A validation study of a high-resolution catchment-scale water quality monitoring framework utilizing a novel bacterial screening device.

UK waterways face significant pollution from both treated and untreated sewage discharges and agricultural runoff. This can lead to serious health issues for water users, environmental damage to ecosystems and losses for the economy via closed bathing waters. Management, however, is hampered by a lack of information on the relative contribution and severity of point and diffuse sources of pollution and their spatial and temporal dynamics. Conventional assessment of water quality, currently undertaken via the EU Water Framework Directive and Bathing Waters Directive, relies on infrequent sampling at limited locations, often failing to capture the dynamic nature of river pollution. In addition, analysis methodologies often require lengthy wait times, limiting the ability to respond to pollution events. To manage water quality sustainably at a catchment scale, and implement effective measures for targeted pollution reduction, there is a clear need for a monitoring framework that allows for more agile, accurate and context-sensitive assessments of water quality and pollution risk.

This study presents a ‘living laboratory’ approach implemented in a small agricultural and sewage-impacted catchment in Southwest England. The work focusses on the application of a novel water quality monitoring technology within a multi-parameter, catchment-scale monitoring framework centred around clear rural (agricultural) and urban (sewage) pollution hotspots for near real-time assessment of bacteriological quality in partnership with community and citizen science groups active within the catchment. This was integrated with high-resolution monitoring undertaken continuously for 12 months alongside frequent in-field sampling for physicochemical, hydrological, nutrient and bacterial measurements to capture baseflow and storm conditions, validated with real-time sewage discharge data.

Elevated nutrient concentrations (>1 mg/L P) showed clear spatial signatures associated with diffuse pollution from small tributaries draining agricultural land, as well as the influence of a rural wastewater treatment works. Discrete pollution signals downstream of an urban centre further reflected the impact of untreated sewage inputs. Additional key findings highlight the ability to generate rapid (13-minute) bacterial measurements comparable to standard reference methods, with wet-weather events exerting the strongest control on bacterial pollution, particularly from small agricultural tributaries where E. coli (CFU/100 mL) concentrations exceeded the Bathing Water Regulation (2013) threshold by up to 30 times. Together, these spatial and temporal patterns provided detailed insight into the source dynamics of both nutrient and bacterial pollution across the catchment. Data fusion across chemical, hydrological, and microbial datasets underpinned the development of a predictive modelling framework, enabling novel rapid bacterial measurements to be evaluated against “gold standard” methods and linked to routine water quality monitoring data.

This work develops a transferable framework for catchment-scale water quality assessment that overcomes delays associated with conventional sample analyses while encouraging stakeholder participation in data collection. The multiple dimensions of this dataset support diagnostic evaluation of the relative importance of agriculture vs sewage pollution sources through space and time, allowing for targeted pollution reduction management and regulatory decision making.