Issue 4, 2024

Performance of machine learning for ozone modeling in Southern California during the COVID-19 shutdown

Abstract

We combine machine learning (ML) and geospatial interpolations to create two-dimensional high-resolution ozone concentration fields over the South Coast Air Basin (SoCAB) for the entire year of 2020. The interpolated ozone concentration fields were constructed using 15 building sites whose daily trends were predicted by random forest regression. Spatially interpolated ozone concentrations were evaluated at 12 sites that were independent from the machine learning sites and historical data to find the most suitable prediction method for SoCAB. Ordinary kriging interpolation had the best performance overall for 2020. The model is best at interpolating ozone concentrations inside the sampling region (bounded by the building sites), with R2 ranging from 0.56 to 0.85 for those sites. All interpolation methods poorly predicted and underestimated ozone concentrations for Crestline during summer, indicating that the site has a distribution of ozone concentrations that is independent from all other sites. Therefore, historical data from coastal and inland sites should not be used to predict ozone in Crestline using data-driven spatial interpolation approaches. The study demonstrates the utility of ML and geospatial techniques for evaluating air pollution levels during anomalous periods. Both ML and the Community Multiscale Air Quality model do not fully capture the irregularities caused by emission reductions during the COVID-19 lockdown period (March–May) in the SoCAB. Including 2020 training data in the ML model training improves the model's performance and its potential to predict future abnormalities in air quality.

Graphical abstract: Performance of machine learning for ozone modeling in Southern California during the COVID-19 shutdown

Supplementary files

Article information

Article type
Paper
Submitted
07 11月 2023
Accepted
22 3月 2024
First published
29 3月 2024
This article is Open Access
Creative Commons BY-NC license

Environ. Sci.: Atmos., 2024,4, 488-500

Performance of machine learning for ozone modeling in Southern California during the COVID-19 shutdown

K. Do, A. K. Yeganeh, Z. Gao and C. E. Ivey, Environ. Sci.: Atmos., 2024, 4, 488 DOI: 10.1039/D3EA00159H

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements