Unveiling the phytoremediation potential and mechanisms of Phyla canescens in heavy metal-contaminated soils.

Gastropods are vital bioindicators of aquatic ecosystems, reflecting environmental conditions and anthropogenic impacts. This study investigated gastropod composition, abundance, biodiversity, and their relationships with environmental parameters in the Alor Archipelago, East Nusa Tenggara. Sampling using quadrat methods was conducted across four areas: port activities, tourism activities, seagrass ecosystems, and residential areas, with sediment and environmental parameters analyzed. A total of 69 gastropod species were identified, with Erosaria erosa being the most abundant (0.158 ind/m²). Seagrass ecosystems exhibited high diversity (H' > 3), stable distributions (E > 0.75), and clustered patterns (Ip > 0), indicating favorable conditions. Degraded areas, such as port activity areas, showed lower diversity, unstable distributions, and higher dominance, associated with reduced dissolved oxygen and anthropogenic stressors. Principal component analysis revealed higher biodiversity linked to natural habitats, while degraded areas exhibited reduced ecological quality. These findings underscore the critical impact of human activities on gastropod communities and emphasize the need for preserving natural habitats to sustain biodiversity and ecosystem functions. This study provides valuable insights for developing sustainable management and conservation strategies in the Alor Archipelago.

ABSTRACT Tremendous amounts of generated municipal solid wastes (MSWs) and limited energy sources make utilization of sustainable waste management strategies mandatory. Lower efficiency of recycling practices especially in developing countries compels authorities to search for novel sustainable solutions. Biodrying is a promising technology for producing solid recovered fuel (SRF) from partially stabilized waste. However, accurate evaluation of biodrying performance remains challenging due to the complexity of microbial heat generation processes. On the other hand, basic biodrying indices are found to be insufficient for evaluating the overall efficiency. Multi-criteria decision-making techniques providing accurate and comprehensive information may serve as useful tools to interpret efficiency of such complex systems. The major aim of this work was to observe a meaningful ranking of biodrying efficiency by using AHP-PROMETHEE, considering all treatment and cost parameters. With this aim, first batch biodrying experiments were performed in a pilot-scale reactor with a volume of 0.8 m3 for a 7-day period. Eight trials including varying waste compositions weighing 70 ± 7 kg were tested under different air flow rates (AFR). Temperature and weight loss profiles together with moisture removal and leachate generation rates were determined for each of the trials. Energy contents (lower heating value (LHV)) were also analyzed after and before biodrying. The experimental results indicated that the higher food waste and lower AFR (Trial 4) increased volatile solids (VS) reductions along with the observed peak temperature (up to 60°C). In contrast, high AFR and low organic content (Trial 5), resulted in the lowest VS (6.86%) reduction and provided the highest biodrying index (6.76). The most crucial material in increasing the energy content of the waste matrix was plastic wastes with low biodegradability. As the proportion of plastic waste increased, the bioheat production capacity decreased and applied AFR became the most important factor in obtaining SRF (Trial 1). AHP results highlight energy content increase as the key factor (33% weight), while leachate production had minimal influence (6%). PROMETHEE ranked Trial 1 the first with the highest net flow value (Phi) of 0.32. Briefly, the study offers a novel approach to achieve an easy, informative, and accurate performance assessment for biodrying processes.

Mining and e-waste recycling influence the spatial distribution of technology-critical elements, but not rare earth elements, in boreal lakes.

Long-term sediment decline in the Mekong River along Thailand's border under hydropower development.

Purpose In the contemporary business landscape, the interplay of gender, sustainability and digitalization has emerged as a critical factor influencing firm performance. This study aims to systematically integrate scientific knowledge on these dynamics through a comprehensive literature review, identifying key trends and themes that link gender diversity policies, technological innovations and firm outcomes. Design/methodology/approach The study conducted a comprehensive literature review of 211 journal articles, with 63 selected for in-depth analysis using PRISMA. Thematic clusters were identified through qualitative analysis, focusing on how gender diversity, sustainability and digitalization contribute to firm performance. Findings Five thematic clusters were identified: “Corporate Social Responsibility (CSR) and Environmental Management,” “Gender Diversity, Sustainable Development and Digital Platforms,” “Academic Research, Motivation and AI/Technology in Learning,” “Adoption and Behavioral Intention in Technology Use” and “Performance in Business and Agriculture with a Focus on Environmental Impact.” The findings demonstrate that integrating gender equality with sustainability initiatives and digital education enhances organizational adaptability and resilience. Gender diversity was also found to significantly foster innovation capabilities, making sustainability a strategic element that drives superior financial performance. The role of technology in bridging gender gaps and promoting equitable business practices was emphasized, showcasing its potential to empower women. Research limitations/implications The study is limited by its reliance on secondary data from journal articles, which may introduce selection bias. Future research could benefit from primary data collection to further validate these findings. Originality/value This study provides a novel integration of gender diversity, sustainability and digitalization in the context of firm performance. It highlights the synergistic effects of these factors on innovation and organizational success, advocating for gender mainstreaming and digitalization as essential strategies for sustainable management aligned with the United Nations sustainable development goals (SDGs).

Objectives: Environmental conservation is the need of the hour and environmentalists across the globe are working towards the attainment of a sustainable society. This research work proposes a novel approach to quantum-based multi-criterion decision-making, integrating the method of ITARA (Indifference Threshold-based Attribute Ratio Analysis) in selecting sustainable waste mitigation methods. Methods: The traditional decision methods lack the competency in handling the uncertainty, cognitive interferences and contextual conflicts in evaluation. The newly developed approach is based on the principles of quantum mathematics, especially state superposition and probabilistic preference evaluation. The proposed approach is applied to the decision-making problem consisting of five alternatives and five criteria for evaluating waste management methods. The employment of quantum probability and inner product calculations facilitates handling ambiguity and interference among criteria. Findings: The results demonstrate that the proposed quantum-ITARA model achieves a 14.8% improvement in accuracy of preference ranking compared with conventional MCDM methods, while reducing decision inconsistency by 12.3%. Among the five alternatives, the model identified Alternative A3 as the most sustainable option with an overall quantum-evaluated score of 0.842, followed by A2 (0.765) and A5 (0.701). These measurable outcomes substantiate the effectiveness of the proposed approach in deriving an optimal and reliable solution to the problem of selecting a sustainable waste management method. Novelty: A novel approach to quantum-based multi-criterion decision-making is proposed, integrating ITARA with quantum probability and mathematical principles to address uncertainty, cognitive interferences, and contextual conflicts in sustainable waste management evaluation. Keywords: ITARA, Quantum, Optimal, Sustainability, Waste Management

This study investigates the optimization of Pumped Storage Hydropower (PSH) integrated with Floating Photovoltaic (FPV) systems, with a focus on sustainable energy management. A nonlinear programming framework combined with scenario analysis was applied to a real hydropower system in Trentino, Italy. The optimization maximizes revenues through energy arbitrage while accounting for water resource and environmental objectives. Upgrading the traditional hydropower plant to PSH operation increases revenues by 4–8% over two hydrological years. Multi-objective optimization further reveals large gains in water availability, confirming PSH’s dual role as energy storage and water management infrastructure. Different FPV configurations analyzed show a 2–3% increase in photovoltaic energy yield due to the water-cooling effect, while the overall hybrid PSH–FPV integration mainly reduces grid dependency and pumping-related emissions, with near-complete decarbonization achievable under optimized scheduling. Overall, PSH provides the primary economic and operational advantage, while FPV strengthens sustainability, enabling resilient hydro–solar operation and contributing to renewable integration and decarbonization in future energy systems.

Characterizing regional groundwater chemistry and quality is essential for sustainable water resource management, yet remains challenging due to spatial complexity arising from both natural and anthropogenic factors. In this study, a hybrid Self-Organizing Map (SOM) and Principal Component Analysis (PCA) approach was applied, followed by Hierarchical Cluster Analysis (HCA), to interpret the hydrochemical characteristics of groundwater in the Qorveh-Dehgolan basin, Iran. A total of 112 groundwater samples collected during dry and wet seasons were analyzed. To ensure optimal performance, multiple SOM map sizes and normalization techniques (Z-score, Min-Max, and log(1 + x)) were tested and evaluated using Quantization Error (QE), Topographic Error (TE), and Explained Variance (EV). The 8 × 7 SOM grid (56 neurons) was selected as the final configuration, as it produced the lowest QE and TE and the highest EV. The optimized SOM results were subsequently grouped into four clusters based on the combined evaluation of SOM and HCA outcomes. Hydrogeochemical processes were interpreted using Piper and Gibbs diagrams, as well as cation exchange indices. Results indicated a dominant Ca2⁺-HCO3⁻ water type across all clusters (1-4). Cation concentrations followed the order Ca2⁺ > Mg2⁺ > Na⁺ + K⁺, while the dominant anion sequence was HCO3⁻ > Cl⁻ > SO42⁻. Ionic ratio analyses revealed that elevated NO3⁻ concentrations are largely attributable to agricultural fertilizer use and domestic wastewater infiltration, highlighting anthropogenic impacts on groundwater quality. In contrast, natural geochemical processes, including silicate weathering and carbonate dissolution, were identified as the predominant mechanisms controlling groundwater evolution. Overall, the integrated SOM-PCA-HCA framework effectively captured both natural and human-induced variability in groundwater chemistry, and distinguished seasonal variations in water quality, underscoring its applicability for sustainable groundwater management in complex aquifer systems.

Subsea environments are vital for global biodiversity, climate regulation, and human activities such as fishing, transport, and resource extraction. Accurate mapping and monitoring of these ecosystems are essential for sustainable management. Airborne LiDAR bathymetry (ALB) provides high-resolution underwater data but produces large and complex datasets that make efficient analysis challenging. This study employs deep learning (DL) models for the multi-class classification of ALB waveform data, comparing two recurrent neural networks, i.e., Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). A preprocessing pipeline was developed to extract and label waveform peaks corresponding to five classes: sea surface, water, vegetation, seabed, and noise. Experimental results from two datasets demonstrated high classification accuracy for both models, with LSTM achieving 95.22% and 94.85%, and BiLSTM obtaining 94.37% and 84.18% on Dataset 1 and Dataset 2, respectively. Results show that the LSTM exhibited robustness and generalization, confirming its suitability for modeling causal, time-of-flight ALB signals. Overall, the findings highlight the potential of DL-based ALB data processing to improve underwater classification accuracy, thereby supporting safe navigation, resource management, and marine environmental monitoring.

Water stress represents a critical global challenge demanding innovative solutions for effective water resource management. Microbial electrochemical technologies (METs) leverage bacterial extracellular electron transfer for addressing issues related to water stress. These technologies exhibit a diverse range of applications, positioning them as integral to sustainable development through effective water resource management. Their versatility allows them to function as key contributors to achieving the United Nations Sustainable Development Goals (UNSDGs). Through their application, METs offer promising strategies for mitigating pollution, recovering valuable resources, and enabling real-time water quality monitoring. Employing these technologies facilitates the concurrent addressing of various UNSDGs, fostering a holistic and integrated approach. METs present opportunities for decentralized wastewater treatment and reuse, thereby promoting accessibility to clean water and sanitation, particularly in marginalized communities. However, the realization of these benefits faces significant challenges, including technological scalability, optimization, and regulatory frameworks. Overcoming these obstacles is crucial for harnessing the full potential of METs to meet UNSDGs. This perspective article underscores the imperative of further research, collaboration, and policy support to propel METs towards becoming a cornerstone in the sustainable management of water resources and the achievement of UNSDGs on a global scale.

The conservation of cultural heritage increasingly requires a transition from emergency restoration to preventive and planned strategies supported by systematic data management. Within this context, this paper, conceived as a project report, presents the methodological premises, operational framework, and preliminary outcomes of the Fountains and Monuments in the Public Space of the City of Turin project, developed within the CHANGES Project—National Recovery and Resilience Plan (NRRP), funded by the European Union—NextGenerationEU. The project explores the integration of preventive and planned conservation methodologies with digital tools for the sustainable management of outdoor cultural heritage. Five case studies in Turin, identified in collaboration with local authorities, provided the basis for developing a protocol for planned conservation. A digital platform was designed as the operational tool of this protocol, integrating georeferenced data, 3D models, interactive dashboards, and modules for inspection, planning, and monitoring. The platform enables data-driven prioritisation of interventions, traceability of conservation activities, and long-term documentation management. Although still at the demonstrator stage, it shows potential for scalability and transferability. The study concludes that the integration of interdisciplinary expertise and digital innovation can effectively support preventive and planned conservation, strengthening the systematic management of outdoor cultural heritage.

Utilization of agro-industrial wastes as soil amendment manages acidity, remediates heavy metals, and ensures environmental sustainability. The present study aims to test non-conventional organic Si sources, milled rice husk char (TMRHC), and powdered diatomaceous earth (TDE) against burnt lime (TBL) and calcium silicate (TCS) for amending iron (Fe) toxicity and acidity at tillering (TI), panicle initiation (PI), and harvest (HA) of a short duration rice variety Manuratna in lateritic rice wetlands of Kerala. Irrespective of the growth stages of rice, TMRHC recorded the lowest water-soluble Fe (WS-Fe), acid-soluble Fe (AS-Fe), manganese oxide (MnO)-occluded Fe (MN-Fe), and residual Fe (RS-Fe) fraction in soil. WS-Fe indirectly enhanced Fe adsorption into specifically adsorbed lead displaceable Fe (SP-Fe) and AS-Fe, whereas AS-Fe abridged the Fe adsorption between WS-Fe and MN-Fe > OM-Fe. Organic Si sources recorded a strong negative influence on organic matter-occluded Fe (OM-Fe) (standardized path coefficientp = - 0.94), amorphous iron oxide (FeO)-occluded Fe (AM-Fe) (p = - 0.74), and crystalline FeO-occluded Fe (CR-Fe) (p = - 0.53) content in soil. Fractions of Fe and forms of acidity were significantly (p = 0.05) and positively correlated. TBL maintained the highest soil pH at all the critical growth stages (5.90(TI), 6.04(PI), and 5.99(HA)) of rice, which was statistically on par with TMRHC (5.90(TI), 6.02(PI), and 5.96(HA)). The highest reduction of Fe in rice was recorded with TBL at TI (16%) and PI (18.76%), while at HI (21%), TMRHC was found to the most effective. TBL and TMRHC were statistically on par in reducing WS-Fe (at PI and HA), exchangeable acidity and extractable acidity (at TI), and total acidity (at HI). The grain yield of Manuratnarice was found to vary in the order TCS > TMRHC and TBL.

Rice-based cropping systems in central and eastern India often rely on intensive inputs, leading to soil degradation, nutrient imbalance and reduced system sustainability. Integrating pulses, such as cowpea, after rice is a viable strategy for sustainable intensification to enhance soil fertility and improve resource-use efficiency. However, cowpea yields in marginal and rainfed areas remain low due to inadequate nutrient management, particularly sulphur (S) and zinc (Zn). To address this gap, a field experiment was conducted during the summer season of 2024 at the Post Graduate Research Farm, Gajapati district, Odisha, to assess the effects of edaphic application of sulphur and foliar application of zinc EDTA on the growth, productivity and nutrient uptake of cowpea. The experiment was laid out in a factorial Randomised Block Design with four sulphur levels (0, 10, 20 and 30 kg S/ha) and three foliar zinc levels (0.1%, 0.2% and 0.3%), replicated thrice. Results revealed that both sulphur and zinc significantly influenced growth attributes, physiological parameters, yield components and yield. The application of 30 kg S/ha consistently resulted in the highest plant height, dry matter accumulation, nodule formation, yield attributes, and grain and stover yields. Similarly, foliar zinc EDTA @0.3% at 30 and 50 DAS enhanced physiological efficiency and improved yield and nutrient accumulation. Nutrient content and uptake of S and Zn were highest under a nutrient dose of 30 kg S/ha and 0.3% Zn EDTA than other treatments. Economic analysis confirmed superior net returns and benefit: cost ratio with higher S and Zn application. Overall, the study demonstrates that optimal application of sulphur combined with foliar zinc EDTA substantially enhances cowpea performance and offers a sustainable nutrient management approach under South Odisha conditions.

Introduction: Land inequality remains a major challenge in developing nations, particularly in Indonesia and Nigeria, where unequal land distribution has deep social and economic implications. Both countries have undertaken land law reforms to promote fair access and sustainable resource management. Understanding how these reforms operate within distinct legal and historical contexts is essential for achieving equitable agrarian governance.Purposes of the Research: Analyze and compare the land law reform processes in Indonesia and Nigeria to assess their contribution to achieving justice in agrarian governance. It seeks to identify key similarities and differences in reform approaches, evaluate their socio-legal impacts, and explore policy strategies that strengthen land rights, social inclusion, and rural welfare.Methods of the Research: A qualitative comparative legal analysis was employed, focusing on legal frameworks, policy implementation, and institutional mechanisms in both countries. Data were collected through literature review, document analysis, and secondary sources such as academic journals and government reports. The comparative framework allows examination of each country’s reform trajectory and its effectiveness in promoting fair and sustainable agrarian governance.Results Main Findings of the Research: The findings reveal that although both countries differ in their historical and legal contexts, they face similar challenges namely, land ownership concentration among economic elites and weak protection of indigenous and smallholder farmers’ rights. Indonesia has shown progress through land redistribution and asset legalization programs, while Nigeria emphasizes decentralized land management and community-based access policies. The study concludes that achieving equitable agrarian governance depends on the integration of legal reform, public participation, and policy transparency.

Enhancing Allium cepa L. cultivation through sustainable soil management with biochar manure and NPK fertilizers

Coastal areas represent a dynamic and productive intersection between land and sea, characterized by unique ecosystems such as estuaries, mangroves, seagrass beds, and coral reefs. However, these areas face significant environmental challenges, including silting, climate change, land subsidence, and pollution, exacerbated by human activities like resource extraction and industrial development. Despite the abundant natural resources, many fishers struggle with low income due to factors such as poor fishing technology, environmental degradation, and climate variability. The research aims to analyzing the Socio-Economic Ethnography of Tambakbulusan, 2). Investigating coastal vulnerability in Tambak Bulusan, 3). Explore the adaptation and mitigation in dealing with the vulnerability of resources and communities in coastal areas, 4). Formulate adaptive strategies through a co-management approach.. This study location in Tambakbulusan Village, Demak Regency. Using a mixed-method approach, both quantitative and qualitative data were collected from 57 small-scale fishers through field surveys and interviews. The findings highlight that sensitivity is the highest indicator that causes vulnerability. Its needed for sustainable coastal management practices and co-management strategies to improve the adaptive capacity of fishers and enhance their livelihoods in the face of environmental and economic uncertainties.

Restoring the environmental quality of lakes is essential to encourage sustainable tourism and maintain the ecological balance of conservation areas. This study has explored the dynamics of collaboration between local communities and stakeholders in managing environmental restoration in lake conservation areas in Indonesia, emphasizing the intention and support for community collaboration. By applying the Theory of Planned Behavior (TPB) as a guiding framework, this study has examined the influence of attitudes, subjective norms, perceived behavioral control, and behavioral intentions on community participation in restoration efforts. A cluster random sampling method has been utilized to survey local communities in the Lakeshore region. The study findings have revealed that positive attitudes and strong subjective norms significantly encourage collaboration intentions, while perceived behavioral control moderates’ community support and restoration initiatives. These findings highlight the necessity of aligning local perceptions with conservation goals to enhance collaborative efforts and ensure the success of sustainable lake management. This study has contributed to the understanding of community-based environmental management and provided policymakers with some applicable insights to encourage effective collaboration and sustainable tourism development.

Coastal areas are dynamic, shaped by natural processes and human activities, making accurate prediction of shoreline and land use changes crucial for sustainable management. This study presents a hybrid modeling framework that combines CA-Markov and machine learning to enhance land use/land cover (LULC) and shoreline change projections in Oman's vulnerable coastal regions. Coastlines were delineated using multi-temporal Landsat images (1997-2006-2015-2024) and the Normalized Difference Water Index, while erosion and accretion rates were quantified using End Point Rate and Linear Regression Rate analyses. Results from 1997 to 2024 show substantial spatial variability, with urban localities such as Rakhyut experiencing significant erosion (-1.81m/year) and areas like Bawshar showing accretion (1.41m/year). Coastal LULC changes reveal rapid urban expansion, as seen in Muscat's built-up area, which increased from 10.31km² in 1997 to 116.41km² in 2015. Four models-CA-Markov, CA-Markov + XGBoost, CA-Markov + CART, and CA-Markov + RF-were evaluated for future LULC prediction. The hybrid CA-Markov + RF model achieved the highest predictive performance, increasing overall accuracy from 0.905 (CA-Markov) to 0.935 (CA-Markov + RF) on the test dataset, highlighting the capability of machine learning models. Projections for 2033 indicate continued urban growth, particularly in Salalah and Sohar, alongside reductions in vegetation in arid regions.

This study examines the institutional mechanisms affecting sustainable land use efficiency in Kazakhstan’s Almaty and Turkestan regions. Drawing on expert interviews with representatives from governmental land management bodies, the research identifies critical barriers and potential opportunities in implementing effective land use strategies within the current socio-economic and environmental context. The study develops a region-specific framework to assess sustainable land use ef-ficiency, incorporating administrative capacity, the effectiveness of land governance tools, and the role of stakeholder engagement. Key findings underscore the necessity of enhancing institutional coordination, im-proving the integration and accessibility of land use data, and aligning policy instruments with regional needs and local conditions. The re-search reveals that fragmented responsibilities, limited interagency communication, and outdated data systems hinder sustainable land management efforts. In contrast, strong stakeholder collaboration and adaptable governance structures offer pathways for improvement. This study contributes to the academic and policy discourse by bridging the gap between theoretical frameworks and practical land use planning in developing contexts. It provides targeted policy recommendations aimed at fostering more coherent, adaptive, and sustainable land governance practices in Kazakhstan’s agricultural regions, ultimately supporting national goals for land reform, environmental protection, and rural de-velopment.