Search Results (742)

The current research aims to develop a simple, sensitive, and green analytical method for the group extraction/monitoring of 19 organochlorine and organophosphorus pesticides from fruit juices using microwave radiation to assist a cloud point extraction (MW-CPE) in combination with re-extraction in hexane and GC-MS/MS detection. The main experimental factors affecting the CPE and re-extraction have been optimized. The matrix-matched calibration was performed, and the limit of quantification (LOQ) for all studied pesticides at optimized conditions ranged between 5 and 47 ng L⁻¹. When applying only 0.25 mL of hexane for re-extraction, the proposed method shows good accuracy and precision. The “greenness” of the developed MW-CPE-GC-MS/MS method was assessed using the AGREE prep software. The method has been successfully implemented in pesticide analysis in commercially available fruit juices (lemon concentrate and red apple juice). The recovery values obtained for most analytes were within the range of 71% and 114% and RSD below 20% (exept Heptahlor, Aldrin, o,p-DDD, p,p-DDD and o,p-DDT, p,p-DDT). The developed method combines a preconcentration with a sample clean-up step due to the extraction of the pigments into the non-polar micelles during the extraction step, and deposition in the intermediate layer of MgSO₄ during the re-extraction step. Full article

In 2024, Brazil experienced record-breaking wildfire activity, underscoring the escalating influence of climate change. This study examines the long-range transport of wildfire-generated aerosol plumes to São Paulo, combining multi-platform observations to trace their origin and properties. During August and September—a period marked by intense fire outbreaks in Pará and Mato Grosso do Sul—lidar measurements performed at São Paulo detected pronounced aerosol plumes. To investigate their source and characteristics, we integrated data from the Earth Cloud Aerosol and Radiation Explorer (EarthCARE) satellite, HYSPLIT back-trajectory modeling, and ground-based AERONET and Raman lidar measurements. Aerosol properties were derived from aerosol optical depth (AOD), Ångström exponent, and lidar ratio (LR) retrievals. Back-trajectory analysis identified three transport pathways originating from active fire zones, with coinciding AOD values (0.7–1.1) and elevated LR (60–100 sr), indicative of dense smoke plumes. Compositional analysis revealed a significant black carbon component, implicating wildfires near Corumbá (Mato Grosso do Sul) and São Félix do Xingu (Pará) as probable emission sources. These findings highlight the efficacy of satellite-based lidar systems, such as Atmospheric Lidar (ATLID) onboard EarthCARE, in atmospheric monitoring, particularly in data-sparse regions where ground instrumentation is limited. Full article

Active galactic nuclei (AGNs) often exhibit broad-line regions (BLRs), populated by high-velocity clouds in approximately Keplerian orbits around the central supermassive black hole (SMBH) at subparsec scales. During episodes of intense accretion at super-Eddington rates, the accretion disk can launch a powerful, radiation-driven wind. This wind may overtake the BLR clouds, forming bowshocks around them. Two strong shocks arise: one propagating into the wind, and the other into the cloud. If the shocks are adiabatic, electrons and protons can be efficiently accelerated via a Fermi-type mechanism to relativistic energies. In sufficiently dense winds, the resulting high-energy photons are absorbed and reprocessed within the photosphere, while neutrinos produced in inelastic $pp$ collisions escape. In this paper, we explore the potential of super-accreting AGNs as neutrino sources. We propose a new class of neutrino emitter: an AGN lacking jets and gamma-ray counterparts, but hosting a strong, opaque, disk-driven wind. As a case study, we consider a supermassive black hole with $M_{\mathrm{BH}}={10}^6{M}_{\odot }$ and accretion rates consistent with tidal disruption events (TDEs). We compute the relevant cooling processes for the relativistic particles under such conditions and show that super-Eddington accreting SMBHs can produce detectable neutrino fluxes with only weak electromagnetic counterparts. The neutrino flux may be observable by the next-generation IceCube Observatory (IceCube-Gen2) in nearby galaxies with a high BLR cloud filling factor. For galaxies hosting more massive black holes, detection is also possible with moderate filling factors if the source is sufficiently close, or at larger distances if the filling factor is high. Our model thus provides a new and plausible scenario for high-energy extragalactic neutrino sources, where both the flux and timescale of the emission are determined by the number of clouds orbiting the black hole and the duration of the super-accreting phase. Full article

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies. Full article

A case study of an incoming biomass burning aerosol plume at Ascension Island is analyzed for the peak of the 2017 fire season using satellites, reanalysis and in situ observations. Measurements from the Atmospheric Radiation Measurement Mobile Facility 1 reveal an abrupt change from relatively clean conditions (~70 parts per billion by volume of carbon monoxide) to a more polluted state (~150 parts per billion by volume of carbon monoxide). Corresponding changes in aerosol size reveal a broadening of size distributions toward larger optical diameters, consistent with the arrival of aged aerosols. Within a 24 h period, black carbon fraction increases ~500% from ~300 ng m^e to ~1500 ng m³, while light absorption coefficients increase ~300%. Long-range transport of these aerosols is primarily confined between 2 and 5 km above sea level along the northwesterly trade winds. Our results show that the primary driver of increases in aerosol loading over Ascension Island is an intensification of the St. Helena high-pressure system (anticyclone) that leads to a weakening of trade winds and increases westward transport on its northern flank. A better understanding of the complex interactions between air quality, meteorology and long-range aerosol transport is important for future modeling studies focused on aerosol–cloud–radiation interactions over the open ocean and reducing its associated uncertainties. Full article

Driven by the rising demand for digitalization and intelligent development of infrared payloads, next-generation systems must be developed within compressed timelines. High-precision digital modeling and simulation techniques offer essential data sources but often falter in complex space-based scenarios due to the limited availability of infrared characteristic data, hindering evaluation of the payload effectiveness. To address this, we propose a digital imaging simulation and verification (DISV) model for high-fidelity infrared image generation and closed-loop validation in the context of cloud–sea target detection. Based on on-orbit infrared imagery, we construct a cloud cluster database via morphological operations and generate physically consistent backgrounds through iterative optimization. The DISV model subsequently calculates scene infrared radiation, integrating radiance computations with an electron-count-based imaging model for radiance-to-grayscale conversion. Closed-loop verification via blackbody radiance inversion is performed to confirm the model’s accuracy. The mid-wave infrared (MWIR, 3–5 µm) system achieves mean square errors (RSMEs) < 0.004, peak signal-to-noise ratios (PSNRs) > 49 dB, and a structural similarity index measure (SSIM) > 0.997. The long-wave infrared (LWIR, 8–12 µm) system yields RMSEs < 0.255, PSNRs > 47 dB, and an SSIM > 0.994. Under 20–40% cloud coverage, the target radiance inversion errors remain below 4.81% and 7.30% for the MWIR and LWIR, respectively. The DISV model enables infrared image simulation across multi-domain scenarios, offering vital support for optimizing on-orbit payload performance. Full article

Monitoring water quality in narrow tributary bays of large river-type reservoirs is hindered by sparse sampling and cloud-limited imagery. This study develops a Trophic State Index (TSI) inversion for Xiangxi Bay, a major tributary bay of the Three Gorges Reservoir, using Landsat data and a backpropagation (BP) neural network, with hyperparameters tuned via a grid search algorithm (GSA). Environmental drivers such as water temperature, solar radiation, and photosynthetically active radiation were combined with Landsat spectral bands. Eleven sites measured monthly in 2009 yielded 98 samples after preprocessing, and training achieved R² = 0.94. Predictions for 2009 show clear spatiotemporal heterogeneity: those for April and September (TSI = 48–59) exceeded those for July and October (46–56), with mid–lower reaches (52–59) being higher than mid–upper reaches (47–54). Out-of-period predictions for April/June 2019 and August/November 2020 were consistent with seasonal expectations, with higher spring–summer TSIs (2019: 50–57; 2020 August: 45–55) than in November 2020 (37–47). Key limitations include the small sample size, cloud-related data gaps, and sensitivity to evolving reservoir operations. This framework demonstrates a practical route to the satellite-based monitoring and mapping of trophic status in narrow reservoir tributaries. Full article

Surface solar irradiance (SSI) is a critical factor influencing the power generation capacity of photovoltaic (PV) power plants. Dynamic changes in cloud cover pose significant challenges to the accurate nowcasting of SSI, which in turn directly affects the reliability and stability of renewable energy systems. However, existing research often simplifies or overlooks changes in the optical and morphological characteristics of clouds, leading to considerable errors in SSI nowcasting. To address this limitation and improve the accuracy of ultra-short-term SSI forecasting, this study first forecasts changes in cloud optical thickness (COT) within the next 3 h based on a spatiotemporal long short-term memory model, since COT is the primary factor determining cloud shading effects, and then integrates the zenith and regional averages of COT, along with factors influencing direct solar radiation and scattered radiation, to achieve precise SSI nowcasting. To validate the proposed method, we apply it to the Albuquerque, New Mexico, United States (ABQ) site, where it yielded promising performance, with correlations between predicted and actual surface solar irradiance for the next 1 h, 2 h, and 3 h reaching 0.94, 0.92, and 0.92, respectively. The proposed method effectively captures the temporal trends and spatial patterns of cloud changes, avoiding simplifications of cloud movement trends or interference from non-cloud factors, thus providing a basis for power adjustments in solar power plants. Full article

The online coupling of aerosols and clouds and its effect on surface global horizontal irradiance (GHI) has not yet been thoroughly investigated in the Weather Research and Forecasting Model with Solar extensions (WRF-Solar), despite its potential significance for solar energy applications. This study addresses this critical gap by implementing a computationally efficient, coupled aerosol–cloud scheme and evaluating its impacts on GHI predictability. Simulations with online aerosol–cloud coupling are systematically compared to uncoupled simulations during March 2021, a period marked by two distinct pollution episodes over north China. The online coupling enhances aerosol optical depth (AOD) simulations, increasing the correlation coefficient from 0.19 to 0.51 while reducing the absolute bias from 0.54 to 0.48 and root mean square error from 0.82 to 0.72, compared to uncoupled simulations. Enhanced cloud microphysics (droplet concentration, water path) yields better cloud optical depth estimates, reducing all-sky GHI bias by 14.5% (63.5 W/m² for the uncoupled scenario and 54.3 W/m² for the coupled scenario) through improved aerosol–cloud–meteorology interactions. Notably, the simultaneous spatiotemporal improvement of both AOD and GHI suggests enhanced internal consistency in aerosol–cloud–radiation interactions, which is crucial for operational solar irradiance forecasting in pollution-prone regions. The results also highlight the practical value of incorporating online aerosol coupling in solar forecasting models. Full article

The direct relationship between aerosols and clouds strongly influences the effects of clouds on the global climate. Aerosol particles act as cloud condensation nuclei (CCN) and ice nuclei (IN), affecting cloud formation, microphysics, and precipitation, as well as increasing the reflection of solar radiation at the cloud tops. Processes such as gas-to-particle conversion and new particle formation (NPF) control aerosol properties that, together with meteorological conditions, regulate cloud droplet nucleation through Köhler theory and related effects. The indirect aerosol effects described by Twomey and Albrecht demonstrate how changes in aerosols impact droplet number, cloud lifetime, and precipitation efficiency. Cloud microphysical processes, including droplet growth, collision-coalescence, and solid-phase mechanisms such as riming, vapor diffusion, and aggregation, shape precipitation development in warm, cold, and mixed-phase clouds. Ice nucleation remains a significant uncertainty due to the diversity of aerosol types and nucleation modes. This work synthesizes these physical interactions to better understand how the chemical and physical properties of aerosols influence cloud and precipitation processes, supporting improvements in weather and climate prediction models despite numerical challenges arising from the complexity of aerosol–cloud interactions. Full article

Surface solar radiation (SSR) is a critical component of the Earth’s energy balance and plays a pivotal role in climate modelling, hydrological processes, and solar energy planning. In data-scarce regions like India, where dense ground-based radiation networks are limited, reanalysis and satellite-derived SSR datasets are often utilized to fill observational gaps. However, these datasets are subject to systematic biases, particularly under diverse sky and seasonal conditions. This study presents a comprehensive evaluation of four widely used SSR datasets: ERA5, IMDAA, MERRA2, and CERES, against high-quality in situ observations from 27 India Meteorological Department (IMD) stations, for the period 1985–2020. The assessment incorporates multi-scale temporal analysis (daily/monthly), spatial validation, and sky-condition stratification via the clearness index (K_t). The results indicate that CERES exhibits the best overall performance with the lowest RMSE (16.30 W/m²), minimal bias (–2.5%), and strong correlation (r = 0.97; p = 0.01), particularly under partly cloudy conditions. ERA5, with a finer spatial resolution, also performs robustly (RMSE = 20.80 W/m²; MBE = –0.8%; r = 0.94; p = 0.01), showing consistent agreement with observed seasonal cycles, though slightly underestimating SSR during monsoonal cloud cover. MERRA2 shows moderate overestimation (+4.4%) with region-specific bias variability, while IMDAA demonstrates persistent overestimation (+10.2%) across all conditions, highlighting limited sensitivity to atmospheric transparency. Importantly, this study reconciles apparent contradictions between monthly and sky condition-based bias analyses, attributing them to aggregation differences. While reanalysis datasets overestimate SSR during the monsoon on average, they tend to underestimate it under heavily overcast conditions. These insights are critical for guiding the selection and application of SSR datasets in solar energy modelling, SPV system design, and climate diagnostics across India’s heterogeneous atmospheric regimes. Full article

The Earth’s Radiation Budget (ERB) is a critical component for understanding the planet’s climate system, as it governs the balance between incoming solar energy and outgoing thermal radiation. Accurate monitoring of the ERB, combined with Ocean Heat Content (OHC) measurements, is essential to assess Earth’s Energy Imbalance (EEI) and its implications for global warming. This paper presents new results on the ERB based on data from the Uvsq-Sat and Inspire-Sat nanosatellite missions, which operated from 2021 to 2024. These satellites constitute the first European constellation demonstrator designed for broadband, Wide Field-Of-View (WFOV) measurements of the ERB. While WFOV instruments provide enhanced temporal and spatial coverage, they do not replace the need for Narrow Field-Of-View (NFOV) measurements, such as those provided by the established Clouds and the Earth’s Radiant Energy System (CERES) instruments. Instead, they are designed to complement them. By using data from both the WFOV constellation and CERES instruments to measure Reflected Solar Radiation (RSR) and Outgoing Longwave Radiation (OLR), we estimate the EEI and monitor its evolution. Our analysis reveals a generally good agreement between Uvsq-Sat and CERES data for EEI from 2021 through the end of 2024. Over this period, EEI derived from Uvsq-Sat averaged +0.87 ± 0.23 ${\mathrm{Wm}}^{-2}$, closely matching the recent CERES trend. Both datasets indicate a peak in EEI in mid-2023, followed by a decline throughout 2024, likely reflecting stabilizing feedbacks triggered by the 2023 El Niño event. Importantly, this short-term decline occurred within a sustained upward trend in EEI since 2013, as shown by CERES observations, with solar activity having a negligible impact. Comparisons with OHC measurements confirm ongoing ocean heat accumulation, consistent with the rising decadal trend in EEI. These insights underscore the importance of continuous, high-frequency observations to capture the complex and rapidly evolving processes influencing Earth’s energy balance. Demonstrations using nanosatellites at different local times illustrate the advantages of small satellite constellations for improved monitoring frequency and coverage, particularly for variables that change over short time scales, such as RSR, also known as Outgoing Shortwave Radiation (OSR). Full article

As key components of the climate system, clouds exert a significant influence on the Earth’s radiation budget and hydrological cycle. However, studies focusing on cloud properties over Central Asia are still limited, and the impacts of cloud variability on regional temperature and precipitation remain poorly understood. This study uses reanalysis and multi-source remote sensing datasets to investigate the spatiotemporal characteristics of clouds and their influence on regional climate. The cloud cover increases from the southwest to the northeast, with mid and low-level clouds predominating in high-altitude regions. All clouds have shown a declining trend during 1981–2020. According to satellite data, the sharpest decline in total cloud cover occurs in summer, while reanalysis data show a more significant reduction in spring. In addition, cloud cover changes influence the local climate through radiative forcing mechanisms. Specifically, the weakening of shortwave reflective cooling and the enhancement of longwave heating of clouds collectively exacerbate surface warming. Meanwhile, precipitation is positively correlated with cloud cover, and its spatial distribution aligns with the cloud water path. The cloud phase composition in Central Asia is dominated by liquid water, accounting for over 40%, a microphysical characteristic that further impacts the regional hydrological cycle. Full article

This study focuses on developing a climate-flood model to investigate and interpret the relationship and impact of climate on runoff/flooding at a sub-regional scale using multiple linear regression (MLR) with 30 years of hydro-climatic data for the Cross River Basin, Nigeria. Data were obtained from Nigerian Meteorological Agency (NIMET) for the following climatic parameters: annual average rainfall, maximum and minimum temperatures, humidity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure. These hydro-meteorological data were analysed and used as parameters input to the climate-flood model. Results from multiple regression analyses were used to develop climate-flood models for all the gauge stations in the basin. The findings suggest that at 95% confidence, the climate-flood model was effective in forecasting the annual runoff at all the stations. The findings also identified the climatic parameters that were responsible for 100% of the runoff variability in Calabar (R² = 1.000), 100% the runoff in Uyo (R² = 1.000), 98.8% of the runoff in Ogoja (R² = 0.988), and 99.9% of the runoff in Eket (R² = 0.999). Based on the model, rainfall depth is the only climate parameter that significantly predicts runoff at 95% confidence intervals in Calabar, while in Ogoja, rainfall depth, temperature, and evaporation significantly predict runoff. In Eket, rainfall depth, relative humidity, solar radiation, and soil temperatures are significant predictors of runoff. The model also reveals that rainfall depth and evaporation are significant predictors of runoff in Uyo. The outcome of the study suggests that climate change has impacted runoff and flooding within the Cross River Basin. Full article

The article is focused on the quantitative assessment of the thermal impact of polar mesospheric clouds (PMCs) on the mesopause caused by the emission of absorbed solar and terrestrial infrared (IR) radiation by cloud particles. For this purpose, a parameterization of mesopause heating by PMC crystals has been developed, the main feature of which is to incorporate the thermal properties of ice and the interaction of cloud particles with the environment. Parametrization is based on PMCs zero-dimensional (0-D) model and uses temperature, pressure, and water vapor data in the 80–90 km altitude range retrieved from Solar Occultation for Ice Experiment (SOFIE) measurements. The calculations are made for 14 PMC seasons in both hemispheres with the summer solstice as the central date. The obtained results show that PMCs can make a significant contribution to the heat balance of the upper atmosphere, comparable to the heating caused, for example, by the dissipation of atmospheric gravity waves (GWs). The interhemispheric differences in heating are manifested mainly in the altitude structure: in the Southern Hemisphere (SH), the area of maximum heating values is 1–2 km higher than in the Northern Hemisphere (NH), while quantitatively they are of the same order. The most intensive heating is observed at the lower boundary of the minimum temperature layer (below 150 K) and gradually weakens with altitude. The NH heating median value is 5.86 K/day, while in the SH it is 5.24 K/day. The lowest values of heating are located above the maximum of cloud ice concentration in both hemispheres. The calculated heating rates are also examined in the context of the various factors of temperature variation in the observed atmospheric layers. It is shown in particular that the thermal impact of PMC is commensurate with the influence of dissipating gravity waves at heights of the mesosphere and lower thermosphere (MLT), which parameterizations are included in all modern numerical models of atmospheric circulation. Hence, the developed parameterization can be used in global atmospheric circulation models for further study of the peculiarities of the thermodynamic regime of the MLT. Full article