Quantifying surface emissions of important trace gases and key air pollutants is essential for understanding atmospheric chemistry and for accurate air-quality forecasts. As such, satellite measurements of trace gas tropospheric vertical columns are often used to provide a ‘top-down’ constraint on surface emissions. However, detection of small localised pollutant sources from space can be particularly difficult and are often dependent on how the satellite observations have been gridded. Although sophisticated gridding techniques are available, most researchers typically average satellite trace gas observations using an ad-hoc ‘drop in the bin’ approach that neglect the observation’s spatial footprint. This limits the grid resolution to which data can be oversampled to, thereby hampering the identification of small localised emissions whose atmospheric signatures can be easily missed.
Hence the main objective of this project is to develop a robust method for identifying small point sources that can be consistently applied to different satellite instruments and atmospheric species.
To achieve this goal the student will use data from the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment 2 (GOME-2), in combination with known emission sources to assess the effectiveness of different gridding methods for source detection within a consistent inverse modelling frame work. Specifically, we will focus on four key pollutants: nitrogen dioxide (NO2), formaldehyde (HCHO), sulphur dioxide (SO2), and glyoxal (CHOCHO). Although each of these reactive species has different sources and sinks, they are all established key indicators of anthropogenic emissions, active photochemistry, and air pollution.
We will aim for two publications: one in Atmospheric Chemistry Physics detailing the gridding algorithm development, and one in Science demonstrating its capability over the Middle East.
This project is relevant and especially timely given that the recent launch of TROPOspheric Monitoring Instrument (TROPOMI; http://www.tropomi.eu/), which offers greater potential for investigating localised emissions.
Initially, the student will install and then run the regional CMAQ model at high horizontal resolutions to simulate trace gas distributions at fine scale. Artificial localised target sources in both background and polluted conditions will be created by perturbing point emissions at individual grid cells. The model fields will then be sampled accordingly to OMI and GOME2 overpasses, and representative noise added to construct a set of pseudo-observations. The student will use the pseudo-observations to remap the simulated trace gas distribution by apply advanced gridding techniques, to evaluate their success at detecting the localised sources. Having characterised the gridding methods using synthetic data, we will use real OMI and GOME2 observations and focus on the Middle East where weak and isolated point signals are easier to detect. Successful detection of surface emissions from these target locations will then confirm if the corresponding gridding algorithm is a viable entity.
Training and Skills
This project offers the candidate an excellent opportunity to work with the latest generation of models, and satellite data, to assess air pollution over localised targets. The student will study a wide variety of topics covering: (1) chemistry-transport modelling, (2) data visualisation & analysis, including validation of model data using in-situ and satellite observations, and (3) remote sensing techniques and interpretation of retrieved quantities. The student will work with existing computer models and add to them under supervised guidance. The student will learn FORTRAN, IDL/python and shell-scripting computer languages, and gain experience of high performance computing.
CENTA training information -
CENTA students are required to complete 45 days training throughout their PhD including a 10 day placement. In the first year, students will be trained as a single cohort on environmental science, research methods and core skills. Throughout the PhD, training will progress from core skills sets to master classes specific to CENTA research themes.
Year 1: Basic research skills training; familiarisation with literature. Install CMAQ & generate trace gas model distributions.
Year 2: Development and testing of gridding algorithms.
Year 3: Application of algorithms to Middle Eastern emissions sources.
Partners and collaboration (including CASE)
Dr Michael Barkley is a past NERC Fellow and a Lecturer, within the College of Science and Engineering, at the University of Leicester. His expertise is in investigating mapping chemical emissions from space using a combination of models and multiple satellite data sets. He has over a decade’s experience of retrieving and interpreting satellite observations of tropospheric chemistry, and a strong background in chemistry-transport modelling. Dr Joshua Vande Hey is a NERC Knowledge Exchange Fellow who specialises in air-quality science, with a particular focus on aerosols & associated health metrics.
Interested applicants are strongly advised to contact Dr Michael Barkley (email@example.com) before applying.