Abstract: Garden pruning waste from Cynodon sp. is a lignocellulosic resource with high lignin content, which limits anaerobic digestion efficiency. While white-rot fungi can delignify biomass through solid-state fermentation (SSF), their efficacy depends on balancing lignin removal with preservation of fermentable carbohydrates. This study evaluated the effect of SSF times (8, 21, and 36 days) with Trametes hirsuta on enzymatic activity and subsequent biogas production. Laccase activity increased progressively, reaching 983.84 U/L at 36 days; whereas manganese and versatile peroxidases peaked at 21 days. Fungal-pretreated samples exhibited lower methane yields, a maximum of 225.32 NmL/gVS at 8 days, compared to untreated biomass (381.66 NmL/gVS). Total lignin content apparently increased across treatments, suggesting pseudo-lignin formation during autoclave sterilization, while glucose and xylose decreased. Biological pretreatment affected methane production by reducing sugar availability, potentially forming inhibitory furanic compounds and antimicrobial metabolites, thereby negating the benefits of enzymatic delignification. These results underscore the complexity of optimizing fungal pretreatment and highlight the need to balance fermentation time to preserve carbohydrates while modifying lignin structure.

Abstract: This research aims to investigate the concept of Industrial Symbiosis as a change agent in the Circular Economy, with its consequent effects on the economy, the environment, and society in terms of sustainable development. This study employs qualitative research with quantitative support from a structured survey of 152 IS project experts, researchers, and practitioners, utilizing a questionnaire comprising Likert-type and multiple-choice questions. Data were aggregated into composite indicators and analyzed by using a log-log regression model. Empirical results reveal that economic benefits are the most significant positive drivers. The actors’ involvement also contributes positively, highlighting the importance of multi-stakeholder collaboration. Conversely, barriers have the strongest negative impact on perceived obstacles and reduce IS synergies on the largest scale. Broader economic and social conditions moderately enhance, while awareness and training show a weaker but positive effect. IS is both economically viable and environmentally necessary, but its expansion depends on reducing financial, regulatory, and infrastructural barriers. Certain economic policy-driven interventions, such as fiscal incentives, regulatory clarity, and investment, enable infrastructure to scale up the adoption of IS.

Abstract: Atmospheric nitrogen dioxide (NO₂) is an important component of reactive nitrogen and plays a key role in the atmospheric nitrogen cycle outside major emission regions. However, its variability under remote background conditions remains poorly characterized, as most observational studies focus on urban or continental environments. This study investigates the background variability of in situ NO₂ measurements at a remote North Atlantic island (Azores) over the period 2015–2024 and examines its association with large-scale atmospheric transport regimes. Monthly NO₂ concentrations were classified into background Atlantic conditions and months influenced by continental air masses using an objective PM₁₀ percentile-based criterion. Differences between regimes were assessed using non-parametric statistics. Although NO₂ concentrations were systematically higher during months associated with continental transport, the differences did not reach statistical significance. Wind speed analysis for the overlapping period 2018–2024 showed consistently higher values during continental transport months, supporting enhanced large-scale advection during these periods. Overall, the results indicate that background NO₂ levels in this remote insular environment exhibit modest but coherent modulation associated with atmospheric transport regimes. These findings contribute to improving the interpretation of reactive nitrogen variability in remote marine settings and highlight the value of island observatories for studying the atmospheric nitrogen cycle.

Abstract: The concepts of anthromes and human appropriation of net primary production (HANPP) are both valuable in understanding our human-dominated planet, yet they have never been integrated theoretically or empirically. Here we utilize an extensive county-level dataset on HANPP and its product-level components to derive, through cluster analysis, ten contemporary US anthromes. From highest to lowest density of harvested HANPP, the anthromes are: Rainfed Corn-Soy, Dairy Fodder, Spring Wheat-Small Grain, Winter Wheat-Sorghum and Corn-Soy Dry Margin, Subtropical Soy-Cotton, Commercial Timber, Mixed Hardwood and Pasture, Recovered Eastern Forest, Prairie-Sagebrush Rangeland, and Arid and Alpine Sparse Grazing. Expanding to thirteen anthromes maintains these, while bifurcating the commercial timber (softwood, hardwood), rainfed corn-soy (core, fringe) and mixed hardwood and pasture anthromes. Cluster analysis was more successful than hierarchical modeling at producing empirically meaningful anthromes.

Abstract: The accumulation of agricultural waste poses significant agronomic and environmental challenges in tropical smallholder farming systems where organic wastes are still underutilized. This study evaluated the effectiveness of vermicomposting with Eisenia fetida to enhance nutrient recovery and the quality of organic fertilizers generated from agricultural waste. Four substrate treatments—cow dung, chicken manure, and vegetable waste (T2), cow dung and vegetable waste (T3), poultry manure and vegetable waste (T1), and a control group free of earthworms (T4)—were assessed during 60 days. Bulk density, pH, electrical conductivity, organic carbon, total nitrogen, accessible phosphorus and potassium, and the C: N ratio were among the significant physicochemical properties that were investigated. Vermicomposting significantly increased the nutritional content; the C: N ratio decreased from 26.43 (control) to 12.23, suggesting improved compost maturity, and T2 contained the greatest quantities of potassium (1.45%), phosphorus (1.21%), and total nitrogen (2.63%). Another sign of improved mineralization efficiency was a significant decrease in organic carbon during the decomposition phase. Vermicomposting improved nutrient availability, compost maturity, and structural quality in comparison to traditional composting. These results show that vermicomposting is a successful resource recovery technique that increases nutrient availability, improves resource efficiency, supports sustainable and climate-resilient agricultural systems, and turns agricultural waste into useful organic fertilizer.

Abstract: The immature stages and biology of Neoplinthus tigratus porculus (Fabricius, 1801) (Coleoptera: Curculionidae: Molytinae) associated with common hop (Humulus lupulus L.) are described for the first time. Morphology and diagnostic characters of mature larvae and pupae are documented and compared with related Molytinae and selected Cleonini (Lixinae). The mature larva generally fits the diagnostic characters of Molytinae larvae but differs in several traits, particularly the very short endocranial line and the relative length of frontal setae, with fs4 distinctly shorter than fs5. Together with the presence of five frontal setae, this combination represents useful characters for distinguishing the species. The larva and pupa are covered with cuticular asperities. Biological observations show that the species develops mainly within the root collar and roots of Humulus lupulus, where larvae feed internally and older instars overwinter. Infested plants are characterized by swollen and weakened roots, often containing multiple larvae.

Abstract: Ammonia volatilization after nitrogen fertilization represents a major pathway of reactive nitrogen loss in cropping systems, reducing nitrogen use efficiency and contributing to environmental impacts. This review analyses fertilizer-based strategies to mitigate these losses from a mechanistic perspective, focusing on the processes governing ammonia formation and emission under field conditions. Approaches such as urease inhibition, pH regulation, ammonium retention, and controlled-release formulations are examined in relation to their effects on hydrolysis, chemical equilibria, and mass transfer. Evidence from field studies and meta-analyses shows strong variability in mitigation performance across soils, climates, and management practices, indicating a high dependence on local conditions. The analysis also identifies trade-offs between nitrogen loss pathways, where reductions in ammonia emissions may influence nitrous oxide emissions or nitrate leaching. A process-based framework is proposed to guide the selection of mitigation strategies according to dominant loss mechanisms, supporting more efficient and con-text-specific nitrogen management in cropping systems.

Abstract: Hydrochar has emerged as a promising carbonaceous amendment to enhance soil quality, yet its short-term effects on soil carbon (C) and nitrogen (N) dynamics and microbial functioning remain poorly understood. Here, a 77-day greenhouse pot experiment was conducted using a Cambisol cultivated with sunflower (Helianthus annuus L.) under two irrigation regimes simulating well-irrigated (WI) and water-deficit (WD) scenarios. Two doses of chicken manure–derived hydrochar (3.25 and 6.5 t ha⁻¹) and mineral fertilizer (MF) treatments providing equivalent N inputs were evaluated. Hydrochar promoted microbial growth and enhanced enzymatic and respiratory activities despite its low apparent C and nutrient input. After 77 days under WI, the addition of 6.5 t ha-1 hydrochar enhanced the activity of phenol oxidase (POA) and acid phosphomonesterase (AcPA). Concomitantly, the availability of soluble C and N increased, whereas total organic C (TOC) and N decreased relative to the initial values. These responses indicate a hydrochar-induced priming effect. The increase in POA relative to β-glucosidase is in line with a functional shift from a predominant degradation of labile compounds towards an increased oxidation of more complex structures. This interpretation is supported by solid-state ¹³C NMR data, revealing a higher degradation index of the soil organic matter. Under WD, these hydrochar-induced effects were attenuated but not suppressed, emphasizing the interactive influence of moisture and amendment dose. Overall, our results show that hydrochar modulates soil biochemical processes primarily through microbially mediated mechanisms rather than through direct nutrient inputs.

Abstract: Remotely Piloted Aircraft (RPAs) equipped with multispectral sensors have emerged as promising tools for estimating foliar nitrogen content (FNC). In this context, this study applied a methodological approach aimed at simulating UAV multispectral data using hyperspectral leaf data obtained in a controlled environment, with the objective of evaluating its predictive potential and its transferability to field data collected by UAVs for FNN estimation. To this end, spectral bands and indices equivalent to those of UAV-mounted sensors were simulated based on hyperspectral data acquired by a benchtop sensor, and subsequently used in modeling via Partial Least Squares Regres-sion (PLSR) and Random Forest (RF). The results showed similar performance across the levels, with R² values of 0.75 and 0.76 for PLSR and RF on the UAV data, and 0.75 and 0.74 for PLSR and RF on the simulated data, respectively. The RF model also performed well in cross-domain validation, with R² = 0.70 when calibrated with simulated data and ap-plied to UAV data. Furthermore, the simulated data maintained high predictive power even with a reduced sample size. It is concluded that spectral simulation constitutes a viable strategy for expanding the applicability of nutritional monitoring using multi-spectral sensors.

Abstract: The present study investigates the statistical distribution of slopes in radar reflectivity [S-Ze] in the lower troposphere at the west coast of India using the C-band radar during pre- monsoon months to monsoon months, which spans the different meteorological conditions, including from a drier atmosphere to moist atmosphere. To investigate the S-Ze, we calculated the difference in Ze between 4 to 2 km altitudes in the lower troposphere. For positive [negative] S-Ze, the Ze decreases [increases] towards the surface. The differences in S-Ze in the lower troposphere during pre-monsoon, monsoon onset and monsoon months reveals the precipitation variability. Among all the months, a higher fraction of +ve S-Ze are observed during March and April months compared to other months, and showed that in drier atmosphere the for most of the time Ze tends to decrease towards the surface. However, the average S-Ze shows the highest -ve average -ve S-Ze, during March and April months near the coastal boundaries and associates with the lesser number of profiles. May and June months have a higher fraction of -ve S-Ze [>60%] is observed over the northern latitudes of the study periods, whereas southern AS has a higher fraction of +ve S-Ze. August has the highest fraction of -ve S-Ze, over land and topographic features. September has the highest fraction of +ve S-Ze at the southern latitudes, and at the same time, the study regions are characterized by the drier atmosphere with less updraft. During the pre-monsoon months thermodynamic conditions are more important, where in the drier atmosphere Ze tends to decrease towards the surface. During the monsoon months the dynamics of convective and stratiform precipitation, and either evaporation during the stratiform precipitation along with the convective outburst may increase the lower level RH. Monsoonal months have the less increase or decrease in the hydrometeors size compared to pre-monsoon months, whereas precipitation is more of a convective nature. The results presented here would be an extension of the study from the satellite based observations, and reveals the extension climatology of inclusion of stratiform precipitation.

Abstract: Conventional clustering techniques for urban water consumption profiling treat each household as an independent entity, thereby disregarding the spatial, socioeconomic, and infrastructural contexts that jointly govern demand behavior. This structural limitation prevents the extraction of contextually coherent consumption profiles—a critical shortcoming for utility managers who must design spatially targeted conservation interventions. To overcome this, we propose Simple GLAC, a novel graph clustering framework that leverages graph neural networks with an adaptive attention mechanism to dynamically model these complex interdependencies. The model’s end-to-end training jointly optimizes a latent representation for cluster cohesion, separation, and spatial homogeneity, where each household’s multi-month consumption record serves as the node feature vector encoding temporal consumption patterns. Evaluated on a large-scale real-world dataset of 4590 residential households across four distinct graph topologies, Simple GLAC consistently achieves superior multi-metric performance over both traditional and graph-based benchmarks, yielding interpretable and operationally actionable consumption profiles aligned with the spatial, administrative, socioeconomic, and infrastructural dimensions of urban water governance. This work provides a powerful, data-driven tool for utility managers to deploy targeted water conservation strategies and optimize urban resource distribution.

Abstract: The 27 day and the 11 year solar cycles in extreme ultraviolet radiation (EUV) of the Sun are influencing the Earth’s middle atmosphere. For the first time, the solar cycle influences on geopotential height (or pressure) are analysed by using the Aura Microwave Limb Sounder (Aura/MLS) observations from 2004 to 2021. Composite analysis shows that the mesospheric 27 day variation of the global mean geopotential height is correlated with the 27 day variation of solar radio flux (F10.7cm index) which is a proxy of solar EUV. The maximum of the geopotential height has a phase lag of 4 days with respect to the maximum of EUV. The 11 year solar cycle has a sensitivity of 492m/100sfu in global mean geopotential height at about 94km height. Similarly, the solar cycle influences of the global means of middle atmospheric temperature, ozone, and water vapour are derived and discussed.

Abstract: Climate warming is reshaping hydrothermal resource allocation worldwide, thereby altering the spatial suitability patterns of rice under different cropping systems. Using the maximum entropy model (MaxEnt), this study characterizes the climatic niche features of four typical rice cropping systems in China—single-season rice, double-season rice, rice–wheat rotation, and rice–maize rotation—based on occurrence points and climatic variables, and evaluates changes in their potential suitable areas during 2081–2100 under the SSP2-4.5 and SSP5-8.5 scenarios. The results show marked differences in the dominant factors controlling the four systems. Single-season rice and rice–maize rotation are mainly constrained by heat accumulation, whereas the suitability boundary of double-season rice is jointly limited by extreme temperature thresholds and precipitation conditions. Rice–wheat rotation is more sensitive to annual temperature range, reflecting its dependence on the seasonal rhythm of cold winters and hot summers. Under future climate scenarios, the potential suitable areas of all four systems generally shift northward and expand eastward, with stronger changes under SSP5-8.5. Suitability increases in parts of Northeast and North China, suggesting theoretical potential for the development of double-cropping systems in these regions, whereas some traditional double-season rice areas in South China may face declining suitability because of increasing high-temperature risk. These findings provide a reference for adjusting cropping systems and enhancing regional adaptation under climate change.

Abstract: Accurate liquid water path estimates derived from backscattered solar radiation require knowledge of the vertical structure of cloud droplet effective radius, yet standard bispectral retrievals assume a vertically homogeneous cloud and overestimate liquid water path by up to 45% compared with in situ measurements. We developed a Gauss-Newton optimal estimation retrieval that simultaneously estimates vertical profiles of cloud droplet effective radius and above-cloud integrated water vapor from hyper-spectral solar backscatter measurements in the visible and shortwave infrared. The retrieval solves for effective radius at cloud top and base, cloud optical thickness, and above-cloud integrated water vapor in logarithmic space, using an a priori covariance matrix with off-diagonal elements derived from VOCALS-REx in-situ measurements. Tested on 69 simulated HySICS reflectance spectra constructed from in situ cloud microphysics, the hyperspectral retrieval reduces the average liquid water path error to 17.7%, compared to 45.2% for the standard bispectral method. Applied to 603 EMIT hyperspectral measurements over the southeast Pacific, MODIS-retrieved liquid water path exceeds the hyperspectral estimate by 25.6% on average. These results demonstrate that simultaneous retrieval of above-cloud water vapor is necessary for accurate droplet profile retrievals, and that the upcoming CLARREO Pathfinder instrument, with its 0.3% radiometric uncertainty, should enable routine vertical profiling of cloud droplet size.

Abstract: Background: The intersection of machine learning (ML) and deep learning (DL) with thermal remote sensing (TRS) has undergone a transformative expansion since 2018, driven by the proliferation of high-resolution satellite missions and open-source deep learning frameworks. Despite this rapid growth, to the best of our knowledge, no comprehensive PRISMA-compliant systematic review has synthesised ML/DL applications specifically within the thermal RS domain across the post-2018 period.Objectives: This review maps the complete landscape of ML/DL applications in thermal RS from January 2018 to March 2026 with five primary objectives: (i) quantify publication trends; (ii) classify the taxonomy of ML/DL architectures; (iii) map application domain coverage; (iv) appraise methodological quality and open science practices; and (v) identify research gaps and future directions.Methods: A systematic electronic search was conducted across Scopus and Google Scholar. Following PRISMA 2020 guidelines (Page et al., 2021), records underwent a structured multi-stage screening process implemented in Python. This consisted of five main screening stages after initial deduplication, followed by a final full-text eligibility assessment for open-access records retrieved via the Unpaywall API. Data extraction employed a structured template covering bibliographic metadata, sensor platforms, ML/DL architecture, application domain, performance metrics, and open science practices.Results: A total of 193 peer-reviewed studies met the inclusion criteria, of which 93 were available as open-access full texts and 100 were accessible through title, abstract, and structured metadata only due to institutional access restrictions. CNNs (43.7%), LSTM/BiLSTM (33.0%), and SVR/SVM (29.1%) were the dominant architectures across the 93 open-access full-text studies from which comprehensive architecture data were extracted. Application domains concentrated on SST forecasting, LST retrieval, LST downscaling, and gap-filling, leaving wildfire detection, evapotranspiration estimation, and permafrost monitoring relatively underrepresented compared to core domains. Code availability was reported in fewer than 5% of included studies.Conclusions: This review reveals a maturing but architecturally conservative field with transformative opportunities in physics-informed neural networks, transformer-based models, and underserved application domains. The persistent open science deficit represents a structural reproducibility challenge that warrants urgent community attention.

Abstract: The textile and clothing industry has historically exerted a significant negative impact on the environment. Excessive water consumption, chemical pollution, and soil degradation are just a few of the pressing environmental concerns linked to this sector. Addressing these issues has become a priority not only for regulatory bodies, at National and European level, but also for the industry itself. More recently, growing attention has turned to reducing the huge volume of waste generated by consumers' unbridled purchase of clothing. Two promising solutions to this challenge are the Circular Economy (CE) model and the implementation of the Digital Product Passport (DPP). CE aims to reintegrate discarded garments back into the value chain, reducing the need for new raw materials and minimizing waste, straightforwarding reuse and recycling. Complementing this, the DPP provides comprehensive information about a garment’s life cycle, enhancing transparency, enabling traceability, and empowering consumers to make more informed choices. In this context, consumers play a pivotal role. They have the responsibility to adopt more sustainable habits, namely purchasing less, choosing eco-friendly options, and extending the lifespan of their clothing through reuse, donation, or recycling. This article proposes a gamified platform designed to inspire and support consumers in embracing more sustainable behaviour. The platform enables users to manage their wardrobes by tracking garment usage and documenting each item’s end-of-life journey. This data not only fosters consumer awareness and accountability but also contributes to the broader implementation of the DPP and CE practices.

Abstract: Chromium is one of the most prevalent toxic heavy metals in the environment and is known to cause cancer, and cellular damage. Various treatments can effectively remove Chromium ion from wastewater. However, majority of those methods are not environmentally friendly. Here, we investigated the efficacy of stem cactus activated carbon (SCAC) and commercial activated carbons (CAC) to remove hexavalent chromium from synthetic and real wastewater. In this study, the stem cactus adsorbent was thoroughly characterized. The effect of initial concentration of Cr (VI), contact time, adsorbent dose, shaking speed, and pH on the adsorption process were examined using Micro Plasma Atomic Emission Spectroscopy and UV-Vis Spectroscopy. The data were analyzed using R-software (version 4.4.3 (2025-02-28)) and Origin (2022). The trends in removal efficiency were examined descriptively using line graph. The adsorption equilibrium isotherms and kinetics models were fitted to the data to evaluate the biosorption mechanisms and compare the sorption capabilities of the two biosorbents (SAC and CAC). Under optimal conditions (0.15g SCAC, pH 2, contact time 60 min, shaking speed 200rpm, and an initial Cr (VI) concentration of 6mg/L), Cr (VI) removal efficiencies reached 98.4% and 99.2% from real and synthetic wastewaters, respectively. The adsorption data fitted both the Langmuir and Freundlich isotherm models, suggesting mixed homogenous and heterogenous surface characteristics on the adsorbent. The adsorption process is an endothermic process and respects the pseudo second order kinetics model. The present study suggests that plant-based adsorbents represent an effective alternative for Cr (VI) ion removal.

Abstract: Communities in Mississippi located near petrochemical refining facilities face ongoing risks from heavy metal contamination in soils, threatening environmental quality, food safety, and public health. This pilot study evaluated the phytoremediation potential of Nerium oleander and cabbage (Brassica oleracea) in a residential fence-line community within the Cherokee Forest subdivision of East Pascagoula, Mississippi, impacted by long- term petrochemical and shipyard activities. Plants were grown directly in contaminated garden soils under natural field conditions. Soil and plant tissue concentrations of lead (Pb), cadmium (Cd), zinc (Zn), and nickel (Ni) were measured using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Phytoremediation effectiveness was assessed through removal efficiency, translocation factor, and bioaccumulation factor. Re- sults showed significant reductions (p < 0.01) in all soil metals, with cadmium removal exceeding 97%. Nerium oleander exhibited substantially higher metal uptake and trans- location capacity than cabbage, achieving a maximum cadmium translocation factor of 9.99 and bioaccumulation factors up to 5.67. In contrast, cabbage showed lower transloca- tion efficiency, indicating limited remediation potential but suitability as a food crop after soil treatment. These findings highlight Nerium oleander as an effective, sustainable, and community-acceptable phytoremediation solution.

Abstract:

Katse Dam(KD), a strategic raw water source to South Africa, is exposed to pollution from mining and aquaculture production. The organic pollution index (OPI), the modified pollution index (MPI), and Carlson's trophic state index (CTSI) have not been previously applied to KD. The current study applies these indices to assess the trophic status of KD in the first decade (FD) (2003-2013), when the intensity of mining and aquaculture activities was minimal, and compares with the second decade (SD) (2014-2024) when production was higher. The Pollution Index of KD revealed that it transitioned from contaminated during the FD to greatly contaminated during SD. KD shifted from eutrophic status to hypereutrophic status in the lacustrine zone during the SD. The cyanobacteria Radiocystis sp. replaced Asterionella sp. and became the most pollution-tolerant algae in the SD, followed by the diatom Flagilaria sp. The pollution index (PI) values of physico-chemical parameters increased from 65 in the FD to 160 in the SD. OPI classifies KD as extremely polluted, with values above the threshold of 5 OPI in the SD. Application of the different indices, attribute mining, and aquaculture as influential to the transition of KD from mesotrophic to eutrophic in the transitional zone. The findings provide environmental managers with a basis to mitigate pollution at source to secure good water quality.

Abstract: Measurements assessing how micrometeorological conditions—driven by the proximity of large buildings—influence PM_2.5 levels were conducted in an urban commune of Santiago, Chile, during the winter and spring seasons. This commune is characterized by a mixture of one story houses and high-rise building. These large building may have an influence on the air quality because they alter wind circulation or lower the temperature due to the shadow of the building. In this work, PM_2.5 and meteorological parameters were measured outside three pairs of houses in winter of 2021, one of which received shadow from a nearby building and the other was under the sun. In one pair, PM_2.5 was larger in the house located under the shadow of the building only during the winter months, the result was attributed to the lower temperature generated by the shadow, which inhibited PM_2.5 dispersion. In another pair of houses, the result was attributed to the difference in wind speed in one of the houses, because the building acts as a windbreak. In the third pair of houses no effect was observed in PM_2.5, this is probably due to the lack of significant micrometeorological differences between the two sites.