Exploring the potential of machine learning for simulations of urban ozone variability

Abstract

Machine learning (ML) has emerged as a powerful technique in the Earth system science, nevertheless, its potential to model complex atmospheric chemistry remains largely unexplored. Here, we applied ML to simulate the variability in urban ozone (O₃) over Doon valley of the Himalaya. The ML model, trained with past variations in O₃ and meteorological conditions, successfully reproduced the independent O₃ data (r² ~ 0.7). Model performance is found to be similar when the variation in major precursors (CO and NO_x) were included in the model, instead of the meteorology. Further the inclusion of both precursors and meteorology improved the performance significantly (r² = 0.86) and the model could also capture the outliers, which are crucial for air quality assessments. We suggest that in absence of high-resolution measurements, ML modeling has profound implications for unraveling the feedback between pollution and meteorology in the fragile Himalayan ecosystem.