Predictive Analysis of Air Pollutants: Harnessing Meteorological Data to Forecast Air Quality Concentrations in Cork, Ireland

Air quality in urban regions worldwide, including Cork, Ireland, is deteriorating dueto pollutants like nitrogen dioxide (NO2) and particulate matter (PM2.5). These pollutants have significant health and environmental implications. This study leveragesrich datasets spanning three decades, from strategic monitoring sites in Cork and meteorological data from Cork Airport. By utilizing machine learning algorithms such asRandom Forests, XGBoost, and CatBoost, this research aims to elucidate how meteorological factors influence NO2 and PM2.5 concentrations and to forecast their futurelevels. The modeling process addresses challenges like missing data and the influenceof external factors like traffic patterns. Initial investigations have shown promising results, especially with ensemble learning models that combine the strengths of individualalgorithms. Ultimately, this research seeks to provide policymakers and urban plannerswith tools to ensure sustainable and healthy urban environments.Keywords: NO2, PM2.5, air quality, Cork, Ireland, meteorological data, machine learning, Random Forests, XGBoost, CatBoost, predictive analysis, urban pollution.