Hasil untuk "Environmental sciences"

U. Beck

Jessica L. Green, B. Bohannan, Rachel J. Whitaker

Han Yin, Yang Xiao, Rohan Kumar Das et al.

Recent progress in audio generation has made it increasingly easy to create highly realistic environmental soundscapes, which can be misused to produce deceptive content, such as fake alarms, gunshots, and crowd sounds, raising concerns for public safety and trust. While deepfake detection for speech and singing voice has been extensively studied, environmental sound deepfake detection (ESDD) remains underexplored. To advance ESDD, the first edition of the ESDD challenge was launched, attracting 97 registered teams and receiving 1,748 valid submissions. This paper presents the task formulation, dataset construction, evaluation protocols, baseline systems, and key insights from the challenge results. Furthermore, we analyze common architectural choices and training strategies among top-performing systems. Finally, we discuss potential future research directions for ESDD, outlining key opportunities and open problems to guide subsequent studies in this field.

W. Michener, Matthew B. Jones

Ecology is evolving rapidly and increasingly changing into a more open, accountable, interdisciplinary, collaborative and data-intensive science. Discovering, integrating and analyzing massive amounts of heterogeneous data are central to ecology as researchers address complex questions at scales from the gene to the biosphere. Ecoinformatics offers tools and approaches for managing ecological data and transforming the data into information and knowledge. Here, we review the state-of-the-art and recent advances in ecoinformatics that can benefit ecologists and environmental scientists as they tackle increasingly challenging questions that require voluminous amounts of data across disciplines and scales of space and time. We also highlight the challenges and opportunities that remain.

Mariana Brüning-González, José Ignacio Arroyo, Pablo A. Marquet et al.

Understanding the quantitative patterns behind scientific disciplines is fundamental for informed research policy. While many fields have been studied from this perspective, Urban Science (USc) and its subfields remain underexplored. As organisms, urban systems rely on materials and energy inputs and transformation (i.e. metabolism) to sustain essential dynamics. This concept has been adopted by various disciplines, including architecture and sociology, and by those focused on metabolic processes, such as ecology and industrial ecology. This study addresses the structure and evolution of Urban Metabolism (UM) and Sustainability research, analyzing articles by disciplines, study subjects (e.g., cities, regions), methodologies, and author diversity (nationality and gender). Our review suggests that UM is an emerging field that grew until 2019, primarily addressed by environmental science and ecology. Common methods include Ecological Network Analysis, and Life Cycle Assessment, and Material Flow Analysis, focusing flows over stocks, ecosystem dynamics and evolutionary perspectives of the urban system. Authors are predominantly from China and the USA, and there are less gender gaps compared to general science research. Our analysis identifies relevant challenges that have become evident in the statistical properties of this scientific field and which might be helpful for the design of improved research policies.

Ahmed Faizul Haque Dhrubo, Mohammad Abdul Qayum

The fast pace of technological growth has created a heightened need for intelligent, autonomous monitoring systems in a variety of fields, especially in environmental applications. This project shows the design process and implementation of a proper dual node (master-slave) IoT-based monitoring system using STM32F103C8T6 microcontrollers. The structure of the wireless monitoring system studies the environmental conditions in real-time and can measure parameters like temperature, humidity, soil moisture, raindrop detection and obstacle distance. The relay of information occurs between the primary master node (designated as the Green House) to the slave node (the Red House) employing the HC-05 Bluetooth module for information transmission. Each node displays the sensor data on OLED screens and a visual or auditory alert is triggered based on predetermined thresholds. A comparative analysis of STM32 (ARM Cortex-M3) and Arduino (AVR) is presented to justify the STM32 used in this work for greater processing power, less energy use, and better peripherals. Practical challenges in this project arise from power distribution and Bluetooth configuration limits. Future work will explore the transition of a Wi-Fi communication protocol and develop a mobile monitoring robot to enhance scalability of the system. Finally, this research shows that ARM based embedded systems can provide real-time environmental monitoring systems that are reliable and consume low power.

Larissa Leão F. de Sousa, Mariana Dias Guilardi, Junior Olimpio Martins et al.

Abstract Background Rabies, a lethal viral zoonotic disease, remains a significant global public health concern. In northeastern Brazil, in particular, its epidemiology is complex and dynamic, characterized by the presence of several reservoirs associated with human rabies infection. Methods This study, conducted from June 2022 to July 2023, was part of a passive epidemiological surveillance initiative under Brazil’s National Rabies Surveillance Program. It investigated the presence of Rhabdovirus (RhabV) in 356 postmortem chiropteran brain samples using three diagnostic techniques for rabies and conducted an evolutionary study on both pan-RhabV- and pan-LYSSAV-positive PCR samples. The samples were collected from 20 bat species and different locations in the State of Ceará, an endemic region for the rabies virus (RABV). Rabies-positive samples were further explored through Bayesian, genetic distance mapping and recombination analyses. Results From a total of 356 samples collected, 43 (12.07%) were positive for direct immunofluorescence (DIF) and 40 (11.23%) for mouse intracerebral inoculation (MIT) tests. Among the positive results, 40 samples were confirmed by both DIF and MIT, while 13 (3.65%) had inconclusive results for one or both techniques. Molecular assays identified 38 rabies-positive samples (10.67%). Members of the Molossidae and Phyllostomidae families had the highest prevalence, highlighting the role of insectivorous and frugivorous bats in the cycle and dynamics of rabies transmission. Phylogenetic reconstructions revealed three distinct and well-supported clusters and clades, indicating the cocirculation of different RABV lineages in the region and shedding light on both intra- and interhost diversity. We also demonstrated genetic distance among the RABV clusters and inferred that their common ancestor originated in Europe, later diversifying across continents. No recombination breakpoints were identified. Conclusions This study highlights the dynamic nature of RABV evolution within individual bat hosts, contributing to the understanding of the genetic diversity of RABV variants found in several bat species in northeastern Brazil. This study provides crucial insights into viral transmission dynamics within and between different host species and is essential for designing effective rabies control and prevention strategies tailored to endemic regions.

Taiwo Bintu Ayinde, Charles F. Nicholson, Benjamin Ahmed

Abstract Achieving Net Zero Emissions in vegetable production systems is a critical challenge in dryland climates of low- and middle-income countries, yet limited data exists to assess the feasibility of such systems. This study employs life cycle inventory methods to evaluate key performance metrics, including yield per land area, production costs, cumulative energy demand (CED), global warming potential (GWP), and water use (WU) for Controlled Environment Agriculture (CEA) in screen houses and field-based tomato production systems in Northern Nigeria. The findings reveal that CEA, despite its high production cost of ₦24,070.80 per m², achieves the highest yield of 28.57 kg per m². Additionally, CEA demonstrates superior efficiency, exhibiting the lowest C ED (0.025 MJ/kg) and GWP (0.76 kg CO₂-eq/kg). In contrast, rainfed field production, while having the lowest cost (₦58.45 per m²), results in the lowest yield (0.08 kg/m²) and the highest GWP (34,545.8%). Irrigated field production performs moderately, with a production cost of ₦150.38 per m², a yield of 0.22 kg per m², and a GWP of 12,572.4%. A key factor influencing yield variation across production systems is the difference in tomato varieties cultivated in open-field and CEA environments. CEA relies on hybrid varieties optimized for controlled conditions, whereas open-field farming utilizes varieties adapted to outdoor environmental fluctuations, contributing to disparities in yield potential. This study highlights the trade-offs between cost, yield, energy efficiency, and environmental impact across different production models. The results underscore the advantages of adopting more efficient and controlled cultivation methods like CEA, offering potential pathways for sustainable and environmentally responsible agricultural practices in regions facing climate and resource constraints.

H. Wheater, E. P. Evans

Abstract Human activities have profoundly changed the land on which we live. In particular, land use and land management change affect the hydrology that determines flood hazard, water resources (for human and environmental needs) and the transport and dilution of pollutants. It is increasingly recognised that the management of land and water are inextricably linked (e.g. Defra, 2004). “Historical context, state of the science and current management issues” section of this paper addresses the science underlying those linkages, for both rural and urban areas. In “Historical context, state of the science and current management issues” section we discuss future drivers for change and their management implications. Detailed analyses are available for flood risk, from the Foresight Future Flooding project (Evans et al., 2004a,b) and other recent studies, and so we use flooding as an exemplar, with a more limited treatment of water resource and water quality aspects. Finally in “Science needs and developments” section we discuss science needs and likely progress. This paper does not address the important topic of water demand except for some reference to the Environment Agency's Water Resources Strategy for England and Wales (Environment Agency, 2009).

Márkus Keller

Changwoo J. Lee, Elaine Symanski, Amal Rammah et al.

Accounting for exposure measurement errors has been recognized as a crucial problem in environmental epidemiology for over two decades. Bayesian hierarchical models offer a coherent probabilistic framework for evaluating associations between environmental exposures and health effects, which take into account exposure measurement errors introduced by uncertainty in the estimated exposure as well as spatial misalignment between the exposure and health outcome data. While two-stage Bayesian analyses are often regarded as a good alternative to fully Bayesian analyses when joint estimation is not feasible, there has been minimal research on how to properly propagate uncertainty from the first-stage exposure model to the second-stage health model, especially in the case of a large number of participant locations along with spatially correlated exposures. We propose a scalable two-stage Bayesian approach, called a sparse multivariate normal (sparse MVN) prior approach, based on the Vecchia approximation for assessing associations between exposure and health outcomes in environmental epidemiology. We compare its performance with existing approaches through simulation. Our sparse MVN prior approach shows comparable performance with the fully Bayesian approach, which is a gold standard but is impossible to implement in some cases. We investigate the association between source-specific exposures and pollutant (nitrogen dioxide (NO$_2$))-specific exposures and birth outcomes for 2012 in Harris County, Texas, using several approaches, including the newly developed method.

Moumita Mondal, Santanu K. Maiti

The specific role of environmental interaction on bias driven circular current in a ring nanojunction is explored within a tight-binding framework based on wave-guide theory. The environmental interaction is implemented through disorder in backbone sites where these sites are directly coupled to parent lattice sites of the ring via single bonds. In absence of backbone disorder circular current becomes zero for a lengthwise symmetric nanojunction, while it increases with disorder which is quite unusual, and after reaching a maximum it eventually drops to zero in the limit of high disorder. The effects of ring-electrode interface configuration, ring-backbone coupling, different types of backbone disorder and system temperature are critically investigated. All the studied results are valid for a broad range of physical parameters, giving us confidence that the outcomes of this theoretical work can be verified experimentally. To make this study self-contained, we also discuss the feasibility of detecting bias-driven circular current and provide design guidelines for implementing our proposed quantum system in a laboratory.

Tomoei Takahashi, George Chikenji, Kei Tokita et al.

How typical elements that shape organisms, such as protein secondary structures, have evolved, or how evolutionarily susceptible/resistant they are to environmental changes, are significant issues in evolutionary biology, structural biology, and biophysics. According to Darwinian evolution, natural selection and genetic mutations are the primary drivers of biological evolution. However, the concept of ``robustness of the phenotype to environmental perturbations across successive generations," which seems crucial from the perspective of natural selection, has not been formalized or analyzed. In this study, through Bayesian learning and statistical mechanics we formalize the stability of the free energy in the space of amino acid sequences that can design particular protein structure against perturbations of the chemical potential of water surrounding a protein as such robustness. This evolutionary stability is defined as a decreasing function of a quantity analogous to the susceptibility in the statistical mechanics of magnetic bodies specific to the amino acid sequence of a protein. Consequently, in a two-dimensional square lattice protein model composed of 36 residues, we found that as we increase the stability of the free energy against perturbations in environmental conditions, the structural space shows a steep step-like reduction. Furthermore, lattice protein structures with higher stability against perturbations in environmental conditions tend to have a higher proportion of $α$-helices and a lower proportion of $β$-sheets. This result is qualitatively confirmed by comparing the histograms of the percentage of secondary structures of evolutionarily robust proteins and randomly selected proteins through an empirical validation using a protein database.

Zihao Wang, Jian Cheng, Liang Xu et al.

A hybrid physics-machine learning modeling framework is proposed for the surface vehicles' maneuvering motions to address the modeling capability and stability in the presence of environmental disturbances. From a deep learning perspective, the framework is based on a variant version of residual networks with additional feature extraction. Initially, an imperfect physical model is derived and identified to capture the fundamental hydrodynamic characteristics of marine vehicles. This model is then integrated with a feedforward network through a residual block. Additionally, feature extraction from trigonometric transformations is employed in the machine learning component to account for the periodic influence of currents and waves. The proposed method is evaluated using real navigational data from the 'JH7500' unmanned surface vehicle. The results demonstrate the robust generalizability and accurate long-term prediction capabilities of the nonlinear dynamic model in specific environmental conditions. This approach has the potential to be extended and applied to develop a comprehensive high-fidelity simulator.

Marco Immanuel B. Rivera, Reinabelle C. Reyes

The future space-borne Laser Interferometer Space Antenna (LISA) is expected to detect gravitational waves (GW) from Extreme Mass Ratio Inspiral (EMRI) binaries which may live in nontrivial environments such as accretion disks. In this work, we apply the Fisher matrix Principal Component Analysis (PCA) method to assess how well LISA observations can jointly constrain the source parameters and environmental densities around EMRIs. Specifically, we calculate the Fisher matrix from the post-Newtonian parameters of an EMRI binary embedded in a fluid with a constant density profile. We determine that the most dominant measurable parameter combination is dominated by contributions from environmental effects, namely, gravitational drag, accretion, and gravitational pull (in order of contribution). The proposed reparameterization of the PN parameters can be used to improve the power and efficiency of future detection and parameter estimation methods.

Tesfaye Bayu Zeleke, Tri Retnaningsih Soeprobowati, Solomon Adissu et al.

The trends of temperature and rainfall are critical indicators of climate change within a certain area. However, the existence of climate change is not locally understood in most parts of the world. This research aims to analyze the trend of temperature and rainfall in the Lake Tana Sub-basin as a means to understand the current and future status of climate change. The trends of temperature and rainfall were analyzed using the modified Mann-Kendall trend test, while the autoregressive integrated moving average model (ARIMA) was used to predict future temperature and rainfall. The findings reveal that monthly temperatures show a significant increasing trend for March, April, May, June, and December with Z-values of 3.96, 3.32, 2.64, 3.21, and 4.6, respectively. Seasonal and annual temperatures also show a significant increasing trend with Z-values of 4, 5.35, 5.08, and 4.41 for spring, autumn, winter, and annual, respectively. The Mann-Kendall trend analysis results show that monthly, seasonal and annual rainfall exhibit significant increasing trends for some months and seasons. The results of the ARIMA model suggest that the predicted values of temperature and rainfall will continue to increase over the next 10 years in the study area. Based on these findings, it can be concluded that there is a significant and increasing trend in temperature and rainfall, which will likely continue over the next decade, indicating the presence of climate change in the study area. The research findings suggest that temperature and rainfall have been increasing over time, leading to climate change in the study area, so sustainable lake management and urban development should be practiced to mitigate and adopt climate change.

Andrzej Klimczuk, Delali A. Dovie, Agnieszka Cieśla et al.

Li Jiaheng, Hu Mengjin, Li Hongkui et al.

Based on the high-resolution satellite image data, the information mining technology of the available surface elements of PV is studied, and the investigation of the available surface elements of PV in 98 counties and cities of South Hebei grid is realized. Based on the large-scale and high-resolution remote sensing data obtained by multi-source remote sensing data fusion technology, the depth-learning-based surface feature recognition technology for photovoltaic development is studied. Based on the method of automatic identification and artificial combination of depth-learning, it can identify the available ground elements (roof, water surface, road surface, dry beach, etc.), the available surface elements of PV in 98 counties and cities of Hebei South Grid were obtained. From the overall point of view, the photovoltaic land, the building occupies the main position, in the four cities are relatively high, are in the 6% ~ 15%

Olawale Emmanuel Rowland

Abstract Meteorological parameters play a major role in air pollutant concentrations as they create conditions that either hinder or facilitate the reaction and dispersion of pollutants in our environments. This is particularly evident in Europe, where frequent alternation of meteorological parameters has the potential to significantly impact pollutant concentrations. This study applied the R openair package to comparatively analyse the relationship between key meteorological parameters and NO2, O3, PM2.5, and PM10 concentrations measured at selected air quality monitoring stations in Krakow, Milan, and Paris in the year 2021. The study made use of meteorological data acquired from National Aeronautics and Space Administration (NASA) Power data repository, and air pollutants data measured at air quality monitoring stations in each of the three cities. The air pollutants data were retrieved from European Environmental Agency’s Airbase. Concentration and correlation analyses were conducted using the relevant functions of the R openair package. Findings in the study revealed a positive relationship between temperature and O3, wind speed and O3; and a negative relationship between temperature and NO2/PM2.5/PM10. The study further revealed a negative relationship between wind speed and NO2/PM2.5/PM10, as well as a negative relationship between precipitation and NO2/PM2.5/PM10. NO2, PM2.5, and PM10 concentrations were higher in winter periods, weekdays, nights, and evenings, but lower in summer periods, weekends, and midday. Whereas O3 concentration was higher in summer periods, weekends, midday, and lower in winter periods, weekdays, nights, and evenings. NO2, PM10, and PM2.5 concentrations were higher during the periods without precipitation than periods with precipitation. In addition, temperature inversions were found to be linked with higher concentrations of NO2, PM2.5, and PM10, but lower concentrations of O3 in Krakow, Paris and Milan. Accordingly, the study recommends effective monitoring, increased awareness, the use of pollutant removing devices, and further research to enhance adaptation and advance knowledge.

Xuanpeng Zhao, Guoyuan Wu, Akula Venkatram et al.

Air quality and human exposure to mobile source pollutants have become major concerns in urban transportation. Existing studies mainly focus on mitigating traffic congestion and reducing carbon footprints, with limited understanding of traffic-related health impacts from the environmental justice perspective. To address this gap, we present an innovative integrated simulation platform that models traffic-related air quality and human exposure at the microscopic level. The platform consists of five modules: SUMO for traffic modeling, MOVES for emissions modeling, a 3D grid-based dispersion model, a Matlab-based concentration visualizer, and a human exposure model. Our case study on multi-modal mobility on-demand services demonstrates that a distributed pickup strategy can reduce human cancer risk associated with PM2.5 by 33.4% compared to centralized pickup. Our platform offers quantitative results of traffic-related air quality and health impacts, useful for evaluating environmental issues and improving transportation systems management and operations strategies.