Human activities have emitted huge amounts of pollutants into the Earth system since the industrial revolution with the usage of fossil fuels. Consequently, these emitted air pollutants deteriorate air quality and exert climate consequences (e.g., global warming and related devastating hazards), which threaten human health and the ecosystem balance on Earth. It is very challenging but extremely important to precisely predict air pollution and climate change in different mitigation scenarios, therefore providing scientific evidence for policymakers to make wise decisions and minimize losses due to unavoidable consequences. In recent years, machine learning attracts increasing attention, as a powerful tool to tackle complex issues and more applications are developed in air pollution prediction and big-data analysis in climate science.
The goal of this Research Topic is to explore the capability of machine learning approaches in improving our understanding of atmospheric processes and tackling climate change issues. As well as, to provide new insights on the co-mitigation of air pollution and climate change in the future. Recently, there is a fast-growing number of research applying machine learning approaches to tackle air pollution and climate change issues, such as the prediction and the assessment of mitigation strategies on air quality, the weather forecast, heatwaves, flood prediction, etc. More innovative explorations of machine learning approaches in understanding atmospheric processes relating to air pollution and climate change are highly encouraged in this collection.
The scope of this Research Topic is to further expand the applications and studies of machine learning approaches in atmospheric and climate science. Within this collection, we invite submissions focusing on, but not limited to, broad applications of machine learning approaches in:
• Air pollution prediction
• Understanding of atmospheric processes
• Mitigation strategies
• Model simulations and capacity expansion
• Aerosol-cloud interactions
• Weather/climate forecast
• Climate projections
Human activities have emitted huge amounts of pollutants into the Earth system since the industrial revolution with the usage of fossil fuels. Consequently, these emitted air pollutants deteriorate air quality and exert climate consequences (e.g., global warming and related devastating hazards), which threaten human health and the ecosystem balance on Earth. It is very challenging but extremely important to precisely predict air pollution and climate change in different mitigation scenarios, therefore providing scientific evidence for policymakers to make wise decisions and minimize losses due to unavoidable consequences. In recent years, machine learning attracts increasing attention, as a powerful tool to tackle complex issues and more applications are developed in air pollution prediction and big-data analysis in climate science.
The goal of this Research Topic is to explore the capability of machine learning approaches in improving our understanding of atmospheric processes and tackling climate change issues. As well as, to provide new insights on the co-mitigation of air pollution and climate change in the future. Recently, there is a fast-growing number of research applying machine learning approaches to tackle air pollution and climate change issues, such as the prediction and the assessment of mitigation strategies on air quality, the weather forecast, heatwaves, flood prediction, etc. More innovative explorations of machine learning approaches in understanding atmospheric processes relating to air pollution and climate change are highly encouraged in this collection.
The scope of this Research Topic is to further expand the applications and studies of machine learning approaches in atmospheric and climate science. Within this collection, we invite submissions focusing on, but not limited to, broad applications of machine learning approaches in:
• Air pollution prediction
• Understanding of atmospheric processes
• Mitigation strategies
• Model simulations and capacity expansion
• Aerosol-cloud interactions
• Weather/climate forecast
• Climate projections
