Plastic pollution is a pervasive global threat, yet efforts to mitigate it are hindered by inconsistent terminology across scientific, industrial, and policy domains. Key terms, such as “polymer,” “plastic,” and “macromolecule” are often used interchangeably despite distinct meanings. This semantic confusion undermines research integrity, muddles regulatory frameworks, and impedes effective environmental management. Without universally accepted definitions or a clear classification system, data comparability, policy implementation, and interdisciplinary collaboration are significantly compromised. This study systematically examines the scope and impact of terminological inconsistencies in plastics discourse. We conducted a structured review of recent (2020 - 2025) peer-reviewed literature spanning polymer science and environmental policy to assess how definitional ambiguity affects research outcomes and decision-making. The findings reveal that ambiguous usage of fundamental terms has led to misinterpretations in scientific studies, inconsistent policy decisions, and fragmented mitigation strategies. In response, we propose a standardization framework guided by the International Union of Pure and Applied Chemistry principles, delineating clear criteria to distinguish polymers, plastics, and macromolecules. We recommend embedding these standardized definitions across academic publications, industry standards, and environmental policies to improve communication, ensure regulatory clarity, and support sustainable management practices. By establishing a coherent global terminology for plastics, this work underscores an urgent call for collective action. Standardizing the language of plastics will not only enhance data comparability and strengthen international policy initiatives, but also ensure that scientists, policymakers, and industry leaders can collaboratively craft effective, evidence-based solutions to plastic pollution.
The rapidly increasing global demand for food security urgently calls for agricultural technologies that can boost productivity without contributing to environmental degradation. A group of fertilizers that combines nanotechnology with beneficial microorganisms - known as nano-biofertilizers - has shown tremendous potential to enhance plant growth, increase yields, and improve soil health. Controlled nutrient release, enhanced microbial activities, and improved nutrient-use efficiency are key benefits of these new fertilizers, collectively leading to higher crop productivity with reduced environmental impact. These advantages contribute to sustainable agriculture by reducing the use of chemical fertilizers, surface soil degradation, nutrient leaching, and greenhouse gas emissions. Recent research findings have also highlighted their role in increasing plant resilience to abiotic stress factors, enhancing soil microbial diversity, and supporting ecological balance. This paper presents a comprehensive analysis of the multifaceted roles of nano-biofertilizers in modern agriculture, focusing on their contributions to plant physiology, soil health, environmental sustainability, and long-term agricultural productivity. Through empirical integration, we argue that nano-biofertilizers could be a transformative tool in advancing sustainable farming and achieving global environmental conservation goals.
Rapid and accurate detection of oil spills is crucial for initiating timely response measures to mitigate environmental impacts. This study proposes an oil spill detection method based on a modified convolutional neural network, termed “SpillNet.” The architecture integrates multiple depthwise separable convolutional layers, batch normalization, and residual connections to enhance feature extraction and learning capabilities. The dataset consists of synthetic aperture radar images obtained from Sentinel-1 satellites, part of the European Space Agency’s Copernicus program. Model training was conducted on an NVIDIA Tesla T4 GPU available on Google Colab, with up to 12GB of random access memory. Programming was carried out in the Python environment using Python 3.7, and all required libraries were installed through pip. The results indicate that the proposed model achieves an accuracy of 0.946947, a mean Intersection over Union of 0.58124, and a mean specificity of 0.944469. These results demonstrate that the proposed model outperforms existing models in the oil spill segmentation task. This study contributes to advancing automated oil spill detection by offering a reliable and efficient solution for early oil spill detection and environmental monitoring.
This study explores the intricate relationship between collection sites and the physiological responses of Suaeda monoica in Jeddah, focusing on photosynthetic pigment levels, osmomodulatory compounds, secondary metabolites, antioxidant potential, nutrient content, and heavy metal accumulation. Site (S)1 sample exhibited the highest chlorophylls a and b contents, whereas the S3 sample showed superior carotenoid levels. Osmoregulatory compounds demonstrated significant site-specific variations, with the S3 sample displaying elevated soluble sugar levels and the S1 sample showing heightened levels of soluble proteins and free amino acids. Secondary metabolites, encompassing phenols, flavonoids, alkaloids, and terpenoids, exhibited distinct accumulation patterns in samples across sites, with phenols being the most prevalent. In addition, the site samples’ antioxidant potential (1,1-diphenyl-2-picrylhydrazyl activity and total antioxidant capacity) varied significantly, with the S1 sample displaying notable antioxidant capacity. The gas chromatography-mass spectrometry analysis of S. monoica leaf ethanolic extracts revealed significant qualitative and quantitative variances linked to the collection sites, revealing a rich diversity of chemical classes. Site-specific phytoconstituents, such as palmitic and nonanoic acids, and unique compounds, such as 2-thiazolamine, characterized the compositions at different sites, suggesting an environmental influence on phytochemical profiles. Furthermore, nitrogen and phosphorus levels varied significantly across sites and plant organs, with the S1 sample showing elevated nitrogen content in roots and phosphorus content in leaves. Heavy metal accumulation varied markedly between soil, roots, and leaves, emphasizing the phytoremediation potential of S. monoica. Collectively, these results illustrated the versatility and phytoremediation potential of S. monoica with its growth habitat.
Municipal solid waste management (MSWM) presents a critical challenge in rapidly urbanizing regions, particularly in developing nations, where population growth and migration place increasing strain on infrastructure. This study examines MSWM practices and public perceptions in north coastal Andhra Pradesh, India, with a focus on waste collection, transportation, generation and reduction, reuse and recycling, composition, and separation. The research aims to assess the effectiveness of current MSWM strategies, identify gaps, and propose improvements based on best management practices. Through an analysis of existing regulations and levels of public engagement, this study provides insights into optimizing waste management approaches within the regulatory framework. The findings contribute to the development of sustainable waste management solutions that enhance environmental quality and urban livability. This research identifies key practices that improve waste management systems and offers recommendations for municipal bodies seeking to enhance efficiency and promote sustainability in waste reduction and resource recovery. The results underscore the importance of integrated solid waste management strategies and public participation in effectively addressing the growing challenges of urban waste management.
The denim sector, particularly in developing nations such as Bangladesh, is drawing increasing attention due to its significant environmental impact. Traditional denim production processes involve high water and energy consumption, synthetic chemicals, and unsustainable raw materials, all of which contribute significantly to pollution and resource depletion. While environmentally friendly substitutes are emerging, their overall environmental performance in the local setting remains underexplored. Following ISO 14040/44 standards and the ReCiPe 2016 Midpoint (H) methodology, this research intends to assess and compare the environmental impacts of traditional and eco-friendly denim manufacturing in Bangladesh through a cradle-to-grave life cycle assessment. Seven key impact categories were analyzed: climate change (greenhouse gas emissions), water consumption, terrestrial acidification, eutrophication, land use, fossil resource scarcity, and human toxicity. The functional unit was defined as 1,000 pairs of denim pants. Data were collected through field surveys, interviews, utility records, and secondary databases. Compared to the traditional model, the environmentally friendly scenario demonstrated significant reductions in environmental impacts, including a 30% reduction in climate effect, 64% reduction in water usage, 50% in acidification, 40% in eutrophication, 41% in fossil resource use, and 50% in human toxicity. Although sustainable production required 10% more land, the advantages in other categories outweighed this trade-off. Monte Carlo simulations were conducted to address uncertainty and validate the robustness of the results. This study offers a context-specific dataset for industry players considering investments in sustainable technology and highlights the clear environmental advantages of adopting greener production techniques. These insights can inform targeted policy changes and assist in the larger shift toward environmentally friendly textile manufacturing in developing countries.
In cheminformatics, predicting molecular properties is crucial for enhancing material research, toxicity assessment, and drug discovery. This research investigates the use of graph neural networks (GNNs) for predicting molecular properties by examining three different architectures: graph convolutional networks (GCNs), graph isomorphism networks (GINs), and graph attention networks (GATs). Employing molecular graph information, these models are evaluated on the MUTEG dataset and measured against key metrics such as accuracy and area under the receiver operating characteristic curve (AUC). Our experimental findings show that GIN has the highest accuracy at 89.2%, exceeding GCN (87.5%) and GAT (88.3%). GIN also achieves the highest AUC of 0.89, whereas the AUCs of GCN and GAT are 0.84 and 0.86, respectively, indicating GIN’s enhanced ability to effectively model graph isomorphisms. We selected GIN for this study because of its proven theoretical and empirical strength in capturing graph-level representations, particularly in domains such as cheminformatics, where molecular structures are naturally modeled as graphs. These results highlight the efficacy of GNNs in predicting molecular properties and position GIN as a favored framework for tasks that demand accurate graph feature extraction. This study further plays a pivotal role in understanding the environmental fate and transport of chemical compounds. We used GIN to identify partition coefficients such as the octanol-water partition coefficient, air-water partition coefficient, and soil-water partition coefficient from the MoleculeNet dataset.
Climate change, low productivity, and environmental degradation are jeopardizing Pakistan’s agricultural sector, whose sustainability and resilience can be potentially improved using agricultural technology (AgriTech). This study examines the relationship between digital technology, precision farming, methane (CH4) and nitrous oxide (N2O) emissions, and Pakistan’s grain crop yields to determine how modern technology impacts ecologically responsible farming. The study used Autoregressive Distributed Lag bounds testing to explore how data analytics, modern farming technologies, and agricultural value-added (AGRI) affect grain crop yields in the short and long run. Long- and short-term crop yields were reduced by AGRI. Data analytics could only produce short-term advantages, but precision agriculture tools and digital technologies assisted in enhancing yields significantly. CH4 and N2O emissions were significantly associated with yield growth, suggesting efficiency trade-offs. This study found that digital technology is an intensive farming method, resulting in higher yields linked to higher input consumption and emissions. The technology also enabled precision agriculture to increase productivity with lower environmental impacts. Taken together, the findings of the current study collectively underline the need to merge smart farming technologies with environmentally friendly methods to boost Pakistan’s agricultural productivity and sustainability.
Climate change has significantly impacted global hydrometeorological variables, placing increasing stress on groundwater resources. This study investigates long-term groundwater level trends in the Republic of Karakalpakstan, Uzbekistan, using a combination of non-parametric statistical models. The Mann-Kendall test, Spearman’s rank correlation, and innovative polygon trend analysis (IPTA) were applied to assess spatiotemporal variations. To address the limitations of parametric methods, this study utilizes robust, assumption-free trend detection techniques. The results reveal statistically significant increasing trends in groundwater levels across most provinces, particularly in Muynak (Z=3.884, p<0.001) and Republic-wide (Z=3.603, p<0.001). In contrast, provinces such as Turtkul, Ellikkala, and Nukus exhibit no significant trends. The IPTA method highlights seasonal fluctuations, with notable decreases in specific months despite the overall upward trend. These findings emphasize the need for localized groundwater management strategies that consider both seasonal dynamics and long-term changes. By integrating multiple statistical techniques, this study provides a comprehensive evaluation of groundwater variability and offers valuable insights for policymakers and water resource managers in arid regions facing climate-induced water challenges.
Faced with the challenges that climate change poses to all human societies, adaptation is becoming a necessity for human survival. In this context, it is necessary to study the climatic phenomena that humans face and that are likely to impact various aspects of life. Therefore, this study sought to analyze the trend of heat waves in Senegal using data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), Canadian Earth System Model Version 5. Three climate scenarios (Shared Socioeconomic Pathway [SSP]1-2.6, SSP2-4.5, and SSP5-8.5) were used, and the study focused on two future climate normals (2020 - 2050, 2050 - 2080). The study first spatialized the 95th percentile of minimum, mean, and maximum temperatures, then analyzed temperature anomalies with the Lamb index before studying the future trend using the Mann-Kendall test. The results obtained reflect an upward trend for all the variables in this study for the two periods combined but with a different level of significance. This increase is greater for minimum temperatures, with rises of 0.43°C for SS1-2.6, 1.06°C for SSP2-4.5, and 2.18°C for SSP5-8.5. In comparison, maximum temperatures rose by 0.50°C, 1.05°C, and 2.03°C, respectively, between the first and second periods. Mean temperatures followed the same dynamic, with 0.48°C for SSP1-2.6, 1.04°C for SSP2-4.5, and 2.16°C for SSP5-8.5. Given these findings, it is important to analyze the behavior of the other CMIP6 models in assessing heat waves in Senegal.
In this article, we consider the Constitution of Ukraine, international agreements, and norms of international law as the foundations of environmental legislation. This study provides a detailed analysis of the provisions and principles enshrined in international legal acts and Ukrainian legislation related to the protection and conservation of nature. The Constitution of Ukraine establishes the state’s environmental strategy, guarantees citizens the right to a safe environment, and mandates state actions to ensure environmental protection. International agreements, which Ukraine has signed and ratified, are equally significant, holding priority over national laws and forming a part of the domestic law. The international legal acts include declarations, principles, and other documents that do not have binding legal force but have significant impacts on the state’s formation and development of environmental law. These acts serve as typical frameworks in environmental protection, granting flexibility in implementation. Therefore, the most effective way to implement such acts into the national legislation of Ukraine is through state adoption based on the text of international legal acts or their individual articles on domestic law. The main ways to solve the problems of implementing international legal acts in environmental protection in Ukraine are as follows: (i) operational addition or amendment of national environmental legislation, (ii) determination of specific mechanisms and ways of implementing international obligations undertaken by Ukraine in environmental protection, and (iii) creation of separate units within the structure of the Ministry of Ecology and Natural Resources of Ukraine, which concentrates their activities on relevant international legal acts and ensures cooperation in this direction.
Located 40 km west of the wilaya of Tlemcen, the Algerian municipality of Maghnia is currently experiencing a severe water shortage that has affected all economic sectors, particularly agriculture. Given that Maghnia is a predominantly agricultural region with 250 hectares of irrigated land, maintaining agricultural productivity under such conditions poses significant challenges. One of the most viable and cost-effective strategies to address this issue is the reuse of non-conventional water sources, specifically the recycling of wastewater treated at the Lagfafe wastewater treatment plant (WWTP). Situated 5 km northwest of the town, the Maghnia WWTP operates using an intensive activated sludge process. This study aims to assess whether the quality of the treated effluent complies with Algerian standards for agricultural reuse. Throughout 2020, wastewater samples were collected and analyzed four times per month at both the inlet and outlet of the treatment plant. The physicochemical parameters examined included temperature, chemical oxygen demand, ortho-phosphates, electrical conductivity, nitrites, nitrates, ammoniacal nitrogen, water potential, dissolved oxygen, suspended solids, and biological oxygen demand. The analytical results indicate that the treated wastewater meets national regulatory standards for agricultural reuse across all evaluated parameters. Consequently, this substantial volume of treated water can be safely used for irrigation, offering a sustainable solution to mitigate the ongoing water crisis affecting the region’s agricultural sector.
Changes in climatic conditions in Ukraine and around the world pose a certain threat to food security and force us to seek new technological opportunities for growing agricultural products. This study aims to identify the optimal cultivation parameters for growing vegetable and berry plants in a portable, compact, and easy-to-maintain hydroponic nutrient film technique (NFT) system. The goal is to achieve high product quality and maximize plant yields by utilizing phytomatrix equipped with red and blue light-emitting diode (LED) elements in a 4:1 ratio, combined with a nutrient solution based on biological products. An experimental portable hydroponic NFT system was developed, incorporating these phytomatrices for additional lighting. This specific red-to-blue light ratio enhances photosynthesis efficiency, promotes yield increases, and ensures the quality development of plant structural parts. In addition, the use of a roller system allows for easy movement of the plants as needed. The study examined the effects of several factors on the growth performance of basil plants (Rutan variety), including seed treatment with the effective microorganisms (EM)-5 concentrate, air and nutrient solution temperature, and nutrient solution concentration. It was determined that the optimal conditions for basil growth include an air and nutrient solution temperature range of 24 - 26°C. Furthermore, increasing the nutrient solution concentration to an optimum level of 2.4 - 2.5 mS/cm resulted in significant improvements across all measured growth parameters of Rutan basil plants. Therefore, growing vegetable crops in a hydroponic installation with phytomatrices with red and blue LED emitting elements in a ratio of 4:1 and using a nutrient solution based on EM preparations will make it possible to increase their productivity.
Vietnam’s rapid urbanization presents a critical challenge at the intersection of economic development and environmental health. This study examines the causal relationships between urbanization, air pollution, and public health outcomes across Vietnam’s major urban centers. Using a comprehensive panel dataset spanning 2013 - 2022 for 10 Vietnamese cities, we employed instrumental variable (IV) techniques to address endogeneity concerns and establish robust causal inference. Our findings revealed that urbanization significantly exacerbates fine particulate matter (PM2.5) air pollution levels, with each percentage point increase in urbanization rate associated with a 0.54 μg/m3 rise in PM2.5 concentration. Across IV analysis using industrial output share as an instrument, we demonstrated that PM2.5 pollution substantially impacts respiratory health outcomes, with each 1 μg/m3 increase leading to approximately 2.5 additional respiratory disease cases per 1,000 population. Environmental policies implemented across Vietnamese cities since 2017 have achieved modest but measurable success in mitigating pollution levels. These results underscore the urgent necessity for integrated sustainable urban planning and robust environmental governance to safeguard public health in Vietnam’s rapidly transforming urban landscape.
Housing availability relative to population size is widely regarded as the main qualitative indicator of housing development standards. In Central Europe, there has been a notable trend of residents relocating from larger urban areas, where most housing is apartment buildings, to smaller satellite towns. Many investors and users are placing greater emphasis on construction methods, material bases, and sustainability. Among the sustainable housing options in Central Europe (V4 countries), single-family wooden houses are gaining popularity. Despite their advantages, the broader adoption of wood-based sustainable construction is hindered by limited public awareness. Hence, this study explores the perception of potential users and investors on wood-based constructions compared to traditional construction methods. Results show that potential users and investors perceive wooden constructions as capable of meeting the current legislative, energy-related, technological, and economic requirements. Correlation analysis of the data revealed differences in the perception of the parameters of constructions among various respondent groups. The primary contribution of this study lies in increasing awareness of wood-based constructions and analyzing the interactions among the selected parameters of constructions. Findings from this study highlight the potential for innovation aimed at enhancing efficiency and sustainability in housing developments.