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...

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Autores principales:	Narendra Ojha, Imran Girach, Kiran Sharma, Amit Sharma, Narendra Singh, Sachin S. Gunthe
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e427481bf236491f82668f22eb027daa
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spelling	oai:doaj.org-article:e427481bf236491f82668f22eb027daa2021-11-21T12:24:22ZExploring the potential of machine learning for simulations of urban ozone variability10.1038/s41598-021-01824-z2045-2322https://doaj.org/article/e427481bf236491f82668f22eb027daa2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01824-zhttps://doaj.org/toc/2045-2322Abstract 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.Narendra OjhaImran GirachKiran SharmaAmit SharmaNarendra SinghSachin S. GuntheNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Narendra Ojha Imran Girach Kiran Sharma Amit Sharma Narendra Singh Sachin S. Gunthe Exploring the potential of machine learning for simulations of urban ozone variability
description	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.
format	article
author	Narendra Ojha Imran Girach Kiran Sharma Amit Sharma Narendra Singh Sachin S. Gunthe
author_facet	Narendra Ojha Imran Girach Kiran Sharma Amit Sharma Narendra Singh Sachin S. Gunthe
author_sort	Narendra Ojha
title	Exploring the potential of machine learning for simulations of urban ozone variability
title_short	Exploring the potential of machine learning for simulations of urban ozone variability
title_full	Exploring the potential of machine learning for simulations of urban ozone variability
title_fullStr	Exploring the potential of machine learning for simulations of urban ozone variability
title_full_unstemmed	Exploring the potential of machine learning for simulations of urban ozone variability
title_sort	exploring the potential of machine learning for simulations of urban ozone variability
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e427481bf236491f82668f22eb027daa
work_keys_str_mv	AT narendraojha exploringthepotentialofmachinelearningforsimulationsofurbanozonevariability AT imrangirach exploringthepotentialofmachinelearningforsimulationsofurbanozonevariability AT kiransharma exploringthepotentialofmachinelearningforsimulationsofurbanozonevariability AT amitsharma exploringthepotentialofmachinelearningforsimulationsofurbanozonevariability AT narendrasingh exploringthepotentialofmachinelearningforsimulationsofurbanozonevariability AT sachinsgunthe exploringthepotentialofmachinelearningforsimulationsofurbanozonevariability
_version_	1718419037749248000

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

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