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 NOx) 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.