Investigating variable importance in ground-level ozone formation with supervised learning

Yu Wen Chen, Sourav Medya, Yi Chun Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Tropospheric ozone (O3) is a greenhouse gas and an influential ground-level air pollutant, thus determining the importance of various factors related to O3 formation is essential. However, O3 concentrations simulated by the available climate models exhibit significant variances, indicating the insufficiency of such models in modeling the O3 formation process accurately. This study used machine learning to identify and understand the impact of various factors on O3 formation and thereby predict O3 concentrations under different climate change and emission reduction scenarios. We employed six supervised learning models to estimate O3 concentrations by using 14 meteorological and chemical variables. We found that deep neural network (DNN) and long short-term memory (LSTM) models could accurately predict O3 concentrations. The variable importance analyses emphasized the significant positive contribution of solar radiation to O3; meanwhile both nitrogen oxides and volatile organic compounds negatively contributed to O3 concentrations. Predicted O3 concentrations based on our DNN and LSTM models in different climate change scenarios showed that increase in water vapor content could offset the effects of raised temperature, and controlling anthropogenic gases, especially carbon monoxide, might have the most bearing on controlling O3 in the future.

Original languageEnglish (US)
Article number119148
JournalAtmospheric Environment
Volume282
DOIs
StatePublished - Aug 1 2022

Keywords

  • Climate change
  • Deep neural network
  • Long short-term memory
  • Ozone formation
  • Supervised learning

ASJC Scopus subject areas

  • Environmental Science(all)
  • Atmospheric Science

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