DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite

Accurate estimation of atmospheric chemical concentrations from multiple observations is crucial for assessing the health effects of air pollution. However, existing methods are limited by imbalanced samples from observations. Here, we introduce a novel deep-learning model-measurement fusion method (DeepMMF) constrained by physical laws inferred from a chemical transport model (CTM) to estimate NO 2 concentrations over the Continental United States (CONUS). By pretraining with spatiotemporally complete CTM simulations, fine-tuning with satellite and ground measurements, and employing a novel optimization strategy for selecting proper prior emission, DeepMMF delivers improved NO 2 estimates, showing greater consistency and daily variation alignment with observations (with NMB reduced from −0.3 to −0.1 compared to original CTM simulations). More importantly, DeepMMF effectively addressed the sample imbalance issue that causes overestimation (by over 100%) of downwind or rural concentrations in other methods. It achieves a higher R 2 of 0.98 and a lower RMSE of 1.45 ppb compared to surface NO 2 observations, overperforming other approaches, which show R 2 values of 0.4−0.7 and RMSEs of 3−6 ppb. The method also offers a synergistic advantage by adjusting corresponding emissions, in agreement with changes (−10% to −20%) reported in the NEI between 2019 and 2020. Our results demonstrate the great potential of DeepMMF in data fusion to better support air pollution exposure estimation and forecasting.

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Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

Byler,

Forland,

McKay

2024

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

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The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use/land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn’t fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

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DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite

Cited by 6 publications

References 60 publications

Artificial intelligence for geoscience: Progress, challenges, and perspectives

Artificial intelligence for geoscience: Progress, challenges, and perspectives

A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO₂ Concentration from Satellite and Ground Monitors

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

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DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite

Cited by 6 publications

References 60 publications

Artificial intelligence for geoscience: Progress, challenges, and perspectives

Artificial intelligence for geoscience: Progress, challenges, and perspectives

A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO2 Concentration from Satellite and Ground Monitors

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

Contact Info

Product

Resources

About

A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO₂ Concentration from Satellite and Ground Monitors