Paper
Fine particulate air pollution estimation in Ouagadougou using satellite aerosol optical depth and meteorological parameters
Framework for analysis of PM2.5 estimates.
Paper
Performance of machine learning for ozone modeling in Southern California during the COVID-19 shutdown
We combine machine learning (ML) and geospatial interpolations to create two-dimensional high-resolution ozone concentration fields over the South Coast Air Basin for the entire year of 2020.
Paper
Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios
Our novel approach leverages accessible datasets and deep learning to achieve accurate air quality modeling in resource-limited environments.
Paper
Emerging investigator series: a machine learning approach to quantify the impact of meteorology on tropospheric ozone in the inland southern California
The role of meteorology in facilitating the formation and accumulation of ground-level ozone is of great theoretical and practical interest, especially due to emissions shifts and changing global climate.
Paper
Precursor apportionment of atmospheric oxygenated organic molecules using a machine learning method
Machine learning is a promising tool in atmospheric chemistry to connect atmospheric oxygenated organic molecules with their precursors.
Paper
Application of machine learning and statistical modeling to identify sources of air pollutant levels in Kitchener, Ontario, Canada
Machine learning is used in air quality research to identify complex relations between pollutant levels, emission sources, and meteorological variables.
Paper
Predicting glass transition temperature and melting point of organic compounds via machine learning and molecular embeddings
We developed tgBoost a machine learning model to predict glass transition temperature (Tg) of organic species considering their molecular structure and functionality for better predictions of the phase state of secondary organic aerosols.
About this collection
This collection highlights leading research published in Environmental Science: Atmospheres which demonstrates the use of Machine Learning techniques in atmospheric science research. Machine learning is an important tool in atmospheric science as it enables the analysis of vast and complex datasets, allowing for the identification of patterns and trends that might be missed by traditional methods, enhancing our understanding of atmospheric processes.