Environmental challenges are often marked by an intergroup dimension. Political conservatives and progressives are divided on their beliefs about climate change, farmers come into conflict with scientists and environmentalists over water allocation or species protection, and communities oppose big business and mining companies that threaten their local environment. These intergroup tensions are reminders of the powerful influence social contexts and group memberships can have on attitudes, beliefs, and actions relating to climate change and the environment more broadly. In this paper, we use social identity theory to help describe and explain these processes. We review literature showing, how conceiving of oneself in terms of a particular social identity influences our environmental attitudes and behaviors, how relations between groups can impact on environmental outcomes, and how the content of social identities can direct group members to act in more or less pro-environmental ways. We discuss the similarities and differences between the social identity approach to these phenomena and related theories, such as cultural cognition theory, the theory of planned behavior, and value-belief-norm theory. Importantly, we also advance social-identity based strategies to foster more sustainable environmental attitudes and behaviors. Although this theoretical approach can provide important insights and potential solutions, more research is needed to build the empirical base, especially in relation to testing social identity solutions.
G. Boussarie, Judith Bakker, O. Wangensteen
et al.
Environmental DNA reveals unsuspected shark diversity and calls for monitoring and protection of residual populations. In the era of “Anthropocene defaunation,” large species are often no longer detected in habitats where they formerly occurred. However, it is unclear whether this apparent missing, or “dark,” diversity of megafauna results from local species extirpations or from failure to detect elusive remaining individuals. We find that despite two orders of magnitude less sampling effort, environmental DNA (eDNA) detects 44% more shark species than traditional underwater visual censuses and baited videos across the New Caledonian archipelago (south-western Pacific). Furthermore, eDNA analysis reveals the presence of previously unobserved shark species in human-impacted areas. Overall, our results highlight a greater prevalence of sharks than described by traditional survey methods in both impacted and wilderness areas. This indicates an urgent need for large-scale eDNA assessments to improve monitoring of threatened and elusive megafauna. Finally, our findings emphasize the need for conservation efforts specifically geared toward the protection of elusive, residual populations.
Petroleum hydrocarbons, oil, heavy metals pollution is becoming additional severe problem due to the growing call for crude oil and crude oil products related products in several fields of application. Such pollution have fascinated much considerations and attractions as it leads to ecological damages in both marines, aquatic and terrestrial ecosystems. Thus, different techniques including chemical surfactants and complex technologies have been proposed for their clean up from the environment, which in turn has detrimental effects on the environment. As of late, biosurfactant compounds have added much deliberation since they are considered as a reasonable option and eco-accommodating materials for remediation technology. The present society is confronting a few difficulties of usage, authorizing ecological protection and environmental change for the next generations. Biosurfactants hold the special property of minimizing and reducing the interfacial tension of liquids. Such features endure biosurfactants to afford a major part in emulsification, de-emulsification, biodegradability, foam formation, washing performance, surface activity, and detergent formulation, which have potential applications in the diverse industrial set-up. Conversations on cost-effective technologies, renewable materials, novel synthesis, downstream, upstream, emerging characterization techniques, molecular, and genetical engineering are substantial to produce biosurfactant of quality and quantity. Therefore, greater attention is being paid to biosurfactant production by identifying their environmental, and biotechnological applications. Be that as it may, the extravagant cost drew in with biosurfactants biotechnological synthesis and recovery can hamper their application in those areas. Notwithstanding these costs, biosurfactants can be used as these parts shows outstandingly high benefits that can at present beat the expenses incurred in the initial purification and downstream processes. Biosurfactant production by microorganisms is relatively considered one of the crucial know-how for improvement, growth, advancement, and environmental sustainability of the 21st century. There is a developing conversation around environmental safety and the significant role that biosurfactants will progressively play soon, for instance, the use of renewable by-products as substrates, potential reduction, re-use and recycling of waste and waste products. The review confers the usefulness of biosurfactants in the removal of environmental contaminants and, consequently, expanding environmental safety and drive towards greener technology.
Conventionally, symmetry-protected topological phases and band crossings are protected by global symmetries acting on the entire system. Here, we show that symmetries preserved only on a partial region of a system, termed local support symmetries, can protect topological features of the full system, even in the presence of symmetry-breaking couplings. We establish a unified framework by deriving explicit conditions for such protection in both insulating and metallic phases and show that destructive interference of Bloch wave functions plays a key role. Using representative tight-binding models, we demonstrate band crossings and topological bands protected by local support crystalline and time-reversal symmetries, and further present a realistic material realization in a fluorinated biphenylene network, where a band crossing is protected by a local support C$_2$ symmetry.
In this paper, our objective is to design, build, and verify a closed-loop environmental control system tailored for small-scale agriculture applications. This project aims to develop a low-cost, safety-critical embedded solution using the Nuvoton NUC140 microcontroller to automate temperature regulation. The goal was to mitigate crop yield losses caused by environmental fluctuations in a greenhouse. Our final implemented system successfully meets all design specifications, demonstrating robust temperature regulation through a PID control loop and ensuring hardware safety through galvanic isolation
IntroductionSafflower (Carthamus tinctorius L.) is a prized medicinal species whose therapeutic value hinges on the abundance of bioactive metabolites. Accumulation of these metabolites are influenced by a range of environmental and edaphic factors, including soil physicochemical parameters, extracellular enzyme activities, composition and function of rhizosphere microbiome. However, how these factors individually and synergistically orchestrate the biosynthesis, transport, and ultimate storage of pharmaceutically active compounds within Safflower tissues remains unknown.MethodsHere, high-throughput amplicon sequencing coupled with comprehensive physiological profiling was employed to investigate soil characteristics, enzyme activities, and rhizosphere microbial communities of safflower across 36 soil samples collected at two distinct altitudes and two growth stages.ResultsThe effective component content was detected in 18 samples, and our results revealed that the safflower stigmas from the high- altitude site (YM) contained significantly elevated levels of hydroxysafflor yellow A (HSYA) compared to those from the lowland site (YF). Soils at the YM site exhibited markedly higher fertility, with available phosphorus, total nitrogen, and organic matter identified as key drivers of HSYA accumulation. Both sites showed high diversity and abundance in rhizosphere microbial communities, with Actinobacteria and Proteobacteria dominating the bacterial communities, and Ascomycota being the predominant fungal phylum.DiscussionTaken together, our findings show that soil properties, microbial communities, and climatic conditions work interactively to influence the buildup of bioactive compounds in safflower. These insights suggest that precise management of soil nutrients and the rhizosphere microbiome can improve medicinal safflower quality.
Daniel Alejandro Valderrama, Marlon Damián Garzón Velasco, Lina Paola Alfonso Chaparro
Environmental Education (EE) is vital for shaping citizens who understand and value sustainability as an epistemological and practical alternative to mitigate current environmental issues. This research was prompted by the exploration of the relationship between EE and the physical sciences, connections that are often overlooked in curriculums and in the teaching processes of both this science and EE. It is essential to emphasize that physics provides conceptual frameworks and methodological tools that can enhance the understanding of environmental phenomena from a broad and multidimensional perspective. To delve into these connections, a study with a hermeneutic interpretative nuance was conducted. Through a questionnaire, the perceptions of prospective teachers in the natural sciences field regarding this topic were gathered. The findings revealed that a significant number of them recognize and value the correlation between physics and EE. From their perspective, this linkage is not only crucial for a comprehensive view of environmental dynamics but also to encourage students to develop critical, articulated, and well-founded thinking about environmental balance. The research also highlighted the didactic opportunities presented when intertwining physics with EE. By associating physical concepts with real environmental issues, learning can be reinforced, making it meaningful and enduring over time. This interdisciplinary fusion also holds the potential to increase students' motivation and interest, fostering a more active and engaged attitude in their educational journey
As human activities intensify, environmental systems such as aquatic ecosystems and water treatment systems face increasingly complex pressures, impacting ecological balance, public health, and sustainable development, making intelligent anomaly monitoring essential. However, traditional monitoring methods suffer from delayed responses, insufficient data processing capabilities, and weak generalisation, making them unsuitable for complex environmental monitoring needs.In recent years, machine learning has been widely applied to anomaly detection, but the multi-dimensional features and spatiotemporal dynamics of environmental ecological data, especially the long-term dependencies and strong variability in the time dimension, limit the effectiveness of traditional methods.Deep learning, with its ability to automatically learn features, captures complex nonlinear relationships, improving detection performance. However, its application in environmental monitoring is still in its early stages and requires further exploration.This paper introduces a new deep learning method, Time-EAPCR (Time-Embedding-Attention-Permutated CNN-Residual), and applies it to environmental science. The method uncovers feature correlations, captures temporal evolution patterns, and enables precise anomaly detection in environmental systems.We validated Time-EAPCR's high accuracy and robustness across four publicly available environmental datasets. Experimental results show that the method efficiently handles multi-source data, improves detection accuracy, and excels across various scenarios with strong adaptability and generalisation. Additionally, a real-world river monitoring dataset confirmed the feasibility of its deployment, providing reliable technical support for environmental monitoring.
Frederik J. Zuiderveen Borgesius, Wilfred Steenbruggen
In the European Union, the General Data Protection Regulation (GDPR) provides comprehensive rules for the processing of personal data. In addition, the EU lawmaker intends to adopt specific rules to protect confidentiality of communications, in a separate ePrivacy Regulation. Some have argued that there is no need for such additional rules for communications confidentiality. This Article discusses the protection of the right to confidentiality of communications in Europe. We look at the right's origins to assess the rationale for protecting it. We also analyze how the right is currently protected under the European Convention on Human Rights and under EU law. We show that at its core the right to communications confidentiality protects three individual and collective values: privacy, freedom of expression, and trust in communication services. The right aims to ensure that individuals and organizations can safely entrust communication to service providers. Initially, the right protected only postal letters, but it has gradually developed into a strong safeguard for the protection of confidentiality of communications, regardless of the technology used. Hence, the right does not merely serve individual privacy interests, but also other more collective interests that are crucial for the functioning of our information society. We conclude that separate EU rules to protect communications confidentiality, next to the GDPR, are justified and necessary.
Miguel Mauricio Aguilera Flores, José Alfonso Flores Aparicio, Fátima Ortiz Gutiérrez
et al.
This work aimed to design a strategy for providing a collection service of urban solid waste in communities without it, using a case study in Sombrerete, Zacatecas, Mexico. The service is provided to the municipal seat and 17 of the 173 communities, resulting in a collection coverage of 10%. Information provided by the Cleaning Department of Sombrerete was collected and analyzed on the number of collection vehicles, communities served, and final waste disposal sites. Communities without urban solid waste collection and disposal services were identified. The strategy was designed to increase the collection coverage using geographic information systems, vehicle routing problem tools, and territory sectorization. Waste collection routes were developed for 11 sectors without service, and final waste disposal sites were evaluated based on environmental protection criteria of the Mexican Official Standard. The technical and economic feasibility of the strategy were analyzed. The results obtained were the design of the collection routes strategy to increase the coverage to 100% in Sombrerete. The designed strategy was feasible since it did not require the purchase of waste collection vehicles and hiring more staff. Approximately MXN 1000 (≈USD 54, EUR 47) in economic benefits were achieved weekly.
Geography. Anthropology. Recreation, Social Sciences
Understanding water and heat transport through soils is vital for managing soil and groundwater resources, agricultural irrigation, and ecosystem protection. This paper aims to explore the potential application of deep learning methods in simulating water and heat transport processes within soils. It also examines the interactions between soil hydrological processes and environmental factors, including meteorological conditions and groundwater levels. To achieve these, we develop a hybrid model Informer–LSTM by combining two powerful architectures: Informer, a Transformer-based model essentially designed for long-sequence time-series forecasting, and Long Short-Term Memory (LSTM), a neural network that is great at learning short-term patterns in sequential data. The model is applied to field measurements from Henan Township in Ordos, Inner Mongolia, China, for training and testing, to simulate three key variables: soil water content, temperature, and heat flux at different depths in two soil columns with different groundwater levels. Our results confirm that Informer–LSTM is highly effective at simulating the soil water and heat transport. Simultaneously, we evaluate its performance by incorporating various combinations of input data including meteorological data, soil hydrothermal dynamics, and groundwater level. This reveals the relationship between soil hydrothermal processes and meteorological data, as well as coupled processes of soil water and heat transport. Moreover, employing SHapley Additive exPlanations (SHAP) analysis, we identify the most influential factors for predicting heat flux in shallow soils. This research demonstrates that deep learning models are a viable and valuable tool for simulating soil hydrothermal processes in arid and semi-arid regions.
Juan Ignacio Ibañez, Aayush Ladda, Paolo Tasca
et al.
The environmental impact of Bitcoin mining has become a significant concern, prompting several governments to consider or implement bans on cryptocurrency mining. However, these well-intentioned policies may lead to unintended consequences, notably the redirection of mining activities to regions with higher carbon intensities. This study aims to quantify the environmental effectiveness of Bitcoin mining bans by estimating the resultant carbon emissions from displaced mining operations. Our findings indicate that, contrary to policy goals, Bitcoin mining bans in low-emission countries can result in a net increase in global carbon emissions, a form of aggravated carbon leakage. We further explore the policy implications of these results, suggesting that more nuanced approaches may be required to mitigate the environmental impact of cryptocurrency mining effectively. This research contributes to the broader discourse on sustainable cryptocurrency regulation and provides a data-driven foundation for evaluating the true environmental costs of Bitcoin regulatory policies.
Drift is a significant issue that undermines the reliability of gas sensors. This paper introduces a probabilistic model to distinguish between environmental variation and instrumental drift, using low-cost non-dispersive infrared (NDIR) CO2 sensors as a case study. Data from a long-term field experiment is analyzed to evaluate both sensor performance and environmental changes over time. Our approach employs importance sampling to isolate instrumental drift from environmental variation, providing a more accurate assessment of sensor performance. The results show that failing to account for environmental variation can significantly affect the evaluation of sensor drift, leading to improper calibration processes.