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River systems in the UK are in poor condition, with sewage discharges and agricultural runoff identified as major contributors to declining river health. Effective assessment and management of river health requires real-time monitoring solutions; however, existing in-situ sensors are largely limited to physiochemical parameters and provide little information on organic pollution input or microbial contamination. This research demonstrates the implementation and deployment of novel multiparameter fluorescence-based sensors capable of measuring bacterial/algal contamination and organic pollution whilst simultaneously correcting for environmental optical interferences in real time. These portable multiparameter fluorometers were deployed as part of a sensing network along the River Dart catchment (UK) in October 2025. We present a dataset collected continuously in real-time over a 3-month period. As part of a managed water quality monitoring programme, continuous data on microbial contamination and organic pollution in the River Dart catchment collected using deployed novel multiparameter fluorescence-based sensors were compared alongside regular field spot sampling and standard laboratory water quality analysis. For the latter, biological oxygen demand, microbial counts and nutrient analysis were performed to contextualise and verify (ground truth) sensing data. Sensing system performance for the detection of organic pollution events and their subsequent impacts on river ecology was evaluated. Our results demonstrate a strong correlation between tryptophan-like-fluorescence and biological oxygen demand, highlighting the ability of the sensor to monitor oxygen demand in real time. Using machine learning and artificial intelligence, we aim to produce a tool capable of detecting pollution events from sensor data and evaluating subsequent impacts on oxygen demand and phytoplankton growth. Our ultimate aim is to deliver a novel validated multiparameter fluorescence-based sensor, integrated within a real-time monitoring network, alongside a tool for interpreting water quality data regarding river health and pollution pressures. We anticipate these outputs combined will enhance potential for early detection of pollution events, facilitate agile decision making and river management and enhance understanding of biogeochemical processing in rivers.