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Isoprene Emissions, Oxidation Chemistry and Environmental Impacts
Summer Diurnal LST Variability Across Local Climate Zones Using ECOSTRESS Data in Lecce and Milan

Journal Description

Atmosphere

Atmosphere is an international, peer-reviewed, open access journal of scientific studies related to the atmosphere published monthly online by MDPI. The Italian Aerosol Society (IAS) and Working Group of Air Quality in European Citizen Science Association (ECSA) are affiliated with Atmosphere and their members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, Inspec, CAPlus / SciFinder, Astrophysics Data System, and other databases.
Journal Rank: CiteScore - Q2 (Environmental Science (miscellaneous))
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.1 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our editors and authors say about the Atmosphere.
Companion journals for Atmosphere include: Meteorology and Aerobiology.

Impact Factor: 2.3 (2024); 5-Year Impact Factor: 2.5 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2073-4433

Latest Articles

29 pages, 4175 KiB

Open AccessArticle

Assessing Long-Term Post-Conflict Air Pollution: Trends and Implications for Air Quality in Mosul, Iraq

by Zena Altahaan and Daniel Dobslaw

Atmosphere 2025, 16(7), 756; https://doi.org/10.3390/atmos16070756 (registering DOI) - 20 Jun 2025

Prolonged conflicts in Iraq over the past four decades have profoundly disrupted environmental systems, not only through immediate post-conflict emissions—such as residues from munitions and explosives—but also via long-term infrastructural collapse, population displacement, and unsustainable resource practices. Despite growing concern over air quality [...] Read more.

Prolonged conflicts in Iraq over the past four decades have profoundly disrupted environmental systems, not only through immediate post-conflict emissions—such as residues from munitions and explosives—but also via long-term infrastructural collapse, population displacement, and unsustainable resource practices. Despite growing concern over air quality in conflict-affected regions, comprehensive assessments integrating long-term data and localized measurements remain scarce. This study addresses this gap by analyzing the environmental consequences of sustained instability in Mosul, focusing on air pollution trends using both remote sensing data (1983–2023) and in situ monitoring of key pollutants—including PM_2.5, PM₁₀, TVOCs, NO₂, SO₂, and formaldehyde—at six urban sites during 2022–2023. The results indicate marked seasonal variations, with winter peaks in combustion-related pollutants (NO₂, SO₂) and elevated particulate concentrations in summer driven by sandstorm activity. Annual average concentrations of all six pollutants increased by 14–51%, frequently exceeding WHO air quality guidelines. These patterns coincide with worsening meteorological conditions, including higher temperatures, reduced rainfall, and more frequent storms, suggesting synergistic effects between climate stress and pollution. The findings highlight severe public health risks and emphasize the urgent need for integrated urban recovery strategies that promote sustainable infrastructure, environmental restoration, and resilience to climate change. Full article

(This article belongs to the Special Issue Emissions of Volatile Organic Compounds (VOCs): Characterization, Environmental Impacts and Control)

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16 pages, 1812 KiB

Open AccessArticle

The Cost of Heat: Health and Economic Burdens in Three Brazilian Cities

by Daniela Debone, Nilton Manuel Évora do Rosário and Simone Georges El Khouri Miraglia

Atmosphere 2025, 16(7), 755; https://doi.org/10.3390/atmos16070755 - 20 Jun 2025

Excess mortality due to heat is a major public health concern globally. In this study, we investigated the association between extreme heat and mortality in three distinct locations in São Paulo state, Brazil—São Paulo city (the capital), Campinas (a large countryside city), and [...] Read more.

Excess mortality due to heat is a major public health concern globally. In this study, we investigated the association between extreme heat and mortality in three distinct locations in São Paulo state, Brazil—São Paulo city (the capital), Campinas (a large countryside city), and Marília (a typical medium-sized rural city)—from 2004 to 2018. We applied a generalized linear model (GLM) with a Poisson distribution and a logarithmic link function for each city, using the excess heat factor (EHF) as the exposure metric. The results showed that increases in the EHF were associated with relative risks of 1.0018 (95% CI: 1.0015–1.0022) in São Paulo, 1.0029 (95% CI: 1.0023–1.0036) in Campinas, and 1.0033 (95% CI: 1.0025–1.0041) in Marília. Altogether, 2319 heat-attributable deaths were estimated, representing an economic burden of USD 6.03 billion based on the value of a statistical life. By integrating economic valuation with mortality risk estimates, our study offers a broader perspective on the consequences of extreme heat, reinforcing the need for public health and policy interventions. Full article

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

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24 pages, 5147 KiB

Open AccessArticle

Research on Air Temperature Inversion Method Based on Land Surface Temperature of Different Land Surface Cover

by Rui Fang, Xiaofang Shan and Qinli Deng

Atmosphere 2025, 16(7), 754; https://doi.org/10.3390/atmos16070754 - 20 Jun 2025

This study explores a method for deriving air temperature (AT) from land surface temperature (LST) based on different urban land-use types, aiming to address the accuracy of urban heat island (UHI) effect measurements. Using Wuhan as a case study, the research integrates remote [...] Read more.

This study explores a method for deriving air temperature (AT) from land surface temperature (LST) based on different urban land-use types, aiming to address the accuracy of urban heat island (UHI) effect measurements. Using Wuhan as a case study, the research integrates remote sensing data with ground meteorological observations to develop various models, analyze their accuracy and applicability, and generate LST and AT maps to validate model reliability. The results indicate that when establishing the LST–AT relationship, polynomial regression performs best for water bodies (R² = 0.905), while random forest yields the highest R² for built-up areas, cropland, and vegetation at 0.942, 0.953, and 0.924, respectively. Due to the characteristics of the algorithms, it is recommended to prioritize random forest for prediction when the sample range covers the observed data range and to use BP neural networks when it does not. The generated maps reveal that in summer, using LST significantly overestimates UHI intensity in the study area, while differences between UHI intensities in winter are negligible. In resource-constrained scenarios, LST can be directly used to assess the UHI effect. Full article

(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)

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12 pages, 2395 KiB

Open AccessArticle

Comparative Analysis of Air Pollution in Beijing and Seoul: Long-Term Trends and Seasonal Variations

by Hana Na and Woo-Sik Jung

Atmosphere 2025, 16(7), 753; https://doi.org/10.3390/atmos16070753 - 20 Jun 2025

This study compares long-term air pollution trends and seasonal patterns in Beijing and Seoul from 2014 to 2024, focusing on PM2.5, PM10, CO, NO₂, SO₂, and O₃. Using statistical analyses including Mann–Kendall tests and generalized additive models, [...] Read more.

This study compares long-term air pollution trends and seasonal patterns in Beijing and Seoul from 2014 to 2024, focusing on PM2.5, PM10, CO, NO₂, SO₂, and O₃. Using statistical analyses including Mann–Kendall tests and generalized additive models, we found that Beijing achieved notable reductions in particulate matter, largely due to stricter industrial controls and reduced coal use, though winter pollution peaks remain. In contrast, Seoul’s improvements were slower, mainly due to persistent vehicular emissions and recurring spring dust storms from northern China. Seasonal analysis showed winter peaks in Beijing linked to coal heating, and spring peaks in Seoul driven by transboundary dust, with higher summer ozone in Seoul reflecting photochemical activity. These findings highlight the need for city-specific air quality management and regional cooperation, recommending further reductions in vehicular emissions for Seoul and continued transition from coal in Beijing to mitigate health impacts. This study identifies specific seasonal trends and pollution sources that require targeted policy interventions to improve air quality. Full article

(This article belongs to the Special Issue Anthropogenic Pollutants in Environmental Geochemistry (2nd Edition))

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22 pages, 4168 KiB

Open AccessArticle

Assessment of CH₄ and CO₂ Emissions from a Municipal Waste Landfill: Trends, Dispersion, and Environmental Implications

by Georgeta Olguta Gavrila, Gabriela Geanina Vasile, Simona Mariana Calinescu, Cristian Constantin, Gheorghita Tanase, Alexandru Cirstea, Valentin Stancu, Valeriu Danciulescu and Cristina Orbeci

Atmosphere 2025, 16(7), 752; https://doi.org/10.3390/atmos16070752 - 20 Jun 2025

The European Union views biogas production from landfills as a crucial element in achieving decarbonization goals by 2050. Biogas is primarily composed of methane (CH₄) and carbon dioxide (CO₂), produced through the anaerobic digestion of various residual materials. This [...] Read more.

The European Union views biogas production from landfills as a crucial element in achieving decarbonization goals by 2050. Biogas is primarily composed of methane (CH₄) and carbon dioxide (CO₂), produced through the anaerobic digestion of various residual materials. This study aimed to investigate CH₄ and CO₂ concentrations from municipal solid waste in biogas capture wells in a landfill in Romania between 2023 and 2024. A peak in CH₄ concentrations occurred in the fall of 2024 (P4 well), while the highest CO₂ content was recorded in the summer of 2023 (P3 well). The Aermod View software platform (version 11.2.0) was employed to model the dispersion of pollutants in the surrounding air. A worst-case scenario was applied to estimate the highest ground-level pollutant concentrations. The highest recorded CH₄ concentration was 90.1 mg/m³, while CO₂ reached 249 mg/m³ within the landfill. The highest CH₄ concentrations were found in the southern part of the site, less than 1 km from the landfill, while CO₂ was highest in the northern area. In conclusion, municipal solid waste landfills behave like unpredictable bioreactors, and without proper management and oversight, they can pose significant risks. An integrated system that combines prevention, reuse, and correct disposal is critical to minimizing these negative effects. Full article

(This article belongs to the Special Issue Anthropogenic Pollutants in Environmental Geochemistry (2nd Edition))

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20 pages, 14382 KiB

Open AccessArticle

Exploring the Causes of Multicentury Hydroclimate Anomalies in the South American Altiplano with an Idealized Climate Modeling Experiment

by Ignacio Alonso Jara, Orlando Astudillo, Pablo Salinas, Limbert Torrez-Rodríguez, Nicolás Lampe-Huenul and Antonio Maldonado

Atmosphere 2025, 16(7), 751; https://doi.org/10.3390/atmos16070751 - 20 Jun 2025

Paleoclimate records have long documented the existence of multicentury hydroclimate anomalies in the Altiplano of South America. However, the causes and mechanisms of these extended events are still unknown. Here, we present a climate modeling experiment that explores the oceanic drivers and atmospheric [...] Read more.

Paleoclimate records have long documented the existence of multicentury hydroclimate anomalies in the Altiplano of South America. However, the causes and mechanisms of these extended events are still unknown. Here, we present a climate modeling experiment that explores the oceanic drivers and atmospheric mechanisms conducive to long-term precipitation variability in the southern Altiplano (18–25° S; 70–65 W; >3500 masl). We performed a series of 100-year-long idealized simulations using the Weather Research and Forecasting (WRF) model, configured to repeat annually the oceanic and atmospheric forcing leading to the exceptionally humid austral summers of 1983/1984 and 2011/2012. The aim of these cyclical experiments was to evaluate if these specific conditions can sustain a century-long pluvial event in the Altiplano. Unlike the annual forcing, long-term negative precipitation trends are observed in the simulations, suggesting that the drivers of 1983/1984 and 2011/2012 wet summers are unable to generate a century-scale pluvial event. Our results show that an intensification of the anticyclonic circulation along with cold surface air anomalies in the southwestern Atlantic progressively reinforce the lower and upper troposphere features that prevent moisture transport towards the Altiplano. Prolonged drying is also observed under persistent La Niña conditions, which contradicts the well-known relationship between precipitation and ENSO at interannual timescales. Contrasting the hydroclimate responses between the Altiplano and the tropical Andes result from a sustained northward migration of the Atlantic trade winds, providing a useful analog for explaining the divergences in the Holocene records. This experiment suggests that the drivers of century-scale hydroclimate events in the Altiplano were more diverse than previously thought and shows how climate modeling can be used to test paleoclimate hypotheses, emphasizing the necessity of combining proxy data and numerical models to improve our understanding of past climates. Full article

(This article belongs to the Special Issue Extreme Climate in Arid and Semi-arid Regions)

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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

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

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

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

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

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

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

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

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

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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24 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

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

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

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

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

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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