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37 pages, 7533 KiB  
Article
Aerosols in the Mixed Layer and Mid-Troposphere from Long-Term Data of the Italian Automated Lidar-Ceilometer Network (ALICENET) and Comparison with the ERA5 and CAMS Models
by Annachiara Bellini, Henri Diémoz, Gian Paolo Gobbi, Luca Di Liberto, Alessandro Bracci and Francesca Barnaba
Remote Sens. 2025, 17(3), 372; https://doi.org/10.3390/rs17030372 - 22 Jan 2025
Abstract
Aerosol vertical stratification significantly influences the Earth’s radiative balance and particulate-matter-related air quality. Continuous vertically resolved observations remain scarce compared to surface-level and column-integrated measurements. This work presents and makes available a novel, long-term (2016–2022) aerosol dataset derived from continuous (24/7) vertical profile [...] Read more.
Aerosol vertical stratification significantly influences the Earth’s radiative balance and particulate-matter-related air quality. Continuous vertically resolved observations remain scarce compared to surface-level and column-integrated measurements. This work presents and makes available a novel, long-term (2016–2022) aerosol dataset derived from continuous (24/7) vertical profile observations from three selected stations (Aosta, Rome, Messina) of the Italian Automated Lidar-Ceilometer (ALC) Network (ALICENET). Using original retrieval methodologies, we derive over 600,000 quality-assured profiles of aerosol properties at the 15 min temporal and 15 metre vertical resolutions. These properties include the particulate matter mass concentration (PM), aerosol extinction and optical depth (AOD), i.e., air quality legislated quantities or essential climate variables. Through original ALICENET algorithms, we also derive long-term aerosol vertical layering data, including the mixed aerosol layer (MAL) and elevated aerosol layers (EALs) heights. Based on this new dataset, we obtain an unprecedented, fine spatiotemporal characterisation of the aerosol vertical distributions in Italy across different geographical settings (Alpine, urban, and coastal) and temporal scales (from sub-hourly to seasonal). Our analysis reveals distinct aerosol daily and annual cycles within the mixed layer and above, reflecting the interplay between site-specific environmental conditions and atmospheric circulations in the Mediterranean region. In the lower troposphere, mixing processes efficiently dilute particles in the major urban area of Rome, while mesoscale circulations act either as removal mechanisms (reducing the PM by up to 35% in Rome) or transport pathways (increasing the loads by up to 50% in Aosta). The MAL exhibits pronounced diurnal variability, reaching maximum (summer) heights of > 2 km in Rome, while remaining below 1.4 km and 1 km in the Alpine and coastal sites, respectively. The vertical build-up of the AOD shows marked latitudinal and seasonal variability, with 80% (30%) of the total AOD residing in the first 500 m in Aosta-winter (Messina-summer). The seasonal frequency of the EALs reached 40% of the time (Messina-summer), mainly in the 1.5–4.0 km altitude range. An average (wet) PM > 40 μg m−3 is associated with the EALs over Rome and Messina. Notably, 10–40% of the EAL-affected days were also associated with increased PM within the MAL, suggesting the entrainment of the EALs in the mixing layer and thus their impact on the surface air quality. We also integrated ALC observations with relevant, state-of-the-art model reanalysis datasets (ERA5 and CAMS) to support our understanding of the aerosol patterns, related sources, and transport dynamics. This further allowed measurement vs. model intercomparisons and relevant examination of discrepancies. A good agreement (within 10–35%) was found between the ALICENET MAL and the ERA5 boundary layer height. The CAMS PM10 values at the surface level well matched relevant in situ observations, while a statistically significant negative bias of 5–15 μg m−3 in the first 2–3 km altitude was found with respect to the ALC PM profiles across all the sites and seasons. Full article
25 pages, 19157 KiB  
Article
Data Augmentation in Earth Observation: A Diffusion Model Approach
by Tiago Sousa, Benoît Ries and Nicolas Guelfi
Information 2025, 16(2), 81; https://doi.org/10.3390/info16020081 - 22 Jan 2025
Abstract
High-quality Earth Observation (EO) imagery is essential for accurate analysis and informed decision making across sectors. However, data scarcity caused by atmospheric conditions, seasonal variations, and limited geographical coverage hinders the effective application of Artificial Intelligence (AI) in EO. Traditional data augmentation techniques, [...] Read more.
High-quality Earth Observation (EO) imagery is essential for accurate analysis and informed decision making across sectors. However, data scarcity caused by atmospheric conditions, seasonal variations, and limited geographical coverage hinders the effective application of Artificial Intelligence (AI) in EO. Traditional data augmentation techniques, which rely on basic parameterized image transformations, often fail to introduce sufficient diversity across key semantic axes. These axes include natural changes such as snow and floods, human impacts like urbanization and roads, and disasters such as wildfires and storms, which limits the accuracy of AI models in EO applications. To address this, we propose a four-stage data augmentation approach that integrates diffusion models to enhance semantic diversity. Our method employs meta-prompts for instruction generation, vision–language models for rich captioning, EO-specific diffusion model fine-tuning, and iterative data augmentation. Extensive experiments using four augmentation techniques demonstrate that our approach consistently outperforms established methods, generating semantically diverse EO images and improving AI model performance. Full article
26 pages, 23622 KiB  
Article
CPS-RAUnet++: A Jet Axis Detection Method Based on Cross-Pseudo Supervision and Extended Unet++ Model
by Jianhong Gan, Kun Cai, Changyuan Fan, Xun Deng, Wendong Hu, Zhibin Li, Peiyang Wei, Tao Liao and Fan Zhang
Electronics 2025, 14(3), 441; https://doi.org/10.3390/electronics14030441 - 22 Jan 2025
Abstract
Atmospheric jets are pivotal components of atmospheric circulation, profoundly influencing surface weather patterns and the development of extreme weather events such as storms and cold waves. Accurate detection of the jet stream axis is indispensable for enhancing weather forecasting, monitoring climate change, and [...] Read more.
Atmospheric jets are pivotal components of atmospheric circulation, profoundly influencing surface weather patterns and the development of extreme weather events such as storms and cold waves. Accurate detection of the jet stream axis is indispensable for enhancing weather forecasting, monitoring climate change, and mitigating disasters. However, traditional methods for delineating atmospheric jets are plagued by inefficiency, substantial errors, and pronounced subjectivity, limiting their applicability in complex atmospheric scenarios. Current research on semi-supervised methods for extracting atmospheric jets remains scarce, with most approaches dependent on traditional techniques that struggle with stability and generalization. To address these limitations, this study proposes a semi-supervised jet stream axis extraction method leveraging an enhanced U-Net++ model. The approach incorporates improved residual blocks and enhanced attention gate mechanisms, seamlessly integrating these enhanced attention gates into the dense skip connections of U-Net++. Furthermore, it optimizes the consistency learning phase within semi-supervised frameworks, effectively addressing data scarcity challenges while significantly enhancing the precision of jet stream axis detection. Experimental results reveal the following: (1) With only 30% of labeled data, the proposed method achieves a precision exceeding 80% on the test set, surpassing state-of-the-art (SOTA) baselines. Compared to fully supervised U-Net and U-Net++ methods, the precision improves by 17.02% and 9.91%. (2) With labeled data proportions of 10%, 20%, and 30%, the proposed method outperforms the MT semi-supervised method, achieving precision gains of 9.44%, 15.58%, and 19.50%, while surpassing the DCT semi-supervised method with improvements of 10.24%, 16.64%, and 14.15%, respectively. Ablation studies further validate the effectiveness of the proposed method in accurately identifying the jet stream axis. The proposed method exhibits remarkable consistency, stability, and generalization capabilities, producing jet stream axis extractions closely aligned with wind field data. Full article
(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)
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18 pages, 6356 KiB  
Article
Modelling Backward Trajectories of Air Masses for Identifying Sources of Particulate Matter Originating from Coal Combustion in a Combined Heat and Power Plant
by Maciej Ciepiela, Wiktoria Sobczyk and Eugeniusz Jacek Sobczyk
Energies 2025, 18(3), 493; https://doi.org/10.3390/en18030493 - 22 Jan 2025
Abstract
The paper analyzes the processes of emission and dispersion of particulate contaminants from a large point source emitter: a hard coal-fired power plant. Reference is made to the European Green Deal and its main objective of reducing anthropogenic particulate and greenhouse gas emissions. [...] Read more.
The paper analyzes the processes of emission and dispersion of particulate contaminants from a large point source emitter: a hard coal-fired power plant. Reference is made to the European Green Deal and its main objective of reducing anthropogenic particulate and greenhouse gas emissions. CHPP, Krakow Combined Heat and Power Plant, Poland, as described in the article, has a strong impact on the mechanisms that shape the microclimatic factors of the Krakow agglomeration. This combined heat and power plant provides heat and electricity for the city, while simultaneously emitting significant amounts of suspended particulate matter into the atmosphere. Due to the adverse impact of non-conventional energy sources on the natural environment and the increasing effects of climate warming, radical changes need to be implemented. The HYSPLIT (Hybrid Single-Particles Lagrangian Integrated Trajectories) model was used to track the movement of contaminated air masses. A 5-day episode of increased hourly concentrations of PM2.5 particulate matter contamination was selected to analyze the backward trajectories of air mass displacement. From 15 August 2022 to 19 August 2022, high 24-h particulate matter concentrations were recorded, measuring around 20 µg/m3. The HYSPLIT model, a unique tool in the precise identification of point sources of pollution and their impact on the air quality of the region, was used to analyze the influx of polluted air masses. A 5-day episode of increased hourly concentrations of PM2.5 pollutants was selected for the study, with values of approximately 20 µg/m3. It was found that low-pressure systems over the North Atlantic brought wet and variable weather conditions, while high-pressure systems in southern and eastern Europe, including Poland, provided stable and dry weather conditions. The simulation results were verified by analyzing synoptic maps of the study area. The image of the displacement of contaminated air masses obtained from the HYSPLIT model was found to be consistent with the synoptic maps, confirming the accuracy of the applied model. This means that the HYSPLIT model can be used to create maps of contaminant dispersion directions. Consequently, it was confirmed that modeling using the HYSPLIT model is an effective method for predicting the displacement directions of particulate contamination originating from coal combustion in a combined heat and power plant. Identifying circulation patterns and front zones during episodes of increased contaminant concentrations is strategic for effective weather monitoring, air quality management, and alerting the public to episodes of increased health risk in a large agglomeration. Full article
(This article belongs to the Collection Feature Papers in Energy, Environment and Well-Being)
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24 pages, 10154 KiB  
Article
Meteorological Changes Across Curiosity Rover’s Traverse Using REMS Measurements and Comparisons with Measurements and MRAMS Model Results
by María Ruíz, Eduardo Sebastián-Martínez, Jose Antonio Rodríguez-Manfredi, Jorge Pla-García, Manuel de la Torre-Juarez and Scot C. R. Rafkin
Remote Sens. 2025, 17(3), 368; https://doi.org/10.3390/rs17030368 - 22 Jan 2025
Abstract
The Curiosity rover, from NASA’s Mars Science Laboratory (MSL), has climbed nearly 740 m from its landing location at −4500.971 m in Gale Crater to a location reached on sol 3967 on the slopes of Mt. Sharp at −3765.27 m. We examine the [...] Read more.
The Curiosity rover, from NASA’s Mars Science Laboratory (MSL), has climbed nearly 740 m from its landing location at −4500.971 m in Gale Crater to a location reached on sol 3967 on the slopes of Mt. Sharp at −3765.27 m. We examine the atmospheric pressure, surface and atmospheric temperatures, relative humidity, and water vapor volume mixing ratios from measurements made by the Rover Environmental Monitoring Station (REMS), taken along the trajectory traveled over 3967 sols spanning from late MY31 to mid-MY37, on an interannual scale. The results help us understand the Martian meteorology inside Gale Crater. The atmospheric pressure and temperature changes caused by the elevation variation of the rover show the impact of the altitude change on the atmospheric dynamics. Regarding the rover’s locations for MY32 and MY36, a detailed comparative analysis of the full diurnal cycle is performed for the solstices and equinoxes. These scenarios are examined using the REMS and the Mars Regional Atmospheric Modeling System (MRAMS) data. We compare the REMS and MRAMS data to evaluate their concordance. We present, for the first time, a hypothesis for the existence of the cold pool phenomenon, which also occurs on Earth, based on REMS data. Full article
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22 pages, 5579 KiB  
Article
Oxygen Nonstoichiometry, Electrical Conductivity, Chemical Expansion and Electrode Properties of Perovskite-Type SrFe0.9V0.1O3−δ
by Aleksei I. Ivanov, Sergey S. Nikitin, Mariya S. Dyakina, Ekaterina V. Tsipis, Mikhail V. Patrakeev, Dmitrii A. Agarkov, Irina I. Zverkova, Andrey O. Zhigachev, Victor V. Kedrov and Vladislav V. Kharton
Materials 2025, 18(3), 493; https://doi.org/10.3390/ma18030493 - 22 Jan 2025
Abstract
X-ray diffraction analysis of the pseudo-binary SrFe1−xVxO3−δ system showed that the solid solution formation limit at atmospheric oxygen pressure corresponds to x ≈ 0.1. SrFe0.9V0.1O3−δ has a cubic perovskite-type structure with the [...] Read more.
X-ray diffraction analysis of the pseudo-binary SrFe1−xVxO3−δ system showed that the solid solution formation limit at atmospheric oxygen pressure corresponds to x ≈ 0.1. SrFe0.9V0.1O3−δ has a cubic perovskite-type structure with the Pmˉ3m space group. The oxygen nonstoichiometry variations in SrFe0.9V0.1O3−δ, measured by coulometric titration in the oxygen partial pressure range of 10−21 to 0.5 atm at 1023–1223 K, can be adequately described using an ideal solution approximation with V5+ as the main oxidation state of vanadium cations. This approach was additionally validated by statistical thermodynamic modeling. The incorporation of vanadium decreases both oxygen deficiency and the average iron oxidation state with respect to undoped SrFeO3−δ. As a result, the electrical conductivity, thermal expansion and chemical expansivity associated with the oxygen vacancy formation all become lower compared to strontium ferrite. At 923 K, the conductivity of SrFe0.9V0.1O3−δ is 14% lower than that of SrFeO3−δ but 21% higher compared to SrFe0.9Ta0.1O3−δ. The area-specific polarization resistance of the porous SrFe0.9V0.1O3−δ electrode deposited onto 10 mol.% scandia- and 1 mol.% yttria-co-stabilized zirconia solid electrolyte with a protective Ce0.9Gd0.1O2−δ interlayer, was 0.34 Ohm×cm2 under open-circuit conditions at 1173 K in air. Full article
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20 pages, 10179 KiB  
Article
Fusion of In-Situ and Modelled Marine Data for Enhanced Coastal Dynamics Prediction Along the Western Black Sea Coast
by Maria Emanuela Mihailov, Alecsandru Vladimir Chirosca and Gianina Chirosca
J. Mar. Sci. Eng. 2025, 13(2), 199; https://doi.org/10.3390/jmse13020199 - 22 Jan 2025
Abstract
This study explores the use of Temporal Fusion Transformers (TFTs), an AI/ML technique, to enhance the prediction of coastal dynamics along the Western Black Sea coast. We integrate in-situ observations from five meteo-oceanographic stations with modelled geospatial marine data from the Copernicus Marine [...] Read more.
This study explores the use of Temporal Fusion Transformers (TFTs), an AI/ML technique, to enhance the prediction of coastal dynamics along the Western Black Sea coast. We integrate in-situ observations from five meteo-oceanographic stations with modelled geospatial marine data from the Copernicus Marine Service. TFTs are employed to refine predictions of shallow water dynamics by considering atmospheric influences, with a particular focus on wave-wind correlations in coastal regions. Atmospheric pressure and temperature are treated as latitude-dependent constants, with specific investigations into extreme events like freezing and solar radiation-induced turbulence. Explainable AI (XAI) is exploited to ensure transparent model interpretations and identify key influential input variables. Data attribution strategies address missing data concerns, while ensemble modelling enhances overall prediction robustness. The models demonstrate a significant improvement in prediction accuracy compared to traditional methods. This research provides a deeper understanding of atmosphere-marine interactions and demonstrates the efficacy of Artificial intelligence (AI)/Machine Learning (ML) in bridging observational and modelled data gaps for informed coastal zone management decisions, essential for maritime safety and coastal management along the Western Black Sea coast. Full article
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15 pages, 2760 KiB  
Article
Thermal Decomposition of Calcium Carbonate at Multiple Heating Rates in Different Atmospheres Using the Techniques of TG, DTG, and DSC
by Dingxiang Zhuang, Zhengzheng Chen and Bin Sun
Crystals 2025, 15(2), 108; https://doi.org/10.3390/cryst15020108 - 22 Jan 2025
Viewed by 102
Abstract
To grasp the decomposition reaction rule of calcium carbonate in cement raw material, the thermogravimetric analyzer (TG), derivative thermogravimetric (DTG), and differential scanning calorimeter (DSC) were used for analysis. Calcium carbonate samples were heated linearly at multiple heating rates of 10, 20, 30, [...] Read more.
To grasp the decomposition reaction rule of calcium carbonate in cement raw material, the thermogravimetric analyzer (TG), derivative thermogravimetric (DTG), and differential scanning calorimeter (DSC) were used for analysis. Calcium carbonate samples were heated linearly at multiple heating rates of 10, 20, 30, and 40 °C/min in the atmospheres of N2 and 70% N2 + 30% O2, respectively. The decomposition kinetics was investigated using a double extrapolation method. Kinetic parameters of the thermal decomposition and the most probable mechanism function were determined in two different atmospheres. The results show that TG, DTG, and DSC curves moved to a higher temperature with the increase in heating rate, and the addition of O2 in the reaction atmosphere had almost no effect on the change in the decomposition curve. Additionally, the activation energy of the initial state in the formation of the new nucleus obtained using the double extrapolation method was 232.13 kJ/mol in the N2 atmosphere, and the most probabilistic mechanistic function was G(α) = 1 − (1 − α)1/2. The chemical reaction process was consistent with the contracted cylinder mechanism model of phase boundary reaction. Moreover, the activation energy of the initial state in the formation of the new nucleus was 233.79 kJ/mol in the 70% N2 + 20% O2 atmosphere, and the chemical reaction process was consistent with that of the N2 atmosphere. Therefore, these results could determine the decomposition temperature and decomposition rate of calcium carbonate. This was important for understanding the thermal stability and processing temperature range of polymer materials, especially the application and potential in production and scientific research. Full article
(This article belongs to the Section Inorganic Crystalline Materials)
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19 pages, 4625 KiB  
Article
Impacts of Physical Parameterization Schemes on Typhoon Doksuri (2023) Forecasting from the Perspective of Wind–Wave Coupling
by Lihua Li, Bo Peng, Weiwen Wang, Ming Chang and Xuemei Wang
J. Mar. Sci. Eng. 2025, 13(2), 195; https://doi.org/10.3390/jmse13020195 - 21 Jan 2025
Viewed by 290
Abstract
Tropical cyclones (TCs) form over warm ocean surfaces and are driven by complex air–sea interactions, posing significant challenges to their forecasting. Accurate parameterization of physical processes is crucial for enhancing the precision of TC predictions. In this study, we employed the Weather Research [...] Read more.
Tropical cyclones (TCs) form over warm ocean surfaces and are driven by complex air–sea interactions, posing significant challenges to their forecasting. Accurate parameterization of physical processes is crucial for enhancing the precision of TC predictions. In this study, we employed the Weather Research and Forecasting model coupled with the Simulating Waves Nearshore (WRF-SWAN) model to forecast Typhoon Doksuri (2023), which exhibited a secondary intensification process in the South China Sea (SCS). We also investigated its sensitivity to various atmospheric physical parameterization schemes (PPS). The findings indicate that improvements in microphysical and cumulus convection parameterizations have significantly enhanced the prediction accuracy of Typhoon Doksuri’s trajectory and intensity. The simulation of sea surface heat flux is primarily influenced by the microphysical scheme, while the cumulus convection scheme substantially affects the representation of the typhoon core’s size and shape. Variations in the wind field induce differences in wave height, potentially reaching up to 2–3 m at any given moment. This study provides valuable insights into the effective selection of physical parameterizations for improving typhoon forecasts. Full article
(This article belongs to the Section Ocean and Global Climate)
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24 pages, 689 KiB  
Article
Modeling the Inter-Arrival Time Between Severe Storms in the United States Using Finite Mixtures
by Ilana Vinnik and Tatjana Miljkovic
Viewed by 234
Abstract
When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate. However, these assumptions are often violated in real-insurance applications. When the rate at which [...] Read more.
When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate. However, these assumptions are often violated in real-insurance applications. When the rate at which events occur changes over time, the exponential distribution becomes unsuitable. In this paper, we study the distribution of inter-arrival times of severe storms, which exhibit substantial variability, violating the assumption of a constant average rate. A new approach is proposed for modeling severe storm recurrence patterns using a finite mixture of log-normal distributions. This approach effectively captures both frequent, closely spaced storm events and extended quiet periods, addressing the inherent variability in inter-event durations. Parameter estimation is performed using the Expectation–Maximization algorithm, with model selection validated via the Bayesian information criterion (BIC). To complement the parametric approach, Kaplan–Meier survival analysis was employed to provide non-parametric insights into storm-free intervals. Additionally, a simulation-based framework estimates storm recurrence probabilities and assesses financial risks through probable maximum loss (PML) calculations. The proposed methodology is applied to the Billion-Dollar Weather and Climate Disasters dataset, compiled by the U.S. National Oceanic and Atmospheric Administration (NOAA). The results demonstrate the model’s effectiveness in predicting severe storm recurrence intervals, offering valuable tools for managing risk in the property and casualty insurance industry. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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16 pages, 4518 KiB  
Article
Inversion of Aerosol Chemical Composition in the Beijing–Tianjin–Hebei Region Using a Machine Learning Algorithm
by Baojiang Li, Gang Cheng, Chunlin Shang, Ruirui Si, Zhenping Shao, Pu Zhang, Wenyu Zhang and Lingbin Kong
Atmosphere 2025, 16(2), 114; https://doi.org/10.3390/atmos16020114 - 21 Jan 2025
Viewed by 356
Abstract
Aerosols and their chemical composition exert an influence on the atmospheric environment, global climate, and human health. However, obtaining the chemical composition of aerosols with high spatial and temporal resolution remains a challenging issue. In this study, using the NR-PM1 collected in the [...] Read more.
Aerosols and their chemical composition exert an influence on the atmospheric environment, global climate, and human health. However, obtaining the chemical composition of aerosols with high spatial and temporal resolution remains a challenging issue. In this study, using the NR-PM1 collected in the Beijing area from 2012 to 2013, we found that the annual average concentration was 41.32 μg·m−3, with the largest percentage of organics accounting for 49.3% of NR-PM1, followed by nitrates, sulfates, and ammonium. We then established models of aerosol chemical composition based on a machine learning algorithm. By comparing the inversion accuracies of single models—namely MLR (Multivariable Linear Regression) model, SVR (Support Vector Regression) model, RF (Random Forest) model, KNN (K-Nearest Neighbor) model, and LightGBM (Light Gradient Boosting Machine)—with that of the combined model (CM) after selecting the optimal model, we found that although the accuracy of the KNN model was the highest among the other single models, the accuracy of the CM model was higher. By employing the CM model to the spatially and temporally matched AOD (aerosol optical depth) data and meteorological data of the Beijing–Tianjin–Hebei region, the spatial distribution of the annual average concentrations of the four components was obtained. The areas with higher concentrations are mainly situated in the southwest of Beijing, and the annual average concentrations of the four components in Beijing’s southwest are 28 μg·m−3, 7 μg·m−3, 8 μg·m−3, and 15 μg·m−3 for organics, sulfates, ammonium, and nitrates, respectively. This study not only provides new methodological ideas for obtaining aerosol chemical composition concentrations based on satellite remote sensing data but also provides a data foundation and theoretical support for the formulation of atmospheric pollution prevention and control policies. Full article
(This article belongs to the Special Issue Atmospheric Pollution in Highly Polluted Areas)
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18 pages, 360 KiB  
Review
Reducing Emissions Using Artificial Intelligence in the Energy Sector: A Scoping Review
by Janne Alatalo, Eppu Heilimo, Mika Rantonen, Olli Väänänen and Tuomo Sipola
Appl. Sci. 2025, 15(2), 999; https://doi.org/10.3390/app15020999 - 20 Jan 2025
Viewed by 356
Abstract
Global warming is a significant threat to the future of humankind. It is caused by greenhouse gases that accumulate in the atmosphere. CO2 emissions are one of the main drivers of global warming, and the energy sector is one of the main [...] Read more.
Global warming is a significant threat to the future of humankind. It is caused by greenhouse gases that accumulate in the atmosphere. CO2 emissions are one of the main drivers of global warming, and the energy sector is one of the main contributors to CO2 emissions. Recent technological advances in artificial intelligence (AI) have accelerated the adoption of AI in numerous applications to solve many problems. This study carries out a scoping review to understand the use of AI solutions to reduce CO2 emissions in the energy sector. This paper follows the PRISMA-ScR guidelines in reporting the findings. The academic search engine Google Scholar was utilized to find papers that met the review criteria. Our research question was “How is artificial intelligence used in the energy sector to reduce CO2 emissions?” Search phrases and inclusion criteria were decided based on this research question. In total, 186 papers from the search results were screened, and 16 papers fitting our criteria were summarized in this study. The findings indicate that AI is already used in the energy sector to reduce CO2 emissions. Three main areas of application for AI techniques were identified. Firstly, AI models are employed to directly optimize energy generation processes by modeling these processes and determining their optimal parameters. Secondly, AI techniques are utilized for forecasting, which aids in optimizing decision-making, energy transmission, and production planning. Lastly, AI is applied to enhance energy efficiency, particularly in optimizing building performance. The use of AI shows significant promise of reducing CO2 emissions in the energy sector. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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24 pages, 13332 KiB  
Article
Corrosion Mechanism of Press-Hardened Steel with Aluminum-Silicon Coating in Controlled Atmospheric Conditions
by Nikola Macháčková, Darja Rudomilova, Tomáš Prošek, Thierry Sturel and Maxime Brossard
Metals 2025, 15(1), 97; https://doi.org/10.3390/met15010097 - 20 Jan 2025
Viewed by 260
Abstract
The effect of various atmospheric parameters on the corrosion mechanism of press-hardened steel (PHS) coated with Al-Si (AS) was studied. Quantitative models of the composition of soluble and stable corrosion products were developed. A high chloride concentration led to a localized corrosion due [...] Read more.
The effect of various atmospheric parameters on the corrosion mechanism of press-hardened steel (PHS) coated with Al-Si (AS) was studied. Quantitative models of the composition of soluble and stable corrosion products were developed. A high chloride concentration led to a localized corrosion due to the presence of cracks in the coating. Increased corrosion resistance of silicon-rich Al8Fe2Si and AlFe at the expense of the Al5Fe2 phase with low silicon content was shown. Under low-chloride-deposition conditions, the coating exhibited good corrosion resistance and provided sufficient protection to the underlying steel. The formation of more local anodes and cathodes under conditions of lower relative humidity led to a reduction in the depth of corrosion pits in the steel substrate. Constant high relative humidity and sulphate deposits on the surface were critical for the acceleration of steel corrosion in coating cracks. Full article
(This article belongs to the Special Issue Metallurgy, Surface Engineering and Corrosion of Metals and Alloys)
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17 pages, 5474 KiB  
Article
Research on Typhoon Prediction by Integrating Numerical Simulation and Deep Learning Methods
by Tianyi Lv, Huaming Yu, Liangshi Lin, Yijun Tao and Xin Qi
Atmosphere 2025, 16(1), 111; https://doi.org/10.3390/atmos16010111 - 20 Jan 2025
Viewed by 305
Abstract
Typhoons rank among the most destructive natural disasters, significantly affecting human activities and daily life. Atmospheric numerical model wind fields, which are widely utilized, often underestimate typhoon intensity. This study proposes a model for predicting typhoon maximum wind speeds using the Long Short-Term [...] Read more.
Typhoons rank among the most destructive natural disasters, significantly affecting human activities and daily life. Atmospheric numerical model wind fields, which are widely utilized, often underestimate typhoon intensity. This study proposes a model for predicting typhoon maximum wind speeds using the Long Short-Term Memory (LSTM) neural network. The model predicts maximum wind speeds based on existing atmospheric numerical forecasts, constructs a parametric wind field model from these predictions, and integrates it with the numerical model wind fields to generate an LSTM-optimized wind field. The results show that the LSTM model accurately predicts typhoon maximum wind speeds, with the predicted extreme values closely aligning with actual observations and capturing the trends of maximum wind speed variations. Compared with the ERA5 typhoon maximum wind speed, the C of the LSTM model for predicting the typhoon maximum wind speed is improved from 0.801 to 0.859, and the RMSE and MAE are reduced by 58% and 64%, respectively. In the simulation of Typhoon DELTA (2020), the LSTM-optimized wind field exhibits substantially higher wind speed intensities in the central region of the typhoon compared to the ERA5 wind field, providing a more accurate representation of the intensity and structure of the typhoon. Full article
(This article belongs to the Section Meteorology)
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20 pages, 8151 KiB  
Article
Numerical Simulation of Tornado-Like Vortices Induced by Small-Scale Cyclostrophic Wind Perturbations
by Yuhan Liu, Yongqiang Jiang, Chaohui Chen, Yun Zhang, Hongrang He, Xiong Chen and Ruilin Zhong
Atmosphere 2025, 16(1), 108; https://doi.org/10.3390/atmos16010108 - 19 Jan 2025
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Abstract
This study introduces a tornado perturbation model utilizing the cyclostrophic wind model, implemented through a shallow-water equation framework. Four numerical experiments were conducted: a single cyclonic wind perturbation (EXP1), a single low-geopotential height perturbation (EXP2), a cyclonic wind perturbation with a 0 Coriolis [...] Read more.
This study introduces a tornado perturbation model utilizing the cyclostrophic wind model, implemented through a shallow-water equation framework. Four numerical experiments were conducted: a single cyclonic wind perturbation (EXP1), a single low-geopotential height perturbation (EXP2), a cyclonic wind perturbation with a 0 Coriolis parameter (EXP3), and a single anticyclonic wind perturbation (EXP4). The outputs showed that in a static atmosphere setting, a small-scale cyclonic wind perturbation generated a tornado-like pressure structure. The centrifugal force in the central area exceeded the pressure gradient force, causing air particles to flow outward, leading to a pressure drop and strong pressure gradient. The effect of the Coriolis force is negligible for meso-γ-scale and smaller systems, while for meso-β-scale and larger systems, it begins to have a significant impact. The results indicate that a robust cyclonic and an anticyclonic wind field can potentially generate a pair of cyclonic and anticyclonic tornadoes when the horizontal vortex tubes in an atmosphere with strong vertical wind shear tilt, forming a pair of positive and negative vorticities. These tornadoes are similar but have different rotation directions. Full article
(This article belongs to the Section Meteorology)
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