Sustainable fish resources are essential for ecological and economic stability, yet many freshwater fish species accumulate toxic contaminants that threaten human health, environment and regional economies. This study analyzes a multi-variable dataset from the Michigan Fish Contaminant Monitoring Program, containing 37 variables across numerous fish species and including key pollutants, such as mercury, perfluorooctane sulfonic acid (PFOS), polychlorinated biphenyls (PCBs), dichlorodiphenyltrichloroethane (DDT), chlordane, and toxaphene. After rigorous preprocessing, including missing-value treatment, outlier removal using the 3-sigma rule, and z-score standardization, principal component analysis (PCA) was applied to characterize contamination patterns. The principal component analysis revealed that the first three principal components explain approximately 55% of total variance, with PC1 reflecting overall contamination and fish size, while PC2 and PC3 distinguish pesticide-driven pollutants from industrial chemicals. These statistically derived contamination regimes highlight biophysical pressures that carry economic consequences. Notably, species such as Lake Whitefish and Lake Trout, which support a $5.1-billion Great Lakes fishery, exhibit contamination patterns with direct implications for market stability and food safety. The findings illustrate how multivariate statistical methods can inform ecological and environmental economic assessments, identifying pollution pathways and supporting circular-economy strategies aimed at reducing contaminant inputs, improving resource efficiency, and safeguarding economically and ecologically valuable fish populations.

The Assaluyeh coastal industrial zone on the Persian Gulf hosts one of Iran’s largest clusters of oil, gas, and petrochemical facilities. Despite decades of project-level Environmental Impact Assessments (EIAs), the region continues to face severe environmental stress, raising important questions about the effectiveness of traditional assessment approaches. This study examines the core limitations of project-based EIA in Assaluyeh and highlights why a shift toward Cumulative Effects Assessment (CEA) is urgently needed. To explore this issue, we reviewed publicly available EIA and Environmental, Social, and Health Impact Assessment (ESHIA) reports, along with scientific publications from 2015 to 2024. Our analysis focused on whether cumulative impacts were acknowledged, the quality and clarity of the methods used, and the overall transparency of documentation. The findings reveal a consistent pattern; most reports limit their evaluations to individual project boundaries and either ignore cumulative impacts or address them only superficially. At the same time, field-based environmental studies draw a much clearer picture of the real situation on the ground. Multiple pollutants, including heavy metals (Pb, Cd, Ni, V), PAHs, BTEX compounds, and high levels of particulate matter, have accumulated across the region. These pollutants originate from numerous sources and interact in complex ways, creating environmental pressures that single-project EIAs are not equipped to capture. Together, these insights point to a crucial gap in current environmental governance. A regional CEA framework, embedded within a Strategic Environmental Assessment (SEA), would provide a more holistic view of industrial development and its long-term consequences. Such an approach can improve coordination among institutions, enhance environmental data sharing, and support more sustainable planning for Iran’s rapidly expanding coastal industrial zones. Overall, this study emphasizes that managing the environmental challenges of Assaluyeh requires moving beyond project-level assessments and adopting tools that can address the full scale of cumulative and synergistic impacts.

Introduction
Photovoltaic power plants are expanding their installed capacity worldwide, accompanied by a growing number of installations. This relates to land-use transformation and to competition among various land-use forms. Specifically, photovoltaic power plants, particularly utility-scale ones, can compete with agriculture and other land uses, such as mining and urban development. Thus, it is important to investigate the intensity of land use and the power density of these generation units to adequately evaluate the impact of this type of renewable energy on land-use structures.

Methods
The works aimed at determining the power density, land-use intensity, or energy density of the power plants in question were considered. Scientific databases (Scopus and Web of Science) were used to find relevant articles. Subsequently, the calculations, which enable comparison of the examined coefficients, were performed.

Results
The results provide important information on environmental impact, enabling evaluation of the scale of potential land-use transformation relative to the planned volume of installed capacity. This study, building on recent work on this topic, examines the land-use intensity of photovoltaic power plants. Moreover, the power density of the considered power plants is determined. This coefficient is essential when comparing different types of generation units, as it allows assessment of the electricity production efficiency of individual technologies in terms of land use. Furthermore, energy density, being a derivative of power density, is presented. In addition, the interrelation of the indicated coefficients is described.

Conclusions: Power density is a parameter strictly related to land-use intensity and varies under specific conditions across regions. Additionally, when evaluating the employment of photovoltaic power plants, the energy density factor should be taken into account.

The assessment of environmental liabilities associated with metallurgy requires integrating chemical, physical, and biological indicators to estimate risks and inform remediation strategies. In this case study, a smelting complex was investigated through georeferenced sampling at a site in southwestern Spain, within the Almadén region, affected by historical metallurgical slags. Part of these slags was reused during a partial reactivation of the area, resulting in heterogeneous materials that may serve as sources of contamination. At present, the site is embedded in a forested environment that supports wildlife, thereby increasing the ecological relevance of the study.

Soil pH (6.26–8.23) and electrical conductivity (30–1,539 µS·cm⁻¹) were determined, together with multielemental composition by XRF-ED and enzymatic bioindicators (acid phosphatase, β-glucosidase, and dehydrogenase). Extremely high concentrations of Pb (up to 121,235 mg·kg⁻¹), Zn (1,525 mg·kg⁻¹), Cu (3,110 mg·kg⁻¹), As (663 mg·kg⁻¹), and Sb (1,510 mg·kg⁻¹) were detected, largely exceeding guideline values for contaminated soils. Multivariate analysis revealed a coherent polymetallic association (Pb-Zn-Cu-As-Sb) associated with the dispersion and reuse of slags, which is distinguishable from the natural geochemical background.

Acid phosphatase and β-glucosidase activities remained at moderate levels even in areas with high metal loads. In contrast, dehydrogenase activity was inhibited by more than 60%, indicating a marked impact on soil biological quality. These findings highlight the need for integrated assessments to effectively manage environmental impacts and ecological risks associated with metallurgy-derived liabilities.

Rhodium (Rh) is a platinum group element (PGE) increasingly found in urban environments due to traffic-related emissions. In this study, Rh was quantified via ICP-MS in 137 topsoil samples from urban, industrial and garden areas in Alcalá de Henares (Spain). Despite being below detection limits in 15.5% of urban samples, Rh concentrations were significantly higher in garden soils (median: 0.168 mg/kg) than in urban parks (0.084 mg/kg; p-value<0.05). Principal component and cluster analyses grouped Rh with molybdenum (Mo) and antimony (Sb), suggesting shared anthropogenic origins, likely related to vehicle emissions and industrial activities. Rh levels showed substantial spatial variability, with high coefficients of variation and skewed distributions that point to localised hotspots. Notably, Rh concentrations were strongly associated with sand and silt fractions, indicating its potential mobility and influence of soil texture. Toxicological characterisation could not be performed due to the absence of US EPA reference values (RfDo/RfCi). Nonetheless, given Rh’s increasing environmental prevalence, potential for human exposure via resuspended particles, and growing use in industrial processes, its monitoring is essential. This study highlights a regulatory and research gap surrounding Rh, emphasising the need for its inclusion in environmental assessments. Further research is warranted to elucidate its toxicokinetics, environmental persistence, and possible health effects, especially in vulnerable populations exposed to urban garden soils.

The Black Sea suffers from pollution originating mainly from land-based sources. The sea receives plastic waste from three major transboundary rivers – the Danube, Don, and Dnieper. Additionally, plastic debris of all sizes (macro, meso, micro, and nanoplastics) can carry various anthropogenic toxicants, such as heavy metals and persistent organic pollutants, over long distances, and they can also leach hazardous substances like bisphenol A and phthalates. The negative effects of MPs on aquatic organisms, resulting from plastic ingestion or entanglement, are already well documented globally. However, comprehensive studies on the ecological status of the Black Sea in Bulgaria are extremely scarce, especially those that include analysis of MP content in commercially important mussel species, surface waters, and sediments. The scientific hypothesis we have formulated is that the Black Sea and mussels are contaminated with MPs. Therefore, the main goal of this project is, for the first time in Bulgaria, to investigate and assess the quantity and composition of MPs in commercially important mussels, waters, and sediments from the Black Sea area, and their negative impact. Emerging contaminants such as MPs require innovative analysis techniques. The environmental samples will be sent to an accredited laboratory , and quantum cascade laser spectroscopy will be used (the Agilent 8700 Laser Direct Infrared Chemical Imaging System). The mussels will also be studied for a set of biomarkers. Last but not least, an assessment will also be conducted on the potential risk to human health associated with the consumption of Black Sea mussels contaminated with MPs.

Acknowledgements: This study is financed by the Department of Scientific Research of University of Plovdiv, project MUPD25BF001 - “Research and assessment of the quantity, composition, and size of microplastic particles in commercially important mussels from the Black Sea area, their negative impact on specific biomarkers, and the risk to human health”.

In the urban and peri-urban areas across Sub-Saharan Africa, rapid urbanization, population growth and changes in consumer behaviour have resulted in volumes of biodegradable waste, most of which is disposed of through open dumping and landfilling. These practices contribute to greenhouse gas emissions, contamination of water bodies and declining soil productivity. This research assesses the impact of vermicomposting technology for the production of biofertilisers inthe environment in Sub-Saharan Africa, with a focus on its role in sustainable waste management, soil health restoration and environmental resilience. Vermicomposting is the biological conversion of organic waste into nutrient-rich vermicompost and vermiwash using earthworms as catalysts. The process is low cost, decentralized and appropriate for rural and urban setup. This research embraces a mixed-methods approach, based on field observations, compost and soil analysis, structured interviews with farmers and waste managers and policy document reviews from case studies in South Africa, Kenya and Ghana. The results indicate that vermicomposting improves soil fertility by increasing organic matter, nitrogen, phosphorus and microbial activity, while improving water retention. It also reduces household organic waste by over 50%, contributing to lower methane emissions and cleaner urban environments. Regardless of ecological advantages , the implementation is still minimal, owing to policy gaps, limited technical skills and inadequate institutional backing. This study helps to understand how soil and waste interact, shows how waste can be turned into useful resources, supports efforts to restore damaged land and points out policy issues that slow down progress. It concludes that vermicomposting technology holds significant potential to improve environmental quality and promote sustainability in Sub-Saharan Africa. Scaling its impact will require integration into national policies, investment in education and community engagement and incentives for decentralized implementation. This approach is able to foster greener and more resilient communities across the region.

Urban districts affected by social fragility and environmental degradation increasingly require predictive and adaptive tools capable of addressing intensifying climate pressures and complex ecological dynamics. This contribution presents a multilayer framework for assessing and enhancing urban environmental resilience by integrating AI-driven environmental analysis, distributed sensing networks and predictive ecological modelling. The proposed approach correlates real-time data on air quality, urban microclimate, mobility flows, land use and public-space conditions with socio-territorial indicators, enabling the early detection of environmental stress patterns in vulnerable neighbourhoods. Machine learning algorithms are employed to identify latent ecological relationships and support dynamic, data-driven interpretations of urban ecosystem behaviour. Experimental application of the framework, based on simulated scenarios and existing urban datasets, indicates a potential 20–25% reduction in urban heat island intensity, improvements in local ecological continuity, and an increase of over 30% in environmental risk prediction accuracy when compared to conventional static assessment methods. These quantitative results demonstrate how AI-based models can effectively support targeted interventions and evidence-based adaptive resilience strategies. The proposed model integrates environmental and socio-territorial indicators within a single dynamic predictive system, oriented towards decision support for urban resilience. By integrating quantitative indicators into adaptive decision-making processes, the research contributes to advancing next-generation approaches for managing environmental vulnerability in contemporary cities.

Introduction, Background, and Objectives

Mining operations continue to depend on linear Maintenance, Repair, and Operations (MRO) structures, limiting the ability to regenerate value from high-capital equipment and intensifying material consumption and carbon emissions. This study investigates how MRO systems can transition toward a digitally enabled circular model that extends component life and reduces material intensity across the equipment lifecycle.

Methods

A qualitative research design was applied using purposive sampling and semi-structured interviews with 22 specialists in reliability engineering, remanufacturing, supply chain, logistics, and sustainability across major mining firms and Original Equipment Manufacturers (OEMs). Interview data were coded and analysed using thematic analysis to identify operational, digital, and contractual constraints that shape the feasibility of circularity in current MRO systems.

Results

The analysis revealed four categories of barriers: (1) fragmented asset-health visibility, (2) inconsistent remanufacturing standards, (3) misaligned OEM–operator incentives, and (4) limited digital traceability for component provenance. Using these empirical insights, the study synthesizes engineering reliability principles, circular-economy logic, digital architecture, and supply-chain coordination theory to propose a Four-Loop Circular MRO Structure. The model formalises four value-regeneration pathways—Reuse, certified material Recovery, load-sensitive Reduction, and OEM-governed Return—each supported by explicit digital enablers: IoT-based condition telemetry, AI-driven remanufacturing candidate identification, Digital Twins for predictive simulation, and blockchain-secured material passports for authenticated component flows.

Contributions

The study provides:

1. A qualitatively validated conceptual model that operationalises circular MRO in heavy-equipment environments.
2. A digital-systems blueprint that integrates reverse logistics, reliability engineering, and OEM–operator coordination.
3. Empirical insights into the real-world organisational and contractual dynamics that influence circularity adoption in mining supply chains.

Conclusion and Future Work

Future research will quantitatively test the model through simulation and digital twin experiments to evaluate its potential impact on emissions reduction, component reliability, and supply chain performance.

Rapid industrialization, urban expansion, and unsustainable exploitation of natural resources have intensified environmental degradation, resulting in biodiversity loss, soil erosion, water scarcity, and climate instability. In this context, Nature-Based Solutions (NbS) have emerged as a transformative framework for addressing environmental challenges through the sustainable management and restoration of natural ecosystems. This assessment highlights the environmental benefits of NbS in promoting environmental recovery across terrestrial, aquatic, and coastal ecosystems. Key strategies such as reforestation, wetland rehabilitation, green infrastructure, sustainable agriculture, and coastal ecosystem restoration are discussed in relation to their ecological, social, and economic impacts. The study also emphasizes the integration of traditional ecological knowledge and modern technologies to enhance restoration efficiency and long-term resilience. Furthermore, it explores the role of NbS in achieving global sustainability goals, particularly in climate adaptation, carbon sequestration, and disaster risk reduction. Despite their growing recognition, the implementation of NbS remains challenged by limited policy support, inadequate funding, and insufficient cross-sector collaboration. Strengthening governance frameworks, public awareness, and scientific innovation is essential to scale up their effectiveness. Overall, this study highlights the critical role of NbS as a bridge between environmental conservation and socioeconomic development, offering a sustainable pathway from degradation to ecological restoration.