Atmosphere

15 pages, 15327 KiB

Open AccessEditor’s ChoiceTechnical Note

Establishment and Operation of an Early Warning Service for Agrometeorological Disasters Customized for Farmers and Extension Workers at Metropolitan-Scale

by Yong-Soon Shin, Hee-Ae Lee, Sang-Hyun Park, Yong-Kyu Han, Kyo-Moon Shim and Se-Jin Han

Atmosphere 2025, 16(3), 291; https://doi.org/10.3390/atmos16030291 - 28 Feb 2025

Viewed by 322

Abstract

A farm-specific early warning system has been developed to mitigate agricultural damage caused by climate change. This system utilizes weather data at the farm level to predict crop growth, forecast weather disaster risks, and provide risk alerts to farmers and local governments. For [...] Read more.

A farm-specific early warning system has been developed to mitigate agricultural damage caused by climate change. This system utilizes weather data at the farm level to predict crop growth, forecast weather disaster risks, and provide risk alerts to farmers and local governments. For effective implementation, local governments must lead operating early warning services that reflect regional agricultural characteristics and farmers’ needs, while the central government provides foundational data. The system connects data from each region to the cloud, enabling the establishment of a nationwide integrated service operation framework that includes the central government, metropolitan cities, municipalities, and farmers. Full article

(This article belongs to the Special Issue ICT-Based High-Resolution Weather and Climate Research for an Early-Warning System for Agricultural Disasters)

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15 pages, 5421 KiB

Open AccessEditor’s ChoiceArticle

Indoor Radon Monitoring in Residential Areas in the Vicinity of Coal Mining Operations in the Mpumalanga Province, South Africa

by Paballo M. Moshupya, Seeke C. Mohuba, Tamiru A. Abiye, Ian Korir and Sifiso Nhleko

Atmosphere 2025, 16(3), 290; https://doi.org/10.3390/atmos16030290 - 28 Feb 2025

Viewed by 397

Abstract

Coal mining and combustion have the potential to increase exposure to radon, a form of radioactive gas recognized as one of the major contributors to lung cancer incidents. In South Africa, coal is used as the primary energy source for producing electricity and [...] Read more.

Coal mining and combustion have the potential to increase exposure to radon, a form of radioactive gas recognized as one of the major contributors to lung cancer incidents. In South Africa, coal is used as the primary energy source for producing electricity and for heating, predominantly in informal settlements and township communities. Most of the existing coal-fired power plants are found in the Mpumalanga province. This paper presents long-term radon (²²²Rn) measurements in dwellings surrounding coal mining centres in the Mpumalanga province and evaluates their contributions to indoor radon exposures. The indoor radon measurements were conducted using solid-state nuclear track detectors and were performed during warm and cold seasons. It was found that the overall indoor radon activity concentrations ranged between 21 Bq/m³ and 145 Bq/m³, with a mean value of 40 Bq/m³. In all the measured dwellings, the levels were below the WHO reference level of 100 Bq/m³ and 300 Bq/m³ reference level recommended by the IAEA and ICRP, with the exception of one dwelling that was poorly ventilated. The results reveal that individuals residing in the surveyed homes are not exposed to radon levels higher than the WHO, ICRP, and IAEA reference levels. The main source influencing indoor radon activity concentrations was found to be primarily the concentration of uranium found in the geological formations in the area, with ventilation being an additional contributing factor of radon levels in dwellings. To maintain good air quality in homes, it is recommended that household occupants should keep their dwellings well ventilated to keep indoor radon levels as low as possible. Full article

(This article belongs to the Special Issue Atmospheric Radon Concentration Monitoring and Measurements (2nd Edition))

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17 pages, 3397 KiB

Open AccessEditor’s ChoiceArticle

A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism

by Bixiong Luo, Peng Zuo, Lijun Zhu and Wei Hua

Atmosphere 2025, 16(3), 266; https://doi.org/10.3390/atmos16030266 - 25 Feb 2025

Viewed by 231

Abstract

Wind power, as a pivotal renewable energy source, is anticipated to play a critical role in ensuring the reliability, security, and stability of the global energy supply system. Accurate prediction of wind power density (WPD) holds significant practical importance for wind farms, grid [...] Read more.

Wind power, as a pivotal renewable energy source, is anticipated to play a critical role in ensuring the reliability, security, and stability of the global energy supply system. Accurate prediction of wind power density (WPD) holds significant practical importance for wind farms, grid operators, and the entire wind power industry, as it facilitates informed decision-making, optimized resource allocation, and enhanced system performance. This paper proposes a novel WPD forecasting model based on RF-DBO-VMD feature selection and BiGRU optimized by an attention mechanism. The proposed model consists of three main stages. First, critical physical features relevant to WPD are identified using random forest (RF), effectively eliminating data redundancy and enhancing prediction efficiency. Second, the variational mode decomposition (VMD) parameters are optimized via the dung beetle optimizer (DBO) algorithm to extract independent intrinsic mode functions (IMFs), which, alongside the original data, serve as temporal feature inputs. Finally, an attention mechanism is employed to identify important information from the outputs of the BiGRU model, and the Grid Search (GS) method is used to optimize the BiGRU-Attention model, yielding optimal predictions. The experimental results demonstrate the model’s high predictive accuracy, evidenced by an R² value of 0.9754. Notably, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) are substantially minimized compared to alternative models. These results highlight the model’s potential to provide effective forecasting insights for future applications, such as energy trading and power system management, which will be further explored in real-world scenarios. Full article

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

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17 pages, 6128 KiB

Open AccessEditor’s ChoiceArticle

Spatiotemporal Characteristics of Mesoscale Convective Systems in the Yangtze River Delta Urban Agglomeration and Their Response to Urbanization

by Xinguan Du, Tianwen Sun and Kyaw Than Oo

Atmosphere 2025, 16(3), 245; https://doi.org/10.3390/atmos16030245 - 21 Feb 2025

Viewed by 372

Abstract

Mesoscale convective systems (MCSs) are major contributors to extreme precipitation in urban agglomerations, exhibiting complex characteristics influenced by large-scale climate variability and local urban processes. This study utilizes a high-resolution MCS database spanning from 2001 to 2020 to investigate the spatiotemporal variations of [...] Read more.

Mesoscale convective systems (MCSs) are major contributors to extreme precipitation in urban agglomerations, exhibiting complex characteristics influenced by large-scale climate variability and local urban processes. This study utilizes a high-resolution MCS database spanning from 2001 to 2020 to investigate the spatiotemporal variations of MCSs in the Yangtze River Delta (YRD) urban agglomeration and assess their response to urbanization. Our analysis reveals significant spatial and temporal differences in MCS activities during the warm season (April to September), including initiation, movement, and lifespan, with notable trends observed over the study period. MCSs are found to contribute substantially to hourly extreme precipitation, accounting for approximately 60%, which exceeds their contribution to total precipitation. Furthermore, the role of MCSs in extreme precipitation has also increased, driven by the intensification of MCS-induced extreme rainfall. Additionally, MCS characteristics exhibit significant regional differences. Urban areas experience more pronounced changes in MCS activity and precipitation compared to the surrounding rural regions. Specifically, urbanization contributes approximately 16% to MCS-related precipitation and 19% to MCS initiation, highlighting its substantial role in enhancing these processes. Moreover, mountainous areas and water bodies surrounding cities show stronger trends in certain MCS characteristics than urban and rural plains. This may be attributed to climatological conditions that favor MCS activity in these regions, as well as the complex interactions between urbanization, topography, and land–sea contrasts. These complicated dynamics warrant further investigation to better understand their implications. Full article

(This article belongs to the Section Meteorology)

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15 pages, 7070 KiB

Open AccessEditor’s ChoiceArticle

Assessment of Fire Dynamics in the Amazon Basin Through Satellite Data

by Humberto Alves Barbosa, Catarina Oliveira Buriti and Tumuluru Venkata Lakshmi Kumar

Atmosphere 2025, 16(2), 228; https://doi.org/10.3390/atmos16020228 - 18 Feb 2025

Cited by 1 | Viewed by 833

Abstract

The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse [...] Read more.

The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, land surface temperature (LST), carbon monoxide (CO), and nitrogen dioxide (NO₂) through geospatial techniques. The results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO₂ emission factors between early and middle dry season fires (July–September). Among these seven variables analyzed, we found a positive relationship between the total biomass burned area and fire-derived GHG emission factors (r² = 0.30) due to the complex dynamics of plant moisture and associated CO and NO₂ emissions generated by fire. Nevertheless, other land surface drivers showed the weakest relationships (r²~0.1) with fire-derived GHG emissions due to other factors that drive their regional distribution. Our analysis suggests the importance of continued research on the response of fire season to other land surface characteristics that represent the processes driving fire over the study region such as fuel load, composition, and structure, as well as prevailing weather conditions. These determinants drive fire-related GHG emissions and fire-related carbon cycling relationships and can, therefore, appropriately inform policy fire-abatement guidelines. Full article

(This article belongs to the Section Air Quality)

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36 pages, 9488 KiB

Open AccessEditor’s ChoiceArticle

New Challenges for Tropical Cyclone Track and Intensity Forecasting in Unfavorable External Environment in Western North Pacific. Part I. Formations South of 20° N

by Russell L. Elsberry, Hsiao-Chung Tsai, Wen-Hsin Huang and Timothy P. Marchok

Atmosphere 2025, 16(2), 226; https://doi.org/10.3390/atmos16020226 - 18 Feb 2025

Viewed by 513

Abstract

A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As [...] Read more.

A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As Typhoon Gaemi (05 W) was moving poleward into an unfavorable environment north of 20° N, a sharp westward turn to cross Taiwan was a challenge to forecast. The pre-Yagi (12 W) westward turn across Luzon Island, re-formation, and then extremely rapid intensification prior to striking Hainan Island were challenges to forecast. The slow intensification of Bebinca (14 W) after moving poleward across 20° N into an unfavorable environment was better forecast by the ECEPS than by the Joint Typhoon Warning Center (JTWC), which consistently over-predicted the intensification. An early westward turn south of 20° N by Kong-Rey (23 W) leading to a long westward path along 17° N and then a poleward turn to strike Taiwan were all track forecasting challenges. Four-dimensional COAMPS-TC Dynamic Initialization analyses utilizing high-density Himawari-9 atmospheric motion vectors are proposed to better define the TC intensities, vortex structure, and unfavorable environment for diagnostic studies and as initial conditions for regional model predictions. In Part 2 study of selected 2024 season TCs that started north of 20° N, more challenging track forecasts and slow intensification rates over an unfavorable TC environment will be documented. Full article

(This article belongs to the Special Issue Typhoon/Hurricane Dynamics and Prediction (2nd Edition))

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30 pages, 32662 KiB

Open AccessEditor’s ChoiceArticle

Air Pollution Trends and Predictive Modeling for Three Cities with Different Characteristics Using Sentinel-5 Satellite Data and Deep Learning

by Salma Alkayal, Hind Almisbahi, Souad Baowidan and Entisar Alkayal

Atmosphere 2025, 16(2), 211; https://doi.org/10.3390/atmos16020211 - 13 Feb 2025

Viewed by 705

Abstract

Accurate air quality forecasting is important in pollution prevention and risk reduction. Effective short-term and long-term forecasting models are needed. This study investigated the need for a new model to forecast air pollution concentrations in three cities with distinct characteristics: a city with [...] Read more.

Accurate air quality forecasting is important in pollution prevention and risk reduction. Effective short-term and long-term forecasting models are needed. This study investigated the need for a new model to forecast air pollution concentrations in three cities with distinct characteristics: a city with high industrial activity, a city with a high population density and urbanization, and an agricultural city. The air pollution data were collected using the Sentinel-5P satellite and Google Earth Engine to apply descriptive analysis and comparison of two years, 2022 and 2023. The studied cities were Al Riyadh (high population), Al Jubail (industrial), and Najran (agricultural) in Saudi Arabia. The selected pollutants were SO₂, NO₂, CO, O₃, and HCHO. In addition, this study investigated the variations observed in all the pollutants during the months of the year, the correlations between the contaminants, and the correlation between NO₂ and the meteorological data. Based on our findings, Al Jubail had the highest level of all the pollutants during the two years, except for NO₂, for which the highest level was observed in Al Riyadh, which has witnessed notable urbanization and development recently. Moreover, this study developed a forecasting model for the concentration of NO₂ based on weather data and the previous values of NO₂ using Long Short-Term Memory (LSTM) and Time2Vec. The modeling proved that any model that is trained on data collected from a specific city is not suitable for predicting the pollution level in another city and the level of another pollutant, as the three cities have different correlations with the pollutants and the weather data. The proposed model demonstrated a superior accuracy in predicting NO₂ concentrations compared to traditional LSTM models, effectively capturing temporal patterns and achieving minimal prediction errors, which contributes to ongoing efforts to understand the dynamics of air pollution based on cities’ characteristics and the period of the year. Full article

(This article belongs to the Special Issue Dispersion and Mitigation of Atmospheric Pollutants)

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22 pages, 11030 KiB

Open AccessEditor’s ChoiceArticle

Adjusting Soil Temperatures with a Physics-Informed Deep Learning Model for a High-Resolution Numerical Weather Prediction System

by Qiufan Wang, Yubao Liu, Yueqin Shi and Shaofeng Hua

Atmosphere 2025, 16(2), 207; https://doi.org/10.3390/atmos16020207 - 12 Feb 2025

Viewed by 583

Abstract

Soil temperature (ST) plays an important role in the surface heat energy balance, and an accurate description of soil temperatures is critical for numerical weather prediction; however, it is difficult to consistently measure soil temperatures. We developed a U-Net-based deep learning model to [...] Read more.

Soil temperature (ST) plays an important role in the surface heat energy balance, and an accurate description of soil temperatures is critical for numerical weather prediction; however, it is difficult to consistently measure soil temperatures. We developed a U-Net-based deep learning model to derive soil temperatures (designated as ST-U-Net) primarily based on 2 m air temperature (T2) forecasts. The model, the domain of which covers the Mt. Lushan region, was trained and tested by utilizing the high-resolution forecast archive of an operational weather research and forecasting four-dimensional data assimilation (WRF-FDDA) system. The results showed that ST-U-Net can accurately estimate soil temperatures based on T2 inputs, achieving a mean absolute error (MAE) of less than 0.8 K on the testing set of 5055 samples. The performance of ST-U-Net varied diurnally, with smaller errors at night and slightly larger errors in the daytime. Incorporating additional inputs such as land uses, terrain height, radiation flux, surface heat flux, and coded time further reduced the MAE for ST by 26.7%. By developing a boundary-layer physics-guided training strategy, the error was further reduced by 8.8%. Full article

(This article belongs to the Section Meteorology)

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19 pages, 4707 KiB

Open AccessEditor’s ChoiceArticle

The Impact of Lightning Rods on the Differential Reflectivity of X-Band Radar

by Hui Wang, Haifeng Yu, Hao Wen and Zhifeng Shu

Atmosphere 2025, 16(2), 204; https://doi.org/10.3390/atmos16020204 - 11 Feb 2025

Viewed by 408

Abstract

Lightning rod configuration is crucial in radar stations. With widespread application of dual-polarisation technology, lightning rods have a significant impact on radar differential reflectivity, particularly for X-band radars with shorter wavelengths. Quantitative analyses and methods for reducing the impact of lightning rods on [...] Read more.

Lightning rod configuration is crucial in radar stations. With widespread application of dual-polarisation technology, lightning rods have a significant impact on radar differential reflectivity, particularly for X-band radars with shorter wavelengths. Quantitative analyses and methods for reducing the impact of lightning rods on radar data quality have become particularly important. In this study, lightning rods of two different sizes were configured on Beijing’s Fangshan X-band radar to perform antenna far-field tests and precipitation process comparative observation tests, and to conduct a quantitative impact assessment of the antenna electrical performance parameters and radar differential reflectivity. First, far-field tests were conducted on the impact of small- and original-diameter lightning rods on the Fangshan X-band radar. The results showed that the horizontal polarisation beam width was reduced by 0.081 and 0.08°, while the vertical polarisation beam width was reduced by 0.02 and 0.11°, respectively. Second, light rain or snowfall with a signal-to-noise ratio greater than 15 dB, and a correlation coefficient greater than 0.985, were selected for comparative observation. When other environmental influences could not be isolated, the original lightning rod showed a maximum ZDR value of 1.32 dB and a maximum azimuth span of 35°. The maximum ZDR value of the small-diameter lightning rod was 0.18 dB and the maximum azimuth span was 20°; however, its deviation from the theoretical maximum value is only 0.05 dB. Therefore, once the system configuration is determined, the design of an appropriate lightning rod scheme can effectively improve radar data quality. Full article

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

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29 pages, 3568 KiB

Open AccessEditor’s ChoiceArticle

Threshold Effects of the Interaction Between Urban Development and Atmospheric Pollution

by Xiaoling Yuan, Hanyu Geng and Zhaopeng Li

Atmosphere 2025, 16(2), 201; https://doi.org/10.3390/atmos16020201 - 10 Feb 2025

Viewed by 595

Abstract

Over the past 70 years since the founding of the People’s Republic of China, urban development has achieved remarkable progress but also encountered severe atmospheric pollution, which has become a significant obstacle to high-quality urban development. Understanding the interaction mechanisms between urban development [...] Read more.

Over the past 70 years since the founding of the People’s Republic of China, urban development has achieved remarkable progress but also encountered severe atmospheric pollution, which has become a significant obstacle to high-quality urban development. Understanding the interaction mechanisms between urban development and atmospheric pollution is thus crucial for promoting sustainable urban construction. This paper explores these mechanisms by analyzing the interplay between urban population, industry, space, social development, and pollution through a theoretical framework. Using a simultaneous equations model and the Three-Stage Least Squares (3SLS) method, it examines these relationships and further investigates threshold effects. The findings reveal a nonlinear relationship with significant thresholds: (1) High levels of PM_2.5, population size, and industrial agglomeration can shift from exacerbating pollution to enabling governance, though excessive thresholds reverse this trend. (2) PM_2.5 mediates the impact of spatial sprawl, environmental regulation, and population dynamics, oscillating between governance and pollution effects. (3) Industrial agglomeration and spatial sprawl show variable impacts on pollution mitigation depending on pollution intensity and urban thresholds. These findings provide critical insights into the intricate dynamics between urban development and atmospheric pollution, emphasizing the importance of adopting differentiated strategies based on specific urban thresholds. Ultimately, this research contributes to the broader goal of harmonizing economic growth, social development, and environmental sustainability in urban areas, serving as a valuable reference for cities worldwide facing similar challenges. Full article

(This article belongs to the Special Issue Air Pollution in China (3rd Edition))

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20 pages, 5287 KiB

Open AccessEditor’s ChoiceArticle

Research on NOx Emissions Testing and Optimization Strategies for Diesel Engines Under Low-Load Cycles

by Fengbin Wang, Jianfu Zhao, Tengteng Li, Peng Guan, Shuangxi Liu, Haiqiao Wei and Lei Zhou

Atmosphere 2025, 16(2), 190; https://doi.org/10.3390/atmos16020190 - 7 Feb 2025

Viewed by 597

Abstract

Under low-load cycles (LLCs), the issue of high NOx emissions from diesel engines is attracting widespread attention. Through a combination of experimental and simulation approaches, the NOx emission behavior under LLC conditions was investigated. Furthermore, the optimization strategies for reducing NOx emissions was [...] Read more.

Under low-load cycles (LLCs), the issue of high NOx emissions from diesel engines is attracting widespread attention. Through a combination of experimental and simulation approaches, the NOx emission behavior under LLC conditions was investigated. Furthermore, the optimization strategies for reducing NOx emissions was studied based on a dual selective catalytic reduction (SCR) after-treatment system. The results indicate that emissions at load rates below 30% during LLCs account for more than 67.5% of the total cycle emissions, particularly under idling and start-stop conditions. Moreover, it was found that NOx emissions decrease significantly by using a pre-positioned dual SCR after-treatment system. And, the closer the SCR is to the engine, the higher the NOx conversion efficiency becomes. As the SCR’s position is adjusted, the rate of NOx removal stabilizes, achieving a maximum reduction in NOx concentration of up to 60.1%. Full article

(This article belongs to the Section Air Pollution Control)

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18 pages, 5015 KiB

Open AccessEditor’s ChoiceArticle

Dissipation Scaling with a Variable C_ϵ Coefficient in the Stable Atmospheric Boundary Layer

by Marta Wacławczyk, Jackson Nzotungishaka, Paweł Jędrejko, Joydeep Sarkar and Szymon P. Malinowski

Atmosphere 2025, 16(2), 188; https://doi.org/10.3390/atmos16020188 - 7 Feb 2025

Viewed by 470

Abstract

This work concerns the Taylor formula for the turbulence kinetic energy dissipation rate in the stable atmospheric boundary layer. The formula relates the turbulence kinetic energy dissipation rate to statistics at large scales, namely, the turbulence kinetic energy and the integral length scale. [...] Read more.

This work concerns the Taylor formula for the turbulence kinetic energy dissipation rate in the stable atmospheric boundary layer. The formula relates the turbulence kinetic energy dissipation rate to statistics at large scales, namely, the turbulence kinetic energy and the integral length scale. In parameterization schemes for atmospheric turbulence, it is usually assumed that the dissipation coefficient

C_{ϵ}

in the Taylor formula is constant. However, a series of recent theoretical works and laboratory experiments showed that

C_{ϵ}

depends on the local Reynolds number. We calculate turbulence statistics, including the dissipation rate, the standard deviation of fluctuating velocities and integral length scales, using observational data from the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition. We show that the dissipation coefficient

C_{ϵ}

varies considerably and is a function of the Reynolds number, however, the functional form of this dependency in the stably stratified atmospheric boundary layer is different than in previous studies. Full article

(This article belongs to the Special Issue Atmospheric Boundary Layer Processes, Characteristics and Parameterization (3rd Edition))

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16 pages, 2878 KiB

Open AccessEditor’s ChoiceArticle

Exploring the Holiday Effect on Elevated Traffic-Related Air Pollution with Hyperlocal Measurements in Chengdu, China

by Sheng Xiang, Jiaojiao Yu, Yu Ting Yu, Pengbo Zhao, Tie Zheng, Jingsong Yue, Yuanyuan Yang and Haobing Liu

Atmosphere 2025, 16(2), 171; https://doi.org/10.3390/atmos16020171 - 2 Feb 2025

Viewed by 929

Abstract

Traffic-related air pollutants (TRAPs) pose significant health risks in megacities, yet fixed monitoring sites often fail to capture their complexity. To characterize the TRAP concentrations which fixed sites cannot address, we employed a mobile platform to effectively capture real-time hyperlocal-scale TRAP variations in [...] Read more.

Traffic-related air pollutants (TRAPs) pose significant health risks in megacities, yet fixed monitoring sites often fail to capture their complexity. To characterize the TRAP concentrations which fixed sites cannot address, we employed a mobile platform to effectively capture real-time hyperlocal-scale TRAP variations in Chengdu, China. A 17-day sampling campaign was conducted covering the National Holiday of China and collected ~1.2 × 10⁵ 1 Hz paired data. We measured particle number concentration (PNC), black carbon (BC), and nitrogen oxides (NO_x) across urban and rural freeway environments to assess the impact of reduced heavy-duty diesel vehicles (HDDVs) during the holiday (i.e., holiday effect). No clear impact of wind direction on TRAP concentrations was found in this study. However, substantial differences (two times) were observed when comparing non-holiday to holiday campaigns. Spearman correlations (0.21–0.56) between TRAPs persistently exceeded Pearson correlations (0.14–0.41), indicating non-linear relationships and suggesting the necessity for data transformations (e.g., logarithms) in TRAP analysis. The comparison of the background subtracted TRAPs concentrations between non-holiday and holidays, revealing approximately a 50% reduction in TRAPs across microenvironments. Among the TRAPs, NO_x emerged as a reliable indicator of HDDV emissions. The study provides insights into vehicle fleet composition impacts, paving the way for enhanced exposure assessment strategies. Full article

(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (3rd Edition))

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23 pages, 11007 KiB

Open AccessEditor’s ChoiceArticle

Research on the Detection Model of Kernel Anomalies in Ionospheric Space Electric Fields

by Xingsu Li, Zhong Li, Jianping Huang, Ying Han, Yumeng Huo, Junjie Song and Bo Hao

Atmosphere 2025, 16(2), 160; https://doi.org/10.3390/atmos16020160 - 31 Jan 2025

Viewed by 569

Abstract

Research has found kernel anomaly regions in the power spectrum images of ionospheric electric fields in space, which are widely distributed. To effectively detect these kernel abnormal regions, this paper proposes a new kernel abnormal region detection method, KANs-Unet, based on KANs and [...] Read more.

Research has found kernel anomaly regions in the power spectrum images of ionospheric electric fields in space, which are widely distributed. To effectively detect these kernel abnormal regions, this paper proposes a new kernel abnormal region detection method, KANs-Unet, based on KANs and U-net networks. The model embeds the KAN-Conv convolutional module based on KANs in the encoder section, introduces the feature pyramid attention module (FPA) at the junction of the encoder and decoder, and introduces the CBAM attention mechanism module in the decoder section. The experimental results show that the improved KANs-Unet model has a mIoU improvement of about 10% compared to the PSPNet algorithm and an improvement of about 7.8% compared to the PAN algorithm. It has better detection performance than the currently popular semantic segmentation algorithms. A higher evaluation index represents that the detected abnormal area is closer to the label value (i.e., the detected abnormal area is more complete), indicating better detection performance. To further investigate the characteristics of kernel anomaly areas and the differences in features during magnetic storms, the author studied the characteristics of kernel anomaly areas during two different intensities of magnetic storms: from November 2021 to October 2022 and from 1 May 2024 to 13 May 2024 (large magnetic storm), and from 11 October 2023 to 23 October 2023 (moderate magnetic storm). During a major geomagnetic storm, the overall distribution of kernel anomaly areas shows a parallel trend with a band-like distribution. The spatial distribution of magnetic latitudes is relatively scattered, especially in the southern hemisphere, where the magnetic latitudes are wider. Additionally, the number of orbits with kernel anomaly areas during ascending increases, especially during peak periods of major geomagnetic storms. The overall spatial distribution of moderate geomagnetic storms does not change significantly, but the global magnetic latitude distribution is relatively concentrated. Full article

(This article belongs to the Special Issue Ionospheric Sounding for Identification of Pre-seismic Activity)

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73 pages, 10996 KiB

Open AccessEditor’s ChoiceReview

The Fluid Ionosphere

by Massimo Materassi

Atmosphere 2025, 16(2), 147; https://doi.org/10.3390/atmos16020147 - 29 Jan 2025

Viewed by 664

Abstract

In this review paper, the equations of motion describing the fluid dynamics of the ionosphere are constructed step by step, so that any master or post-graduate student may get familiar with the general theory of the “traditional” approach to Ionospheric Physics, in which [...] Read more.

In this review paper, the equations of motion describing the fluid dynamics of the ionosphere are constructed step by step, so that any master or post-graduate student may get familiar with the general theory of the “traditional” approach to Ionospheric Physics, in which chemicals forming the Earth’s upper atmosphere are represented as fluids in mutual interaction. The hypotheses on which the smooth-field fluid representation is based are discussed in terms of microscopic dynamics of the gas particles; this discussion is oriented to prepare the reader for the post-fluid approaches to the physics of turbulence. The fluid-dynamical picture of the ionosphere is the most classical and conceptually simple one, and this makes it extremely widespread in terms of theoretical models and applications. The advantages and achievements of this theory are highlighted, its limits are discussed, and perspectives to go beyond it sketched. Full article

(This article belongs to the Section Upper Atmosphere)

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18 pages, 2034 KiB

Open AccessEditor’s ChoiceArticle

Comparison of Identified Ice Supersaturated Regions for Contrail Avoidance Using Three Standard Weather Forecast Databases

by Amy Tal Rose-Tejwani, Lance Sherry and Kayla Ebright

Atmosphere 2025, 16(2), 149; https://doi.org/10.3390/atmos16020149 - 29 Jan 2025

Viewed by 563

Abstract

Contrails form as a result of water vapor bonding with soot emitted from jet engines at cruise altitudes, leading to contrail formation in Ice Supersaturated Regions (ISSRs). Contrails are estimated to contribute approximately 2% to total anthropogenic global warming. Some researchers have developed [...] Read more.

Contrails form as a result of water vapor bonding with soot emitted from jet engines at cruise altitudes, leading to contrail formation in Ice Supersaturated Regions (ISSRs). Contrails are estimated to contribute approximately 2% to total anthropogenic global warming. Some researchers have developed simulation models to estimate the frequency, duration, and spatial distribution of contrails. Other researchers have identified issues with the accuracy of the data for predicting the timing and precise geographic positioning of ISSRs. This study presents a systematic review of 22 peer-reviewed articles that included detailed models of ISSR identification, identifying three atmospheric data sources, four parameters, and two equations for calculating the parameters derived. A further analysis revealed differences in the temperature and RH_W readings across the three databases, resulting in differences in the RH_I calculations and the identification of ISSRs. Over an 18-month period in Sterling, Virginia, USA, the radiosonde data and two atmospheric forecast databases identified the ISSR conditions on 44%, 47%, and 77% of days, respectively. Broken down by a flight level between 30,000 and 39,999 feet in altitude, these differences are highlighted further. The forecast databases overestimated the presence of ISSRs compared to the radiosonde data. These findings underscore the variability inherent in atmospheric datasets and the conversion methods, highlighting potential areas for refinement in ISSR prediction, notably in the development of ensemble forecasts based on several atmospheric databases. The implications of these results, the limitations of this study, and future work are discussed. Full article

(This article belongs to the Special Issue Advance in Transportation Meteorology (2nd Edition))

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16 pages, 4222 KiB

Open AccessEditor’s ChoiceArticle

Modeling Return Levels of Non-Stationary Rainfall Extremes Due to Climate Change

by Mahin Razi Ghalavand, Manuchehr Farajzadeh and Yousef Ghavidel Rahimi

Atmosphere 2025, 16(2), 136; https://doi.org/10.3390/atmos16020136 - 27 Jan 2025

Viewed by 744

Abstract

Global warming increases evaporation and atmospheric water vapor, leading to more extreme events in both spatial and temporal domains. This study conducts a non-stationary extreme value analysis of the annual daily maximum at 36 meteorological stations over Iran from 1960 to 2021. We [...] Read more.

Global warming increases evaporation and atmospheric water vapor, leading to more extreme events in both spatial and temporal domains. This study conducts a non-stationary extreme value analysis of the annual daily maximum at 36 meteorological stations over Iran from 1960 to 2021. We applied stationary and non-stationary Generalized Extreme Value (GEV) models within a Bayesian framework to estimate return levels for rainfall extremes, along with 90% confidence intervals. Our findings indicate that non-stationary models are not prominently evident based on AIC at most stations; however, non-stationary Generalized Extreme Value (GEV) models outperform stationary models based on RMSE and NSE evaluation criteria that sufficiently capture variations in extremes. Furthermore, most observed changes in extreme events exhibit a non-stationary pattern. Non-stationary analysis indicates that the frequency and severity of rainfall extremes have shown both increasing and decreasing trends, characterized by inconsistent spatial patterns. Full article

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

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12 pages, 4383 KiB

Open AccessEditor’s ChoiceArticle

Decadal Regime Shifts in Sea Fog Frequency over the Northwestern Pacific: The Influence of the Pacific Decadal Oscillation and Sea Surface Temperature Warming

by Shihan Zhang, Liguo Han, Jingchao Long, Lingyu Dong, Pengzhi Hong and Feng Xu

Atmosphere 2025, 16(2), 130; https://doi.org/10.3390/atmos16020130 - 26 Jan 2025

Viewed by 470

Abstract

Sea fog significantly impacts marine activities, ecosystems, and radiation balance. We analyzed the decadal variation characteristics of sea fog frequency (SFF) over the northwestern Pacific and investigated the roles of the Pacific decadal oscillation (PDO) and sea surface temperature (SST) warming in driving [...] Read more.

Sea fog significantly impacts marine activities, ecosystems, and radiation balance. We analyzed the decadal variation characteristics of sea fog frequency (SFF) over the northwestern Pacific and investigated the roles of the Pacific decadal oscillation (PDO) and sea surface temperature (SST) warming in driving these changes. The results show that SFF experienced a significant and sudden decadal increase around 1978 (up by 12.9%) and a prominent decadal decrease around 1999 (down by 7.8%). The sudden increase in SFF around 1978 was closely related to the PDO. A positive PDO phase induced unusual anticyclonic circulation and southerly winds over the northwestern Pacific, enhancing low-level atmospheric stability and moisture supply, thus facilitating sea fog formation. Nevertheless, the decrease in SFF around 1999 was related to SST warming in the north Pacific. The rise in sea temperatures weakened the SST front south of the foggy region, reducing the cooling and condensation of warm air necessary for sea fog formation. This study enhances the understanding of the decadal variability mechanism of SFF over the northwestern Pacific regulated by large-scale circulation systems and provides a reference for future sea fog forecasting work. Full article

(This article belongs to the Section Meteorology)

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16 pages, 4518 KiB

Open AccessEditor’s ChoiceArticle

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 838

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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21 pages, 16278 KiB

Open AccessEditor’s ChoiceArticle

Synoptic and Mesoscale Atmospheric Patterns That Triggered the Natural Disasters in the Metropolitan Region of Belo Horizonte, Brazil, in January 2020

by Thaís Aparecida Cortez Pinto, Enrique Vieira Mattos, Michelle Simões Reboita, Diego Oliveira de Souza, Paula S. S. Oda, Fabrina Bolzan Martins, Thiago Souza Biscaro and Glauber Willian de Souza Ferreira

Atmosphere 2025, 16(1), 102; https://doi.org/10.3390/atmos16010102 - 18 Jan 2025

Viewed by 702

Abstract

Between 23 and 25 January 2020, the Metropolitan Region of Belo Horizonte (MRBH) in Brazil experienced 32 natural disasters, which affected 90,000 people, resulted in 13 fatalities, and caused economic damages of approximately USD 250 million. This study aims to describe the synoptic [...] Read more.

Between 23 and 25 January 2020, the Metropolitan Region of Belo Horizonte (MRBH) in Brazil experienced 32 natural disasters, which affected 90,000 people, resulted in 13 fatalities, and caused economic damages of approximately USD 250 million. This study aims to describe the synoptic and mesoscale conditions that triggered these natural disasters in the MRBH and the physical properties of the associated clouds and precipitation. To achieve this, we analyzed data from various sources, including natural disaster records from the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), GOES-16 satellite imagery, soil moisture data from the Soil Moisture Active Passive (SMAP) satellite mission, ERA5 reanalysis, reflectivity from weather radar, and lightning data from the Lightning Location System. The South Atlantic Convergence Zone, coupled with a low-pressure system off the southeast coast of Brazil, was the predominant synoptic pattern responsible for creating favorable conditions for precipitation during the studied period. Clouds and precipitating cells, with cloud-top temperatures below −65 °C, over several days contributed to the high precipitation volumes and lightning activity. Prolonged rainfall, with a maximum of 240 mm day⁻¹ and 48 mm h⁻¹, combined with the region’s soil characteristics, enhanced water infiltration and was critical in triggering and intensifying natural disasters. These findings highlight the importance of monitoring atmospheric conditions in conjunction with soil moisture over an extended period to provide additional information for mitigating the impacts of natural disasters. Full article

(This article belongs to the Special Issue Prediction and Modeling of Extreme Weather Events)

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18 pages, 7881 KiB

Open AccessEditor’s ChoiceArticle

Effect of Multiple Injection Strategy Under High Ammonia Ratio on Combustion and Emissions of Liquid Ammonia/Diesel Dual DI Engine

by Zhenbin Chen, Yudong Wan, Omar I. Awad and Zhiqiang Pan

Atmosphere 2025, 16(1), 94; https://doi.org/10.3390/atmos16010094 - 16 Jan 2025

Viewed by 719

Abstract

With the increasingly prominent environmental and energy issues, emission regulations are becoming more stringent. Ammonia diesel dual fuel (ADDF) engine is one of the effective ways to reduce carbon emissions. This study investigated the effect of multiple injection strategy on the combustion and [...] Read more.

With the increasingly prominent environmental and energy issues, emission regulations are becoming more stringent. Ammonia diesel dual fuel (ADDF) engine is one of the effective ways to reduce carbon emissions. This study investigated the effect of multiple injection strategy on the combustion and emission characteristics of liquid ammonia/diesel dual direct injection (DI) engines through numerical simulation. The results showed that under the condition of maintaining the same pre injection diesel fuel and high ammonia energy ratio (80%), with the introduction of multiple injection, the peak cylinder pressure decreased and the peak phase advanced, the combustion start angle (CA10) advanced, the heat release showed a multi-stage pattern. The times of injection (TSOI) has a significant effect on combustion and emissions. As TSOI increased, ignition delay decreased, the combustion duration is shortened, and the combustion is accelerated. Notably, overall emissions of NOx and N₂O have decreased, but the emissions of unburned NH₃ have increased. Optimized the state of ammonia injection (SOAI) timing and ammonia injection pressure (AIP), showed that advancing SOAI timing and increasing AIP improved combustion. Advanced the SOAI timing to −8 °CA ATDC, resulted in a significant NOx emissions decrease with an increase in TSOI, reaching over 50%. Although increasing injection pressure can improve combustion, it also results in higher N₂O emissions. Full article

(This article belongs to the Special Issue Renewable Strategies for Emission Reduction: A Multisectoral Approach)

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56 pages, 48151 KiB

Open AccessEditor’s ChoiceArticle

Excitation of ULF, ELF, and VLF Resonator and Waveguide Oscillations in the Earth–Atmosphere–Ionosphere System by Lightning Current Sources Connected with Hunga Tonga Volcano Eruption

by Yuriy G. Rapoport, Volodymyr V. Grimalsky, Andrzej Krankowski, Asen Grytsai, Sergei S. Petrishchevskii, Leszek Błaszkiewicz and Chieh-Hung Chen

Atmosphere 2025, 16(1), 97; https://doi.org/10.3390/atmos16010097 - 16 Jan 2025

Viewed by 770

Abstract

The simulations presented here are based on the observational data of lightning electric currents associated with the eruption of the Hunga Tonga volcano in January 2022. The response of the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system to unprecedented lightning currents is theoretically investigated at low frequencies, [...] Read more.

The simulations presented here are based on the observational data of lightning electric currents associated with the eruption of the Hunga Tonga volcano in January 2022. The response of the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system to unprecedented lightning currents is theoretically investigated at low frequencies, including ultra low frequency (ULF), extremely low frequency (ELF), and very low frequency (VLF) ranges. The electric current source due to lightning near the location of the Hunga Tonga volcano eruption has a wide-band frequency spectrum determined in this paper based on a data-driven approach. The spectrum is monotonous in the VLF range but has many significant details at the lower frequencies (ULF, ELF). The decreasing amplitude tendency is maintained at frequencies exceeding 0.1 Hz. The density of effective lightning current in the ULF range reaches the value of the order of 10⁻⁷ A/m². A combined dynamic/quasi-stationary method has been developed to simulate ULF penetration through the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system. This method is suitable for the ULF range down to 10⁻⁴ Hz. The electromagnetic field is determined from the dynamics in the ionosphere and from a quasi-stationary approach in the atmosphere, considering not only the electric component but also the magnetic one. An analytical/numerical method has been developed to investigate the excitation of the global Schumann resonator and the eigenmodes of the coupled Schumann and ionospheric Alfvén resonators in the ELF range and the eigenmodes of the Earth–ionosphere waveguide in the VLF range. A complex dispersion equation for the corresponding disturbances is derived. It is shown that oscillations at the first resonance frequency in the Schumann resonator can simultaneously cause noticeable excitation of the local ionospheric Alfvén resonator, whose parameters depend on the angle between the geomagnetic field and the vertical direction. VLF propagation is possible over distances of 3000–10,000 km in the waveguide Earth–ionosphere. The results of simulations are compared with the published experimental data. Full article

(This article belongs to the Special Issue Feature Papers in Upper Atmosphere (2nd Edition))

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21 pages, 5166 KiB

Open AccessEditor’s ChoiceArticle

Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses

by Masashi Hayakawa, Shinji Hirooka, Koichiro Michimoto, Stelios M. Potirakis and Yasuhide Hobara

Atmosphere 2025, 16(1), 88; https://doi.org/10.3390/atmos16010088 - 15 Jan 2025

Viewed by 705

Abstract

The purpose of this paper is to discuss the effect of earthquake (EQ) preparation on changes in meteorological parameters. The two physical quantities of temperature (T)/relative humidity (Hum) and atmospheric chemical potential (ACP) have been investigated with the use of the Japanese meteorological [...] Read more.

The purpose of this paper is to discuss the effect of earthquake (EQ) preparation on changes in meteorological parameters. The two physical quantities of temperature (T)/relative humidity (Hum) and atmospheric chemical potential (ACP) have been investigated with the use of the Japanese meteorological “open” data of AMeDAS (Automated Meteorological Data Acquisition System), which is a very dense “ground-based” network of meteorological stations with higher temporal and spatial resolutions than the satellite remote sensing open data. In order to obtain a clearer identification of any seismogenic effect, we have used the AMeDAS station data at local midnight (LT = 01 h) and our initial target EQ was chosen to be the famous 1995 Kobe EQ of 17 January 1995 (M = 7.3). Initially, we performed conventional statistical analysis with confidence bounds and it was found that the Kobe station (very close to the EQ epicenter) exhibited conspicuous anomalies in both physical parameters on 10 January 1995, just one week before the EQ, exceeding m (mean) + 3σ (standard deviation) in T/Hum and well above m + 2σ in ACP within the short-term window of one month before and two weeks after an EQ. When looking at the whole period of over one year including the day of the EQ, in the case of T/Hum only we detected three additional extreme anomalies, except in winter, but with unknown origins. On the other hand, the anomalous peak on 10 January 1995 was the largest for ACP. Further, the spatial distributions of the anomaly intensity of the two quantities have been presented using about 40 stations to provide a further support to the close relationship of this peak with the EQ. The above statistical analysis has been compared with an analysis with recent machine/deep learning methods. We have utilized a combinational use of NARX (Nonlinear Autoregressive model with eXogenous inputs) and Long Short-Term Memory (LSTM) models, which was successful in objectively re-confirming the anomalies in both parameters on the same day prior to the EQ. The combination of these analysis results elucidates that the meteorological anomalies on 10 January 1995 are considered to be a notable precursor to the EQ. Finally, we suggest a joint examination of our two meteorological quantities for their potential use in real short-term EQ prediction, as well as in the future lithosphere–atmosphere–ionosphere coupling (LAIC) studies as the information from the bottom part of LAIC. Full article

(This article belongs to the Section Meteorology)

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34 pages, 773 KiB

Open AccessEditor’s ChoiceReview

Machine Learning Methods for Weather Forecasting: A Survey

by Huijun Zhang, Yaxin Liu, Chongyu Zhang and Ningyun Li

Atmosphere 2025, 16(1), 82; https://doi.org/10.3390/atmos16010082 - 14 Jan 2025

Cited by 3 | Viewed by 6791

Abstract

Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically examines machine learning [...] Read more.

Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically examines machine learning (ML)-based weather forecasting methods, which demonstrate exceptional capability in handling complex, high-dimensional datasets and leveraging large volumes of historical and real-time data, enabling the identification of subtle patterns and relationships among weather variables. Research on specific tasks such as global weather forecasting, downscaling, extreme weather prediction, and how to combine machine learning methods with physical principles are very active in the current field. However, several unresolved or challenging issues remain, including the interpretability of models and the ability to predict rare weather events. By identifying these gaps, this research provides a roadmap for advancing machine learning-based weather forecasting techniques to complement and enhance weather prediction results. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)

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19 pages, 1296 KiB

Open AccessEditor’s ChoiceArticle

MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting

by Shaohan Li, Min Chen, Lu Yi, Qifeng Lu and Hao Yang

Atmosphere 2025, 16(1), 67; https://doi.org/10.3390/atmos16010067 - 9 Jan 2025

Viewed by 528

Abstract

Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as [...] Read more.

Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as difficulty in capturing complex spatio-temporal dependencies. To address these issues, this study introduces a novel short-term wind speed forecasting model named as MIESTC. The proposed model employs an independent encoder to extract features from each meteorological variable, mitigating the issues of noise that are caused by variable mixing. Then, a multivariate spatio-temporal correlation module is used to capture the global spatio-temporal dependencies between variables and model their interactions. Experimental results on the ERA5-LAND dataset show that, compared to the ConvLSTM, UNET, and SimVP models, the MIESTC model reduces RMSE by 14.60%, 8.64%, and 10.41%, respectively, for a 1 h prediction duration. For a 6 h prediction duration, the corresponding reductions are 13.91%, 8.20%, and 6.95%, validating its superior performance in short-term wind speed forecasting. Furthermore, an analysis of variable impacts reveals that U10, V10, and T2M play dominant roles in wind speed prediction, while TP exhibits a relatively lower impact, aligning with the results of the correlation analysis. These findings underscore the potential of MIESTC as an effective and reliable tool for short-term wind speed prediction. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)

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31 pages, 3113 KiB

Open AccessEditor’s ChoiceArticle

Automatic Threshold Selection for Generalized Pareto and Pareto–Poisson Distributions in Rainfall Analysis: A Case Study Using the NOAA NCDC Daily Rainfall Database

by Roberto Mínguez

Atmosphere 2025, 16(1), 61; https://doi.org/10.3390/atmos16010061 - 8 Jan 2025

Viewed by 835

Abstract

Both extreme-excess modeling and extreme-value analysis of precipitation events frequently utilize the Generalized Pareto (GP) distribution to model peaks above a selected threshold. However, selecting an appropriate threshold remains a complex and challenging task, which has discouraged many practitioners from employing Pareto or [...] Read more.

Both extreme-excess modeling and extreme-value analysis of precipitation events frequently utilize the Generalized Pareto (GP) distribution to model peaks above a selected threshold. However, selecting an appropriate threshold remains a complex and challenging task, which has discouraged many practitioners from employing Pareto or Pareto–Poisson distributions for extreme-value analysis. Recent analyses of threshold selection methods proposed in the technical literature, particularly when applied to rainfall records with high quantization levels, have shown that nonparametric methods are often unreliable. Additionally, methods relying on the asymptotic properties of the GP distribution tend to produce unrealistically high threshold estimates. In contrast, graphical methods and goodness-of-fit (GoF) metrics that account for the pre-asymptotic behavior of the GP distribution have demonstrated better performance. Despite these improvements, there remains no automatic and statistically robust methodology for threshold selection. This study develops an automatic, statistically sound procedure for optimal threshold selection, leveraging weighted mean square errors and internally studentized residuals. The proposed method outperforms existing approaches in terms of accuracy, as demonstrated through numerical experiments and its application to real-world data from the NOAA NCDC Daily Rainfall Database. Results indicate that the method not only improves threshold estimation precision but also enhances the reliability of extreme-value analysis for precipitation records, making it a valuable tool for hydrological applications. The findings emphasize the practical implications of the method for analyzing extreme rainfall events and its potential for broader climatological studies. Full article

(This article belongs to the Special Issue Precipitation Observations and Prediction (2nd Edition))

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20 pages, 3405 KiB

Open AccessEditor’s ChoiceArticle

ICOS Potenza (Italy) Atmospheric Station: A New Spot for the Observation of Greenhouse Gases in the Mediterranean Basin

by Emilio Lapenna, Antonella Buono, Alessandro Mauceri, Isabella Zaccardo, Francesco Cardellicchio, Francesco D’Amico, Teresa Laurita, Davide Amodio, Canio Colangelo, Gianluca Di Fiore, Antonella Gorga, Ermann Ripepi, Francesco De Benedictis, Silvana Pirelli, Liborio Capozzo, Vincenzo Lapenna, Gelsomina Pappalardo, Serena Trippetta and Lucia Mona

Atmosphere 2025, 16(1), 57; https://doi.org/10.3390/atmos16010057 - 8 Jan 2025

Cited by 3 | Viewed by 1314

Abstract

The Integrated Carbon Observation System (ICOS) is the reference Research Infrastructure (RI) for the observation of greenhouse gases (GHGs) across Europe, providing standardised, long-term and high-precision measurements of the most relevant species (CO₂, CH₄, CO, etc.). The ICOS Atmosphere [...] Read more.

The Integrated Carbon Observation System (ICOS) is the reference Research Infrastructure (RI) for the observation of greenhouse gases (GHGs) across Europe, providing standardised, long-term and high-precision measurements of the most relevant species (CO₂, CH₄, CO, etc.). The ICOS Atmosphere network currently extends throughout the continent, although the density of stations in the Mediterranean area is still low compared to Central and Northern Europe. In this context, the recently implemented class 1 continental station near Potenza in Basilicata, Italy—station code: POT—represents an important step forward in the extension of the ICOS atmosphere domain across the South, reducing the large spatial gaps existing between ICOS sites within the Mediterranean basin. Herein, we provide a description of the new ICOS POT station and the site where it operates, focusing mostly on the technical setup of the sampling system which plays a key role in GHG measurements. With a strong technical connotation, the present paper aims to be beneficial for the ICOS atmosphere community and those stations that intend to join the network in the future, providing an accurate description of the station at the level of single components. Moreover, a brief overview of the peculiarities of the site and the scientific perspectives to be pursued, together with very preliminary data collected at the new ICOS station, are presented. Preliminary data collected during a short campaign are compared with STILT (Stochastic Time-Inverted Lagrangian Transport) model results as a first test of the measurements and to provide a first insight of the specific Potenza situation in terms of GHG concentrations. Full article

(This article belongs to the Section Air Quality)

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22 pages, 10003 KiB

Open AccessEditor’s ChoiceArticle

Spatial Downscaling of Daily Temperature Minima Using Machine Learning Methods and Application to Frost Forecasting in Two Alpine Valleys

by Sudheer Bhakare, Michael Matiu, Alice Crespi and Dino Zardi

Atmosphere 2025, 16(1), 38; https://doi.org/10.3390/atmos16010038 - 1 Jan 2025

Viewed by 1060

Abstract

This study examines the performance of three machine learning models—namely, Artificial Neural Network (ANN), Random Forest (RF), and Convolutional Neural Network (CNN)—for spatial downscaling of seasonal forecasts of daily minimum temperature from 12 km to 250 m horizontal resolution. Downscaling is carried out [...] Read more.

This study examines the performance of three machine learning models—namely, Artificial Neural Network (ANN), Random Forest (RF), and Convolutional Neural Network (CNN)—for spatial downscaling of seasonal forecasts of daily minimum temperature from 12 km to 250 m horizontal resolution. Downscaling is carried out with a one-month lead time, with analysis split into short-term (1 to 8 days) and extended (9 to 28 days) forecast periods, allowing a detailed assessment of the performance of models over time. Results suggest that CNN outperforms ANN and RF, achieving lower Root Mean Square Error (ranging from 2.04 °C to 2.66 °C) and Mean Absolute Error (1.59 °C to 2.03 °C) along with higher correlation (0.75 to 0.88) and reduced bias (−0.38 °C to −0.68) across all seasons, for the short term. The CNN model also exhibits superior performance in frost prediction, with the highest F1 score (0.78) and lowest False Discovery Rate (0.30) in predicting frost events, particularly in early spring for the short-term forecast period over 2010–2018. However, errors increase in transitional months, like April, and in the extended forecast period, confirming the intrinsic challenges inherent to predicting frost events in these months. Despite the decreased skills for extended forecast periods, results suggest that the CNN model’s effectiveness for spatial downscaling of minimum temperature and frost forecasting over complex terrain provides a valuable tool for frost risk management. Full article

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

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24 pages, 5694 KiB

Open AccessEditor’s ChoiceArticle

Investigating the Temporal and Spatial Characteristics of Lower Atmospheric Ducts in the Arctic via Long-Term Numerical Simulations

by Jinyue Wang, Xiaofeng Zhao, Jing Zou, Pinglv Yang, Bo Wang, Shuai Yang, Zhijin Qiu, Zhiqian Li, Tong Hu and Miaomiao Song

Atmosphere 2025, 16(1), 11; https://doi.org/10.3390/atmos16010011 - 26 Dec 2024

Viewed by 569

Abstract

In this study, a diagnostic model for lower atmospheric ducts was developed using the polar weather research and forecasting model. A five-year simulation was then conducted across the entire Arctic region to investigate the temporal and spatial characteristics of lower atmospheric ducts. The [...] Read more.

In this study, a diagnostic model for lower atmospheric ducts was developed using the polar weather research and forecasting model. A five-year simulation was then conducted across the entire Arctic region to investigate the temporal and spatial characteristics of lower atmospheric ducts. The model demonstrated excellent performance in simulating modified atmospheric refractivity, with root mean square errors ranging from 0 M to 5 M. The five-year simulation results revealed that duct occurrence rates across the Arctic region were all below 1% and exhibited a negative relationship with latitude. Regarding the difference between surface ducts and elevated ducts, a higher frequency of surface ducts was detected in the Arctic region. The height and thickness of surface ducts were generally lower than those of elevated ducts, but the strength of surface ducts was slightly greater. Regionally, surface ducts mainly occurred in the land areas surrounding the Arctic Ocean, while more elevated ducts were found in the North Atlantic Sea area. Additionally, a negative correlation was observed between the polar vortex indices and the characteristics of ducts, particularly for surface ducts. The ducts in Greenland were notably influenced by polar vortex activity, whereas the ducts in other regions, such as the Norwegian Sea and Kara Sea, were less affected. Full article

(This article belongs to the Special Issue Advances in Understanding Extreme Weather Events in the Anthropocene)

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12 pages, 3372 KiB

Open AccessEditor’s ChoiceArticle

Lightning Current Distribution of the First and Subsequent Strokes Based on the Lightning Location System: Survey in Yunnan Power Grid

by Yutang Ma, Hongchun Shu, Changxin Xiao, Gaohui Yang, Chengwei Xie, Mengmeng Zhu and Pulin Cao

Atmosphere 2025, 16(1), 15; https://doi.org/10.3390/atmos16010015 - 26 Dec 2024

Viewed by 747

Abstract

Lightning is an electrical discharge phenomenon in the atmosphere caused by charge separation in clouds, which is divided into cloud-to-ground (CG) and cloud-to-cloud (CC) lightning. In recent years, research on the characteristics of multiple-stroke ground lightning and the amplitude of lightning currents has [...] Read more.

Lightning is an electrical discharge phenomenon in the atmosphere caused by charge separation in clouds, which is divided into cloud-to-ground (CG) and cloud-to-cloud (CC) lightning. In recent years, research on the characteristics of multiple-stroke ground lightning and the amplitude of lightning currents has attracted significant attention. The amplitude of lightning currents serves as fundamental data for lightning protection in power systems. Its accurate measurement is crucial for designing and safeguarding power systems. This paper obtains data from a lightning location system and analyzes the probability density distribution of lightning current amplitudes. It is found that the median of lightning currents gradually decreases with an increasing number of multiple strokes, and there is a trend in the change of lightning current steepness. As the number of strokes increases, the median value of amplitude distribution gradually decreases, while the steepness coefficient shows an increasing trend. These research findings contribute to a deeper understanding of the characteristics of lightning and provide important references for lightning prevention and disaster reduction. Full article

(This article belongs to the Special Issue Atmospheric Electricity (2nd Edition))

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13 pages, 3943 KiB

Open AccessEditor’s ChoiceArticle

Investigating the Applicability of the Peak Density Thickness Parameter over the Equatorial Region

by Mohamed O. Shammat, Bodo W. Reinisch, Ivan Galkin, Philip J. Erickson, Jay A. Weitzen and William C. Rideout

Atmosphere 2025, 16(1), 10; https://doi.org/10.3390/atmos16010010 - 26 Dec 2024

Viewed by 519

Abstract

The Peak Density Thickness (PDT) refers to a vertical region in the ionosphere encompassing the F2 peak, where electron density is at its maximum, and extending upward—maintaining a constant density—for a fixed altitude beyond this peak. This study builds on the previously established [...] Read more.

The Peak Density Thickness (PDT) refers to a vertical region in the ionosphere encompassing the F2 peak, where electron density is at its maximum, and extending upward—maintaining a constant density—for a fixed altitude beyond this peak. This study builds on the previously established PDT concept, initially explored at midlatitudes using data from Millstone Hill, by evaluating its applicability and effectiveness over equatorial latitudes using data from the Jicamarca Incoherent Scatter Radar (ISR) in Lima, Peru. A comprehensive analysis of electron density profiles measured by the Jicamarca ISR, spanning 1997 to 2020, was conducted using the Madrigal database to extract the PDT parameter for the F2 layer. Findings from the Jicamarca ISR indicate that the PDT parameter peaks around solar noon, aligning with observations from Millstone Hill. For selected case studies, the Vary-Chap topside model was employed to reconstruct the ionospheric profile above the F2 peak and PDT, demonstrating the model’s enhanced effectiveness when incorporating the PDT parameter over equatorial regions. This research confirms the presence of PDT in equatorial regions, consistent with its behavior at midlatitudes, and underscores the importance of PDT in refining predictive ionospheric models across different latitudes. Full article

(This article belongs to the Special Issue A Dynamical System for the Earth's Ionosphere—Space Weather through Complex System Science)

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16 pages, 6328 KiB

Open AccessEditor’s ChoiceArticle

Gas Transport Arising from the Decomposition of Methane Hydrates in the Sediments of the Arctic Shelf to the Atmosphere: Numerical Modeling

by Mariia Trimonova, Nikolay Baryshnikov and Sergey Turuntaev

Atmosphere 2025, 16(1), 9; https://doi.org/10.3390/atmos16010009 - 26 Dec 2024

Viewed by 630

Abstract

This study investigates the transport of methane released from gas hydrate decomposition through sedimentary layers to quantify its flux into the atmosphere, a critical process given methane’s role as a major greenhouse gas. A novel methodology was developed to model two-phase, unsteady gas [...] Read more.

This study investigates the transport of methane released from gas hydrate decomposition through sedimentary layers to quantify its flux into the atmosphere, a critical process given methane’s role as a major greenhouse gas. A novel methodology was developed to model two-phase, unsteady gas flow in regions of hydrate decomposition, incorporating key factors such as relative permeability curves, capillary pressure, hydrostatics, and gas diffusion. Numerical simulations revealed that to achieve a gas front rise rate of 7 m/year, the gas accumulation rate must not exceed 10⁻⁸ kg/m³·s. At higher accumulation rates (10⁻⁶ kg/m³·s), gas diffusion has minimal impact on the saturation front movement, whereas at lower rates (10⁻⁸ kg/m³·s), diffusion significantly affects the front’s behavior. The study also established that the critical gas accumulation rate required to trigger sediment blowout in the hydrate decomposition zone is approximately 10⁻⁶ kg/m³·s, several orders of magnitude greater than typical bubble gas fluxes observed at the ocean surface. The proposed model improves the ability to predict the contribution of Arctic shelf methane hydrate decomposition to atmospheric methane concentrations. Full article

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

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19 pages, 6206 KiB

Open AccessEditor’s ChoiceArticle

Assessing Wildfire Risk in South Korea Under Climate Change Using the Maximum Entropy Model and Shared Socioeconomic Pathway Scenarios

by Jukyeong Choi and Heemun Chae

Atmosphere 2025, 16(1), 5; https://doi.org/10.3390/atmos16010005 - 25 Dec 2024

Viewed by 1284

Abstract

For effective management and prevention, wildfire risk prediction needs to consider the substantial impacts of climate change on wildfire patterns. This study analyzed the probability of wildfire occurrence in South Korea using the Maximum Entropy (MaxEnt) model and predicted future wildfire occurrence under [...] Read more.

For effective management and prevention, wildfire risk prediction needs to consider the substantial impacts of climate change on wildfire patterns. This study analyzed the probability of wildfire occurrence in South Korea using the Maximum Entropy (MaxEnt) model and predicted future wildfire occurrence under shared socioeconomic pathway (SSP) climate change scenarios. The model utilized historical fire occurrence data and was trained using 12 environmental variables encompassing climate, topography, vegetation, and socioeconomic factors. Future wildfire risk was predicted under the SSP2-4.5 and SSP5-8.5 scenarios for 2041–2060 and 2081–2100. Increased average temperature and solar radiation were key drivers of elevated wildfire risk, whereas increased precipitation and relative humidity reduced this risk. Under current conditions, 367,027 ha (6.52%) within the study area were classified as high-risk based on the MaxEnt model output (p > 0.6). Under both SSP scenarios, a decline in the at-risk area was observed over time. This study provides fundamental data for wildfire management and prevention strategies in South Korea and provides quantitative evidence on the potential impact of climate-related environmental changes on wildfires. Full article

(This article belongs to the Section Climatology)

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15 pages, 1622 KiB

Open AccessEditor’s ChoiceArticle

Trends in the Occurrence of Compound Extremes of Temperature and Precipitation in Côte d’Ivoire

by Elisée Yapo Akobé, Adama Diawara, Fidèle Yoroba, Benjamin K. Kouassi, Assi Louis Martial Yapo, Ibrahima Diba, Kouakou Kouadio, Dro T. Tiémoko, Dianikoura Ibrahim Koné and Arona Diedhiou

Atmosphere 2025, 16(1), 3; https://doi.org/10.3390/atmos16010003 - 24 Dec 2024

Viewed by 623

Abstract

The aim of this study is to characterize the compound extremes of rainfall and temperature in Côte d’Ivoire. For this purpose, we analyzed the outputs of fourteen (14) climate models from the CORDEX-Africa project. Results show an increase (approximately 4.3 °C) in the [...] Read more.

The aim of this study is to characterize the compound extremes of rainfall and temperature in Côte d’Ivoire. For this purpose, we analyzed the outputs of fourteen (14) climate models from the CORDEX-Africa project. Results show an increase (approximately 4.3 °C) in the surface temperature and a decrease (5.90%) of the mean rainfall in the near (2036–2065) and far futures (2071–2100) over Côte d’Ivoire during the January–February–March (JFM) period. The analysis of the compound extremes of the wet/warm type highlights an increase in the frequency of this climatic hazard in the northern and central parts of the country during the January–March (JFM) season in the near and far futures. The dry/warm mode will increase in the central and southern parts of the country in the near future and in the whole country in the far future. These increases in compound extremes could lead to an increase in droughts and natural disasters across the country and could have a negative impact on socio-economic activities, such as transportation and agricultural production. This work could provide decision support for political decision-makers in formulating future public policies for managing agricultural production, food security, and natural disasters. Full article

(This article belongs to the Section Meteorology)

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20 pages, 288 KiB

Open AccessEditor’s ChoiceArticle

The Impacts of Environmental Assessment and Public Appeal on Air Quality: Evidence from the Chinese Provinces

by Zhi Li, Wen Wang and Zuo Zhang

Atmosphere 2024, 15(12), 1539; https://doi.org/10.3390/atmos15121539 - 22 Dec 2024

Viewed by 742

Abstract

Local governments excessively pursued an economic growth-oriented incentive system while neglecting air pollution control for a long time in China. The impacts of environmental assessment and public appeal could potentially promote environmental governance, thus reducing air pollution. Based on panel data of 30 [...] Read more.

Local governments excessively pursued an economic growth-oriented incentive system while neglecting air pollution control for a long time in China. The impacts of environmental assessment and public appeal could potentially promote environmental governance, thus reducing air pollution. Based on panel data of 30 Chinese provinces from 2003 to 2021, we obtain results using the OLS and GLS methods indicating that environmental assessment and public appeal significantly impact both local environmental governance investments and environmental legislation, with environmental legislation having a more significant effect. Compared to environmental letters and visits, environmental proposals from NPC (National People’s Congress) deputies and CPPCC (Chinese People’s Political Consultative Conference) members, as well as public online environmental concerns, have more significant positive impacts on environmental governance. Environmental governance can indeed reduce air pollution and is also affected by the personal characteristics of the officials. Officials who are in their second term or have transferred from other provinces are more willing to implement environmental governance. Older officials and those with higher education are also inclined towards environmental governance. Compared to provincial governors, the results are more significant for CCP (China’s Communist Party) secretaries. We also further perform a series of robustness tests and find that the effect still exists. The presented results provide valuable insights for the optimization of the roles of environmental assessment and public participation, contributing to reforming the environmental governance system in China. Full article

(This article belongs to the Section Air Quality)

23 pages, 9787 KiB

Open AccessEditor’s ChoiceArticle

Monitoring Ionospheric and Atmospheric Conditions During the 2023 Kahramanmaraş Earthquake Period

by Serkan Doğanalp and İrem Köz

Atmosphere 2024, 15(12), 1542; https://doi.org/10.3390/atmos15121542 - 22 Dec 2024

Viewed by 938

Abstract

Recent advancements have led to a growing prevalence of studies examining ionospheric and atmospheric anomalies as potential precursors to earthquakes. In this context, the study involved analyzing variations in ionospheric total electron content (TEC), investigating anomalies, assessing space weather conditions, and examining changes [...] Read more.

Recent advancements have led to a growing prevalence of studies examining ionospheric and atmospheric anomalies as potential precursors to earthquakes. In this context, the study involved analyzing variations in ionospheric total electron content (TEC), investigating anomalies, assessing space weather conditions, and examining changes in atmospheric parameters to evaluate potential precursors and post-seismic effects related to the Mw 7.7 and Mw 7.6 earthquakes that struck Kahramanmaraş consecutively in 2023. To compute the total electron content (TEC) values, data from 29 GNSS receivers covering a period of approximately 49 days were processed. In addition, since identical code signals were not available among all receiver stations, the study conducted an analysis of TEC estimations applying different GPS codes. To analyze space weather conditions, which are considered the main source of changes in the ionosphere, variations in sunspot number, solar activity index, magnetic activity indices (Kp and Dst), and geomagnetic field components were examined across the relevant period. To assess the potential presence of a distinct relationship between seismic activity at the Earth’s surface and ionospheric conditions, atmospheric parameters including temperature, relative humidity, and pressure were meticulously monitored and evaluated. As a result of the study, it was determined that TEC anomalies that could be evaluated as earthquake precursors independent of space weather conditions were observed starting from the 3rd day before the earthquake, and high positive TEC anomalies occurred immediately after the earthquakes. In atmospheric parameters, the change in behavior, particularly in temperature value, 10 days before the earthquake, is noteworthy. Full article

(This article belongs to the Special Issue Observations and Analysis of Upper Atmosphere)

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13 pages, 10017 KiB

Open AccessEditor’s ChoiceArticle

Estimation of Nitrous Oxide Emissions from Agricultural Sources and Characterization of Spatial and Temporal Changes in Anhui Province (China)

by Zhou Ye, Yujuan Sun, Xianglin Zhang and Youzhi Yao

Atmosphere 2024, 15(12), 1538; https://doi.org/10.3390/atmos15121538 - 22 Dec 2024

Cited by 1 | Viewed by 672

Abstract

To evaluate the estimation and spatiotemporal variation characteristics of nitrous oxide emissions from agricultural sources in Anhui Province, the nitrous oxide emissions generated during crop cultivation and manure management were assessed based on the recommended methods in the “Guidelines for Provincial Greenhouse Gas [...] Read more.

To evaluate the estimation and spatiotemporal variation characteristics of nitrous oxide emissions from agricultural sources in Anhui Province, the nitrous oxide emissions generated during crop cultivation and manure management were assessed based on the recommended methods in the “Guidelines for Provincial Greenhouse Gas Inventories” and official statistical data. The results showed that the overall emission of nitrous oxide from agricultural land showed a downward trend, reaching a valley value in 2019 with an emission of 2.83 × 10⁴ tons. The annual average emissions of nitrous oxide from agricultural land and manure management account for 80.98% and 19.02% of the total annual average emissions of nitrous oxide from agricultural activities in Anhui Province, respectively. Both agricultural land emissions and livestock manure management show a trend of nitrous oxide emissions decreasing from the northern region of Anhui > central region of Anhui > southern region of Anhui. In this paper, we explored and discussed the intrinsic driving factors behind the spatiotemporal changes in nitrous oxide emissions, and analyzed the potential for future emission reductions. It is suggested that the emissions of nitrous oxide from agricultural sources can be reduced through measures such as reasonable nitrogen application, adjustment of aquaculture structures, and the improvement of manure treatment methods, providing a theoretical reference for the estimation of greenhouse gas emissions from agricultural sources. Full article

(This article belongs to the Section Air Quality)

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24 pages, 7431 KiB

Open AccessEditor’s ChoiceArticle

Cyclone Classification over the South Atlantic Ocean in Centenary Reanalysis

by Eduardo Traversi de Cai Conrado, Rosmeri Porfírio da Rocha, Michelle Simões Reboita and Andressa Andrade Cardoso

Atmosphere 2024, 15(12), 1533; https://doi.org/10.3390/atmos15121533 - 21 Dec 2024

Viewed by 916

Abstract

Since the beginning of the satellite era, only three tropical cyclones have been recorded over the South Atlantic Ocean. To investigate the potential occurrence of such systems since the 1900s, ERA20C, a centennial reanalysis, was utilised. This study first evaluates the performance of [...] Read more.

Since the beginning of the satellite era, only three tropical cyclones have been recorded over the South Atlantic Ocean. To investigate the potential occurrence of such systems since the 1900s, ERA20C, a centennial reanalysis, was utilised. This study first evaluates the performance of ERA20C in reproducing the climatology of all cyclone types over the southwestern South Atlantic Ocean by comparing it with a modern reanalysis (ERA5) for the period 1979–2010. Despite its simpler construction, ERA20C is able to reproduce key climatological features, such as frequency, location, seasonality, intensity, and thermal structure of cyclones similar to ERA5. Then, the Cyclone Phase Space (CPS) methodology was applied to determine the thermal structure at each time step for every cyclone between 1900 and 2010 in ERA20C. The cyclones were then categorised into different types (extratropical, subtropical, and tropical), and systems exhibiting a warm core at their initial time step were classified as tropical cyclogenesis. Between 1900 and 2010, 96 cases of tropical cyclogenesis were identified over the South Atlantic. Additionally, throughout the lifetime of all cyclones, a total of 1838 time steps exhibited a tropical structure, indicating that cyclones can acquire a warm core at different stages of their lifecycle. The coasts of southeastern and southern sectors of northeast Brazil emerged as the most favourable for cyclones with tropical structures during their lifecycle. The findings of this study highlight the occurrence of tropical cyclones in the South Atlantic prior to the satellite era, providing a foundation for future research into the physical mechanisms that enabled these events. Full article

(This article belongs to the Special Issue Cyclones: Types and Phase Transitions)

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12 pages, 2736 KiB

Open AccessEditor’s ChoiceArticle

Impact of Nanoparticles as an Air Pollutant on Angulin-1/Lipolysis-Stimulated Lipoprotein Receptor in Asthma

by DaYeon Hwang, Min-Hyeok An, Pureun-Haneul Lee, Jung-Hyun Kim, Yunha Nam, Shinhee Park, Ae-Rin Baek and An-Soo Jang

Atmosphere 2024, 15(12), 1532; https://doi.org/10.3390/atmos15121532 - 20 Dec 2024

Viewed by 711

Abstract

Background: The tricellular tight junction protein angulin-1/lipolysis-stimulated lipoprotein receptor (LSR) is linked to numerous signal transduction pathways that govern gene expression, epithelial cell function, and morphogenesis. The effect of titanium dioxide (TiO₂) on LSR and asthma remains unknown. The objective of [...] Read more.

Background: The tricellular tight junction protein angulin-1/lipolysis-stimulated lipoprotein receptor (LSR) is linked to numerous signal transduction pathways that govern gene expression, epithelial cell function, and morphogenesis. The effect of titanium dioxide (TiO₂) on LSR and asthma remains unknown. The objective of the present study was to evaluate the impact of TiO₂ on LSR expression in asthma. Methods: A TiO₂-induced animal model of asthma was established using BALB/c mice and cell lines using normal human bronchial epithelial (NHBE) lung cells and we examined LSR, RAGE, and TGFβ expression using this model. Additionally, we analyzed plasma-LSR concentrations and their correlation with clinical variables in asthma patients and control subjects. Results: The LSR concentrations in patients with asthma were lower compared to controls, and were correlated with lung function and inflammatory cell ratio. In NHBE cells treated with Derp1, LSR protein expression was reduced and changed by exposure to TiO₂, whereas TGFβ expression was increased and changed. In mouse lungs, LSR expression was significantly reduced in OVA mice and changed in OVA/TiO₂ mice. Conclusion: Circulating LSR levels were decreased and correlated with clinical variables in patients with asthma, and they were influenced by TiO₂ exposure in mice, suggesting the potential involvement of LSR in asthma pathogenesis. Full article

(This article belongs to the Special Issue Research on Air Pollution and Human Exposures)

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23 pages, 11421 KiB

Open AccessEditor’s ChoiceArticle

Simulation and Assessment of Episodic Dust Storms in Eastern Saudi Arabia Using HYSPLIT Trajectory Model and Satellite Observations

by Abdulrahman Suhail Alzaid, Ismail Anil and Omer Aga

Atmosphere 2024, 15(12), 1515; https://doi.org/10.3390/atmos15121515 - 18 Dec 2024

Viewed by 1114

Abstract

The “dust belt” region extending from the western Sahara to the Gobi Desert frequently generates severe dust storms that cause hazardous air quality and disrupt daily activities. Dust storm management systems with proactive mitigation strategies can minimize the detrimental impacts of dust storms. [...] Read more.

The “dust belt” region extending from the western Sahara to the Gobi Desert frequently generates severe dust storms that cause hazardous air quality and disrupt daily activities. Dust storm management systems with proactive mitigation strategies can minimize the detrimental impacts of dust storms. This study applies the HYSPLIT model to simulate dust storms in Saudi Arabia, specifically targeting the eastern region. The study’s main objective is to calibrate and validate the model’s dust storm prediction module for the eastern region of Saudi Arabia. The validated HYSPLIT model, with optimized parameters such as threshold friction velocity, particle release rate, and dry deposition velocity from model calibration studies, showed a strong linear correlation between measured and predicted values. It achieved an R² of 0.9965, indicating excellent model accuracy. The main findings of the source apportionment approach, employing air particle backward trajectories and frequency analyses, indicated that the northern regions, specifically Iraq and Syria, were the primary sources of the severe dust storms observed in the receptor area. The outcomes of this study will be a reference for future research aimed at improving dust storm management systems and selecting sites for tree-planting campaigns under the “Saudi & Middle East Green Initiatives”. Full article

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

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19 pages, 7527 KiB

Open AccessEditor’s ChoiceArticle

Satellite Signatures of Pre-Seismic Atmospheric Anomalies of 6 February 2023 Türkiye Earthquakes

by Maria Zoran, Dan Savastru and Marina Tautan

Atmosphere 2024, 15(12), 1514; https://doi.org/10.3390/atmos15121514 - 18 Dec 2024

Viewed by 789

Abstract

Time series satellite data, coupled with available ground-based observations, enable geophysicists to survey earthquake precursors in areas of strong geotectonic activity. This paper is focused on pre-seismic atmospheric disturbances resulting from the stress accumulated during the seismogenic process related to the 6 February [...] Read more.

Time series satellite data, coupled with available ground-based observations, enable geophysicists to survey earthquake precursors in areas of strong geotectonic activity. This paper is focused on pre-seismic atmospheric disturbances resulting from the stress accumulated during the seismogenic process related to the 6 February 2023 Kahramanmaras doublet earthquake sequence in Türkiye. We investigated the pre- and post-seismic anomalies of multiple precursors of different spatiotemporal patterns from MODIS Terra/Aqua and NOAA-AVHRR satellite data (air temperature at 2 m height—AT, air relative humidity—RH, and air pressure—AP, surface outgoing long-wave radiation—OLR, and land surface temperature—LST). Pre-seismic recorded anomalies of AT within seven months and OLR within one month before the main shocks suggested the existence of the preparatory process of the Kahramanmaras doublet earthquake. The 8-Day LST_Day and LST_night data evidenced pre-seismic and post-seismic thermal anomalies for both the Pazarcik and Elbistan earthquakes. The results of this study highlight that the spatiotemporal evolution of earthquake precursors can be important information for updating the seismic hazard in geotectonic active areas. Full article

(This article belongs to the Special Issue Ionospheric Sounding for Identification of Pre-seismic Activity)

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11 pages, 1757 KiB

Open AccessEditor’s ChoiceArticle

High-Altitude Discharges and Whistlers of Volcanic Thunderstorms

by Evgeniy I. Malkin, Boris M. Shevtsov, Nina V. Cherneva, Evgeniy A. Kazakov and János Lichtenberger

Atmosphere 2024, 15(12), 1503; https://doi.org/10.3390/atmos15121503 - 17 Dec 2024

Viewed by 670

Abstract

The results of the observations of atmospherics and whistlers initiated by high-altitude electrical discharges that occurred during the eruption of the Kamchatka volcanoes (Bezymianny and Shiveluch (Russia)) on 7 and 10 April 2023 are presented. Recording of atmospherics and associated whistlers was carried [...] Read more.

The results of the observations of atmospherics and whistlers initiated by high-altitude electrical discharges that occurred during the eruption of the Kamchatka volcanoes (Bezymianny and Shiveluch (Russia)) on 7 and 10 April 2023 are presented. Recording of atmospherics and associated whistlers was carried out by a VLF (very low frequencies) radio direction finder. Two-hop whistlers were identified by dispersion coefficient, which corresponded to the double passage of the signal from Kamchatka to Australia and back. The heights of the electric discharges were determined by means of interferograms of direct and reflected from the ionosphere radiofrequency atmospherics. The high-altitude distribution of an electric discharge is obtained, the penetration of which into the ionosphere is responsible for the generation of whistlers. The characteristics of volcanic electrical discharges and whistlers can be used to estimate the height of an explosive eruption. Full article

(This article belongs to the Special Issue Atmospheric Electricity (2nd Edition))

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29 pages, 3942 KiB

Open AccessEditor’s ChoiceArticle

Evidence and Explanation for the 2023 Global Warming Anomaly

by Roger N. Jones

Atmosphere 2024, 15(12), 1507; https://doi.org/10.3390/atmos15121507 - 17 Dec 2024

Viewed by 4427

Abstract

In 2023, the rapid increase in global temperature of around 0.25 °C caught the scientific community by surprise. Its cause has been investigated largely by exploring variations on a long-term trend, with little success. Building on previous work, this paper proposes an alternative [...] Read more.

In 2023, the rapid increase in global temperature of around 0.25 °C caught the scientific community by surprise. Its cause has been investigated largely by exploring variations on a long-term trend, with little success. Building on previous work, this paper proposes an alternative explanation—on decadal timescales, observed temperature shows a complex, nonlinear response to forcing, stepping through a series of steady-state regimes. The 2023 event is nominated as the latest in the sequence. Step changes in historical and modeled global mean surface temperatures (GMSTs) were detected using the bivariate test. Each time series was then separated into gradual (trends) and rapid components (shifts) and tested using probative criteria. For sea surface, global and land surface temperatures from the NOAA Global Surface Temperature Dataset V6.0 1880–2022, the rapid component of total warming was 94% of 0.72 °C, 78% of 1.16 °C and 74% of 1.93 °C, respectively. These changes are too large to support the gradual warming hypothesis. The recent warming was initiated in March 2023 by sea surface temperatures (SSTs) in the southern hemisphere, followed by an El Niño signal further north. Global temperatures followed, then land. A preceding regime shift in 2014 and subsequent steady-state 2015–2022 was also initiated and sustained by SSTs. Analysis of the top 100 m annual average ocean temperature from 1955 shows that it forms distinct regimes, providing a substantial ‘heat bank’ that sustains the changes overhead. Regime shifts are also produced by climate models. Archived data show these shifts emerged with coupling of the ocean and atmosphere. Comparing shifts and trends with equilibrium climate sensitivity (ECS) in an ensemble of 94 CMIP5 RCP4.5 models 2006–2095 showed that shifts had 2.9 times the influence on ECS than trends. Factors affecting this relationship include ocean structure, initialization times, physical parameters and model skill. Single model runs with skill ≥75 showed that shifts were 6.0 times more influential than trends. These findings show that the dominant warming mechanism is the sudden release of heat from the ocean rather than gradual warming in the atmosphere. The model ensemble predicted all regime changes since the 1970s within ±1 year, including 2023. The next shift is projected for 2036, but current emissions are tracking higher than projected by RCP4.5. Understanding what these changes mean for the estimation of current and future climate risks is an urgent task. Full article

(This article belongs to the Section Climatology)

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26 pages, 6195 KiB

Open AccessEditor’s ChoiceArticle

Vegetation Effects on Air Pollution: A Comprehensive Assessment for Two Italian Cities

by Mihaela Mircea, Gino Briganti, Felicita Russo, Sandro Finardi, Camillo Silibello, Rossella Prandi, Giuseppe Carlino, Massimo D’Isidoro, Andrea Cappelletti and Giuseppe Cremona

Atmosphere 2024, 15(12), 1511; https://doi.org/10.3390/atmos15121511 - 17 Dec 2024

Cited by 1 | Viewed by 780

Abstract

The role of urban vegetation in urban air quality is usually assessed by considering only the pollutant removal capacity of the plants. This study aims to show, for the first time, the effects of vegetation on air pollutant concentrations through its effects on [...] Read more.

The role of urban vegetation in urban air quality is usually assessed by considering only the pollutant removal capacity of the plants. This study aims to show, for the first time, the effects of vegetation on air pollutant concentrations through its effects on meteorology, separately from its biogenic emissions. It also investigates how air quality changes when only biogenic emissions are altered by using plants with different emission factors, as well as the potential effects of introducing new vegetation into urban areas. These assessments were conducted using atmospheric modelling systems currently employed for air quality forecasting and planning, configured specifically for the cities of Bologna and Milan. Simulations were performed for two representative months, July and January, to capture summer and winter conditions, respectively. The variability in air concentrations of ozone (O₃), nitrogen dioxide (NO₂), and particulate matter (PM10) within the municipal boundaries was assessed monthly. When evaluating the impact of future vegetation, changes in temperature, wind speed, and relative humidity were also considered. The results indicate that vegetation influences air quality more significantly through changes in meteorological conditions than through biogenic emissions. Changes in biogenic emissions result in similar behaviours in O₃ and PM10 concentrations, with the latter being affected by the changes in the concentrations of secondary biogenic aerosols formed in the atmosphere. Changes in NO₂ concentrations are controlled by the changes in O₃ concentrations, increasing where O₃ concentrations decrease, and vice versa, as expected in highly polluted areas. Meteorologically induced vegetation effects also play a predominant role in depositions, accounting for most of the changes; however, the concentrations remain high despite increased deposition rates. Therefore, understanding only the removal characteristics of vegetation is insufficient to quantify its effects on urban air pollution. Full article

(This article belongs to the Section Air Quality)

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22 pages, 7731 KiB

Open AccessEditor’s ChoiceArticle

Determining the PM₁₀ Pollution Sources near the Copper Smelter in Bor, Serbia

by Renata Kovačević, Bojan Radović, Dragan Manojlović, Tamara Urošević, Tatjana Apostolovski-Trujić, Viša Tasić and Milena Jovašević-Stojanović

Atmosphere 2024, 15(12), 1498; https://doi.org/10.3390/atmos15121498 - 16 Dec 2024

Viewed by 825

Abstract

The EPA Positive Matrix Factorization (PMF) 5.0 model was applied to determine the sources and characteristics of PM₁₀ collected near the copper smelter in Bor, Serbia, from September 2009 to July 2010. For a better understanding of the industrial sources of PM [...] Read more.

The EPA Positive Matrix Factorization (PMF) 5.0 model was applied to determine the sources and characteristics of PM₁₀ collected near the copper smelter in Bor, Serbia, from September 2009 to July 2010. For a better understanding of the industrial sources of PM₁₀ pollution, the dataset was divided into four observation periods: heating season (HS), non-heating season (NHS), copper smelter in work (SW), and copper smelter out of work (SOW). The daily limit for the PM₁₀ fraction of 50 μg/m³ was exceeded on one-sixth of days in the NHS, about half the days in the HS, and about one-third of days during the SOW and SW period. The nine different sources of PM₁₀ were identified: fuel combustion, industrial dust, dust from tailings, storage and preparation of raw materials, secondary nitrate, Cu smelter, traffic, cadmium, and plant for the production of precious metals. The contribution of factors related to the activities in the copper smelter complex to the total mass of PM₁₀ was 83.1%. When the copper smelter was out of work the contribution of all the factors related to PM₁₀ pollution from the copper smelter to the total mass of the PM₁₀ was 2.3-fold lower, 35.8%, compared with the period when the copper smelter was in work. This study is the first attempt to use PMF receptor modeling to determine the air pollution sources and their contribution to ambient air pollution in the city of Bor, Serbia. Full article

(This article belongs to the Special Issue Atmospheric Particulate Matter: Origin, Sources, and Composition)

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21 pages, 7255 KiB

Open AccessEditor’s ChoiceArticle

Evaluating Ionospheric Total Electron Content (TEC) Variations as Precursors to Seismic Activity: Insights from the 2024 Noto Peninsula and Nichinan Earthquakes of Japan

by Karan Nayak, Rosendo Romero-Andrade, Gopal Sharma, Charbeth López-Urías, Manuel Edwiges Trejo-Soto and Ana Isela Vidal-Vega

Atmosphere 2024, 15(12), 1492; https://doi.org/10.3390/atmos15121492 - 14 Dec 2024

Cited by 3 | Viewed by 1453

Abstract

This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and [...] Read more.

This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and temporal pattern assessments over a 30-day period before the earthquake. The time series for TEC at the closest station to the epicenter, USUD, reveals a localized decline, with a significant negative anomaly exceeding 5 TECU observed 22 and 23 days before the earthquake, highlighting the potential of TEC variations as seismic precursors. Similar patterns were observed at a nearby station, MIZU, strengthening the case for a seismogenic origin. Positive anomalies were linked to intense space weather episodes, while the most notable negative anomalies occurred under geomagnetically calm conditions, further supporting their seismic association. Using Kriging interpolation, the anomaly zone was shown to closely align with the earthquake’s epicenter. To assess the consistency of TEC anomalies in different seismic events, the study also examines the Mw 7.1 Nichinan earthquake in August 2024. The results reveal a prominent negative anomaly, reinforcing the reliability of TEC depletions in seismic precursor detection. Additionally, spatial correlation analysis of Pearson correlation across both events demonstrates that TEC coherence diminishes with increasing distance, with pronounced correlation decay beyond 1000–1600 km. This spatial decay, consistent with Dobrovolsky’s earthquake preparation area, strengthens the association between TEC anomalies and seismic activity. This research highlights the complex relationship between ionospheric anomalies and seismic events, underscoring the value of TEC analysis as tool for earthquake precursor detection. The findings significantly enhance our understanding of ionospheric dynamics related to seismic events, advocating for a comprehensive, multi-station approach in future earthquake prediction efforts. Full article

(This article belongs to the Special Issue Electromagnetic Observations and Their Applications in Earthquake Research)

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14 pages, 3274 KiB

Open AccessEditor’s ChoiceArticle

Reconstructed Phase Space of Tropical Cyclone Activity in the North Atlantic Basin for Determining the Predictability of the System

by Sarah M. Weaver, Christopher A. Steward, Jason J. Senter, Sarah S. Balkissoon and Anthony R. Lupo

Atmosphere 2024, 15(12), 1488; https://doi.org/10.3390/atmos15121488 - 12 Dec 2024

Viewed by 952

Abstract

Tropical cyclone prediction is often described as chaotic and unpredictable on time scales that cross into stochastic regimes. Predictions are bounded by the depth of understanding and the limitations of the physical dynamics that govern them. Slight changes in global atmospheric and oceanic [...] Read more.

Tropical cyclone prediction is often described as chaotic and unpredictable on time scales that cross into stochastic regimes. Predictions are bounded by the depth of understanding and the limitations of the physical dynamics that govern them. Slight changes in global atmospheric and oceanic conditions may significantly alter tropical cyclone genesis regions and intensity. The purpose of this paper is to characterize the predictability of seasonal storm characteristics in the North Atlantic basin by utilizing the Largest Lyapunov Exponent and Takens’ Theorem, which is rarely used in weather or climatological analysis. This is conducted for a post-weather satellite era (1960–2022). Based on the accumulated cyclone energy (ACE) time series in the North Atlantic basin, cyclone activity can be described as predictable at certain timescales. Insight and understanding into this coupled non-linear system through an analysis of time delay, embedded dimension, and Lyapunov exponent-reconstructed phase space have provided critical information for the system’s predictability. Full article

(This article belongs to the Special Issue Advances in Tropical Cyclone Prediction: Observation, Simulation, and Verification)

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19 pages, 906 KiB

Open AccessEditor’s ChoiceArticle

Forecasting of Local Lightning Using Spatial–Channel-Enhanced Recurrent Convolutional Neural Network

by Wei Zhou, Jinliang Li, Hongjie Wang, Donglai Zhang and Xupeng Wang

Atmosphere 2024, 15(12), 1478; https://doi.org/10.3390/atmos15121478 - 11 Dec 2024

Viewed by 963

Abstract

Lightning is a hazardous weather phenomenon, characterized by sudden occurrences and complex local distributions. It poses significant challenges for accurate forecasting, which is crucial for public safety and economic stability. Deep learning methods are often better than traditional numerical weather prediction (NWP) models [...] Read more.

Lightning is a hazardous weather phenomenon, characterized by sudden occurrences and complex local distributions. It poses significant challenges for accurate forecasting, which is crucial for public safety and economic stability. Deep learning methods are often better than traditional numerical weather prediction (NWP) models at capturing the spatiotemporal predictors of lightning events. However, these methods struggle to integrate predictors from diverse data sources, which leads to lower accuracy and interpretability. To address these challenges, the Multi-Scale Spatial–Channel-Enhanced Recurrent Convolutional Neural Network (SCE-RCNN) is proposed to improve forecasting accuracy and timeliness by utilizing multi-source data and enhanced attention mechanisms. The proposed model incorporates a multi-scale spatial–channel attention module and a cross-scale fusion module, which facilitates the integration of data from diverse sources. The multi-scale spatial–channel attention module utilizes a multi-scale convolutional network to extract spatial features at different spatial scales and employs a spatial–channel attention mechanism to focus on the most relevant regions for lightning prediction. Experimental results show that the SCE-RCNN model achieved a critical success index (CSI) of 0.83, a probability of detection (POD) of 0.991, and a false alarm rate (FAR) reduced to 0.351, outperforming conventional deep learning models across multiple prediction metrics. This research provides reliable lightning forecasts to support real-time decision-making, making significant contributions to aviation safety, outdoor event planning, and disaster risk management. The model’s high accuracy and low false alarm rate highlight its value in both academic research and practical applications. Full article

(This article belongs to the Special Issue The Challenge of Weather and Climate Prediction)

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16 pages, 2182 KiB

Open AccessEditor’s ChoiceArticle

Enhancements of Triethanolamine CO₂ Absorption Rate and Degradation in the Presence of Nickel Nanoparticles Catalysts

by Harold W. Orendi, Kevin Joby and Lidija Šiller

Atmosphere 2024, 15(12), 1479; https://doi.org/10.3390/atmos15121479 - 11 Dec 2024

Viewed by 792

Abstract

Here, the catalytic and degradation effect of nickel nanoparticles (NiNPs) on triethanolamine (TEA) with CO₂ at 20 °C and 50 °C and a range of TEA concentrations (3–30 wt%) was studied. We show that TEA absorption rate of CO₂ can be [...] Read more.

Here, the catalytic and degradation effect of nickel nanoparticles (NiNPs) on triethanolamine (TEA) with CO₂ at 20 °C and 50 °C and a range of TEA concentrations (3–30 wt%) was studied. We show that TEA absorption rate of CO₂ can be enhanced with NiNPs, the maximum enhancement was 8.3% when compared to a control solution found at 50 °C with 30 wt% TEA alone. Additionally, the time for TEA to be fully loaded with CO₂ is reduced; compared to the control, NiNPs enhanced solutions were up to 26.3% faster. Also, to the best of our knowledge, this is the first time the degradation of TEA with NiNPs has been studied. TEA was subject to both oxygen (30 wt%, 55 °C, 0.35 L/min of air, 0.4 mol_CO2/mol_TEA, 7.5 mL/min of CO₂) and thermal degradation with and without NiNPs (30 wt%, 0.5 mol_CO2/mol_TEA, 135 °C). In both degradation experiments, surprisingly, there was no significant difference in TEA degradation in the presence of NiNPs. At high temperature (135 °C), the solution lost 19.2% and 20.3% of the original TEA, with and without NiNPs, respectively. In the presence of oxygen, the solution lost 30.5% and 33.6% of the original TEA, with and without NiNPs, respectively. This indicates that TEA or its mixture with other amines and NiNPs could improve post-combustion CO₂ capture. Full article

(This article belongs to the Special Issue Advances in CO₂ Capture and Absorption)

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20 pages, 477 KiB

Open AccessEditor’s ChoiceArticle

Children’s Exposure to Volatile Organic Compounds: A Comparative Analysis of Assessments in Households, Schools, and Indoor Swimming Pools

by Marta Fonseca Gabriel, Fátima Felgueiras and Manuel Feliciano

Atmosphere 2024, 15(12), 1471; https://doi.org/10.3390/atmos15121471 - 9 Dec 2024

Cited by 2 | Viewed by 906

Abstract

Chemical pollution is an increasing worldwide concern, with children being especially vulnerable to the harmful effects of air pollution. This study aimed to characterize the mixture of volatile organic compounds (VOCs) present in indoor air across residential, educational, and recreational settings. It analyzed [...] Read more.

Chemical pollution is an increasing worldwide concern, with children being especially vulnerable to the harmful effects of air pollution. This study aimed to characterize the mixture of volatile organic compounds (VOCs) present in indoor air across residential, educational, and recreational settings. It analyzed data on VOC concentrations from previous sampling campaigns conducted in households with children, primary schools, and indoor swimming pools (70 buildings, 151 indoor spaces) in northern Portugal. The findings reveal the co-occurrence of 16 VOCs (1,2,4-trimethylbenzene, benzene, ethylbenzene, m/o/p-xylenes, styrene, toluene, tetrachloroethylene, 2-ethylhexanol, butanol, acetophenone, ethyl acetate, benzaldehyde, decanal, nonanal, 1-methoxy-2-propanol and limonene) across all three settings, primarily associated to emissions from building materials and detergents. However, distinct patterns were also observed in the VOCs detected across the three indoor environments: in homes, the predominant VOCs were primarily released from cleaning and fragranced products; in schools, from ammonia-based cleaners and occupant activities; and in swimming pools, the predominant airborne chemicals were disinfection by-products resulting from the chemical dynamics associated with water disinfection. Overall, the findings highlight the need for additional research to deepen our understanding of the risks posed by combined exposure to multiple indoor air chemicals for children. These results also underscore the importance of developing and enforcing regulations to monitor VOC levels in environments frequented by children and implementing preventive measures to minimize their exposure to harmful chemicals. Full article

(This article belongs to the Special Issue Enhancing Indoor Air Quality: Monitoring, Analysis and Assessment)

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