Atmosphere

25 pages, 18025 KB

Open AccessArticle

Joint Modeling of Pixel-Wise Visibility and Fog Structure for Real-World Scene Understanding

by Jiayu Wu, Jiaheng Li, Jianqiang Wang, Xuezhe Xu, Sidan Du and Yang Li

Atmosphere 2025, 16(10), 1161; https://doi.org/10.3390/atmos16101161 (registering DOI) - 4 Oct 2025

Reduced visibility caused by foggy weather has a significant impact on transportation systems and driving safety, leading to increased accident risks and decreased operational efficiency. Traditional methods rely on expensive physical instruments, limiting their scalability. To address this challenge in a cost-effective manner, [...] Read more.

Reduced visibility caused by foggy weather has a significant impact on transportation systems and driving safety, leading to increased accident risks and decreased operational efficiency. Traditional methods rely on expensive physical instruments, limiting their scalability. To address this challenge in a cost-effective manner, we propose a two-stage network for visibility estimation from stereo image inputs. The first stage computes scene depth via stereo matching, while the second stage fuses depth and texture information to estimate metric-scale visibility. Our method produces pixel-wise visibility maps through a physically constrained, progressive supervision strategy, providing rich spatial visibility distributions beyond a single global value. Moreover, it enables the detection of patchy fog, allowing a more comprehensive understanding of complex atmospheric conditions. To facilitate training and evaluation, we propose an automatic fog-aware data generation pipeline that incorporates both synthetically rendered foggy images and real-world captures. Furthermore, we construct a large-scale dataset encompassing diverse scenarios. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both visibility estimation and patchy fog detection. Full article

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

18 pages, 5845 KB

Open AccessArticle

Olive and Grass Pollen Concentrations: Evaluation of Forecast Models with Real Observations as Standard in the Évora Region, Portugal

by Ana Galveias, Hélder Fraga, Ana Rodrigues Costa and Célia M. Antunes

Atmosphere 2025, 16(10), 1160; https://doi.org/10.3390/atmos16101160 (registering DOI) - 4 Oct 2025

Abstract

Background: The CAMS Regional System provides crucial, reliable pollen forecasts for allergenic pollen types. These robust predictions support the scientific and medical communities, aiding in the diagnosis, evaluation, and protection of allergic populations. So, the main goal of this study was to evaluate [...] Read more.

Background: The CAMS Regional System provides crucial, reliable pollen forecasts for allergenic pollen types. These robust predictions support the scientific and medical communities, aiding in the diagnosis, evaluation, and protection of allergic populations. So, the main goal of this study was to evaluate which model, or models best represent and simulate the olive and grass pollen data of the Évora region in the years 2021 to 2024. Results: The results showed that there are statistically significant differences between the data of the models and between the years for each of the pollen types considered. These differences were not just in pollen concentrations; they also appeared in characteristics of the pollen season, like its duration, maximum peak concentration, start date and exposure level. According to Taylor diagrams, applying moving average for normalized data, it was shown that MOCAGE best represents and simulates olive concentration data. For grass pollen SILAM, EURAD-IM and MOCAGE were the best performers. Conclusions: CAMS data can enhance the quality of life of the allergic population, as well as support the scientific and medical community to improve, assist and create mitigation measures that reduce exposure and consequently significantly reduce the occurrence of allergic disease. Full article

(This article belongs to the Special Issue Atmospheric Bioaerosols: Detection, Characterization and Modelling (2nd Edition))

17 pages, 1935 KB

Open AccessArticle

Analysis of Stratospheric Ozone and Nitrogen Dioxide over Mid-Brazil for a Period from 2005 to 2020

by Elvira Kovač-Andrić, Vlatka Gvozdić, Brunislav Matasović, Nikola Sakač and Amaury de Souza

Atmosphere 2025, 16(10), 1159; https://doi.org/10.3390/atmos16101159 - 3 Oct 2025

Abstract

This study analyses the stratospheric concentrations of ozone (O₃) and nitrogen dioxide (NO₂) over a 16-year period (2005 to 2020) over central Brazil using satellite data with the aim of determining the influence of NO₂ on ozone distribution [...] Read more.

This study analyses the stratospheric concentrations of ozone (O₃) and nitrogen dioxide (NO₂) over a 16-year period (2005 to 2020) over central Brazil using satellite data with the aim of determining the influence of NO₂ on ozone distribution and the impact of fires and volcanic eruptions on these gases. The analysis shows that ozone and NO₂ follow seasonal patterns, with the highest concentrations occurring in September and October and the lowest from January to June. A positive correlation was found between the concentrations of ozone and NO₂, and the results of the Fourier analysis indicate semi-annual and annual cycles in the concentrations of these gases. Although there was an increase in the number of fires in the last 11 years of the study, this increase did not lead to significant changes in ozone or NO₂ concentrations, indicating the stability of these parameters in the observed area. It is presumed that the reason for the lack of changes is lower intensity of fires despite their increased number. Regarding wind patterns, it is observed that they do not differ much either which is in accordance with the fact that the monitored area is fairly close to the equator. Full article

(This article belongs to the Section Upper Atmosphere)

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15 pages, 1348 KB

Open AccessArticle

Carbon Emission Accounting and Emission Reduction Path of Container Terminal Under Low-Carbon Perspective

by Bingbing Li, Long Cheng, Huangqin Wang, Jiaren Li, Zhenyi Xu and Chengrong Pan

Atmosphere 2025, 16(10), 1158; https://doi.org/10.3390/atmos16101158 - 3 Oct 2025

Abstract

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored [...] Read more.

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored to the operational characteristics of Shanghai Port container terminals. The Ship Traffic Emission Assessment Model (STEAM) is applied to estimate emissions during berthing, while a bottom-up method is employed for mobile-mode container handling operations. Targeted mitigation strategies—such as shore power adoption, operational optimization, and “oil-to-electricity” or “oil-to-gas” transitions—are evaluated through comparative analysis. Results show that vessels generate substantial emissions during erthing, which can be significantly reduced (by over 60%) through shore power usage. In terminal operations, internal transport trucks have the highest emissions, followed by straddle carriers, container tractors, and forklifts; in stacking, tire cranes dominate emissions. Comprehensive comparisons indicate that “oil-to-electricity” can reduce total emissions by approximately 39%, while “oil-to-gas” can achieve reductions of about 73%. These findings provide technical and policy insights for supporting the green transformation of container terminals under the national dual-carbon strategy. Full article

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

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43 pages, 89605 KB

Open AccessArticle

Mesoscale Convective Systems over Ecuador: Climatology, Trends and Teleconnections

by Leandro Robaina, Lenin Campozano, Marcos Villacís and Amanda Rehbein

Atmosphere 2025, 16(10), 1157; https://doi.org/10.3390/atmos16101157 - 3 Oct 2025

Abstract

Research on Mesoscale Convective Systems (MCSs) in Ecuador has focused on regional studies. However, it lacks a thorough and general examination of their relationship with the nation’s diverse orography and large-scale phenomena. This study conducts a climatological analysis of MCS occurrence throughout Ecuador’s [...] Read more.

Research on Mesoscale Convective Systems (MCSs) in Ecuador has focused on regional studies. However, it lacks a thorough and general examination of their relationship with the nation’s diverse orography and large-scale phenomena. This study conducts a climatological analysis of MCS occurrence throughout Ecuador’s natural regions. We perform this study using Sen’s Slope and the Mann–Kendall test. Teleconnections from the Pacific and Atlantic Oceans are studied through wavelet decomposition between time series and Pacific and Atlantic oceanic indices. The main factors that control MCS formation depend on the region. The Intertropical Convergence Zone (ITCZ) at the large scale affects the entire territory. In western Ecuador, MCS formation is mostly related to the El Niño current and the Chocó Low-Level Jet (CLLJ). The Orinoco Low-Level Jet (OLLJ) and evapotranspiration and nocturnal convection display the largest roles in the east. A progressive intensification of activity from Highlands-North in SON is detected (0.143 MCSs per year). MCSs contribute 26% of total precipitation on average, with regional variations from Coast-South (16.41%) to Amazon-North (44.13%). The research confirms existing knowledge about El Niño’s strong relationship (ρ = 0.7) with MCS occurrence in coastal areas while uncovering new complex patterns. The Trans-Nino Index (TNI) functions as a critical two-sided modulator that conventional analysis methods fail to detect. It produces null correlations over conventional time series of MCS occurrence yet emerges as a primary driver of low-frequency variability in the proposed six natural zones of Ecuador. Wavelet decomposition reveals contrasting TNI responses: Amazon-North shows positive correlation (0.73) while Amazon-South exhibits negative correlation (−0.70) at low frequencies. This affects Walker circulations dynamics over the Pacific Ocean. This research establishes fundamental knowledge about MCSs in Ecuador. It builds on a database with strong methodology as a backbone. The research provides essential information about the factors leading to convection in the country. This will help improve seasonal forecast accuracy and risk management effectiveness. Full article

(This article belongs to the Section Meteorology)

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Graphical abstract

19 pages, 14588 KB

Open AccessArticle

Research on Evaporation Duct Height Prediction Modeling in the Yellow and Bohai Seas Using BLA-EDH

by Xiaoyu Wu, Lei Li, Zheyan Zhang, Can Chen and Haozhi Liu

Atmosphere 2025, 16(10), 1156; https://doi.org/10.3390/atmos16101156 - 2 Oct 2025

Abstract

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited [...] Read more.

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems. Full article

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

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21 pages, 4247 KB

Open AccessArticle

Diverging Carbon Balance and Driving Mechanisms of Expanding and Shrinking Cities in Transitional China

by Jiawei Lei, Keyu Luo, Le Xia and Zhenyu Wang

Atmosphere 2025, 16(10), 1155; https://doi.org/10.3390/atmos16101155 - 1 Oct 2025

Abstract

The synergy between carbon neutrality and urbanization is essential for effective climate governance and socio-ecological intelligent transition. From the perspective of coupled urban dynamic evolution and carbon metabolism systems, this study integrates the Sen-MK trend test and the geographical detector model to explore [...] Read more.

The synergy between carbon neutrality and urbanization is essential for effective climate governance and socio-ecological intelligent transition. From the perspective of coupled urban dynamic evolution and carbon metabolism systems, this study integrates the Sen-MK trend test and the geographical detector model to explore the spatial–temporal differentiation patterns and driving mechanisms of carbon balance across 337 prefecture-level cities in China from 2012 to 2022. The results reveal a spatial–temporal mismatch between carbon emissions and carbon storage, forming an asymmetric carbon metabolism pattern characterized by “expansion-dominated and shrinkage-dissipative” dynamics. Carbon compensation rates exhibit a west–high to east–low gradient distribution, with hotspots of expansionary cities clustered in the southwest, while shrinking cities display a dispersed pattern from the northwest to the northeast. Based on the four-quadrant carbon balance classification, expansionary cities are mainly located in the “high economic–low ecological” quadrant, whereas shrinking cities concentrate in the “low economic–high ecological” quadrant. Industrial structure and population scale serve as the dual-core drivers of carbon compensation. Expansionary cities are positively regulated by urbanization rates, while shrinking cities are negatively constrained by energy intensity. These findings suggest that differentiated regulation strategies can help optimize carbon governance within national territorial space. Full article

(This article belongs to the Section Air Quality)

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31 pages, 489 KB

Open AccessSystematic Review

Explainable Artificial Intelligence and Machine Learning for Air Pollution Risk Assessment and Respiratory Health Outcomes: A Systematic Review

by Israel Edem Agbehadji and Ibidun Christiana Obagbuwa

Atmosphere 2025, 16(10), 1154; https://doi.org/10.3390/atmos16101154 - 1 Oct 2025

Abstract

Air pollution is a leading environmental risk that causes respiratory morbidity and mortality. The increasing availability of high-resolution environmental data and air pollution-related health cases have accelerated the use of machine learning models (ML) to estimate environmental exposure–response relationships, forecast health risks and [...] Read more.

Air pollution is a leading environmental risk that causes respiratory morbidity and mortality. The increasing availability of high-resolution environmental data and air pollution-related health cases have accelerated the use of machine learning models (ML) to estimate environmental exposure–response relationships, forecast health risks and call for the needed policy and practical interventions. Unfortunately, ML models are opaque, in a sense that, it is unclear how these models combine various data inputs to make a concise decision. Thus, limiting its trust and use in clinical matters. Explainable artificial intelligence (xAI) models offer the necessary techniques to ensure transparent and interpretable models. This systematic review explores online data repositories through the lens of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline to synthesize articles from 2020 to 2025. Various inclusion and exclusion criteria were established to narrow the search to a final selection of 92 articles, which were thoroughly reviewed by independent researchers to reduce bias in article assessment. Equally, the ROBINS-I (Risk Of Bias In Non-randomized Studies of Interventions) domain strategy was helpful in further reducing any possible risk in the article assessment and its reproducibility. The findings reveal a growing adoption of ML techniques such as random forests, XGBoost, parallel lightweight diagnosis models and deep neural networks for health risk prediction, with SHAP (SHapley Additive exPlanations) emerging as the dominant technique for these models’ interpretability. The extremely randomized tree (ERT) technique demonstrated optimal performance but lacks explainability. Moreover, the limitations of these models include generalizability, data limitations and policy translation. Conclusion: This review’s outcome suggests limited research on the integration of LIME (Local Interpretable Model-Agnostic Explanations) in the current ML model; it recommends that future research could focus on causal-xAI-ML models. Again, the use of such models in respiratory health issues may be complemented with a medical professional’s opinion. Full article

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

21 pages, 6647 KB

Open AccessArticle

Evaluation and Projection of Degree-Days and Degree-Days Categories in Southeast Europe Using EURO-CORDEX

by Hristo Chervenkov and Kiril Slavov

Atmosphere 2025, 16(10), 1153; https://doi.org/10.3390/atmos16101153 - 1 Oct 2025

Abstract

The temperature-based indicators heating and cooling degree days, are frequently utilized to quantitatively link indoor energy demand and outdoor thermal conditions, especially in the context of climate change. We present a comprehensive study of the heating and cooling degree-days and the degree-days categories [...] Read more.

The temperature-based indicators heating and cooling degree days, are frequently utilized to quantitatively link indoor energy demand and outdoor thermal conditions, especially in the context of climate change. We present a comprehensive study of the heating and cooling degree-days and the degree-days categories for the near past (1976–2005), and the AR5 RCP4.5 and RCP8.5 scenario-driven future (2066–2095) over Southeast Europe based on an elaborated methodology and performed using a 19 combinations of driving global and regional climate models from EURO-CORDEX with horizontal resolution of 0.11°. Alongside the explicit focus of the degree-days categories and the finer grid resolution, the study benefits substantially from the consideration of the monthly, rather than annual, time scale, which allows the assessment of the intra-annual variations of all analyzed parameters. We provide evidences that the EURO-CORDEX ensemble is capable of simulating the spatiotemporal patterns of the degree-days and degree-day categories for the near past period. Generally, we demonstrate also a steady growth in cooling and a decrease in heating degree-days, where the change of the former is larger in relative terms. Additionally, we show an overall shift toward warmer degree-day categories as well as prolongation of the cooling season and shortening of the heating season. As a whole, the magnitude of the projected long-term changes is significantly stronger for the ’pessimistic’ scenario RCP8.5 than the ’realistic’ scenario RCP4.5. These outcomes are consistent with the well-documented general temperature trend in the gradually warming climate of Southeast Europe. The patterns of the projected long-term changes, however, exhibit essential heterogeneity, both in time and space, as well as among the analyzed parameters. This finding is manifested, in particular, in the coexistence of opposite tendencies for some degree-day categories over neighboring parts of the domain and non-negligible month-to-month variations. Most importantly, the present study unequivocally affirms the significance of the anticipated long-term changes of the considered parameters over Southeast Europe in the RCP scenario-driven future with all subsequent and far-reaching effects on the heating, cooling, and ventilation industry. Full article

(This article belongs to the Section Climatology)

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20 pages, 8772 KB

Open AccessArticle

An Assessment of the Applicability of ERA5 Reanalysis Boundary Layer Data Against Remote Sensing Observations in Mountainous Central China

by Jinyu Wang, Zhe Li, Yun Liang and Jiaying Ke

Atmosphere 2025, 16(10), 1152; https://doi.org/10.3390/atmos16101152 - 1 Oct 2025

Abstract

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected [...] Read more.

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected from a high-precision remote sensing platform located in a representative mountainous valley (Xinyang city) in central China, spanning December 2024 to February 2025. Our findings indicate that both horizontal and vertical wind speeds from the ERA5 dataset exhibit diminishing deviations as altitude increases. Significant biases are observed below 500 m, with horizontal mean wind speed deviations ranging from −4 to −3 m/s and vertical mean wind speed deviations falling between 0.1 and 0.2 m/s. Conversely, minimal biases are noted near the top of the boundary layer. Both ERA5 and observations reveal a dominance of northeasterly and southwesterly winds at near-surface levels, which aligns with the valley orientation. This underscores the substantial impact of heterogeneous mountainous terrain on the low-level dynamic field. At an altitude of 1000 m, both datasets present similar frequency patterns, with peak frequencies of approximately 15%; however, notable discrepancies in peak wind directions are evident (north–northeast for observations and north–northwest for ERA5). In contrast to dynamic variables, ERA5 temperature deviations are centered around 0 K within the lower layers (0–500 m) but show a slight increase, varying from around 0 K to 6.8 K, indicating an upward trend in deviation with altitude. Similarly, relative humidity (RH) demonstrates an increasing bias with altitude, although its representation of moisture variability remains insufficient. During a typical cold event, substantial deviations in multiple ERA5 variables highlight the needs for further improvements. The integration of machine learning techniques and mathematical correction algorithms is strongly recommended as a means to enhance the accuracy of ERA5 data under such extreme conditions. These findings contribute to a deeper understanding of the use of ERA5 datasets in the mountainous areas of central China and offer reliable scientific references for weather forecasting and climate modelings in these areas. Full article

(This article belongs to the Special Issue Data Analysis in Atmospheric Research)

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17 pages, 1225 KB

Open AccessArticle

Assessment of the ZJWARMS Forecast Model’s Adaptability and AI-Based Bias Correction over Complex Terrain

by Qi Zhang, Yiwen Shi, Yifan Wang, Shiyun Mou, Zhidan Zhu, Tu Qian, Zhijun Mao, Shujie Yuan, Lin Han and Xiaocan Lao

Atmosphere 2025, 16(10), 1151; https://doi.org/10.3390/atmos16101151 - 1 Oct 2025

Abstract

This study assesses the efficacy of the ZJWARMS model’s AI-based post-processing correction method for temperature and wind speed forecasts in complex terrain. By analyzing 72 h forecasts at four stations with varying elevations (from 273 m to 1327 m) in the Liuchun Lake [...] Read more.

This study assesses the efficacy of the ZJWARMS model’s AI-based post-processing correction method for temperature and wind speed forecasts in complex terrain. By analyzing 72 h forecasts at four stations with varying elevations (from 273 m to 1327 m) in the Liuchun Lake region during December 2021–December 2022, the study found that AI-based corrections substantially enhanced both forecast accuracy and stability. The results indicate that, after correction, temperature forecast accuracy at all stations exceeded 99%, with the most notable relative gains at higher elevations (up to 48.1%). The mean absolute error (MAE) for temperature declined from 3.08 °C to below 0.8 °C at Octagonal Palace, and from 3.29 °C to below 0.6 °C at Mountaintop. Wind speed forecast accuracy also increased from approximately 60–70% to nearly 100%, with MAE generally constrained to the range of 0.2–0.4 m/s. In terms of extreme error control, the number of samples with temperature errors exceeding ±2 °C was markedly reduced. For instance, at Mountainside, the count dropped from 127 to 0. Extreme wind speed errors were also effectively eliminated. After correction, error distributions became more concentrated, and both temporal stability and spatial consistency showed notable improvement. These gains enhance operational forecasting and risk management in mountainous regions, for example, through threshold-based wind-hazard alerts and support for mountain-road icing, by providing more reliable, high-confidence guidance. Full article

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

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20 pages, 25657 KB

Open AccessArticle

Regional Divergence in Long-Term Trends of the Marine Heatwave over the East China Sea

by Qun Ma, Zhao-Jun Liu, Wenbin Yin, Ming-Xuan Lu and Jun-Bo Ma

Atmosphere 2025, 16(10), 1150; https://doi.org/10.3390/atmos16101150 - 1 Oct 2025

Abstract

Marine heatwaves (MHWs) pose a serious threat to the marine ecosystems and fishery resources in the East China Sea (ECS). Based on National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature High Resolution version 2 data, this study investigated the regional divergence [...] Read more.

Marine heatwaves (MHWs) pose a serious threat to the marine ecosystems and fishery resources in the East China Sea (ECS). Based on National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature High Resolution version 2 data, this study investigated the regional divergence in long-term trends of MHWs in the ECS from 1982 to 2023. The principal findings were as follows. Concerning MHWs, the coastal waters of China from northern Jiangsu coast to northeast of Taiwan Island experienced a relatively high annual average frequency, the longest duration, largest number of total days, strongest intensity, and the most pronounced seasonal signals. Additionally, the areas along the Kuroshio path showed significant levels of frequency, duration, and total days, but with comparatively weak intensity. In the empirical orthogonal function (EOF) analysis, EOF1 of the total days and cumulative intensity exhibited notable variation along the path of the Kuroshio and its offshoots, and in Chinese coastal areas. EOF2 showed significantly more conspicuous variation in areas extending from the Yangtze River Estuary to the northern Jiangsu coast. Furthermore, the MHW indices generally showed a positive trend in the ECS from 1982 to 2023. Importantly, the regions with high annual average MHW indices were also characterized by a significantly positive increasing trend. Moderate (79.10%) and strong (19.94%) events were most prevalent, whereas severe (0.82%) and extreme (0.14%) events occurred infrequently. The enhanced solar radiation and the reduced latent heat loss were the main contributing factors of MHWs in the ECS. These findings provide valuable insights into the ecological environment and resources of the ECS as a marine pastoral area. 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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23 pages, 6455 KB

Open AccessArticle

Detection of Sulfur from Industrial Emissions Across a Complex Mountainous Landscape: An Isotope Approach Using Plant-Based Biomonitors in Winter

by Ann-Lise Norman, Sunita LeGallou, Erin E. Caldwell, Patrick M. Blancher, Jelena Matic and Ralph Cartar

Atmosphere 2025, 16(10), 1149; https://doi.org/10.3390/atmos16101149 - 30 Sep 2025

Abstract

Tree rings, tree needles, and moss can be used as biomonitors to evaluate atmospheric pollutant concentrations and deposition patterns spanning different timescales. This study compares output from air quality modeling and measurements to patterns observed using a combination of sulfur concentration and isotope [...] Read more.

Tree rings, tree needles, and moss can be used as biomonitors to evaluate atmospheric pollutant concentrations and deposition patterns spanning different timescales. This study compares output from air quality modeling and measurements to patterns observed using a combination of sulfur concentration and isotope composition in moss (using moss bags and controls) as biomonitors in a region of southern Alberta, Canada influenced by industrial emissions. Tree rings allow comparisons of historical to current sulfur deposition patterns. Moss, which integrates atmospheric nutrients during growth, allows for concurrent comparisons. The contrast of inorganic and organic sulfur within conifer tree needles provides a measure of pollutant uptake over their short lifespans. Sulfur uptake within biomonitors in a southern Alberta ecosystem allow assessment of the presence (in moss, needles) and effects (on conifer growth) of atmospheric sulfur deposition from industrial emissions. These data were examined relative to California Puff (CALPuff) model projections and traditional active and passive air quality sampling. Patterns in sulfur isotope abundance (δ³⁴S) from moss bags placed throughout the eastern slopes of the southern Alberta foothills of the Rocky Mountains implicate local industry as the dominant atmospheric sulfur source over winter, with the tissues of conifers (needles and cores) and moss decreasing with distance from industrial emissions. This was consistent with apportionment calculations based on active and passive sampling, which also showed a surprising trend of sulfur deposition upwind of the industrial stack in the mountains to the west. δ³⁴S values for pine needles and tree rings were consistent with greater sulfur stress and reductions in tree growth associated with increased industrial sulfur concentrations and deposition. We conclude that plant biomonitors are effective short-term (tree needles and moss) and long-term (tree cores) indicators of sulfur pollution in a complex, mountainous landscape. Full article

(This article belongs to the Special Issue Biomonitoring—an Effective Tool for Air Pollution Assessment (2nd Edition))

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15 pages, 1897 KB

Open AccessArticle

Sources and Reactivity of Ambient VOCs on the Tibetan Plateau: Insights from a Multi-Site Campaign (2012–2014) for Assessing Decadal Change

by Fangkun Wu, Jie Sun, Yinghong Wang and Zirui Liu

Atmosphere 2025, 16(10), 1148; https://doi.org/10.3390/atmos16101148 - 30 Sep 2025

Abstract

Investigating atmospheric volatile organic compounds (VOCs) is critical for understanding their sources, chemical reactivity, and impacts on air quality, climate, and human health, especially in remote regions like the Tibetan Plateau where baseline data remains scarce. In this study, ambient VOCs species were [...] Read more.

Investigating atmospheric volatile organic compounds (VOCs) is critical for understanding their sources, chemical reactivity, and impacts on air quality, climate, and human health, especially in remote regions like the Tibetan Plateau where baseline data remains scarce. In this study, ambient VOCs species were simultaneously measured at four remote background sites on the Tibetan Plateau (Nyingchi, Namtso, Ngari, and Mount Everest) from 2012 to 2014 to investigate their concentration, composition, sources, and chemical reactivity. Weekly integrated samples were collected and analyzed using a Gas Chromatograph-Mass Spectrometer/Flame Ionization Detector (GC-MS/FID) system. The total VOC mixing ratios exhibited site-dependent variability, with the highest levels observed in Nyingchi, followed by Mount Everest, Ngari and Namtso. The VOC composition in those remote sites was dominated by alkanes (25.7–48.5%) and aromatics (11.4–34.7%), followed by halocarbons (19.1–28.1%) and alkenes (11.5–18.5%). A distinct seasonal trend was observed, with higher VOC concentrations in summer and lower levels in spring and autumn. Source analysis based on correlations between specific VOC species suggests that combustion emissions (e.g., biomass burning or residential heating) were a major contributor during winter and spring, while traffic-related emissions influenced summer VOC levels. In addition, long-range transport of pollutants from South Asia also significantly impacted VOC concentrations across the plateau. Furthermore, reactivity assessments indicated that alkenes were the dominant contributors to OH radical loss rates, whereas aromatics were the largest drivers of ozone formation potential (OFP). These findings highlight the complex interplay of local emissions and regional transport in shaping VOC chemistry in this high-altitude background environment, with implications for atmospheric oxidation capacity and secondary pollutant formation. Full article

(This article belongs to the Special Issue Atmospheric Chemistry in Urban Environments: Insights into Organic Compounds, Aerosols, and Haze Formation Mechanisms)

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14 pages, 5022 KB

Open AccessArticle

PM_2.5 Concentration Prediction Model Utilizing GNSS-PWV and RF-LSTM Fusion Algorithms

by Mingsong Zhang, Li Li, Galina Dick, Jens Wickert, Huafeng Ma and Zehua Meng

Atmosphere 2025, 16(10), 1147; https://doi.org/10.3390/atmos16101147 - 30 Sep 2025

Abstract

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along [...] Read more.

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along with air quality and meteorological data collected in Suzhou city from February 2021 to July 2023, were employed in this study. The Spearman correlation analysis and Random Forest (RF) feature importance assessment were used to select key input features, including PWV, PM₁₀, O₃, atmospheric pressure, temperature, and wind speed. Based on RF, Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP) algorithms, four PM_2.5 concentration prediction models were developed using sliding window and fusion algorithms. Experimental results show that the root mean square error (RMSE) of the 1 h PM_2.5 concentration prediction model using the RF-LSTM fusion algorithm is 4.36

μ g / m^{3}

, while its mean absolute error (MAE) and mean absolute percentage error (MAPE) values are 2.63

μ g / m^{3}

and 9.3%. Compared to the individual LSTM and MLP algorithms, the RMSE of the RF-LSTM PM_2.5 prediction model improves by 34.7% and 23.2%, respectively. Therefore, the RF-LSTM fusion algorithm significantly enhances the prediction accuracy of the 1 h PM_2.5 concentration model. As for the 2 h, 3 h, 6 h, 12 h, and 24 h PM_2.5 prediction models using the RF-LSTM fusion algorithm, their RMSEs are 5.6

μ g / m^{3}

, 6.9

μ g / m^{3}

, 9.9

μ g / m^{3}

, 12.6

μ g / m^{3}

, and 15.3

μ g / m^{3}

, and their corresponding MAPEs are 13.8%, 18.3%, 28.3%, 38.2%, and 48.2%, respectively. Their prediction accuracy decreases with longer forecasting time, but they can effectively capture the fluctuation trends of future PM_2.5 concentrations. The RF-LSTM PM_2.5 prediction models are efficient and reliable for early warning systems in Suzhou city. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment (2nd Edition))

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14 pages, 3399 KB

Open AccessArticle

On the Quasi-Steady Vorticity Balance in the Mature Stage of Hurricane Irma (2017)

by Jasper de Jong, Aarnout J. van Delden and Michiel L. J. Baatsen

Atmosphere 2025, 16(10), 1146; https://doi.org/10.3390/atmos16101146 - 29 Sep 2025

Abstract

Vorticity budgets in traditional height or pressure coordinates are commonly examined to help explain how tropical cyclones evolve over time. One disadvantage of using these coordinates is that the vorticity flux due to diabatic heating cannot be easily assessed. Isentropic coordinates naturally lend [...] Read more.

Vorticity budgets in traditional height or pressure coordinates are commonly examined to help explain how tropical cyclones evolve over time. One disadvantage of using these coordinates is that the vorticity flux due to diabatic heating cannot be easily assessed. Isentropic coordinates naturally lend themselves to determine the effect of diabatic heating—the vorticity budget simplifies, and a clear-cut distinction can be made between adiabatic (advective) and diabatic vorticity fluxes. Above the boundary layer, advective vorticity fluxes alone would lead to a quick spin-down of the mature tropical cyclone. Do diabatic processes prevent this from happening? If so, how? This paper investigates the vorticity budget of Hurricane Irma (2017) in its mature quasi-steady phase. We analyse a simulation of Irma with an operational high-resolution weather forecasting model. During Irma’s remarkably long period (37 h) of steady peak intensity, the radially outward advective isentropic vorticity flux in the eyewall above the boundary layer is balanced by a radially inward diabatic isentropic vorticity flux. Frictional effects and asymmetrical flow properties are of little importance to the maintenance of cyclone intensity in its mature phase, provided enough latent heat is released in the eyewall to maintain an inward vorticity flux that balances the advective flux. Full article

(This article belongs to the Section Meteorology)

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17 pages, 4320 KB

Open AccessArticle

Can Heat Waves Fully Capture Outdoor Human Thermal Stress? A Pilot Investigation in a Mediterranean City

by Serena Falasca, Ferdinando Salata, Annalisa Di Bernardino, Anna Maria Iannarelli and Anna Maria Siani

Atmosphere 2025, 16(10), 1145; https://doi.org/10.3390/atmos16101145 - 29 Sep 2025

Abstract

In addition to air temperature and personal factors, other weather quantities govern the outdoor human thermal perception. This study provides a new targeted approach for the evaluation of extreme events based on a specific multivariable bioclimate index. Heat waves (HWs) and outdoor human [...] Read more.

In addition to air temperature and personal factors, other weather quantities govern the outdoor human thermal perception. This study provides a new targeted approach for the evaluation of extreme events based on a specific multivariable bioclimate index. Heat waves (HWs) and outdoor human thermal stress (OHTS) events that occurred in downtown Rome (Italy) over the years 2018–2023 are identified, characterized, and compared through appropriate indices based on the air temperature for HWs and the Mediterranean Outdoor Comfort Index (MOCI) for OHTS events. The overlap between the two types of events is evaluated for each year through the hit (HR) and false alarm rates. The outcomes reveal severe traits for HWs and OHTS events and higher values of HR (minimum of 66%) with OHTS as a predictor of extreme conditions. This pilot investigation confirms that the use of air temperature threshold underestimates human physiological stress, revealing the importance of including multiple parameters, such as weather variables (temperature, wind speed, humidity, and solar radiation) and personal factors, in the assessment of hazards for the population living in a specific geographical region. This type of approach reveals increasingly critical facets and can provide key strategies to establish safe outdoor conditions for occupational and leisure activities. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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37 pages, 3856 KB

Open AccessArticle

Urban Health Assessment Through a Planetary Health Perspective: Methods and First Results from the Rome NBFC Experiment

by Carmina Sirignano, Daiane De Vargas Brondani, Gianluca Di Iulio, Chiara Anselmi, Stefania Argentini, Alessandro Bracci, Carlo Calfapietra, Silvia Canepari, Giampietro Casasanta, Giorgio Cattani, Simona Ceccarelli, Hellas Cena, Tony Christian Landi, Rosa Coluzzi, Rachele De Giuseppe, Stefano Decesari, Annalisa Di Cicco, Alessandro Domenico Di Giosa, Luca Di Liberto, Alessandro Di Menno di Bucchianico, Marisa Di Pietro, Oxana Drofa, Simone Filardo, Raffaela Gaddi, Alessandra Gaeta, Clarissa Gervasoni, Alessandro Giammona, Michele Pier Luca Guarino, Laura De Gara, Maria Cristina Facchini, Vito Imbrenda, Antonia Lai, Stefano Listrani, Alessia Lo Dico, Lorenzo Marinelli, Lorenzo Massimi, Maria Cristina Monti, Luca Mortarini, Marco Paglione, Ferdinando Pasqualini, Danilo Ranieri, Laura Restaneo, Matteo Rinaldi, Eleonora Rubin, Andrea Scartazza, Rosa Sessa, Alice Traversa, Lina Fusaro, Annamaria Altomare, Gloria Bertoli and Francesca Costabile Show full author list Hide full author list

Atmosphere 2025, 16(10), 1144; https://doi.org/10.3390/atmos16101144 - 29 Sep 2025

Abstract

Addressing the planetary crisis associated with climate change, biodiversity loss, global pollution, and public health requires novel and holistic approaches. Here, we present the methodology and initial results of an experiment conducted in Rome within the framework of the National Biodiversity Future Center [...] Read more.

Addressing the planetary crisis associated with climate change, biodiversity loss, global pollution, and public health requires novel and holistic approaches. Here, we present the methodology and initial results of an experiment conducted in Rome within the framework of the National Biodiversity Future Center (NBFC) project, Spoke 6. The major objective of this study was to outline the planetary health approach as a lens to assess urban health. This transdisciplinary case study explored the relationship between urban traffic-related external exposome and pro-oxidative responses in humans and plants. This methodology is based on the integration of atmospheric dynamics modeling, state-of-the-art aerosol measurements, biomonitoring in human cohorts, in vitro cellular assays, and the assessment of functional trait markers in urban trees. The results indicate that short-term exposure to urban aerosols, even at low concentrations, triggers rapid oxidative and inflammatory responses in bronchial epithelial cells, modulates gene and miRNA expression, alters gut microbiota diversity, and induces functional trait changes in urban trees. This study also highlights the feedback mechanisms between vegetation and atmospheric conditions, emphasizing the role of urban greenery in modulating microclimate and exposure. The methodology and initial results presented here will be further analyzed in future studies to explore proof of a cause–effect relationship between short-term exposure to traffic-related environmental stressors in urban areas and oxidative stress in humans and plants, with implications for chronic responses. In a highly urbanized world, this evidence could be pivotal in motivating the widespread implementation of planetary health approaches for assessing urban health. Full article

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

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18 pages, 15249 KB

Open AccessArticle

Responses of the East Asian Winter Climate to Global Warming in CMIP6 Models

by Yuxi Jiang, Yutao Chi, Weidong Wang, Wenshan Li, Hui Wang and Jianxiang Sun

Atmosphere 2025, 16(10), 1143; https://doi.org/10.3390/atmos16101143 - 29 Sep 2025

Abstract

Global warming has been altering the East Asian climate at an unprecedented rate since the 20th century. In order to evaluate the changes in the East Asian winter climate (EAWC) and support policy-making for climate mitigation and adaptation strategies, this paper utilizes the [...] Read more.

Global warming has been altering the East Asian climate at an unprecedented rate since the 20th century. In order to evaluate the changes in the East Asian winter climate (EAWC) and support policy-making for climate mitigation and adaptation strategies, this paper utilizes the multimodel ensemble from the Couple Model Intercomparison Project 6 and a temperature threshold method to investigate the EAWC changes during the period 1979–2100. The results show that the EAWC has been undergoing widespread and robust changes in response to global warming. The winter length in East Asia has shortened and will continue shortening owing to later onsets and earlier withdrawals, leading to a drastic contraction in length from 100 days in 1979 to 43 days (27 days) in 2100 under SSP2-4.5 (SSP5-8.5). While most regions of the East Asian continent are projected to become warmer in winter, the Japan and marginal seas of northeastern Asia will face the risks from colder winters with more frequent extreme cold events, accompanied by less precipitation. Meanwhile, the Tibetan Plateau is very likely to have colder winters in the future, though its surface snow amounts will significantly decline. Greenhouse gas (GHG) emissions are found to be responsible for the EAWC changes. GHG traps heat inside the Earth’s atmosphere and notably increases the air temperature; moreover, its force modulates large-scale atmospheric circulation, facilitating an enhanced and northward-positioned Aleutian low together with a weakened Siberian high, East Asian trough, and East Asian jet stream. These two effects work together, resulting in a contracted winter with robust and uneven regional changes in the EAWC. This finding highlights the urgency of curbing GHG emissions and improving forecasts of the EAWC, which are crucial for mitigating their major ecological and social impacts. Full article

(This article belongs to the Section Climatology)

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