Search Results (3,866)

The conventional sulfidization–xanthate flotation of oxide copper minerals is often limited by reagent consumption and the mismatch between surface generation and sulfidization when sulfidization is carried out only during flotation conditioning. To address these limitations, this study proposes an in situ sulfidization strategy during the grinding stage and investigates the combined regulation of grinding media (conventional steel vs. 18% Cr cast iron) and atmosphere (ambient air vs. N₂ purging) on the flotation of a synthetic malachite–dolomite ore. Real-time pulp chemistry monitoring and ethylenediaminetetraacetic acid (EDTA) extraction indicate that the conventional grinding environment has two main disadvantages: Fe dissolution from steel media increases Fe-related surface contamination, while the air-ground pulp promotes the oxidation and consumption of active sulfidizing species. The combined use of high-Cr media and an N₂ atmosphere improved the chemical environment for grinding-stage sulfidization. Specifically, the high-Cr media reduced Fe release and associated surface contamination, while N₂ purging shifted the pulp to a lower-potential environment that was more favorable for preserving active sulfide species. Under standardized reagent conditions, the optimized in situ sulfidization protocol increased copper recovery from 29.79% to 57.47% and improved the concentrate grade from 9.71% to 11.11%. These results suggest that regulating the grinding environment can enhance in situ sulfidization flotation of malachite by reducing Fe interference and controlling pulp redox conditions, providing useful guidance for the efficient beneficiation of oxide copper minerals. Full article

Air pollution stands as one of the most critical environmental challenges confronting nations worldwide, and investigating the differential impacts of diverse environmental governance modes holds pivotal implications for global sustainability. Based on panel data of 284 cities in China from 2012 to 2024, this research takes the Central Environmental Protection Inspection and the Air Pollution Comprehensive Control Action Plan in Autumn and Winter as quasi-natural experiments representing hierarchical intervention and horizontal collaboration, respectively, and employs a difference-in-differences model to evaluate their impacts on atmospheric pollution. Results show that (1) while both modes significantly curb atmospheric pollution, hierarchical intervention presents a weaker effect than horizontal collaboration; (2) hierarchical intervention exhibits greater effectiveness in low-pollution and high-development areas, whereas horizontal collaboration yields more pronounced effects in low-pollution and low-development areas; (3) hierarchical intervention suppresses pollution by intensifying promotional pressure, while horizontal collaboration exerts effects by advancing environmental decentralization; (4) the former exacerbates border pollution, whereas the latter does not. These findings provide substantial implications for policymakers seeking to scientifically utilize environmental policy tools to promote green and sustainable development. Full article

Soybean (Glycine max) is a key crop in China grown as a source of edible oil and plant-derived protein, and its production is closely linked to national food security. Insufficient nitrogen availability remains a key constraint on soybean yield. In legumes, symbiotic nitrogen fixation (SNF) enables the conversion of atmospheric nitrogen into bioavailable forms, thereby reducing reliance on synthetic fertilizers and improving soil quality. Nitrogenase activity is a central determinant of SNF efficiency; however, its regulatory mechanisms in soybean nodules are not yet fully understood. In this study, transcriptomic data from two soybean accessions with contrasting SNF performance (Suinong 14 and ZYD00006) were analyzed, leading to the identification of Glyma.04G013500 as a member of the GmRD22 gene family. This study verified that this gene is potentially associated with nitrogenase activity. Functional characterization revealed that this gene acts as a negative regulator of nodulation by influencing the expression of genes associated with nodule development. Haplotype analysis further uncovered a pattern consistent with domestication, as the elite haplotype (HapI) with enhanced nitrogen fixation capacity, exhibited a progressive increase in frequency from wild soybean populations to landraces and modern cultivars. These findings suggest that GmRD22 has undergone directional selection during soybean domestication and improvement. Overall, these results offer new insights into the genetic control of SNF and establish promising targets for breeding soybean varieties with improved nitrogen fixation efficiency. Full article

This study investigates the effects of phosphorus (P) on the phases developed during sintering and their impact on the stability of key phases in iron ore sinter, particularly the silico-ferrite of calcium and aluminum (‘SFCA’) series of phases. Two complementary systems were studied: an industrially representative blend of natural iron ores (JSM) and a synthetic high-purity SFCA analogue (SA) system designed to promote controlled SFCA formation. Phosphorus was added as hydroxyapatite (HA) at levels of 0.5, 1.0, 1.5 and 5 wt.%. To simulate a standard sintering profile, experiments were conducted over a range of temperatures for 3 min in a controlled low-oxygen-potential atmosphere of pO₂ = 5 × 10⁻³ atm. A modified Bond Abrasion test was used to evaluate the tumble index (TI) strength of the samples, and the chemistry, mineralogy and microstructure of all sintered products were analyzed. Results indicated that all P-doped JSM samples fired within the temperature range of 1300 to 1330 °C met the minimum strength requirement (TI = 80%) for producing high-quality sinters. Adding small to medium amounts of HA (≤1.5 wt.%) to both compositions had a limited impact on the overall mineral phases. Conversely, adding a high amount of HA (5 wt.%) encouraged the creation of Ca–Si–P phases. Analysis of the microstructure, minerals, and microchemistry indicated that P tended to segregate phases rich in phosphorus by interacting with calcium oxide and silica. The findings from the study highlight that at the low levels of P typically found in iron ores, there is no significant impact on the strength, mineralogy and phases formed during sintering. Full article

Located in the northeastern Tibetan Plateau, the Qaidam Basin hosts abundant strategic lithium (Li) and boron (B) salt lake resources crucial for national resource security. Existing studies focus on individual lakes, lacking systematic Li-B geochemical and source–transport–sink research across the geothermal–river–lake system. Based on 40 water samples from 16 lakes and multi-isotope and hydrochemical data, this study explores Li-B spatial distribution, isotopic evolution and enrichment rules. The results reveal prominent spatial heterogeneity of Li and B distributions. The contents of riverine Li and B are higher than the global average level, and terminal salt lakes show the highest enrichment degree. Specifically, southern lakes are Li-dominant, while northern lakes are B-dominant, with both reaching industrial exploitation grades. Significant Li and B isotopic fractionation occurs throughout the hydrological system, with geothermal fluids presenting depleted isotopic compositions and lake waters showing enriched features. H-O isotopic evidence and Gibbs diagram analysis indicate that surface waters in the basin are primarily recharged by atmospheric precipitation, and their hydrochemical compositions are jointly controlled by rock weathering and strong evaporative concentration, accompanied by distinct north–south hydrogeological zonation differences. Source analysis demonstrates that Li is mainly derived from high-temperature water–rock interactions of Li-rich volcanic and granitic rocks in the southern East Kunlun Mountains, whereas B originates from ultrahigh-pressure B-rich metamorphic rocks along the northern North Qaidam margin. The migration and accumulation sequence of Li and B follows the pathway: geothermal fluid emission → fluvial transportation → terminal lake enrichment. Evaporation and mineral precipitation are the dominant factors controlling elemental enrichment and isotopic fractionation. This basin-wide study supplements salt lake critical mineral metallogenic theories and guides efficient Li-B exploration and sustainable development. Full article

This paper presents the design, construction, and experimental evaluation of a low-cost, portable solar tracking system for monitoring ultraviolet solar radiation in real time. It integrates a Raspberry Pi Zero 2W as the embedded control unit, an AS7331 spectral sensor to measure UVA, UVB, and UVC irradiance, two 270° servomotors to position the system toward the sun, an NEO-6M GPS module to geolocate the system, and a DS3231 real-time clock to synchronize the time. To enable autonomous outdoor operation, a multistage power supply architecture based on a solar panel, a rechargeable battery, and LM2596 and MP1584EN DC-DC regulators was implemented. The tracking algorithm uses astronomical equations to estimate the solar azimuth and elevation and updates the sensor orientation during daylight hours. This allows the UV sensor to remain approximately normal to the incoming solar radiation. Experimental tests were conducted in Arequipa, Peru. The recorded data included UVA, UVB, and UVC irradiance; sensor temperature; geographic coordinates; time; and solar angles. The measured UV profiles exhibited the anticipated diurnal behavior: maximum values around solar noon, higher UVA levels than UVB levels, and minimal UVC levels due to atmospheric absorption. We compared the radiometric response with reference information from EarthKit, PVGIS 5.3, SAMPA, and a Davis Vantage Pro 2 weather station. We evaluated the solar positioning performance against Stellarium, NOAA, and the NREL Solar Position Algorithm. Across the complete five-day validation at three daily evaluation times, the maximum percentage errors were 0.0584% for azimuth and 0.5059% for elevation relative to the NREL SPA, NOAA, and Stellarium reference calculations. The results demonstrate that the proposed system constitutes an embedded, portable, autonomous, and low-cost platform for in situ monitoring of solar ultraviolet radiation. Due to its modular architecture, georeferencing capability, time synchronization, and independent power supply, the prototype can be used as a mobile measurement unit or as part of a distributed network of UV stations at various locations in Arequipa. In this regard, the system enables multipoint measurement campaigns, complements fixed weather stations, validates solar models, and generates local experimental data for the spatial and temporal assessment of the solar UV resource under real-world field conditions. Full article

Urban air pollution poses significant risks to human health, ecosystems, and the environment, highlighting the need for accurate monitoring of atmospheric pollutants. This study investigated the spatial and temporal variability of key tropospheric pollutants, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), nitrous acid (HONO), formaldehyde (HCHO), and ozone (O₃), in Brighton, UK, using an integrated approach that combined ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), Long-Path Differential Optical Absorption Spectroscopy (LP-DOAS), and Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) observations. Ground-based measurements comprised four MAX-DOAS campaigns conducted between 2021 and 2024 and a long-term LP-DOAS dataset spanning 2017–2023, complemented by coincident TROPOMI observations. The datasets were spatially co-located, temporally aligned, quality-controlled, and analysed using statistical methods, time-series analysis, and polar plot techniques to assess pollutant variability, identify emission sources, and evaluate the agreement between satellite and ground-based observations. The results revealed clear seasonal and diurnal variations in pollutant levels, with elevated NO₂ during winter and enhanced O₃ during summer, reflecting the influence of anthropogenic emissions and photochemical processes. Polar plot analysis further identified distinct wind-dependent pollutant patterns, indicating the importance of local emission sources. Comparisons between ground-based and satellite observations showed that TROPOMI successfully captured the temporal variability of NO₂ measured by means of LP-DOAS, with a moderate positive correlation (r_s = 0.55), but underestimated NO₂ relative to MAX-DOAS observations (r_s = 0.38), reflecting differences in measurement geometry, spatial resolution, and retrieval sensitivity. The overall findings demonstrate that integrating ground-based and satellite observations provides a more comprehensive understanding of urban air quality than either approach alone. This combined monitoring framework improves confidence in satellite-derived atmospheric products and supports more effective air quality assessment and management in Brighton and similar urban environments. Full article

This study investigates hydroclimatic variability and water balance dynamics in the Akmola region during 2003–2023 using observations from 16 meteorological stations. The study evaluates changes in air temperature, precipitation, reference evapotranspiration (ET₀), climatic water balance, and drought conditions. Reference evapotranspiration was calculated using the FAO-56 Penman–Monteith method, while drought variability was assessed using the 12-month Standardized Precipitation–Evapotranspiration Index (SPEI-12). Temporal trends were analyzed using the Mann–Kendall test and Sen’s slope estimator. The results revealed significant spatial heterogeneity in hydroclimatic conditions across the region. Air temperature showed a consistent increasing trend at most stations, accompanied by increasing atmospheric evaporative demand. All stations were characterized by a persistently negative climatic water balance, with mean annual values of approximately −750 mm, indicating a regional moisture deficit. Reference evapotranspiration exhibited significant spatial variability, with the highest values observed in the southern and central parts of the region. Precipitation remained the dominant control of water balance variability (r = 0.79–0.96), while increasing temperature intensified moisture deficits through increased evapotranspiration. SPEI-12 indicated recurrent drought episodes and increasing drought vulnerability associated with warming-induced atmospheric water demand. The study demonstrates that increasing evapotranspiration is becoming a major driver of water stress in the Akmola region and highlights the importance of integrating climatic water balance and SPEI indicators for drought monitoring and climate adaptation in semi-arid steppe regions. Full article

The following research focuses on the study of the behavior of air plasma developed inside a 200 kW arc-jet constrictor. The scope of the analysis performed in the present document is focused specifically on the velocity fields and their characteristics inside the constrictor domain. Among them, the velocity magnitude, the distribution of the radial profiles at different stations along the length of the constrictor, and the axial velocity profile distribution. The analytical study considers mathematical models, which include air properties like specific heat, thermal conductivity, electric conductivity and enthalpy. The axial enthalpy distribution is shown at different radii of the constrictor in order to explain the presence of the velocity fields inside the control volume of interest. In the present work, it was found that the magnitude of the air plasma velocity fields increases due to the contact with the electric arc along the constrictor, reaching maximum velocity values of 5000 m/s at 10,000 K in the center of the constrictor and gradually decreasing when the constrictor radius approaches the wall. These findings provide insight into performance aspects of an arc jet constrictor using atmospheric air as a propellant. A key limitation of the present study stems from the initial assumptions and restrictions detailed in the Materials and Methods section. These constraints inherently alter several plasma flow parameters, namely the Lorentz force, the radial variation of the electric potential, etc. Full article

Wetlands are the largest natural source of atmospheric methane, but the socioeconomic consequences of future wetland CH₄ emissions remain poorly quantified. We combined projected wetland CH₄ pathways with the PAGE-ICE integrated assessment model to estimate their contributions to atmospheric CH₄ concentration, radiative forcing, warming, and socioeconomic damages under SSP1-2.6, SSP2-4.5, and SSP5-8.5. By 2100, wetland CH₄ emissions increased atmospheric CH₄ concentrations by 782, 868, and 1207 ppb under the three pathways, respectively. The corresponding global warming increments were 0.190, 0.170, and 0.166 °C, showing that larger atmospheric CH₄ contributions do not translate linearly into larger temperature responses. Wetland-attributable annual global damages reached 2.79, 5.91, and 6.49 trillion USD per year⁻¹ by 2100, while discounted cumulative damages over 2020–2100 reached 79.96, 127.24, and 127.33 trillion USD under SSP1-2.6, SSP2-4.5, and SSP5-8.5. Regionally, Africa, the Middle East, and India accounted for the largest absolute damages, whereas Eastern Europe and the former Soviet Union had the highest losses relative to GDP and population. A sensitivity analysis identified transient climate response, damage-function curvature, and the pure time preference rate as the main controls on valuation uncertainty. These results indicate that projected wetland CH₄ emissions can make a measurable marginal contribution to future climate damage and should be incorporated into sustainability assessments, methane-related climate-risk evaluations, and long-term adaptation planning. Full article

Vegetation photosynthesis is jointly regulated by precipitation supply, atmospheric water demand, and vertically stratified soil moisture, but their relative roles across vegetation types and hydroclimatic backgrounds remain unclear. Using monthly satellite solar-induced chlorophyll fluorescence (SIF), ERA5-Land hydroclimatic variables, and vegetation-type data across China from 2000 to 2024, this study combined monthly anomaly analysis, lagged partial correlation, and XGBoost–SHAP to quantify moisture-response patterns in cropland, forest, grassland, and shrubland along an arid–humid gradient. Vegetation SIF increased overall from 2000 to 2024, with a clear northwest–southeast gradient and higher multiyear mean SIF in forests and shrublands than in croplands and grasslands. XGBoost–SHAP results showed that vapor pressure deficit (VPD) was the largest single contributor to SIF anomalies at the national scale, accounting for 31.8% of total moisture-factor contribution, followed by precipitation, shallow soil moisture, and middle- plus deep-layer soil moisture. The VPD contribution differed among vegetation types, reaching 43.3% in grassland, 32.5% in cropland, 30.4% in shrubland, and 27.5% in forest. Lagged responses also varied with soil depth and hydroclimatic background. Precipitation and shallow soil moisture mainly produced concurrent or 1-month lagged responses, whereas middle- and deep-layer soil moisture generated 2–3-month delayed effects in some arid and semi-arid cropland and woody vegetation. Across the arid–humid gradient, dominant moisture constraints shifted from deeper soil-water regulation in arid regions toward VPD control in transitional zones and precipitation or shallow-soil-moisture control in humid ecosystems. These findings demonstrate that vegetation SIF anomalies are shaped by coupled atmospheric demand, precipitation input, and soil-water storage, with vegetation traits and hydroclimatic background jointly determining the dominant moisture-response pathway. Full article

Disentangling the relative roles of emissions and atmospheric processes in controlling air-pollutant concentrations remains a central challenge in air-quality management. Although machine-learning (ML) models can accurately predict pollutant concentrations, the temporal evolution of the importance of individual drivers is often difficult to interpret. Here, we introduce a framework for reconstructing time-resolved feature importance (FI) in ML air-quality models. Hourly NO₂ and PM_2.5 concentrations were simulated across clusters of observations, defined along concentration trajectories in a state–space spanned by source intensity and meteorological variables. Within each cluster, predictor importance is quantified and mapped back onto the corresponding time points, yielding continuous FI time series for all predictors. The framework is demonstrated using observations from the nationwide air-quality network in Israel, together with traffic-related source indicators and meteorological parameters. The dominant drivers differ markedly between the two pollutants: NO₂ variability is primarily associated with local emissions, mechanical transport, and turbulent mixing, whereas PM_2.5 variability reflects predictors that are related to nucleation, coagulation, hygroscopic growth, long-range transport, and chemical transformation. The feature importance exhibits pronounced seasonal, regional, and diurnal variability, including modulation around traffic rush hours. These results demonstrate the value of time-resolved interpretability for diagnosing drivers of air-pollutant variability and improving the representation of processes in statistical air-quality models. Full article

Snow depth and persistence strongly influence ground–atmosphere energy exchange, meltwater input, and the thermal regime of rock glacier systems, yet high-resolution snow monitoring remains scarce in the Southern Carpathians. This study uses multi-temporal Unmanned Aerial Vehicle (UAV) Structure-from-Motion (SfM) photogrammetry to map snow-depth variability and microtopographic controls on the Galeșu Rock Glacier, Retezat Mountains. Eight UAV surveys were conducted between 2023 and 2025, including seven snow-covered acquisitions and one snow-free reference survey in August 2025. Snow depth was derived by DEM differencing and analyzed against morphometric indices, mainly profile curvature and relative topographic position. Results reveal strong spatial heterogeneity, with recurrent snow accumulation in furrowed, concave, and depressional sectors and reduced snow depth on local topographic highs. The 2024 surveys showed substantially deeper snow than 2025, with mean snow depths of 1.82 m in February and 1.62 m in March 2024, compared with 0.72 m and 0.83 m in February and March 2025. April 2023 displayed the deepest snowpack, with a mean snow depth of 2.27 m. Class-based analysis showed median contrasts of 2.30 m in 2024 and 1.20 m in 2025 between strong negative and strong positive curvature classes. These findings demonstrate that rock glacier microtopography exerts a first-order control on snow accumulation and persistence, providing a basis for future studies linking snow redistribution to ground thermal regimes, meltwater pathways, and ground-ice preservation in marginal periglacial environments. Full article

This study presents two workflows for leveraging longitudinal computed tomography (CT) datasets when developing deep learning-based detection systems for gradually developing postharvest disorders. Workflow 1 (Longitudinal Benchmarking) benchmarks neural networks by training and testing them on images from different stages of disorder progression. It examines the trade-off between detecting a disorder early or accurately and evaluates whether neural networks can generalize across time points. Workflow 2 (Longitudinal eXplainable Artificial Intelligence (XAI) Heatmaps) provides heatmaps that indicate how changes over time affect the outcomes of neural networks. It uses image registration to align an earlier-acquired image and then uses it as a baseline when calculating the heatmap. The workflows are demonstrated on a dataset of ‘Braeburn’ apples that were CT-scanned multiple times while developing internal browning during controlled-atmosphere (CA) storage and shelf life. The Longitudinal Benchmarking workflow was used to investigate whether images acquired immediately after CA storage can be used to predict the eventual browning after a shelf-life period, which is highly relevant in industrial practice. Moreover, the longitudinal XAI heatmaps avoided artifacts caused by out-of-distribution baselines or identical baseline regions, which occurred with conventional black or zero baselines. Full article

Methane emissions from plugged and unplugged abandoned wells dilute rapidly with air, causing conventional detection methods to fall short of detecting methane leaks from these sources. Commonly used technologies for methane emission detection often fall short of the detection limit required for low-rate emissions from abandoned wells, and are either dependent on environmental conditions or costly and highly energy consuming, barring them from becoming a scalable solution to this problem. We introduce here a new system for outdoor testing and show how the flux chamber detection limit is progressively reduced from 700 g/h to 2 g/h and ultimately to 1 g/h, meeting the US Department of the Interior (DOI) standard for monitoring equipment used on abandoned wells. Field deployment on an actual abandoned well also revealed intermittent emissions, which may serve as an indicator of deteriorating well integrity over time when monitored periodically. To forecast the emission event timing and intensity, a Liquid Time-Constant (LTC) and a gated recurrent neural network were trained on methane concentration time series collected during field deployment. As available well sites for physical testing are limited and atmospheric conditions are not controllable, a computational fluid dynamics (CFD) simulation framework integrated with machine learning (ML) was developed to optimize wellhead chamber geometry and size for both detectability and safety. This chamber is designed to be used for testing well sites in mass number that helps well abandonment ranking systems for both operators and regulatory bodies. Full article