NERC-NSFGEO SMARTWATER: Diagnosing controls of pollution hot spots and hot moments and their impact on catchment water quality
Lead Research Organisation:
University of Birmingham
Department Name: Sch of Geography, Earth & Env Sciences
Abstract
Planetary boundaries of river water pollution are at risk of being breached, with dangerous consequences for human and environmental health, economic prosperity, and water security. The current paradigm for environmental management is predicated on understanding of average conditions. However, we know environmental pollution can vary markedly in space and time. This interdisciplinary Large Grant (co-created with non-academic partners and as NERC-NSF collaboration) will pioneer innovations in experimental analytics, data science and mathematical modelling to yield new mechanistic understandings of the dynamic drivers of multi-contaminant pollution hotspots (spaces) and hot moments (times) in a changing water world.
The diagnosis of the impact of these locations and periods when average pollution conditions are far exceeded on large scale and long-term river basin water quality is critical to inform local and global adaptation and mitigation strategies for river pollution and develop interventions to keep within a safe(r) 'operating space' and improve water quality for people and the environment. SMARTWATER will therefore integrate environmental sensing, network and data science innovations, and mathematical modelling with stakeholders' catchment knowledge to transform the way we diagnose, understand, predict, and manage water pollution hotspots and hot moments.
We will:
1. Pioneer the application of scalable field diagnostic technologies for water quality sensing and sampling for identifying and characterising multi-pollution hotspots and hot moments for emerging (e.g., wastewater indicators, pharmaceuticals, pesticides) and legacy (e.g., nutrients) contaminants.
2. Develop smart water quality monitoring network solutions at river basin scale based on integrating high-resolution networks of proxy water pollution indicators with multivariate UAV boat-based longitudinal river network sampling to understand the footprint, propagation and persistence of pollution hotspots and hot moments in river basins.
3. Develop and apply data science innovations integrating deep machine learning and artificial intelligence approaches for pollution source attribution and to identify how hotspots and hot moments of multi-pollutions dynamics results from pollution source activation, connectivity and river network transport and transformation.
4. Demonstrate the utility of the new generation of smart pollution data to improve the capacity of integrated river basin scale water quality models to adequately present and predict the emergence of pollution hotspots and hot moments including their large-scale footprint and longer-term relevance for catchment water pollution.
5. Co-create with our stakeholder community pathways for successfully implementing practical and policy relevant changes in water quality management practice and use the interdisciplinary and inter-sectoral expertise of our broad stakeholder base to inform knowledge generation and dissemination pipelines in SMARTWATER.
The mechanistic process understanding and integrated technological and management solutions that will be developed in SMARTWATER will allow a step change in the diagnostics, prediction and management of water pollution and transform our ability to understand and tackle pollution pressures of increasing complexity in a rapidly changing environment.
The diagnosis of the impact of these locations and periods when average pollution conditions are far exceeded on large scale and long-term river basin water quality is critical to inform local and global adaptation and mitigation strategies for river pollution and develop interventions to keep within a safe(r) 'operating space' and improve water quality for people and the environment. SMARTWATER will therefore integrate environmental sensing, network and data science innovations, and mathematical modelling with stakeholders' catchment knowledge to transform the way we diagnose, understand, predict, and manage water pollution hotspots and hot moments.
We will:
1. Pioneer the application of scalable field diagnostic technologies for water quality sensing and sampling for identifying and characterising multi-pollution hotspots and hot moments for emerging (e.g., wastewater indicators, pharmaceuticals, pesticides) and legacy (e.g., nutrients) contaminants.
2. Develop smart water quality monitoring network solutions at river basin scale based on integrating high-resolution networks of proxy water pollution indicators with multivariate UAV boat-based longitudinal river network sampling to understand the footprint, propagation and persistence of pollution hotspots and hot moments in river basins.
3. Develop and apply data science innovations integrating deep machine learning and artificial intelligence approaches for pollution source attribution and to identify how hotspots and hot moments of multi-pollutions dynamics results from pollution source activation, connectivity and river network transport and transformation.
4. Demonstrate the utility of the new generation of smart pollution data to improve the capacity of integrated river basin scale water quality models to adequately present and predict the emergence of pollution hotspots and hot moments including their large-scale footprint and longer-term relevance for catchment water pollution.
5. Co-create with our stakeholder community pathways for successfully implementing practical and policy relevant changes in water quality management practice and use the interdisciplinary and inter-sectoral expertise of our broad stakeholder base to inform knowledge generation and dissemination pipelines in SMARTWATER.
The mechanistic process understanding and integrated technological and management solutions that will be developed in SMARTWATER will allow a step change in the diagnostics, prediction and management of water pollution and transform our ability to understand and tackle pollution pressures of increasing complexity in a rapidly changing environment.
Organisations
- University of Birmingham (Lead Research Organisation)
- Zone Atelier Bassin Du Rhone (Project Partner)
- National Trust (Project Partner)
- Swedish University of Agricultural Sciences (Project Partner)
- In-Situ Europe Ltd (Project Partner)
- Earthwatch Europe (Project Partner)
- Warwickshire Wildlife Trust (Project Partner)
- Indian Institute of Technology Roorkee (Project Partner)
- Southern University of Science and Technology (Project Partner)
- Natural Resources Wales (Project Partner)
- Bihar State Pollution Control Board (Project Partner)
- RS Hydro (Project Partner)
- UK Water Industry Research (Project Partner)
- Arup Group (United Kingdom) (Project Partner)
- Severn Trent (United Kingdom) (Project Partner)
- Scottish Environment Protection Agency (Project Partner)
- YTL (United Kingdom) (Project Partner)
- Helmholtz Ctr for Environmental Research (Project Partner)
- Argonne National Laboratory (Project Partner)
- The Rivers Trust (Project Partner)
- Thames21 (Project Partner)
- HydroJules (Project Partner)
- United Nations Educational, Scientific and Cultural Organization (Project Partner)
- Chelsea Technologies (United Kingdom) (Project Partner)
- Kelda Group (United Kingdom) (Project Partner)
- National Ecological Observatory Network (Project Partner)
Publications

Gehmlich K
(2024)
Gender Distribution of Scientific Prizes Is Associated with Naming of Awards after Men, Women or Neutral
in Data

Gehmlich K
(2024)
How we name academic prizes matters.
in Nature human behaviour