Search Results (180)

The induced polarisation (IP) technique is a geophysical method used to measure chargeability and resistivity, providing crucial insights into subsurface geological structures. Traditionally, IP measurements have been instrumental in exploring disseminated sulphide deposits, leveraging the strong polarisation response of metallic particles. It provides valuable insights about rock mineralisation, matrix composition, and formation polarizability by analysing electrical parameters. However, their potential to predict metallurgical performance remains largely unexplored. This study evaluates whether IP parameters—chargeability and resistivity—can serve as geometallurgical indicators for copper sulphide ores. The evaluation integrates IP measurements with mineralogical and flotation data. Artificial pyrite–sand mixtures and five real ore samples from Mount Isa were analysed using the sample core IP tester and mineral liberation analysis, followed by collectorless flotation tests. Statistical analysis demonstrated a strong correlation between resistivity and chalcopyrite recovery (R² = 0.90, p = 0.99), as well as a moderate correlation between chargeability and chalcopyrite selectivity (R² = 0.72, p = 0.93). These findings demonstrate that IP captures key textural and electrochemical features governing flotation behaviour, including pyrite abundance, mineral liberation, and galvanic interactions. The results highlight IP as a promising rapid-assessment tool for identifying ore variability and forecasting flotation response, with potential integration into geometallurgical models and mine-to-mill optimisation. Further validation across broader ore domains is recommended to refine the predictive capability of IP-based indicators. Full article

Bioaerosols released during wildland and prescribed fires may influence ecosystems, air quality, and microbial dispersal, yet their transport and deposition remain poorly understood. This study combined infrared uncrewed aircraft system (UAS) observations of a prescribed burn with the coupled fire–atmosphere model WRF-SFIRE and a Lagrangian particle model in order to evaluate how uncertainties in simulated fire behavior affect predicted bioaerosol (bacterial cell) transport and deposition. A reconstruction of the observed spatiotemporal evolution of the fire was derived from thermal UAS measurements acquired during the burn and incorporated into a WRF-SFIRE simulation, in which the modeled fire spread was constrained to follow this reconstructed progression. This benchmark run was compared with two unconstrained, fully coupled simulations that used a low and a high estimate of fuel moisture content (FMC) to represent typical uncertainty in fire rate of spread (ROS) prediction. Despite substantial differences in fire intensity and plume dynamics among the simulations, the resulting bioaerosol transport pathways and deposition patterns were broadly consistent across cases. The horizontal transport of the bioaerosols was dominated by the ambient Easterly wind and the bioaerosols were lofted by fire-affected updrafts—some exceeding 10 m/s—within the buoyant plume structure resolved in WRF-SFIRE. Deposition hot-spots appeared in consistent locations in the three simulations, especially regions where topography forced up-slope transport. Although the most intense fire produced slightly greater local deposition—likely due to a combination of stronger fire-induced downdrafts and overturning from penetration into strong vertical wind shear above the boundary layer—differences were small relative to the overall deposition footprint. These results suggested that, for burns of this scale, bioaerosol transport and deposition predictions are relatively robust to realistic uncertainties in fire-behavior modeling. This finding indicates that coupled fire–atmosphere and particle-transport modeling frameworks could be employed to quantitatively forecast microbial transport and deposition during future controlled burn experiments. Full article

Oceanfront relief varies along coastlines and serves as the first barrier to wave and surge damage. However, forecasted increases in storm frequency and sea levels are anticipated to enhance coastal erosion, potentially weakening this protection. The land–sea transition is variable along the New England coast, USA, and this variability has produced a range of coastal morphologies that can vary over short distances. It is important to track the beach transition zone to better understand transformations of the system and related hazard risks. A combination of field and computer-based methods was used to evaluate the beach transition zone of southwestern Rhode Island to determine alongshore variability and dynamics. More specifically, a decadal-scale study was conducted to examine changes in morphology from 2011 to 2022, and a short-term study at South Kingstown Town Beach examined changes from November 2023 to January 2024 using time-series drone-derived elevations. Classification of over 500 cross-shore transects illustrated the dominance of sedimentary shorelines, with smaller areas of rocky outcrops and hardening. Analysis of four different years (2011, 2014, 2018, and 2022) determined that beaches with dune morphology were the most common type of transition zone (41–47% of the transects) and transects with a high bank upland were the next most frequent class (34–41%). Following Hurricane Sandy in 2012, a 6% decrease in the number of dune-classified transects was measured; however, one-third of those recovered dune morphology by 2022. The greatest beach transformations over the short-term study occurred in response to strong storms in the 2023–2024 winter season, during which lateral beach movement (erosion) exceeded 15 m in portions of South Kingstown Town Beach. Dune erosion was accompanied by overwash flooding and deposition, and the area remained low-lying and thus vulnerable to future impacts. The beach transition zone classification and insights from this research will be informative for future planning by coastal communities by determining at-risk shorelines based on underlying geology and the stability of morphological features. Full article

Aerosol dry deposition is an important sink for particulate matter and a source of uncertainty in air quality modeling. Using the Weather Research and Forecasting model coupled with CUACE (WRF-CUACE), we quantified how three aerosol dry deposition schemes and satellite-based leaf area index (LAI) information affected PM_2.5 dry removal and near-surface PM_2.5 over central and eastern China in January 2022. The schemes were abbreviated as Z01, E20, and PZ10, respectively. A fourth simulation (PZ10_MLAI) used PZ10 but replaced the baseline LAI dataset with a Moderate Resolution Imaging Spectroradiometer (MODIS) constrained LAI field. Hourly PM_2.5 was evaluated with the China National Environmental Monitoring Center network. The schemes produced pronounced, size-dependent differences in deposition velocities, with a pronounced spread in the 0 to 2.5 µm average and more than one order of magnitude spread in the accumulation mode diagnostic, leading to distinct regional mean PM_2.5 dry deposition fluxes. The mean PM_2.5 flux increased by 5.9% in E20 relative to Z01 and decreased by 54.4% in PZ10. The MODIS LAI adjustment changed the PZ10 mean flux by 0.42%. The flux contrasts yielded coherent PM_2.5 responses, with E20 reducing near-surface concentrations by about 10 to 30% and PZ10 increasing them by about 20 to 60%, reaching about 80 to 100% in parts of southern China. Domain mean correlations ranged from 0.61 to 0.65 and PZ10-based simulations exhibited near-zero mean bias. Although MODIS LAI effects were modest for this winter month, local PM_2.5 differences commonly remained within about 4% and approached 6 to 10%, indicating that satellite LAI constraints can be important for multi-year and decadal applications. Full article

Chromium (Cr) exists in multiple oxidation states, with Cr(III) and Cr(VI) being the most environmentally and industrially relevant due to their distinct toxicity profiles and chemical behaviour. This study presents a comprehensive method that combines chemical simulation modelling, emission spectroscopy for quantification, and the controlled laboratory production of Cr species. Key findings include that acid digestion effectively extracted the Cr(III) and total Cr species, while thermodynamic modelling forecasted their stability and speciation under various environmental conditions. Thematic analysis indicates that the current quantification of Cr species is still in early development and remains centralized. Mineralogical and surface investigations showed that samples 1 and 2 have a BET surface area below 1 m²/g, whereas samples 3 and 4 exceed this. All samples are crystalline, with approximately 54.3 weight percent Cr₂O₃, 7.3 weight percent SiO₂, 17.75 weight percent of MgO, and 8.3 weight percent Al₂O₃, suggesting Al and Fe²⁺ replacement of Cr in the spinel structure. Computational fluid dynamics (CFD) modelling revealed that longer residence times are necessary for higher Cr metallization under H₂-CH₄-reducing conditions, and accurately predicted carbon deposition on pellets. These results demonstrate that CFD can optimize the H₂:CH₄ ratio to minimize carbon deposition and enhance gas transport to reaction sites. Full article

The economic importance of Carlin-type gold deposits is complicated by the concealed nature of stratiform gold-bearing zones and their occurrence at depths of several tens of meters or more below the present-day surface. This necessitates the use of a wide range of technologies and unconventional, including cost-effective and environmentally friendly, exploration methods to delineate potentially prospective areas. This study explores the possibilities of applying remote sensing methods to organize prospecting and exploration activities for targeting Carlin-type deposits in a more efficient and cost-effective way. The location of Carlin-type gold deposits within areas of orogenic and post-orogenic magmatism, mantle plumes, and linear crustal structures—as demonstrated by previous research in the Nevada and South China metallogenic provinces—may serve as a basis for developing a conceptual model of their distribution. To this end, we developed the GeoNEM (Geodynamic Numeric Environmental Modeling) software in Python, which enables the analysis of the formation of fold and fault structures, melt emplacement and contamination, as well as the duration and rate of geodynamic processes. GeoNEM is based on the computational geodynamics “marker-in-cell” (MIC) method, which treats geological media as extremely high-viscosity fluids. Locations of the brittle deformations of the crust, the formation of which was simulated numerically, can be detected through lineament analysis of remote sensing images. The spatial distribution of such structures—lineaments—serves as a predictive criterion for assessing the prospectivity of territories for Carlin-type gold deposits. It has been demonstrated that remote sensing provides a modern level of efficiency, cost-effectiveness, and comprehensiveness in approaching the exploration and assessment of new Carlin-type gold deposits. This is particularly important in the context of rational resource utilization and cost reduction. Full article

The rapid mobilization of sediment stored behind dams, in amounts that are large relative to mean annual sediment loads, can jumpstart river restoration but can also adversely impact habitat, infrastructure, land, and water use upstream of, within, and downstream of the former impoundment. A wide range of geomorphic and engineering assessment tools were applied to help manage sediment-related risks associated with the removal of two dams from the Elwha River in Washington State and the release of roughly 21 million m³ of sediment. Each of these tools had its strengths and weaknesses, which are explored here. The processes of sediment erosion, transport and deposition were complex. No one model was able to fully simulate all these with the accuracy necessary for predicting the magnitude and timing of coarse and fine sediment release from the reservoir. Collectively, however, the model outputs provided enough information to guide the adaptive sediment management process during dam removal. When the complexity of the morphodynamic responses to dam removal and the associated risks exceeded the capacity of any one tool to adequately assess, synoptic forecasting proved useful. The lessons learned on the Elwha have provided insights into how to use a variety of modeling techniques to address sediment management issues as dam removal scale, complexity and risk increase. Full article

A $5\mathrm{n}\mathrm{m}$ thick polycrystalline $N{i}_{81}F{e}_{19}$ film was sputter-deposited onto a circular 3-inch diameter, $390\mathsf{\mu }\mathrm{m}$ thick single-crystal wafer with $Si{O}_2$ surface layers. The magnetoresistance (MR) of the sample was analyzed as a function of applied DC magnetic field and temperature using the Van der Pauw technique. Magnetic measurements were carried out over a temperature range of 25 °C to 350 °C using a Lake Shore Hall Effect Measurement System (HEMS). An external magnetic field ranging from $+14\mathrm{k}\mathrm{G}$ to $-14\mathrm{k}\mathrm{G}$ was applied at each temperature value to observe changes in resistance. Hall coefficients and resistance were obtained by applying current in both directions with different contact configurations. Machine learning techniques, including Random Forest Regression, were employed to predict magnetoresistivity beyond 350 °C; the best-performing model achieved $R^2$ values up to $0.9449$ with MSE as low as $0.0071$, and enabled Curie temperature estimation with $T_C\approx 590.97°\mathrm{C}$. This study highlights the potential of machine learning in accurately forecasting material properties beyond experimental limits, providing enhanced predictive models for the magnetoresistive behavior and critical temperature transitions of $N{i}_{81}F{e}_{19}$ . Full article

Microscopic Particulate Matter (PM) below 10 µm can enter the respiratory system and affect human health in the short and long term. Railway enclosures are sites with high concentrations of fine PM and technical solutions like mechanical filtration exist to increase the air quality. However, several crucial factors must be evaluated and optimized like energy consumption, maintenance cost/interval, design and control. A fast and adaptable evaluation of decontamination solutions is required to find the optimal solution. To answer this, a 1D multizone model based on station discretization aligned with the track direction is proposed to precisely place decontamination systems along the station. In each zone, a set of ordinary differential equations is used to forecast the daily progression of PM concentrations, based on physical parameters (air and train velocities, and train traffic) used to describe the different physical phenomena (resuspension, deposition, ventilation and generation). Three-dimensional CFD (Computational Fluid Dynamics) simulations are used to characterize the efficiency and range of decontamination products and reproduce their effect in the 1D model. This approach allows for flexible optimization of local and global decontamination efficiencies with multiple parameter changes. PM₁₀ and PM_2.5 (below 10 and 2.5 µm) are studied here as they are often monitored. Full article

The stage-discharge relationship in the Jingjiang Reach of the Yangtze River has undergone significant alterations due to post-Three Gorges Reservoir (TGR) operation effects, notably bed erosion and roughness variation. This study employs a calibrated 1D hydrodynamic model based on Saint-Venant equations. The model was validated with high accuracy (Nash-Sutcliffe efficiency >0.94 at key stations) using long-term hydrological data (1996–2022). Four scenarios were simulated: pre-dam conditions, post-dam topography with pre-dam roughness, pre-dam topography with increased roughness, and coupled post-dam changes. A novel scenario-based decomposition framework was developed to isolate individual and coupled factor contributions, advancing beyond traditional descriptive approaches. The results indicate that upstream water level changes are mainly controlled by riverbed erosion (e.g., at the Zhicheng Station: the topographic contribution rate exceeds 80% at a flow rate of 5000 m³/s, resulting in a water level drop of approximately 1.7 m), while downstream, an increase in roughness becomes the dominant factor (e.g., at the Jianli Station: causing a water level rise of about 1.0 m at a flow rate of 13,000 m³/s, with such changes being particularly pronounced under low-flow conditions). Spatially, topographic influence attenuates downstream, whereas roughness sensitivity amplifies in high-sinuosity reaches (bend coefficient: 3.0). Seasonally, the topographic contribution rate remains stable overall during the low-flow period, e.g., within a narrow range of 0.88–0.98 at Zhicheng Station, while roughness effects exhibit negative values in dry periods (November) due to fine sediment deposition. The coupling effect in mid-discharge ranges (15,000–20,000 m³/s) at Jianli partially offsets stage reductions. These findings not only provide critical insights for flood forecasting and navigation management in the Jingjiang Reach but also offer a transferable methodology for quantifying hydro-morphodynamic interactions in global regulated rivers, highlighting the model’s utility in predictive water resource management. Full article

This study presents a comparative performance modeling and optimization framework for a 5 kWp rooftop photovoltaic (PV) system in Qatar, using two widely adopted simulation tools, PVsyst and the System Advisor Model (SAM). The research addresses a key limitation in existing PV modeling practice: the restricted capability of standard software to represent site-specific soiling and dynamic albedo effects under arid climatic conditions. To overcome these limitations, the Humboldt State University (HSU) soiling model was calibrated using field measurements from a DustIQ sensor, and its parameters, rainfall cleaning threshold and particulate deposition velocity were optimized through a Differential Evolution algorithm. Additionally, the study utilized dynamic albedo inputs to better account for ground-reflectance effects in energy yield simulations. The optimized approach reduced the root mean square error (RMSE) of predicted soiling ratios from 7.30 to 1.93 and improved the agreement between simulated and measured monthly energy yields for 2024, achieving normalized RMSE values of 4.66% in SAM and 4.86% in PVsyst. The findings demonstrate that coupling data-driven soiling optimization with refined albedo representation modernizes the predictive capabilities of PVsyst and SAM, yielding more reliable performance forecasts. This methodological advancement supports better-informed design and operation of rooftop PV systems in desert environments where soiling and reflectivity effects are pronounced. Full article

Amid accelerating global nitrogen deposition, China has emerged as the world’s third-largest hotspot after Western Europe and North America. Disentangling how elevated N inputs interact with intensifying precipitation and silvicultural practices is therefore essential for forecasting forest responses to ongoing climate change. Taking advantage of the “canopy-simulated nitrogen deposition” platform in Jigongshan National Nature Reserve, we compared tree-level census data from 2012 and 2022 to quantify decadal shifts in neighborhood competition under seven nitrogen addition and rainfall enhancement regimes. After using ordered-sample clustering to identify eight competitors as the optimal neighborhood size, we applied the Hegyi family of competitive indices (CI, CI1, CI2, mCI, mCI1 and mCI2) to analyze competitive responses at three hierarchical levels: the entire stand, all surviving trees and three dominant species (Quercus acutissima, Quercus variabilis, and Liquidambar formosana). The results indicate: (1) Nitrogen addition and rainfall enhancement did not alter the dominant tree species of the stand, which remained primarily Q. acutissima, Q. variabilis, and L. formosana. (2) The competition indices based on all trees showed an upward trend, whereas those calculated for surviving trees and for dominant species declined markedly (surviving trees p < 0.1, L. formosana CI1 p < 0.05). (3) Although nitrogen addition treatments did not alter overall stand competition intensity, it relieved competitive pressure on surviving trees by suppressing interspecific interactions (CI2 and mCI2); intraspecific competition also weakened, but at a slower rate. (4) Interspecific competition intensity for surviving L. formosana decreased significantly, whereas competition indices for Q. acutissima and Q. variabilis remained statistically unchanged. (5) Nitrogen addition methods (canopy vs. understory) had no significant effect on competition indices, while nitrogen addition intensity exhibited a dose-dependent effect: high nitrogen addition significantly reduced interspecific competition intensity more than low nitrogen addition (p < 0.05). In summary, nitrogen deposition primarily regulates interspecific competition through concentration rather than pathway, providing scientific basis for adaptive management of broad-leaved mixed forests in transitional zones under sustained nitrogen deposition scenarios. Full article

Air quality monitoring and forecasting have become increasingly critical in urban environments due to rising pollution levels and their impact on public health. Recent advances in Internet of Things (IoT) technology and machine learning offer promising alternatives to traditional monitoring stations, which are limited by high costs and sparse deployment. This paper presents the development of a real-time, low-cost air quality forecasting system that integrates IoT-based sensing units with predictive machine learning algorithms. The proposed system employs low-cost gas sensors and microcontroller-based hardware to monitor pollutants such as particulate matter, carbon monoxide, carbon dioxide and volatile organic compounds. A fully functional prototype device was designed and manufactured using Fused Deposition Modeling (FDM) with modular and scalable features. The data acquisition pipeline includes on-device adjustment, local smoothing, and cloud transfer for real-time storage and visualization. Advanced feature engineering and a multi-model training strategy were used to generate accurate short-term forecasts. Among the models tested, the GRU-based deep learning model yielded the highest performance, achieving $R^2$ values above 0.93 and maintaining latency below 130 ms, suitable for real-time use. The system also achieved over 91% accuracy in health-based AQI category predictions and demonstrated stable performance without sensor saturation under high-pollution conditions. This study demonstrates that combining embedded hardware, real-time analytics, and ML-driven forecasting enables robust and scalable air quality management solutions, contributing directly to sustainable development goals through enhanced environmental monitoring and public health responsiveness. Full article

Qualitative schematic models of the Mishrif Formation, which have previously dominated the research, are inadequate for predicting reservoir production performance due to their inability to quantify spatial heterogeneity. In contrast to these earlier approaches, this study integrates core analysis, wireline logs, and 3D seismic data to not only describe but also quantitatively characterize the depositional elements and their spatial distribution. A novel methodology was developed to define nine distinct depositional elements from cored wells and then continuously identify them in uncored wells using unique pseudo-wireline log responses, a step not achieved in prior work. Furthermore, moving beyond previous qualitative models, 3D quantitative versions were constructed using Sequential Indicator Simulation (SIS) explicitly constrained by depositional geometries derived from 3D seismic inversion volumes. For the first time, these models reveal the quantitative spatial extent and evolution of these elements. Updating the 3D petrophysical property model using this new depositional framework resulted in a 15% increase in successful production history matches, demonstrating the direct and superior predictive power of this integrated quantitative approach for forecasting oil reservoir production performance. Full article

Understanding and predicting the distribution of mineral dust in the atmosphere remains a major scientific challenge due to the complex nature of dust emission, transport, and deposition processes. Dust aerosols have a profound impact on climate, air quality, and biogeochemical cycles, making their accurate representation in models critical. In this study, we employ the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate dust events over the Mediterranean. To reduce model uncertainties, we assimilate satellite-derived dust optical depth observations from the MIDAS (Mineral Dust Aerosol Satellite) dataset. The assimilation of MIDAS data leads to significant improvements in the spatial and temporal accuracy of dust forecasts. The enhanced model outputs offer continuous in time and space dust fields that are particularly valuable for applications such as air quality management and the optimization of solar energy systems. Full article