IAP-26-132

Determining the Widespread Risk of Atmospheric Aerosols Released During Environmental Hazard Events, using Satellite-based Observations.

Environmental hazards such as volcanic eruptions, wildfires and even dust storms have the potential to contribute significant volumes of aerosols to the atmosphere. These aerosols can contribute to environmental degradation, through the widespread deposition of particles and impacts to local-global radiation budgets. The grounding of particles can impact polar regions where particles can trigger rapid glacial melting[1] or trigger health crises in those susceptible to pulmonary conditions[2]. While aerosols such as volcanic sulphates can produce acid rain that devastate crop yields and ultimately the economic stability of the impacted region[3]. It has also been observed that the resulting imbalance in the radiation budget on a regional to global scale can cause variations in climatic conditions[4]. As these aerosols and particles can travel significant distances, they can impact communities beyond the immediate area of the environmental hazard. Therefore, a synoptic scale view is needed to assess their impacts. For this reason, satellite-based remote sensing represents a powerful resource in the assessment of the broader impacts of emitted particulates.

Expected responsibilities:
Within this research project the candidate will be responsible for compiling information on the extent and composition of aerosols and/or particles emitted from volcanoes, wildfires and/or during dust storms. Individual events will be tracked and the composition of the particles will be inferred using existing techniques[5], where these are available. The primary focus of this research will be to develop new methods of detecting and tracking emitted aerosols and particles, through the development of a multi-sensor methodology and incorporating new data, such as that from NASA’s PACE sensor (Plankton, Aerosol, Cloud, ocean Ecosystem: https://pace.oceansciences.org/about.htm). This project represents an opportunity for the selected PhD candidate to develop novel methods for conducting remote observations of environmental hazards with global applicability. Where opportunities arise, there may be scope for  responsive fieldwork (e.g., the collection of drone-based wildfire plume multispectral observations, to validate satellite based retrievals), which can be used to inform and refine developed algorithm.

Technical Skills:
It is expected that the candidate will have a strong interest in environmental hazards that generate atmospheric plumes (e.g., volcanoes, wildfires and/or dust storms). The candidate should have experience with the processing of remote sensing data and be proficient in geospatial analysis techniques. Due to the intention to develop novel single- or multi-sensor algorithms for categorising atmospheric plumes, it is expected that the student will have a proficiency with coding, or the capability to refine and develop these skills during their PhD.

Deliverables: The primary deliverable of this project will be an algorithm, and associated case study analyses, for conducting remote assessment of aerosols using free to access data. It is expected that this will be user friendly and made publicly available upon completion of the PhD, facilitating its widespread use and improving global assessment capabilities.

Novelty: The project will utilise cutting-edge satellite technology to develop new techniques for the analysis of environmental hazard events. The outputs generated as part of this project have the potential to be beneficial to the global hazard assessment community.

Data: Archived and ongoing observations from PACE (NASA), Sentinel-2, -3 and -5p (European Space Agency) and legacy data from MISR, MODIS, ASTER (NASA) & Landsat (United State Geological Service) where relevant. Depending on prior experiences of the successful candidate SAR observations (e.g., Sentinel-1) may also be incorporated, with additional support provided from the wider Stirling supervisory team.

Location: The project will primarily be desk based. Primary data sources will be satellite-based remote sensing data. For established sensor data, algorithms will be available, and it is expected that you will become proficient with the processing of these data. For sensors where the expectation is the development of dedicated algorithms, there may be opportunities to collaborate (e.g., to gain additional training; or participation in responsive field campaigns) with national/international research partners, either remotely or in person, where funds allow.

Field opportunities: In the event that local (Scottish) wildfires provide the opportunity to collect multispectral data for use as a validation data set for the developed algorithm, the successful candidate would have the opportunity to use existing or sourced drone platforms to collect this data in collaboration with University of Stirling staff and/or external collaborators. In this scenario, the student will be offered the opportunity to complete drone pilot training.

Year 1

Preparing literature review on related topics. Begin downloading and processing remote sensing data using existing techniques, to become familiar with current capabilities. It is expected that this will be done through the selection and investigation of a relevant wildfire events. The output of these analyses will result in the submission of a journal article.

Year 2

Develop multi-sensor methods for the assessment of an environmental hazard event(s) using novel technologies and/or methodologies, refined during the first year of study. It is expected that the developed technique will be written up as a journal article and submitted after the second year. Engagement with opportunistic field-based activities (e.g., multispectral drone observations of local wildfire plumes in collaboration with Stirling staff or external collaborators) and training opportunities (depending on student engagement with wider collaborative partners)

Year 3

The final year will continue the algorithm development, refinement and validation process and will likely result in the publication of at least one case study analysis using the multi-sensor technique devised. If fieldwork is undertaken it is expected this may delay year 3 output (no more than 2 months).

Year 3.5

Engage with end-users to refine the algorithm and user interface developed, complete & submit remaining papers and final thesis, and undertake viva examination.

This is a multi-disciplinary project encompassing a) geology/atmospheric/environmental science, b) remote sensing, c) data analysis & d) algorithm development.
The successful candidate will have the opportunity to develop skills in these areas through engagement with their interdisciplinary research supervisors. Additionally, attending training courses, such as those operated through the ESA EO Science for Society or similar sources, will be encouraged.
The candidate will also be encouraged to take non-subject specific trainings to enable the development of a broader skill set beyond their academic studies, such as those provided through the University of Stirling Institute of Advanced Studies (IAS).