Last Updated: July 20, 2022
Our research focuses on better understanding the chemical composition of the atmosphere, its perturbation by human activity, and the implications for life on Earth. We use advanced models of atmospheric composition to interpret observations from satellites, aircraft, ground networks, and other platforms. We view our models as part of an integrated observing system to increase fundamental knowledge and address pressing environmental issues.
We have a large number of ongoing projects at any given time, addressing broad research themes and organized in five subgroups (scroll down for more information):
- Air Quality - Leaders: Shixian Zhai and Xu Feng
- Chemistry - Leaders: Kelvin Bates and Ruijun Dang
- Climate and Health - Leader: Loretta Mickley
- Methane - Leaders: Zhen Qu and Daniel Varon
- Model Development - Leader: Sebastian Eastham (joint with MIT)
In addition we have three subgroups focused on practices:
- Machine Learning & Data Science - Leaders: Daniel Varon, Makoto Kelp, Drew Pendergrass
- Diversity, Inclusion, and Belonging - Leaders: Nadia Colombi and Drew Pendergrass
- GEOS-Chem Support Team - Bob Yantosca, Melissa Sulprizio, Lizzie Lundgren
China and Korea have a very severe particulate matter (PM) and ozone air pollution problem. Surface air quality networks together with satellite and aircraft allow us to track changes in air quality and the responses to emission controls, to better understand the chemical processes controlling PM and ozone formation, and to guide future emission control strategies.
Satellites are increasingly powerful resources for monitoring air quality but are highly sensitive to background concentrations of pollutants above the surface. This background is poorly understood; it may result from natural sources or from pollution transported on international/intercontinental scales. Better understanding of background influences is critical for interpreting satellite observations and for developing strategies to improve air quality.
Tropospheric oxidants (ozone and OH) are of central importance for global atmospheric chemistry. The hydroxyl radical (OH) is the main atmospheric oxidant responsible for removal of atmospheric gases yet we have little understanding of the factors controlling it and the associated long-term trends. Ozone is a major greenhouse gas in the middle/upper troposphere, a toxic gas at the surface, and the primary source of OH, but the factors controlling its global distribution and long-term trends are poorly understood. Chemical production and loss of ozone and OH involve complex nonlinear mechanisms coupled to transport on all scales. Key questions relate to the sources and chemistry of nitrogen oxides (NOx), oxygenated volalile organic compounds (VOCs), and halogens. The role of peroxyacetylnitrate (PAN) as a reservoir for long-range transport of NOx is critical.
The atmospheric chemistry of volatile organic compounds (VOCs) has major implications for oxidant concentrations and for formation of organic aerosol. VOCs are emitted from a range of biogenic and anthropogenic sources. They undergo various oxidation cascades in the atmosphere, producing increasingly substituted and cleaved compounds, terminating eventually in the formation of CO2, the formation of secondary organic aerosol (SOA) or the removal by deposition. Most of the species and reactions involved have never been measured and must be inferred indirectly. Multigenerational products of VOC oxidation may be responsible for an "organic soup" in the remote troposphere that is suggested by observations but not understood at all. Representing organic chemistry in models is a major challenge.
Anthropogenic emissions of methane come from a very large number of relatively small point sources including coal mine vents, oil/gas production and processing facilities, stockyards, landfills, etc. These sources have highly variable emissions, and can spike under abnormal conditions. Satellites provide a unique vantage point for detecting the atmospheric plumes originating from large point sources and thus enable action to shut down these emissions.
Methane is the second most important anthropogenic greenhouse gas after CO2. It is particularly important for near-term (~20 years) climate change. Methane is emitted by a wide range of processes including oil/gas systems, livestock, landfills, wastewater, rice cultivation, and wetlands. The magnitudes of these sources, their spatial distributions, and their temporal trends are poorly understood. Improving the "bottom-up" inventories that relate emissions to activity levels is imperative for climate policy. This can be done by using state-of-science activity and emission factor data, evaluating the resulting inventories with atmospheric observations through "top-down"inverse analyses, and using the results of these inverse analyses to further improve the emission inventories. This partnership between bottom-up and top-down approaches is key to improving understanding of methane emissions in a way that can enable policy action.
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