NERC-NSFGEO SMARTWATER: Diagnosing controls of pollution hot spots and hot moments and their impact on catchment water quality

Lead Research Organisation: UK CENTRE FOR ECOLOGY & HYDROLOGY
Department Name: Water Resources (Wallingford)


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 ca 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
understanding 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.


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