RESEARCH

Keywords

Air quality monitoring, Climate change mitigation

Atmospheric chemistry, Chemical transport modeling, Environmental satellites

Air quality assessment

  Short-lived air pollutants such as particulate matter (PM), ozone (O3), and their precursor gases including nitrogen oxides (NOx) and volatile organic compounds (VOCs) have adverse effects on human health (Oak et al., 2023) and plant growth (Feng et al., 2022). They are also climate forcers that can affect the Earth’s radiation budget. Therefore, it is essential to continuously monitor and understand the impacts and drivers of air pollution. 

  We use advanced numerical models of atmospheric chemistry to test our current understanding of the system (Oak et al., 2019; Oak et al., 2022) and interpret observations from satellites, aircraft, and ground networks (Park et al., 2021; Yang et al., 2024). We also apply data-driven approaches like machine learning to improve the information content and data quality of Earth observations (Oak et al., 2024; Pendergrass et al., 2025). These data and tools allow us to track changes in air quality, better understand the chemical processes that drive changes, and to guide future emission control strategies (Oak et al., 2025).

On-going and future work

• Improving model simulations on regional air quality

• Machine learning approaches to improve environmental satellite observations and produce relevant surface concentration fields

• Top-down quantification of NOx and VOC emissions

Greenhouse gas monitoring

  Once emitted into the atmosphere, greenhouse gases including carbon dioxide (CO2) and methane (CH4) reside for years, with lifetimes of +100 years and 9 years, respectively. These long-lived gases travel around the Earth trapping heat, and raise surface temperature, leading to global warming. Recently, methane has been recognized as the best lever to slow near-term climate change because it is relatively easy to control compared to CO2. However, methane is emitted by a wide range of sources including fuel exploitation, livestock, landfills, rice cultivation, and natural wetlands and the magnitudes and distributions are still widely unknown. 

  We use advanced numerical models and satellite observations as well as ground-based flux measurements to better quantify the sources of atmospheric methane. Using a Bayesian inversion approach we aim to improve current bottom-up inventories and understand the drivers of observed methane trends (Jacob et al., 2022). We will also include CO2 in our inversion framework to provide a transparent system to track the progress of climate policies, as well as guide future directions toward carbon neutrality, and better understand the global carbon cycle. 

On-going and future work

• Top-down quantification of methane emissions

• Trend analysis of total carbon emissions

• Integrated carbon monitoring and carbon cycle modeling