Leveraging Deep Learning for Physical Model Bias of Global Air Quality Estimates

TL;DR

This paper introduces a deep learning approach using CNNs to correct biases in physical models of surface ozone, improving accuracy and understanding of urban air quality for health and policy applications.

Contribution

It presents a novel CNN-based method to estimate and correct physical model residuals of surface ozone, incorporating high-resolution land use data for enhanced accuracy.

Findings

CNN-based bias correction outperforms traditional methods.

Incorporating satellite land use data improves model estimates.

Results enhance understanding of urban ozone bias factors.

Abstract

Air pollution is the world's largest environmental risk factor for human disease and premature death, resulting in more than 6 million permature deaths in 2019. Currently, there is still a challenge to model one of the most important air pollutants, surface ozone, particularly at scales relevant for human health impacts, with the drivers of global ozone trends at these scales largely unknown, limiting the practical use of physics-based models. We employ a 2D Convolutional Neural Network based architecture that estimate surface ozone MOMO-Chem model residuals, referred to as model bias. We demonstrate the potential of this technique in North America and Europe, highlighting its ability better to capture physical model residuals compared to a traditional machine learning method. We assess the impact of incorporating land use information from high-resolution satellite imagery to improve model estimates. Importantly, we discuss how our results can improve our scientific understanding of the factors impacting ozone bias at urban scales that can be used to improve environmental policy.