Search Results (1,519)

Methane emissions from freshwater reservoirs represent a globally relevant greenhouse gas source, which are estimated to range from 3% to 10% of all global anthropogenic methane emissions. However, there is high uncertainty in estimating reservoir emissions on local to global scales due to a combination of data paucity in key regions, particularly in the Southern Hemisphere, and challenges monitoring emission pathways. The key to improved spatially and temporally representative estimation of emission rates is to better understand the primary drivers of emission pathways, in particular, ebullition. We examine ebullition from 15 freshwater storages located in the Southern Hemisphere subtropical (South East Queensland) and temperate (Tasmania) regions using a combination of optical methane detection to develop the high-resolution mapping of ebullition zones and floating chamber incubation within ebullition zones to quantify areal emission rates. We demonstrate the equivalent water level, through air pressure or physical water level change, as a key driver of ebullition and examine the implications for spatially and temporally representative estimation of reservoir emissions. This study represents the largest broadscale ebullition survey undertaken across Australian temperate and subtropical reservoirs. The study findings are of broad relevance to scientists and corporate and government entities navigating the complexities of estimating greenhouse gas emissions from reservoirs and related policy instruments. Full article

In many hardwood forests, resprouting is a common response to disturbance and basal resprouts may represent a substantial component of the forest understory, especially post-wildfire. Despite this, resprouts are often overlooked in biomass assessments and drivers of resprouting responses in certain species are still unknown. These knowledge gaps are problematic as the contribution of resprouts to understory fuel loads are needed for wildfire risk modeling and effective forest stewardship. Here, we validated the handheld mobile laser scanning (HMLS) of basal resprout volume and field measurements of stem count and clump height as methods to estimate the mass of California Bay Laurel (Umbellularia californica) basal resprouts at Pepperwood and Saddle Mountain Preserves, Sonoma County, California. In addition, we examined the role of tree size and wildfire severity in predicting post-wildfire resprouting response. Both field measurements (clump height and stem count) and remote sensing (HMLS-derived volume) effectively estimated dry mass (total, leaf and wood) of U. californica resprouts, but underestimated dry mass for a large resprout. Tree size was a significant factor determining post-wildfire resprouting response at Pepperwood Preserve, while wildfire severity significantly predicted post-wildfire resprout size at Saddle Mountain. These site differences in post-wildfire basal resprouting predictors may be related to the interactions between fire severity, tree size, tree crown topkill, and carbohydrate mobilization and point to the need for additional demographic and physiological research. Monitoring post-wildfire changes in U. californica will deepen our understanding of resprouting dynamics and help provide insights for effective forest stewardship and wildfire risk assessment in fire-prone northern California forests. Full article

Antimicrobial resistance (AMR) presents a global health challenge, necessitating comprehensive surveillance and intervention strategies. Wastewater-based epidemiology (WBE) is a promising tool that can be utilized for AMR monitoring by offering population-level insights into microbial dynamics and resistance gene dissemination in communities. This review (n = 29 papers) examines the current landscape of utilizing WBE for AMR surveillance with a focus on methodologies, findings, and gaps in understanding. Reported methods from the reviewed literature included culture-based, PCR-based, whole genome sequencing, mass spectrometry, bioinformatics/metagenomics, and antimicrobial susceptibility testing to identify and measure antibiotic-resistant bacteria and antimicrobial resistance genes (ARGs) in wastewater, as well as liquid chromatography-tandem mass spectrometry to measure antibiotic residues. Results indicate Escherichia coli, Enterococcus spp., and Pseudomonas spp. are the most prevalent antibiotic-resistant bacterial species with hospital effluent demonstrating higher abundances of clinically relevant resistance genes including bla, bcr, qnrS, mcr, sul1, erm, and tet genes compared to measurements from local treatment plants. The most reported antibiotics in influent wastewater across studies analyzed include azithromycin, ciprofloxacin, clindamycin, and clarithromycin. The influence of seasonal variation on the ARG profiles of communities differed amongst studies indicating additional factors hold significance when examining the conference of AMR within communities. Despite these findings, knowledge gaps remain, including longitudinal studies in multiple and diverse geographical regions and understanding co-resistance mechanisms in relation to the complexities of population contributors to AMR. This review underscores the urgent need for collaborative and interdisciplinary efforts to safeguard public health and preserve antimicrobial efficacy. Further investigation on the use of WBE to understand these unique population-level drivers of AMR is advised in a proposed framework to inform best practice approaches moving forward. Full article

Space weather exerts a significant influence on the Earth’s atmosphere, driving a variety of physical processes, including the production of cosmogenic radionuclides. Among these, ⁷Be is a naturally occurring radionuclide formed through spallation reactions induced by cosmic-ray showers interacting with atmospheric constituents, primarily oxygen and nitrogen. Over long timescales, the atmospheric concentration of ⁷Be exhibits a direct correlation with the cosmic-ray flux reaching the Earth and an inverse correlation with solar activity, which modulates this flux via variations of the heliosphere. The large availability of ⁷Be concentration data, resulting from its use as a natural tracer employed in atmospheric transport studies and in monitoring the fallout from radiological incidents such as the Chernobyl disaster, can also be exploited to investigate the impact of space weather conditions on the terrestrial atmosphere and related geophysical processes. The present study analyzes a long-term dataset of monthly ⁷Be activity concentrations in air samples collected at ground level since 1987 at the ENEA Casaccia Research Center in Rome, Italy. In particular, the linear correlation of this time series with the galactic cosmic ray flux on Earth and solar activity have been investigated. Data from a ground-based neutron monitor and sunspot numbers have been used as proxies for galactic cosmic rays and solar activity, respectively. A centered running-mean low-pass filter was applied to the monthly ⁷Be time series to extract its low-frequency component associated with cosmic drivers, which is partially hidden by high-frequency modulations induced by atmospheric dynamics. For Solar Cycles 22, 23, 24, and partially 25, the analysis shows that a substantial portion of the relationship between stratospheric ⁷Be concentrations and cosmic drivers is captured by linear correlation. Within a statistically consistent framework, the evidence supports a correlation between ⁷Be and cosmic drivers consistent with solar-cycle variability. The ⁷Be radionuclide can therefore be regarded as a reliable atmospheric tracer of cosmic-ray variability and, indirectly, of solar modulation. Full article

Tropical and subtropical forests account for approximately one-third of global terrestrial gross primary productivity (GPP), and the diurnal patterns of GPP strongly regulate the land–atmosphere CO₂ interactions and feedback to the climate. Combined with ground eddy-covariance (EC) flux towers, geostationary satellites offer significant advantages for continuously monitoring these diurnal variations in the “breathing of biosphere”. Here we utilized half-hourly optical signals from the Himawari-8 Advanced Himawari Imager (H8/AHI) geostationary satellite and tower-based EC flux data to investigate the diurnal variations in subtropical forest GPP and its drivers. Results showed that three machine learning models well estimated the diurnal patterns of subtropical forest GPP, with the determination coefficient (R²) ranging from 0.71 to 0.76. Photosynthetically active radiation (PAR) is the primary driver of the diurnal cycle of GPP, modulated by temperature, soil water content, and vapor pressure deficit. Moreover, the effect magnitude of PAR on GPP varies across three timescales. This study provides robust technical support for diurnal forest GPP estimations and the possibility for large-scale estimations of diurnal GPP over tropics in the future. Full article

Truck drivers play a pivotal role in global freight transport systems, yet their occupational and behavioral risk exposures make them a priority population in road safety research. This systematic review examines the factors influencing risky driving behaviors among truck drivers and their impacts on road safety outcomes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the review aimed to identify hazardous driving behaviors, the internal and external factors contributing to these behaviors, and their consequences for traffic safety. Inclusion criteria targeted original research published in English between 2009 and 2024 specifically focused on truck driver behavior and road safety outcomes. Systematic searches across PubMed, Scopus, Web of Science, and IEEE Xplore yielded 104 studies meeting these criteria. The synthesis revealed prevalent risky behaviors—such as speeding, fatigue-related impairments, distracted driving, and substance use—driven by internal factors (e.g., health conditions, psychological stress) and external pressures (e.g., occupational demands, regulatory constraints). These behaviors were consistently associated with increased crash risk. Nonetheless, limitations including the exclusion of non-English studies, reliance on self-reported data, and lack of standardized metrics constrained cross-study comparability and generalizability. Effective interventions identified include fatigue management programs, driver monitoring technologies, and positive safety climates. Findings underscore the urgent need for evidence-based, multifaceted strategies to enhance truck driver safety and inform policy, industry practices, and future research. Full article

In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that generates vibrations and acoustic emissions, directly affecting passenger comfort and accelerating infrastructure deterioration. This work presents a methodology for the automatic detection of corrugated track sections, based on the mathematical modeling of the spectral content of onboard-recorded acoustic signals. The hypothesis is that these defects produce characteristic peaks in the frequency domain, whose position depends on speed but whose wavelength remains constant. The novelty of the proposed approach lies in the formulation of two functional spectral indices—IIAPD (permissive) and EWISI (restrictive)—that combine power spectral density (PSD) and fast Fourier transform (FFT) analysis over spatial windows, incorporating adaptive frequency bands and dynamic prominence thresholds according to train speed. This enables robust detection without manual intervention or subjective interpretation. The methodology was validated under real operating conditions on a commercially operated metro line and compared with two reference techniques. The results show that the proposed approach achieved up to 19% higher diagnostic accuracy compared to the best-performing reference method, maintaining consistent detection performance across all evaluated speeds. These results demonstrate the robustness and applicability of the method for integration into autonomous trains as an onboard diagnostic system, enabling reliable, continuous monitoring of rail corrugation severity using reproducible mathematical metrics. Full article

Assessing fish biodiversity is essential for freshwater ecosystem conservation. This study compares environmental DNA (eDNA) metabarcoding and traditional morphological surveys to investigate fish communities in the Gaya River, China. A total of 42 fish species were identified, with 13 detected only by eDNA, 7 exclusively by morphology, and 11 by both methods. A comparative analysis of species composition, functional diversity, and phylogenetic diversity revealed significant differences between the two approaches. Notably, eDNA data indicated higher phylogenetic diversity (PD), while morphological surveys captured greater functional evenness (FEve). Multivariate analyses indicated that total phosphorus (TP), total suspended solids (TSS), electrical conductivity (EC), temperature (T), and pH significantly influenced fish community composition, while dissolved oxygen (DO) was a key driver of species richness (SR), functional richness (FRic), and PD. These findings highlight the methodological differences and complementary strengths of eDNA and morphological approaches in biodiversity assessments. By providing comparative insights into fish diversity patterns, this study underscores the importance of using multi-method approaches to improve freshwater biodiversity monitoring and conservation strategies. Full article

Illegal wildlife trade is a major global driver of biodiversity loss, shaped by high consumer demand, transboundary networks, and uneven enforcement. In the Middle East, particularly the Gulf Cooperation Council (GCC) region, factors such as high purchasing power, cultural traditions (e.g., falconry, prestige pets), and expanding digital marketplaces sustain both legal and illegal flows. We present a nine-year (2017–2025) assessment based on weekly, repeated field surveys at the Friday Market, adjacent pet shops, and private farms, complemented by systematic monitoring of online advertisements on Haraj.com.sa. We recorded 1063 individual animals across 88 species, birds (39.4%), reptiles (52.0%), and mammals (8.6%), and analyzed prices, conservation status, and venue-specific patterns. The most frequently recorded taxa included the white-eared bulbul (Pycnonotus leucotis), common slider (Trachemys scripta), and Egyptian mastigure (Uromastyx aegyptia). Mammals, though fewer in number, commanded the highest prices, particularly cheetahs (Acinonyx jubatus) and lions (Panthera leo). About 26% of species were IUCN-listed as threatened, with CITES Appendix I taxa fetching higher prices. Findings underscore the need for real-time monitoring, targeted enforcement, and cross-border collaboration to address escalating trade in rare and protected species. Full article

To address urban flooding challenges exacerbated by climate change and urbanization, this study develops an integrated assessment framework combining the analytic hierarchy process (AHP), entropy weight method, and cloud model to quantify urban flood resilience. Resilience is deconstructed into resistance, adaptability, and recovery and evaluated through 24 indicators spanning water resources, socio-economic systems, and ecological systems. Subjective (AHP) and objective (entropy) weights are optimized via minimum information entropy, with the cloud model enabling qualitative–quantitative resilience mapping. Analyzing 2014–2024 data from 27 Chinese sponge city pilots, the results show resilience improved from “poor to average” to “good to average”, with a 2.89% annual growth rate. Megacities like Beijing and Shanghai excel in resistance and recovery due to infrastructure and economic strengths, while cities like Sanya enhance resilience via ecological restoration. Key drivers include water allocation (27.38%), economic system (18.41%), and social system (17.94%), with critical indicators being population density, secondary industry GDP ratio, and sewage treatment rate. Recommendations emphasize upgrading rainwater storage, intelligent monitoring networks, and resilience-oriented planning. The model offers a scientific foundation for urban disaster risk management, supporting sustainable development. This approach enables systematic improvements in adaptive capacity and recovery potential, providing actionable insights for global flood-resilient urban planning. Full article

As a critical ecological security node in North China, the Baiyangdian Basin underpins regional water resources, biodiversity conservation, and environmental risk mitigation. Its ecological integrity is fundamental to the sustainable development of the Beijing–Tianjin–Hebei (BTH) megaregion. This study leveraged Google Earth Engine (GEE) to quantify spatiotemporal ecosystem dynamics within the Baiyangdian watershed from 1990 to 2023, utilizing the Remote Sensing Ecological Index (RSEI). The primary drivers influencing the watershed’s ecological and environmental quality were subsequently analyzed. The results show that the ecological quality of the Baiyangdian Basin showed fluctuating changes from 1990 to 2023. Overall, the northwestern part of the Baiyangdian Basin improved significantly, while the southeastern part was slightly degraded, and the intensity of the change between different RSEI grades was low, mainly fluctuating between poor, medium, and good grades. Both anthropogenic and natural factors have high explanatory power for the ecological quality of the Baiyangdian watershed, and the land use type in particular is the main driver of changes in the RSEI area. The explanatory power of these factors was significantly enhanced by the interaction between them, especially the interaction between the land use type and other drivers. Within the drivers of the land use type, the cropland area, woodland area, shrub area, and grassland area have a significant influence. In summary, the area change in different land use types is the main factor influencing the ecological quality of the Baiyangdian watershed. This study has demonstrative value and implications for large-scale shallow lakes and wetlands, ecological barriers in rapidly urbanizing regions, the integrated management of cross-administrative watersheds, and the use of the GEE platform for long time-series and large-scale ecological monitoring and assessment. Full article

Driving fatigue is a crucial factor affecting road traffic safety. Accurately assessing the driver’s fatigue status is critical for accident prevention. This paper explores the assessment methods of driving fatigue under different conditions based on multimodal physiological and behavioral data. Physiological data such as heart rate, brainwave, electromyography, and pupil diameter were collected through experiments, as well as behavioral data such as posture changes, vehicle acceleration, and throttle usage. The results show that physiological and behavioral indicators have significant sensitivity to driving fatigue, and the fusion of multimodal data can effectively improve the accuracy of fatigue detection. Based on this, a comprehensive driving fatigue assessment model was constructed, and its applicability and reliability in different driving scenarios were verified. This study provides a theoretical basis for the development and application of driver fatigue monitoring systems, helping to achieve real-time fatigue warnings and protections, thereby improving driving safety. Full article

In deep-sea mining operations, rigid risers operate in a complex and uncertain ocean environment where vessel–riser interactions present significant structural challenges. This study develops a coupled dynamic modeling framework that integrates vessel motions and environmental loads to evaluate the probabilistic risk of riser failure. Using frequency-domain RAOs derived from AQWA and time-domain simulations in OrcaFlex 11.0, we analyze the riser’s effective tension, bending moment, and von Mises stress under a range of wave heights, periods, and directions, as well as varying current and wind speeds. A Monte Carlo simulation framework based on Latin hypercube sampling is used to generate 10,000 sea state scenarios. The response distributions are approximated using probability density functions to assess structural reliability, and global sensitivity is evaluated using a Sobol-based approach. Results show that the wave height and period are the primary drivers of riser dynamic response, both with sensitivity indices exceeding 0.7. Transverse wave directions exert stronger dynamic excitation, and the current speed notably affects the bending moment (sensitivity index = 0.111). The proposed methodology unifies a coupled time-domain simulation, environmental uncertainty analysis, and reliability assessment, enabling clear identification of dominant factors and distribution patterns of extreme riser responses. Additionally, the workflow offers practical guidance on key monitoring targets, alarm thresholds, and safe operation to support design and real-time decision-making. Full article

Accurate prediction of wheat storage quality is essential for ensuring storage safety and providing early warnings of quality deterioration. However, existing methods focus solely on storage environmental conditions, neglecting the spatial distribution of temperature within grain piles, lacking interpretability, and generally failing to provide reliable forecasts of future quality changes. To overcome these challenges, an interpretable prediction framework for wheat storage quality based on stacked ensemble learning is proposed. Three key features, Effective Accumulated Temperature (EAT), Cumulative High Temperature Deviation (CHTD), and Cumulative Temperature Gradient (CTG), were derived from grain temperature data to capture the spatiotemporal dynamics of the internal temperature field. These features were then input into the stacked ensemble learning model to accurately predict historical quality changes. In addition, future grain temperatures were predicted with high precision using a Graph Convolutional Network-Temporal Fusion Transformer (GCN-TFT) model. The temperature prediction results were then employed to construct features and were fed into the stacked ensemble learning model to enable future quality change prediction. Baseline experiments indicated that the stacked model significantly outperformed individual models, achieving R² = 0.94, MAE = 0.44 mg KOH/100 g, and RMSE = 0.59 mg KOH/100 g. SHAP interpretability analysis revealed that EAT constituted the primary driver of wheat quality deterioration, followed by CHTD and CTG. Moreover, in future quality prediction experiments, the GCN-TFT model demonstrated high accuracy in 60-day grain temperature forecasts, and although the prediction accuracy of fatty acid value changes based on features derived from predicted temperatures slightly declined compared to features based on actual temperature data, it remained within an acceptable precision range, achieving an MAE of 0.28 mg KOH/100 g and an RMSE of 0.33 mg KOH/100 g. The experiments validated that the overall technical route from grain temperature prediction to quality prediction exhibited good accuracy and feasibility, providing an efficient, stable, and interpretable quality monitoring and early warning tool for grain storage management, which assists managers in making scientific decisions and interventions to ensure storage safety. Full article

Remote photoplethysmography (rPPG) offers a promising solution for non-contact driver monitoring by detecting subtle blood flow-induced facial color changes from video. However, motion artifacts in dynamic driving environments remain key challenges. This study presents an rPPG framework that combines signal processing techniques before and after applying Eulerian Video Magnification (EVM) for pulse rate (PR) estimation in driving simulators. While not novel, the approach offers insights into the efficiency of the EVM method and its time complexity. We compare results of the proposed rPPG approach against reference Empatica E4 data and also compare it with existing achievements from the literature. Additionally, the possible bias of the Empatica E4 is further assessed using an independent dataset with both the Empatica E4 and the Faros 360 measurements. EVM slightly improves PR estimation, reducing the mean absolute error (MAE) from 6.48 bpm to 5.04 bpm (the lowest MAE (~2 bpm) was achieved under strict conditions) with an additional time required for EVM of about 20 s for 30 s sequence. Furthermore, statistically significant differences are identified between younger and older drivers in both reference and rPPG data. Our findings demonstrate the feasibility of using rPPG-based PR monitoring, encouraging further research in driving simulations. Full article