High frequency water quality sensor networks for monitoring hot spots and hot moments of urban and rural water pollution in real-time

Urban water pollution and quality concerns within densely populated river catchments are amongst the most pressing environmental and public health challenges faced by societies on the planet, impacting ecosystem functioning and water and food security. Recent advances in field-based environmental analytical monitoring capacity have yielded first insights into the highly dynamic behaviour of water pollution problems in urban and peri-urban places. These include the identification of hotspots, as areas of increased pollution levels and hot moments, as periods of intensive pollution, that over-proportionally affect catchment water quality. However, the relative importance of these pollution hotspots and hot moments as well as the conditions that cause this behaviour have yet to be determined.

We are currently experiencing a technological revolution in environmental monitoring, changing paradigms in water quality and pollution sensing to new frontiers that open unprecedented opportunities for taking the pulse of water quality extremes in complex landscapes. Instead of taking water samples in the field and transporting them back into the laboratory for subsequent analysis, the recent sensor revolution enables the monitoring of water quality in-situ, that is, in real-time and directly where it occurs. These technological advances enable to more adequately capture the event characteristics of dynamic flow and pollution events, including water quality extremes. Recent interdisciplinary research has also triggered the development of useful metrics for the identification of pollution source zone activation in river basins.

This project will work at the forefront of these developments to directly improve the way we detect, monitor, and prevent pollution of urban rivers. This PhD project will pioneer the combined and integrated development of novel types of water quality sensor networks and numerical models of in order improve the mechanistic understanding of the evolution of source area activation along urban-rural gradients. It will therefore push current paradigms in in-situ water quality monitoring technologies (such as absorbance and fluorescence probes, as well as river basin scale water quality monitoring in order to identify event-based dynamics of pollution sources (Figure 1 right). The findings of this study will directly support the development of more evidence-based prediction and management of river basin management. 

Methodology:

This PhD project will develop innovative methodologies for monitoring urban water quality in real-time and in-situ based on absorbance and fluorescence-based sensor network technologies and participatory approaches for bid-data generation in water pollution surveys. Observation results will be interrogated using big-data analyses tools such as deep machine learning and artificial intelligence and integrated with numerical modelling approaches to quantify river basin wide fate and transport of pollutants. To capture the spatial evolution and interactions of pollution source zone activations along complex river network structures, this study will combine field experimental findings with the development of numerical water quality models. 

 Training and skills:

Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.

Students will receive expert training in the development and field applications of absorbance and fluorescence based in-situ water quality monitoring. Training will be provided by interdisciplinary experts and benefit from the Birmingham Summer School. There will also be the opportunity for collaboration and training with a range of international partners (including through the UNESCO UniTwin Network on Ecohydrological Interfaces led by Krause and Hannah and Critical Zone Observatories in the US and University of Lyon, France). This project offers the unique opportunity to help translate science into basin management practice, through the close involvement and collaboration with the Environment Agency.

 Partners and collaboration (including CASE):

The project is directly supported by the EA (Environment Agency) with a CASE studentship (including £1,000 p.a.) and will be supported by close collaborations with the British Geological Survey (BGS) and Centre for Ecology and Hydrology (CEH) in sensor network development and design.

COVID-19 Resilience of the Project:

The majority of experimental field and laboratory work will be conducted at existing field sites of the research group and partners that have a management plan involving COVID resilience as well as the University of Birmingham’s EcoLab (which is outdoor and thus less subject to restrictions). The local nature of the research minimises the need for travel if this should be restricted and reducing interactions with others to controlled laboratory and experimental facility environments where full H&S procedures are in place. Short-term disruptions to fieldwork because of local or national lockdowns can be accommodated by flexibility in the project timeline. 

Possible timeline:

Year 1: Analysis of existing high-frequency water quality data from urban and rural test catchments and identification of distinct hot moments in water pollution, alongside development of in-situ water quality sensing protocol extending existing monitoring networks

Year 2: Conduction of field monitoring through in-situ real-time water quality sensor network and integration with distributed sampling campaigns. Initial data interrogation and interpretations.

Year 3: Development and analysis of event-based water quality metrics based on high-frequency sensor results and identification of time-dynamic pollution source zone activation. Development of catchment fate and transport models for simulation of river-basin wide pollution fate and transport for the prediction of water quality fluctuations during extreme events (storm flow and droughts). 

The project will integrate cutting-edge field-based sensor network technologies with adaptive modelling and data-analysis techniques in an interdisciplinary approach geared to create transformative research with direct environmental and societal impact.

Please email potential supervisor Prof Krause ([Email Address Removed]) for more information.