predicting PM2.5 concentrations, mirroring findings from deep learning approaches. However, the study underscores the need for larger and more diverse datasets to enhance predictive accuracy and generalize findings. Future research directions include exploring hybrid deep learning algorithms, refining machine learning models, and expanding data collection efforts across various locations and time periods to better understand the dynamics of air quality. Overall, this research contributes to a deeper understanding of air quality dynamics in Leeds and offers insights into mitigation strategies and policy interventions to address air pollution challenges effectively.