Hasil untuk "Environmental Science"

G. Cooper

The advent of generative artificial intelligence (AI) offers transformative potential in the field of education. The study explores three main areas: (1) How did ChatGPT answer questions related to science education? (2) What are some ways educators could utilise ChatGPT in their science pedagogy? and (3) How has ChatGPT been utilised in this study, and what are my reflections about its use as a research tool? This exploratory research applies a self-study methodology to investigate the technology. Impressively, ChatGPT’s output often aligned with key themes in the research. However, as it currently stands, ChatGPT runs the risk of positioning itself as the ultimate epistemic authority, where a single truth is assumed without a proper grounding in evidence or presented with sufficient qualifications. Key ethical concerns associated with AI include its potential environmental impact, issues related to content moderation, and the risk of copyright infringement. It is important for educators to model responsible use of ChatGPT, prioritise critical thinking, and be clear about expectations. ChatGPT is likely to be a useful tool for educators designing science units, rubrics, and quizzes. Educators should critically evaluate any AI-generated resource and adapt it to their specific teaching contexts. ChatGPT was used as a research tool for assistance with editing and to experiment with making the research narrative clearer. The intention of the paper is to act as a catalyst for a broader conversation about the use of generative AI in science education.

C. Conrad, Krista G. Hilchey

T. Forsyth

D. Guston

M. Cristescu, P. Hebert

The study of environmental DNA (eDNA) has the potential to revolutionize biodiversity science and conservation action by enabling the census of species on a global scale in near real time. To achieve this promise, technical challenges must be resolved. In this review, we explore the main uses of eDNA as well as the complexities introduced by its misuse. Current eDNA methods require refinement and improved calibration and validation along the entire workflow to lessen false positives/negatives. Moreover, there is great need for a better understanding of the “natural history” of eDNA—its origins, state, lifetime, and transportation—and for more detailed insights concerning the physical and ecological limitations of eDNA use. Although eDNA analysis can provide powerful information, particularly in freshwater and marine environments, its impact is likely to be less significant in terrestrial settings. The broad adoption of eDNA tools in conservation will largely depend on addressing current uncertainties in data interpretation.

Michael J. O. Pocock, J. Tweddle, J. Savage et al.

Citizen science—the involvement of volunteers in data collection, analysis and interpretation—simultaneously supports research and public engagement with science, and its profile is rapidly rising. Citizen science represents a diverse range of approaches, but until now this diversity has not been quantitatively explored. We conducted a systematic internet search and discovered 509 environmental and ecological citizen science projects. We scored each project for 32 attributes based on publicly obtainable information and used multiple factor analysis to summarise this variation to assess citizen science approaches. We found that projects varied according to their methodological approach from ‘mass participation’ (e.g. easy participation by anyone anywhere) to ‘systematic monitoring’ (e.g. trained volunteers repeatedly sampling at specific locations). They also varied in complexity from approaches that are ‘simple’ to those that are ‘elaborate’ (e.g. provide lots of support to gather rich, detailed datasets). There was a separate cluster of entirely computer-based projects but, in general, we found that the range of citizen science projects in ecology and the environment showed continuous variation and cannot be neatly categorised into distinct types of activity. While the diversity of projects begun in each time period (pre 1990, 1990–99, 2000–09 and 2010–13) has not increased, we found that projects tended to have become increasingly different from each other as time progressed (possibly due to changing opportunities, including technological innovation). Most projects were still active so consequently we found that the overall diversity of active projects (available for participation) increased as time progressed. Overall, understanding the landscape of citizen science in ecology and the environment (and its change over time) is valuable because it informs the comparative evaluation of the ‘success’ of different citizen science approaches. Comparative evaluation provides an evidence-base to inform the future development of citizen science activities.

Zhimin Luo, Jianhua Chen, Yongjie Zhang et al.

Defects in pipes adversely affect both the jacking construction process and long-term operational safety, yet their specific impacts on mechanical properties remain unclear. This study investigates pipe jacking segments under deflection, using the Changsha Meixi Lake project as a case study. Similar model tests combined with digital image correlation were employed to examine the evolution of stress and deformation under various deflection angles and defect conditions. The reliability of the laboratory tests was verified through theoretical stress calculations under the non-deflection condition. The credibility of the laboratory test results was further enhanced by employing a numerical model and normalized parameters. Key findings reveal that stress distribution characteristics are jointly determined by the deflection mode and load. Co-directional deflection exhibits a more significant stress concentration effect; under identical load and angle conditions, it results in higher stress levels due to a superposition effect, whereas diagonal deflection shows a weakening effect. Joint deformation progresses through three distinct stages. The linear growth stage exhibits an initial linear strain–load relationship under stable deflection (load < 2 kN). The accelerated deformation stage is characterized by nonlinear strain growth with a slowing deformation rate (2–4 kN). The deformation deceleration stage finally shows a slow linear strain increment (load > 4 kN). Increasing load and deflection angle significantly amplify axial deformation, particularly revealing a “thick-in-the-middle, thin-at-the-sides” compression characteristic in the 45° vault zones. Furthermore, segment defects markedly exacerbate stress non-uniformity. Defect angles ≥ 60° substantially increase the frequency and amplitude of compressive stress in the vault, accelerate the decay of tensile stress at the bottom, and critically reduce structural stability. These new findings provide significant insights for deflection control and structural safety assessment in pipe jacking engineering. The experimental framework provides fundamental insights into construction operations in upper-soft and lower-hard strata tunneling.

Zakia H. Alhashem, Hasna Abdullah Alali, Shehab A. Mansour et al.

The process of hydrogen peroxide decomposition, facilitated by copper oxide nanoparticles, produces reactive oxidants that possess the ability to oxidize multiple pollutants. CuO/Cu₂O hybrid nanoparticles were successfully synthesized through a thermal decomposition route and applied as a heterogeneous catalytic oxidant for a fluorescent dye, namely Basic Violet 10 (BV10) dye. The microstructure and morphology of the prepared catalyst were evaluated via X-ray diffraction (XRD) and a field-emission scanning electron microscope (FE-SEM), respectively. The produced nanoparticles (NPs) were induced through ultraviolet light as a green photodecomposition technology. The system parameters were investigated, and the optimal initial NP concentration, H₂O₂ concentration, and pH were assessed. The highest removal rate corresponding to 82% was achieved when 40 and 400 mg/L of NPs and H₂O₂ were introduced, respectively. The system could operate at various pH values, and the alkaline pH (8.0) was efficient in proceeding with the oxidation system that overcomes the limitation of the homogeneous acidic Fenton catalyst. The introduced catalyst demonstrated consistent sustainability, achieving a notable removal rate of 68% even after six consecutive cycles of use. This innovative technique’s accomplishment examines the feasibility of utilizing copper as a replacement for iron in the Fenton reaction, demonstrating efficacy over an extended pH range. Finally, the temperature effectiveness of the reaction showed that the reaction is exothermic in nature, working at a low energy barrier (20.4 kJ/mol) and following the pseudo-second-order kinetic model.

Julian Schön, Lena Hoffmann, Nikolas Becker

Artificial intelligence (AI) is often presented as a key tool for addressing societal challenges, such as climate change. At the same time, AI's environmental footprint is expanding increasingly. This report describes the systemic environmental risks of artificial intelligence, in particular, moving beyond direct impacts such as energy and water usage. Systemic environmental risks of AI are emergent, cross-sector harms to climate, biodiversity, freshwater, and broader socioecological systems that arise primarily from AI's integration into social, economic, and physical infrastructures, rather than its direct resource use, and that propagate through feedbacks, yielding nonlinear, inequitable, and potentially irreversible impacts. While these risks are emergent and quantification is uncertain, this report aims to provide an overview of systemic environmental risks. Drawing on a narrative literature review, we propose a three-level framework that operationalizes systemic risk analysis. The framework identifies the structural conditions that shape AI development, the risk amplification mechanisms that propagate environmental harm, and the impacts that manifest as observable ecological and social consequences. We illustrate the framework in expert-interview-based case studies across agriculture and biodiversity, oil and gas, and waste management.

Xiangyu Lyu, Hongyu Chen, En-Kun Li et al.

The space-borne gravitational wave detectors such as TianQin offers a new window to test General Relativity by observing the early inspiral phase of stellar-mass binary black holes. A key concern arises if these stellar-mass binary black holes reside in gaseous environments such as active galactic nucleus accretion disks, where environmental effects imprint detectable modulations on the gravitational waveform. Using Bayesian inference on simulated signals containing both environmental and dipole deviation, we have assessed the extent to which the presence of environmental effects affects the detectability of dipole radiation. Our results demonstrate that even in the presence of strong environmental coupling, the dipole parameter can be recovered with high precision, and the evidence for dipole radiation remains distinguishable. Crucially, we find that the existence of environmental effects does not fundamentally impede the identification of dipole radiation, provided both effects are simultaneously modelled in the inference process. This study establishes that future tests of modified gravity with space-borne observatories can remain robust even for sources in astrophysical environments.

Mizanur Rahman, Amran Bhuiyan, Mohammed Saidul Islam et al.

Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by integrating planning, tool use, and multimodal reasoning across text, code, tables, and visuals. This survey presents the first comprehensive, lifecycle-aligned taxonomy of data science agents, systematically analyzing and mapping forty-five systems onto the six stages of the end-to-end data science process: business understanding and data acquisition, exploratory analysis and visualization, feature engineering, model building and selection, interpretation and explanation, and deployment and monitoring. In addition to lifecycle coverage, we annotate each agent along five cross-cutting design dimensions: reasoning and planning style, modality integration, tool orchestration depth, learning and alignment methods, and trust, safety, and governance mechanisms. Beyond classification, we provide a critical synthesis of agent capabilities, highlight strengths and limitations at each stage, and review emerging benchmarks and evaluation practices. Our analysis identifies three key trends: most systems emphasize exploratory analysis, visualization, and modeling while neglecting business understanding, deployment, and monitoring; multimodal reasoning and tool orchestration remain unresolved challenges; and over 90% lack explicit trust and safety mechanisms. We conclude by outlining open challenges in alignment stability, explainability, governance, and robust evaluation frameworks, and propose future research directions to guide the development of robust, trustworthy, low-latency, transparent, and broadly accessible data science agents.

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

Tabea Turrini, Daniel Dörler, Anett Richter et al.

Abstract Citizen science offers significant innovation potential in science, society and policy. To foster environmental and conservation goals, citizen science can (i) generate new knowledge, (ii) enhance awareness raising and facilitate in-depth learning as well as (iii) enable civic participation. Here, we investigate how these aims are realised in citizen science projects and assess needs and challenges for advancing citizen science and stimulating future initiatives. To this end, we conducted a quantitative, web-based survey with 143 experts from the environmental and educational sector in Germany, Austria and Switzerland. Our findings show that citizen science project managers pursue goals related to all three areas of potential impact. Interestingly, enabling civic participation was considered slightly less important in relation to generating new knowledge and creating learning opportunities. Different areas of necessary action emerge from our analysis. To fully realize the potential of citizen science for generating knowledge, priority should be given to enhance capacities to more effectively share research results with the scientific community through publication, also in scientific journals. Systematic evaluation is needed to gain a better understanding of citizen science learning outcomes, for which criteria need to be developed. Fostering project formats that allow participants to get involved in the whole research process – from posing the study question to implementing results – could enhance the transformative aspect of citizen science at a societal level. Important structural aspects that need to be addressed include adjustments in funding schemes, facilitation of communication between citizens and academia-based scientists, and offers for training, guidance and networking.

Tae Hyeon Heo, Hyeon Park, Nam-Wook Kim et al.

Waxy maize is mainly cultivated in South Korea for the production of food and snacks, and colored maize with increased anthocyanin content is used in the production of functional foods and medicinal products. Association mapping analysis (AMA) is supported as the preferred method for identifying genetic markers associated with complex traits. Our study aimed to identify molecular markers associated with two anthocyanin content and six seed coat color traits in near-isogenic lines (NILs) of colored waxy maize assessed through AMA. We performed AMA for 285 SSR loci and two anthocyanin content and six seed coat color traits in 10 NILs of colored waxy maize. In the analysis of population structure and cluster formation, the two parental lines (HW3, HW9) of “Mibaek 2ho” variety waxy maize and the 10 NILs were clearly divided into two groups, with each group containing one of the two parental inbred lines. In the AMA, 62 SSR markers were associated with two seed anthocyanin content and six seed coat color traits in the 10 NILs. All the anthocyanin content and seed coat color traits were associated with SSR markers, ranging from 2 to 12 SSR markers per characteristic. The 12 SSR markers were together associated with both of the two anthocyanin content (kuromanin and peonidin) traits. Our current results demonstrate the effectiveness of SSR analysis for the examination of genetic diversity, relationships, and population structure and AMA in 10 NILs of colored waxy maize and the two parental lines of the “Mibaek 2ho” variety waxy maize.

Julian R. Greenwood, Vanica Lacorte-Apostol, Thomas Kroj et al.

Abstract A critical step to maximize the usefulness of genome-wide association studies (GWAS) in plant breeding is the identification and validation of candidate genes underlying genetic associations. This is of particular importance in disease resistance breeding where allelic variants of resistance genes often confer resistance to distinct populations, or races, of a pathogen. Here, we perform a genome-wide association analysis of rice blast resistance in 500 genetically diverse rice accessions. To facilitate candidate gene identification, we produce de-novo genome assemblies of ten rice accessions with various rice blast resistance associations. These genome assemblies facilitate the identification and functional validation of novel alleles of the rice blast resistance genes Ptr and Pia. We uncover an allelic series for the unusual Ptr rice blast resistance gene, and additional alleles of the Pia resistance genes RGA4 and RGA5. By linking these associations to three thousand rice genomes we provide a useful tool to inform future rice blast breeding efforts. Our work shows that GWAS in combination with whole-genome sequencing is a powerful tool for gene cloning and to facilitate selection of specific resistance alleles for plant breeding.

C. P. B. O'Malley, C. P. B. O'Malley, G. G. Roberts et al.

<p>The modern state of the mantle and its evolution on geological timescales are of widespread importance for the Earth sciences. For instance, it is generally agreed that mantle flow is manifest in topographic and drainage network evolution, glacio-eustasy, and the distribution of sediments. There are now a variety of theoretical approaches to predict histories of mantle convection and its impact on surface deflections. A general goal is to make use of observed deflections to identify Earth-like simulations and constrain the history of mantle convection. Several important insights into the role of radial and non-radial viscosity variations, gravitation, and the importance of shallow structure already exist. Here we seek to bring those insights into a single framework to elucidate the relative importance of popular modeling choices for predicted instantaneous vertical surface deflections. We start by comparing results from numeric and analytic approaches to solving the equations of motion that are ostensibly parameterized to be as similar as possible. Deflections predicted by such numeric and analytic models can vary by <span class="inline-formula">∼</span> 10 %, and the difference increases to <span class="inline-formula">∼</span> 25 % when viscosity is temperature-dependent. Including self-gravitation and the gravitational potential of the deflected surface is a relatively small source of discrepancy. However, spherical harmonic correlations between model predictions decrease dramatically with the removal of shallow structure to increasing depths and when radial viscosity structure is modified. The results emphasize the sensitivity of instantaneous surface deflections to density and viscosity anomalies in the upper mantle. They reinforce the view that a detailed understanding of lithospheric structure is crucial for relating mantle convective history to observations of vertical motions at Earth's surface.</p>

Arne Tobian, Dieter Gerten, Ingo Fetzer et al.

The planetary boundaries framework defines a safe operating space for humanity. To date, these boundaries have mostly been investigated separately, and it is unclear whether breaching one boundary can lead to the transgression of another. By employing a dynamic global vegetation model, we systematically simulate the strength and direction of the effects of different transgression levels of the climate change boundary (using climate output from ten phase 6 of the Coupled Model Intercomparison Project models for CO _2 levels ranging from 350 ppm to 1000 ppm). We focus on climate change-induced shifts of Earth’s major forest biomes, the control variable for the land-system change boundary, both by the end of this century and, to account for the long-term legacy effect, by the end of the millennium. Our simulations show that while staying within the 350 ppm climate change boundary co-stabilizes the land-system change boundary, breaching it (>450 ppm) leads to critical transgression of the latter, with greater severity the higher the ppm level rises and the more time passes. Specifically, this involves a poleward treeline shift, boreal forest dieback (nearly completely within its current area under extreme climate scenarios), competitive expansion of temperate forest into today’s boreal zone, and a slight tropical forest extension. These interacting changes also affect other planetary boundaries (freshwater change and biosphere integrity) and provide feedback to the climate change boundary itself. Our quantitative process-based study highlights the need for interactions to be studied for a systemic operationalization of the planetary boundaries framework.

Benaissa Chaimae, Rossi Abdelhamid, Bouhmadi Belkacem et al.

This study aims to investigate the physical, chemical, and bacteriological quality of water derived from both a well and a spring across three distinct periods (2008, 2012, and 2021) in both summer and winter. These sampling points are situated within the urbanized area of Al Hoceima and serve as crucial sources of drinking water for a substantial portion of the city's population due to their proximity to the city center. The water hardness values observed at these natural points ranged from 5.9 to 82 (°F), categorizing the water from these sources as very hard. Furthermore, the Piper diagram revealed chemical facies characterized by chlorinated sodium and calcium magnesium sulfate. The elevated concentrations of sodium and chloride were attributed to the proximity of the Mediterranean Sea shoreline. Analysis of bacteriological parameters in these waters uncovered notable contamination by fecal germs. Principal Component Analysis (PCA) of the water samples identified two primary groups, elucidated by two factors that collectively account for 79.37% of the variance. The first factor (50.11%) is linked to gypsum dissolution and marine intrusion, while the second factor (29.26%) is associated with external contributions such as anthropogenic pollution.

Tai Dinh, Wong Hauchi, Daniil Lisik et al.

This paper explores the critical role of data clustering in data science, emphasizing its methodologies, tools, and diverse applications. Traditional techniques, such as partitional and hierarchical clustering, are analyzed alongside advanced approaches such as data stream, density-based, graph-based, and model-based clustering for handling complex structured datasets. The paper highlights key principles underpinning clustering, outlines widely used tools and frameworks, introduces the workflow of clustering in data science, discusses challenges in practical implementation, and examines various applications of clustering. By focusing on these foundations and applications, the discussion underscores clustering's transformative potential. The paper concludes with insights into future research directions, emphasizing clustering's role in driving innovation and enabling data-driven decision-making.

Pranav Gupta, Raunak Sharma, Rashmi Kumari et al.

Environment Sound Classification has been a well-studied research problem in the field of signal processing and up till now more focus has been laid on fully supervised approaches. Over the last few years, focus has moved towards semi-supervised methods which concentrate on the utilization of unlabeled data, and self-supervised methods which learn the intermediate representation through pretext task or contrastive learning. However, both approaches require a vast amount of unlabelled data to improve performance. In this work, we propose a novel framework called Environmental Sound Classification with Hierarchical Ontology-guided semi-supervised Learning (ECHO) that utilizes label ontology-based hierarchy to learn semantic representation by defining a novel pretext task. In the pretext task, the model tries to predict coarse labels defined by the Large Language Model (LLM) based on ground truth label ontology. The trained model is further fine-tuned in a supervised way to predict the actual task. Our proposed novel semi-supervised framework achieves an accuracy improvement in the range of 1\% to 8\% over baseline systems across three datasets namely UrbanSound8K, ESC-10, and ESC-50.