Uncover the physical and chemical drivers that are responsible for observed distributions of air pollution over Asia using new geostationary satellite observations and a cutting-edge atmospheric chemistry transport model and its adjoint model.
Deteriorating air quality levels is an ongoing challenge for all major global economies. Air pollution is now acknowledged to be the largest environmental stressor on human health (Cohen et al, 2017), with the effects most acutely felt by those living in the world’s largest cities. There is a pressing need for improved scientific understanding of atmospheric chemistry to underpin numerical models that can inform and guide global urban development choices over the next two decades to help mitigate the worst effects of air pollution.
There have been exceptional advances over the last two decades in both the ability to measure and computationally model the atmosphere. Satellite instruments now provide global views of air pollution as seen from space, but they have been difficult to put into context of data collected on the ground within cities because of differences in spatial and temporal sampling. Here we study new satellite observations of atmospheric pollution from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS) (Kim et al., 2020) that sits in a geostationary orbit. The orbit is particularly relevant to this project because it effectively hovers over Asia and collects data throughout the day. This represents an unprecedented opportunity to study the evolution of air pollution over Asia. GEMS will eventually be joined by the NASA TEMPO (focused over North America) and European Sentinel-4 (focused on Europe) geostationary instruments, but for now GEMS provides unique insights.
The project takes advantage of an established link between the Universities of Edinburgh and Leeds and Seoul National University, three highly ranked universities, and builds on a long-standing collaboration between Professors Palmer and Park.
1) Is our current knowledge, embodied by the GEOS-Chem atmospheric chemistry transport model, consistent with the air pollutant chemistry measurements collected by GEMS?
2) To what extent are sparse ground-based observation networks consistent with data collected by the GEMS satellite?
3) Are there significant transboundary air quality gradients within Asian countries and across national borders?
4) How can we effectively use data collected by GEMS to quantify air pollution emissions over Asia and study the resulting regional and local atmospheric transport of pollutants?
You will use the established GEOS-Chem atmospheric chemistry transport model (http://acmg.seas.harvard.edu/geos/) that has played an important role in interpreting air pollutant chemistry data from GEMS and from ground-based data, e.g., Marvin et al, 2020. In the first instance, you will use the model as an intermediary to relate measurements collected by sparse ground-based and aircraft sensors to those collected by the GEMS satellite. You will also use the adjoint model for GEOS-Chem, which provides an efficient and advanced numerical method for quantifying the sensitivity of observed quantities to precursor emissions and atmospheric chemistry (e.g., Parrington et al, 2012).
Year 1: Research training. Familiarization with the GEOS-Chem model and the principles of satellite remote sensing. Drive a nested version of GEOS-Chem (centred over Asia) with established emission inventories and produce preliminary comparisons with data from GEMS and ground-based and airborne sensors.
Year 2: Use GEOS-Chem to explore observed spatial and temporal variations of air pollution across Asia, with a particular focus on variations during daylight hours that are unique to the GEMS satellite.
Year 3: Examine the role of the variations studied in Year 2 to improve emission inventories and our ability to describe the transport of pollutants.
A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills. Specialist training will include: (1) remote sensing data and (2) computer modelling of tropospheric ozone chemistry. The successful student will also spend an extended period in Year 2/Year 3 at the Seoul National University, working alongside Professor Park and the GEMS data groups.
The successful candidate will have a degree in the physical sciences and most likely physics, chemistry, or applied mathematics. This is a computational project: no prior computing experience is necessary, but some knowledge of coding would be useful (e.g., Python, FORTRAN).
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning and field training. All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
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