Tai‐Long He
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View article: High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport Open
Quantifying greenhouse gas (GHG) emissions is critically important for projecting future climate and assessing the impact of environmental policy. Estimating GHG emissions using atmospheric observations is typically done using source–recep…
View article: Deep learning-based surface O3 responses to anthropogenic and meteorological changes
Deep learning-based surface O3 responses to anthropogenic and meteorological changes Open
The applications of deep learning (DL) technique in atmospheric environment research are expanding rapidly. Here we developed a DL framework to quantify the responses of surface ozone (O3) to anthropogenic and meteorological changes in Chi…
View article: Assessing Methane Detection Capabilities of Operational Satellite Sensors using Controlled Release Experiments
Assessing Methane Detection Capabilities of Operational Satellite Sensors using Controlled Release Experiments Open
Detecting, reporting, and mitigating fugitive methane leaks has been identified as one way of  lowering national methane emissions in the United States.  To that effect, the United States Environmental Protection Agency has launc…
View article: FootNet v1.0: development of a machine learning emulator of atmospheric transport
FootNet v1.0: development of a machine learning emulator of atmospheric transport Open
There has been a proliferation of dense observing systems to monitor greenhouse gas (GHG) concentrations over the past decade. Estimating emissions with these observations is often done using an atmospheric transport model to characterize …
View article: Challenges and Opportunities Offered by Geostationary Space Observations for Air Quality Research and Emission Monitoring
Challenges and Opportunities Offered by Geostationary Space Observations for Air Quality Research and Emission Monitoring Open
Space-borne remote sensing of atmospheric chemical constituents is crucial for monitoring and better understanding global and regional air quality. Since the 1990s, the continuous development of instruments onboard low-Earth orbiting (LEO)…
View article: High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport Open
Quantifying greenhouse gas (GHG) emissions is critically important for projecting future climate and assessing the impact of environmental policy. Estimating GHG emissions using atmospheric observations is typically done using source-recep…
View article: Increased methane emissions from oil and gas following the Soviet Union’s collapse
Increased methane emissions from oil and gas following the Soviet Union’s collapse Open
Global atmospheric methane concentrations rose by 10 to 15 ppb/y in the 1980s before abruptly slowing to 2 to 8 ppb/y in the early 1990s. This period in the 1990s is known as the “methane slowdown” and has been attributed in part to the co…
View article: The capability of deep learning model to predict atmospheric compositions across spatial and temporal domains
The capability of deep learning model to predict atmospheric compositions across spatial and temporal domains Open
Machine learning (ML) techniques have been extensively applied in the field of atmospheric science. It provides an efficient way of integrating data and predicting atmospheric compositions. However, whether ML predictions can be extrapolat…
View article: Deep learning-derived anthropogenic and meteorological drivers of surface ozone change in China
Deep learning-derived anthropogenic and meteorological drivers of surface ozone change in China Open
Urban air pollution continues to pose a significant health threat, despite regulations to control emissions. Here we present a comparative analysis of the anthropogenic and meteorological drivers of surface ozone (O3) change in China by in…
View article: The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe
The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe Open
Data‐driven methods have been extensively applied to predict atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O 3 ) predictions across continents in China, the United States (US) and Eu…
View article: FootNet v1.0: Development of a machine learning emulator of atmospheric transport
FootNet v1.0: Development of a machine learning emulator of atmospheric transport Open
There has been a proliferation of dense observing systems to monitor greenhouse gas (GHG) concentrations over the past decade. Estimating emissions with these observations is often done using an atmospheric transport model to characterize …
View article: Data‐ and Model‐Based Urban O<sub>3</sub> Responses to NO<sub>x</sub> Changes in China and the United States
Data‐ and Model‐Based Urban O<sub>3</sub> Responses to NO<sub>x</sub> Changes in China and the United States Open
Urban air pollution continues to pose a significant health threat, despite regulations to control emissions. Here we present a comparative analysis of urban ozone (O 3 ) responses to nitrogen oxide (NO x ) changes in China and the United S…
View article: Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021
Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021 Open
Surface ozone (O3) concentrations in China have increased largely in the past decade. An accurate understanding of O3 pollution evolution is critical for making effective regulatory policies. Here we integrate data- and process-based model…
View article: Spaceborne assessment of the Soviet Union's role in the 1990s methane slowdown
Spaceborne assessment of the Soviet Union's role in the 1990s methane slowdown Open
Methane is the second most important anthropogenic greenhouse gas, amounting to 60% of the radiative forcing from CO2 since pre-industrial times based on emitted compound. Global atmospheric methane concentrations rose by 10-15 ppb/yr in t…
View article: Inverse modelling of Chinese NO <sub> <i>x</i> </sub> emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis
Inverse modelling of Chinese NO <sub> <i>x</i> </sub> emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis Open
Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observational coverag…
View article: Authors' response to referee comments on acp-2022-251
Authors' response to referee comments on acp-2022-251 Open
Abstract. Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOxâ=âNOâ+âNO2). However, the utility of these measurements is impacted by reduced obs…
View article: A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage
A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage Open
Uptake of atmospheric carbon by the ocean, especially at high latitudes, plays an important role in offsetting anthropogenic emissions. At the surface of the Southern Ocean south of 30 ∘ S, the ocean carbon uptake, which had been weakening…
View article: Comment on acp-2022-251
Comment on acp-2022-251 Open
Abstract. Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observation…
View article: Comment on acp-2022-251
Comment on acp-2022-251 Open
Abstract. Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observation…
View article: A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China
A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China Open
The applications of novel deep learning (DL) techniques in atmospheric science are rising quickly. Here we build a hybrid DL model (hyDL-CO), based on convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks, …
View article: Inverse modeling of Chinese NO <sub>x</sub> emissions using deep learning: Integrating in situ observations with a satellite-based chemical reanalysis
Inverse modeling of Chinese NO <sub>x</sub> emissions using deep learning: Integrating in situ observations with a satellite-based chemical reanalysis Open
Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observational coverag…
View article: Comment on gmd-2021-420
Comment on gmd-2021-420 Open
Abstract. The applications of novel deep learning (DL) techniques in atmospheric science are rising quickly. Here we build a hybrid DL model (hyDL-CO), based on convolutional neural networks (CNNs) and long short-term memory (LSTM) neural …
View article: Can the data assimilation of CO from MOPITT or IASI constrain high-latitude wildfire emissions? A Case Study of the 2017 Canadian Wildfires
Can the data assimilation of CO from MOPITT or IASI constrain high-latitude wildfire emissions? A Case Study of the 2017 Canadian Wildfires Open
Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Geophysical Research - Atmospheres. ESSOAr is a venue for early communication or feedback before peer review. Data may be pre…
View article: A comparative analysis for deep learning model (hyDL-CO v1.0) and Kalman Filter to predict CO in China
A comparative analysis for deep learning model (hyDL-CO v1.0) and Kalman Filter to predict CO in China Open
The applications of novel deep learning techniques in atmospheric science are rising quickly. Here we build a hybrid deep learning (DL) model (hyDL-CO), based on convolutional neural networks (CNN) and long short-term memory (LSTM) neural …
View article: Experimental Investigations of the Resistance Performance of Commercial Cylinder Filters and Effect Factors under Humid Airflows
Experimental Investigations of the Resistance Performance of Commercial Cylinder Filters and Effect Factors under Humid Airflows Open
Experiments were performed to summarize the variations in the resistance of commercial cylinder filters applied in the air intake filtration systems of gas power plants under humid airflows. Seven clean cylinder filters composed of various…
View article: Deep learning to evaluate US NOx emissions using surface ozone predictions
Deep learning to evaluate US NOx emissions using surface ozone predictions Open
Emissions of nitrogen oxides (NOx = NO + NO2) in the United States have declined significantly during the past three decades. However, satellite observations since 2009 indicate total column NO2 is no longer declining even as bottom-up inv…