- Development of robust model capable of simulating solute and microplastic transport processes, nutrient removal efficiency, and hydraulic efficiency in constructed wetlands.
- Development of a machine learning-based Bayesian approach uncertainty quantification methodology to investigate the key modelling parameters, and inversely quantify and reduce the uncertainties of the numerical modelling tool developed within this study. We will use existing field-based data for validation and calibration processes.
- Undertaking scenario modelling to Investigate the effects of climate variability, seasonal and interannual variation in vegetation composition, as well as the geometrical design of the pond on the performance of the constructed wetlands and their pollution removal efficiency.
Constructed wetlands (CWs) are ecologically engineered systems that use soil, vegetation, and organisms to treat water and remove solute and solid pollution. It is the interplay between water-vegetation-soil that governs the wetland physical, chemical, and biological treatment processes. These ‘Natural Capital’ assets are one of the most effective measures to treat municipal and industrial wastewater, greywater, and storm-water runoff. Dynamics of water movement plays a key role in the removal of pollutants, as it influences the hydraulic residence time for treating the pollutants. Plant communities have a prominent effect on the wetland hydrodynamics and performance, as they generate flow resistance, changes the velocity field, and affect mixing characteristics, enabling suspended material to fall to the wetland bed. Seasonal variation in vegetation growth and die-back influences the performance of the system. In addition, the microbial community will respond over time to the organic and metal pollutants that are constituents of the effluents. In recent years, several studies investigated the pollution transport mechanisms in constructed wetlands. However, critical knowledge gaps remain in modelling the physical, biological processes affecting solute and solid transport in wetlands.
The main aim is to develop a robust numerical simulation tool capable of accurately modelling pollution transport and storage processes in the constructed wetlands to quantify the nutrient removal efficiency, and the hydraulic efficiency of constructed wetlands. Development of such model is critical for optimal and efficient design, operation, and maintenance of these natural capital assets. The model validation and fine-tunning with be undertaken using field-based tracer study data from our existing project in collaboration with the Norfolk Rivers Trust. This project will also investigate and quantify the effects of climate variability and seasonal and interannual variation in vegetation composition on the performance, mixing and dispersion processes within the CWs to inform the design and operation of CWs. Hence, this project will provide a step change in environmental protection and integrated catchment management by modelling and optimising the performance of constructed wetland natural capital assets, and significantly, be influential at a time of considerable investment in these systems by the water industry.
HostUniversity of Warwick
- Climate and Environmental Sustainability
- Organisms and Ecosystems
- Dr Soroush Abolfathi
- Dr Jonathan Pearson, Prof Gary Bending
- Industry Partner: Dr Geoff Brighty, Norfolk Rivers Trust
This project will undertake advanced numerical modelling techniques to simulate pollutant transport processes (solute and microplastics), nutrient removal efficiency and hydraulic performance of constructed wetlands. The model developed within this study will be validated and calibrated using fluorometric tracer study undertaken at our pilot study CW located in Norfolk, UK. State of the art machine learning-based Bayesian approach uncertainty quantification study will be undertaken to investigate the effects of key modelling parameters on the performance of the model. The uncertainty analysis model will quantify and reduce the uncertainties of the numerical modelling tool developed within this study. A series of scenarios with variations in climatic conditions, geometrical design, seasonal and interannual variation in vegetation composition, will be simulated to quantify the performance and pollution removal efficiency of wetlands under a range of design and operational conditions. The analysis of the scenario modelling results will provide comprehensive new knowledge on the optimal design and operational conditions of constructed wetlands in changing climate conditions.
Training and skills
Training will be provided in a wide range of numerical modelling techniques and CFD tools. Advanced machine learning techniques and computational skills training needed for the project will be provided. The successful applicant will be trained in cutting-edge hydrodynamic and tracer modelling techniques. Through our industrial partners a range of training will be provided on catchment planning and management, pollution risk management, habitat improvement, communication and public understanding of science. In addition, the researcher will be able to work closely with the wetland design and creation team, ensuring that their science will be applied and validated at full field-scale
Partners and collaboration
This PhD project benefits from supervision by internationally leading groups at University of Warwick including, Warwick Water (Engineering), Warwick Centre for Predictive Modelling (interdisciplinary) and Microbial Diversity and Functioning (Life Sciences). The research team are internationally recognized for their research into fate and transport of contaminants in aquatic and ecologically sensitive systems. Besides the standard NERC PhD-funding, the project is supported by Norfolk Rivers Trust (NRT) and Anglian Water. The successful applicant will have the opportunity of data collection in wetlands operated by NRT and Anglian Water. This PhD project also benefits from a unique opportunity to use world-class high-performance computing facility at the Centre for Scientific Computing, University of Warwick. Furthermore, there will be internship and placement opportunity for the student at NRT to engage with projects in pollution risk-management, catchment planning and management.
For further inquiries about the project, you can contact Dr Soroush Abolfathi ([email protected]).
Norfolk Rivers Trust.
Norfolk Rivers Trust have created innovative natural treatment plant for over a million litres of water a day to help improve the quality of water that is returned to the River Ingol, one of Norfolk’s precious chalk streams.
Frogshall: Creating an Integrated Constructed Wetland (ICW)
If you would like to apply to the project please visit: https://warwick.ac.uk/fac/sci/lifesci/study/pgr/studentships/nerccenta/
Basic research skill training; literature review and familiarisation with existing datasets and analysis techniques for hydrodynamic and fluorometric data. Preparation for model development.
Development, validation and calibration of pollution transport model to simulate solute and microplastic transport in constructed wetland under a range of geometrical design and operational conditions. Sensitivity analysis and uncertainty quantification study for the model output.
Detailed scenario modelling and analysis of the numerical data. Writing the thesis will take place during the final year.
Besseling, E., Quik, J.T.K., Sun, M. and Koelmans, A.A. (2017) ‘Fate of nano- and microplastic in freshwater systems: A modeling study’, Environmental Pollution 220, 540-548.
Ballent, A., Pando, S., Purser, A., Juliano, M.F. and Thomsen, L. (2013) ‘Modelled transport of benthic marine microplastic pollution in the Nazaré Canyon’, Biogeosciences 10(12), 7957-7970.
The first year of this PhD is designed to train the student with a range of numerical and machine learning skills and to conduct a comprehensive gap analysis on the existing study and data. The nature of activities in the Year1 of this PhD allow us to operate remotely and supervision can be through online platforms if necessary. No major laboratory or fieldwork is needed for this PhD and as such this is an ideal project for remote working to mitigate the risk of COVID19.