Scottish Water is a statutory corporation that provides water and sewerage services across Scotland. It is accountable to the public through the Scottish Government.
The water industry faces increasing challenges in ensuring efficient and sustainable delivery of safe drinking water through ageing infrastructure while meeting operational, regulatory, and customer demands. Large volumes of data are generated but are stored and utilised in disconnected silos. Such fragmentation limits the potential for comprehensive analysis, informed decision-making, and operational optimisation. To better understand the impact of this fragmentation, we developed a three-layer data and process connectivity framework, a novel graph-database-driven approach designed to identify, analyse, and address gaps in data utilisation and data process efficiency. The framework systematically maps critical data flows and reveals interdependencies between data, intermediary processes, and business applications. We validated the framework through a case study with Scottish Water, constructing a graph comprising 127 nodes and 911 relationships across water distribution data and processes. Using node centrality measures, relationship-based graph querying, and impact simulations, we demonstrate how this approach exposes inefficiencies, uncovers overlooked data relationships, and generates actionable insights for process integration. The findings highlight the potential of graph-database-driven approaches to overcome data fragmentation, bridge silos, and enable more data-driven and strategically optimised operations, with applicability across the wider water sector and other industries.
UV-C photodetectors are poised to play an increasingly important role in future photonic technologies, driven by the rapid emergence of UV-C light sources and new wide bandgap semiconductors. These advances are enabling new levels of spectral selectivity, radiation hardness, sensitivity, and device integration, while opening opportunities across a broad range of applications. This roadmap provides a comprehensive overview of the current landscape of UV-C photodetection, spanning established and emerging material platforms (Ga2O3, AlGaN, BN, diamond, MgZnO, 2-dimensional materials, metal halide perovskites, micro-electromechanical systems), and their applications in metrology, astronomy, communications, environmental monitoring, fire detection, missile warning, gas sensing, and medical diagnostics. By identifying opportunities, bottlenecks, and future directions, this roadmap aims to support both newcomers and established researchers, with the aim of accelerating the translation of UV-C photodetectors into impactful technologies.
Pharmaceuticals and metabolites excreted by humans are a threat to aquatic ecosystems globally. Gaps exist in environmental data sets which can be filled by predicting concentrations using prescribing data available at suitable spatial (e.g., wastewater treatment plant [WWTP] catchment area) and temporal scale (e.g., monthly). The aim was to improve the accuracy of predicted pharmaceutical concentrations in untreated influent wastewater. A conventional prediction approach of applying human excretion information to prescribing data found three of 12 analytes (metformin, desmethylvenlafaxine and clarithromycin) had acceptable predictions (within +/- 50% of their measured concentration) at three WWTPs of varying size. Several analytes had systematic underestimated predictions across WWTPs related to low analyte excretion rates. Laboratory-scale gravity sewer experiments revealed an important contributor was glucuronide metabolite deconjugation back to their parent pharmaceutical which could not be accounted for using excretion information. Therefore, numerical correction factors were derived (0.027-0.86) from prescribing and wastewater data to establish the fraction of prescribed pharmaceuticals found in wastewater. These account for changes between prescription of a pharmaceutical and its presence in wastewater (e.g., human metabolism and in-sewer transformation) without the need to quantify and correct for them individually. This enabled acceptable predictions for another six analytes (carbamazepine, propranolol, venlafaxine, fluoxetine, norfluoxetine and desmethylclarithromycin). Therefore, incorporating this approach in prediction models for treated effluents and river water can improve their accuracy for improved risk assessment. This is key to identify where subsequent technological or 'upstream' intervention is needed to target pharmaceutical environmental impacts.
Active pharmaceutical ingredients (APIs) are present in aquatic environments at concentrations that can impact ecosystems. Their major pathway to the environment is in the discharge of wastewater effluents from centralised wastewater treatment works (WWTWs). However, in most rural areas where houses are not connected to a public sewage network, single households or groups of houses rely on onsite wastewater treatment works (OWTWs), mainly septic tanks (STs). Therefore, STs are reviewed as a pathway for APIs to the aquatic environment. Despite STs being extensively used globally, there is a geographical bias in available data with most studies conducted in the USA. Furthermore, studies focus on a few APIs (e.g., carbamazepine, sulfamethoxazole and paracetamol) and the impact of STs to groundwater. Previous reliance on grab sampling and the heterogenous composition of influent wastewater characterised by the small contributing populations makes assessing ST removal efficiency challenging. Available data suggests little or no removal of APIs in the anaerobic environment of STs. Conducting an intensive monitoring approach (e.g., continuous 24 h composite sampling) for durations several times the ST hydraulic retention time will help benchmark their performance for API removal against other processes. Recent studies show surface waters receiving ST discharges have API concentrations exceeding their predicted no effect concentration (PNEC, the concentration below which no adverse effect is expected). Mean concentrations of 17β-estradiol, ampicillin, ibuprofen, memantine, palmitamid, paracetamol and trihexyphenidyl all exceeded their PNEC by up to 50 times. However, there is a lack of data for several APIs identified to be of possible environmental concern in prioritisation watch lists such as those outlined by the EU (e.g., amoxicillin, clarithromycin, desmethylvenlafaxine and clindamycin). Receiving surface waters can be small ecologically important streams, demanding the need for further monitoring and intervention. Other than adopting secondary treatment (e.g., constructed wetlands where possible) or alternative OWTWs which achieve greater API removal, sustainable medicine use is proposed as a viable means of reducing the environmental impact of ST discharges where risks are identified. Utilising environmentally informed prescribing and other 'upstream' interventions such as return schemes for unused pharmaceuticals, have great potential for success where small populations of people can be specifically targeted.
Biofiltration offers a sustainable, low-energy solution for drinking water treatment but suffers from inconsistent performance due to complex microbial dynamics. Current studies lack insight into early biofilter microbial community assembly. Here we perform a high-resolution spatial and temporal investigation of biomass accumulation and community development within biological activated carbon (BAC) filters over the first 6 months of operation. We found that initial biomass accumulation is not linear, instead characterised by periods of growth and decay. Mass balance identified an estimated + 6.54 × 108 new cells daily during the growth phase (days 34–62), falling to a loss of 1.69 × 109 by the decay phase (days 83–162). There was no significant increase in richness until the decay phase (ANOVA p values > 0.05 between days 34, 62 and 83). Significant stratification (ANOVA p values < 0.05) was observed with bed depth with 79
