Search Results (507)

This study investigates the effects of key atmospheric features on mineral dust emissions and transport in the Sahara–Sahel region, focusing on the Bodélé Depression, during June 2006 and 2011. We use a combination of high-resolution atmospheric simulations (AROME model), satellite observations (MODIS), and reanalysis data (ERA5, ECMWF) to examine the roles of the low-level jet (LLJ), Saharan heat low (SHL), Intertropical Discontinuity (ITD), and African Easterly Jet (AEJ) in modulating dust activity. Our results reveal significant interannual variability in aerosol optical depth (AOD) between the two periods, with a marked decrease in June 2011 compared to June 2006. The LLJ emerges as a dominant factor in dust uplift over Bodélé, with its intensity strongly influenced by local topography, particularly the Tibesti Massif. The position and intensity of the SHL also play crucial roles, affecting the configuration of monsoon flow and Harmattan winds. Analysis of wind patterns shows a strong negative correlation between AOD and meridional wind in the Bodélé region, while zonal wind analysis emphasizes the importance of the AEJ and Tropical Easterly Jet (TEJ) in dust transport. Surprisingly, we observe no significant correlation between ITD position and AOD measurements, highlighting the complexity of dust emission processes. This study is the first to combine climatological context and case studies to demonstrate the effects of African monsoon variability on dust uplift at intra-seasonal timescales, associated with the modulation of ITD latitude position, SHL, LLJ, and AEJ. Our findings contribute to understanding the complex relationships between large-scale atmospheric features and dust dynamics in this key source region, with implications for improving dust forecasting and climate modeling efforts. Full article

Atmospheric corrosion significantly impacts infrastructure worldwide, with traditional assessment methods being time-intensive and costly. This study developed a comprehensive machine learning framework for predicting atmospheric corrosion rates using environmental and material parameters. Three regression models (Linear Regression, Random Forest, and Gradient Boosting) were trained on a scientifically informed synthetic dataset incorporating established corrosion principles from ISO 9223 standards and peer-reviewed literature. The Gradient Boosting model achieved superior performance with cross-validated R² = 0.835 ± 0.024 and RMSE = 98.99 ± 16.62 μm/year, significantly outperforming the Random Forest (p < 0.001) and Linear Regression approaches. Feature importance analysis revealed the copper content (30%), exposure time (20%), and chloride deposition (15%) as primary predictors, consistent with the established principles of corrosion science. Model diagnostics demonstrated excellent predictive accuracy (R² = 0.863) with normally distributed residuals and homoscedastic variance patterns. This methodology provides a systematic framework for ML-based corrosion prediction, with significant implications for protective coating design, material selection, and infrastructure risk assessment, pending comprehensive experimental validation. Full article

Traffic-related pollutants remain a challenging global issue, with significant policy implications. Within the European Union, Romania has the highest yearly societal cost per capita due to air pollution, which kills 29,000 Romanians every year, whereas the health and economic costs are also significant. In this context, municipal authorities in the country, particularly in high-density areas, should place a strong focus on mitigating air pollution. In particular, the capital city, Bucharest, ranks among the most congested cities in the world while registering the highest pollution index in Romania, with traffic pollution responsible for two-thirds of its air pollution. Consequently, studies that assess and model pollution trends are paramount to inform local policy-making processes and assist pollution-mitigation efforts. In this paper, a generalized additive modeling (GAM) framework is employed to model hourly concentrations of nitrogen dioxide (NO₂), i.e., a relevant traffic-pollution proxy, at a busy urban traffic location in central Bucharest, Romania. All models are developed on a wide, fine-granularity dataset spanning January 2017–December 2022 and include extensive meteorological covariates. Model robustness is assured by switching between the generalized additive model (GAM) framework and the generalized additive mixed model (GAMM) framework when the residual autoregressive process needs to be specifically acknowledged. Results indicate that trend GAMs explain a large amount of the hourly variation in traffic pollution. Furthermore, meteorological factors contribute to increasing the models’ explanation power, with wind direction, relative humidity, and the interaction between wind speed and the atmospheric pressure emerging as important mitigators for NO₂ concentrations in Bucharest. The results of this study can be valuable in assisting local authorities to take proactive measures for traffic pollution control in the capital city of Romania. Full article

Flammulina velutipes is a widely cultivated edible mushroom in East Asia, recognized for its nutritional benefits and distinct morphology characterized by a long stipe and a compact, hemispherical pileus. The pileus not only plays a critical biological role in reproduction through spore formation but also serves as a key commercial trait influencing consumer preference and market value. Despite its economic importance, pileus development in F. velutipes is highly sensitive to environmental factors, among which carbon dioxide (CO₂) concentration is particularly influential under indoor cultivation conditions. While previous studies have reported that elevated CO₂ levels can inhibit pileus expansion in other mushroom species, the molecular mechanisms by which CO₂ affects pileus growth in F. velutipes remain poorly understood. In this study, we investigated the impact of CO₂ concentration on pileus morphology and gene expression in F. velutipes by cultivating fruiting bodies under two controlled atmospheric conditions: low (1000 ppm) and high (10,000 ppm) CO₂. Morphometric analysis revealed that elevated CO₂ levels significantly suppressed pileus expansion, reducing the average diameter by more than 50% compared to the low CO₂ condition. To elucidate the underlying genetic response, we conducted RNA sequencing and identified 102 differentially expressed genes (DEGs), with 78 being downregulated under elevated CO₂. Functional enrichment analysis highlighted the involvement of cyclin-dependent protein kinase regulatory pathways in this response. Two cyclin genes were found to be significantly downregulated under elevated CO₂ conditions, and their suppression was validated through quantitative real-time PCR. These genes, possessing conserved cyclin_N domains, are implicated in the regulation of the eukaryotic cell cycle, particularly in mitotic growth. These results indicate that CO₂-induced downregulation of cyclin genes may underlie cell cycle arrest, contributing to inhibited pileus development. This study is the first to provide transcriptomic evidence that elevated CO₂ concentrations specifically repress PHO80-like cyclin genes in F. velutipes, revealing a molecular mechanism by which CO₂ stress inhibits pileus development. These findings suggest that elevated CO₂ triggers a morphogenetic checkpoint by repressing PHO80-like cyclins, thereby modulating cell cycle progression during fruiting body development. This study provides the first evidence of such a transcriptional response in edible mushrooms and offers promising molecular targets for breeding CO₂-resilient strains and optimizing commercial cultivation conditions. Full article

The nitrogen cycle (N-cycle) is a cornerstone of global biogeochemistry, regulating nitrogen availability and affecting atmospheric chemistry, agricultural productivity, and ecological balance. Central to this cycle is the reversible interplay between nitrate (NO₃⁻) and nitrite (NO₂⁻), mediated by molybdenum-dependent enzymes—Nitrate reductases (NARs) and Nitrite oxidoreductases (NXRs). Despite catalyzing opposite reactions, these enzymes exhibit remarkable structural and mechanistic similarities. This review aims to elucidate the molecular underpinnings of nitrate reduction and nitrite oxidation by dissecting their enzymatic architectures, redox mechanisms, and evolutionary relationships. By focusing on recent structural, spectroscopic, and thermodynamic data, we explore how these two enzyme families represent “two sides of the same coin” in microbial nitrogen metabolism. Special emphasis is placed on the role of oxygen atom transfer (OAT) as a unifying mechanistic principle, the influence of environmental redox conditions, and the emerging evidence of bidirectional catalytic potential. Understanding this dynamic enzymatic interconversion provides insight into the flexibility and resilience of nitrogen-transforming pathways, with implications for environmental management, biotechnology, and synthetic biology. Full article

The accelerated increase in atmospheric CO₂ concentration is one of the most pressing problems at present. It is possible that this increase causes slight modifications in intracellular CO₂. The aim of this work was to determine whether CO₂ at different concentrations can affect the oxidative damage caused by ciprofloxacin (CIP) in Escherichia coli and to evaluate the possible implications of this effect for human health. To identify the effects of CO₂ on the action of CIP, reactive oxygen (ROS) and reactive nitrogen (RNS) species were measured at two different CO₂ concentrations while monitoring the bacterial antioxidant response. These assays showed that CO₂ led to a decrease in ROS formation relative to that under atmospheric conditions (ACs), while it had the opposite effect on RNS formation, which increased relative to that under ACs. Under CO₂ conditions, antioxidant defenses were less activated, with superoxide dismutase, catalase, and ferric reducing assay potency decreasing compared to those under ACs; however, reduced glutathione exhibited the opposite behavior. In the presence of CO₂, the activity of CIP against E. coli was reduced relative to that under ACs. In conclusion, CO₂ interferes with the action of CIP in bacterial cells, generating changes in oxidative stress. Full article

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

This study examines the impact of geomagnetic disturbances quantified by the Kp and Dst indices on the accuracy of single-frequency GPS positioning across mid-latitudes and the equatorial zone, with a focus on temporal and spatial positioning errors variability. GNSS data from a globally distributed network of 14 IGS stations were analyzed for September 2017, featuring significant geomagnetic activity. The selection of stations encompassed equatorial and mid-latitude regions (approximately ±45°), strategically aligned with the distribution of the Dst index during geomagnetic storms. Satellite navigation data were processed using RTKLIB software in standalone mode with standardized atmospheric and orbital corrections. The GPS was chosen over GLONASS following preliminary testing, which revealed a higher sensitivity of GPS positional accuracy to variations in geomagnetic indices such as Kp and Dst, despite generally lower total error magnitudes. The ECEF coordinate system calculates the total GPS error as the vector sum of deviations in the X, Y, and Z axes. Statistical evaluation was performed using One-Way Repeated Measures ANOVA to determine whether positional error variances across geomagnetic activity phases were significant. The results of the variance analysis confirm that the variation in the total GPS positioning error is non-random and can be attributed to the influence of geomagnetic storms. However, regression analysis reveals that the impact of geomagnetic storms (quantified by Kp and Dst) displays spatiotemporal variability, with no consistent correlation to GPS positioning error dynamics. The findings, as well as the developed methodology, have qualitative implications for GNSS-dependent operations in sensitive sectors such as navigation, timing services, and geospatial monitoring. 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

Land Surface Temperature (LST) is essential for understanding the interactions between the land surface and the atmosphere. This study presents a comprehensive evaluation of machine learning (ML)-based downscaling algorithms to enhance the spatial resolution of MODIS LST data from 960 m to 30 m, leveraging auxiliary variables including vegetation indices, terrain parameters, and land surface reflectance. By establishing non-linear relationships between LST and predictive variables through eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms, the proposed framework was rigorously validated using in situ measurements across China’s Heihe River Basin. Comparative analyses demonstrated that integrating multiple vegetation indices (e.g., NDVI, SAVI) with terrain factors yielded superior accuracy compared to factors utilizing land surface reflectance or excessive variable combinations. While slope and aspect parameters marginally improved accuracy in mountainous regions, including them degraded performance in flat terrain. Notably, land surface reflectance proved to be ineffective in snow/ice-covered areas, highlighting the need for specialized treatment in cryospheric environments. This work provides a reference for LST downscaling, with significant implications for environmental monitoring and urban heat island investigations. Full article

The plasma in Earth’s magnetosphere is comprised of ions from the solar wind and from Earth’s polar wind, with the orientation of the interplanetary magnetic field (IMF) acting to modulate the relative contributions from these two sources. Although ion composition and charge state are strong indicators of ion provenance, here we consider isotope ratios as a possible additional method for tracing plasma provenance. Solar wind isotope ratios have been well characterized, but isotope ratios have not been measured for magnetospheric plasma, and only a few measurements have been made for Earth’s ionosphere. Accounting for diffusive separation in the ionosphere, and using a magnetospheric source flux model, we estimate isotope ratios for several light ions (H⁺, He⁺, N⁺ and O⁺) in the magnetosphere. The primary source of N and O magnetospheric ions is the polar wind, and He ions come primarily from the solar wind. H ions come from both polar and solar winds. The extreme diffusive separation of O⁺ isotopes argues against the polar wind as a significant source of O to the lunar regolith during the passage of the Moon through the magnetotail. Full article

Non-thermal processing encompasses a range of emerging food technologies, including high-pressure processing (HPP), pulsed electric field (PEF), cold atmospheric plasma (CAP), high-pressure carbon dioxide (HPCD), and ultrasound (US). Unlike traditional thermal processing or chemical preservatives, these methods offer advantages such as lower energy consumption, enhanced environmental sustainability, and effective microbial inactivation, thereby extending food shelf life. Moreover, they can better preserve the nutritional integrity, color, flavor, and texture of food products. However, a critical concern associated with non-thermal processing is its potential to induce microorganisms into a viable but nonculturable (VBNC) state. These VBNC cells evade detection via conventional culturing techniques and may remain metabolically active and retain virulence, posing hidden food safety risks. Despite these implications, comprehensive reviews addressing the induction of a VBNC state by non-thermal treatments remain limited. This review systematically summarizes the microbial inactivation effects and mechanisms of non-thermal processing techniques, the VBNC state, and their associated hazards. This review aims to support technological innovation and sustainable advancement in non-thermal food processing. Full article

This study utilized Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite level-2 data with high-confidence cloud–aerosol discrimination (|CAD| > 70) to investigate the optical properties, vertical distributions, seasonal variations, and aerosol interactions of near-surface cloud layers (cloud base height < 2.5 km) over Australia from 2006 to 2021. This definition encompasses both traditional low clouds and part of mid-level clouds that extend into the lower troposphere, enabling a comprehensive view of cloud systems that interact most directly with boundary-layer aerosols. The results showed that the optical depth of low clouds (CODL) exhibited significant spatial heterogeneity, with higher values in central and eastern regions (often exceeding 6.0) and lower values in western plateau regions (typically 4.0–5.0). CODL values demonstrated clear seasonal patterns with spring peaks across all regions, contrasting with traditional summer-maximum expectations. Pronounced diurnal variations were observed, with nighttime CODL showing systematic enhancement effects (up to 19.29 maximum values compared to daytime 11.43), primarily attributed to surface radiative cooling processes. Cloud base heights (CBL) exhibited counterintuitive nighttime increases (41% on average), reflecting fundamental differences in cloud formation mechanisms between day and night. The geometric thickness of low clouds (CTL) showed significant diurnal contrasts, decreasing by nearly 50% at night due to enhanced atmospheric stability. Cloud layer number (CN) displayed systematic nighttime reductions (18% decrease), indicating dominance of single stratiform cloud systems during nighttime. Regional analysis revealed that the central plains consistently exhibited higher CODL values, while eastern mountains showed elevated cloud heights due to orographic effects. Correlation analysis between cloud and aerosol layer properties revealed moderate but statistically significant relationships (|R| = 0.4–0.6), with the strongest correlations appearing between cloud layer heights and aerosol layer heights. However, these correlations represent only partial influences among multiple factors controlling cloud development, suggesting measurable but modest aerosol effects on cloud properties. This study provides comprehensive observational evidence for cloud optical property variations and aerosol–cloud interactions over Australia, contributing to an improved understanding of Southern Hemisphere cloud systems and their climatic implications. Full article

The Eemian interglacial (~130–116 ka) is a period characterized by a significantly warmer climate than the pre-industrial era, providing a valuable opportunity to study natural climate variability and its implications for the future. We studied the Eemian climate in Europe by applying an interactive downscaling to our Earth system model (iLOVECLIM) to increase its horizontal atmospheric resolution from 5.56° to 0.25° latitude-longitude. A transient simulation was conducted for both the standard version of the model and with an interactive downscaling applied for the Eemian (127–116 ka). Our simulations suggest that the magnitude of temperature and precipitation varied across different regions of Europe, with some areas experiencing more pronounced warming and precipitation changes than others. The latitudinal pattern in our simulation during the Eemian shows that the warming in Europe was stronger at high latitudes than at mid-latitudes. Relative to the pre-industrial climate, our downscaling scheme simulates at 127 ka higher temperatures between 3–4 °C in the northern part of Europe and higher precipitation values between 150–300 mm/yr. Our results indicate that, in comparison to the standard model, the downscaled simulations offer spatial variability that is more in line with proxy-based reconstructions and other climate models. Full article

This article examines the energy efficiency and climate impact of various heating methods commonly employed across industrial sectors. Fossil fuel combustion heat sources, which are predominantly employed for industrial heating, contribute significantly to atmospheric pollution and associated asset losses. The electrification of industrial heating has the potential to substantially reduce the total energy consumed in industrial heating processes and significantly mitigate the rate of global warming. Advances in electrical heating technologies are driven by enhanced energy conversion, compactness, and precision control capabilities, ensuring attractive financial payback periods for clean, energy-efficient equipment. These advancements stem from the use of improved performance materials, process optimization, and waste heat utilization practices, particularly at high temperatures. The technical challenges associated with large-scale, heavy-duty electric process heating are addressed through the novel innovations discussed in this article. Electrification and the corresponding energy efficiency improvements reduce the water consumed for industrial steam requirements. The article reviews new technologies that replace conventional process gas heaters and pressure boilers with efficient electric process gas heaters and instant steam generators, operating in the high kilowatt and megawatt power ranges with very high-temperature capabilities. Financial payback calculations for energy-optimized processes are illustrated with examples encompassing a range of comparative energy costs across various temperatures. The economics and implications of waste heat utilization are also examined in this article. Additionally, the role of futuristic, radical technical innovations is evaluated as a sustainable pathway that can significantly lower energy consumption without compromising performance objectives. The potential for a new paradigm of self-organization in processes and final usage objectives is briefly explored for sustainable innovations in thermal engineering and materials development. The policy implications and early adoption of large-scale, energy-efficient thermal electrification are discussed in the context of temperature segmentation for industrial-scale processes and climate-driven asset losses. Policy shifts towards incentivizing energy efficiency at the manufacturing level of heater use are recommended as a pathway for deep decarbonization. Full article