Atmosphere

14 pages, 1670 KiB

Open AccessArticle

Inhibiting the Production of Polychlorinated Organic Pollutants in the Hydrolysis Oxidation Process of 1,2-Dichlorobenzene

by Yuqing Li, Bisi Lv, Na Li, Yingjie Li, Wenjie Song and Jiahui Zhou

Atmosphere 2025, 16(6), 750; https://doi.org/10.3390/atmos16060750 - 19 Jun 2025

Viewed by 273

Abstract

The hydrolysis oxidation of 1,2-chlorobenzene (1,2-DCB) over Pd-Ti-Ni/ZSM-5(25) catalysts has been investigated as a safe and environmentally friendly method for the removal of chlorinated aromatic organic compounds. Experimental results demonstrate that hydrolysis oxidation technology can effectively suppress the formation of polychlorinated organic compounds. [...] Read more.

The hydrolysis oxidation of 1,2-chlorobenzene (1,2-DCB) over Pd-Ti-Ni/ZSM-5(25) catalysts has been investigated as a safe and environmentally friendly method for the removal of chlorinated aromatic organic compounds. Experimental results demonstrate that hydrolysis oxidation technology can effectively suppress the formation of polychlorinated organic compounds. Among the catalysts studied, the 0.5%Pd-2%Ti-8%Ni/ZSM-5(25) catalyst exhibited optimal hydrolysis oxidation performance, achieving complete conversion of 1,2-DCB at 425 °C. Notably, this technology significantly inhibits the formation of polychlorinated organic by-products during the catalytic degradation of 1,2-DCB. Although trace amounts of chlorobenzene were still detected, the overall reduction in hazardous by-products is remarkable. Characterization techniques, including X-Ray Diffraction (XRD), X-Ray Photoelectron Spectroscopy (XPS), Pyridine adsorption infrared Spectroscopy (pyridine IR) and Fourier transform infrared spectroscopy (FT-IR) analysis, revealed that the acidity and redox properties of the catalyst surface play a pivotal role in the hydrolysis oxidation process. The hydrolysis oxidation of chlorinated volatile organic compounds not only effectively reduces pollutant concentrations but also prevents the generation of more toxic by-products. This dual benefit not only protects the environment but also minimizes ecological risks, highlighting the potential of this technology for sustainable environmental remediation. Full article

(This article belongs to the Special Issue Emission Characteristics and Control Technology of Volatile Organic Compounds)

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20 pages, 3194 KiB

Open AccessArticle

Emission Rates for Light-Duty Truck Towing Operations in Real-World Conditions

by Bumsik Kim, Rohit Jaikumar, Rodolfo Souza, Minjie Xu, Jeremy Johnson, Carl R. Fulper, James Faircloth, Madhusudhan Venugopal, Chaoyi Gu, Tara Ramani, Michael Aldridge, Richard W. Baldauf, Antonio Fernandez, Thomas Long, Richard Snow, Craig Williams, Russell Logan and Heidi Vreeland

Atmosphere 2025, 16(6), 749; https://doi.org/10.3390/atmos16060749 - 19 Jun 2025

Viewed by 311

Abstract

Light-duty trucks (LDTs) are often used to tow trailers. Towing increases the load on the engine, and this additional load can affect exhaust emissions. Although heavy-duty towing impacts are widely studied, data on LDT towing impacts is sparse. In this study, portable emissions [...] Read more.

Light-duty trucks (LDTs) are often used to tow trailers. Towing increases the load on the engine, and this additional load can affect exhaust emissions. Although heavy-duty towing impacts are widely studied, data on LDT towing impacts is sparse. In this study, portable emissions measurement systems (PEMSs) were used to measure in-use emissions from three common LDTs during towing and non-towing operations. Emission rates were characterized by operating modes defined in the Environmental Protection Agency’s (EPA’s) MOVES (MOtor Vehicle Emissions Simulator) model. The measured emission rates were compared to the default rates used by MOVES, revealing similar overall trends. However, discrepancies between measured rates and MOVES predictions, especially at high speed and high operating modes, indicate a need for refinement in emissions modeling for LDTs under towing operations. Results highlight a general trend of increased CO₂, CO, HC, and NOx when towing a trailer compared to non-towing operations across nearly all operating modes, with distinct CO and HC increases in the higher operating modes. Although emissions were observed to be notably higher in a handful of scenarios, results also indicate that three similar LDTs can have distinctly different emission profiles. Full article

(This article belongs to the Section Air Quality)

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15 pages, 5319 KiB

Open AccessArticle

Assessing the Reliability of Seasonal Data in Representing Synoptic Weather Types: A Mediterranean Case Study

by Alexandros Papadopoulos Zachos, Kondylia Velikou, Errikos-Michail Manios, Konstantia Tolika and Christina Anagnostopoulou

Atmosphere 2025, 16(6), 748; https://doi.org/10.3390/atmos16060748 - 18 Jun 2025

Viewed by 295

Abstract

Seasonal climate forecasts are an essential tool for providing early insight into weather-related impacts and supporting decision-making in sectors such as agriculture, energy, and disaster management. Accurate representation of atmospheric circulation at the seasonal scale is essential, especially in regions such as the [...] Read more.

Seasonal climate forecasts are an essential tool for providing early insight into weather-related impacts and supporting decision-making in sectors such as agriculture, energy, and disaster management. Accurate representation of atmospheric circulation at the seasonal scale is essential, especially in regions such as the Eastern Mediterranean, where complex synoptic patterns drive significant climate variability. The aim of this study is to perform a comparison of weather type classifications between ERA5 reanalysis and seasonal forecasts in order to assess the ability of seasonal data to capture the synoptic patterns over the Eastern Mediterranean. For this purpose, we introduce a regional seasonal forecasting framework based on the state-of-the-art Advanced Research WRF (WRF-ARW) model. A series of sensitivity experiments were also conducted to evaluate the robustness of the model’s performance under different configurations. Moreover, the ability of seasonal data to reproduce observed trends in weather types over the historical period is also examined. The classification results from both ERA5 and seasonal forecasts reveal a consistent dominance of anticyclonic weather types throughout most of the year, with a particularly strong signal during the summer months. Model evaluation indicates that seasonal forecasts achieve an accuracy of approximately 80% in predicting the daily synoptic condition (cyclonic or anticyclonic) up to three months in advance. These findings highlight the promising skill of seasonal datasets in capturing large-scale circulation features and their associated trends in the region. Full article

(This article belongs to the Section Climatology)

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17 pages, 15168 KiB

Open AccessArticle

Variability in Summer Rainfall and Rain Days over the Southern Kalahari: Influences of ENSO and the Botswana High

by Bohlale Kekana, Ross Blamey and Chris Reason

Atmosphere 2025, 16(6), 747; https://doi.org/10.3390/atmos16060747 - 18 Jun 2025

Viewed by 355

Abstract

Rainfall variability in the sensitive Kalahari semi-desert in Southern Africa, a region of strong climatic gradients, has not been much studied and is poorly understood. Here, anomalies in rainfall totals and moderate and heavy rain day frequencies are examined for both the summer [...] Read more.

Rainfall variability in the sensitive Kalahari semi-desert in Southern Africa, a region of strong climatic gradients, has not been much studied and is poorly understood. Here, anomalies in rainfall totals and moderate and heavy rain day frequencies are examined for both the summer half of the year and three bi-monthly seasons using CHIRPS rainfall data and ERA5 reanalysis. Peak rainfall occurs in January–February, with anomalously wet summers marked by a significant increase in the number of rainy days rather than rainfall intensity. Wet summers are linked to La Niña events, cyclonic anomalies over Angola, and a weakened Botswana High, which enhances low-level moisture transport and convergence over the region as well as mid-level uplift. Roughly the reverse patterns are found during anomalously dry summers. On sub-seasonal scales, ENSO and the Botswana High (the Southern Annular Mode) are negatively (positively) significantly correlated with early summer rainfall, while in mid-summer, and for the entire November–April season, only ENSO and the Botswana High are correlated with rainfall amounts. In the late summer, weak negative correlations remain with the Botswana High, but they do not achieve 95% significance. Full article

(This article belongs to the Section Climatology)

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21 pages, 4801 KiB

Open AccessArticle

Projection of Cloud Vertical Structure and Radiative Effects Along the South Asian Region in CMIP6 Models

by Praneta Khardekar, Hemantkumar S. Chaudhari, Vinay Kumar and Rohini Lakshman Bhawar

Atmosphere 2025, 16(6), 746; https://doi.org/10.3390/atmos16060746 - 18 Jun 2025

Viewed by 275

Abstract

The evaluation of cloud distribution, properties, and their interaction with the radiation (longwave and shortwave) is of utmost importance for the proper assessment of future climate. Therefore, this study focuses on the Coupled Model Inter-Comparison Project Phase-6 (CMIP6) historical and future projections using [...] Read more.

The evaluation of cloud distribution, properties, and their interaction with the radiation (longwave and shortwave) is of utmost importance for the proper assessment of future climate. Therefore, this study focuses on the Coupled Model Inter-Comparison Project Phase-6 (CMIP6) historical and future projections using the Shared Socio-Economic Pathways (SSPs) low- (ssp1–2.6), moderate- (ssp2–4.5), and high-emission (ssp5–8.5) scenarios along the South Asian region. For this purpose, a multi-model ensemble mean approach is employed to analyze the future projections in the low-, mid-, and high-emission scenarios. The cloud water content and cloud ice content in the CMIP6 models show an increase in upper and lower troposphere simultaneously in future projections as compared to ERA5 and historical projections. The longwave and shortwave cloud radiative effects at the top of the atmosphere are examined, as they offer a global perspective on radiation changes that influence atmospheric circulation and climate variability. The longwave cloud radiative effect (44.14 W/m²) and the shortwave cloud radiative effect (−73.43 W/m²) likely indicate an increase in cloud albedo. Similarly, there is an expansion of Hadley circulation (intensified subsidence) towards poleward, indicating the shifting of subtropical high-pressure zones, which can influence regional monsoon dynamics and cloud distributions. The impact of future projections on the tropospheric temperature (200–600 hPa) is studied, which seems to become more concentrated along the Tibetan Plateau in the moderate- and high-emission scenarios. This increase in the tropospheric temperature at 200–600 hPa reduces atmospheric stability, allowing stronger convection. Hence, the strengthening of convective activities may be favorable in future climate conditions. Thus, the correct representation of the model physics, cloud-radiative feedback, and the large-scale circulation that drives the Indian Summer Monsoon (ISM) is of critical importance in Coupled General Circulation Models (GCMs). Full article

(This article belongs to the Section Climatology)

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18 pages, 4964 KiB

Open AccessArticle

Multi-Model Simulations of a Mediterranean Extreme Event: The Impact of Mineral Dust on the VAIA Storm

by Tony Christian Landi, Paolo Tuccella, Umberto Rizza and Mauro Morichetti

Atmosphere 2025, 16(6), 745; https://doi.org/10.3390/atmos16060745 - 18 Jun 2025

Viewed by 262

Abstract

This study investigates the impact of desert dust on precipitation patterns using multi-model simulations. Dust-based processes of formation/removal of ice nuclei (IN) and cloud condensation nuclei (CCN) are investigated by using both the online access model WRF-CHIMERE and the online integrated model WRF-Chem. [...] Read more.

This study investigates the impact of desert dust on precipitation patterns using multi-model simulations. Dust-based processes of formation/removal of ice nuclei (IN) and cloud condensation nuclei (CCN) are investigated by using both the online access model WRF-CHIMERE and the online integrated model WRF-Chem. Comparisons of model predictions with rainfall measurements (GRISO: Spatial Interpolation Generator from Rainfall Observations) over the Italian peninsula show the models’ ability to reproduce heavy orographic precipitation in alpine regions. To quantify the impact of the mineral dust transport concomitant to the atmospheric river (AR) on cloud formation, a sensitivity study is performed by using the WRF-CHIMERE model (i) by setting dust concentrations to zero and (ii) by modifying the settings of the Thompson Aerosol-Aware microphysics scheme. Statistical comparisons revealed that WRF-CHIMERE outperformed WRF-Chem. It achieved a correlation coefficient of up to 0.77, mean bias (MB) between +3.56 and +5.01 mm/day, and lower RMSE and MAE values (~32 mm and ~22 mm, respectively). Conversely, WRF-Chem displayed a substantial underestimation, with an MB of −25.22 mm/day and higher RMSE and MAE values. Our findings show that, despite general agreement in spatial precipitation patterns, both models significantly underestimated the peak daily rainfall in pre-alpine regions (e.g., 216 mm observed at Malga Valine vs. 130–140 mm simulated, corresponding to a 35–40% underestimation). Although important instantaneous changes in precipitation and temperature were modeled at a local scale, no significant total changes in precipitation or air temperature averaged over the entire domain were observed. These results underline the complexity of aerosol–cloud interactions and the need for improved parameterizations in coupled meteorological models. Full article

(This article belongs to the Section Aerosols)

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21 pages, 11264 KiB

Open AccessArticle

Comparative Analysis of Perturbation Characteristics Between LBGM and ETKF Initial Perturbation Methods in Convection-Permitting Ensemble Forecasts

by Jiajun Li, Chaohui Chen, Xiong Chen, Hongrang He, Yongqiang Jiang and Yanzhen Kang

Atmosphere 2025, 16(6), 744; https://doi.org/10.3390/atmos16060744 - 18 Jun 2025

Viewed by 282

Abstract

This study investigates an extreme squall line event that occurred in northern Jiangxi Province, China on 30–31 March 2024. Based on the WRF model, convection-permitting ensemble forecast experiments were conducted using two distinct initial perturbation approaches, namely, the Local Breeding of Growing Modes [...] Read more.

This study investigates an extreme squall line event that occurred in northern Jiangxi Province, China on 30–31 March 2024. Based on the WRF model, convection-permitting ensemble forecast experiments were conducted using two distinct initial perturbation approaches, namely, the Local Breeding of Growing Modes (LBGM) and the Ensemble Transform Kalman Filter (ETKF), to compare their perturbation structures, spatiotemporal evolution, and precipitation forecasting capabilities. The experiments demonstrated the following: (1) The LBGM method significantly improved the root mean square error (RMSE) of mid-upper tropospheric variables, particularly demonstrating superior performance in low-level temperature field forecasts, but the overall ensemble spread of the system was consistently smaller than that of ETKF. (2) The evolution of dynamical spread within the squall line system confirmed that ETKF generated greater spread growth in low-level wind fields, while LBGM exhibited better spatiotemporal alignment between mid-upper tropospheric wind field spread and the synoptic system evolution. (3) Vertical profiles of total moist energy revealed that ETKF initially exhibited higher total moist energy than LBGM. Both methods showed increasing total moist energy with forecast lead time, displaying a bimodal structure dominated by kinetic energy in upper layers (300–100 hPa) and balanced kinetic energy and moist physics terms in lower layers (1000–700 hPa), with ETKF demonstrating larger growth rates. (4) Kinetic energy spectrum analysis indicated that ETKF exhibited significantly higher perturbation energy than LBGM in the 100–1000 km mesoscale range and superior small- to medium-scale perturbation characterization at the 6–60 km scales initially. Precipitation and radar echo verification showed that ETKF effectively corrected positional biases in precipitation forecasts, while LBGM more accurately reproduced the bow-shaped echo structure near Nanchang due to its precise simulation of leading-edge vertical updrafts and rear-sector low pseudo-equivalent potential temperature regions. Full article

(This article belongs to the Special Issue Numerical Weather Prediction Models and Ensemble Prediction Systems (2nd Edition))

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18 pages, 2528 KiB

Open AccessArticle

Characterization of Historical Aerosol Optical Depth Dynamics Using LSTM and Peak Enhancement Techniques

by Horia-Alexandru Cămărășan, Alexandru Mereuță, Lucia-Timea Deaconu, Horațiu-Ioan Ștefănie, Andrei-Titus Radovici, Camelia Botezan, Zoltán Török and Nicolae Ajtai

Atmosphere 2025, 16(6), 743; https://doi.org/10.3390/atmos16060743 - 18 Jun 2025

Viewed by 294

Abstract

This study addresses the challenges of characterizing aerosol optical depth (AOD) dynamics from satellite observations, which are often hindered by data gaps and variability. A long short-term memory (LSTM) network was trained on an extended AOD dataset from Sicily to capture temporal patterns. [...] Read more.

This study addresses the challenges of characterizing aerosol optical depth (AOD) dynamics from satellite observations, which are often hindered by data gaps and variability. A long short-term memory (LSTM) network was trained on an extended AOD dataset from Sicily to capture temporal patterns. The trained model was then applied to AOD data from distinct geographical regions: Cluj-Napoca and the central Mediterranean Sea. While the LSTM effectively captured general seasonal trends, it tended to smooth extreme AOD events. To mitigate this, a post-processing algorithm was developed to enhance the representation of AOD peaks and valleys. This enhancement method refines the characterization of historical AOD, providing a more accurate representation of observed atmospheric variability, particularly in capturing high and low AOD episodes. The results demonstrate the efficacy of the hybrid approach in improving the characterization of AOD dynamics across different regions. Full article

(This article belongs to the Special Issue Recent Advances in Atmospheric Optics: From Advanced Instrumentation and Techniques to Applications in Aerosol Monitoring)

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23 pages, 3859 KiB

Open AccessArticle

Temporal and Latitudinal Occurrences of Geomagnetic Pulsations Recorded in South America by the Embrace Magnetometer Network

by Jose Paulo Marchezi, Odim Mendes and Clezio Marcos Denardini

Atmosphere 2025, 16(6), 742; https://doi.org/10.3390/atmos16060742 - 18 Jun 2025

Viewed by 253

Abstract

This study investigates the occurrence and distribution of geomagnetic pulsations (Pc2–Pc5) over South America during 2014, analyzing their dependence on magnetic latitude, local time, and geomagnetic activity. Geomagnetic field data were obtained from the Embrace magnetometer network, which spans Brazil and Argentina and [...] Read more.

This study investigates the occurrence and distribution of geomagnetic pulsations (Pc2–Pc5) over South America during 2014, analyzing their dependence on magnetic latitude, local time, and geomagnetic activity. Geomagnetic field data were obtained from the Embrace magnetometer network, which spans Brazil and Argentina and includes regions influenced by the Equatorial Electrojet (EEJ) and the South Atlantic Magnetic Anomaly (SAMA). Both continuous and discrete wavelet transforms (CWT and DWT) were employed to analyze non-stationary signals and reconstruct pulsation activity during quiet and disturbed geomagnetic periods. The results reveal that Pc5 and Pc3 pulsations exhibit a pronounced diurnal peak around local noon, with significantly stronger and more widespread activity under storm conditions. Spatial analyses highlight localized enhancements near the dip equator during quiet times and broader latitudinal spread during geomagnetic disturbances. These findings underscore the strong modulation of pulsation activity by geomagnetic conditions and offer new insights into wave behavior at low and mid-latitudes. This work contributes to understanding magnetosphere–ionosphere coupling and has implications for space weather prediction and geomagnetically induced current (GIC) risk assessment in the South American sector. Full article

(This article belongs to the Special Issue Ionospheric Disturbances and Space Weather)

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23 pages, 6031 KiB

Open AccessArticle

Assessment of the PPP-AR Strategy for ZTD and IWV in Africa: A One-Year GNSS Study

by Moustapha Gning Tine, Pierre Bosser, Ngor Faye, Lila Jean-Louis and Mapathé Ndiaye

Atmosphere 2025, 16(6), 741; https://doi.org/10.3390/atmos16060741 - 17 Jun 2025

Viewed by 345

Abstract

With the increasing demand for near real-time atmospheric water vapor monitoring, this study evaluates the performance of the open-source PRIDE PPP-AR software (version 3.0.5) for retrieving Zenith Total Delay (ZTD) and Integrated Water Vapor (IWV) over the African continent over a one-year period. [...] Read more.

With the increasing demand for near real-time atmospheric water vapor monitoring, this study evaluates the performance of the open-source PRIDE PPP-AR software (version 3.0.5) for retrieving Zenith Total Delay (ZTD) and Integrated Water Vapor (IWV) over the African continent over a one-year period. PRIDE PPP-AR is compared with established PPP-AR and PPP solutions, including CSRS-PPP, IGN-PPP, and NGL and using GipsyX, ERA5, and IGS products as references. A robust methodology combining time series processing and statistical evaluation was adopted. Multiple tools were leveraged to ensure a comprehensive performance analysis of GNSS data from seven stations in Africa, where such studies remain scarce. The results show that PRIDE PPP-AR achieves ZTD accuracy comparable to GipsyX (RMSE < 6 mm, R² ≈ 0.99) and performs at a similar level to NGL and CSRS-PPP. Compared to the other solutions, PRIDE PPP-AR has an accuracy similar to CSRS-PPP and NGL, but slightly better than IGN-PPP, in line with ERA5 and IGS references. For IWV retrieval, comparisons with ERA5 indicate RMSE values of about 1.5 to 2.7 kg/m², depending on station location and climatic conditions. IWV variability tends to increase towards the equator, where the recorded fluctuations are higher than in subtropical zones. In addition, collocated radiosonde (RS) measurements in Abidjan confirm good agreement, further validating the reliability of the software. This study highlights the potential of GNSS meteorology, in providing reliable spatiotemporal IWV monitoring and indicates that the PRIDE PPP-AR is ready for the high precision meteorological applications in African regions. These results offer promising prospects for spatiotemporal studies through African multi-GNSS networks and the PRIDE PPP-AR approach. Full article

(This article belongs to the Special Issue Advances in Methods for the Investigation of the Atmospheric Water Cycle)

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17 pages, 2555 KiB

Open AccessArticle

A Bibliometric Analysis of the Impact of Extreme Weather on Air Transport Operations

by Kristína Kováčiková, Andrej Novák, Martina Kováčiková and Alena Novak Sedlackova

Atmosphere 2025, 16(6), 740; https://doi.org/10.3390/atmos16060740 - 17 Jun 2025

Viewed by 345

Abstract

Extreme weather events pose increasing risks to air transport operations, affecting flight safety, scheduling, and infrastructure resilience. This paper provides a comprehensive bibliometric analysis of scientific literature addressing the impacts of extreme weather on aviation, based on 1000 documents retrieved from the Web [...] Read more.

Extreme weather events pose increasing risks to air transport operations, affecting flight safety, scheduling, and infrastructure resilience. This paper provides a comprehensive bibliometric analysis of scientific literature addressing the impacts of extreme weather on aviation, based on 1000 documents retrieved from the Web of Science Core Collection (2010–2024). Using VOSviewer software, keyword co-occurrence, overlay visualization, co-authorship networks, and citation analyses were conducted. Results revealed a clear thematic shift from environmental impact assessments toward research emphasizing operational resilience, technological adaptation, and mitigation strategies. Collaboration networks highlighted strong international cooperation, particularly among institutions in the United States, Germany, and the United Kingdom, with growing contributions from emerging research regions. Highly cited studies predominantly focused on emissions modeling and operational mitigation measures. Despite notable advances, the field remains fragmented and geographically uneven, underscoring the need for broader interdisciplinary integration and empirical validation of adaptation strategies. This paper offers a systematic overview of the evolving research landscape and identifies critical directions for future efforts to enhance the resilience and sustainability of global air transport systems under increasing climatic volatility. Full article

(This article belongs to the Section Meteorology)

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27 pages, 5450 KiB

Open AccessArticle

A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment

by Ioannis Stergiou, Nektaria Traka, Dimitrios Melas, Efthimios Tagaris and Rafaella-Eleni P. Sotiropoulou

Atmosphere 2025, 16(6), 739; https://doi.org/10.3390/atmos16060739 - 17 Jun 2025

Viewed by 973

Abstract

Accurate air quality forecasting is essential for environmental management and health protection. However, conventional air quality models often exhibit systematic biases and underpredict pollution events due to uncertainties in emissions, meteorology, and atmospheric processes. Addressing these limitations, this study introduces a hybrid deep [...] Read more.

Accurate air quality forecasting is essential for environmental management and health protection. However, conventional air quality models often exhibit systematic biases and underpredict pollution events due to uncertainties in emissions, meteorology, and atmospheric processes. Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. The model is trained here, using data from ten stations in Texas, enabling it to capture both spatial and temporal patterns in atmospheric behavior. Performance evaluation shows notable improvements, with a Root Mean Square Error (RMSE) reduction ranging from 34.11% to 71.63%. F1 scores for peak detection improved by up to 37.38%, Dynamic Time Warping (DTW) distance decreased by 72.77%, the Index of Agreement rose up to 90.09%, and the R² improved by up to 188.80%. A comparison of four loss functions—Mean Square Error (MSE), Huber, Asymmetric Mean Squared Error (AMSE), and Quantile Loss—revealed that MSE offered balanced performance, Huber Loss achieved the highest reduction in systematic RMSE, and AMSE performed best in peak detection. Additionally, four deep learning architectures were evaluated: baseline CNN-LSTM, a hybrid model with attention mechanisms, a transformer-based model, and an End-to-End framework. The hybrid attention-based model consistently outperformed others across metrics while maintaining lower computational demands. Full article

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

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29 pages, 4817 KiB

Open AccessArticle

Comprehensive Analysis of the Driving Forces Behind NDVI Variability in China Under Climate Change Conditions and Future Scenario Projections

by Ao Li, Shuai Yin, Nan Li and Chong Shi

Atmosphere 2025, 16(6), 738; https://doi.org/10.3390/atmos16060738 - 17 Jun 2025

Viewed by 336

Abstract

Climate change has a significant impact on vegetation development. While existing studies provide some insights, long-term trend analysis and multifactor driver assessments for China are still lacking. At the same time, research on the future vegetation development under different climate change scenarios needs [...] Read more.

Climate change has a significant impact on vegetation development. While existing studies provide some insights, long-term trend analysis and multifactor driver assessments for China are still lacking. At the same time, research on the future vegetation development under different climate change scenarios needs further strengthening. In response to these issues, this study analyzed China’s normalized difference vegetation index (NDVI) data from 2001 to 2023, exploring vegetation cover trends, driving factors, and predicting the impact of future climate change. Firstly, this study decomposed the time series data into seasonal, trend, and residual components using the Seasonal–Trend decomposition using Loess (STL) decomposition method, quantifying vegetation changes across different climate zones. Partial least squares (PLS) regression analysis was then used to examine the relationship between NDVI and driving factors, and the contribution of these factors to NDVI variation was determined through the variable importance in projection (VIP) score. The results show that NDVI has significantly increased over the past two decades, especially since 2010. Further analysis revealed that vegetation growth is primarily influenced by soil moisture, shortwave radiation, and total precipitation (VIP scores > 0.8). Utilizing machine learning with Coupled Model Intercomparison Project Phase 6 (CMIP6) multimodel data, this study predicts NDVI trends from 2023 to 2100 under four emission scenarios (SSP126, SSP245, SSP370, SSP585), quantifying future meteorological factors such as temperature, precipitation, and radiation to NDVI. Findings indicate that under high-emission scenarios, the vegetation greenness in some regions may experience improved vegetation conditions despite global warming challenges. Future land management strategies must consider climate change impacts on ecosystems to ensure sustainability and enhance ecosystem services. Full article

(This article belongs to the Section Air Quality and Health)

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21 pages, 3911 KiB

Open AccessArticle

Trends in Annual, Seasonal, and Daily Temperature and Its Relation to Climate Change in Puerto Rico

by José J. Hernández Ayala, Rafael Méndez Tejeda, Fernando L. Silvagnoli Santos, Nohán A. Villafañe Rolón and Nickanthony Martis Cruz

Atmosphere 2025, 16(6), 737; https://doi.org/10.3390/atmos16060737 - 17 Jun 2025

Viewed by 397

Abstract

Puerto Rico has experienced recent increases in annual, seasonal and daily temperatures that have been associated with climate change. More recently, the island has been experiencing an increase in the frequency of extremely warm days that are causing significant environmental and socio-economic impacts. [...] Read more.

Puerto Rico has experienced recent increases in annual, seasonal and daily temperatures that have been associated with climate change. More recently, the island has been experiencing an increase in the frequency of extremely warm days that are causing significant environmental and socio-economic impacts. This study focuses on examining how annual, seasonal and daily temperatures have changed over recent decades in 12 historical sites spread across the island for the 1970–2024 period and how it relates to climate change. The Mann–Kendall tests for trends were employed for the annual and seasonal series to identify areas of the island where warming has been found to be statistically significant. The 90th, 95th, and 99th percentiles of daily temperature series were also analyzed. This study found that Puerto Rico has experienced significant warming from 1970 to 2024, with the most consistent increases in minimum temperatures, especially during the summer and nighttime hours. The frequency of extreme heat events has increased across nearly all stations in different areas of the island. Stepwise regression models identified surface air temperature (SAT), sea surface temperature (SST), and total precipitable water (TPW) as the most influential regional climate predictors driving mean temperature trends and the occurrence of extreme heat events. Full article

(This article belongs to the Section Climatology)

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16 pages, 1754 KiB

Open AccessArticle

The Impact of Air Pollution on Morbidity in the Industrial Areas of the East Kazakhstan Region

by Gulnaz Sadykanova, Sanat Kumarbekuly and Ayauzhan Yessimbekova

Atmosphere 2025, 16(6), 736; https://doi.org/10.3390/atmos16060736 - 17 Jun 2025

Viewed by 685

Abstract

Atmospheric air pollution is a major environmental and public health concern, particularly in industrialized regions. The East Kazakhstan Region exhibits high rates of oncological, cardiovascular, and respiratory diseases. However, the specific impact of industrial emissions on morbidity remains insufficiently studied. This study employed [...] Read more.

Atmospheric air pollution is a major environmental and public health concern, particularly in industrialized regions. The East Kazakhstan Region exhibits high rates of oncological, cardiovascular, and respiratory diseases. However, the specific impact of industrial emissions on morbidity remains insufficiently studied. This study employed correlation and regression analyses using data on pollutant emissions and population morbidity indicators from 2014 to 2023. Correlation and regression methods, along with geoinformation technologies, were applied. A moderate positive correlation was found between industrial emission volumes and the incidence of neoplasms (r = 0.59, R² = 0.35), especially in areas with high concentrations of sulfur dioxide, nitrogen oxides, and particulate matter. The findings confirm the significant influence of polluted air—particularly mixed pollutants—on the increase in cancer-related diseases. The conclusions emphasize the urgent need to implement emission reduction measures, enhance environmental monitoring and disease prevention, and carry out further epidemiological research. Full article

(This article belongs to the Section Air Quality and Health)

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8 pages, 1020 KiB

Open AccessArticle

Forbush Effects Associated with Disappeared Solar Filaments

by Olga Kryakunova, Botakoz Seifullina, Maria Abunina, Nataly Shlyk, Artem Abunin, Nikolay Nikolayevskiy and Irina Tsepakina

Atmosphere 2025, 16(6), 735; https://doi.org/10.3390/atmos16060735 - 17 Jun 2025

Viewed by 260

Abstract

The Forbush effects (FEs) in cosmic rays associated with interplanetary disturbances caused by the disappearance of solar filaments (DSFs) outside active regions (ARs) are considered. In total, 481 FEs were detected for 1995–2023 using the database of Forbush Effects and Interplanetary Disturbances (FEID). [...] Read more.

The Forbush effects (FEs) in cosmic rays associated with interplanetary disturbances caused by the disappearance of solar filaments (DSFs) outside active regions (ARs) are considered. In total, 481 FEs were detected for 1995–2023 using the database of Forbush Effects and Interplanetary Disturbances (FEID). The behavior of the cosmic ray density was calculated using the Global Survey Method (GSM). The distributions of the FE numbers depending on their duration and magnitude, as well as on the characteristics of the interplanetary and near-Earth medium, were obtained. It is found that the average duration of such FEs (33.4 ± 0.5 h) is almost the same as for events associated with CMEs from ARs, but the average magnitude is much smaller (0.83 ± 0.03%). It is also shown that coronal mass ejections (CMEs) caused by DSFs are often low-speed interplanetary disturbances (with an average maximum SW speed of 423.2 ± 3.5 km/s), the velocities of which are close to the speed of the background solar wind (SW). During FEs associated with CMEs after DSFs outside ARs, on average, unsettled geomagnetic activity is observed. Magnetic storms were recorded only in 19% of events. Lower values of FE magnitude and geomagnetic activity are associated with weakened magnetic fields and low speeds of such interplanetary disturbances. Full article

(This article belongs to the Section Planetary Atmospheres)

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21 pages, 4469 KiB

Open AccessArticle

Assessment of PM₁₀ and PM_2.5 Concentrations in Santo Domingo: A Comparative Study Between 2019 and 2022

by Carime Matos-Espinosa, Ramón Delanoy, Claudia Caballero-González, Anel Hernández-Garces, Ulises Jauregui-Haza, Solhanlle Bonilla-Duarte and José-Ramón Martínez-Batlle

Atmosphere 2025, 16(6), 734; https://doi.org/10.3390/atmos16060734 - 16 Jun 2025

Viewed by 452

Abstract

This study analyzes the spatial and temporal variability of PM₁₀ and PM_2.5 concentrations in Santo Domingo, Dominican Republic, based on short-term sampling campaigns conducted in 2019 and 2022. In 2019, PM₁₀ levels averaged 38.14 µg/m³, while in 2022 [...] Read more.

This study analyzes the spatial and temporal variability of PM₁₀ and PM_2.5 concentrations in Santo Domingo, Dominican Republic, based on short-term sampling campaigns conducted in 2019 and 2022. In 2019, PM₁₀ levels averaged 38.14 µg/m³, while in 2022 they rose significantly to 62.18 µg/m³. PM_2.5 in 2022 averaged 30.37 µg/m³. These differences are likely influenced by meteorological variability, including increased transport of Saharan dust in mid-2022, and seasonal factors. Although local emission changes were not directly assessed, they may have also played a role in the observed trends. Statistical analyses revealed that aerosol optical depth (AOD), air pressure, and rainfall were significant predictors of PM₁₀ in 2022, explaining up to 75% of the variance. Correlations and regression models confirmed a robust association between AOD and PM levels on a weekly timescale. These findings highlight the importance of integrating remote sensing and meteorological data to improve air quality monitoring and inform environmental policy in Caribbean urban areas. Full article

(This article belongs to the Section Air Quality)

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23 pages, 5083 KiB

Open AccessArticle

Current Status, Trends, and Future Directions in Chilean Air Quality: A Data-Driven Perspective

by Kevin Basoa, Zoё L. Fleming, Manuel A. Leiva, Carolina Concha and Camilo Menares

Atmosphere 2025, 16(6), 733; https://doi.org/10.3390/atmos16060733 - 16 Jun 2025

Viewed by 579

Abstract

Air pollution is one of the main problems facing humanity today. Megacities and large urban areas are pollution hotspots. Measuring pollutants and recording these measurements is key to developing effective strategies to reduce the pollution levels to which people are exposed. However, air [...] Read more.

Air pollution is one of the main problems facing humanity today. Megacities and large urban areas are pollution hotspots. Measuring pollutants and recording these measurements is key to developing effective strategies to reduce the pollution levels to which people are exposed. However, air quality monitoring presents significant challenges (e.g., investment costs, maintenance), which can lead to limited monitoring equipment. Despite this, Chile has an extensive air quality monitoring network, known as the National Air Quality Information System (SINCA in Spanish). This network has more than 200 monitoring stations that measure, record, and display information on pollution levels in different locations in Chile. In this study, all the information available from the SINCA network was systematized to evaluate the completeness of the records and the current trends of several pollutants in Chile. The main results show that most measurements focus on particulate matter and sulfur dioxide concentrations, and many of the measurement stations are located in the central part of the country (32° S–38° S). However, by splitting the data into five macrozones, one can see the regional air quality characteristics and changes. Furthermore, there are significant data gaps at some monitoring stations, which makes it difficult to elaborate a robust analysis. Regarding pollution levels, a significant decrease is observed for the peak Particulate Matter (PM_2.5) concentrations, with decreases of nearly 40% compared to concentrations at the beginning of the 2000s. This is consistent with the concentration trends, which show negative trends in most cases. Full article

(This article belongs to the Section Air Quality)

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17 pages, 2925 KiB

Open AccessArticle

Ionospheric Time Series Prediction Method Based on Spatio-Temporal Graph Neural Network

by Yifei Chen, Yang Liu, Kunlin Yang, Lanhao Li, Chao Xiong and Jinling Wang

Atmosphere 2025, 16(6), 732; https://doi.org/10.3390/atmos16060732 - 16 Jun 2025

Viewed by 318

Abstract

Predicting global ionospheric total electron content (TEC) is critical for high-precision GNSS applications, but some existing models fail to jointly capture spatial heterogeneity and multiscale temporal trends. To address the problem, this work proposes a spatio-temporal graph neural network (STGNN) that addresses these [...] Read more.

Predicting global ionospheric total electron content (TEC) is critical for high-precision GNSS applications, but some existing models fail to jointly capture spatial heterogeneity and multiscale temporal trends. To address the problem, this work proposes a spatio-temporal graph neural network (STGNN) that addresses these limitations through (1) a trainable positional attention mechanism to dynamically infer node dependencies without fixed geographical constraints and (2) a GRU–Transformer sequential module to hierarchically model local and global temporal patterns. The proposed network is validated by different solar and geomagnetic activities. With a training dataset with a time span between 2008 and 2018, the proposed model is tested in a high solar phase for the year 2015 and a low solar phase for the year 2018. For 2015, the experimental results show a 21.9% RMSE reduction at low latitudes compared to the results of the iTransformer model. For the geomagnetic storm event, the proposed STGNN achieves 16.0% higher stability. For the one-week (84 step) prediction test, the STGNN shows a 27.0% lower error compared to the MLPMultivariate model. The model’s self-adaptive spatial learning and multiscale temporal modeling uniquely enable TEC forecasting under diverse geophysical conditions. Full article

(This article belongs to the Special Issue Advanced GNSS for Ionospheric Sounding and Disturbances Monitoring)

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21 pages, 1937 KiB

Open AccessArticle

Digital Twin-Based Framework for Real-Time Monitoring and Analysis of Urban Mobile-Source Emissions

by Peter Zhivkov, Stefka Fidanova and Ivan Dimov

Atmosphere 2025, 16(6), 731; https://doi.org/10.3390/atmos16060731 - 16 Jun 2025

Viewed by 306

Abstract

This study introduces a digital twin paradigm that uses both stationary and mobile sensors and cutting-edge machine learning for urban air quality monitoring. By boosting

R^{2}

values from 0.29 to 0.87–0.95, our two-step calibration method increased the accuracy of low-cost PM sensors, [...] Read more.

This study introduces a digital twin paradigm that uses both stationary and mobile sensors and cutting-edge machine learning for urban air quality monitoring. By boosting

R^{2}

values from 0.29 to 0.87–0.95, our two-step calibration method increased the accuracy of low-cost PM sensors, showing the possibility of growing monitoring networks without sacrificing measurement accuracy. Significant temporal and spatial variability in PM concentrations was found by mobile sensor deployments, with variations of up to 300% over short distances, predominantly during heavy traffic. During rush hours, peak concentrations were found on multi-lane boulevards and intersections, indicating important exposure concerns usually overlooked by stationary monitoring networks. According to our Graph Neural Network model, which successfully described pollutant dispersion patterns, road dust resuspension predominates in residential areas, while vehicle emissions account for 65% of PM_2.5 along high-traffic corridors. Urban green areas lower PM levels by 30%, yet when the current low-emission zones were first implemented, they had no discernible effect on air quality. Municipal authorities can use this digital twin strategy to acquire practical insights for focused air quality improvements. The method helps make evidence-based traffic management and urban planning judgments by identifying unidentified pollution hotspots and source contributions. The technique offers a scalable option for establishing healthier urban development and marks a substantial leap in environmental monitoring. Full article

(This article belongs to the Special Issue Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources)

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17 pages, 3375 KiB

Open AccessArticle

Influence of Clouds and Aerosols on Solar Irradiance and Application of Climate Indices in Its Monthly Forecast over China

by Shuting Zhang and Xiaochun Wang

Atmosphere 2025, 16(6), 730; https://doi.org/10.3390/atmos16060730 - 16 Jun 2025

Viewed by 232

Abstract

Based on the Clouds and the Earth’s Radiant Energy System (CERES) satellite data from 2001 to 2023 and the climate indices from the National Oceanic and Atmospheric Administration (NOAA), this study analyzes the solar irradiance over mainland China and the impacts of clouds [...] Read more.

Based on the Clouds and the Earth’s Radiant Energy System (CERES) satellite data from 2001 to 2023 and the climate indices from the National Oceanic and Atmospheric Administration (NOAA), this study analyzes the solar irradiance over mainland China and the impacts of clouds and aerosols on it and constructs monthly forecasting models to analyze the influence of climate indices on irradiance forecasts. The irradiance over mainland China shows a spatial distribution of being higher in the west and lower in the east. The influence of clouds on irradiance decreases from south to north, and the influence of aerosols is prominent in the east. The average explained variance of clouds on irradiance is 86.72%, which is much higher than that of aerosols on irradiance, 15.62%. Singular Value Decomposition (SVD) analysis shows a high correlation between the respective time series of irradiance and cloud influence, with the two fields having similar spatial patterns of opposite signs. The variation in solar irradiance can be attributed mainly to the influence of clouds. Empirical Orthogonal Function (EOF) analysis indicates that the variation in the first mode of irradiance is consistent in most parts of China, and its time coefficient is selected for monthly forecasting. Both the traditional multiple linear regression method and the Long Short-Term Memory (LSTM) network are used to construct monthly forecast models, with the preceding time coefficient of the first EOF mode and different climate indices as input. Compared with the multiple linear regression method, LSTM has a better forecasting skill. When the input length increases, the forecasting skill decreases. The inclusion of climate indices, such as the Indian Ocean Basin (IOB), El Nino–Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), can enhance the forecasting skill. Among these three indices, IOB has the most significant improvement effect. The research provides a basis for accurate forecasting of solar irradiance over China on monthly time scale. Full article

(This article belongs to the Special Issue Solar Radiation: Measurements and Model Studies—Progress and Perspectives (2nd Edition))

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20 pages, 1258 KiB

Open AccessArticle

Upscaling the Uptake of Climate-Smart Agriculture in Semi-Arid Areas of South Africa

by Gugulethu Zuma-Netshiukhwi, Jan Jacobus Anderson, Carel Hercules Wessels and Ernest Malatsi

Atmosphere 2025, 16(6), 729; https://doi.org/10.3390/atmos16060729 - 16 Jun 2025

Viewed by 376

Abstract

Efforts to counteract climate change-induced challenges and increase agricultural productivity are growing across Africa. The Southern African region has observed a continuous myriad of weather extremes and hazard occurrences, impacting agrifood systems. The decline in agrifood systems results in food insecurities. The adoption [...] Read more.

Efforts to counteract climate change-induced challenges and increase agricultural productivity are growing across Africa. The Southern African region has observed a continuous myriad of weather extremes and hazard occurrences, impacting agrifood systems. The decline in agrifood systems results in food insecurities. The adoption of Climate-Smart Agriculture (CSA) technologies is key to building climate-resilient agricultural systems. CSA adoption is limited by several factors, including a lack of institutional support, deficiencies in policy integration, and insufficient numbers of agricultural advisors. This study was conducted in semi-arid areas in the Free State and Limpopo provinces, South Africa. This manuscript presents the upscaling of CSA towards the enhancement of sustainable agrifood systems. The respondents included of 196 smallholder farmers and 125 agricultural advisors who participated in CSA training. CSA practices include agroecological cropping systems and micro-catchments. Technology transfer requires qualitative and quantitative approaches for adoption efficacy. The CSA Acceptance Model has missing factors that were modified, including usability, profitability, sustainability, and the perceived cost of acceptance. The participatory living laboratory approach was key to using demonstration trials, on-farm training, and training of intermediaries. Through the effectiveness of technology transfer and reciprocal systems, smallholder farmers can transition to commercial levels and contribute to sustainable agrifood systems. Full article

(This article belongs to the Special Issue Meteorological Extremes and Their Impacts on Agricultural Productivity and Food Security)

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18 pages, 6644 KiB

Open AccessArticle

Air Quality and Social Vulnerability: Estimating Mining-Induced PM₁₀ Pollution in Tula, Mexico

by Osiel O. Mendoza-Lara, Andrés O. López-Pérez, Claudia Yazmín Ortega-Montoya, Adria Imelda Prieto Hinojosa and J. M. Baldasano

Atmosphere 2025, 16(6), 728; https://doi.org/10.3390/atmos16060728 - 16 Jun 2025

Viewed by 404

Abstract

The Tula Metropolitan Area in Mexico is characterized by significant industrial activity, including thermoelectric power plants, refineries, cement plants, and mining operations. While the impact of mining on air quality has been less studied compared to other industries, this research aims to estimate [...] Read more.

The Tula Metropolitan Area in Mexico is characterized by significant industrial activity, including thermoelectric power plants, refineries, cement plants, and mining operations. While the impact of mining on air quality has been less studied compared to other industries, this research aims to estimate the contribution of mining areas to PM₁₀ air pollution in the region. Using the AERMOD dispersion model coupled with the WRF meteorological model, emission areas were identified through GIS analysis, and specific emission factors for mining activities were applied. The results indicate that mining areas can contribute up to 40 µg/m³ of PM₁₀, exceeding both national and international air quality standards. Monitoring data suggests that mining activities account for approximately 30% of the measured PM₁₀ concentrations in the area. Furthermore, spatial analysis using the Urban Marginalization Index (UMI) revealed that areas with high PM₁₀ concentrations often coincide with regions of high social vulnerability, particularly in communities with elevated levels of marginalization. This study concludes that mining operations significantly contribute to air pollution in the Tula Metropolitan Area, highlighting the need for targeted mitigation measures and public policies that address both environmental and social vulnerabilities. Full article

(This article belongs to the Special Issue Atmospheric Pollution in Mining Areas)

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23 pages, 8042 KiB

Open AccessArticle

Numerical Analysis of Air Quality Improvement and Thermal Comfort in a Classroom Using Organic Air Cleaners

by Enrique Cruz-Octaviano, Guillemo Efren Ovando-Chacon, Abelardo Rodriguez-Leon and Sandy Luz Ovando-Chacon

Atmosphere 2025, 16(6), 727; https://doi.org/10.3390/atmos16060727 - 15 Jun 2025

Viewed by 338

Abstract

Evaluating ventilation behavior inside classrooms in hot climates is fundamental to ensure good indoor air quality and proper thermal comfort, thus guaranteeing a healthy environment for the users. This study analyzes the impact of mixed ventilation strategies, which combine mechanical extractors and organic [...] Read more.

Evaluating ventilation behavior inside classrooms in hot climates is fundamental to ensure good indoor air quality and proper thermal comfort, thus guaranteeing a healthy environment for the users. This study analyzes the impact of mixed ventilation strategies, which combine mechanical extractors and organic air cleaners (OACs), on CO₂ concentration and temperature distribution in an air-conditioned classroom with closed doors and windows. We used computational fluid dynamics simulations to analyze the effect of different extractor and OACs configurations on airflow distribution and average temperature, as well as the temporal evolution of average CO₂ concentrations inside the classroom. The configuration with one extractor and two OACs reduces CO₂ concentrations to 613 ppm, representing an effective solution with lower energy consumption. These findings demonstrate that hybrid ventilation systems can significantly improve IAQ and maintain thermal comfort, offering viable and energy-efficient alternatives for enclosed classrooms in hot climate regions. Full article

(This article belongs to the Section Air Quality)

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22 pages, 10230 KiB

Open AccessArticle

Near-Surface Water Vapor Content Based on SPICAV IR/VEx Observations in the 1.1 and 1.18 μm Transparency Windows of Venus

by Daria Evdokimova, Anna Fedorova, Nikolay Ignatiev, Oleg Korablev, Franck Montmessin and Jean-Loup Bertaux

Atmosphere 2025, 16(6), 726; https://doi.org/10.3390/atmos16060726 - 15 Jun 2025

Viewed by 316

Abstract

The SPICAV IR spectrometer aboard the Venus Express orbiter measured spectra of the 1.1 and 1.18 μm atmospheric transparency windows at the Venus night side in 2006–2014. The long-term measurements encompassed the major part of the Venus globe, including polar latitudes. For the [...] Read more.

The SPICAV IR spectrometer aboard the Venus Express orbiter measured spectra of the 1.1 and 1.18 μm atmospheric transparency windows at the Venus night side in 2006–2014. The long-term measurements encompassed the major part of the Venus globe, including polar latitudes. For the first time, the H₂O volume mixing ratio in the deep Venus atmosphere at about 10–16 km has been retrieved for the entire SPICAV IR dataset using a radiative transfer model with multiple scattering. The retrieved H₂O volume mixing ratio is found to be sensitive to different approximations of the H₂O and CO₂ absorption lines’ far wings and assumed surface emissivity. The global average of the H₂O abundance retrieved for different parameters ranges from 23.6 ± 1.0 ppmv to 27.7 ± 1.2 ppmv. The obtained values are consistent with recent studies of water vapor below the cloud layer, showing the H₂O mixing ratio below 30 ppmv. Within the considered dataset, the zonal mean of the H₂O mixing ratio does not vary significantly from 60° S to 75° N, except for a 2 ppmv decrease noted at high latitudes. The H₂O local time distribution is also uniform. The 8-year observation period revealed no significant long-term trends or periodicities. Full article

(This article belongs to the Section Planetary Atmospheres)

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17 pages, 12483 KiB

Open AccessArticle

Southeast Asia’s Extreme Precipitation Response to Solar Radiation Management with GLENS Simulations

by Heri Kuswanto, Fatkhurokhman Fauzi, Brina Miftahurrohmah, Mou Leong Tan and Hong Xuan Do

Atmosphere 2025, 16(6), 725; https://doi.org/10.3390/atmos16060725 - 15 Jun 2025

Viewed by 498

Abstract

This study evaluates the impacts of Solar Radiation Management (SRM) on precipitation-related climate extremes in Southeast Asia. Using simulations from the Geoengineering Large Ensemble (GLENS), we assess spatial anomalies and differences in extreme precipitation indices—number of wet days (RR1), very heavy precipitation days [...] Read more.

This study evaluates the impacts of Solar Radiation Management (SRM) on precipitation-related climate extremes in Southeast Asia. Using simulations from the Geoengineering Large Ensemble (GLENS), we assess spatial anomalies and differences in extreme precipitation indices—number of wet days (RR1), very heavy precipitation days (R20mm), maximum 5-day precipitation (Rx5day), consecutive dry days (CDD), and consecutive wet days (CWD)—relative to historical (1980–2009) and Representative Concentration Pathway 8.5 (RCP8.5) baselines. The results reveal that SRM induces highly heterogeneous precipitation responses across the region. While SRM increases rainfall frequency in parts of Indonesia, it reduces the number of wet days and lengthens dry spells over Vietnam, Thailand, and the Philippines. Spatial variations are also observed in changes to heavy precipitation days and multi-day rainfall events, with potential implications for flood and drought risks. These findings highlight the complex trade-offs in hydrological responses under SRM deployment, with important considerations for agriculture, water resource management, and climate adaptation strategies in Southeast Asia. Full article

(This article belongs to the Section Climatology)

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27 pages, 2653 KiB

Open AccessArticle

Temporal and Machine Learning-Based Principal Component and Clustering Analysis of VOCs and Their Role in Urban Air Pollution and Ozone Formation

by Balendra V. S. Chauhan, Maureen J. Berg, Ajit Sharma, Kirsty L. Smallbone and Kevin P. Wyche

Atmosphere 2025, 16(6), 724; https://doi.org/10.3390/atmos16060724 - 15 Jun 2025

Viewed by 453

Abstract

This study investigates the temporal dynamics, sources, and photochemical behaviour of key volatile organic compounds (VOCs) along Marylebone Road, London (1 January 2015–1 January 2023), a heavily trafficked urban area. Hourly measurements of benzene, toluene, ethylbenzene, ethene, propene, isoprene, propane, and ethyne, alongside [...] Read more.

This study investigates the temporal dynamics, sources, and photochemical behaviour of key volatile organic compounds (VOCs) along Marylebone Road, London (1 January 2015–1 January 2023), a heavily trafficked urban area. Hourly measurements of benzene, toluene, ethylbenzene, ethene, propene, isoprene, propane, and ethyne, alongside ozone (O₃) and meteorological data, were analysed using correlation matrices, regression, cross-correlation, diurnal/seasonal analysis, wind-sector analysis, PCA (Principal Component Analysis), and clustering. Strong inter-VOC correlations (e.g., benzene–ethylbenzene: r = 0.86, R² = 0.75; ethene–propene: r = 0.68, R² = 0.53) highlighted dominant vehicular sources. Diurnal peaks of benzene, toluene, and ethylbenzene aligned with rush hours, while O₃ minima occurred in early mornings due to NO titration. VOCs peaked in winter under low mixing heights, whereas O₃ was highest in summer. Wind-sector analysis revealed dominant VOC emissions from SSW (south-southwest)–WSW (west-southwest) directions; ethyne peaked from the E (east)/ENE (east-northeast). O₃ concentrations were highest under SE (southeast)–SSE (south-southeast) flows. PCA showed 39.8% of variance linked to traffic-related VOCs (PC1) and 14.8% to biogenic/temperature-driven sources (PC2). K-means clustering (k = 3) identified three regimes: high VOCs/low O₃ in stagnant, cool air; mixed conditions; and low VOCs/high O₃ in warmer, aged air masses. Findings highlight complex VOC–O₃ interactions and stress the need for source-specific mitigation strategies in urban air quality management. Full article

(This article belongs to the Special Issue Air Pollution: Emission Characteristics and Formation Mechanisms)

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22 pages, 2562 KiB

Open AccessArticle

Investigation of the Regularities of the Influence of Meteorological Factors on Avalanches in Eastern Kazakhstan

by Marzhan Rakhymberdina, Natalya Denissova, Yerkebulan Bekishev, Gulzhan Daumova, Milan Konečný, Zhanna Assylkhanova and Azamat Kapasov

Atmosphere 2025, 16(6), 723; https://doi.org/10.3390/atmos16060723 - 15 Jun 2025

Viewed by 350

Abstract

This paper studies the influence of meteorological factors on avalanche occurrence in East Kazakhstan using modern data analysis methods. A dataset of 111 avalanche events in nine avalanche-prone areas of the region, recorded between 2012 and 2023, was compiled. Primary data on avalanche [...] Read more.

This paper studies the influence of meteorological factors on avalanche occurrence in East Kazakhstan using modern data analysis methods. A dataset of 111 avalanche events in nine avalanche-prone areas of the region, recorded between 2012 and 2023, was compiled. Primary data on avalanche dates were obtained from the Department of Emergency Situations of East Kazakhstan Region (DES EKR), and meteorological data were sourced from the Kazhydromet website. Descriptive statistics, correlation analysis, principal component analysis (PCA), as well as K-means clustering and DBSCAN algorithms, were used for the analysis. During the analysis of meteorological conditions preceding avalanches at nine avalanche-prone areas in Eastern Kazakhstan, using PCA (Principal Component Analysis), the main weather factors affecting avalanche formation were determined. Clustering of 111 avalanches using the K-Means method allowed the identification of four scenario types: gradual snow accumulation without wind (33 cases), upper layer thawing due to warming (34), high snow cover (28), and storm impact (16). The DBSCAN method revealed two anomalous cases related to extreme snow depth. Correlation analysis revealed significant relationships between avalanches and meteorological parameters such as air temperature, snow cover depth, wind speed and direction, precipitation, and relative humidity. Correlation analysis revealed both negative and positive relationships between meteorological parameters. Principal component analysis identified the most significant variables affecting avalanche activity, with temperature, snow cover height, and wind making the greatest contributions. Cluster analysis demonstrated that avalanches could occur under different combinations of weather conditions within the same areas, confirming the complex nature of avalanche-forming processes. The results emphasize the need for an integrated approach to avalanche forecasting that accounts for the multi-parametric interactions of meteorological factors, and may contribute to the improvement of avalanche risk monitoring and mitigation systems in mountain regions. Full article

(This article belongs to the Section Meteorology)

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19 pages, 18325 KiB

Open AccessArticle

Thermodynamic Study of a Mediterranean Cyclone with Tropical Characteristics in September 2020

by Sotirios T. Arsenis, Angelos I. Siozos and Panagiotis T. Nastos

Atmosphere 2025, 16(6), 722; https://doi.org/10.3390/atmos16060722 - 14 Jun 2025

Viewed by 441

Abstract

This study examines the evolution, structure, and dynamic and thermodynamic mechanisms of a Mediterranean tropical-like cyclone (TLC), or medicane (from Mediterranean–Hurricane), that occurred in the central Mediterranean region from 15 to 19 September 2020. This event is considered an extreme meteorological phenomenon, particularly [...] Read more.

This study examines the evolution, structure, and dynamic and thermodynamic mechanisms of a Mediterranean tropical-like cyclone (TLC), or medicane (from Mediterranean–Hurricane), that occurred in the central Mediterranean region from 15 to 19 September 2020. This event is considered an extreme meteorological phenomenon, particularly impacting the Greek area and affecting the country’s economic and social structures. It is one of the most significant recorded Mediterranean cyclone phenomena in the broader Mediterranean region. The synoptic and dynamic environment, as well as the thermodynamic structure of this atmospheric disturbance, were analyzed using thermodynamic parameters. The system’s development can be described through three distinct phases, characterized by its symmetrical structure and warm core, as illustrated in the phase space diagrams and further supported by dynamical analysis. During the first phase, on 15 September, the structure of the upper tropospheric layers began to strengthen the parent barometric low, which had been in the Sirte Bay region since 13 September. The influence of upper-level dynamical processes was responsible for the reconstruction of the weakened barometric low. In the second phase, during the formation of the Mediterranean cyclone, low-level diabatic processes determined the evolution of the surface cyclone without significant support from upper-tropospheric baroclinic processes. Therefore, in this phase, the system is characterized as barotropic. In the third phase, the system remained barotropic but showed a continuous weakening tendency as the sea surface pressure steadily increased. This comprehensive analysis highlights the intricate processes involved in the development and evolution of Mediterranean cyclones with tropical characteristics. Full article

(This article belongs to the Special Issue Climate and Weather Extremes in the Mediterranean)

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19 pages, 4960 KiB

Open AccessArticle

Long-Term Fine Particulate Matter (PM_2.5) Trends and Exposure Patterns in the San Joaquin Valley of California

by Ricardo Cisneros, Donald Schweizer, Marzieh Amiri, Gilda Zarate-Gonzalez and Hamed Gharibi

Atmosphere 2025, 16(6), 721; https://doi.org/10.3390/atmos16060721 - 14 Jun 2025

Viewed by 478

Abstract

Since 1989, California pollution control efforts have caused annual PM_2.5 averages to decrease. Despite the decline in ambient air concentrations of PM_2.5, the San Joaquin Valley (SJV) of California continues to violate the federal standard for PM_2.5. This [...] Read more.

Since 1989, California pollution control efforts have caused annual PM_2.5 averages to decrease. Despite the decline in ambient air concentrations of PM_2.5, the San Joaquin Valley (SJV) of California continues to violate the federal standard for PM_2.5. This study evaluated PM_2.5 trends, diurnal and seasonal patterns, pollution sources, and air quality improvements from 2000 to 2022 in the SJV. Hourly and daily PM_2.5 data from CARB and EPA-certified monitors were analyzed using regression models, polar plots, and Air Quality Index (AQI) classification methods. Monthly PM_2.5 concentrations peaked in winter (November–January) and during commute periods, with higher levels observed on Fridays and Saturdays. In this study, the highest daily PM_2.5 levels observed in Fresno and Bakersfield occurred during the autumn, most likely due to agricultural activities and higher wind speeds, with daily values greater than 25 µgm⁻³ and 50 µgm⁻³, respectively. In contrast, in Clovis, the highest daily PM_2.5 concentrations occurred in the winter during episodes characterized by low wind speeds, with values greater than 22 µgm⁻³. While PM_2.5 has declined since 1999, progress has slowed significantly since 2010. However, all sites exceeded the new EPA standard of 9 µgm⁻³. Without substantial changes to emission sources, meeting federal standards will be difficult. Full article

(This article belongs to the Special Issue New Insights into Exposure and Health Impacts of Air Pollution (2nd Edition))

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