Atmosphere

24 pages, 8188 KiB

Open AccessArticle

Top of the Atmosphere Reflected Shortwave Radiative Fluxes from ABI on GOES-18

by Yingtao Ma, Rachel T. Pinker, Wen Chen, Istvan Laszlo, Hye-Yun Kim, Hongqing Liu and Jaime Daniels

Atmosphere 2025, 16(8), 979; https://doi.org/10.3390/atmos16080979 (registering DOI) - 17 Aug 2025

Abstract

In this study, we describe the derivation and evaluation of Top of the Atmosphere (TOA) Shortwave Radiative (SWR) Fluxes from the Advanced Baseline Imager (ABI) sensor on the GOES-18 satellite. The TOA estimates use narrowband observations from ABI that are transformed to broadband [...] Read more.

In this study, we describe the derivation and evaluation of Top of the Atmosphere (TOA) Shortwave Radiative (SWR) Fluxes from the Advanced Baseline Imager (ABI) sensor on the GOES-18 satellite. The TOA estimates use narrowband observations from ABI that are transformed to broadband (NTB), based on simulations and adjusted to total fluxes using Angular Distribution Models (ADMs). Subsequently, the GOES-18 estimates are evaluated against the Clouds and the Earth’s Radiant Energy System (CERES) data, the only observed SWR broadband flux dataset. The importance of agreement at the TOA is that most methodologies to derive surface SWR start with the satellite observation at the TOA. Moreover, information needed to compute radiative fluxes at both boundaries (TOA and surface) is needed for estimating the energy absorbed by the atmosphere. The methodology described was comprehensively evaluated, and possible sources of errors were identified. The results of the evaluation for the four seasonal months indicate that by using the best available auxiliary data, the accuracy achieved in estimating TOA SWR at the instantaneous scale ranges between 0.55 and 17.14 W m⁻² for the bias and 22.21 to 30.64 W m⁻² for the standard deviation of biases (differences are ABI minus CERES). It is believed that the high bias of 17.14 for July is related to the predominantly cloudless sky conditions, when the used ADMs do not perform as well as for cloudy conditions. Full article

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

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16 pages, 2856 KiB

Open AccessArticle

Dynamic Risk Assessment of Collapse Geological Hazards on Highway Slopes in Basalt Regions During Rainy Seasons

by Lihui Qian, Peng Zhao and Zhongshui Li

Atmosphere 2025, 16(8), 978; https://doi.org/10.3390/atmos16080978 (registering DOI) - 17 Aug 2025

Abstract

Anchored in the four-factor theory of natural hazard risk, this study presents a dynamic risk assessment of collapse geological hazards (CGHs) using the S3K highway slope in Changbai Korean Autonomous County, China, as a case study. Building on previous research, the methodological framework [...] Read more.

Anchored in the four-factor theory of natural hazard risk, this study presents a dynamic risk assessment of collapse geological hazards (CGHs) using the S3K highway slope in Changbai Korean Autonomous County, China, as a case study. Building on previous research, the methodological framework consists of three sequential stages: (1) critical indicators for CGHs in basalt regions are identified, with iron-staining anomalies—a hallmark of such terrains—innovatively integrated as a slope stability metric; (2) a system dynamics (SD) model is developed in Vensim to quantify dynamic feedback mechanisms, focusing on the “rock weathering–rainfall triggering–slope instability” nexus, and time-varying parameters are introduced to enable monthly-scale risk prediction; and (3) a 500 m × 500 m grid system is established using ArcGIS 10.4, and a computer program is developed to achieve SD-GIS coupling and calculate grid parameters. The information value method is then employed to determine risk thresholds, thereby completing CGH risk assessment and prediction. The results indicate that over the next five years, high-risk areas will exhibit spatial agglomeration when monthly rainfall exceeds approximately 130 mm (July and August). Conversely, when monthly rainfall is below around 60 mm, the entire region will display low or no risk. Model simulations reveal that risks during the rainy season over the next five years will exhibit insignificant variability, prompting simplification of the resultant cartography. Field validation corroborates the robustness of the model. This research overcomes the primary limitations of conventional static assessment models by improving the dynamic predictability and the applicability to basalt terrains. The integrated SD-GIS framework presents a novel methodological paradigm for dynamic CGH risk analysis and offers support for the formulation of targeted disaster mitigation strategies. Full article

(This article belongs to the Section Climatology)

19 pages, 3981 KiB

Open AccessArticle

Dataset Construction for Radiative Transfer Modeling: Accounting for Spherical Curvature Effect on the Simulation of Radiative Transfer Under Diverse Atmospheric Scenarios

by Qingyang Gu, Kun Wu, Xinyi Wang, Qijia Xin and Luyao Chen

Atmosphere 2025, 16(8), 977; https://doi.org/10.3390/atmos16080977 (registering DOI) - 17 Aug 2025

Abstract

Conventional radiative transfer (RT) models often adopt the plane-parallel (PP) approximation, which neglects Earth’s curvature and leads to significant optical path errors under large solar or sensor zenith angles, particularly for high-latitude regions and twilight conditions. The spherical Monte Carlo method offers high [...] Read more.

Conventional radiative transfer (RT) models often adopt the plane-parallel (PP) approximation, which neglects Earth’s curvature and leads to significant optical path errors under large solar or sensor zenith angles, particularly for high-latitude regions and twilight conditions. The spherical Monte Carlo method offers high accuracy but is computationally expensive, and the commonly used pseudo-spherical (PSS) approximation fails when the viewing zenith angle exceeds 80°. With the increasing application of machine learning in atmospheric science, the efficiency and angular limitations of spherical RT simulations may be overcome. This study provides a physical and quantitative foundation for developing a hybrid RT framework that integrates physical modeling with machine learning. By systematically quantifying the discrepancies between PP and spherical RT models under diverse atmospheric scenarios, key influencing factors—including wavelength, solar and viewing zenith angles, aerosol properties (e.g., single scattering albedo and asymmetry factor), and PP-derived radiance—were identified. These variables significantly affect spherical radiative transfer and serve as effective input features for data-driven models. Using the corresponding spherical radiance as the target variable, the proposed framework enables rapid and accurate inference of spherical radiative outputs based on computationally efficient PP simulations. Full article

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

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17 pages, 2419 KiB

Open AccessArticle

Variations in the Surface Atmospheric Electric Field on the Qinghai–Tibet Plateau: Observations at China’s Gar Station

by Jia-Nan Peng, Shuai Fu, Yan-Yan Xu, Gang Li, Tao Chen and En-Ming Xu

Atmosphere 2025, 16(8), 976; https://doi.org/10.3390/atmos16080976 (registering DOI) - 17 Aug 2025

Abstract

The Qinghai-Tibet Plateau, known as the “third pole” of the Earth with an average elevation of approximately 4500 m, offers a unique natural laboratory for probing the dynamic behavior of the global electric circuit. In this study, we conduct a comprehensive analysis of [...] Read more.

The Qinghai-Tibet Plateau, known as the “third pole” of the Earth with an average elevation of approximately 4500 m, offers a unique natural laboratory for probing the dynamic behavior of the global electric circuit. In this study, we conduct a comprehensive analysis of near-surface vertical atmospheric electric field (AEF) measurements collected at the Gar Station (80.1° E, 32.5° N; 4259 m a.s.l.) on the western Tibetan Plateau, spanning the period from November 2021 to December 2024. Fair-weather conditions are imposed. The annual mean AEF at Gar is ∼0.331 kV/m, significantly higher than values observed at lowland and plain sites, indicating a pronounced enhancement in atmospheric electricity associated with high-altitude conditions. Moreover, the AEF exhibits marked seasonal variability, peaking in December (∼0.411–0.559 kV/m) and valleying around July–August (∼0.150–0.242 kV/m), yielding an overall amplitude of approximately 0.3 kV/m. We speculate that this seasonal pattern is primarily driven by variations in aerosol concentration. During winter, increased aerosol loading from residential heating and vehicle emissions due to incomplete combustion reduces atmospheric conductivity by depleting free ions and decreasing ion mobility, thereby enhancing the near-surface AEF. In contrast, lower aerosol concentrations in summer lead to weaker AEF. This seasonal decline in aerosol levels is likely facilitated by stronger winds and more frequent rainfall in summer, which enhance aerosol dispersion and wet scavenging, whereas weaker winds and limited precipitation in winter favor near-surface aerosol accumulation. On diurnal timescales, the Gar AEF curve deviates significantly from the classical Carnegie curve, showing a distinct double-peak and double-trough structure, with maxima at ∼03:00 and 14:00 UT and minima near 00:00 and 10:00 UT. This deviation may partly reflect local influences related to sunrise and sunset. This study presents the longest ground-based AEF observations over the Qinghai–Tibet Plateau, providing a unique reference for future studies on altitude-dependent AEF variations and their coupling with space weather and climate processes. Full article

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24 pages, 3586 KiB

Open AccessArticle

Energy Sustainability of Urban Areas by Green Systems: Applied Thermodynamic Entropy and Strategic Modeling Means

by Carla Balocco, Giacomo Pierucci, Michele Baia, Costanza Borghi, Saverio Francini, Gherardo Chirici and Stefano Mancuso

Atmosphere 2025, 16(8), 975; https://doi.org/10.3390/atmos16080975 (registering DOI) - 17 Aug 2025

Abstract

Global warming, anthropogenic pressure, and urban expansion at the expense of green spaces are leading to an increase in the incidence of urban heat islands, creating discomfort and health issue for citizens. This present research aimed at quantifying the impact of nature-based solutions [...] Read more.

Global warming, anthropogenic pressure, and urban expansion at the expense of green spaces are leading to an increase in the incidence of urban heat islands, creating discomfort and health issue for citizens. This present research aimed at quantifying the impact of nature-based solutions to support decision-making processes in sustainable energy action plans. A simple method is provided, linking applied thermodynamics to physics-informed modeling of urban built-up and green areas, high-resolution climate models at urban scale, greenery modeling, spatial georeferencing techniques for energy, and entropy exchanges evaluation in urban built-up areas, with and without vegetation. This allows the outdoor climate conditions and thermo-hygrometric well-being to improve, reducing the workload of cooling plant-systems in buildings and entropy flux to the environment. The finalization and post-processing of obtained results allows the definition of entropy footprints. The main findings show a decrease in greenery’s contribution for different scenarios, referring to a different climatological dataset, but an increase in entropy that becomes higher for the scenario with higher emissions. The comparison between the entropy footprint values for different urban zones can be a useful support to public administrations, stakeholders, and local governments for planning proactive resilient cities and anthropogenic impact reduction and climate change mitigation. Full article

(This article belongs to the Special Issue Advanced Studies on Climate Change in Urban Areas: Emerging Technologies and Strategies to Address Heat Waves and Improve Thermo-Hygrometric Comfort)

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22 pages, 3221 KiB

Open AccessArticle

Exploring NDVI Responses to Regional Climate Change by Leveraging Interpretable Machine Learning: A Case Study of Chengdu City in Southwest China

by Ying Xiang, Guirong Hou, Junjie Li, Yidan Zhang, Jie Lu, Zhexiu Yu, Fabao Niu and Hanqing Yang

Atmosphere 2025, 16(8), 974; https://doi.org/10.3390/atmos16080974 (registering DOI) - 17 Aug 2025

Abstract

Regional extreme climate change remains a major environmental issue of global concern. However, in the context of the joint effects of urban expansion and the urban ecological environment, the responses of the normalized difference vegetation index (NDVI) to regional climate change and its [...] Read more.

Regional extreme climate change remains a major environmental issue of global concern. However, in the context of the joint effects of urban expansion and the urban ecological environment, the responses of the normalized difference vegetation index (NDVI) to regional climate change and its driving mechanism remain unclear. This study takes Chengdu as an example, selects the air temperature (Ta), precipitation (P), wind speed (WS), and soil water content (SWC) within the period from 2001 to 2023 as influencing factors, and uses Theil-Sen median trend analysis and interpretable machine learning models (random forest (RF), BP neural network, support vector machine (SVM), and extreme gradient boosting (XG-Boost) models). The average absolute value of Shapley additive explanations (SHAPs) is adopted as an indicator to explore the key mechanism driving regional climate change in Chengdu in terms of NDVI changes. The analysis results reveal that the NDVI exhibited an extremely significant increasing trend during the study period (p = 8.6 × 10⁻⁶ < 0.001), and that precipitation showed a significant increasing trend (p = 1.2 × 10⁻⁴ < 0.001); however, the air temperature, wind speed, and soil-relative volumetric water content all showed insignificant increasing trends. A simulation of interpretable machine learning models revealed that the random forest (RF) model performed exceptionally well in terms of simulating the dynamics of the urban NDVI (R² = 0.746), indicating that the RF model has an excellent ability to capture the complex ecological interactions of a city without prior assumptions. The dependence relationship between the simulation results and the main driving factors indicates that the Ta and P are the main factors affecting the NDVI changes. In contrast, the SWC and WS had relatively small influences on the NDVI changes. The prediction analysis results reveal that a monthly average temperature of 25 °C and a monthly average precipitation of approximately 130 mm are conducive to the stability of the NDVI in the study area. This study provides a reference for exploring the responses of NDVI changes to regional climate change in the context of urban expansion and urban ecological construction. Full article

(This article belongs to the Special Issue Vegetation–Atmosphere Interactions in a Changing Climate)

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19 pages, 6372 KiB

Open AccessArticle

Diagnosing Tibetan Plateau Summer Monsoon Variability Through Temperature Advection

by Xueyi Xun, Zeyong Hu, Fei Zhao, Zhongqiang Han, Min Zhang and Ruiqing Li

Atmosphere 2025, 16(8), 973; https://doi.org/10.3390/atmos16080973 (registering DOI) - 16 Aug 2025

Abstract

It has always been a research topic for some meteorologists to design a new and reasonable calculation scheme of the intensity of the Tibetan Plateau (TP) summer monsoon (TPSM). Existing indices are defined based on dynamic factors. However, the intensity of the TPSM [...] Read more.

It has always been a research topic for some meteorologists to design a new and reasonable calculation scheme of the intensity of the Tibetan Plateau (TP) summer monsoon (TPSM). Existing indices are defined based on dynamic factors. However, the intensity of the TPSM can also be influenced by thermal factors. We therefore propose defining a TPMI in terms of horizontal temperature advection within the main body of the TP. This provides a new index that directly quantifies the extent to which the thermal forcing in the TP region regulates the monsoon system. The new index emphasizes the importance of the atmospheric asymmetry structure in measuring TPSM strength, represents the variability of the TPSM circulation system, effectively reflects the meteorological elements, and accurately represents the climate variation. Tropospheric temperature (TT) and TPSM are linked by the new index. These significant centers of correlation are characterized by alternating positive and negative phases along the Eastern European Plain, across the Turan Plain, and into southwestern and northeastern China. The correlation coefficients are found to be significantly out of phase between high and low altitudes in the vertical direction. This research broadens our minds and helps us to develop a new approach to measuring TPSM strength. It can also predict extreme weather events in advance based on TPMI changes, providing a scientific basis for disaster warnings and the management of agriculture and water resources. Full article

(This article belongs to the Section Climatology)

15 pages, 1990 KiB

Open AccessArticle

Real-Time Insights into Indoor Air Quality in University Environments: PM and CO₂ Monitoring

by Dan-Marius Mustață, Daniel Bisorca, Ioana Ionel, Ahmed Adjal and Ramon-Mihai Balogh

Atmosphere 2025, 16(8), 972; https://doi.org/10.3390/atmos16080972 (registering DOI) - 16 Aug 2025

Abstract

This study presents real-time measurements of particulate matter (PM₁, PM₂.₅, PM₁₀) and carbon dioxide (CO₂) concentrations across five university indoor environments with varying occupancy levels and natural ventilation conditions. CO₂ concentrations frequently [...] Read more.

This study presents real-time measurements of particulate matter (PM₁, PM₂.₅, PM₁₀) and carbon dioxide (CO₂) concentrations across five university indoor environments with varying occupancy levels and natural ventilation conditions. CO₂ concentrations frequently exceeded the 1000 ppm guideline, with peak values reaching 3018 ppm and 2715 ppm in lecture spaces, whereas one workshop environment maintained levels well below limits (mean = 668 ppm). PM concentrations varied widely: PM₁₀ reached 541.5 µg/m³ in a carpeted amphitheater, significantly surpassing the 50 µg/m³ legal daily limit, while a well-ventilated classroom exhibited lower levels despite moderate occupancy (PM₁₀ max = 116.9 µg/m³). Elevated PM values were strongly associated with flooring type and occupant movement, not just activity type. Notably, window ventilation during breaks reduced CO₂ concentrations by up to 305 ppm (p < 1 × 10⁻⁴⁷) and PM₁₀ by over 20% in rooms with favorable layouts. These findings highlight the importance of ventilation strategy, spatial orientation, and surface materials in shaping indoor air quality. The study emphasizes the need for targeted, non-invasive interventions to reduce pollutant exposure in historic university buildings where mechanical ventilation upgrades are often restricted. Full article

(This article belongs to the Special Issue Physical and Chemical Characterization of Particulate Matter: Ambient, Personal, and Indoor Perspectives)

18 pages, 2613 KiB

Open AccessArticle

Synergistic Effects of Ambient PM_2.5 and O₃ with Natural Temperature Variability on Non-Accidental and Cardiovascular Mortality: A Historical Time Series Analysis in Urban Taiyuan, China

by Huan Zhou, Hong Geng, Jingjing Tian, Li Wu, Zhihong Zhang and Daizhou Zhang

Atmosphere 2025, 16(8), 971; https://doi.org/10.3390/atmos16080971 (registering DOI) - 15 Aug 2025

Abstract

Climate change and air pollution are associated with a range of health outcomes, including cardiovascular and respiratory disease. Evaluation of the synergic effects of air pollution and increasing natural temperature on mortality is important for understanding their potential joint health effects. In this [...] Read more.

Climate change and air pollution are associated with a range of health outcomes, including cardiovascular and respiratory disease. Evaluation of the synergic effects of air pollution and increasing natural temperature on mortality is important for understanding their potential joint health effects. In this study, the modification effects of air temperature on the short-term association of ambient fine particulate matter (PM_2.5) and ozone (O₃) with non-accidental death (NAD) and cardiovascular disease (CVD) mortality were evaluated by using the generalized additive model (GAM) combined with the distributed lag nonlinear model (DLNM) in urban areas of Taiyuan, a representative of energy and heavy industrial cities in Northern China. The data on the daily cause-specific death numbers, air pollutants concentrations, and meteorological factors were collected from January 2013 to December 2019, and the temperature was divided into low (<25th percentile), medium (25–75th percentile), and high (>75th percentile) categories. Significant associations of PM_2.5 and O₃ with NAD and CVD mortality were observed in single-effect analysis. A statistically significant increase in the effect estimates of PM_2.5 and O₃ on NAD and CVD mortality was also observed on high-temperature days. But the associations of those were not statistically significant on medium- and low-temperature days. At the same temperature level, the effects of PM_2.5 and O₃ on the CVD mortality were larger than those on NAD (1.74% vs. 1.21%; 1.67% vs. 0.57%), and the elderly and males appeared to be more vulnerable to both higher temperatures and air pollution. The results suggest that the acute effect of PM_2.5 and O₃ on NAD and CVD mortality in urban Taiyuan was enhanced by increasing temperatures, particularly for the elderly and males. It highlights the importance of reducing PM_2.5 and O₃ exposure in urban areas to reduce the public health burden under the situation of global warming. Full article

(This article belongs to the Special Issue Composition Analysis and Health Effects of Atmospheric Particulate Matter (2nd Edition))

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21 pages, 3373 KiB

Open AccessArticle

The Impact of Photochemical Loss on the Source Apportionment of Ambient Volatile Organic Compounds (VOCs) and Their Ozone Formation Potential in the Fenwei Plain, Northern China

by Yanan Tao, Qi Xiong, Yawei Dong, Jiayin Zhang, Lei Cao, Min Zhu, Qiaoqiao Wang and Jianwei Gu

Atmosphere 2025, 16(8), 970; https://doi.org/10.3390/atmos16080970 - 15 Aug 2025

Abstract

The Fenwei Plain (FWP), one of China’s most polluted regions, has experienced severe ozone (O₃) pollution in recent years. Volatile organic compounds (VOCs), key O₃ precursors, undergo significant photochemical degradation, yet their loss and the implications for source apportionment and [...] Read more.

The Fenwei Plain (FWP), one of China’s most polluted regions, has experienced severe ozone (O₃) pollution in recent years. Volatile organic compounds (VOCs), key O₃ precursors, undergo significant photochemical degradation, yet their loss and the implications for source apportionment and ozone formation potential (OFP) in this region remain unclear. This study conducted summertime VOC measurements in two industrial cities in the FWP, Hancheng (HC) and Xingping (XP), to quantify photochemical losses of VOCs and assessed their impact on source attribution and OFP with photochemical age-based parameterization methods. Significant VOC photochemical losses were observed, averaging 3.6 ppbv (7.1% of initial concentrations) in HC and 1.9 ppbv (5.6%) in XP, with alkenes experiencing the highest depletion (22–30%). Source apportionment based on both initial (corrected) and observed concentrations revealed that industrial sources (e.g., coking, coal washing, and rubber manufacturing) dominated ambient VOCs. Ignoring photochemical losses underestimated contributions from natural gas combustion and biogenic sources, while it overestimated the secondary source. OFP calculated with lost VOCs (OFP_loss) reached 34 ppbv in HC and 15 ppbv in XP, representing 20% and 25% of OFP based on observed concentrations, respectively, with reactive alkenes accounting for over 90% of OFPloss. The results highlight the importance of accounting for VOC photochemical losses for accurate source identification and developing effective O₃ control strategies in the FWP. Full article

(This article belongs to the Section Air Quality)

15 pages, 3075 KiB

Open AccessArticle

Influence of Atmospheric Circulation on Seasonal Temperatures in Serbia

by Suzana Putniković

Atmosphere 2025, 16(8), 969; https://doi.org/10.3390/atmos16080969 - 15 Aug 2025

Abstract

An objective classification scheme by Jenkinson and Collison is applied to the period 1961–2010 to statistically model the temperatures over Serbia. The originally identified 26 weather types (WTs) are reorganised into 10 basic types. This discussion includes the synoptic characteristics, frequency and trends [...] Read more.

An objective classification scheme by Jenkinson and Collison is applied to the period 1961–2010 to statistically model the temperatures over Serbia. The originally identified 26 weather types (WTs) are reorganised into 10 basic types. This discussion includes the synoptic characteristics, frequency and trends of the 10 WTs as well as the trends of seasonal mean, maximum and minimum temperatures in Serbia. In this area, the anticyclonic weather type is predominant throughout the year, and its negative trend is significant in summer and autumn. The relationship between air temperature and atmospheric circulation types is investigated by analysing the mean and anomalies of mean, maximum and minimum temperatures for each individual atmospheric circulation type and by stepwise regression. The multiple regression models developed for six stations using circulation WTs as predictors showed the best performance in modelling winter mean temperatures for Zlatibor and Loznica compared to the other stations, while the models for other seasons proved to be inadequate. Full article

(This article belongs to the Special Issue Air Temperature and Precipitation and Relationship to Atmospheric Circulation (2nd Edition))

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17 pages, 10829 KiB

Open AccessArticle

Vertical Profiling of PM₁ and PM_2.5 Dynamics: UAV-Based Observations in Seasonal Urban Atmosphere

by Zhen Zhao, Yuting Pang, Bing Qi, Chi Zhang, Ming Yang and Xuezhu Ye

Atmosphere 2025, 16(8), 968; https://doi.org/10.3390/atmos16080968 - 15 Aug 2025

Abstract

Urban particulate matter (PM) pollution critically impacts public health and climate. However, traditional ground-based monitoring fails to resolve vertical PM distribution, limiting understanding of transport and stratification-coupled mechanisms. Vertical profiles collected by an unmanned aerial vehicle (UAV) over Hangzhou, a core megacity in [...] Read more.

Urban particulate matter (PM) pollution critically impacts public health and climate. However, traditional ground-based monitoring fails to resolve vertical PM distribution, limiting understanding of transport and stratification-coupled mechanisms. Vertical profiles collected by an unmanned aerial vehicle (UAV) over Hangzhou, a core megacity in China’s Yangtze River Delta, reveal the spatiotemporal heterogeneity and multi-scale drivers of regional PM pollution during two intensive ten-day campaigns capturing peak pollution scenarios (winter: 17–26 January 2019; summer: 21–30 August 2019). Results show stark seasonal differences: winter PM₁ and PM_2.5 averages were 2.6- and 2.7-fold higher (p < 0.0001) than summer. Diurnal patterns were bimodal in winter and unimodal (single valley) in summer. Vertically consistent PM₁ and PM_2.5 distributions featured sharp morning (08:00) concentration increases within specific layers (winter: 250–325 m; summer: 350–425 m). Analysis demonstrates multi-scale coupling of synoptic systems, boundary layer processes, and vertical wind structure governing pollution. Key mechanisms include a winter “Transport-Accumulation-Reactivation” cycle driven by cold air, and summer typhoon circulation influences. We identify hygroscopic growth triggered by inversion-high humidity coupling and sea-breeze-driven secondary aerosol formation. Leveraging UAV-based vertical profiling over Hangzhou, this study pioneers a three-dimensional dissection of layer-coupled PM dynamics in the Yangtze River Delta, offering a scalable paradigm for aerial–ground networks to achieve precision stratified control strategies in megacities. Full article

(This article belongs to the Special Issue Air Pollution in China (4th Edition))

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18 pages, 4355 KiB

Open AccessArticle

The Evaluation of ERA5’s Applicability in Nearshore Western Atlantic Regions During Hurricanes—“ISAIAS” 2020

by Zhiyong Xu, Biyun Guo, Guiting Song, Venkata Subrahmanyam Mantravadi, Wenjing Xu, Cheng Wan and John Sikule Sabuyi

Atmosphere 2025, 16(8), 967; https://doi.org/10.3390/atmos16080967 - 15 Aug 2025

Abstract

Hurricanes cause significant destruction, disrupting transportation, and resulting in loss of life and property. High-precision marine meteorological data are essential for understanding hurricanes. ERA5 provides high temporal resolution and global coverage of analytical data; however, the accuracy of the data during hurricanes is [...] Read more.

Hurricanes cause significant destruction, disrupting transportation, and resulting in loss of life and property. High-precision marine meteorological data are essential for understanding hurricanes. ERA5 provides high temporal resolution and global coverage of analytical data; however, the accuracy of the data during hurricanes is uncertain. To investigate the applicability of ERA5 during hurricanes, this study used buoy data as reference values and assessed the applicability of ERA5 sea-surface wind speed (WS), sea-surface temperature (SST), and sea-surface pressure (SSP) during the 2020 Atlantic hurricane “ISAIAS” through spatial distribution and error analysis. The results indicate that there is a positive correlation and consistency between the trends of ERA5 and reference values. The average correlation coefficients for SSP, WS, and SST are 0.953, 0.822, and 0.607, respectively. Nearshore topography has a significant impact on data accuracy, resulting in greater errors compared to open-water areas. This study provides a theoretical basis for the application of ERA5 data during hurricanes. Full article

(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change (2nd Edition))

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19 pages, 11804 KiB

Open AccessArticle

Assessing the Impact of Ammonia Emissions from Mink Farming in Denmark on Human Health and Critical Load Exceedance

by Lise Marie Frohn, Jesper Leth Bak, Jørgen Brandt, Jesper Heile Christensen, Steen Gyldenkærne and Camilla Geels

Atmosphere 2025, 16(8), 966; https://doi.org/10.3390/atmos16080966 - 15 Aug 2025

Abstract

In this study, the objective is to assess the impacts of NH₃ emissions from mink farming on human health and nature, which are sensitive to atmospheric nitrogen deposition. The impact-pathway approach is applied to follow the emissions from source to impact on [...] Read more.

In this study, the objective is to assess the impacts of NH₃ emissions from mink farming on human health and nature, which are sensitive to atmospheric nitrogen deposition. The impact-pathway approach is applied to follow the emissions from source to impact on human health in Europe (including Denmark) and from source to critical nitrogen load exceedances for NH₃-sensitive nature in Denmark. The Danish Eulerian Hemispheric Model (DEHM) is used for modelling the air pollution concentrations in Europe and nitrogen depositions on land and water surfaces in Denmark arising from NH₃ emissions from mink farming in Denmark. The Economic Valuation of Air (EVA) pollution model system is applied for deriving the health effects and corresponding socio-economic costs in Denmark and Europe arising from the emissions from mink farming. On a local scale in Denmark, the deposition resulting from the NH₃ emissions from mink farming is modelled using the results from the OML-DEP model at a high resolution to derive the critical nitrogen load exceedances for Danish nature areas sensitive to NH₃. From the analysis of the impacts through human exposure to the air pollutants PM_2.5, NO₂, and O₃, it is concluded that in total, ~60 premature deaths annually in Europe, including Denmark, can be attributed to the emissions of NH₃ to the atmosphere from the mink farming sector in Denmark. This corresponds to annual socio-economic costs on the order of EUR 142 million. From the analysis of critical load exceedances, it is concluded that an exceedance of the critical load of nitrogen deposition of ~14,600 hectares (ha) of NH₃-sensitive nature areas in Denmark can be attributed to NH₃ emissions from mink farming. The cost for restoring nature areas of this size, damaged by eutrophication from excess nitrogen deposition, is estimated to be ~EUR 110 million. In 2020, the mink sector in Denmark was shut down in connection with the COVID-19 pandemic. All mink were culled by order of the Danish Government, and now in 2025, the process of determining the level of financial compensation to the farmers is still ongoing. The socio-economic costs following the impacts on human health in Europe and nitrogen-sensitive nature in Denmark of NH₃ emissions from the now non-existing mink sector can therefore be viewed as socio-economic benefits. In this study, these benefits are compared with the expected level of compensation from the Danish Government to the mink farmers, and the conclusion is that the compensation to the mink farmers breaks even with the benefits from reduced NH₃ emissions over a timescale of ~20 years. Full article

(This article belongs to the Special Issue Effects of Natural and Anthropogenic Factors on Climate and Environment (2nd Edition))

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17 pages, 3520 KiB

Open AccessArticle

A Hybrid Air Quality Prediction Model Integrating KL-PV-CBGRU: Case Studies of Shijiazhuang and Beijing

by Sijie Chen, Qichao Zhao, Zhao Chen, Yongtao Jin and Chao Zhang

Atmosphere 2025, 16(8), 965; https://doi.org/10.3390/atmos16080965 - 15 Aug 2025

Abstract

Accurate prediction of the Air Quality Index (AQI) is crucial for protecting public health; however, the inherent instability and high volatility of AQI present significant challenges. To address this, the present study introduces a novel hybrid deep learning model, KL-PV-CBGRU, which utilizes Kalman [...] Read more.

Accurate prediction of the Air Quality Index (AQI) is crucial for protecting public health; however, the inherent instability and high volatility of AQI present significant challenges. To address this, the present study introduces a novel hybrid deep learning model, KL-PV-CBGRU, which utilizes Kalman filtering to decompose AQI data into features and residuals, effectively mitigating volatility at the initial stage. For residual components that continue to exhibit substantial fluctuations, a secondary decomposition is conducted using variational mode decomposition (VMD), further optimized by the particle swarm optimization (PSO) algorithm to enhance stability. To overcome the limited predictive capabilities of single models, this hybrid framework integrates bidirectional gated recurrent units (BiGRU) with convolutional neural networks (CNNs) and convolutional attention modules, thereby improving prediction accuracy and feature fusion. Experimental results demonstrate the superior performance of KL-PV-CBGRU, achieving R² values of 0.993, 0.963, 0.935, and 0.940 and corresponding MAE values of 2.397, 8.668, 11.001, and 14.035 at 1 h, 8 h, 16 h, and 24 h intervals, respectively, in Shijiazhuang—surpassing all benchmark models. Ablation studies further confirm the critical roles of both the secondary decomposition process and the hybrid architecture in enhancing predictive accuracy. Additionally, comparative experiments conducted in Beijing validate the model’s strong transferability and consistent outperformance over competing models, highlighting its robust generalization capability. These findings underscore the potential of the KL-PV-CBGRU model as a powerful and reliable tool for air quality forecasting across varied urban settings. Full article

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

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24 pages, 9802 KiB

Open AccessArticle

Threshold Dynamics of Vegetation Carbon Sink Loss Under Multiscale Droughts in the Mongolian Plateau

by Hongguang Chen, Mulan Wang, Fanhao Meng, Chula Sa, Min Luo, Wenfeng Chi and Sonomdagva Chonokhuu

Atmosphere 2025, 16(8), 964; https://doi.org/10.3390/atmos16080964 - 14 Aug 2025

Abstract

Gross primary productivity (GPP) is a key carbon flux in the global carbon cycle, and understanding the inhibitory effects of drought on GPP and its underlying mechanisms is crucial for understanding carbon–climate feedback. However, current research has not sufficiently addressed the threshold dynamics [...] Read more.

Gross primary productivity (GPP) is a key carbon flux in the global carbon cycle, and understanding the inhibitory effects of drought on GPP and its underlying mechanisms is crucial for understanding carbon–climate feedback. However, current research has not sufficiently addressed the threshold dynamics and regional differentiation of GPP responses to the synergistic effects of meteorological drought (MD) and soil moisture drought (SD), particularly in the drought-sensitive Mongolian Plateau. This study focuses on the Mongolian Plateau from 1982 to 2021, using the standardized precipitation index (SPI) and standardized soil moisture index (SSI) to characterize MD and SD, respectively. The study combines the three-threshold run theory, cross-wavelet analysis, Spearman correlation analysis, and copula models to systematically investigate the variation characteristics, propagation patterns, and the probability and thresholds for triggering GPP loss under different time scales (monthly, seasonal, semi-annual, and annual). The results show that (1) both types of droughts exhibited significant intensification trends, with SD intensifying at a faster rate (annual scale SSI12 trend: −0.34/10a). The intensification trend strengthened with increasing time scales. MD exhibited high frequency, short duration, and low intensity, while SD showed the opposite characteristics. The most significant aridification occurred in the central region. (2) The average propagation time from MD to SD was 11.22 months. The average response time of GPP to MD was 10.46 months, while the response time to SD was significantly shorter (approximately 2 months on average); the correlation between SSI and GPP was significantly higher than that between SPI and GPP. (3) The conditional probability of triggering mild GPP loss (e.g., <40^th percentile) was relatively high for both drought types, and the probability of loss increased as the time scales extended. Compared to MD, SD was more likely to induce severe GPP loss. Additionally, the drought intensity threshold for triggering mild loss was lower (i.e., mild drought could trigger it), while higher drought intensity was required to trigger severe and extreme losses. Therefore, this study provides practical guidance for regional drought early-warning systems and ecosystem adaptive management, while laying an important theoretical foundation for a deeper understanding of drought response mechanisms. Full article

(This article belongs to the Special Issue The Response of Plateau Vegetation to Climatic and Anthropogenic Drivers)

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45 pages, 50650 KiB

Open AccessArticle

Spatiotemporal Patterns of 45-Day Precipitation in Rio Grande Do Sul State, Brazil: Implications for Adaptation to Climate Variation

by Luana Centeno Cecconello, Angela Maria de Arruda, André Becker Nunes and Tirzah Moreira Siqueira

Atmosphere 2025, 16(8), 963; https://doi.org/10.3390/atmos16080963 - 12 Aug 2025

Abstract

Understanding precipitation variability is essential for assessing climate dynamics and their impacts on agriculture, water resources, and infrastructure. This study analyzes subseasonal precipitation patterns in Rio Grande do Sul, Brazil, using 45-day accumulated intervals over a 17-year period (2006–2022), a timescale critical for [...] Read more.

Understanding precipitation variability is essential for assessing climate dynamics and their impacts on agriculture, water resources, and infrastructure. This study analyzes subseasonal precipitation patterns in Rio Grande do Sul, Brazil, using 45-day accumulated intervals over a 17-year period (2006–2022), a timescale critical for understanding drivers of extreme events like the catastrophic floods of 2024. A total of 138 precipitation fields were generated from 670 spatial points. Spatial analysis revealed median precipitation values ranging from 130 to 329 mm/45 days, with the northeast showing the highest accumulations and the southwest showing the driest conditions. Temporal variability was marked by abrupt anomalies, with median peaks up to 462 mm and minima of 33 mm. Significant temporal autocorrelation (lag-1, 45 days) was identified in the central and northern regions, while lag-2 (90 days) showed inverse patterns in the south (correlation coefficient ≈ −0.45). Principal component analysis (KMO = 0.909; Bartlett’s χ² = 187,990.945; p < 0.05) identified seven dominant modes, with PC1 explaining 26% of total variance and highlighting extremely wet anomalies (e.g., SPI > 2.0). Correlation with the Oceanic Niño Index revealed heterogeneous responses to ENSO phases, with strong El Niño episodes (2009, 2015–2016) associated with precipitation peaks up to 966 mm/45 days. These results underscore the importance of subseasonal scales for understanding climate anomalies and support the development of regional forecast strategies and water management policies under increasing climate variability. Full article

(This article belongs to the Topic Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges)

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17 pages, 7328 KiB

Open AccessArticle

Coastal Wind in East Iceland Using Sentinel-1 and Model Data Reanalysis

by Eduard Khachatrian, Yngve Birkelund and Andrea Marinoni

Atmosphere 2025, 16(8), 962; https://doi.org/10.3390/atmos16080962 - 12 Aug 2025

Abstract

This study evaluates three wind data sources in East Iceland’s coastal environment: the high-resolution Synthetic Aperture Radar (SAR)-based Sentinel-1, the regional reanalysis Copernicus Arctic Regional Reanalysis (CARRA), and the global reanalysis ECMWF Reanalysis v5 (ERA5). We focus on assessing the advantages and limitations [...] Read more.

This study evaluates three wind data sources in East Iceland’s coastal environment: the high-resolution Synthetic Aperture Radar (SAR)-based Sentinel-1, the regional reanalysis Copernicus Arctic Regional Reanalysis (CARRA), and the global reanalysis ECMWF Reanalysis v5 (ERA5). We focus on assessing the advantages and limitations of each dataset, especially considering their differences in spatial and temporal resolutions. While ERA5 aligns well with CARRA and Sentinel-1 offshore, it tends to underestimate wind speeds and misrepresent wind directions near complex coastlines and fjords, with Root Mean Squared Difference (RMSD) values reaching up to 3.98 m/s in these areas. CARRA’s higher resolution allows it to better capture coastal wind dynamics and shows strong agreement with Sentinel-1. Sentinel-1 excels in revealing detailed local wind features, such as katabatic winds in fjords, highlighting the value of satellite observations in complex terrain. By combining these complementary datasets, this study enhances understanding of coastal wind variability and supports improved hazard assessment in Iceland’s challenging coastal environments. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))

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12 pages, 2439 KiB

Open AccessArticle

Research on Temperature Prediction of Passion Fruit Planting Bases in Southwest Fujian Province

by Shiyun Mou, Shujie Yuan, Yuchen Shi, Lin Han, Kai Yang and Hongyi Li

Atmosphere 2025, 16(8), 961; https://doi.org/10.3390/atmos16080961 - 12 Aug 2025

Abstract

This article utilized hourly temperature, humidity, pressure and wind speed data from passion fruit meteorological observation stations in three southwestern cities of Fujian Province (Longyan, Sanming, Zhangzhou) from 2020 to 2022, as well as national ground conventional meteorological observation stations. BP neural network [...] Read more.

This article utilized hourly temperature, humidity, pressure and wind speed data from passion fruit meteorological observation stations in three southwestern cities of Fujian Province (Longyan, Sanming, Zhangzhou) from 2020 to 2022, as well as national ground conventional meteorological observation stations. BP neural network and stepwise regression method were applied to construct temperature prediction models for the passion fruit planting bases. The results showed that: (1) The simulation effect of the passion fruit station temperature prediction model based on BP neural network (referred to as BP model) was better than that of the model based on stepwise regression method (referred to as regression model). The average absolute error (MSE) of BP model (2.75–3.42 °C) was smaller than that of regression model (3.32–3.94 °C). (2) For the simulation results of daily temperature changes in the passion fruit station, the difference in hourly average temperature between the BP model predictions (regression model predictions) and observed temperatures at passion fruit station was −4.1–4.4 °C (−6.0–10.2 °C). The BP model showed a daily temperature trend that was closer to the measured values; (3) For the simulation results of high and low temperatures in the passion fruit station, the BP neural network model (regression model) showed a prediction error range of −5.6 °C to 5.2 °C compared to observed temperatures, while the stepwise regression model’s error range was −4.1 °C to 8.8 °C. The BP model’s predicted temperature trend was closer to the measured values. (4) Both models have significant shortcomings in the prediction of high-temperature individual cases and hourly averages, with relatively large errors (generally exceeding 3 °C), especially during the period from 10 to 16 o’clock. The future version needs to be optimized. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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