Hasil untuk "Environmental Science"

J. Willard, X. Jia, Shaoming Xu et al.

There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques. This article provides a structured overview of such techniques. Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks are described. We then provide a taxonomy of these existing techniques, which uncovers knowledge gaps and potential crossovers of methods between disciplines that can serve as ideas for future research.

Y. Kovas, C. Haworth, Philip S. Dale et al.

Despite the importance of learning abilities and disabilities in education and child development, little is known about their genetic and environmental origins in the early school years. We report results for English (which includes reading, writing, and speaking), mathematics, and science as well as general cognitive ability in a large and representative sample of U.K. twins studied at 7, 9, and 10 years of age. Although preliminary reports of some of these data have been published, the purpose of this monograph is to present new univariate, multivariate, and longitudinal analyses that systematically examine genetic and environmental influences for the entire sample at all ages for all measures for both the low extremes (disabilities) and the entire sample (abilities). English, mathematics, and science yielded similarly high heritabilities and modest shared environmental influences at 7, 9, and 10 years despite major changes in content across these years. We draw three conclusions that go beyond estimating heritability. First, the abnormal is normal: Low performance is the quantitative extreme of the same genetic and environmental influences that operate throughout the normal distribution. Second, continuity is genetic and change is environmental: Longitudinal analyses suggest that age-to-age stability is primarily mediated genetically, whereas the environment contributes to change from age to age. Third, genes are generalists and environments are specialists: Multivariate analyses indicate that genes largely contribute to similarity in performance within and between the three domains--and with general cognitive ability--whereas the environment contributes to differences in performance. These conclusions have far-reaching implications for education and child development as well as for molecular genetics and neuroscience.

W. Dashek

This annotated resource list was developed in response to an expressed need by King Drew Medical Magnet High School science teachers for curriculum-related, web-based science information that is reliable, up-to-date, and content and age-appropriate to the high school student. Aided through our year-long collaboration with National Library of Medicine through the Distance Learning Project, we were able to evaluate the best of the best available on the web, and in many instances this meant using NLM’s vast network of resources. A pre-eminent resource for information in the sciences, NLM served as the standard in selecting all other websites. Before each site made it to these pages, we looked for these indicators of quality: Accuracy, Authority, Currency, and Coverage. (See Appendix: Criteria for Evaluating Internet Sites). This web resource is a work in progress as we will continue making revisions and additions to it as needed.

E. S. E. Rentier, L. H. Cammeraat

The demand for construction-grade sand is growing at a tremendous rate and the world is expected to run out of this resource by 2050. Construction-grade sand, hereafter referred to as 'sand', can be found in (former) aquatic environments, such as rivers and is a provisioning ecosystem service. Even under controlled circumstances, the practice of extracting the sand from the riverbed and -banks impacts the environment. Unfortunately, many countries lack sand mining regulation policies and in combination with a high demand, this results in indiscriminate and illegal mining. To create effective policies for sustainable extraction of river sand, there is a need for both qualitative and quantitative data on the effects of river sand mining. This paper brings together the effects of river sand mining on the physical, biological, chemical, and anthropogenic environment through a systematic literature review. The effects found are widespread and often cumulative. In the physical environment, the primary effects are riverbed widening and lowering. In the biological environment, the overarching effect is a reduced biodiversity and stretches from the aquatic and shoreline flora and fauna to the whole floodplain area. The effects on the chemical environment are a reduced water, air and soil quality through pollution. The effects on the anthropogenic environment comprise of damaged infrastructure, bad working circumstances for workers, limited access to water and agricultural losses. The findings of this research emphasize the complexity and cascading nature of the effects of river sand mining, as well as the severity and urgency of the problem. Based on the effects found and the four environments, a set of guidelines are proposed at the end of this paper to be used for global agenda making regarding sustainable sand extraction. Future research should prioritise quantifying the observed effects and developing science-based policies for sustainable mining.

Aniko Konya, Peyman Nematzadeh

The rapid development and efficiency of Artificial Intelligence (AI) tools have made them increasingly popular in various fields and research domains. The environmental discipline is now experiencing an exponential interest in harnessing the potential of AI over the past decade. We have reviewed the latest applications of AI tools in the environmental disciplines, highlighting the opportunities they present and discussing their advantages and disadvantages in this field. After the emergence of deep learning algorithms in 2010, interest in using AI tools for environmental tasks has grown exponentially. Among the studied articles, over 65 % of environmental tasks that demonstrate interest in using AI tools initially relied on conventional statistical and mathematical models. Using AI tools can greatly benefit the areas of environmental science and engineering. One of the main advantages of utilizing AI tools is their ability to analyze and process large amounts of data efficiently. Recently, the European Union established a European supercomputing ecosystem program to advance science and enhance the quality of life for its citizens. Nine of these projects prioritize environmental and sustainable goals. Despite the benefits of AI, it is still in its early stages of development, which comes with environmental concerns. The amount of power consumed and the time required to train an AI model can greatly affect the carbon emissions it produces, exacerbating the challenges posed by climate change. Efforts are currently underway to develop AI technology that is environmentally sustainable, minimizes energy consumption, and has a low carbon footprint. Selecting the appropriate AI model architecture can reduce energy consumption by almost 90 %. The main finding suggests that collaboration between environmental and AI professionals becomes crucial in leveraging the full potential of AI in addressing pressing environmental challenges.

Raffaella Maria Ribatti, Luciana de Gennaro, Alessia Daponte et al.

Abstract Substance Use Disorders (SUDs) are a significant public health concern with complex etiologies involving genetic, environmental, and psychological factors. Here, we present BioSUD, a biobank that, by integrating genomic data with comprehensive phenotypic assessments, including sociodemographic, psychosocial, and addiction-related variables, was designed to investigate the etiology of SUDs within the Southern Italian population. We assessed a cohort of 1,806 participants (1,508 controls and 298 individuals with SUD diagnosis). Genomic analyses of the newly generated genotypes showed a predominantly Southern Italian ancestry for the BioSUD cohort. Admixture analysis reveals a complex history of genetic admixture in Southern Italian populations, exhibiting Southern European, African, and other ancestries. This results in significant genetic variation, potentially limiting the applicability of translational studies primarily based on Northern European ancestries. From a social and psychological perspective, individuals with SUDs exhibited lower socioeconomic status, increased exposure to adverse experiences, and compromised familial and peer relationships relative to controls. These results show that the BioSUD cohort is valuable for studying SUD-associated complex behavioral traits.

Marwan Fahs, Behzad Ataie-Ashtiani, Behzad Ataie-Ashtiani et al.

Sifan Gu, Zhengyu Liu, Ning Zhao et al.

Abstract The rapid CO2 rise during the early deglaciation is often linked to enhanced ventilation by intensified Antarctic Bottom Water (AABW) overturning. The recorded radiocarbon ventilation seesaw during the early deglaciation, which describes improved Southern Ocean and reduced North Atlantic abyssal radiocarbon ventilation, has been interpreted as intensified AABW and reduced North Atlantic Deep Water convections. However, abyssal radiocarbon records also reflect changes in surface reservoir ages and interior water mass mixing. Using isotope-enabled simulations, we show that this seesaw results from weakened AABW overturning and decreased Southern Ocean surface reservoir age. With AABW occupying the abyssal ocean, weakened AABW overturning increases transit time, with the magnitude increasing northward. This transit time increase outpaced the declining $$\Delta ^{14}C_{{atm}}$$ Δ 14 C a t m induced Southern Ocean surface reservoir age decrease in the abyssal North Atlantic, but not in the abyssal Southern Ocean, thus producing a radiocarbon ventilation seesaw. Our results suggest sluggish deep water overturning from both poles during the early deglaciation.

Robert Mzungu Runya, Chris McGonigle, Rory Quinn et al.

ABSTRACT Understanding the spatial dynamics of harbour porpoise (Phocoena phocoena) is crucial for effective conservation and management. The study presents a multidisciplinary approach to modelling and analysing the site occurrence and habitat use of Phocoena phocoena within the Skerries and Causeway Special Area of Conservation (SAC), identifying areas where they were seen surfacing and/or spending the most time. Using data derived from multibeam echosounders (MBES), particle size analysis of sediments, hydrodynamic modelling, and theodolite tracking observations, the study examines the influence of local hydrodynamics and environmental conditions on the spatial distribution of harbour porpoises. Kernel density analysis of 451 porpoise sightings over an 11‐day survey demonstrated that dense clusters and higher aggregations occurred within ~500 m of the shoreline. Generalised Additive Models (GAMs) identified slope, aspect, backscatter intensity and sediment grain size as the most significant environmental predictors, accounting for 47.6% of the deviance in harbour porpoise distribution. Porpoises' occurrence was particularly spatially coincident with coarser sediments (4.25–5 mm), and their distribution was highly concentrated around headlands, shoreline and within a 3‐h window before and after high water. Overall, these findings highlight the dynamic nature of harbour porpoises' use of habitat in space and time, with models predicting a high probability of porpoise encounters (> 0.6) nearshore, particularly in headland areas characterised by local flow acceleration and coarser seabeds. The study presents a robust workflow for developing a porpoise‐specific monitoring program. By leveraging multidisciplinary methodological approaches, the study provides a scientific basis for refining marine conservation measures, delivering long‐term protection for harbour porpoise habitats under existing legal and management frameworks both within and beyond the SAC boundaries.

Puja Das, Auroop R. Ganguly

Abstract Earth system models inform water policy and interventions, but knowledge gaps in hydrologic representations limit the credibility of projections and impacts assessments. The literature does not provide conclusive evidence that incorporating higher resolutions, comprehensive process models, and latest parameterization schemes, will result in improvements. We compare hydroclimate representations and runoff projections across two generations of Coupled Modeling Intercomparison Project (CMIP) models, specifically, CMIP5 and CMIP6, with gridded runoff from Global Runoff Reconstruction (GRUN) and ECMWF Reanalysis V5 (ERA5) as benchmarks. Our results show that systematic embedding of the best available process models and parameterizations, together with finer resolutions, improve runoff projections with uncertainty characterizations in 30 of the largest rivers worldwide in a mechanistically explainable manner. The more skillful CMIP6 models suggest that, following the mid-range SSP370 emissions scenario, 40% of the rivers will exhibit decreased runoff by 2100, impacting 850 million people.

McLuret, S. Joe Patrick Gnanaraj, Vanthana Jeyasingh

This study focuses on optimizing IoT-enabled stepped basin solar stills by integrating the Taguchi method, Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms. The objective was to enhance distillate yield, thermal efficiency, and system performance by optimizing key parameters—water depth, basin material, phase change material (PCM) type, and reflector angle. The Taguchi orthogonal array minimized experimental runs, while PSO and ABC algorithms refined parameter selection. Experimental results showed that a combination of 5 mm water depth, black copper basin, salt hydrate PCM, and a 45° internal reflector angle achieved a distillate yield of 3200 ml/day with 78.05 % efficiency, nearing the theoretical maximum of 4100 ml/day. Real-time IoT monitoring enabled dynamic adjustments, further improving efficiency. The findings highlight the effectiveness of combining smart monitoring and advanced optimization techniques to create scalable and sustainable solar desalination solutions for water-scarce regions.

A.Y.A. Mohamed, Laurence Gill, Alejandro Monleon et al.

Microbial aggregates of different sizes in aerobic granular sludge (AGS) systems have been shown to exhibit distinct microbial community compositions. However, studies comparing the microbial activities of different-sized aggregates in AGS systems remain limited. In this study, genome-resolved metatranscriptomics was used to investigate microbial activity patterns within differently sized aggregates in a full-scale AGS plant. Our analysis revealed a weak correlation between the relative abundance of metagenome-assembled genomes (MAGs) and their transcriptomic activity, indicating that microbial abundance does not directly correspond to metabolic activity within the system. Flocculent sludge (FL; <0.2 mm) predominantly featured active nitrifiers and fermentative polyphosphate-accumulating organisms (PAOs) from Candidatus Phosphoribacter, while small granules (SG; 0.2–1.0 mm) and large granules (LG; >1.0 mm) hosted more metabolically active PAOs affiliated with Ca. Accumulibacter. Differential gene expression analysis further supported these findings, demonstrating significantly higher expression levels of key phosphorus uptake genes associated with Ca. Accumulibacter in granular sludge (SG and LG) compared to flocculent sludge. Conversely, Ca. Phosphoribacter showed higher expression of these genes in the FL fraction. This study highlights distinct functional roles and metabolic activities of crucial microbial communities depending on aggregate size within AGS systems, offering new insights into optimizing wastewater treatment processes.

J. J. Carrera-Hernández

<p>This work presents Mexico's High Resolution Climate Database (MexHiResClimDB), which is a newly developed gridded, high-resolution climate dataset comprised of daily, monthly and yearly precipitation and temperature (<span class="inline-formula"><i>T</i>_min</span>, <span class="inline-formula"><i>T</i>_max</span>, <span class="inline-formula"><i>T</i>_avg</span>). This new database provides the largest temporal coverage of the aforementioned climate variables at the highest spatial resolution (20 arcsec, or 560 m on Mexico's CCL projection) when compared to the other currently available gridded datasets for Mexico and its development has allowed for the analysis of the country's climate extremes for the 1951–2020 period. By comparing the spatial distribution of precipitation from the MexHiResClimDB with other gridded data (Daymet, L15, CHIRPS and PERSIANN CDR), it was found that the precipitation provided by this new dataset adequately represents the spatial variation of extreme precipitation events, in particular for the precipitation that occurred during 15–16 September 2013, caused by the presence of Tropical storm Manuel in the Pacific Ocean and Hurricane Ingrid (Cat 1) in the Gulf of Mexico. Using data from 61 days retrieved from Automated Weather Stations located throughout Mexico – and correspoding to the two months with the largest precipitation in Mexico – it was found that precipitation data from MexHiResClimDB has the lowest MAE (8.7 mm), compared to those of L15 (9.5 mm), Daymet (10.1 mm) and CHIRPS (11.7 mm). For <span class="inline-formula"><i>T</i>_min</span> and <span class="inline-formula"><i>T</i>_max</span>, the lowest MAE was obtained with MexHiResClimDB (1.7 and 1.8 °C, respectively), followed by Daymet (2.0 °C for both temperatures) and L15 (2.4 and 2.5 °C). With this new database an analysis of the extreme events of precipitation and temperature in Mexico for the 1951–2020 period was undertaken: the wettest year was 1958, the wettest day 26 September 1970, and September of 2013 the wettest month. It was also found that eight out of the ten days with the highest <span class="inline-formula"><i>T</i>_min</span> occurred in 2020, the two months with the highest <span class="inline-formula"><i>T</i>_min</span> were July and August of 2020 and that the six years with the highest <span class="inline-formula"><i>T</i>_min</span> were 2015–2020. When <span class="inline-formula"><i>T</i>_max</span> was analysed, it was found that the hottest day was 15 June 1998, while June of 1998 was the hottest month and 2020 the hottest year, and that the four hottest years occurred between 2011–2020. Nationwide (and considering 1961–1990 as the baseline period), <span class="inline-formula"><i>T</i>_min</span>, <span class="inline-formula"><i>T</i>_avg</span> and <span class="inline-formula"><i>T</i>_max</span> have increased, with their anomalies drastically increasing in recent years and reaching values above 1.0 °C in 2020. At the same time, precipitation has also decreased in recent years – which combined with the increase in temperature will have severe impacts on water availability. This new database provides a tool to quantify – in detail – the spatio-temporal variability of climate throughout Mexico.</p> <p>The MexHiResClimDB entire dataset is available on Figshare (<a href="https://doi.org/10.6084/m9.figshare.c.7689428.v2">https://doi.org/10.6084/m9.figshare.c.7689428.v2</a>, <span class="cit" id="xref_altparen.1"><a href="#bib1.bibx16">Carrera-Hernández</a>, <a href="#bib1.bibx16">2025</a><a href="#bib1.bibx16">a</a></span>).</p>

Fiona Preston-Whyte , Toshka Barnardo , Danica Marlin et al.

Data gaps limit solutions and policy development for environmental issues. Citizen science offers a possible solution to reduce data gaps at a limited cost while enhancing environmental education (EE). While highly effective in the latter, citizen science campaigns rarely produce reliable, comparable, and meaningful data. This often results from fragmented awareness, varying data collection methods, and little training prior to data collection. This article explores how Massive Open Online Courses (MOOCs) can be used to train citizen scientists, increase the value of citizen science data, and ensure that resources invested in citizen science initiatives are used more efficiently. We use a beach macrolitter monitoring course developed by Sustainable Seas Trust (SST) (NGO/NPO) and GRID-Arendal (a research foundation) as a case study in Africa, since the marine litter issue has widespread public support, and beaches are pleasant locations that attract potential citizen scientists. Beach macrolitter surveys utilise everyday equipment, and monitoring methods are simple if individuals are supported with appropriate training. This is especially relevant in Africa, where plastic pollution is forecasted to increase faster than other regions, and resources for research can be limited. This article gives a modified problemsolution model (mPSM) perspective, considering the challenges and solutions of MOOC development by two organisations working in the same space with limited resources. Challenges to inclusivity for online training in Africa included language barriers and limited technological access. Using Africa as a case study, we show that by combining professional abilities, inclusive digital education can be achieved using data-light MOOCs, offline engagement and other inclusive strategies to overcome the challenges of m- (mobile) and e- (electronic) learning. This kind of EE can be a powerful tool in developing reliable data while enhancing citizens’ agency in working towards Sustainable Development Goals (SDGs).

Yofri Furqani Hakim, Fuan Tsai

The demand for large-scale topographic maps in Indonesia has significantly increased due to the implementation of several government initiatives that necessitate the utilization of spatial data in development planning. Currently, the national production capacity for large-scale topographic maps in Indonesia is 13,000 km²/year using stereo-plotting/mono-plotting methods from photogrammetric data, Lidar, high-resolution satellite imagery, or a combination of the three. In order to provide the necessary data to the respective applications in a timely manner, one strategy is to only generate critical layers of the maps. One of the topographic map layers that is often needed is land cover. This research focuses on providing land cover to support the accelerated provision of topographic maps. The data used are very-high-resolution satellite images. The method used is a deep learning approach to classify very-high-resolution satellite images into land cover data. The implementation of the deep learning approach can advance the production of topographic maps, particularly in the provision of land cover data. This significantly enhances the efficiency and effectiveness of producing large-scale topographic maps, hence increasing productivity. The quality assessment of this study demonstrates that the AI-assisted method is capable of accurately classifying land cover data from very-high-resolution images, as indicated by the Kappa values of 0.81 and overall accuracy of 86%, respectively.

Lanbo Lai, Xiaolin Wang, Gholamreza Kefayati et al.

The indirect evaporative cooling system, which exploits the water evaporation process to generate cooling loads without introducing additional moisture, has been recognised as a viable alternative to conventional air-conditioning systems. This acknowledgment is due to its attributes of energy efficiency and environmental friendliness. The meticulous selection of wetting materials for an indirect evaporative cooler is of paramount importance as it significantly influences the heat and mass transfer performance of the system. Therefore, this paper experimentally examined a novel material produced by laser-resurfaced technology, and this material was compared with four other distinct materials (kraft paper, cotton fibre, polyester fibre, and polypropylene + nylon fibre) while considering the wicking ability, water-holding capacity, and thermal response performance. The results revealed that the fabric materials, specifically cotton fibre and polyester fibre, exhibited outstanding water-wicking ability, with a vertical wicking distance exceeding 16 cm. Cotton fibre also demonstrated an exceptional water-holding ability, registering a value of 0.0754 g/cm². In terms of thermal response performance, polypropylene + nylon fibre and the laser-resurfaced polymer achieved stable conditions within one minute, which could be attributed to the absence of a mechanical support plate and adhesive layer. All five materials attained stability after 4.2 min. Cotton and polyester fibres exhibited advantages in the duration of the evaporation process, maintaining stable conditions for 24 and 90 min, respectively. Based on the experimental results, appropriate water-spray strategies are proposed for each material.

Nghiem Thanh Pham, Tung Kieu, Duc-Manh Nguyen et al.

Small Language Models (SLMs) offer computational efficiency and accessibility, yet a systematic evaluation of their performance and environmental impact remains lacking. We introduce SLM-Bench, the first benchmark specifically designed to assess SLMs across multiple dimensions, including accuracy, computational efficiency, and sustainability metrics. SLM-Bench evaluates 15 SLMs on 9 NLP tasks using 23 datasets spanning 14 domains. The evaluation is conducted on 4 hardware configurations, providing a rigorous comparison of their effectiveness. Unlike prior benchmarks, SLM-Bench quantifies 11 metrics across correctness, computation, and consumption, enabling a holistic assessment of efficiency trade-offs. Our evaluation considers controlled hardware conditions, ensuring fair comparisons across models. We develop an open-source benchmarking pipeline with standardized evaluation protocols to facilitate reproducibility and further research. Our findings highlight the diverse trade-offs among SLMs, where some models excel in accuracy while others achieve superior energy efficiency. SLM-Bench sets a new standard for SLM evaluation, bridging the gap between resource efficiency and real-world applicability.

Kaiyuan Tian, Linbo Qiao, Baihui Liu et al.

Scientific research faces high costs and inefficiencies with traditional methods, but the rise of deep learning and large language models (LLMs) offers innovative solutions. This survey reviews transformer-based LLM applications across scientific fields such as biology, medicine, chemistry, and meteorology, underscoring their role in advancing research. However, the continuous expansion of model size has led to significant memory demands, hindering further development and application of LLMs for science. This survey systematically reviews and categorizes memory-efficient pre-training techniques for large-scale transformers, including algorithm-level, system-level, and hardware-software co-optimization. Using AlphaFold 2 as an example, we demonstrate how tailored memory optimization methods can reduce storage needs while preserving prediction accuracy. By bridging model efficiency and scientific application needs, we hope to provide insights for scalable and cost-effective LLM training in AI for science.

Xue-Geng Hong, Mei-Qi Zhang, Fang Tang et al.

Toscana virus (TOSV), a member of the <i>Phlebovirus</i> genus transmitted by sandflies, is acknowledged for its capacity to cause neurological infections and is widely distributed across Mediterranean countries. The potential geographic distribution and risk to the human population remained obscure due to its neglected nature. We searched PubMed and Web of Science for articles published between 1 January 1971 and 30 June 2023 to extract data on TOSV detection in vectors, vertebrates and humans, clinical information of human patients, as well as the occurrence of two identified sandfly vectors for TOSV. We further predicted the global distribution of the two sandfly vectors, based on which the global risk of TOSV was projected, after incorporating the environmental, ecoclimatic, biological, and socioeconomic factors. A total of 1342 unique studies were retrieved, among which 389 met the selection criteria and were included for data extraction. TOSV infections were documented in 10 sandfly species and 14 species of vertebrates, as well as causing a total of 7571 human infections. The occurrence probabilities of two sandfly vectors have demonstrated the greatest contributions to the potential distribution of TOSV infection risk. This study provides a comprehensive overview of global TOSV distribution and potential risk zones. Future surveillance and intervention programs should prioritize high-risk areas based on updated quantitative analyses.

Jan Pišl, Marc Rußwurm, Lloyd Haydn Hughes et al.

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drivers vary in space and time, producing accurate spatially explicit maps with regular temporal updates is essential. Drivers can be recognized from satellite imagery but the scale of tropical deforestation makes it unfeasible to do so manually. Machine learning opens up possibilities for automating and scaling up this process. In this study, we developed and trained a deep learning model to classify the drivers of any forest loss—including deforestation—from satellite image time series. Our model architecture allows understanding of how the input time series is used to make a prediction, showing the model learns different patterns for recognizing each driver and highlighting the need for temporal data. We used our model to classify over $588^{^{\prime}}000$ sites to produce a map detailing the drivers behind tropical forest loss. The results confirm that the majority of it is driven by agriculture, but also show significant regional differences. Such data is a crucial source of information to enable targeting specific drivers locally and can be updated in the future using free satellite data.