Environmental Sustainability

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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
A critical review of conventional and emerging technologies for the detection of contaminants, allergens and adulterants in plant-based milk alternatives
(Elsevier, 2025) Karimi, Zahra; Campbell, Katrina; Kevei, Zoltan; Patriarca, Andrea; Koidis, Anastasios; Anastasiadi, Maria
The increasing popularity of plant-based milk alternatives (PBMAs) necessitates effective safety and authentication measures to ensure food product integrity and maintain consumer trust. This review aims to offer a comprehensive overview of potential contaminants, allergens, and adulterants in PBMAs, and the analytical methodologies employed for their detection and quantitation. It details the advantages and limitations of widely employed testing techniques, such as chromatography, spectroscopy, immunoassays and PCR. In addition, it explores recent advancements in portable detection methods based on novel technologies such as CRISPR and biosensor systems that offer new opportunities for rapid and precise analysis. Despite these technological innovations, important challenges remain, particularly in optimizing sample preparation protocols and improving DNA-based methods efficiency. The integration of multiple detection strategies and the development of rapid, cost-effective analytical tools are critical steps towards enhancing both industry compliance and consumer confidence. Furthermore, green analytical methods — such as solvent-free extraction, AI-driven spectroscopy, and sustainable sample preparation techniques — pave the way toward eco-friendly and more efficient PBMA safety testing.
Open Access
Prolonged heat stress in Brassica napus during flowering negatively impacts yield and alters glucosinolate and sugars metabolism
(Frontiers, 2025-01-01) Kourani, Mariam; Anastasiadi, Maria; Hammond, John P.; Mohareb, Fady
Oilseed rape (Brassica napus), one of the most important sources of vegetable oil worldwide, is adversely impacted by heatwave-induced temperature stress especially during its yield-determining reproductive stages. However, the underlying molecular and biochemical mechanisms are still poorly understood. In this study, we investigated the transcriptomic and metabolomic responses to heat stress in B. napus plants exposed to a gradual increase in temperature reaching 30°C in the day and 24°C at night for a period of 6 days. High-performance liquid chromatography (HPLC) and liquid chromatography–mass spectrometry (LC-MS) was used to quantify the content of carbohydrates and glucosinolates, respectively. Results showed that heat stress reduced yield and altered oil composition. Heat stress also increased the content of carbohydrate (glucose, fructose, and sucrose) and aliphatic glucosinolates (gluconapin and progoitrin) in the leaves but decreased the content of the indolic glucosinolate (glucobrassicin). RNA-Seq analysis of flower buds showed a total of 1,892, 3,253, and 4,553 differentially expressed genes at 0, 1, and 2 days after treatment (DAT) and 4,165 and 1,713 at 1 and 7 days of recovery (DOR), respectively. Heat treatment resulted in downregulation of genes involved in respiratory metabolism, namely, glycolysis, pentose phosphate pathway, citrate cycle, and oxidative phosphorylation especially after 48 h of heat stress. Other downregulated genes mapped to sugar transporters, nitrogen transport and storage, cell wall modification, and methylation. In contrast, upregulated genes mapped to small heat shock proteins (sHSP20) and other heat shock factors that play important roles in thermotolerance. Furthermore, two genes were chosen from the pathways involved in the heat stress response to further examine their expression using real-time RT-qPCR. The global transcriptome profiling, integrated with the metabolic analysis in the study, shed the light on key genes and metabolic pathways impacted and responded to abiotic stresses exhibited as a result of exposure to heat waves during flowering. DEGs and metabolites identified through this study could serve as important biomarkers for breeding programs to select cultivars with stronger resistance to heat. In particular, these biomarkers can form targets for various crop breeding and improvement techniques such as marker-assisted selection.
Open Access
Nanomaterials as a new frontier platform: metal-doped and hybrid carbon dots as enzyme mimics for environmental applications
(Frontiers, 2025-01-01) Yousaf, Aiman; Imran, Muhammad; Farooq Warsi, Muhammad; Alsafari, Ibrahim A.; Khan, Farhan A.; Parra-Saldívar, Roberto; Gutiérrez-Soto, Guadalupe; Iqbal, Hafiz M. N.
Environmental pollution has become an inexorable problem for the planet Earth. The precise detection and degradation of heavy metals, pesticides, industrial-, pharmaceutical- and personal care- products is needed. Nanotechnology holds great promise in addressing global issues. Over the past decades, nanozymic nanomaterials have exceptionally overcome the intrinsic limitations of natural enzymes. Carbon dots (CDs) exhibit unique structures, surface properties, high catalytic activities, and low toxicity. Different techniques, such as doping or surface passivation, can enhance these exceptional properties. Doping modifies CDs’ electronic, magnetic, optical, and catalytic properties considerably. Metal doping, a more significant strategy, involves the introduction of metallic impurities, which offer insight into enhancing the physicochemical properties of CDs. Metal-doped CDs exhibit higher optical absorbance and catalytic performance than pristine CDs. The literature shows that researchers have utilized various synthetic approaches to fabricate CDs-Metal nanozymes. Researchers have reported the metal-doped and hybrid CDs’ peroxidase, catalase, laccase, and superoxide dismutase-like activities. These metal-doped nanozymes put forward substantial environmental remediations and applications such as sensing, photocatalytic degradation, adsorption, and removal of environmental contaminants. This review thoroughly discussed the metal-based functionalization of CDs, the enzyme-like properties, and the ecological applications of metal-doped and hybrid enzymes. The review also presents the current novelties, remaining challenges, and future directions with key examples.
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
Solidago canadensis modifies microbial community and soil physicochemical properties through litter leachates and root exudates
(Oxford University Press (OUP), 2025-04) Bo, Yanwen; Liao, Yali; Pawlett, Mark; Akbar, Rasheed; Girkin, Nickolas T.; Sun, Jianfan; Ali, Amjad; Ahmad, Naushad; Liu, Wei; Wang, Xiaoyan; Du, Daolin
Invasive plant inputs alter soil microbial communities via chemical compounds in litter, root exudates, and leachate, impacting a range of soil processes, but precise effects are poorly understood. We examined Solidago canadensis, a common invasive species in China, and its litter effects on soil microbial communities under natural conditions. Experimental treatments included S. canadensis seedling density (1 and 2 plants/pot) and quantity of litter (10 and 20 g/pot), with control groups that contained no plants or litter. After 120 days, soil samples were analyzed for physico-chemical properties, GC-MS chemical composition, and bacterial community composition using high-throughput sequencing. Results showed that S. canadensis seedlings and litter inputs increased soil pH, organic matter (SOM), and nitrogen (TN), while phosphorus and potassium remained unchanged. We identified 66 chemical compounds, predominantly ketones, alcohol, aldehyde, hydrocarbon, ester, acid, terpenoids, and alkaloids, associated with the presence of the invasive species, alongside shifts in dominant bacterial genera including Sphingomonas, Acidobacteriales, and Gemmatimonas. Rarer genera under the invasive treatment species, such as Candidatus, Rhodoplanes and Novosphingobium, correlated positively with soil TN, pH, and SOM. Collectively, our results demonstrate how the increased presence of allelochemicals from S. canadensis litter significantly impact soil properties and bacterial communities, and may therefore have implications for ecosystem dynamics.
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
The known unknowns of petrogenic organic carbon in soils
(American Geophysical Union (AGU), 2025-04-01) Evans, Daniel L.; Doetterl, Sebastian; Gallarotti, Nora; Georgiadis, Eleanor; Nabhan, Sami; Wartenweiler, Stephan H.; Rhyner, Timo M. Y.; Mittelbach, Benedict V. A.; Eglinton, Timothy I.; Hemingway, Jordon D.; Blattmann, Thomas M.
Intensifying effects of global climate change have spurred efforts to enhance carbon sequestration and the long‐term storage of soil organic carbon (OC). Current soil carbon models predominantly assume that inputs of OC are biospheric, that is, primarily derived from plant decomposition. However, these overlook the contribution of OC from soil parent material, including petrogenic organic carbon (OCpetro) from OC‐bearing (meta‐)sedimentary bedrock. To our knowledge, no soil carbon model accounts for the inputs of OCpetro to soils, resulting in significant gaps in our understanding about the roles OCpetro plays in soils. Here, we call for cross‐disciplinary research to investigate the transport and stability of OCpetro across the bedrock–soil continuum. We pose four key questions as motivation for this effort. Ignoring the inputs of OCpetro to soils has significant implications, including overestimating biospheric carbon stocks and turnover times. Furthermore, we lack information on the role that OCpetro may play in priming microbial communities, as well as the impacts of land management on OCpetro stocks.
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
Predicted yield and soil organic carbon changes in agroforestry, woodland, grassland, and arable systems under climate change in a cool temperate Atlantic climate
(Springer, 2025-05) Giannitsopoulos, Michail L.; Burgess, Paul J.; Graves, Anil R.; Olave, Rodrigo J.; Eden, Jonathan M.; Herzog, Felix
The impact of a changing climate on crop and tree growth remains complex and uncertain. Whilst some areas may benefit from longer growing seasons and increased CO2 levels, others face threats from more frequent extreme weather events. Models can play a pivotal role in predicting future agricultural and forestry scenarios as they can guide decision-making by investigating the interactions of crops, trees, and the environment. This study used the biophysical EcoYield-SAFE agroforestry model to account for the atmospheric CO2 fertilization and calibrated the model using existing field measurements and weather data from 1989 to 2021 in a case study in Northern Ireland. The study then looked at two future climate scenarios based on the representative concentration pathways (RCP 4.5 and RCP 8.5) for 2020–2060 and 2060–2100. The predicted net impacts of future climate scenarios on grass and arable yields and tree growth were positive with increasing CO2 fertilization, which more than offset a generally negative effect of increased temperature and drought stress. The predicted land equivalent ratio remained relatively constant for the baseline and future climate scenarios for silvopastoral and silvoarable agroforestry. Greater losses of soil organic carbon were predicted under arable (1.02–1.18 t C ha−1 yr−1) than grassland (0.43–0.55 t C ha−1 yr−1) systems, with relatively small differences between the baseline and climate scenarios. However, the predicted loss of soil organic carbon was reduced in the long-term by planting trees. The model was also used to examine the effect of different tree densities on the trade-offs between timber volume and understory crop yields. To our best knowledge this is the first study that has calibrated and validated a model that accounts for the effect of CO2 fertilization and determined the effect of future climate scenarios on arable, grassland, woodland, silvopastoral, and silvoarable systems at the same site in Europe.
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.

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