Abstract: This review focuses on the integration of thermochemical and biochemical processes as a transformative approach to biomass conversion. By combining technologies such as anaerobic digestion, hydrothermal liquefaction, pyrolysis, and syngas fermentation, this review highlights how hybrid systems maximize resource recovery and improve energy efficiency. Key examples include the use of digestate from anaerobic digestion as a feedstock for pyrolysis or hydrothermal carbonization, enhancing biochar and hydrochar production while improving nutrient recycling. Similarly, the integration of syngas fermentation with gasification demonstrates how thermochemical products can be further valorized into biofuels under milder biochemical conditions. This review also addresses the reuse of by-products, such as the aqueous phase from hydrothermal processes, in nutrient recovery and algae cultivation, showcasing the circular potential of these systems. By emphasizing the technical and economic synergies of integrating diverse technologies, this paper outlines a clear pathway for industrial-scale adoption, contributing to sustainable energy production and reduced greenhouse gas emissions.

Abstract: Accurate estimating green canopy cover in rubber plantations is crucial for monitoring vegetation health and assessing stress impacts. This study validates satellite-derived canopy cover estimates by integrating UAV-based measurements, ground observations, remote sensing, and machine learning approaches. Sentinel-2 and Landsat imagery were utilized to derive spectral vegetation indices (SVIs) under varying stress conditions, while UAV-based canopy cover assessments provided high-resolution reference data for validation. The findings revealed that while certain SVIs exhibited strong cor-relations with canopy density under stable conditions, their predictive accuracy declined significantly during extreme stress events, such as Pestalotiopsis outbreaks and seasonal leaf fall periods. To improve estimation accuracy, machine learning models were developed, with Random Forest (RF) outperforming Support Vector Machines (SVM), Classification and Regression Trees (CART), and Linear Regression (LR). RF achieved the highest predictive accuracy (R² = 0.82, RMSE = 6.48, MAE = 4.97), demonstrating its reliability in capturing non-linear interactions between canopy heterogeneity and environmental stressors. These results highlight the limitations of traditional vegetation indices and emphasize the importance of multi-sensor integration and advanced modeling techniques for more precise canopy monitoring. This study contributes to developing robust remote sensing frameworks for early stress detection and sustainable plantation management in tropical rubber ecosystems.

Abstract: Maize is an important food and feed crop for smallholder farmers in Vietnam and is affected by changing growing and crop management conditions under ongoing climate change posing potential production risks in the future. This study analyses future maize growing conditions in Northern Vietnam based on seven main agrometeorological indicators by applying the agrometeorological indicator software (AGRICLIM) on climate projections based on one global circulation model with an embedded regional climate model for two emission pathways (RCP4.5 and RCP8.5) from 1951 towards 2100. The results were analyzed for four time slices in annual, seasonal, and monthly scales and reveal that the future agrometeorological conditions for three local main maize growing seasons (winter, spring, and forage maize season) in general become more extreme compared to current conditions. In particular, the calculated increase in heat stress days, heavy precipitation events, and drought stress days for maize show different trends for the specific maize growing seasons. For example, drought and heat stress conditions may occur more frequently during the spring and forage maize season while the risk for soil erosions and N-leaching may rise in the winter and forage maize season. These findings will support the development of adaptation options under more adverse weather conditions for maize growing systems in Northern Vietnam.

Abstract: This paper presents an algorithm for evaluating the environmental impact of clothing swaps, promoting extended use and responsible consumption. Implemented in an online swapping platform, the algorithm quantifies reductions in environmental impact due to extended clothing lifespan and avoided purchase of new garment, promoting swapping activities. Developed through scientific literature analysis, Life Cycle Assessment (LCA), and swapping practice studies, the algorithm uses key environmental indicators: carbon footprint, water use, energy consumption, and land use. It integrates consumer behaviour insights and uses both default and user-entered clothing data to calculate environmental savings. Results show that clothing impact varies by fabric. Viscose and polyester garments have the lowest environmental impact, while swapping cotton and wool items yields the highest savings, as these materials are more resource intensive. The platform-integrated algorithm recorded 251 swaps over two months, preventing 4,137 kg CO₂ emissions, 6,809 m³ of water use, 3.08 m²a crop eq of land use, and 87.23 GJ of energy consumption. These findings highlight the significant environmental benefits of prolonging clothing use through swapping instead of throwing clothes away.

Abstract: Artificial ocean alkalinization (AOA) is one of the most promising marine carbon dioxide removal technologies. We applied the results from the 6th Coupled Model Intercomparison Project (CMIP6) to characterize the temporal and spatial variabilities of future marine carbon chemistry under the implementation of AOA. Our study shown the carbon system varied widely under the implement of AOA, but some efficiencies may be covered up by the forcing of high carbon emission scenario. Basing on the CMIP6 protocol, which added 0.14 Pmol alkalinity into the ocean ever year, AOA promoted the increase of DIC, delayed the rise of pCO2, and restrained the aggravation of pH and Ω, actually. The temperate oceans in both hemispheres were the most significant impacted basins, whereas the Southern Ocean were the less affected region. In the present century, the oceanic carbon sink would intensify rapidly until the year around 2080, and then weaken slowly. Implementation of AOA merely changed the relative strength of oceanic sink rather than its variation pattern. In deep ocean, what’ more, the effectiveness of AOA did exist, but was quite little for the mitigation of ocean acidification.

Abstract: Plant functional traits are critical indicators of ecosystem health, yet predicting aquatic leaf traits via spectral reflectance remains challenging due to limited sample sizes and the underrepresentation of rare species. We hypothesized that dominant species’ spectral models could infer rare species’ traits even with constrained data. To test this, we measured leaf reflectance spectra and eleven functional traits across diverse freshwater macrophyte species, developing Partial Least Squares Regression (PLSR) models under varying species combinations (All-families, Dominant-families, Non-Cyperaceae, etc.) and sample sizes (40–240). Results demonstrated that species composition exerted greater influence than sample size on validation accuracy for most traits when samples ranged from 120 to 240. A minimum threshold of 160 samples was identified for robust trait prediction, though model performance diverged significantly between All-families and dominant-family combinations, suggesting dominant taxa alone inadequately represent quadrat-level trait diversity. These findings challenge assumptions that dominant species compensate for rare species’ scarcity in spectral modeling. We advocate prioritizing rare species sampling to enhance model generalizability in wetland ecosystems. This work establishes actionable guidelines for scaling spectral trait prediction in marshes, advancing ecological monitoring and restoration efforts.

Abstract: This paper presents an innovative service for river flow forecasting and its demonstration in two dam-controlled rivers in Portugal, Tejo and Mondego rivers, based on using Multilayer Perceptron (MLP) models to predict and forecast river flow. The main goal is to create and improve AI models that operate as remote services, providing precise and timely river flow predictions for the next 3 days. This paper examines the use of MLP architectures to predict river discharge using comprehensive hydrological data from Portugal’s National Water Resources Information System (Sistema Nacional de Informação de Recursos Hídricos, SNIRH), demonstrated for the Tejo and Mondego river basins. The methodology is described in detail, including data preparation, model training, and forecasting processes, and provides a comparative study of the MLP model’s performance in both case studies. The analysis shows that MLP models attain reasonable accuracy in short-term river flow forecasts for the selected scenarios and datasets, adeptly reflecting discharge patterns and peak occurrences. These models seek to enhance water resource management and decision-making by amalgamating modern data-driven methodologies with established hydrological and meteorological data sources, facilitating better flood mitigation and sustainable water resource planning as well as accurate boundary conditions for downstream forecast systems.

Abstract: Global water demand due to population growth and agricultural development, has led to widespread overexploitation of groundwater, particularly in semi-arid regions. Traditional hydrochemistry monitoring system still suffers from limited laboratory accessibility and high costs. This study aims to predict major ions of groundwater, including Ca²⁺, Mg²⁺, Na⁺, SO₄²⁻, Cl⁻, K⁺, HCO₃⁻, and NO₃⁻, utilizing two field measurable parameters (i.e., total dissolved solids (TDS) and mineralization (MIN)) in Aflou_Syncline region, Algeria. A multilayer perceptron (MLP) model optimized with the Levenberg-Marquardt backpropagation (LMBP) provided the most predictive accuracy for the different ions of SO₄²⁻, Mg²⁺, Na⁺, Ca²⁺, and Cl⁻ with R2 = (0.842, 0.980, 0.759, 0.945, 0.895) and RMSE = (53.660, 12.840, 14.960, 36.460, 30.530) (mg/L) in the testing phase, respectively. However, the predictive accuracy for the remaining ions of K⁺, HCO₃⁻, and NO₃⁻ was supplied as R² = (0.045, 0.366, 0.004) and RMSE = (6.480, 41.720, 40.460) (mg/L), respectively. The performance of our model (LMBP-MLP) was validated in similar geological areas in the adjacent area, including Aflou, Madna, and Ain Madhi. In addition, LMBP-MLP showed very promising results, with performance similar to the original research area.

Abstract: The Yana-Kolyma collision orogen is one of world-class gold economic belts, where the large gold deposits are localized, mainly in the Upper Paleozoic and Lower Mesozoic clastic rocks. Dikes-hosted orogenic gold deposits have been to a lesser extent, but they are important for analyzing the structural control of mineralization within the framework of the tectonic evolution of the host orogen. Orogenic gold deposits of the Vyun ore field are hosted in Titonian mafic and felsic dike, but they have no genetic connection with dikes. The late formation of deposits leads to the fact that previously reactivated polydeformed structures turn out to be mineralized. Study of the structural control of mineralization is also complicated by superimposed late tectonic events. Based on the analysis of collected field materials, the paper presents the results of the study of deformation structures of the Vyun ore field within the framework of the Mesozoic evolution history throughout the geological time of the eastern convergent margin of the Siberian craton. Four stages of deformations are identified. The pre-mineralization deformations, metamorphic and magmatic events share a common NE-SW shortening (D1), which is related to the subduction of the Oymyakon oceanic slab and collision of the Kolyma-Omolon superterrane from the eastern margin of the Siberian Craton. These deformations are characterized by multiphase history of superposition of several tectonic events under conditions of compression and progressive deformations (D1/1 and D1/2). Ore mineralization was formed at the end of compression in the same stress field (D1/2). Its structural control is determined by reactivation of older dikes and faults. Dikes are areas of heterogeneous stress and heterogeneous strain, being favorable for the concentration of ore fluids. The metallogenic time of formation of the gold mineralization is synchronous to the tectonic event likely reflects the final stages of the Kolyma–Omolon microcontinent – Siberian Craton collision of the Valanginian during crustal thickening. The main impulse of Au mineralization coincided with a slowdown in convergence. Postmineralization tectonic regime was related to the Aptian-Late Cretaceous tectonic transition from compression to transpression. Transpressional tectonics was determined accordingly by W-E (D2) and N-S (D3) stress fields caused by several accretion events in the Cretaceous on the northern and eastern margins of Siberia. D4 tension strains are caused by the opening of the Eurasian Oceanic basin in the Arctic in the Paleocene. The obtained results of the relation between polydeformed structures and associated mineralization have important implications to contribute to a proper understanding of the structural control of orogenic gold deposits and their relationship to the evolution of the host orogen and the conceptual exploration targeting orogenic gold deposits in Phanerozoic terranes of craton margins.

Abstract: Mapping and profiling of clay resources were essential for numerous industries due to clay's unique properties and widespread availability. This study employed GPS and QGIS to map clay resources in Brgy. Bugas-Bugas, Placer, and Brgy. Cabugo, Claver, Surigao del Norte. Laboratory analysis was conducted to evaluate elemental and mineral composition, plasticity, shrinkage, water absorption, porosity, color characteristics, and flexural strength of the clay samples. Significant variations in clay properties were observed, with chemical analysis revealing higher silica content in Claver and Placer clay, indicating potential strength and lower shrinkage. Conversely, Kauswagan clay exhibited higher levels of aluminum oxide and iron oxide, suggesting increased water absorption and darker coloration after firing. XRD analysis identified montmorillonite as the main mineral found in the Claver clay sample, while the Placer clay sample from Brgy. Bugas-Bugas was identified as dickite clay, containing significant quartz and anorthite, a variety of plagioclase, indicative of a siliceous secondary clay. Plasticity tests demonstrated high plasticity for Kauswagan and moderate plasticity for Claver and Placer, while shrinkage tests indicated low drying shrinkage for Placer and high total linear shrinkage for Kauswagan. Kauswagan clay exhibited the highest water absorption rates, whereas Placer and Claver clays were suitable for tile manufacturing due to their lower water absorption rates. Porosity was highest in Kauswagan, followed by Placer. Color analysis revealed that fired Placer samples were lighter in color compared to Claver samples, with both showing a slight shift towards green and a more yellowish hue in Placer. Placer exhibited higher flexural strength compared to Kauswagan. These findings significantly contributed to understanding clay resources in the area, facilitating informed decision-making for their development and utilization of clay across a wide range of industries vital for efficient and effective resource management.

Abstract: This study proposes an eco-friendly approach to zeolite agglomeration for petroleum sorbents. The novelty lies in integrating agglomeration and deagglomeration within a single high-pressure grinding roll (HPGR) system, enhancing sorption capacity by creating a secondary porosity network. This eliminates energy-intensive calcination, making it a sustainable alternative to wet granulation. We examine the impact of binder and water dosages on sorption capacity, mechanical resistance, and textural properties of roll-compacted zeolite agglomerates. Feed materials were characterized using N₂ adsorption, XRD, XRF, particle size distribution, and SEM. Structural and functional properties were assessed via mercury intrusion porosimetry, petroleum sorption efficiency (Westinghouse test), sorption capacity, gravitational drop tests, and SEM. All sorbents (0.5–1 mm) met the 50 wt.% oil absorbency threshold for petroleum spill cleanup in Poland. The fabricated zeolite agglomerates exhibited superior sorption capacities compared to zeolite powder, Na-P1, and commercial sorbents. The optimal feed composition yielded sorbents with the best properties and versatile performance. An analysis of sorption characteristics and pore size distribution showed that a higher proportion of 10–100 µm pores improved efficiency. This study addresses the knowledge gap in zeolite powder agglomeration and demonstrates the effectiveness of integrating agglomeration and deagglomeration in a high-pressure roller press.

Abstract: The dynamic relationship between microplastics (MPs) in the air and on the Earth’s surface involves both natural and anthropogenic forces. MPs are transported from the ocean to the air by bubble scavenging and seaspray formation and released from land sources by wind and human activities. Up to 8.6 megatons of MPs per year have been estimated to be in air above the oceans. They are distributed by wind, water and passive vectors and returned to the Earth’s surface via rainfall and passive deposition, but can escape to the stratosphere, where they may exist for months. Anthropogenic sprays, such as paints, agrochemicals, personal care and cosmetic products, and domestic and industrial procedures (e.g., air conditioning, vacuuming and washing, waste disposal, manufacture of plastic-containing objects) add directly to the airborne MP load, which is higher in internal than external air. Atmospheric MPs are less researched than those on land and in water, but, in spite of the major problem of lack of standard methods for determining MP levels, the clothing industry is commonly considered the main contributor to the external air pool, while furnishing fabrics, artificial ventilation devices, and presence and movement of human beings are the main source of indoor MPs. The majority of airbourne plastic particles are fibers and fragments; air currents enable them to reach remote environments, potentially traveling thousands of kilometers through the air, before being deposited in the various forms of precipitation (rain, snow, or “dust”). The increasing preoccupation of the populace and greater attention being paid to Industrial Ecology may help to reduce the concentration and spread of MPs and nanoparticles from domestic and industrial activities in the future.

Abstract: Understanding how extreme weather events (EWEs) impact vegetation phenology is crucial for assessing ecosystem stability under climate change. This study systematically investigated the ecosystem growing season length (GL) response to four types of EWEs- extreme heat, extreme cold, extreme wetness (surplus precipitation), and extreme drought (lack of precipitation). The EWEs extremity thresholds were found statistically using detrended long time series (2000-2022) ERA5 meteorological data through z-score transformation. The analysis was based on grassland ecosystem in the Mongolian Plateau (MP) from 2000 to 2022. Using solar-induced chlorophyll fluorescence data and event coincidence analysis, we evaluated the probability of GL anomalies coinciding with EWEs and assessed vegetation sensitivity to climate variability. Our results showed negative GL anomalies exhibited a stronger association with EWEs than positive anomalies, particularly in arid and cold regions of the MP, where extreme drought and extreme cold events predominantly shortened the GL. Conversely, extreme heat and extreme wet events played dominant role in warmer and wetter regions, influencing both GL lengthening and shortening. The regional background hydrothermal conditions modulated vegetation sensitivity, with warmer regions being more susceptible to extreme heat stress and drier regions exhibiting higher vulnerability to extreme drought conditions. Furthermore, grassland ecosystems demonstrated lower resilience to extreme drought than forest ecosystems likely due to differences in root structure and water use efficiency. These findings emphasize the importance of regional weather variability and climate characteristics in shaping vegetation phenology and provide new insights into how weather extremes impact ecosystem stability in semi-arid and arid regions. Future research should explore extreme weather events and the role of human activities to enhance predictions of vegetation-climate interactions in grassland ecosystems of the MP.

Abstract: Extreme precipitation events are one of the common hazards in eastern Texas, generating a large amount of storm water. Water running off urban areas may bring non-point source (NPS) pollution to natural resources such as rivers and lakes. Urbanization exacerbates this issue by increasing impervious surfaces that prevent natural infiltration. This study evaluates the efficacy of rain gardens, a natural-based best management practice (BMP), in mitigating NPS pollution from urban stormwater runoff. Stormwater samples were collected at inflow and outflow points of three rain gardens and analyzed for various water quality parameters, including pH, electrical conductivity, fluoride, chloride, nitrate, nitrite, phosphate, sulfate, salts, carbonates, bicarbonates, sodium, potassium, aluminum, boron, calcium, mercury, arsenic, copper iron lead magnesium, manganese and zinc. Removal efficiencies for nitrate, phosphate, and zinc exceeded 70%, while heavy metals such as lead achieved reductions up to 80%. However, certain parameters, such as calcium, magnesium and conductivity showed increased outflow concentrations, attributed to substrate leaching. These increases resulted in a higher outflow pH. Overall, the pollutants were removed with an efficiency exceeding 50%. These findings demonstrate that rain gardens are an effective and sustainable solution for managing urban stormwater runoff and mitigating NPS pollution in eastern Texas, particularly in regions vulnerable to extreme precipitation events.

Abstract: Extreme wind waves occurring during storms are a major natural hazard for the maritime economy on the one hand and a driver of various natural processes, e. g. the erosion of coastlines, on the other. Assessing the extreme values of wind wave heights and their long-term trends is crucial for coastal and offshore engineering and climate change studies. This study is dedicated to the values and long-term trends of extreme wave heights in the Black Sea. A 73-years long reanalysis is applied for the study. In order to additionally validate the reanalysis, data on wave heights form the Saral/AltiKa satellite altimeter is used. 19 locations along the coastline of the Black Sea are selected for the analysis of wind wave data. Maximal significant wave heights exceed 8.5 m along the southwestern coast of the Crimean Peninsula. Besides, 99.9th, 99th and 95th percentiles of significant wave heights are assessed. The long-term linear trends of these values are in general not statistically significant except of one location in case of maximal significant wave heights and two locations in case of 95th percentile of significant wave heights.

Abstract: Fish physiology is a key field of study in fisheries science, providing a solid foundation for understanding aquatic ecosystems and driving innovation in the aquaculture sector. As aquaculture continues to be a key source of food worldwide, study in this subject has increasingly focused on the implications of climate change, which poses considerable threats to fish and other aquatic species. This review outlines current studies on fish physiological responses to several stressors related with climate change, such as changes in temperature, salinity, disease occurrence, and oxygen levels. The combination of these abiotic and biotic variables is vital, as climate change accelerates pathogen fate and dispersion and alters nutrient dynamics, influencing fish growth and survival. This study also reviews mesocosm experiments and modelling research to illustrate the significance of these interactions in developing sustainable fisheries management and enhancing aquaculture methods. Furthermore, the study identifies current research trends and recommends areas for future research to address the ongoing problems posed by climate change to aquatic resources.

Abstract: Urban greenery and cooling initiatives have become top priorities for municipalities worldwide, as they contribute to improved environmental quality and urban resilience. This study leverages advancements in remote sensing (RS) and cloud-based processing to assess and monitor changes in public urban green spaces (PUGS) in Sanandaj, Iran. Using high-resolution Sentinel-2 imagery (10 m) processed in Google Earth Engine (GEE), we calculated and mapped the normalized difference vegetation index (NDVI) across 20 major PUGSs over a five-year period from 2019 to 2023. A total of 507 Sentinel-2 images were analyzed, offering a comprehensive view of seasonal and annual greenness trends. Our findings reveal that May is the peak month for greenery, while February consistently shows the lowest NDVI values, indicating seasonal greenness variability. Specifically, the mean NDVI of PUGSs decreased significantly between 2019 and 2022, with values recorded at 0.735, 0.737, 0.622, 0.417, and 0.570 in the greenest month of each respective year, highlighting a noticeable decline in vegetation health and extent. This reduction can be attributed to water scarcity and suboptimal management practices, as evidenced by dried or underperforming green spaces in recent years. Our results underscore the potential of integrating NDVI-based assessments within urban development frameworks to more accurately define and sustain PUGSs in Sanandaj. This methodology provides a replicable approach for cities aiming to optimize urban greenery management through RS technology.

Abstract: The 210Pb-based dating method provides absolute ages determination in recent aquatic sediments at centennial scales. It is widely used to support a large variety of environmental studies. However, any empirical data set is compatible with an infinite number of chronologies that need to be constrained by a series of assumptions (models) on the particular sedimentary conditions of the studied environment, and validated with independent chronostratigraphic markers. During five decades, about thirty models have been developed to cope with the wide diversity of natural conditions, a good number of them appearing in recent years, along with new concepts such as model errors, attractors for χ-mapping, or kinetic reactive transport, which have changed common views and practices. This paper aims to present a comprehensive review of this dating method to provide to final users updated tools and a renewed understanding to improve the reliability of their applications. Models are classified in terms of their assumptions on the sedimentary systems, which are better understood from a revisited theory of early compaction and the description of the microcosms of saturated porous media, where composite fluxes of tracers undergo different deposition pathways in terms of physical and kinetic reactive transport. The article reviews empirical evidence on the natural variability in mass flows and initial activity concentrations. Some models allow analytical solutions, while others require numerical techniques. The review is illustrated with examples from real case studies.

Abstract: Background: Modern agriculture relies heavily on chemicals to ensure high yields and food security, but their overuse has led to health issues and pest resistance. Researchers are now exploring natural, eco-friendly alternatives for pest control. Methods: This study evaluated two ethanol-based formulations (12.5% and 25% v/v) derived from the tangerine peel (Citrus reticulata L. var 'Clementina') against conventional chemical treatments and untreated control in potato (Solanum tubevar. L. var. ‘Capiro’) cultivation. A randomised block design with three blocks per treatment, each containing 45 plants, was used during the wet season (February–April 2023). Results: Visual inspections and yellow traps followed weekly application from day 30 to 105 post-planting to monitor pest (e.g., Frankliniella occidentalis, Aphididae) and beneficial insect (e.g., Coccinellidae, Aphis mellifera) populations. The 25% formulation performed similarly to chemical treatments against pests but was harmless to beneficial insects. Post-harvest analysis showed that the formulations achieved 73% of conventional yields, with comparable tuber damage and Premnotrypes vorax larvae levels. Conclusions: Toxicological tests confirmed the formulations' eco-friendliness, making them suitable for small-scale Andean "chakras" for organic farming and honey production without chemicals.

Abstract: The valorization of food waste, particularly in a manner that is decentralized has significant potential to enhance to reuse of organic wastes. A novel technology which converts raw organic waste, particularly vegetable and food waste a de-graded material in approximately 24 hours as a bio fertiliser. The material which is pathogen free and stable and has a texture similar to humic soil conditioner and an aromatic characteristic similar to that of coffee grind . This material is created through the use of a bio-digester. During the process, the food digester or bio-digester does not require any additional fresh water and has little to no grey water discharge to the sewers, environmental conditions dependent. A cultivated and unique consortium of hydrolytic and mesophilic enzymes are added to the pre-conditioned chamber that aggressively breaks down the raw material. This chamber is controlled and monitored to maintain optimum mesophilic conditions throughout the 24-hour period. During the processing of food waste, emissions were recorded as being minimal. The addition of the enzyme had a significant effect on the organic matter characteristics and on the nutrient profile on the material. The nutrient profile, pH, electrical conductivity, heavy metal content, and other parameters of the material produced from the process over a number of years was determined. These data indicate very low levels of heavy metals and reasonable levels of nitrogen (N), phosphorus (P) and potassium (K). Controlled growth studies were conducted to ascertain the comparative growth performance of a model crop, perennial rye grass when grown in soil treated with the material, biowaste compost (BWC) and composted green waste (CGW), all applied at three rates. Overall, the biomass from pots treated with degraded food waste material were higher than pots treated with a biowaste and green waste compost at com-parative application rates on a volume basis. Analysis of the grass for agronom-ically important macronutrients, such as N, P and K, indicated higher uptake rates of these nutrients in the experimental material treated pots over four harvests. Despite higher uptake the residual total nitrogen (N) and available phosphorus (P) and potassium (K) were higher in the soil after the 4th harvest. The product produced from the food waste processing has a high potential as a viable organ-ic/biofertilizer fertilizer and fits in with the EU strong policy of the Circular Economy and new EU Fertiliser Regulations which promotes organic fertilizer as against mineral fertilizer. The most substantial benefits of such a technology are both the very short turn around and the reduced emissions of GHG gasses re-leased during the processing.