Search Results (3,862)

The hydrosystem composed of Lake Togo, Lagoon of Togoville, and Lagoon of Aného is located in the coastal zone of Togo and receives important and different kinds of mining waste that cause its degradation. This study aims to evaluate the physicochemical and metallic quality of these waters and determine the possible sources of these contaminants using geostatistical, multivariate, and special analysis methods. These waters were very mineralized according to the average conductivity (15.51 mS/cm). Average contents (μg/L) in trace elements varied from 2.46 μg/L for As to 141.63 μg/L for Pb. Average levels of Cd, Pb, Cr, and Ni were significantly higher than the WHO standards. Trace elements and physicochemical parameters showed strong spatial variations with the highest values recorded downstream of the hydrosystem. The main possible source of trace element pollution was the intrusion of seawater loaded with phosphate effluent, followed by atmospheric deposition and soil leaching. This hydrosystem, therefore, deserves special attention for better planning its management. Full article

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

Population growth has led to an increased volume of wastewater from industrial, domestic, and municipal sources, contaminating aquatic bodies in the state of Veracruz. This study aimed to assess the efficacy of a water treatment system incorporating a DAF stage, followed by the cultivation of a microalgal consortium to eliminate pollutants from the blended effluent. The cultivation of Nannochloropsis oculata in wastewater entailed the assessment of a single variable (operating pressure) within the DAF system, in conjunction with two supplementary variables (residence time and F:M ratio), resulting in removal efficiencies of 70% for CODt, 77.24% for CODs, 78.34% for nitrogen, and 77% for total organic carbon. The water sample was found to contain elevated levels of organic matter and pollutants, beyond the permitted limits set forth in NOM-001-SEMARNAT-2021. The obtained removal percentages indicate that the suggested physicochemical–biological process (DAF-microalgae) is a suitable method for treating mixed wastewater. This approach reduces atmospheric pollution by sequestering greenhouse gases such as carbon dioxide through the photosynthetic activity of N. oculata cells, so facilitating the production of oxygen and biomass while limiting their accumulation in the atmosphere. Full article

The conversion of CO₂ into CH₄ through the Sabatier reaction is one of the key processes that can reduce CO₂ emissions into the atmosphere. This work aims to develop Ni-Al aerogel-based thermo-photocatalysts with large specific surface areas prepared using a sol–gel method and subsequent supercritical drying in CO₂. Different Al/Ni molar ratios were selected for the development of the catalysts, characterized using ICP-OES, N₂ adsorption–desorption isotherms, XRD, H₂-TPR, TEM, UV-Vis DRS, and XPS techniques. Thermo-photocatalytic activity tests were performed in a photoreactor with two different light sources (λ = 365 nm, λ = 470 nm) at a temperature range from 300 °C to 450 °C and a pressure of 10 bar. The catalyst with the highest Ni loading (AG 1/3) produced the best catalytic results, reaching CO₂ conversion and CH₄ selectivity levels of 82% and 100%, respectively, under visible light at 450 °C. In contrast, the catalysts with the lowest nickel loading produced the lowest results, most likely due to their low amounts of active Ni. These results suggest that supercritical drying is an efficient method for developing active thermo-photocatalysts with high Ni dispersion, suitable for Sabatier reactions under mild reaction conditions. Full article

Sulfate as a potential pollution source in the water environment of the basin, identifying sulfate sources and migration mechanisms is essential for protecting the water environment and ensuring sustainable water management. Liuyang River is a primary tributary of the Xiangjiang River. It has experienced progressively intensifying anthropogenic influences in recent decades, manifested by sustained sulfate concentration increases. However, the sulfate sources and their contributions were not clear. This study used hydrochemistry and multi-isotopes methods combined with Simmr model to study the hydrochemical characteristics, sulfate sources, and migration–transformation processes of surface water and groundwater. The results showed that the hydrochemical types of surface water were HCO₃-Ca and HCO₃·SO₄-Ca·Mg, and groundwater were HCO₃-Ca, HCO₃-Ca·Mg, and HCO₃·SO₄-Ca. Ions in the water primarily originated from carbonate and silicate rocks dissolution and sulfide oxidation, augmented by mining operations, sewage discharge, and chemical production. The analyses of hydrochemistry, isotopes, and Simmr model revealed that surface water sulfate originated from soil sulfate (35.70%), sulfide oxidation (26.56%), sewage (16.58%), and atmospheric precipitation (12.45%). Groundwater sulfate was derived predominantly from sewage (34.96%), followed by soil sulfate (28.09%), atmospheric precipitation (17.35%), and sulfide oxidation (12.25%). Sulfate migration and transformation were controlled by the natural environment and anthropogenic impacts. When unaffected by human activities, sulfate mainly originated from soil and atmospheric precipitation, relating to topography, geological conditions, agricultural activities, and precipitation intensity. However, in regions with intense human activities, contributions from sewage and sulfide oxidation significantly increased due to the influences of mining and industrial activities. Full article

Accurate short-term forecasting of photovoltaic power generation is vital for the operational stability of isolated energy systems, especially in regions with increasing renewable energy penetration. This study presents a novel AI-based forecasting framework applied to the island of Cyprus. Using machine learning methods, particularly regression trees, the proposed approach evaluates the impact of key environmental variables on PV performance, with an emphasis on atmospheric dust transport and air composition variability. A distinguishing feature of this work is the integration of cross-continental dust events and diverse atmospheric parameters into a structured forecasting model. A new clustering methodology is introduced to classify these inputs and analyze their correlation with PV output, enabling improved feature selection for model training. Importantly, all input parameters are sourced from publicly accessible, internet-based platforms, facilitating wide reproducibility and operational application. The obtained results demonstrate that incorporating dust deposition and air composition features significantly enhances forecasting accuracy, particularly during severe dust episodes. This research not only fills a notable gap in the PV forecasting literature but also provides a scalable model for other dust-prone regions transitioning to high levels of solar energy integration. Full article

Timber and other biobased materials store carbon that has been captured from the atmosphere during photosynthesis and plant growth. The estimation of these biogenic carbon stocks in the harvested wood products (HWP) pool has received increasing attention since its inclusion in greenhouse gas reporting by the IPCC. It is of particular interest for long service life products such as timber in buildings; however, some aspects require further thought—in particular the handling of service lives as opposed to half-lives. The most commonly used model for calculating changes in the HWP pool uses first order decay based on half-lives. However other approaches are based on average service lives and estimates of residence times in the product pool, enabling different mathematical functions to be used. This paper considers the evolution of the two concepts and draws together data from a wide range of sources to consider service life estimation, which can be either related to design life or practical observations such as local environmental conditions, decay risk or consumer behaviour. As an increasing number of methods emerge for calculating HWP pool dynamics, it is timely to consider how these numerical inputs from disparate sources vary in their assumptions, calculation types, accuracy and results. Two groups are considered: half-lives for first order decay models, and service life and residence time population distributions within models based on other functions. A selection of examples are drawn from the literature to highlight emerging trends and discuss numerical constraints, data availability and areas for further study. The review indicated that issues exist with inconsistent use of nomenclature for half-life, average service life and peak flow from the pool. To ensure better sharing of data between studies, greater clarity in reporting function types used is required. Full article

The amount of waste generated by society is constantly increasing. Consequently, there is a need to develop new and better methods of treating it. A significant part of municipal waste is biowaste, which can be treated as a source of valuable resources such as nutrients, organic matter, and energy. The present work aims to determine the properties of the tested household biowaste and the possibility of using it as feedstock in slow pyrolysis to obtain biochar. The slow pyrolysis process of the biowaste was carried out in an electrically heated Horizontal Tube Furnace (HTF) at temperatures of 400 °C, 500 °C, and 600 °C in a nitrogen atmosphere. The analysis showed that depending on the type and composition of the biowaste, its properties are different. All the biowaste tested has a high moisture content (between 63.51% and 81.53%), which means that the biowaste needs to be dried before the slow pyrolysis process. The characteristics of kitchen biowaste are similar to those of food waste studied by other researchers in different regions of the world. In addition, the properties of kitchen biowaste are similar to those of the typical biomasses used to produce biochar via slow pyrolysis, such as wood, almond shells, and rice husks. Both kinds of garden biowaste tested may have been contaminated (soil, rocks) during collection, which affected the high ash content of spring (17.75%) and autumn (43.83%) biowaste. This, in turn, affected all the properties of the garden biowaste, which differed significantly from both the literature data of other garden wastes and from the properties of typical biomass feedstocks used to produce biochar in slow pyrolysis. For all biowaste tested, it was shown that as the pyrolysis temperature increases, the yield of biochar decreases. The maximum mass yield of biochar for kitchen, spring garden, and autumn garden biowaste was 36.64%, 66.53%, and 66.99%, respectively. Comparing the characteristics of biowaste before slow pyrolysis, biochar obtained from kitchen biowaste had a high carbon content, fixed carbon, and a higher HHV. In contrast, biochar obtained from garden biowaste had a lower carbon content and a lower HHV. Full article

This study presents AD-MRCNN, an advanced deep learning framework for automated atmospheric front detection that addresses two critical limitations in existing methods. First, current approaches directly input raw meteorological data without optimizing feature compatibility, potentially hindering model performance. Second, they typically only provide frontal category information without identifying individual frontal systems. Our solution integrates two key innovations: 1. An intelligent adapter module that performs adaptive feature fusion, automatically weighting and combining multi-source meteorological inputs (including temperature, wind fields, and humidity data) to maximize their synergistic effects while minimizing feature conflicts; the utilized network achieves an average improvement of over 4% across various metrics. 2. An enhanced instance segmentation network based on Mask R-CNN architecture that simultaneously achieves (1) precise frontal type classification (cold/warm/stationary/occluded), (2) accurate spatial localization, and (3) identification of distinct frontal systems. Comprehensive evaluation using ERA5 reanalysis data (2009–2018) demonstrates significant improvements, including an 85.1% F1-score, outperforming traditional methods (TFP: 63.1%) and deep learning approaches (Unet: 83.3%), and a 31% reduction in false alarms compared to semantic segmentation methods. The framework’s modular design allows for potential application to other meteorological feature detection tasks. Future work will focus on incorporating temporal dynamics for frontal evolution prediction. Full article

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts. Full article

The 2019 Antarctic sudden stratospheric warming (SSW) is well captured by the specified dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (SD-WACCM-X). This SSW is dominated by a strong quasi-stationary planetary wave with zonal wavenumber 1 (SPW1) activity, and nonmigrating tides show great variations. The nonlinear interactions between SPW1 and diurnal, semidiurnal and terdiurnal migrating tides triggered by this SSW also have significant impacts on the variabilities of corresponding nonmigrating tides. This is clearly proven by the fact that the variations of the secondary nonmigrating tides, generated by the nonlinear interaction, show higher correlation during this SSW than those during the non-SSW period. Meanwhile, the SPW1 dominates the nonlinear interactions with diurnal, semidiurnal and terdiurnal migrating tides, and the corresponding secondary nonmigrating tides show concurrent increases with SPW1. In the ionosphere, the nonmigrating tidal oscillations exhibit consistent temporal variabilities with those shown in the neutral atmosphere, which demonstrates the neutral–ion coupling through nonmigrating tides and that nonmigrating tides are significant sources for the short-term ionospheric variability during this SSW event. Specifically, the enhancement of the ionospheric longitudinal wavenumber 4 structure coincides with the increase of the eastward-propagating diurnal tide with zonal wavenumber 3 (DE3), semidiurnal tide with zonal wavenumber 2 (SE2) and terdiurnal tide with zonal wavenumber 1 (TE1). Also, DE3 dominates the influence of nonmigrating tides on the ionospheric longitudinal wavenumber 4 structure during this SSW. Full article

The air quality in the Beijing–Tianjin–Hebei region of China has markedly improved in recent decades. Characterizing current PM_2.5 transmission between cities in light of the continuous reduction in emissions from various sources is of great significance for the formulation of future regional joint prevention and control strategies. To address these issues, a WRF-CAMx modeling project was implemented to explore the pollution characteristics from the perspectives of transport flux, regional source apportionment, and the comprehensive impact of multiple pollutants from 2013 to 2020. It was found that the net PM_2.5 transport flux among cities declined considerably during the study period and was positively affected by the continuous reduction in emission sources. The variations in local emissions and transport contributions in various cities from 2013 to 2020 revealed differences in emission control policies and efforts. It is worth noting that under polluted weather conditions, obvious interannual differences in PM_2.5 transport fluxes in the BTH region were observed, emphasizing the need for more scientifically based regional collaborative control strategies. The change in the predominant precursor from SO₂ to NOx has posed new challenges for emission reduction. NOx emission reductions will significantly decrease PM_2.5 concentrations, while SO₂ and NH₃ reductions show limited effects. The reduction in NOx emissions might have a fluctuating impact on the generation of SOAs, possibly due to changes in atmospheric oxidation. However, the deep treatment of NOx has a positive effect on the synergistic improvement of multiple air pollutants. This emphasizes the need to enhance the reduction in NOx emissions in the future. The results of this study can serve as a reference for the development of effective PM_2.5 precursor control strategies and regional differentiation optimization improvement policies in the BTH region. Full article

The Nanmiao Emergency Groundwater Source Area, rich in H₂SiO₃, serves as a strategic freshwater reserve zone in western Jiangxi Province. However, the mechanisms underlying groundwater formation in this area remain unclear. This study applied a combination of statistical analysis, isotopic tracing, and hydrochemical modeling to reveal the hydrochemical characteristics and origins of groundwater in the region. The results indicate that Na⁺ and Ca²⁺ dominate the cations, while HCO₃⁻ and Cl⁻ dominate the anions. Groundwater from descending springs is characterized by low mineralization and weak acidity, with hydrochemical types of primarily HCO₃–Na·Mg and HCO₃–Mg·Na·Ca. Groundwater from boreholes is weakly mineralized and neutral, with dominant hydrochemical types of HCO₃–Ca·Na and HCO₃–Ca·Na·Mg, suggesting a deep circulation hydrogeochemical process. Hydrogen and oxygen isotope analysis indicates that atmospheric precipitation is the primary recharge source. The chemical composition of groundwater is mainly controlled by rock weathering, silicate mineral dissolution, and cation exchange processes. During groundwater flowing, water and rock interactions, such as leaching, cation exchange, and mixing, occur. This study identifies the recharge sources and circulation mechanisms of regional groundwater, offering valuable insights for the sustainable development and protection of the emergency water source area. Full article

The Flexible Combined Imager (FCI), developed as the next-generation imager for the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellite series, represents a significant advancement over its predecessor, SEVIRI, on the Meteosat Second Generation (MSG) satellites. FCI offers more spectral bands, higher spatial resolution, and faster imaging capabilities, supporting a wide range of applications in weather forecasting, climate monitoring, and environmental analysis. On 13 January 2024, the FCI onboard MTG-I1 (renamed Meteosat-12 in December 2024) experienced a critical anomaly involving the failure of its onboard Calibration and Obturation Mechanism (COM). As a result, the use of the COM was discontinued to preserve operational safety, leaving the instrument dependent on alternative calibration methods. This loss of onboard calibration presents immediate challenges, particularly for the infrared channels, including image artifacts (e.g., striping), reduced radiometric accuracy, and diminished stability. To address these issues, EUMETSAT implemented an external calibration approach leveraging algorithms from the Global Space-based Inter-Calibration System (GSICS). The inter-calibration algorithm transfers stable and accurate calibration from the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral instrument aboard Metop-B and Metop-C satellites to FCI’s infrared channels daily, ensuring continued data quality. Comparisons with Cross-track Infrared Sounder (CrIS) data from NOAA-20 and NOAA-21 satellites using a similar algorithm is then used to validate the radiometric performance of the calibration. This confirms that the external calibration method effectively compensates for the absence of onboard blackbody calibration for the infrared channels. For the visible and near-infrared channels, slower degradation rates and pre-anomaly calibration ensure continued accuracy, with vicarious calibration expected to become the primary source. This adaptive calibration strategy introduces a novel paradigm for in-flight calibration of geostationary instruments and offers valuable insights for satellite missions lacking onboard calibration devices. This paper details the COM anomaly, the external calibration process, and the broader implications for future geostationary satellite missions. Full article

The observations of atmospheric, oceanic, and sea ice data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition were used to analyze the influence of snow redistribution and melt-pond processes on the evolution of sea ice thickness (SIT) in 2019 and 2020. To mitigate the effect of missing atmospheric observations from the time of the expedition, we used ERA5 atmospheric reanalysis along the MOSAiC drift trajectory to force the single-column sea ice model Icepack. SIT simulations from six combinations of two melt-pond schemes and three snow-redistribution configurations of Icepack were compared with observations and analyzed to investigate the sources of model–observation discrepancies. The three snow-redistribution configurations are the bulk scheme, the snwITDrdg scheme, and one simulation conducted without snow redistribution. The bulk scheme describes snow loss from level ice to leads and open water, and snwITDrdg describes wind-driven snow redistribution and compaction. The two melt-pond schemes are the TOPO scheme and the LVL scheme, which differ in the distribution of melt water. The results show that Icepack without snow redistribution simulates excessive snow–ice formation, resulting in an SIT thicker than that observed in spring. Applying snow-redistribution schemes in Icepack reduces snow–ice formation while enhancing the congelation rate. The bulk snow-redistribution scheme improves the SIT simulation for winter and spring, while the bias is large in simulations using the snwITDrdg scheme. During the summer, Icepack underestimates the sea ice surface albedo, resulting in an underestimation of SIT at the end of simulation. The simulations using the TOPO scheme are characterized by a more realistic melt-pond evolution compared to those using the LVL scheme, resulting in a smaller bias in SIT simulation. Full article