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Mapping per- and polyfluoroalkyl substances contamination in England's surface waterbodies: Urban water cycle pathways and governance challenges

Wed, 15 Apr 2026 00:00:00 GMT

Title: Mapping per- and polyfluoroalkyl substances contamination in England's surface waterbodies: Urban water cycle pathways and governance challenges Authors: García Herrera, A; Iacovidou, E; Giakoumis, T Abstract: Per- and polyfluoroalkyl substances (PFAS) contamination has emerged as a major international environmental and regulatory challenge, with PFAS increasingly detected across freshwater systems worldwide. However, in countries with limited PFAS manufacturing, such as England, it remains unclear whether surface waterbodies contamination reflects diffuse consumer-driven pollution, sectoral pressures, or dominant point-source pathways of PFAS pollution, such as Wastewater Treatment Works (WWTWs). In this study, we address this gap by providing the first surface-waterbody-level characterisation of PFAS contamination across England, drawing on the Environment Agency 's 2024 national dataset. Linking PFAS detections with sectoral pressure classifications, the study makes the following contributions: 1) quantifies the associations between individual compounds and human activities, 2) assesses WWTWs as pathways for PFAS release, and 3) maps detected PFAS to sector-specific product applications. Our analysis reveals that 92% of monitored waterbodies contain at least one of thirty-four detected PFAS, with multiple compounds co-occurring (mean ∼ 6.5) and PFOS frequently exceeding its Environmental Quality Standard. Water Industry/Domestic/General Public pressures showed strong positive associations with 11 PFAS compounds, with effect sizes of 2.9–9.9 (FDR < 0.05). After adjusting for overlapping sectoral influences, significant positive associations remained for PFHxS.L, PFBS, PFHpA, PFOS..B, PFOS..L and PFOS_combined, with odds ratios between 2.0 and 3.0 (FDR < 0.05). PFAS were also routinely present in WWTWs effluents, where removal efficiencies were often low or negative, indicating that WWTWs function as chronic point sources. Persistent PFOS detections in WWTWs effluents long after its restriction reflect that PFAS are now deeply embedded within the built environment, recirculating through the urban water cycle. These findings underscore the necessity for a comprehensive, system-level governance approach for PFAS that transcends single-compound restrictions and advocates for a fair allocation of mitigation responsibilities. Description: Highlights: • PFAS contamination is widespread, detected in 92% of England's monitored waterbodies. • Water Industry/Domestic/General Public key pathways of PFAS leaching to waterbodies. • PFOS routinely present in Wastewater Treatment Works effluents despite regulatory ban. • Wastewater Treatment Works showed low or negative removal efficiencies for many PFAS. • Stronger source controls and polluter-pays governance are essential for protection.; Data availability: Data will be made available on request.; Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0048969726004432?via%3Dihub#s0075 .

Multi-Scale Decoupling of Industrial Dynamics Via Trend-Fluctuation Interaction-Aware Transformer for Quality Prediction

Mon, 13 Apr 2026 00:00:00 GMT

Title: Multi-Scale Decoupling of Industrial Dynamics Via Trend-Fluctuation Interaction-Aware Transformer for Quality Prediction Authors: Xiao, L; Wang, P; Fang, Y; Wang, Z Abstract: Accurate prediction of key quality variables is crucial for monitoring and optimizing modern industrial processes. However, most existing methods remain constrained by single-scale modeling, making it difficult to capture long-term global trends and short-term local fluctuations simultaneously. In addition, the dynamic couplings between these multi-scale components are often overlooked, leading to insufficient feature extraction. To address these limitations, a multi-scale trend-fluctuation interaction-aware transformer (MTI-Former) is proposed in this paper. First, a decoupling layer based on discrete wavelet transform (DWT) is designed to decompose industrial data into low-frequency trend and high-frequency fluctuation signals. Then, an adaptive high-pass enhancement filter is introduced to amplify critical high-frequency details and improve the perception of local disturbances. Cross-scale coupling is modeled through a trend-fluctuation interaction-aware attention module, which captures dynamic interactions between trends and fluctuations. Subsequently, a trend-fluctuation decoupling attention module applies a dual-path cross-attention mechanism to separately extract global dependencies and local variations. Finally, a gating mechanism fuses these representations to generate comprehensive multi-scale temporal predictions. The effectiveness of MTI-Former is verified on two real industrial datasets, and extensive results show that it outperforms several state-of-the-art methods in industrial quality prediction.

Tldiag: a federated learning-based DB-mixer for privacy-preserving and fault diagnosis in transmission lines

Sat, 21 Mar 2026 00:00:00 GMT

Title: Tldiag: a federated learning-based DB-mixer for privacy-preserving and fault diagnosis in transmission lines Authors: Mao, C; Wen, C; Wang, Z; Liu, W; Yang, J Abstract: As a critical component of smart grids where faults occur frequently, the rapid and accurate diagnosis of transmission line faults is essential for enhancing system reliability and response efficiency. However, existing centralized diagnostic methods face significant challenges, including data privacy concerns, limited feature representation capabilities, and class imbalance issues in real-world power grids. To address these issues, this paper proposes an adaptive diagnostic framework based on federated learning and a dual-branch mixer (TLDiag) to address the privacy-sensitive cross-domain fault diagnosis problem in dynamic grid environments. Specifically, a distributed federated learning architecture is designed to ensure data privacy by enabling localized model training through collaborative client–server interactions. Furthermore, a dual-branch mixer, i.e., DB-Mixer, guided by a dual-branch attention mechanism (DBAM), is developed to enhance feature representation by jointly modeling spatial and channel-wise information. Additionally, an adaptive dual-field loss (ADF loss) is introduced, incorporating dynamic task weighting and physical constraints to effectively mitigate class imbalance and improve diagnostic robustness. Extensive experiments conducted on the IEEE 5-bus system demonstrate that TLDiag achieves superior performance in both fault type classification (97.53%) and fault location identification (98.34%), while exhibiting stable convergence across varying client scales. Compared to baseline methods such as MDCNN and CNN-LSTM, TLDiag significantly outperforms in accuracy and robustness. By deeply integrating federated learning with the physical characteristics of power systems, this approach offers a high-accuracy, privacy-preserving solution for real-time fault diagnosis in smart grid scenarios. Description: Data availability: The data supporting the findings of this study are publicly available from the IEEE Data Port (https://ieee-dataport.org/documents/transmission-line-fault-using-line-voltages-and-currents-features).

Extracting Meaningful Insights from User Research Videos

Fri, 06 Feb 2026 00:00:00 GMT

Title: Extracting Meaningful Insights from User Research Videos Authors: Ghatoray, SK; Li, Y Abstract: Recognising and tracking user emotions in research videos is vital to understanding user needs and expectations. Limited research exists on automating emotion extraction from multimodal videos in user experience (UX). This study proposes a conceptual framework for automated extraction of actionable insights using facial, speech-to-text, and text-based emotion recognition to capture nuanced emotional data. The multimodal approach integrates visible and spoken cues through temporal alignment and fusion techniques, enabling robust behavioural pattern detection. An interactive AI analyst tool is used to query the integrated data in natural language, reduce manual workload, and improve the efficiency and scalability of UX evaluation. A case study of the implementation of the proposed framework is also provided with details of individual components, such as facial emotion recognition, speech-to-text, text-based emotion recognition, temporal alignment and fusion, and insight extraction via interactive AI.