Combining Machine Learning and Satellite Observations to Predict Spatial and Temporal Variation of near Surface OH in North American Cities
Abstract
The hydroxyl radical (OH) is the primary cleansing agent in the atmosphere. The abundance of OH in cities initiates the removal of local pollutants; therefore, it serves as the key species describing the urban chemical environment. We propose a machine learning (ML) approach as an efficient alternative to OH simulation using a computationally expensive chemical transport model. The ML model is trained on the parameters simulated from the WRF-Chem model, and it suggests that six predictive parameters are capable of explaining 76% of the OH variability. The parameters are the tropospheric NO₂ column, the tropospheric HCHO column, J(O¹D), H₂O, temperature, and pressure. We then use observations of the tropospheric NO₂ column and HCHO column from OMI as input to the ML model to enable measurement-based prediction of daily near surface OH at 1:30 pm local time across 49 North American cities over the course of 10 years between 2005 and 2014. The result is validated by comparing the OH predictions to measurements of isoprene, which has a source that is uncorrelated with OH and is removed rapidly and almost exclusively by OH in the daytime. We demonstrate that the predicted OH is, as expected, anticorrelated with isoprene. We also show that this ML model is consistent with our understanding of OH chemistry given the solely data-driven nature.
Additional Information
© 2022 American Chemical Society. Received: August 20, 2021; Revised: March 7, 2022; Accepted: March 8, 2022; Published: March 18, 2022. The authors gratefully acknowledge support from NASA Grant 80NSSC19K0945. We acknowledge high-performance computing support from the Savio computational cluster resource provided by the Berkeley Research Computing program at the University of California, Berkeley (supported by the UC Berkeley Chancellor, Vice Chancellor for Research, and Chief Information Officer). We acknowledge use of the WRF-Chem preprocessor tools MOZBC provided by the Atmospheric Chemistry Observations and Modeling (ACOM) laboratory of NCAR. The authors declare no competing financial interest.Attached Files
Supplemental Material - es1c05636_si_001.pdf
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Additional details
- Eprint ID
- 113964
- DOI
- 10.1021/acs.est.1c05636
- Resolver ID
- CaltechAUTHORS:20220318-222979098
- 80NSSC19K0945
- NASA
- Created
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2022-03-18Created from EPrint's datestamp field
- Updated
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2022-07-15Created from EPrint's last_modified field