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.

This project aims to make use of lots of networked GridStix depth sensors to improve predictions of flood inundation and water level elevation at important locations with a view to improving flood warning capabilities. The project involves improving the software that links the sensors and distributed computing resources. This will allow distributed hydraulic routing models to be run, with the possibility of reducing the uncertainty in their predictions by using the sensor information in real-time. Since the GridStix also have on-board computing capabilities there is also a possibility of building a cheap local forecasting system for specific points at risk of flooding. The science questions involved include how best to make the netwroking robust, how best to constrain the uncertainty in flood routing models and improve their predictions, and how best to implement the local flood forecasting models. The research will be implemented on the River Ribble, subject to regular fluvial flooding, and the tidal system of the River Dee Estuary. The research represents a collaboration between Lancaster and Bristol Universities, the Proudman Oceanographic Laboratory and the Environment Agency.

Manufactured chemicals are essential for the maintenance of public health, food production and quality of life, including a diverse range of pharmaceuticals, pesticides, and personal care products. The use of these compounds throughout society has led to increasing concentrations and chemodiversity in the environment. Whilst there has been focus on understanding the impacts of chemicals on a subset of freshwater biodiversity (particularly invertebrates and fish), we understand less about how chemical pollution impacts freshwater microbes. These microbial communities (the 'microbiome') number in the millions to billions of cells per millilitre of water or gram of sediment and form the most biodiverse and functionally important component of freshwater ecosystems. The biogeochemical and ecological functions delivered by freshwater microbes are essential to wider freshwater ecosystem health. The PAthways of Chemicals Into Freshwaters and their ecological ImpaCts (PACIFIC) project will focus on understanding the link between sources of anthropogenic chemicals and their pathways, fate and ecological impacts in freshwater ecosystems, with an emphasis on freshwater microbial ecosystems and the functions they perform. We will investigate the relationship between predicted diffuse and point source chemical pathways and measured chemical concentrations in water and sediments at locations across the Thames and Bristol Avon catchments, chosen to represent gradients of diffuse pollution sources. These locations will be chosen to coincide with Wastewater Treatment Works (WwTWs) to understand how sewage effluent contributes to chemical burden across these gradients. Liquid chromatography coupled with (high resolution) tandem mass spectrometry and QTOF (quadrupole Time-of-Flight) mass spectrometry will be used to perform targeted and untargeted profiling of chemical groups proven and suspected to impact freshwater ecology. A range of microbial community ecosystem endpoints will also be measured at each location to identify the impact of chemical exposure, including bacterial and fungal community composition via DNA sequencing, the expression of nutrient cycling and chemical stress and resistance genes, the production of extracellular enzymes involved with biogeochemical cycling, and the functional gene repertoire of whole microbial communities. We will perform experimental microcosm exposures on freshwater microbial communities, with increasing complexity and realism, deploying high-throughput screening to identify novel chemical groups (and their structural features) with the capacity to restructure these communities. Exemplar microbial community modifying chemicals will be investigated in more detail by applying cutting-edge molecular techniques to determine ecological exposure thresholds that represent different taxonomic and functional aspects of freshwater microbial ecosystems. Novel field based mesocosms will be used to explore wastewater exposures in more realistic, but controlled settings, allowing us to explore how chemical pollution may interact with other ecological drivers such as nutrients and temperature, and how microbial responses scale-up to higher trophic levels and alter ecosystem functioning. Spatially and temporally up-scaled models of diffuse and point source chemical pollution pathways will be combined with novel thresholds developed from the lab and field exposures, to determine chemical threats to freshwater microbes, supporting the development of tools for the better management of the risks of chemical pollution to freshwater ecosystem health. These will be combined with future hydrological, climate and socio-economic scenarios, informed by responses in our experiments, and co-developed with project collaborators, the Environment Agency, to explore future threats to microbial freshwater ecosystems and wider ecosystem health.

This project aims to make use of lots of networked GridStix depth sensors to improve predictions of flood inundation and water level elevation at important locations with a view to improving flood warning capabilities. The project involves improving the software that links the sensors and distributed computing resources. This will allow distributed hydraulic routing models to be run, with the possibility of reducing the uncertainty in their predictions by using the sensor information in real-time. Since the GridStix also have on-board computing capabilities there is also a possibility of building a cheap local forecasting system for specific points at risk of flooding. The science questions involved include how best to make the netwroking robust, how best to constrain the uncertainty in flood routing models and improve their predictions, and how best to implement the local flood forecasting models. The research will be implemented on the River Ribble, subject to regular fluvial flooding, and the tidal system of the River Dee Estuary. The research represents a collaboration between Lancaster and Bristol Universities, the Proudman Oceanographic Laboratory and the Environment Agency.

Measurements of ambient air-quality have been made routinely in the UK for many decades. The number of measurements has expanded substantially in the past decade following the implementation of the National Air Quality Strategy. This has increased the number of sites and pollutants measured and the number of local meteorological records taken to help interpret air-quality data. The collected air-quality data are generally used to check if the local pollution climate complies with air-quality standards. For this purpose they are summarised as annual statistics e.g. as annual-average concentrations, or as the total hours per year above a designated concentration value. Although such statistics serve to check compliance, they only use part of the information embedded in the air-quality and meteorological data for the purpose of assessing the performance of sources and policies. There have been several attempts to make better use of routine air-quality monitoring data for purposes for tracking the performance of individual sources and for managing air-quality more effectively. Although such studies have shown the advantages of better methods for presenting and interpreting data (e.g. polar plots of concentrations and wind speed) these advantages have not been generally recognised or the methods transferred into regular use by practitioners. This is in spite of the fact that such information would lead to more robust, rapid and cost-effective decisions for air quality management. Furthermore, few attempts have been made to apply novel forms of aerometric analysis to modelled data. When comparing predictions against observations it is important to check that a model 'gives the right answer for the right reasons'. Opportunities now exist to subject the latest generation of 'one atmosphere' models to rigorous forms of aerometric evaluation. This knowledge transfer proposal therefore aims to demonstrate the advantages of 'smarter' forms of aerometric analysis to a wide range of air-quality practitioners. We will show these advantages in a range of practical air-quality situations both for traditional community pollutants (e.g. SO2, NO2, PM10) and 'new priority pollutants' (e.g. methane) so that such methods become established in regular use. We will show how existing and novel techniques can be used to exploit air-quality data more fully and rigorously, and crucially how the extra information can benefit operational and policy decisions e.g. by giving earlier and clearer advice on the performance of individual sources, or on the progress of specific policies. The methods will not only enable measured concentrations to be better exploited, but will also be applied to modelled concentrations - so helping to improve prediction and management of air quality in future. We will disseminate our methods to practitioners via a range of mechanisms including (i) a website for announcements, progress reports and archived resources, (ii) case summaries & evaluation meetings, (iii) handouts & presentations to user bodies, (iv) conference posters/papers, (v) peer-reviewed publications, (vi) a final report and (vi) a closing workshop. In order to transfer the methods into regular use, we will show users that they can inform practical decisions on air quality (e.g. in management areas), resource use (e.g. fuels, abatement costs), societal behaviours (e.g. on transport, waste), health, (e.g. particulates) and quality of life. Our team has well-established links to professional air-quality bodies including: the Institute of Air-Quality Management, the UK's Atmospheric Dispersion Modelling Liaison Committee, and Environmental Protection UK / with its specialist Dispersion Modellers' User Group. We will use these links to consult on the selection of cases studies, to give information on project progress, and to show air-quality practitioners how their decisions can benefit from improved air-quality analysis techniques.