Staff publications (ES)

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Open Access
Municipal wastewater treatment with anaerobic membrane Bioreactors for non-potable reuse: a review
(Taylor & Francis, 2024-05-18) Huang, Yu; Jeffrey, Paul; Pidou, Marc
Anaerobic membrane bioreactors (AnMBRs) are seen as a promising technology for application in water reuse schemes. However, the evidence base for their potential and efficacy in this regard is fragmented. We draw together this disparate knowledge base to offer a state of the art review of municipal wastewater treatment with AnMBRs and evaluate the technology’s potential application for water reuse. Water quality regulations and standards from different regions of the world are used as performance metrics to compare and contrast the treatment performance of pilot and laboratory scale AnMBR systems reported in the literature (n = 50). Findings indicate that under stable operation, AnMBRs have the potential to produce water for agricultural reuse. However, without post-treatment, AnMBRs are incapable of delivering water that meets other non-potable reuse standards across a range of important parameters such as COD, BOD5, NH3-N and TP. Analysis of key operational parameters determine the operation of AnMBR for non-potable reuse purpose cover influent water matrix, pH, temperature, hydraulic retention time, system and membrane configuration. An assessment of candidate post-treatment technologies suggests a tradeoff between the cost and effluent quality based on the reuse application requirement. We conclude by discussing a number of challenges and limitations to the use of AnMBRs for reuse applications in order to outline a pathway to maturity for effective treatment trains.
Open Access
Artificial intelligence-driven innovation in Ganoderma spp.: potentialities of their bioactive compounds as functional foods
(Royal Society of Chemistry (RSC), 2025) Khanal, Sonali; Sharma, Aman; Pillai, Manjusha; Thakur, Pratibha; Tapwal, Ashwani; Kumar, Vinod; Verma, Rachna; Kumar, Dinesh
Ganoderma spp., which are essential decomposers of lignified plant materials, can affect trees in both wild and cultivated settings. These fungi have garnered significant global interest owing to their potential to combat several chronic, complicated, and infectious diseases. As technology progresses, researchers are progressively employing artificial intelligence (AI) for studying various fungal strains. This novel approach has the potential to accelerate the knowledge and application of Ganoderma spp. in the food industry. The development of extensive Ganoderma databases has markedly expedited research on them by enhancing access to information on bioactive components of Ganoderma and promoting collaboration with the food sector. Progress in AI techniques and enhanced database quality have further advanced AI applications in Ganoderma research. Techniques such as machine learning (ML) and deep learning employing various methods, including support vector machines (SVMs), Bayesian networks, artificial neural networks (ANNs), random forests (RFs), and convolutional neural networks (CNNs), are propelling these advancements. Although AI possesses the capacity to transform Ganoderma research by tackling significant difficulties, continuous investment in research, data dissemination, and interdisciplinary collaboration are necessary. AI could facilitate the development of customized functional food products by discerning patterns and correlations in customer data, resulting in more specific and accurate solutions. Thus, the future of AI in Ganoderma research looks auspicious, presenting prospects for ongoing advancement and innovation in this domain.
Open Access
Rivers as natural capital assets: a quick scoping review to assess the evidence linking river asset condition to changes in the flow of ecosystem services
(Wiley, 2025) Zini, Valentina; Johnson, Natalie; Crouch, Alice; Lenagan, Gerard; Cooper, Chris; Naura, Marc; Speck, Imogen; Rouquette, Jim
River managers are beginning to adopt natural capital approaches in practice. However, while it is crucial for river management, the link between river asset condition and the flow of ecosystem services is poorly understood. In this study, we conducted a Quick Scoping Review (QSR) of the research into river asset condition and ecosystem service delivery to explore the current state of knowledge. The review team developed a PICO (Population, Intervention, Control, Outcome) model to transpose the concepts of the research enquiry into a search strategy for the evidence base and used a Delphi screening exercise to prioritise a subset of literature for the narrative findings. VOSviewer was used to analyse the high‐level linguistic themes from the full list of references. This co‐designed, collaborative and objective QSR approach allowed us to examine a large body of literature in a reproducible manner while minimising bias, demonstrating best practice for evidence review that should be continuously updated, generating a ‘living evidence’ knowledge asset. The results of the review demonstrate there is some knowledge of the mechanisms linking the condition of river assets to the delivery of ecosystem services for the majority of the broad range of ecosystem services analysed, with the exception of some of the cultural services, where comparatively fewer studies explore this link. However, a clear understanding of the quantitative evidence of the relationships between condition and ecosystem service delivery is missing for all of the ecosystem services. This gap stems from a lack of standardised methodologies used across the studies and a focus on a narrow range of definitions of condition. The gap needs to be addressed in future research on the topic, and a first step is to adopt more standardised indicators of river asset condition.
Open Access
An overview of non-destructive technologies for postharvest quality assessment in horticultural crops
(Taylor & Francis, 2025) O’Brien, Ciara; Alamar, M. Carmen
Artificial intelligence and machine vision are increasingly popular within food supply chains for automated decision making in quality grading and disease identification. There are many types of data that these models can be trained on, and choosing which information is needed is a critical factor in minimising both food loss and cost, while maximising the impact on food quality. Non-destructive technologies give information about crop phenotypes (e.g. external colour, oil content, sweetness) without damaging the crop, allowing a greater and more representative proportion the stored food to be analysed. These non-destructive technologies use different methods to analyse the product, each with different intrinsic capabilities and limitations. Therefore, choosing which technology is most appropriate for each application is a complex and costly decision. This mini-review summarises the physical and chemical basis of how some popular non-destructive technologies function, and how these different methods give unique advantages and limitations. The most popular technologies summarised include Red-Green-Blue (RGB) imaging, visible and near-infrared spectroscopy, and vibrometry. We also review technologies that are growing in popularity, including X-ray imaging, ultraviolet spectroscopy, and magnetic resonance imaging.
Open Access
Perennial flower strips can be a cost‐effective tool for pest suppression in orchards
(Wiley, 2025) Howard, Charlotte; Burgess, Paul J.; Fountain, Michelle T.; Brittain, Claire; Garratt, Michael P. D.
Flower strips can provide many economic benefits in commercial orchards, including reducing crop damage by a problematic pest, rosy apple aphid (Dysaphis plantaginea [Passerini]). To explore the financial costs and benefits of this effect, we developed a bio‐economic model to compare the establishment and opportunity costs of perennial wildflower strips with benefits derived from increased yields due to reduced D. plantaginea fruit damage under high and low pest pressure. This was calculated across three scenarios: (1) a flower strip on land that would otherwise be an extension of the standard grass headland, (2) a flower strip on land that could otherwise be used to produce apples and (3) a flower strip in the centre of an orchard. Through reduction of D. plantaginea fruit damage alone, our study shows that flower strips on the headland can be a positive financial investment. If non‐crop land was not available, establishment of a flower strip in the centre of an orchard, instead of the edge, could recoup opportunity costs by providing benefits to crops on both sides of the flower strip. Our study can help guide the optimal placement of flower strips and inform subsidy value for these schemes.
Open Access
Investigation on the protection ability of two commonly packaging methods to apples during express transportation
(Elsevier, 2025-03-01) Yu, Jincheng; Qiang, Hongli; Shi, Mingwei; Li, Zhiguo; Fadiji, Tobi; Wani, Ali Abas; Burgeon, Clément
Packaging plays a vital role in the post-harvest sales process of apples. This study conducted express transportation tests to evaluate the protective effectiveness of two commonly used packaging methods for apples. Key parameters assessed included real-time changes in temperature, humidity, vibration load, and CO₂ levels inside the packaging boxes during transit, as well as the storage quality of apples after transportation. Results showed significant variations in load distribution within corrugated partition-based cardboard boxes (CP combination packaging). Conversely, foam holder-based cardboard boxes (FP combination packaging) exhibited CO₂ accumulation. Furthermore, mechanical damage was predominantly localized to the fruit belly. Compared to CP combination packaging box, FP combination packaging box provided more stable shock resistance at lower vibration forces (< 10 N) across transit routes, likely due to its EPS foam design, which restricted fruit movement and absorbed external vibrations. Post-storage analysis showed that damaged apples experienced a 0.16 % increase in mass loss, a 0.83 % rise in soluble solids content (SSC), and a 0.19 MPa reduction in firmness compared to undamaged controls. These findings provide valuable insights into optimizing packaging design to minimize transport-induced damage and enhance apple preservation.
Open Access
Peanut value chain development: the case of Lower Lake Victoria Basin of Kenya
(MDPI, 2025-03-25) Odunga, George Okoth; Bidzakin, John K.; Okaka, Philip; Okoth, Sheila; Mutsotso, Beneah; Graves, Anil R.
Peanut is Kenya’s second most important legume after beans, primarily grown in the Nyanza and Western regions. This study maps the peanut value chain in Kenya, aiming to identify key actors, quantify costs and value addition, and outline constraints and opportunities, with a view to upgrading the chain. A cross-sectional survey was conducted among value chain actors in Karachuonyo and Nyakach sub-counties, complemented by secondary data sources. Descriptive statistics were used to analyze socio-economic characteristics, production volumes, pricing, demand trends, and policy-related factors. The findings indicate a predominance of female farmers (68%) in peanut production, though few use improved technologies; only 26% were aware of improved seed varieties, and just 1.5% reported using them. Fertilizer usage was absent, attributed to high costs, soil conditions, and limited knowledge. The wholesale and processing segments are male-dominated, largely due to capital intensity and travel requirements, while female traders dominate the retail sector. Strengths Weaknesses Opportunity and Threats (SWOT) analysis highlighted the significant potential of favorable production ecologies, processing options, and robust demand in local and international markets. Key constraints identified include limited seed availability, high fertilizer costs, pest issues, and declining soil fertility. Policy implications include increasing access to affordable inputs, promoting gender-inclusive programs, investing in agricultural research and infrastructure, supporting sustainable farming practices, and fostering public-private partnerships to expand processing and market access.
Open Access
Engineering biology applications for environmental solutions: potential and challenges
(Springer Nature, 2025-04) Lea-Smith, David J.; Hassard, Francis; Coulon, Frederic; Partridge, Natalie; Horsfall, Louise; Parker, Kyle D. J.; Smith, Robert D. J.; McCarthy, Ronan R.; McKew, Boyd A.; Gutierrez, Tony; Kumar, Vinod; Dotro, Gabriella; Yang, Zhugen; Curtis, Thomas P.; Golyshin, Peter; Heaven, Sonia; Jefferson, Bruce; Jeffrey, Paul; Jones, Davey L.; Le Corre Pidou, Kristell; Liu, Yongqiang; Lyu, Tao; Smith, Cindy; Yakunin, Alexander; Zhang, Yue; Krasnogor, Natalio
Engineering biology applies synthetic biology to address global environmental challenges like bioremediation, biosequestration, pollutant monitoring, and resource recovery. This perspective outlines innovations in engineering biology, its integration with other technologies (e.g., nanotechnology, IoT, AI), and commercial ventures leveraging these advancements. We also discuss commercialisation and scaling challenges, biosafety and biosecurity considerations including biocontainment strategies, social and political dimensions, and governance issues that must be addressed for successful real-world implementation. Finally, we highlight future perspectives and propose strategies to overcome existing hurdles, aiming to accelerate the adoption of engineering biology for environmental solutions.
Open Access
Precision laser manufacturing and metrology of nature-inspired bioactive surfaces for antibacterial medical implants
(Elsevier, 2025-04-01) Hawi, Sara; Goel, Saurav; Kumar, Vinod; Giusca, Claudiu; Pearce, Oliver; Ayre, Wayne Nishio
Femtosecond laser ablation presents a highly promising method to create bioactive nano/micro-structured metallic surfaces, offering numerous avenues for fabricating diverse types of surface structures. However, the relationship between surface properties and biological functionality, leading to the observed bioactivity remains unclear. This study aimed to investigate the relationship between structured/patterned steel surfaces and bioactivity, identifying key factors that enhance their performance. As opposed to the commonly used controversial parameter, arithmetic surface roughness (Ra), fractal dimension analysis was discovered to be strongly representative in quantifiably evaluating the adhesion of Staphylococcus aureus NCTC 7791 and MG-63 osteoblast-like cells. Surface chemistry and surface energy of structured surfaces showed no significant influence on bacterial adhesion. A specific type of laser-induced periodic structured surfaces with sub-micron wavelengths, high fractal dimension, and high texture aspect ratio demonstrated a 63 % reduction in bacterial adhesion compared to flat surfaces while avoiding cytotoxicity to MG-63 cells. Our findings underline the importance of scale-dependent analysis and the use of fractal analysis in evaluating the effectiveness of laser-structured surfaces for orthopaedic implant applications.
Open Access
Usability of agricultural drought vulnerability and resilience indicators in planning strategies for small farms: a principal component approach
(Elsevier, 2025-04-01) Sarmah, Tanaya; Balta-Ozkan, Nazmiye; Konak, Abdullah; Shrimpton, Elisabeth; Sass, Karina Simone; De Macedo, Marina Batalini; Mendiondo, Eduardo Mario; Nardocci, Adelaide Cassia; Huo, Da; Jacobson, Michael Gregory
Water-related stresses and risks of droughts, exacerbated by climate change, have been extensively documented. These studies often rely on various indicators to monitor and forecast the impacts of droughts. However, current literature on the usability of these indicators for modelling drought risk and in decision-making processes is fragmented and lacks a clear, systematic, and methodological approach. Usability, in this context, refers to the relevance, accessibility, clarity, and practicality of indicators for guiding planning strategies. To address this knowledge gap, the Management of Disaster Risk and Societal Resilience (MADIS)1 project aims to collate and assess drought vulnerability and resilience indicators from existing literature to support decision-makers in improving policies related to agricultural droughts on small farms. The MADIS project identified over 100 indicators, from which 36 were selected for further analysis. A global online survey using the Delphi technique was conducted, and the resulting data was used to perform a Principal Component Analysis (PCA). Findings revealed that these 36 indicators could be reduced and grouped up to ten principal components, each corresponding to a theme across five categories: relevancy, understanding, accessibility, objectivity, and temporal. This study, therefore, highlights the practical usability of these indicators for developing context-specific and efficient resilience strategies. Indicators related to water management were found to be crucial and applicable across all five categories, as the availability, quality, and source of water are essential for monitoring and mitigating drought hazards. Conversely, indicators related to rural development and demographics, while quantifiable and collected at different temporal scales, were deemed less understandable and accessible by experts. Grouping indicators under common themes reduces the complexity of evaluating similar indicators and aids in selecting the most relevant ones for different contexts. This approach simplifies indicator selection and enables decision-makers to formulate resilience policies more efficiently and comprehensively.
Open Access
Robustness and resilience of different solid-liquid separation technologies for tertiary phosphorus removal to low levels by coagulation
(Elsevier, 2025-04-25) Murujew, Olga; Wilson, Andrea; Vale, Peter C. J.; Bajón Fernández, Yadira; Jefferson, Bruce; Pidou, Marc
In this study, three tertiary solid separation technologies were assessed on their robustness and resilience against an effluent phosphorus target of <0.3 mg P/L at steady state and dynamic conditions. The ballasted flocculation system was found to be very robust at delivering the low P target. Alternatively, cloth filtration provided a more sustainable option for less strict consents of sub 0.5 mg P/L. The effluent from the membrane system was more variable but it was shown to meet the low consents even with increased phosphorus and solids content in the feed. A molar ratio of 1.37 Fe: P was shown to be sufficient to meet the P target at short contact times as with the ballasted flocculation process. It was highlighted that optimisation of up-stream flocculation can be a considerable factor for consistent performance. Overall, the study determined key attributes of the different technologies tested providing valuable insights for technology selection at full scale.
Open Access
Enhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: an advanced approach toward sustainable and carbon-neutral wastewater treatment
(Elsevier, 2025-05) Cairone, Stefano; Oliva, Giuseppina; Romano, Fabiana; Pasquarelli, Federica; Mariniello, Aniello; Zorpas, Antonis A.; Pollard, Simon J. T.; Choo, Kwang-Ho; Belgiorno, Vincenzo; Zarra, Tiziano; Naddeo, Vincenzo
Integrating carbon capture and utilization (CCU) technologies into wastewater treatment plants (WWTPs) is essential for mitigating greenhouse gas (GHG) emissions and enhancing environmental sustainability, but further advancements in process monitoring and control are critical to optimizing treatment performance. This study investigates the application of artificial intelligence (AI) modeling to enhance process monitoring and control in a novel integrated CCU biotechnology with a moving bed biofilm reactor (MBBR) sequenced with an algal photobioreactor (aPBR). This system reduces GHG and odour emissions simultaneously. Several machine learning (ML) models, including artificial neural networks (ANNs), support vector machines (SVM), random forest (RF), and least-squares boosting (LSBoost), were tested. The LSBoost was the most suitable for modeling the MBBR + aPBR system, exhibiting the highest accuracy in predicting CO2 (R2 = 0.97) and H2S (R2 = 0.95) emissions from the MBBR. LSBoost also achieved the highest accuracy for predicting CO2 (R2 = 0.85) and H2S (R2 = 0.97) outlet concentrations from the aPBR. These findings underscore the importance of aligning AI algorithms to the characteristics of the treatment technology. The proposed AI models outperformed conventional statistical methods, demonstrating their ability to capture the complex, nonlinear dynamics typical of processes in environmental technologies. This study highlights the potential of AI-driven monitoring and control systems to significantly improve the efficiency of CCU biotechnologies in WWTPs for climate change mitigation and sustainable wastewater management.
Open Access
Artificial intelligence for prediction of shelf-life of various food products: recent advances and ongoing challenges
(Elsevier, 2025-05-01) Rashvand, Mahdi; Ren, Yuqiao; Sun, Da-Wen; Senge, Julia; Krupitzer, Christian; Fadiji, Tobi; Miró, Marta Sanzo; Shenfield, Alex; Watson, Nicholas J.; Zhang, Hongwei
Background: Accurate estimation of shelf-life is essential to maintain food safety, reduce wastage, and improve supply chain efficiency. Traditional methods such as microbial and chemical analysis, and sensory evaluation provide reproducible results but require time and labor and may not be suitable for real-time or high-throughput applications. The integration of artificial intelligence (AI) with advanced analysis techniques offers a suitable alternative for rapid, data-driven estimation of shelf-life in dynamic storage environments. Approach and scope: The current review assesses the application of AI-based techniques such as machine learning (ML), deep learning (DL), and hybrid approaches in food product shelf life prediction. This study highlights how AI can be utilized to examine data from non-destructive testing methods like hyperspectral imaging, spectroscopy, machine vision, and electronic sensors to enhance predictive performance. The review also describes how AI-based techniques contribute to managing food quality, reduce economic losses, and enhance sustainability by ensuring optimized food distribution and reducing waste. Key findings and conclusions: AI techniques overcome conventional techniques by considering intricate, multi-sourced information capturing microbiological, biochemical, and environmental factors influencing food spoilage. Meat, dairy, fruits and vegetables, and beverage case studies illustrate AI techniques' superiority in real-time monitoring and quality assessment. It also identifies limitations such as data availability, model generalizability, and computational cost, constraining extensive applications. Cloud and Internet of Things (IoT) platform integration into future applications has to be considered to enable real-time decision-making and adaptive modeling. AI can be a paradigm-changing tool in food industries with intelligent, scalable, and low-cost interventions in food safety, waste reduction, and sustainability.
Open Access
Unifying nucleation and crystal growth mechanisms in membrane crystallisation
(Elsevier, 2025-05-01) Mapetere, A.; Di Profio, Gianluca; Curcio, Efrem; Campo Moreno, Pablo; McAdam, Ewan J.
While several mechanisms have been proposed to describe crystallisation processes in membrane distillation, it has not been possible to provide a definitive description since the nucleation kinetics are difficult to measure. This study therefore introduced non-invasive techniques to measure induction time within two discrete domains (the membrane surface and bulk solution) and was complemented by the introduction of a modified power law relation between supersaturation and induction time, that enables mass and heat transfer processes in the boundary layer to be directly related to classical nucleation theory (CNT). Temperature (T, 45–60 °C) and temperature difference (ΔT, 15–30 °C) were used to adjust boundary layer properties, which established a log-linear relation between the nucleation rate and the supersaturation level in the boundary layer at induction, which is characteristic of CNT. Crystal size distribution analysis demonstrated how nucleation rate and crystal growth rate could be adjusted using ΔT and T respectively. Consequently, ΔT and T can be used collectively to fix the supersaturation set point within the boundary layer to achieve the preferred crystal morphology. However, at higher supersaturation levels, scaling was observed. Discrimination of the primary nucleation mechanisms, using measured induction times, revealed scaling to be formed homogeneously, which indicates exposure of the pores to extremely high supersaturation levels. Morphological analysis of scaling indicated growth to be dominated by secondary nucleation mechanisms, that resulted in a habit that is distinctive from the crystal phase formed in the bulk solution. From this analysis, a critical supersaturation threshold was identified, below which kinetically controlled scaling can be ‘switched-off’, leaving crystals to form solely in the bulk solution comprising the preferred cubic morphology. This study serves to unify understanding on nucleation and growth mechanisms to enhance control over crystallisation in membrane systems.
Open Access
On the role of crystal-liquid interfacial energy in determining scaling, nucleation and crystal growth in membrane distillation crystallisation
(Elsevier, 2025-05-01) Vasilakos, Konstantinos; Thomas, Navya; Hermassi, Mehrez; Campo Moreno, Pablo; McAdam, Ewan J.
While the interfacial energy (σ) of a solute contributes toward the excess surface free energy requirement for nucleation, its role in determining scaling, nucleation and crystal growth processes within membrane distillation has yet to be described. Highly soluble salts (low σ) are generally understood to possess a low nucleation energy, where the limited relative supersaturation (Δc/c∗) can favour a heterogeneous primary nucleation mechanism. This was indicated by scaling, which is generally presumed to occur in response to the membrane substrate lowering the critical Gibbs free energy requirement for nucleation (ΔG∗). For less soluble salts (high σ), primary nucleation was not observed until Δc/c∗ exceeded a threshold of 1. It was postulated that the excess chemical potential available was sufficient to favour homogeneous primary nucleation in the bulk solution, which mitigates scale formation on the membrane. In-situ characterisation methods also established how nucleation rate and crystal size could be directly attributed to the σ, which is compatible with the crystallisation literature on aqueous salts within a comparable range of solubilities. While crystallisation tends to be controlled by a combination of thermodynamic and kinetic processes, this study illustrates how interfacial energy (a thermodynamic quantity) can be used to anticipate nucleation and crystal growth mechanisms in membrane crystallisation.
Open Access
Safe faecal sludge emptying and transport: compliance challenges and models for a public good
(IWA Publishing, 2025-05-01) Grisaffi, Claire; Leinster, Paul; Mugo, Kariuki; Drabble, Sam; Parker, Alison
In the 81 countries where most urban dwellers rely on faecal sludge (FS) emptying and transport, services are frequently provided by a heterogeneous private sector. Considering the responses of service providers is essential to ensuring that the regulatory frameworks put into place achieve their intended outcomes and safeguard public and environmental health. Combining a literature review and expert practitioner input, we identify priority challenges for scaling safe FS emptying and transport (E&T) services and use these to adapt a holistic model of business compliance. We confirm well-documented challenges such as cost structures for compliance with regulation, the perception of services as low status, and an inadequate enabling environment. We identify the importance of trust in building voluntary compliance as a novel issue for sanitation but widely discussed in the regulation literature. We also identify a distinct role for the regulator as a catalyst for change. The role of disgust as a policy barrier and the application of behavioural theory to building compliance are areas warranting further research. This is the first paper to explicitly consider the regulation of FS E&T through a compliance lens, linking established areas of the regulation literature to new findings in urban sanitation.
Open Access
Machine learning-driven sensor array based on luminescent metal–organic frameworks for simultaneous discrimination of multiple anions
(Elsevier, 2025-05-15) Wei, Dali; Xu, Cheng; Wang, Ying; Feng, Weiwei; Deng, Chunmeng; Wu, Xiangyang; Deng, Yibin; Yang, Zhugen; Zhang, Zhen
Due to the high correlation of anions in waters to environmental quality and human health, thus there is urgent need for developing simple and effective sensors to discriminate multiple anions. Herein, a machine learning-assisted fluorescent sensor array based on two luminescent metal–organic frameworks (LMOFs, UiO-66-NH2 and UiO-66-OH) was developed for simultaneous discrimination of five anions (F−, PO43−, ClO44−, NO3−, and SO42−). Wherein, UiO-66-NH2 and UiO-66-OH were designed by anchoring 2,5-diaminoterephthalic acid and 2,5-dihydroxyterephthalic acid on UiO-66, respectively, which exhibited blue and green fluorescence emission, possessing good fluorescence property. Interestingly, the anions could effectively enhance the fluorescence intensity of UiO-66-NH2 and UiO-66-OH to generate diverse fluorescence responses and unique fingerprints, which could be utilized to develop a fluorescence sensor array for the rapid identification of five anions. Under the optimized conditions, the proposed sensor array showed good performance for identifying multiple anions and their mixtures with satisfactory sensitivity. More importantly, the integration of machine learning algorithm and sensor array has successfully achieved accurate identification and prediction of five anions in real water samples, affirming its practicability in actual samples. Our findings provided a promising tool for detecting multiple anions, and inspired potentials of the combination of sensor arrays and machine learning algorithm for pollution control in real waters.
Open Access
An analysis of factors that influence the spatial pattern of faecal matter flow in unsewered cities
(Elsevier, 2025-05-25) Sultana, M. Sufia; Waine, Toby W.; Bari, Niamul; Tyrrel, Sean
The management of sanitation systems in unsewered cities in low and middle income countries is a critical issue, yet it is unclear where the risk hotspots are and where interventions should be focused. This study utilised a prototype model, developed by the authors, to map the spatial pattern of faecal flow in Rajshahi city, a secondary city in northwest Bangladesh with a population around a million. This city serves as a representative example of 60 such secondary cities in Bangladesh and hundreds more in the economically developing region in Asia, Africa and Latin America. The model relies on assumptions that carry significant uncertainties; hence, the study employed a sensitivity analysis with multiple plausible scenarios to characterise these uncertainties, aiming to identify ways to improve the model further. Five major influencing factors on the spatial pattern of faecal flow were identified: the emptying of septic tanks, the use of soak pits, and sludge removal from drains, variations in faecal matter production by building types, and the presence or absence of toilets. These factors were shown to collectively have a significant impact (almost 50 % changed) on the model outcome, depending upon the assumptions made. The study offers insights that will guide future data collection efforts by emphasising the need to understand these specific influencing factors and their spatial pattern. Consequently, this research has broader implications for urban sanitation management as well as associated public health research like wastewater surveillance, risk assessment, and disease dynamics in similar urban settings, offering insights into areas of uncertainty that need to be addressed in future modelling efforts.
Open Access
Developing a multifunctional indicator framework for soil health
(Elsevier, 2025-06) Hannam, Jacqueline A.; Harris, Maddie; Deeks, Lynda K.; Hoskins, Hannah; Hutchison, James; Withers, Amy J; Harris, Jim A.; Way, Lawrence; Rickson, R. Jane
We developed a proof-of-concept indicator framework to monitor soil health based on the delivery of ecosystem services. Instead of distilling soil health to one metric, the framework enables simultaneous comparison of the delivery and trade-offs between different ecosystem services that are delivered by soils, accounting for inherent capability determined by soil type and land use. The framework has potential to explore a whole systems approach, ascertaining soil system response in real time that can detect emergent properties of the system. Initial development of the framework ranked salient soil properties known to be linked and pertinent to the delivery of ecosystem services. These key soil properties, together with other environmental variables were used to create simple conceptual models representing a causal network for soils’ contributions to the ecosystem services of climate regulation, food production, water regulation and below-ground biodiversity. The conceptual models were developed into Bayesian Belief Networks populated with relevant national data and expert judgement. The resulting outputs gave an indication of how well (i.e. healthy) a soil can deliver each ecosystem service at a land parcel scale presented in a dashboard app. The output at a specific location can be contextualised or benchmarked against to the range of values for areas with similar soil and land use types. The idea was to build the model with readily available data and knowledge but with flexibility for iterative development to refine the framework and models and improve outputs over time. This enables indicator updates using inputs of local knowledge of land management, or when additional soil data becomes available, or when soil policy drivers change, or our understanding of the conceptual and statistical models are improved. The indicator framework can be applied and adapted for use in multiple contexts from reporting national policy targets on soil health to determining soil health for a farmer at the field level.
Open Access
Development and application of DNA hydrogels in biosensing: current status and future implications
(Elsevier, 2025-06-01) Chen, Zhuo; Mao, Kang; Xue, Jiaqi; Feng, Rida; Zhang, Kuankuan; Su, Junxia; Du, Wei; Ran, Jiabing; Yang, Changying; Yang, Zhugen; Zhang, Hua
As emerging biopolymer materials, DNA hydrogels quickly respond to external stimuli to specifically recognize DNA through base pairing and have become widely used in the field of biosensors. Unlike traditional biosensing strategies, biosensors based on DNA hydrogels are highly specific, programmable and degradable. In this work, based on the advantages and wide application of DNA hydrogels in the field of biosensors, the progress of DNA hydrogel biosensors is systematically summarized in terms of the types of DNA hydrogels, detection principles and biosensor device integration. First, the types of DNA hydrogels used in biosensors are briefly introduced. Next, we thoroughly demonstrate the detection principles of DNA hydrogel biosensors; the detection principles depend on the recognition elements, signal elements, and transduction types of the DNA hydrogel used in the biosensor. In particular, we demonstrate the great potential of integrated devices and techniques used in DNA hydrogel biosensors, such as microfluidics and portable devices. Finally, the challenges and future development of DNA hydrogels in biosensing are discussed. This work can be used as a reference for research on biosensing analysis using DNA hydrogels.

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