exposed to during their daily lives.

This PhD project will develop an advanced data-fusion framework to map, understand and predict hourly PM2.5 exposure across megacities. It will combine emerging geostationary satellite observations with ground-based air quality measurements, traffic activity, meteorological data, satellite-derived trace-gas products and demographic information. Unlike conventional satellite approaches that commonly provide daily estimates, geostationary satellites offer the potential to observe rapid changes in atmospheric composition throughout the day. The project will explore how these observations can be used to identify evolving pollution hotspots, localised micro-exposure environments, and populations at greatest shortterm exposure risk.

The student will develop physically informed data-fusion methods to address key challenges in satellitebased air-quality assessment, including missing or incomplete aerosol and trace-gas observations caused by cloud cover, retrieval limitations and variable atmospheric conditions. These methods will be designed to reconstruct satellite information using constraints from atmospheric reanalysis, meteorology and complementary observations, before integrating the resulting products into high-resolution PM2.5 mapping and forecasting models.

A central aim will be to move beyond concentration mapping towards predictive exposure management. The project will investigate how dynamic PM2.5 hotspots interact with population distribution and mobility, enabling estimates of population exposure and acute health-risk indicators at high spatial and temporal resolution. It will also evaluate whether the framework can support practical, targeted exposure-reduction guidance, such as identifying locations, times or population groups where interventions may have the greatest benefit.

The research will be jointly supervised between The University of Manchester and Peking University. At Manchester, the student will benefit from expertise in environmental data science and atmospheric environmental modelling. At Peking University, the student will work with internationally recognised expertise in satellite remote sensing, PM2.5 retrieval and the fusion of satellite and ground-based observations in complex urban environments.

The project is suited to candidates with backgrounds in environmental science, atmospheric science, geography, engineering, computer science, data science or related disciplines. It will provide interdisciplinary training in satellite remote sensing, data science, spatiotemporal modelling, air quality science and environmental health applications, preparing the student for a career at the interface of environmental research, data science and urban sustainability.

exposed to during their daily lives.

This PhD project will develop an advanced data-fusion framework to map, understand and predict hourly PM2.5 exposure across megacities. It will combine emerging geostationary satellite observations with ground-based air quality measurements, traffic activity, meteorological data, satellite-derived trace-gas products and demographic information. Unlike conventional satellite approaches that commonly provide daily estimates, geostationary satellites offer the potential to observe rapid changes in atmospheric composition throughout the day. The project will explore how these observations can be used to identify evolving pollution hotspots, localised micro-exposure environments, and populations at greatest shortterm exposure risk.

The student will develop physically informed data-fusion methods to address key challenges in satellitebased air-quality assessment, including missing or incomplete aerosol and trace-gas observations caused by cloud cover, retrieval limitations and variable atmospheric conditions. These methods will be designed to reconstruct satellite information using constraints from atmospheric reanalysis, meteorology and complementary observations, before integrating the resulting products into high-resolution PM2.5 mapping and forecasting models.

A central aim will be to move beyond concentration mapping towards predictive exposure management. The project will investigate how dynamic PM2.5 hotspots interact with population distribution and mobility, enabling estimates of population exposure and acute health-risk indicators at high spatial and temporal resolution. It will also evaluate whether the framework can support practical, targeted exposure-reduction guidance, such as identifying locations, times or population groups where interventions may have the greatest benefit.

The research will be jointly supervised between The University of Manchester and Peking University. At Manchester, the student will benefit from expertise in environmental data science and atmospheric environmental modelling. At Peking University, the student will work with internationally recognised expertise in satellite remote sensing, PM2.5 retrieval and the fusion of satellite and ground-based observations in complex urban environments.

The project is suited to candidates with backgrounds in environmental science, atmospheric science, geography, engineering, computer science, data science or related disciplines. It will provide interdisciplinary training in satellite remote sensing, data science, spatiotemporal modelling, air quality science and environmental health applications, preparing the student for a career at the interface of environmental research, data science and urban sustainability.