PhD: Mapping river pollutants in space & time with inverse modelling

Project Description

Pollution is the largest pressure affecting the health of English rivers and an area of major public interest. To mitigate a polluted river, the sources of contaminants must be identified and quantified, termed ‘source apportionment’ . A challenge is quantifying pollution sources that are unknown or difficult to monitor. An exciting approach to overcome this challenge is to use “inverse modelling”, traditionally used in geophysics. Inverse approaches work “backwards” from observations to parametrise water quality models that best fit the data. In this project, you will develop this nascent approach to generate new and better insights into pollution sources into waterways. One direction is to build high-resolution geospatial data (e.g., land-use) into inverse models using Bayesian or Machine Learning approaches. Another is developing an inverse framework to interpret pollution sources that vary in time (e.g., sewage spills), using the growing amounts of time-series water-quality data. Depending on interest there is scope to generate new data to test your models using in-situ sensors and samples e.g., in urban rivers in London. You will develop open-source tools that can be used by researchers, regulators or river protection groups to interpret water-quality data. The methods you develop will also have applications in the wider Earth Sciences such as in geochemistry & geomorphology. Ultimately, your work will contribute to solving the freshwater contamination crisis.

Research themes

Environmental Hazards & Pollution

Project Specific Training

In this project you will develop skills from the supervisory team in developing high-quality research software, primarily in python. You will gain experience using version control and collaborative development tools (git, github) widely used in industry. You will also gain experience in using command-line geospatial processing pipelines such as GDAL. Depending on the interest of the student, there is scope to develop skills and field experience (from the supervisory team) in gathering water-quality and hydrochemical datasets via sampling or deployment of water-quality sensors.

Potential Career Trajectory

In this computational project you will develop a range of useful “data science” and software development skills that are desirable to many quantitative industries inside and outside of the environmental sciences. The specific project area of water-quality data-analysis will also provide you with expertise that could be applied in environmental regulation, the water industry, or in the environmental 3rd sector.