Atmosphere

16 pages, 2934 KiB

Open AccessArticle

Assessment of the Area of Heavy Metals and Radionuclides Deposition on the Environment of the Household Waste Landfill on the 9th km of Vilyuisky Tract in Yakutsk City

by Sargylana Mamaeva, Marina Frontasyeva, Kristina Petrova, Vassiliy Kolodeznikov, Galina Ignatyeva, Eugenii Zakharov and Vladlen Kononov

Atmosphere 2025, 16(7), 816; https://doi.org/10.3390/atmos16070816 - 3 Jul 2025

Abstract

For the first time, the deposition area of heavy metals and other trace elements (Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, P, Pb, S, Sr, Sb, V, Zn, and Hg) on the territory surrounding a landfill of domestic (municipal) waste at [...] Read more.

For the first time, the deposition area of heavy metals and other trace elements (Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, P, Pb, S, Sr, Sb, V, Zn, and Hg) on the territory surrounding a landfill of domestic (municipal) waste at the 9th km of the Vilyuisky tract of Yakutsk within a radius of 51 km was assessed using the method of moss biomonitors and ICP-OES as an analytical technique. Mosses were analyzed for radionuclide content (⁴⁰K, ¹³⁷Cs, ²¹² Pb, ²¹⁴Pb, ²¹²Bi, ²¹⁴Bi, ²⁰⁸Tl, ⁷Be, and ²²⁸Ac) in a number of selected samples by semiconductor gamma spectrometry. The results of the examination of moss samples by ICP-OES indicate the presence of large amounts of toxic Ba and metal debris (Al, Co, Cr, Fe, S, and Pb) at the landfill. In addition, it is shown that the investigated samples contain elements such as Cd, Co, Cr, Cu, Cu, Mn, Ni, Pb, Sr, V, Zn, and Hg. The method of gamma spectrometry revealed that the studied samples contain such radioactive elements as ¹³⁷Cs, daughter products of ²³⁸U and ²³²Th. Detection of the same heavy metals and radionuclides in the atmospheric air of the city and in the vegetation near the landfill may indicate that one of the sources of environmental pollution may be products of incineration of the landfill contents at the 9th km of the Vilyuisky tract. Full article

(This article belongs to the Section Air Quality)

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22 pages, 5767 KiB

Open AccessArticle

Influence of Humidity on the Electric Field, Filtration Efficiency, and Flow Velocity in Electret Filter Media: Direct Numerical Simulation

by Daniel Stoll and Sergiy Antonyuk

Atmosphere 2025, 16(7), 815; https://doi.org/10.3390/atmos16070815 - 3 Jul 2025

Abstract

Electret filter media are electrostatically charged during the manufacturing process to activate effective electrical separation mechanisms. In order to investigate the influence of humidity on these mechanisms, the electric field, and filtration efficiency, a Direct Numerical Simulation (DNS) study of the aerosol deposition [...] Read more.

Electret filter media are electrostatically charged during the manufacturing process to activate effective electrical separation mechanisms. In order to investigate the influence of humidity on these mechanisms, the electric field, and filtration efficiency, a Direct Numerical Simulation (DNS) study of the aerosol deposition within wetted fibrous nonwoven filter media used in masks was carried out. Initial experimental investigations determined key properties of the filter material, including porosity, fiber diameter, and surface charge density. Using Micro-Computed Tomography (µCT), preferred locations for droplet deposition within the filter were identified. Additional experiments quantified the amount of water absorbed by the filter medium and assessed its impact on the existing electric field. Numerical simulations examined various models with differing porosity and fiber diameter, incorporating different levels of water content to analyze the changes in the electric field, flow velocity, and resulting filtration efficiency. The results provide valuable insights into the significant effects of fiber change on filtration performance, demonstrating the electret filter’s ability to partially compensate for the negative impacts of water. Full article

(This article belongs to the Special Issue Electrostatics of Atmospheric Aerosols (2nd Edition))

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26 pages, 5006 KiB

Open AccessArticle

Kilometer-Scale Regional Modeling of Precipitation Projections for Bulgaria Using HPC Discoverer

by Rilka Valcheva and Ivan Popov

Atmosphere 2025, 16(7), 814; https://doi.org/10.3390/atmos16070814 - 3 Jul 2025

Abstract

The main goal of this study is to present future changes in various precipitation indices at a kilometer-scale resolution for Bulgaria on an annual and seasonal basis. Numerical simulations were conducted using the Non-Hydrostatic Regional Climate Model version 4 (RegCM4-NH) following the Coordinated [...] Read more.

The main goal of this study is to present future changes in various precipitation indices at a kilometer-scale resolution for Bulgaria on an annual and seasonal basis. Numerical simulations were conducted using the Non-Hydrostatic Regional Climate Model version 4 (RegCM4-NH) following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol for three 10-year periods (1995–2004, 2041–2050, and 2090–2099), with horizontal grid resolutions of 15 km and 3 km, on the petascale supercomputer HPC Discoverer at Sofia Tech Park. Data from the Hadley Centre Global Environment Model version 2 (HadGEM2-ES), based on the Representative Concentration Pathway 8.5 (RCP8.5) scenario, were used as boundary conditions for the regional climate model (RCM) simulations, which were subsequently downscaled to the kilometer-scale (3 km) simulations using a one-way nesting approach. High-resolution model data were compared with high-resolution observational datasets as well as lower-resolution (15 km) data. Future changes in precipitation indices were analyzed on both annual and seasonal scales, including mean daily and hourly precipitation, the frequency and intensity of wet days (>1 mm/day) and wet hours (>0.1 mm/hour), extreme daily precipitation (99th percentile, p99), and extreme hourly precipitation (99.9th percentile, p99.9) for both future periods. Additionally, changes in near-surface (2 m) temperature and surface snow amount were also presented. There is no substantial difference in projected temperature change between the resolutions. A positive trend in annual mean precipitation is expected in the near future. Extreme precipitation (p99 and p99.9) is projected to increase in spring and winter, accompanied by a rise in daily and hourly precipitation intensity across both future periods. An increase in surface snow amount is observed in the central Danubian Plain, Thracian Lowland, and parts of the Rila and Pirin mountains for the near-future period. However, surface snow amount is expected to decrease by the end of the century. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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21 pages, 11460 KiB

Open AccessArticle

Changes in the Intra-Annual Precipitation Regime in Poland from 1966 to 2024

by Joanna Wibig and Joanna Jędruszkiewicz

Atmosphere 2025, 16(7), 813; https://doi.org/10.3390/atmos16070813 - 3 Jul 2025

Abstract

Many studies relate to long-term changes in annual precipitation in Poland, yet most of them were statistically insignificant. The primary objective of this research was to investigate the precipitation regime during the year in the context of climate change, which is more crucial [...] Read more.

Many studies relate to long-term changes in annual precipitation in Poland, yet most of them were statistically insignificant. The primary objective of this research was to investigate the precipitation regime during the year in the context of climate change, which is more crucial than annual averages from the perspectives of agriculture and plant growth, as well as for the industrial sector and human access to clean water. For this reason, we used daily precipitation data from the Institute of Meteorology and Water Management—National Research Institute from 1966 to 2024. Each month of the study was examined for changes in monthly totals, the number of dry and wet days, precipitation intensities, and extremes. In the cold season, a considerable shift in precipitation patterns was found between November and December, which became drier, and January and February, which became wetter with more intense and extreme precipitation. Pronounced changes were also noticed in April and June, when not only the monthly totals but also the number of wet days and precipitation intensity decreased. These two months, together with winter, are essential for plant growth. On the contrary, July became slightly wetter. Interesting changes were also observed in September, including an increase in dry days and more intense rainfall. With the increase in temperature and changes in the advection of air masses, September became more similar to summer than to autumn months. The key factors driving shifts in precipitation regimes during the year were a warmer atmosphere and changes in circulation patterns. Full article

(This article belongs to the Section Climatology)

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20 pages, 3602 KiB

Open AccessArticle

Dust Aerosol Classification in Northwest China Using CALIPSO Data and an Enhanced 1D U-Net Network

by Xin Gong, Delong Xiu, Xiaoling Sun, Ruizhao Zhang, Jiandong Mao, Hu Zhao and Zhimin Rao

Atmosphere 2025, 16(7), 812; https://doi.org/10.3390/atmos16070812 - 2 Jul 2025

Abstract

Dust aerosols significantly affect climate and air quality in Northwest China (30–50° N, 70–110° E), where frequent dust storms complicate accurate aerosol classification when using CALIPSO satellite data. This study introduces an Enhanced 1D U-Net model to enhance dust aerosol retrieval, incorporating Inception [...] Read more.

Dust aerosols significantly affect climate and air quality in Northwest China (30–50° N, 70–110° E), where frequent dust storms complicate accurate aerosol classification when using CALIPSO satellite data. This study introduces an Enhanced 1D U-Net model to enhance dust aerosol retrieval, incorporating Inception modules for multi-scale feature extraction, Transformer blocks for global contextual modeling, CBAM attention mechanisms for improved feature selection, and residual connections for training stability. Using CALIPSO Level 1B and Level 2 Vertical Feature Mask (VFM) data from 2015 to 2020, the model processed backscatter coefficients, polarization characteristics, and color ratios at 532 nm and 1064 nm to classify aerosol types. The model achieved a precision of 94.11%, recall of 99.88%, and F1 score of 96.91% for dust aerosols, outperforming baseline models. Dust aerosols were predominantly detected between 0.44 and 4 km, consistent with observations from CALIPSO. These results highlight the model’s potential to improve climate modeling and air quality monitoring, providing a scalable framework for future atmospheric research. Full article

(This article belongs to the Section Aerosols)

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16 pages, 26026 KiB

Open AccessArticle

Computational Fluid Dynamics-Based Modeling of Methane Flows Around Oil and Gas Equipment

by Abhinav Anand, Stuart Riddick, Kira B. Shonkwiler, Aashish Upreti, Michael Moy, Elijah Kiplimo, Mercy Mbua and Daniel J. Zimmerle

Atmosphere 2025, 16(7), 811; https://doi.org/10.3390/atmos16070811 - 2 Jul 2025

Abstract

Recent studies estimate that emissions from oil and gas production facilities contribute between 20 and 50% of the total methane (

C H_{4}

) emitted in the US; therefore, quantifying and reducing these emissions are crucial for achieving climate goals. Methane quantification [...] Read more.

Recent studies estimate that emissions from oil and gas production facilities contribute between 20 and 50% of the total methane (

C H_{4}

) emitted in the US; therefore, quantifying and reducing these emissions are crucial for achieving climate goals. Methane quantification depends on both measuring methane concentrations and converting them to emissions through a modeling framework. Currently, simple atmospheric dispersion models are primarily used to quantify emissions and concentrations, but these estimates are highly uncertain when quantifying emissions from complex aerodynamic sources, such as oil and gas facilities. This investigation used a CFD modeling approach, which can account for aerodynamic complexity but has hitherto not been used to model methane concentrations downwind of a methane release of a known rate, and compared it against in situ measurements. High-time-resolution (1 Hz) methane concentration and meteorological data were measured during experiments conducted at the METEC on 21 March and 11 July 2024. The METEC site configuration, measured wind data, and controlled emission rates were used as input for the CONVERGE CFD model to model downwind

C H_{4}

concentration. The modeling was carried out between 20 and 70 m, from two different points of release in two separate controlled-release experiments, one from a separator and another from a wellhead. In these experiments, we found that the CFD model could predict the

C H_{4}

concentrations downwind of the release to a good degree. The model was evaluated on multiple metrics to assess its performance in estimating methane concentrations at typical fence line distances (∼30 m). These results help us to understand external flows and the ability of CFD models to predict downwind concentrations in aerodynamically complex environments. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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17 pages, 5264 KiB

Open AccessCommunication

Some Interesting Observations of Cross-Mountain East-to-Southeasterly Flow at Hong Kong International Airport and Their Numerical Simulations

by Pak-Wai Chan, Ping Cheung, Kai-Kwong Lai, Jie-Lan Xie and Yan-Yu Leung

Atmosphere 2025, 16(7), 810; https://doi.org/10.3390/atmos16070810 - 1 Jul 2025

Abstract

With the availability of more ground-based remote-sensing meteorological equipment at Hong Kong International Airport, many more interesting features of terrain-disrupted airflow have been observed, such as the applications of short-range Doppler LIDAR. This paper documents a number of new features observed at the [...] Read more.

With the availability of more ground-based remote-sensing meteorological equipment at Hong Kong International Airport, many more interesting features of terrain-disrupted airflow have been observed, such as the applications of short-range Doppler LIDAR. This paper documents a number of new features observed at the airport area, such as the hydraulic jump-like feature, vortex, and extensive mountain wake/reverse flow. The technical feasibility of using a numerical resolution weather prediction model to simulate such features is also explored. It is found that the presently available input data and numerical model may not be able to capture the fine features of the atmospheric boundary layer, and thus they are not very successful in reproducing many small-scale terrain-disrupted airflow features downstream of an isolated hill. On the other hand, more larger-scale terrain-disrupted flow features may be better captured, but there are still limitations with the available turbulence parameterization schemes. This paper aims at documenting the newly observed flow features at the Hong Kong International Airport, enhancing the understanding of low-level windshear, and evaluating the outputs of numerical resolution simulations for reproducing such observed features and its technical feasibility on short-term forecasting. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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17 pages, 2698 KiB

Open AccessArticle

An Integrated Hydrological–Hydrodynamic Model Based on GPU Acceleration for Catchment-Scale Rainfall Flood Simulation

by Ruixiao Ma, Hao Han and Zhaoan Zhang

Atmosphere 2025, 16(7), 809; https://doi.org/10.3390/atmos16070809 - 1 Jul 2025

Abstract

Extreme rainstorms are difficult to predict and often result in catchment-scale rainfall flooding, leading to substantial economic losses globally. Enhancing the numerical computational efficiency of flood models is essential for improving flood forecasting capabilities. This study presents an integrated hydrological–hydrodynamic model accelerated using [...] Read more.

Extreme rainstorms are difficult to predict and often result in catchment-scale rainfall flooding, leading to substantial economic losses globally. Enhancing the numerical computational efficiency of flood models is essential for improving flood forecasting capabilities. This study presents an integrated hydrological–hydrodynamic model accelerated using GPU (Graphics Processing Unit) technology to perform high-efficiency and high-precision rainfall flood simulations at the catchment scale. The model couples hydrological and hydrodynamic processes by solving the fully two-dimensional shallow water equations (2D SWEs), incorporating GPU-accelerated parallel computing. The model achieves accelerated rainstorm flooding simulations through its implementation on GPUs with parallel computing technology, significantly enhancing its computational efficiency and maintaining its numerical stability. Validations are conducted using an idealized V-shaped catchment and an experimental benchmark, followed by application to a small catchment on the Chinese Loess Plateau. The computational experiments reveal a strong positive correlation between grid cell numbers and GPU acceleration efficiency. The results also demonstrate that the proposed model offers better computational accuracy and acceleration performance than the single-GPU model. This GPU-accelerated hydrological–hydrodynamic modeling framework enables rapid, high-fidelity rainfall flood simulations and provides critical support for timely and effective flood emergency decision making. Full article

(This article belongs to the Special Issue Advances in Rainfall-Induced Hazard Research)

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15 pages, 3985 KiB

Open AccessArticle

Interaction Between Radon, Air Ions, and Ultrafine Particles Under Contrasting Atmospheric Conditions in Belgrade, Serbia

by Fathya Shabek, Predrag Kolarž, Igor Čeliković, Milica Ćurčić and Aco Janičijević

Atmosphere 2025, 16(7), 808; https://doi.org/10.3390/atmos16070808 - 1 Jul 2025

Abstract

Radon’s radioactive decay is the main natural source of small air ions near the ground. Its exhalation from soil is affected by meteorological factors, while aerosol pollution reduces air ion concentrations through ion-particle attachment. This study aimed to analyze correlations between radon, ions, [...] Read more.

Radon’s radioactive decay is the main natural source of small air ions near the ground. Its exhalation from soil is affected by meteorological factors, while aerosol pollution reduces air ion concentrations through ion-particle attachment. This study aimed to analyze correlations between radon, ions, and air pollution under varying conditions and to assess potential health impacts. Measurements were taken at two sites: in early autumn at a suburban part of Belgrade with relatively clean air, and in late autumn in central Belgrade under polluted conditions, with low temperatures and high humidity. Parameters measured included radon, small air ions, particle size distribution, PM mass concentration, temperature, humidity, and pressure. Results showed lower radon concentrations in late autumn due to high soil moisture and absence of nocturnal inversions. Radon and air ion concentrations exhibited a strong positive correlation for both polarities under suburban conditions, whereas measurements in the urban setting revealed a weak negative correlation, despite radon concentrations in soil gas being approximately equal at both sites. Small ion levels were also reduced, mainly due to suppressed radon exhalation and increased aerosol concentrations, especially ultrafine particles. A strong negative correlation (r < −0.5) was found between small air ion concentrations and particle number concentrations in the 20–300 nm range, while larger particles (300–1000 nm and >1 µm) showed weak or no correlation due to their lower and more stable concentrations. In contrast, early autumn measurements showed a diurnal cycle of radon, characterized by nighttime maxima and daytime minima, unlike the consistently low values observed in late autumn. Full article

(This article belongs to the Special Issue Outdoor and Indoor Air Ions, Radon, and Ozone)

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17 pages, 2124 KiB

Open AccessArticle

Soiling Forecasting for Parabolic Trough Collector Mirrors: Model Validation and Sensitivity Analysis

by Areti Pappa, Johannes Christoph Sattler, Siddharth Dutta, Panayiotis Ktistis, Soteris A. Kalogirou, Orestis Spiros Alexopoulos and Ioannis Kioutsioukis

Atmosphere 2025, 16(7), 807; https://doi.org/10.3390/atmos16070807 - 1 Jul 2025

Abstract

Parabolic trough collector (PTC) systems, often deployed in arid regions, are vulnerable to dust accumulation (soiling), which reduces mirror reflectivity and energy output. This study presents a physically based soiling forecast algorithm (SFA) designed to estimate soiling levels. The model was calibrated and [...] Read more.

Parabolic trough collector (PTC) systems, often deployed in arid regions, are vulnerable to dust accumulation (soiling), which reduces mirror reflectivity and energy output. This study presents a physically based soiling forecast algorithm (SFA) designed to estimate soiling levels. The model was calibrated and validated using three meteorological data sources—numerical forecasts (YR), METAR observations, and on-site measurements—from a PTC facility in Limassol, Cyprus. Field campaigns covered dry, rainy, and red-rain conditions. The model demonstrated robust performance, particularly under dry summer conditions, with normalized root mean square errors (NRMSE) below 1%. Sedimentation emerged as the dominant soiling mechanism, while the contributions of impaction and Brownian motion varied according to site-specific environmental conditions. Under dry deposition conditions, the reflectivity change rate during spring and autumn was approximately twice that of summer, indicating a need for more frequent cleaning during transitional seasons. A red-rain event resulted in a pronounced drop in reflectivity, showcasing the model’s ability to capture abrupt soiling dynamics associated with extreme weather episodes. The proposed SFA offers a practical, adaptable tool for reducing soiling-related losses and supporting seasonally adjusted maintenance strategies for solar thermal systems. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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18 pages, 49730 KiB

Open AccessArticle

High-Resolution Daily XCH₄ Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data

by Mohamad M. Awad and Saeid Homayouni

Atmosphere 2025, 16(7), 806; https://doi.org/10.3390/atmos16070806 - 1 Jul 2025

Abstract

Atmospheric methane (CH₄) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH₄ is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This growing concentration contributes to environmental [...] Read more.

Atmospheric methane (CH₄) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH₄ is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This growing concentration contributes to environmental degradation, including ocean acidification, accelerated climate change, and a rise in natural disasters. The column-averaged dry-air mole fraction of methane (XCH₄) is a crucial indicator for assessing atmospheric CH₄ levels. In this study, the Sentinel-5P TROPOMI instrument was employed to monitor, map, and estimate CH₄ concentrations on both regional and global scales. However, TROPOMI data exhibits limitations such as spatial gaps and relatively coarse resolution, particularly at regional scales or over small areas. To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. The CNN-AE model demonstrated the highest accuracy in regional-scale analysis, achieving a Mean Absolute Error (MAE) of 28.48 ppb and a Root Mean Square Error (RMSE) of 30.07 ppb. This was followed by the Random Forest (RF) regressor (MAE: 29.07 ppb; RMSE: 36.89 ppb), GridData Nearest Neighbor Interpolator (NNI) (MAE: 30.06 ppb; RMSE: 32.14 ppb), and the Radial Basis Function (RBF) Interpolator (MAE: 80.23 ppb; RMSE: 90.54 ppb). On a global scale, the CNN-AE again outperformed other methods, yielding the lowest MAE and RMSE (19.78 and 24.7 ppb, respectively), followed by RF (21.46 and 27.23 ppb), GridData NNI (25.3 and 32.62 ppb), and RBF (43.08 and 54.93 ppb). Full article

(This article belongs to the Special Issue Advanced Remote Sensing Techniques in Application of Air Quality and Climate Study)

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14 pages, 4788 KiB

Open AccessArticle

Heat Impact Assessment and Heat Prevention Suggestions for Thermal Comfort at Large-Area and Long-Duration Outdoor Sport Events in Taiwan

by Si-Yu Yu, Tzu-Ping Lin and Andreas Matzarakis

Atmosphere 2025, 16(7), 805; https://doi.org/10.3390/atmos16070805 - 1 Jul 2025

Abstract

This study aims to (1) analyze thermal comfort at outdoor sport events held outside of fixed venues or locations; (2) establish a method for evaluating environmental thermal comfort for large-scale, long-term outdoor activities; and (3) provide suggestions for the arrangement of shifts in [...] Read more.

This study aims to (1) analyze thermal comfort at outdoor sport events held outside of fixed venues or locations; (2) establish a method for evaluating environmental thermal comfort for large-scale, long-term outdoor activities; and (3) provide suggestions for the arrangement of shifts in routes and participants for heat warning and mitigation. Taiwan ReAnalysis Downscaling (TReAD) data, Sky View Factors (SVFs), GSV2SVF tool, and RayMan Pro were applied to analyze and evaluate thermal comfort at the 2021 Torch Relay Round the Island, Taiwan. In this study, modified Physiologically Equivalent Temperature (mPET), Wet Bulb Globe Temperature (WBGT), and Universal Thermal Climate Index (UTCI) were estimated and selected as thermal indicators for the purpose of obtaining a more comprehensive perspective. We also define and present thermal performance with a simple traffic light symbol (green: comfortable/yellow: warm/red: hot) and try to go beyond the concept of heat and visualize it in an easy-to-understand way. Full article

(This article belongs to the Special Issue Climate Change and Health: Enhancing Coherence of the Knowledge Base, Policies and Strategies into a Healthy, Sustainable and Resilient Future (2nd Edition))

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16 pages, 10829 KiB

Open AccessArticle

Combination of High-Rate Ionosonde Measurements with COSMIC-2 Radio Occultation Observations for Reference Ionosphere Applications

by Iurii Cherniak, David Altadill, Irina Zakharenkova, Víctor de Paula, Víctor Navas-Portella, Douglas Hunt, Antoni Segarra and Ivan Galkin

Atmosphere 2025, 16(7), 804; https://doi.org/10.3390/atmos16070804 - 1 Jul 2025

Abstract

Knowledge of ionospheric plasma altitudinal distribution is crucial for the effective operation of radio wave propagation, communication, and navigation systems. High-frequency sounding radars—ionosondes—provide unbiased benchmark measurements of ionospheric plasma density due to a direct relationship between the frequency of sound waves and ionospheric [...] Read more.

Knowledge of ionospheric plasma altitudinal distribution is crucial for the effective operation of radio wave propagation, communication, and navigation systems. High-frequency sounding radars—ionosondes—provide unbiased benchmark measurements of ionospheric plasma density due to a direct relationship between the frequency of sound waves and ionospheric electron density. But ground-based ionosonde observations are limited by the F2 layer peak height and cannot probe the topside ionosphere. GNSS Radio Occultation (RO) onboard Low-Earth-Orbiting satellites can provide measurements of plasma distribution from the lower ionosphere up to satellite orbit altitudes (~500–600 km). The main goal of this study is to investigate opportunities to obtain full observation-based ionospheric electron density profiles (EDPs) by combining advantages of ground-based ionosondes and GNSS RO. We utilized the high-rate Ebre and El Arenosillo ionosonde observations and COSMIC-2 RO EDPs colocated over the ionosonde’s area of operation. Using two types of ionospheric remote sensing techniques, we demonstrated how to create the combined ionospheric EDPs based solely on real high-quality observations from both the bottomside and topside parts of the ionosphere. Such combined EDPs can serve as an analogy for incoherent scatter radar-derived “full profiles”, providing a reference for the altitudinal distribution of ionospheric plasma density. Using the combined reference EDPs, we analyzed the performance of the International Reference Ionosphere model to evaluate model–data discrepancies. Hence, these new profiles can play a significant role in validating empirical models of the ionosphere towards their further improvements. Full article

(This article belongs to the Special Issue Variability and Predictability of Space Weather and the Ionosphere: Recent Advances (2nd Edition))

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20 pages, 6259 KiB

Open AccessArticle

Remediation Effects of Potamogeton crispus on Nitrogen-Loaded Water Bodies and Its Greenhouse Gas Emission Mechanisms

by Xiaoyi Li, Xiaoxiu Lun, Jianzhi Niu, Lumin Zhang, Bo Wu and Xinyue Wang

Atmosphere 2025, 16(7), 803; https://doi.org/10.3390/atmos16070803 - 1 Jul 2025

Abstract

Potamogeton crispus (P. crispus), with strong nitrogen uptake capacity, plays an important ecological role during winter and early spring when most aquatic plants are inactive. Its presence can also influence microbial denitrification in sediments by regulating oxygen levels and organic carbon [...] Read more.

Potamogeton crispus (P. crispus), with strong nitrogen uptake capacity, plays an important ecological role during winter and early spring when most aquatic plants are inactive. Its presence can also influence microbial denitrification in sediments by regulating oxygen levels and organic carbon availability. In this study, an indoor hydroponic simulation system was used to systematically evaluate the effects of P. crispus under different nitrogen-loading conditions on nitrogen removal from water, changes in sediment carbon and nitrogen fractions, microbial community structure, and greenhouse gas fluxes. The results showed that P. crispus effectively removed TN, NH₄⁺-N, NO₃⁻-N, and NO₂⁻-N, maintaining strong denitrification capacity even under high-nitrogen loading. Under all nitrogen conditions, TN removal exceeded 80%, while NH₄⁺-N and NO₃⁻-N removal efficiencies surpassed 90%, with effective suppression of NO₂⁻-N accumulation. Rhizosphere-mediated regulation by P. crispus enhanced the transformation and stabilization of DOC and NO₃⁻-N in sediments, while also mitigating nitrogen-induced disturbances to carbon–nitrogen balance. The plant also exhibited strong CO₂ uptake capacity, low CH₄ emissions with a slight increase under higher nitrogen loading, and N₂O fluxes that were significantly affected by nitrogen levels—showing negative values under low nitrogen and sharp increases under high-nitrogen conditions. Correlation analyses indicated that CO₂ and N₂O emissions were mainly regulated by microbial taxa involved in carbon and nitrogen transformation, while CH₄ emissions were primarily driven by methanogenic archaea and showed weaker correlations with environmental factors. These findings highlight the importance of water restoration during low-temperature seasons and provide a theoretical basis for integrated wetland management strategies aimed at coordinated pollution reduction and carbon mitigation. Full article

(This article belongs to the Special Issue Interactions of Urban Greenings and Air Pollution)

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24 pages, 5555 KiB

Open AccessArticle

A Signal Processing-Guided Deep Learning Framework for Wind Shear Prediction on Airport Runways

by Afaq Khattak, Pak-wai Chan, Feng Chen, Hashem Alyami and Masoud Alajmi

Atmosphere 2025, 16(7), 802; https://doi.org/10.3390/atmos16070802 - 1 Jul 2025

Abstract

Wind shear at the Hong Kong International Airport (HKIA) poses a significant safety risk due to terrain-induced airflow disruptions near the runways. Accurate assessment is essential for safeguarding aircraft during take-off and landing, as abrupt changes in wind speed or direction can compromise [...] Read more.

Wind shear at the Hong Kong International Airport (HKIA) poses a significant safety risk due to terrain-induced airflow disruptions near the runways. Accurate assessment is essential for safeguarding aircraft during take-off and landing, as abrupt changes in wind speed or direction can compromise flight stability. This study introduces a hybrid framework for short-term wind shear prediction based on data collected from Doppler LiDAR systems positioned near the central and south runways of the HKIA. These systems provide high-resolution measurements of wind shear magnitude along critical flight paths. To predict wind shear more effectively, the proposed framework integrates a signal processing technique with a deep learning strategy. It begins with optimized variational mode decomposition (OVMD), which decomposes the wind shear time series into intrinsic mode functions (IMFs), each capturing distinct temporal characteristics. These IMFs are then modeled using bidirectional gated recurrent units (BiGRU), with hyperparameters optimized via the Tree-structured Parzen Estimator (TPE). To further enhance prediction accuracy, residual errors are corrected using Extreme Gradient Boosting (XGBoost), which captures discrepancies between the reconstructed signal and actual observations. The resulting OVMD–BiGRU–XGBoost framework exhibits strong predictive performance on testing data, achieving R² values of 0.729 and 0.926, RMSE values of 0.931 and 0.709, and MAE values of 0.624 and 0.521 for the central and south runways, respectively. Compared with GRUs, LSTM, BiLSTM, and ResNet-based baselines, the proposed framework achieves higher accuracy and a more effective representation of multi-scale temporal dynamics. It contributes to improving short-term wind shear prediction and supports operational planning and safety management in airport environments. Full article

(This article belongs to the Special Issue Aviation Meteorology: Developments and Latest Achievements)

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13 pages, 7849 KiB

Open AccessArticle

Effect of Vegetation on the Intensity of Low-Level Coastal Jets: The Case of Tenerife

by Emilio Megías and Manuel García-Román

Atmosphere 2025, 16(7), 801; https://doi.org/10.3390/atmos16070801 - 1 Jul 2025

Abstract

Low-level coastal jets are a common climatic phenomenon along much of the coast of Tenerife. However, there are large differences in intensity and frequency between the northern and southeastern facades of the island, even though both are exposed to the trade winds. This [...] Read more.

Low-level coastal jets are a common climatic phenomenon along much of the coast of Tenerife. However, there are large differences in intensity and frequency between the northern and southeastern facades of the island, even though both are exposed to the trade winds. This study analyzes how a local factor, such as the existing vegetation cover, influences this phenomenon. Comparing the wind records and the Normalized Difference Vegetation Index of both exposed facades, it is observed that the lower the values of this index of vegetation cover, the greater the increase in the mean diurnal wind speed. It can be stated, from nine years of study, that there are clear indications of a relationship between the vegetation index used and the low-level coastal jet intensity. Full article

(This article belongs to the Section Climatology)

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15 pages, 8481 KiB

Open AccessArticle

Mitigating Model Biases in Arid Region Precipitation over Northwest China Through Dust–Cloud Microphysical Interactions

by Anqi Wang, Xiaoning Xie, Zhibao Dong, Xiaoyun Li, Ke Shang, Xiaokang Liu and Zhijing Xue

Atmosphere 2025, 16(7), 800; https://doi.org/10.3390/atmos16070800 - 1 Jul 2025

Abstract

Accurate projection of future climate trends in arid regions critically depends on reliable precipitation simulations. However, most Coupled Model Intercomparison Project Phase 6 (CMIP6) models exhibit systematic overestimations of precipitation in Northwest China, a bias that undermines the credibility of climate projections for [...] Read more.

Accurate projection of future climate trends in arid regions critically depends on reliable precipitation simulations. However, most Coupled Model Intercomparison Project Phase 6 (CMIP6) models exhibit systematic overestimations of precipitation in Northwest China, a bias that undermines the credibility of climate projections for this vulnerable region. This persistent bias likely stems from the omission of key physical processes in traditional models. In this study, we incorporate a dust–ice-cloud interaction scheme into the Community Atmosphere Model version 5 (CAM5) model to investigate its role in regulating precipitation over dust-rich arid regions. This physical mechanism, which is rarely included in conventional models, is particularly relevant for Northwest China where dust aerosols are abundant. Our results show that accounting for dust-induced ice nucleation leads to a significant reduction in total precipitation, especially in the convective component, thereby alleviating the longstanding wet bias in the region. These findings underscore the critical importance of dust–ice-cloud interactions in simulating precipitation in arid environments. To improve the accuracy of future climate projections in Northwest China, climate models must incorporate realistic representations of dust-related microphysical processes. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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34 pages, 8670 KiB

Open AccessArticle

Assessing Climate Impact on Heritage Buildings in Trentino—South Tyrol with High-Resolution Projections

by Camille Luna Stella Blavier, Elena Maines, Piero Campalani, Harold Enrique Huerto-Cardenas, Claudio Del Pero and Fabrizio Leonforte

Atmosphere 2025, 16(7), 799; https://doi.org/10.3390/atmos16070799 - 1 Jul 2025

Abstract

Climate variations impact the preservation of heritage buildings, necessitating a strategic understanding of potential effects to effectively guide preservation efforts. This study analyzes temperature- and precipitation-dependent climate-heritage indices in Trentino–South Tyrol using EURO-CORDEX regional climate models for the period 1971–2100 under RCP 4.5 [...] Read more.

Climate variations impact the preservation of heritage buildings, necessitating a strategic understanding of potential effects to effectively guide preservation efforts. This study analyzes temperature- and precipitation-dependent climate-heritage indices in Trentino–South Tyrol using EURO-CORDEX regional climate models for the period 1971–2100 under RCP 4.5 and RCP 8.5 scenarios. The selected indices were calculated with climdex-kit and relied on bias-adjusted temperature and precipitation data with a 1 km spatial resolution. The obtained results indicate a geographically punctuated increase in biomass accumulation on horizontal surfaces, a slight decreasing trend in freeze–thaw events, an increase in growing degree days indicating a small, heightened insect activity, and a rise in heavy precipitation days. The Scheffer Index shows a significantly increased potential for wood degradation, particularly under the RCP 8.5 scenario, while the Wet-Frost Index remains consistently low. Finally, according to each identified hazard, adaptive solutions are suggested. These findings provide critical insights into future climate impacts on heritage buildings in the region, aiding stakeholders in planning targeted interventions. The study emphasizes the crucial role of integrating detailed climate data into heritage preservation strategies, advocating for the inclusion of future risk analysis in the “knowledge path” in order to enhance the resilience of buildings. Full article

(This article belongs to the Special Issue Climate Change Challenges for Heritage Architecture)

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15 pages, 737 KiB

Open AccessArticle

An Octant-Based Multi-Objective Optimization Approach for Lightning Warning in High-Risk Industrial Areas

by Marcos Antonio Alves, Bruno Alberto Soares Oliveira, Douglas Batista da Silva Ferreira, Ana Paula Paes dos Santos, Osmar Pinto, Jr., Fernando Pimentel Silvestrow, Daniel Calvo and Eugenio Lopes Daher

Atmosphere 2025, 16(7), 798; https://doi.org/10.3390/atmos16070798 - 30 Jun 2025

Abstract

Lightning strikes are a major hazard in tropical regions, especially in northern Brazil, where open-area industries such as mining are highly exposed. This study proposes an octant-based multi-objective optimization approach for spatial lightning alert systems, focusing on minimizing both false alarm rate (FAR) [...] Read more.

Lightning strikes are a major hazard in tropical regions, especially in northern Brazil, where open-area industries such as mining are highly exposed. This study proposes an octant-based multi-objective optimization approach for spatial lightning alert systems, focusing on minimizing both false alarm rate (FAR) and failure-to-warn (FTW). The method uses NSGA-III to optimize a configuration vector consisting of directional radii and alert thresholds, based solely on historical lightning location data. Experiments were conducted using four years of cloud-to-ground lightning data from a mining area in Pará, Brazil. Fifteen independent runs were executed, each with 96 individuals and up to 150 generations. The results showed a clear trade-off between FAR and FTW, with optimal solutions achieving up to 16% reduction in FAR and 50% reduction in FTW when compared to a quadrant-based baseline. The use of the hypervolume metric confirmed consistent convergence across runs. Sensitivity analysis revealed spatial patterns in optimal configurations, supporting the use of directional tuning. The proposed approach provides a flexible and interpretable model for risk-based alert strategies, compliant with safety regulations such as NBR 5419/2015 and NR-22. It offers a viable solution for automated alert generation in high-risk environments, especially where detailed meteorological data is unavailable. Full article

(This article belongs to the Special Issue Recent Insights and Advances in Lightning and Other Thunderstorm-Related Transient Events)

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