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.
Highlights
models[19,37,39,40,41]
We further investigate the sensitivity of model performance to the fraction of available data being used for the training
Our study unravels the strong potential of machine learning (ML) modeling for computationally inexpensive simulations of urban O3 variability in the Himalayan foothills region
Summary
The conventional models need intensive computing resources which poses further limitation in conducting high-resolution simulation. Recent studies utilized AI/ML modeling in the analyses of extreme whether events and prediction of oceanic phenomenon as well as atmospheric composition[46,47,48]. These studies have shown that ML models trained with data from observations or physical models can produce reliable simulations without intensive high-end computing. Considering the scientific and societal implications, lack of measurements, and limitations of conventional models over Himalayan region, the objectives of this study are as follows:. (2) To study the effects of meteorological and chemical variables on model performance. Model simulations and results are presented in the “Model simulations and results” section, followed by “Discussion” section
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