How polluted is our World? Investigating air pollutant long-range transport using state-of-the-art geostationary satellite constellations and chemistry transport modelling!

Poor air quality is one of the largest environmental stresses on human health (Cohen et al., 2017) and most severely impacts on countries with large dense populations and intense industrial/economic activities. While there has been much study and investigation into the drivers of global and regional air pollution (Lelieveld et al., 2015; Pope et al., 2018), further and improved scientific understanding is needed to help develop policies to tackle and mitigate against the harmful impacts it has on human health.

Historically, surface observations and infrequent aircraft campaigns have provided important information on air pollution, but the limited spatiotemporal coverage of these measurements have presented substantial challenges to fully understand the balance of processes leading to air pollution. In the recent decades, there has been rapid development and advancements in satellite technologies improving our ability to monitor air pollution. The most advance platforms are the South Korean Geostationary Environmental Monitoring Spectrometer (GEMS; Kim et al., 2020) and American (NASA) Tropospheric Emissions: Monitoring of Pollution (TEMPO; Zoogman et al., 2017) geostationary satellites, launched in February 2020 and April 2023. The European Space Agency (ESA) Sentinel-4 (S4; Copernicus, 2024) is planned for launch in late 2025. We can now monitor continental air pollution hotspots, spatial gradients, diurnal cycles and long-range transport at unparalleled spatial and temporal resolutions.

Combined with advanced regional and global atmospheric chemistry models, the synergy of current Earth observation and modelling capabilities allows for the robust, novel and timely investigation and quantification of key processes controlling air quality. Given the geostationary orbits of GEMS, TEMPO and S4, and the ongoing air pollution challenges over Asian, North America and Europe, these regions represent the key scientific focus of this PhD project. The research aims of this PhD are:

1. To what extent do these new geostationary platforms provide robust and important quantification of Asian, North American and European air pollution hotspots, spatial gradients and diurnal cycles?

2. Can state-of-the-art atmospheric chemistry models simulate the observed spatiotemporal distribution of observed air pollution and help underpin the key processes driving it?

3. Can a synergy of models and satellite data be used to constrain regional emission of key air pollutants?

4. How do the interactions of emissions, atmospheric chemistry and meteorology contribute to the regional and hemispheric (i.e. Asian to North America) transboundary issues of air pollution?

To tackle these research questions, the student will use a combination of the geostationary satellite air quality products and atmospheric chemistry models (e.g. the global TOMCAT model (Monks et al., 2017) and/or the regional WRF-Chem model (Graham et al., 2021). A potential outline of activities for each year could be:

Year 1: Exploring the GEMS, TEMPO and S4 air pollution data, developing skills and codes to investigate Aim 1. Gain experience running atmospheric chemistry models.

Year 2: Undertake comprehensive model-satellite comparisons to understand the model skill/deficiencies and thus improving the model’s representation of key processes and quantifying their importance to controlling air quality.

Year 3: Use the model for sensitivity experiences to constrain the regional emission inventories and understand the importance of regional pollution sources on local and transboundary air quality.

The student will gain training in using large satellite data sets, running atmospheric chemistry models and translating their research results into scientific publications. The student may also be able to join in with specialised training from Research Centres (e.g. the National Centre for Earth Observation) and DTP/CDT courses on data assimilation and machine learning etc.

More information can be found at:

https://yes-dtn.ac.uk/research/how-polluted-is-our-world-investigating-air-pollutant-long-range-transport-using-state-of-the-art-geostationary-satellite-constellations-and-chemistry-transport-modelling/