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Global microplastic contamination in freshwater lakes: Spatial patterns, environmental drivers, and methodological challenges
(Elsevier, 2026) Jachimowicz, Piotr; Babkiewicz, Ewa; Gavlová, Anna; Lang, Jaroslav; Madzielewska, Weronika Irena; Maszczyk, Piotr; Mierzyńska, Karolina; Zieliński, Piotr
Microplastic (MP) pollution in freshwater lakes is an emerging global concern, yet comprehensive assessments remain limited. This review systematically analyzes 84 studies comprising 1268 individual sampling points across over 300 lakes worldwide, selecting only data based on FTIR and Raman spectroscopy to ensure identification reliability. MP concentrations in surface waters ranged from below 0.001 to over 200 MP/L, with the highest levels observed in shallow, lowland, and eutrophic systems. Fibers and fragments dominated MP shapes in both water and sediments, and polyethylene, polypropylene, and polyethylene terephthalate were the most commonly detected polymers, mirroring global plastic production trends. Environmental parameters such as trophic state, shoreline urbanization index and lake morphology were identified as key drivers of MP abundance and characteristics. A clear horizontal gradient was observed, with MP concentrations decreasing from shorelines toward lake centers. However, methodological inconsistencies remain a major obstacle to accurate assessments, including the dominance of surface-only sampling (96.5 % of lakes), limited spatial replication (over 70 % single-point sampling), and the frequent omission of MPs <300 mu m. These shortcomings highlight the urgent need for standardized, multi-depth, and year-round sampling strategies, as well as harmonized size fractionation and validation protocols, to ensure robust and comparable future assessments of MP pollution in freshwater ecosystems.
Shining the dynamics of the Economic Complexity Index on the European Union's climate change strategy: Evidence from the novel approach of MMQR
(Elsevier, 2026) Kömürcüoglu, Ömer Faruk; Kömürcüoglu, Elif Duygu; Koçak, Sinem; Çi̇l, Dilek; Karis, Çiğdem; Güven, Aykut Fatih; Bajaj, Mohit; Blažek, Vojtěch
For the European countries, the issue of combating climate change has become a matter of existence. Therefore, it is of extreme importance to present economic-based evidence for these countries' climate action. One emerging yet underexplored area is the environmental implications of the Economic Complexity Index (ECI), which reflects the knowledge intensity embedded in a country's production structure. Despite its relevance, studies examining the relationship between ECI and environmental degradation (ED) in the European context remain scarce. This paper aims to fill this gap by investigating the impact of ECI on ED between 1995 and 2021, focusing on the European Union countries recognized for their environmental sustainability efforts. For this purpose, the relationship between ECI and two of the pioneer indicators of ED-ecological footprint (EFP) and carbon emissions (CO2)-is assessed through two separate models. To address the dynamic and heterogeneous structure of the relationship, the novel Method of Moments Quantile Regression (MMQR) approach is employed. Empirical evidence suggests that ECI contributes to ED, with a stronger impact observed on CO2 emissions than on EFP. Another key finding is that higher levels of ED limit the negative environmental effects of ECI. However, the robustness of the findings is confirmed using the Driscoll-Kraay (D-K) standard error estimator and also, the symmetric causality test of Dumitrescu-Hurlin (D-H). As global leaders in environmental initiatives, EU countries must guarantee the availability and variety of green financing sources to expedite the transition to sustainable production methods in sectors impacting the ECI index via the European Investment Bank and the EU Innovation Fund. Policymakers can provide favorable tax incentives to industries that implement eco-friendly production methods to lower their expenses, thereby rewarding these industries and fostering acceptance of this strategy among sectors beyond this framework. Achieving higher ECI scores through the integration of renewable energy and green technologies is therefore essential for EU countries striving for a greener and more resilient future.
Multi-step-ahead forecasting of bike-sharing demand using multilayer perceptron model with additional timestamp features
(2026) Alfian, Ganjar; Saputra, Yuris Mulya; Ramadhani, Wildan Dzaky; Atmaji, Fransiskus Tatas Dwi; Farooq, Umar; Beneš, Filip; Fitriyani, Norma Latif; Syafrudin, Muhammad
Bike sharing is increasingly gaining popularity as an affordable and environmentally friendly mode of transportation in urban areas. However, the nature of bike sharing, where users can pick up and return bikes at different stations, often results in an uneven distribution of bikes across stations. Consequently, accurately predicting the future number of rented bikes at each station becomes crucial for bike-sharing operators to optimize the bike inventory at each location. This study introduces a multi-step-ahead forecasting model that employs machine learning methods to predict the hourly demand for rented bikes. We utilize information on rented bikes from the preceding day to forecast the forthcoming counts of rented bikes for the next 1, 3, 6, 12, and 24 h. Additional features extracted from timestamps are incorporated to enhance the accuracy of the model. We compare the proposed model, based on multilayer perceptron (MLP), with various machine learning prediction algorithms, including Support Vector Regression (SVR), K-Nearest Neighbor (KNN), Decision Tree (DT), Adaptive Boosting (AdaBoost), Random Forest (RF), and Linear Regression (LR). Applying the proposed MLP model to the Seoul bike-sharing dataset demonstrates a positive outcome, indicating a reduction in prediction error compared to other forecasting models. The proposed model achieves the highest R-2 (coefficient of determination) values when compared to other models, with values of 0.973, 0.882, 0.82, 0.807, and 0.79 for prediction horizons of 1, 3, 6, 12, and 24 h, respectively. By obtaining future values for predicted rented bikes, the trained model is anticipated to assist in optimizing the number of available bikes for bike-sharing companies.
Effect of NaBH4 loading and reduction temperature on defect-driven CO2 photoreduction over TiO2
(Elsevier, 2026) Ricka, Rudolf; Wanag, Agnieszka; Kusiak-Nejman, Ewelina; Reli, Martin; Filip Edelmannová, Miroslava; Łapiński, Marcin; Słowik, Grzegorz; Morawski, Antoni W.; Kočí, Kamila
This study investigates the role of defect engineering in enhancing TiO2-based photocatalysts for CO2 photoreduction through a systematically controlled synthesis. In contrast to previous reports focused on Ti3+ doping of commercial TiO2, here we combine sol-gel synthesis with post-synthetic chemical reduction using sodium borohydride (NaBH4) to obtain TiO2 materials with tunable concentrations of surface defects, specifically oxygen vacancies and Ti3+ sites. By varying both the reduction temperature and NaBH4 dosage, we introduce a new level of control over defect formation. The materials were characterized by X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), nitrogen physisorption, and photoelectrochemical measurements. Photocatalytic performance was assessed via CO2 photoreduction under UV-vis irradiation. The sample reduced at 350 degrees C with 1.5 g NaBH4 showed the highest activity and selectivity toward CH4 and CO, clearly surpassing the performance of commercial TiO2 (P25) and a sol-gel reference without chemical reduction (W-TiO2_350 degrees C). The improved performance is attributed to a synergistic balance of Ti3+ sites, oxygen vacancies, and surface hydroxyls, which enhance charge separation and CO2 activation. This work introduces new synthesis-structure-activity relationships and demonstrates the potential of defect-tuned TiO2 materials for efficient and selective CO2 valorization.
Zinc and copper metallic instability: Investigating altered metal functionality in both human and animal studies
(Springer Nature, 2026) Bhardwaj, Nidhi; Bhardwaj, Vandna; Choudhary, Ambika; Choudhary, Monika; Bhardwaj, Indu; Dulta, Kanika; Nagraik, Rupak; Ravi, Karthikeyan; Sharma, Avinash; Aman, Junaid
Homeostasis is the regulatory mechanism for the expression of all genes, the function of all metabolic pathways, the utilization of any essential trace element (TEs), while its disruptions lead to many pathological states. The pathologies include cardiovascular disease, anaemia, diabetes, neurological disorders, and cell death. For this, copper and zinc are two of the major TEs involved in controlling the physiological and pathological processes in both humans and animals. Zinc deficiency, for instance, is linked with decreased body weight, decreased ability to metabolize glucose, and impaired immune function. By contrast, deficiency of copper can lead to several neurological disorders, oxidative stress, mitochondrial dysfunction, and changes in lipid metabolism. On the other hand, there excessive exposure can have adverse effects on health, including the development of epilepsy, neuronal excitability, genotoxic effects, and cellular toxicity. Moreover, dual biological functions of zinc further complicate the understanding of their roles in both health and disease. Such as, zinc has a neuromodulatory function and helps to control excitably in neurons, but sometimes zinc in the synapse, inhibit the functioning of inhibitory neurotransmitter and cause damage to the neurons. Likewise, in metabolic diseases, particularly diabetes mellitus, there is often dysregulation of the levels of zinc and copper, resulting in steel-like interactions; elevated levels of copper and reduced levels of zinc contribute towards the pathogenesis of both the disease and the progression of dementia. Despite this antagonistic relationship, both trace metals act synergistically as necessary derivatives of superoxide dismutase; therefore, both play a vital role in maintaining cellular antioxidant defense systems. Therefore, this review covers published articles from 1992-2025 with regard to zinc and copper in their dietary and nanoparticle forms in animal and human models to demonstrate their differing roles and how they complement one another, or conflict with one another.Graphical AbstractA graphical summary of the percentage of publications (A), as well as the mechanism of neurotransmission by zinc ions (B), and the regulation of Zn2+ and Cu+ ions in both humans and animals, through either positive (regulation) or negative (regulation) pathways (C).