Marginal reduction in surface NO2 attributable to airport shutdown: A machine learning regression-based approach

Revealing the Covariation of Atmospheric O2 and Pollutants in an Industrial Metropolis by Explainable Machine Learning

“…Machine learning (ML) models have been demonstrated to be a powerful tool for reconstructing, simulating, and predicting atmospheric pollution, including PM 2.5 , − O 3 , , NO x , , etc., outperforming finely designed chemical transport models . The use of ML models provides greater flexibility and efficiency when utilizing real-world data and is especially adept at revealing complex and hidden nonlinear correlations , that might not be easily identified using traditional physical models, providing new insights into the underlying mechanisms of the studied phenomena .…”