The aim of this bibliometric analysis was to evaluate the evolution of scientific research in hydrogen, focusing on green hydrogen production, storage, and utilization. Articles from prominent databases were analyzed, revealing a strong emphasis on sustainable hydrogen technologies through keywords like “hydrogen production”, “green hydrogen”, and “solar power generation”. Mature research areas include production methods and electrolysis, while emerging topics such as energy efficiency and policy are gaining traction. The most-cited papers, from Energy Conversion and Management to the International Journal of Hydrogen Energy, cover techno-economic assessments and case studies on deploying hydrogen technologies. Key findings highlight the variability of the Levelized Cost of Hydrogen (LCOH) across technologies and regions. Deep learning applications, including Fast Fourier Transform-based forecasting and explainable AI models, are transforming production optimization, while Life Cycle Assessment (LCA) emphasizes renewable energy’s role in reducing carbon intensity and resource consumption. Diverse strategies, such as fiscal incentives for wind energy and use of urban waste, underline the importance of local solutions. This analysis reflects the rapid growth of hydrogen research, driven by international collaboration and innovations in sustainable production, storage, and optimization. It is hoped that this paper will help to shed more light on the current and future understanding of green hydrogen.

Environmental pollution from microplastics (MPs), an anthropogenically mediated menace to the biosphere, is a global concern. This research conducted a bibliometric analysis of MPs in soil and terrestrial ecosystems using R software and biblioshiny to analyze descriptive statistics, author contributions, documents, and conceptual structures. The Scopus database was selected, and various keywords optimized the search. An analysis of research trends divided the timeline into three periods: period I (2012–2018), period II (2019–2021), and period III (2022). Results showed Wang J. as the author with the highest h-index, while Science of the Total Environment had an h-index of 43. In period I, research focused on MPs and plastic pollution, raising questions about their impacts on terrestrial ecosystems. Research found that MPs negatively affected soil organisms by decreasing growth rates and causing weight loss. In period II, research diversified, exploring interactions between MPs and heavy metals, bioaccumulation, phytotoxicity, soil pollution, and gut microbiota. Research revealed that MPs could influence heavy metal uptake by plants and affect bacterial community structures in soil. In period III, research emphasized oxidative stress, a common ecotoxicity response in plants and animals. MPs induced oxidative stress, leading to reactive oxygen species (ROS) buildup. Polyethylene MPs were more toxic to plant seedlings than polystyrene and polypropylene, causing significant oxidative stress. Similarly, oxidative stress impacts on soil organisms were highlighted when exposed to MPs and pesticides. Research in this area is expected to continue growing, aiding in delineating distinct research paths, particularly focusing on oxidative stress, bioaccumulation, and heavy metals.

This study evaluated the phytoremediation of mine tailing-contaminated soils in Quiulacocha, Peru, using the combined application of biochar and compost, with Zea mays L. (maize) serving as the phytoremediator due to its high biomass production and stress tolerance. A factorial experimental design was implemented, varying two main factors: the mining tailings dose (30% and 60% w/w) and the biochar pyrolysis temperature (300 °C and 500 °C). The mine tailings were characterized by high concentrations of heavy metals and unfavourable physico-chemical properties (pH, low organic matter), whereas the biochar, produced from pine forest residues, and the compost, derived from urban organic waste, exhibited attributes that enhance soil quality. During the pot experiment, response variables including the Bioconcentration Factor (BCF) and Translocation Factor (TF) for various metals were evaluated to assess the capacity for contaminant immobilization and their distribution between plant roots and aerial tissues. The results demonstrated that the incorporation of biochar and compost significantly improved soil quality by increasing pH, cation exchange capacity, and nutrient retention, while simultaneously reducing the bioavailability of heavy metals and limiting their translocation to the aerial parts of maize. Factorial analysis further indicated that both the tailings dose and biochar pyrolysis temperature significantly influenced the efficacy of the phytoremediation process. In conclusion, the combined application of biochar and compost presents an effective and sustainable strategy for rehabilitating mine tailing-contaminated soils by stabilizing heavy metals and promoting the safe growth of Zea mays L.

This study presents a scientometric analysis of renewable energy applications in low-temperature regions, focusing on green hydrogen production, carbon storage, and emerging trends. Using bibliometric tools such as RStudio and VOSviewer, the research evaluates publication trends from 1988 to 2024, revealing an exponential growth in renewable energy studies post-2021, driven by global policies promoting carbon neutrality. Life cycle assessment (LCA) plays a crucial role in evaluating the environmental impact of energy systems, underscoring the need to integrate renewable sources for emission reduction. Hydrogen production via electrolysis has emerged as a key solution in decarbonizing hard-to-abate sectors, while carbon storage technologies, such as bioenergy with carbon capture and storage (BECCS), are gaining traction. Government policies, including carbon taxes, fossil fuel phase-out strategies, and renewable energy subsidies, significantly shape the energy transition in cold regions by incentivizing low-carbon alternatives. Multi-objective optimization techniques, leveraging artificial intelligence (AI) and machine learning, are expected to enhance decision-making processes, optimizing energy efficiency, reliability, and economic feasibility in renewable energy systems. Future research must address three critical challenges: (1) strengthening policy frameworks and financial incentives for large-scale renewable energy deployment, (2) advancing energy storage, hydrogen production, and hybrid energy systems, and (3) integrating multi-objective optimization approaches to enhance cost-effectiveness and resilience in extreme climates. It is expected that the research will contribute to the field of knowledge regarding renewable energy applications in low-temperature regions.

This research reports a bibliometric analysis using the bibliometrix package with information from the SCOPUS database. A descriptive analysis was performed to provide an overview of the countries, institutions, authors, journals and keywords of 80 manuscripts published between 1997 and 2022 in the field of study of emergy and its application in wastewater treatment. The results showed 2 sources with the highest h and g impact index (Ecological Engineering and Journal of Cleaner Production) with a significant growth since 2009. The most cited keywords were "emergy" and "wastewater", and the countries with the most important scientific networks were China, Japan, Saudi Arabia, USA, Brazil, Colombia and Italy. In addition, it shows that collaborative groups among the authors from China and USA are the ones that consolidate the main research network, as well as the largest scientific production with the most cited articles in the field of study of emergy in wastewater treatment. Therefore, the application of the emergy method in wastewater treatment requires extensive research in different types of treatments. This type of analysis would allow the development of a new approach in the application of wastewater treatment in terms of sustainability and the introduction of Latin American countries in this emerging field.

Chemically demethoxylated and Ca-cross-linked orange-peel waste was engineered as a biosorbent for Mn(II) removal from water. A three-factor Box–Behnken design (biosorbent dose 3–10 g L⁻¹, initial Mn²⁺ 100–300 mg L⁻¹, contact time 3–8 h; pH 5.5 ± 0.1, 25 °C) required only 16 runs to locate the optimum (10 g L⁻¹, 100 mg L⁻¹, 8 h), at which the material removed 94.8% ± 0.3% manganese removal under the optimized conditions (10 g L⁻¹, 100 mg L⁻¹, 8 h, pH 5.5) of dissolved manganese and reached a Langmuir capacity of 29.7 mg g⁻¹. Equilibrium data fitted the Freundlich (R² = 0.968) and Sips (R² = 0.969) models best, indicating a heterogeneous surface, whereas kinetic screening confirmed equilibrium within 6 h. FTIR and SEM–EDX verified abundant surface –COO⁻/–OH groups and showed Mn deposits that partially replaced residual Ca, supporting an ion-exchange component in the uptake mechanism. A preliminary cost analysis ( 90% regeneration efficiency over three cycles highlight the economic and environmental promise of this modified agro-waste for polishing Mn-laden effluents.