Atmosphere

18 pages, 1736 KB

Open AccessArticle

A Hybrid Statistical-Machine Learning Framework for Risk-Based Screening of High-Frequency Carbon Emission Data Under Emissions Trading Systems

by Changyi Weng, Zhenghua Shu, Jueying Qian, Jingwei Fan and Xiaohu Luo

Atmosphere 2026, 17(6), 624; https://doi.org/10.3390/atmos17060624 - 22 Jun 2026

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Abstract

Reliable carbon emission data are essential for the effective operation of emissions trading systems (ETS), especially as China’s ETS expands to include energy-intensive industries. This study proposes a hybrid, risk-based anomaly detection framework for high-frequency CO₂ emission data by cross-validating material-based emissions [...] Read more.

Reliable carbon emission data are essential for the effective operation of emissions trading systems (ETS), especially as China’s ETS expands to include energy-intensive industries. This study proposes a hybrid, risk-based anomaly detection framework for high-frequency CO₂ emission data by cross-validating material-based emissions with flue gas-based monitoring data. Under normal operating conditions, the ratio of material-based to flue gas-based emissions is expected to remain within a relatively stable distribution. Potential high-risk periods can therefore be identified when this relationship is distorted or when local temporal patterns deviate from expected behavior. The framework combines Hartigan’s dip test with a window-based Random Forest (RF) classifier, which is suitable for continuous monitoring data that may exhibit temporal dependence. The framework was evaluated using 15-min CO₂ emission data from a cement production facility, with simulations of anomaly magnitude, duration, and mode. Results show that the dip test performs well for long-lasting or strong anomalies, whereas the RF model is more sensitive to subtle, short-term deviations. In the integrated framework, 94.7% of anomalous periods were detected by at least one method and flagged as potential data-quality risks, whereas normal periods were not flagged, supporting its use to prioritize verification efforts. Full article

(This article belongs to the Section Air Quality)

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26 pages, 5163 KB

Open AccessArticle

Climate Change Impacts on Diurnal Temperature Range and Thermal Discomfort and Their Association in Selected Eastern Mediterranean Cities Using CMIP6 Projections

by George Katavoutas, Konstantinos V. Varotsos and Christos Giannakopoulos

Atmosphere 2026, 17(6), 623; https://doi.org/10.3390/atmos17060623 - 22 Jun 2026

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Abstract

Climate projections indicate significant changes in temperature patterns and other meteorological parameters under different climate change scenarios, with temperature receiving special attention due to its influence on thermal conditions and human discomfort. This study examines the relationship between diurnal temperature range (DTR) and [...] Read more.

Climate projections indicate significant changes in temperature patterns and other meteorological parameters under different climate change scenarios, with temperature receiving special attention due to its influence on thermal conditions and human discomfort. This study examines the relationship between diurnal temperature range (DTR) and thermal discomfort in the five largest cities of Greece during summer. Thermal discomfort is assessed using Thom’s discomfort index (DI), where values ≥ 21 indicate the onset of thermal discomfort, focusing on thermal conditions at the upper (DI_h) and lower (DI_c) boundaries of daily variability. The analysis uses multiple CMIP6 projections for the reference period (1981–2010) and the near future (2031–2060) under the SSP2-4.5 and SSP5-8.5, representing intermediate and high greenhouse gas forcing pathways, respectively. The study aims to investigate associations between DTR and DI-based thermal discomfort. DTR is projected to increase in most cities in the near future relative to the reference period. This reflects a regional specific response that differs from the global tendency reported in the literature for minimum air temperatures (T_min) to increase faster than maximum air temperatures (T_max). Effect size analysis of DTR indicates generally small effects in Thessaloniki, medium to large effects in Larissa depending on the scenario, and large effects in Heraklion, Athens and Patra. Projected differences in DTR are consistent with the asymmetrical response of air temperature, specifically to the higher increase rate in T_max than in T_min in most cities. DI-based thermal discomfort shows a clear contrast between upper (DI_h) and lower (DI_c) boundaries of daily variability, reflected in higher discomfort classes for DI_h and lower classes for DI_c. Higher DTR values are associated with higher DI_h-based thermal discomfort, while the corresponding association between DTR and DI_c is weak or absent. The positive association observed for the DI_h-based conditions is largely governed by the shared contribution of T_max to both DTR and the discomfort index, whereas the absent or weak association for DI_c-based conditions may reflect the weaker association between DTR and T_min as well as the relatively smaller variability of T_min. Full article

(This article belongs to the Special Issue The Drivers and Impacts of Climate Change Over the Eastern Mediterranean)

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25 pages, 56520 KB

Open AccessArticle

A Tropospheric Delay Model for InSAR in Alpine Canyon Regions Through Incorporation of Time-Varying Gaussian Coefficients and Coupled ZWD

by Jihong Zhang, Xiaoqing Zuo, Shipeng Guo, Cheng Huang and Xuefu Yue

Atmosphere 2026, 17(6), 622; https://doi.org/10.3390/atmos17060622 - 22 Jun 2026

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Abstract

This study addresses the stratified and turbulent tropospheric delays that impede interferometric synthetic aperture radar (InSAR) deformation monitoring in alpine canyon regions. We introduce a tropospheric delay model that incorporates time-varying Gaussian coefficients and coupled zenith wet delay (ZWD) by combining diverse multi-source [...] Read more.

This study addresses the stratified and turbulent tropospheric delays that impede interferometric synthetic aperture radar (InSAR) deformation monitoring in alpine canyon regions. We introduce a tropospheric delay model that incorporates time-varying Gaussian coefficients and coupled zenith wet delay (ZWD) by combining diverse multi-source data. This model was incorporated into StaMPS for InSAR processing. Evaluation results demonstrated that (1) the model accurately captured seasonal and diurnal tropospheric variations, achieving a root mean squared error (RMSE) of 2.01 cm relative to the GNSS reference data; (2) the model corrected stratified and turbulent delays and reduced interferometric phase standard deviation (STD) by 9.28% compared to the Generic Atmospheric Correction Online Service (GACOS); and (3) the deformation accuracy improved by 19.07% over GACOS. Discussion results indicate that accounting for time-varying Gaussian coefficients is essential and that coupling ZWD to rectify turbulent delays outperformed the filtering method. The observed negative interferogram corrections result from the random intensity of turbulent delays. These findings confirm the effectiveness of the proposed model for high-precision InSAR deformation monitoring in complex alpine terrains. The proposed model aims to enhance studies of tropospheric delay variations in alpine canyon regions and to mitigate such delays in InSAR-based geological hazard monitoring. Full article

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

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14 pages, 1724 KB

Open AccessArticle

Measurement Uncertainty and Detection Limits in Radon Concentration Assessment Using CR-39 Nuclear Track Detectors

by Filomena Loffredo and Maria Quarto

Atmosphere 2026, 17(6), 621; https://doi.org/10.3390/atmos17060621 - 22 Jun 2026

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Abstract

Radon is a naturally occurring radioactive gas present in soil, rocks, and water, and is one of the main sources of exposure to natural radiation. It is the second leading cause of lung cancer after smoking. An accurate assessment of indoor radon concentrations [...] Read more.

Radon is a naturally occurring radioactive gas present in soil, rocks, and water, and is one of the main sources of exposure to natural radiation. It is the second leading cause of lung cancer after smoking. An accurate assessment of indoor radon concentrations is therefore essential for radiation protection and risk management. This study presents a metrological analysis of indoor radon measurements performed using CR-39 nuclear track detectors exposed over varying exposure times. A dataset of 90 measurements was analyzed in accordance with ISO 11929 and ISO 11665-4, with particular attention to the combined use of measurement uncertainty and characteristic limits (decision threshold and detection limit). The results show that characteristic limits allow a statistically consistent discrimination between true radon signals and background fluctuations, while measurement uncertainty provides a quantitative description of the reliability of individual results. The combined interpretation of these quantities enables a more accurate assessment of the validity of the measurements, particularly for values close to the detection limit. In addition, a dimensionless Reliability Ratio (R), defined as the ratio of the measured concentration to the detection limit, is introduced as an operational indicator for evaluating the reliability of individual measurements and comparing results obtained under different exposure times. The proposed framework is demonstrated using real measurement data and highlights the practical role of metrological concepts in supporting decision-making processes in indoor radon risk assessment and mitigation strategies. Full article

(This article belongs to the Section Air Pollution Control)

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20 pages, 21082 KB

Open AccessArticle

Forecasting Human Bioclimatic Comfort in a Hot–Dry Climate Using Sarimax Machine Learning: Diyarbakır, Turkey

by Ahmet Koç, Murat Uçan, Sülem Şenyiğit Doğan, Mehmet Kaya, Gökhan Şahin and Erdal Akin

Atmosphere 2026, 17(6), 620; https://doi.org/10.3390/atmos17060620 - 20 Jun 2026

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Abstract

Climate, and especially cities with hot climatic conditions, directly impact human life. In this study, hourly datasets from the central meteorological station in Diyarbakır city center for the years 1990–2022 were utilized. These data were analyzed using RayMan Pro-2.1 software, and Physiological Equivalent [...] Read more.

Climate, and especially cities with hot climatic conditions, directly impact human life. In this study, hourly datasets from the central meteorological station in Diyarbakır city center for the years 1990–2022 were utilized. These data were analyzed using RayMan Pro-2.1 software, and Physiological Equivalent Temperature values were derived. The obtained Physiological Equivalent Temperature values were analyzed using the SARIMAX model implemented on a machine learning infrastructure to uncover the changes between 2022 and 2050. According to the results obtained, the Physiological Equivalent Temperature value, which was 15.42 °C in 1990 in real terms, increased by 21.3% to 18.66 °C in 2022. According to the SARIMAX model predictions, Physiological Equivalent Temperature values in 2022 are estimated to rise to 21.42 °C by 2050, reflecting an increase of 14.79%. The aim of this study is to examine the temporal variations in human bioclimatic comfort values and provide a foundation for future predictions. This will contribute to the development of urban master plans by local and administrative authorities. Full article

(This article belongs to the Special Issue Urban Air Quality, Green Spaces, and Microclimate Analysis)

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21 pages, 3449 KB

Open AccessArticle

Burden of Mortality Attributable to Long-Term Exposure to PM_2.5 in Addis Ababa, Ethiopia: A Health Impact Assessment Using AirQ+

by Andualem Ayele Mengistu, Andualem Mekonnen Hiruy, Eyale Bayable Tegegne, Marc N. Fiddler and Solomon Bililign

Atmosphere 2026, 17(6), 619; https://doi.org/10.3390/atmos17060619 - 19 Jun 2026

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Abstract

Health impact assessments of ambient particulate matter remain far less developed in sub-Saharan African cities, despite fine particulate matter (PM_2.5) being a significant contributor to premature mortality globally. This study quantified the public health burdens of adult mortality associated with long-term [...] Read more.

Health impact assessments of ambient particulate matter remain far less developed in sub-Saharan African cities, despite fine particulate matter (PM_2.5) being a significant contributor to premature mortality globally. This study quantified the public health burdens of adult mortality associated with long-term PM_2.5 exposure in Addis Ababa, Ethiopia, under different counterfactual air quality scenarios. Hourly PM_2.5 data were collected across nine monitoring stations from 2022 to 2023. AirQ+ tool was utilized to estimate attributable natural-cause and cardiovascular disease (CVD) mortality among adults aged ≥ 30 years. Spatial analysis showed mean concentrations ranging from 15 µg/m³ to 33 µg/m³, with an overall mean of 26.74 µg/m³, exceeding the WHO annual guideline by more than fivefold. Seasonal peaks occurred from June to August and diurnal maxima at 7:00 AM. In 2022, attributable natural-cause deaths ranged from 1489 (6.16%) at the less stringent WHO Interim Target 3 (15 µg/m³) to 3169 (13.11%) at the WHO Air Quality Guidelines (5 µg/m³). In 2023, the range was 1544 (6.40%) to 3218 (13.33%). For specific chronic endpoints, PM_2.5 concentration level was responsible for between 509 and 1071 CVD deaths in 2022, and between 535 and 1126 CVD deaths in 2023 across the counterfactual scenario. These results highlight the substantial health burden posed by ambient PM₂.₅ in Addis Ababa and emphasize the urgent need for targeted interventions. Full article

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

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9 pages, 440 KB

Open AccessBrief Report

Trends in the 10-Year Record of Airborne Cryptomeria japonica Pollen Concentrations in Jeju, Korea

by Young Jong Han, Mae Ja Han, Seungbum Kim, Jae-Won Oh and Kyu Rang Kim

Atmosphere 2026, 17(6), 618; https://doi.org/10.3390/atmos17060618 - 19 Jun 2026

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Abstract

Cryptomeria japonica (Japanese cedar) is extensively planted as windbreaks in Jeju, Korea, producing highly allergenic pollen that significantly affects local populations. This study analyzed 10-year trends of airborne C. japonica pollen concentrations and their relationship with meteorological factors in Jeju to provide essential [...] Read more.

Cryptomeria japonica (Japanese cedar) is extensively planted as windbreaks in Jeju, Korea, producing highly allergenic pollen that significantly affects local populations. This study analyzed 10-year trends of airborne C. japonica pollen concentrations and their relationship with meteorological factors in Jeju to provide essential data for allergy management and climate adaptation strategies. Daily airborne pollen sampling was conducted using Burkard traps from 2015 to 2024 at a monitoring site in Jeju. Meteorological data, including temperature, wind speed, relative humidity, precipitation, solar radiation, and cloud amount, were obtained from the Korea Meteorological Administration. Temporal trends were analyzed using linear regression and the Mann–Kendall test, while correlations between pollen parameters and meteorological variables were calculated using Spearman’s correlation coefficients. Over the 10-year period, annual pollen integral (APIn) and peak concentrations showed statistically significant increasing trends. Pollen season start dates demonstrated a tendency toward earlier occurrence. Season onset was strongly negatively correlated with pre-season temperatures in January and February. January solar radiation showed positive correlations with both season end and period duration. C. japonica pollen concentrations in Jeju demonstrate significant increasing trends with earlier seasonal onset, primarily driven by pre-season warming in January and February. These changes may lead to prolonged allergen exposure periods, necessitating enhanced public health preparedness and adaptation of clinical management strategies for allergic populations. Full article

(This article belongs to the Special Issue Pollen Monitoring and Health Risks)

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17 pages, 7567 KB

Open AccessReview

Airborne Antibiotic-Resistant Bacteria—Challenge for Healthcare Environments

by Katarzyna Kauch, Anna Mainka and Ewa Brągoszewska

Atmosphere 2026, 17(6), 617; https://doi.org/10.3390/atmos17060617 - 18 Jun 2026

Viewed by 363

Abstract

Antimicrobial resistance (AMR) is a growing global public health challenge. Its development is strongly associated with the inappropriate and excessive use of antimicrobial agents, leading to reduced treatment effectiveness, limited availability of therapeutic options, constraints on medical procedures, and an increasing economic burden. [...] Read more.

Antimicrobial resistance (AMR) is a growing global public health challenge. Its development is strongly associated with the inappropriate and excessive use of antimicrobial agents, leading to reduced treatment effectiveness, limited availability of therapeutic options, constraints on medical procedures, and an increasing economic burden. This narrative review synthesizes current knowledge on antibiotic-resistant bacteria detected in airborne samples from healthcare environments and examines their reported resistance profiles. The review focused on the bacterial species identified, methods used for antimicrobial susceptibility assessment, types of healthcare facilities investigated, and environmental and behavioral factors influencing the occurrence and dissemination of airborne antibiotic-resistant bacteria. The clinical relevance of the reported pathogens was discussed in the context of the WHO Bacterial Priority Pathogens List (BPPL), while the WHO AWaRe classification and TrACSS framework were used as complementary interpretative tools to contextualize resistance patterns and their implications for antimicrobial stewardship and AMR surveillance. The reviewed studies showed that airborne bacterial communities in healthcare settings were dominated by Gram-positive bacteria, particularly Staphylococcus spp. and Bacillus spp., while clinically relevant pathogens such as methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa, and Acinetobacter baumannii were also frequently detected. Resistance to β-lactam antibiotics was the most frequently reported resistance pattern. Considerable heterogeneity in sampling strategies, antimicrobial susceptibility testing methods, and interpretive criteria limited direct comparison among studies. The findings highlight the need for standardized monitoring methods, long-term surveillance, and integrated environmental and clinical research to support infection prevention strategies and mitigate antimicrobial resistance. Full article

(This article belongs to the Section Aerosols)

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21 pages, 6896 KB

Open AccessArticle

MFD-DF: A PM_2.5 Concentration Prediction Method Based on Multimodal Feature Decomposition and Dynamic Fusion

by Chen Song, Quanbo Long, Zhaobo Su, Yanchao Jiang, Li Wan, Xiankun Zhang, Tiantian Lv, Wenhu Hao and Zuxuan Shi

Atmosphere 2026, 17(6), 616; https://doi.org/10.3390/atmos17060616 - 18 Jun 2026

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Abstract

Accurate air pollutant concentration prediction is crucial for public health and sustainable urban development. Existing methods predominantly rely on single-modal data, resulting in inadequate representation of pollutant spatiotemporal evolution, poor prediction accuracy, and limited generalization capabilities. To address these challenges, this research proposes [...] Read more.

Accurate air pollutant concentration prediction is crucial for public health and sustainable urban development. Existing methods predominantly rely on single-modal data, resulting in inadequate representation of pollutant spatiotemporal evolution, poor prediction accuracy, and limited generalization capabilities. To address these challenges, this research proposes a novel PM_2.5 prediction framework termed MFD-DF that integrates ground-station time series and satellite remote sensing images. In feature extraction, learnable decomposition and deformable convolution are introduced, and a Cross-Modal Slot Attention module explicitly decomposes features to resolve information blurring. Subsequently, a dynamic cross-modal alignment mechanism is designed alongside a learnable Time-Expansion Network (TEN) to ensure fine-grained interaction. Furthermore, a local-global attention feature fusion mechanism is proposed to optimize data integration efficacy. Experimental results demonstrate that in single-step PM_2.5 prediction tasks, the proposed MFD-DF achieves significant improvements of approximately 10–20% in MAE, RMSE, and MAPE compared to state-of-the-art baselines. In multi-step PM_2.5 prediction, it effectively alleviates the error accumulation problem in long-sequence forecasting, demonstrating superior robustness and accuracy. Full article

(This article belongs to the Section Air Quality)

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24 pages, 4006 KB

Open AccessArticle

Benchmarking Landsat-8 Collection 2 Level-2 Land Surface Temperature Accuracy Using SURFRAD Stations: Effects of Seasonality and Atmospheric Water Vapor

by Almustafa AbdElkader Ayek, Mohannad Ali Loho, Nasser Ibrahem, Afnan Abdullah Alturki, Youssef M. Youssef and Mayada Abdelkader Abdelaziz

Atmosphere 2026, 17(6), 615; https://doi.org/10.3390/atmos17060615 - 18 Jun 2026

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Abstract

Land Surface Temperature (LST) is essential for climate monitoring, drought assessment, and urban heat analysis. Despite its importance, the Landsat-8 Collection 2 Level-2 (C2L2) LST product has not been rigorously validated using ground measurements—a critical gap this study addresses. We present the first [...] Read more.

Land Surface Temperature (LST) is essential for climate monitoring, drought assessment, and urban heat analysis. Despite its importance, the Landsat-8 Collection 2 Level-2 (C2L2) LST product has not been rigorously validated using ground measurements—a critical gap this study addresses. We present the first comprehensive accuracy assessment using 382 coincident satellite–ground observations collected from seven Surface Radiation Budget Network (SURFRAD) stations distributed across diverse climatic regions of the United States during the period 2023–2025. The validation results indicate strong overall agreement between satellite-derived and ground-measured temperatures, yielding an RMSE of 4.20 °C, a coefficient of determination (R²) of 0.91, and a Pearson correlation coefficient (r) of 0.98. These statistics demonstrate the high reliability of the C2L2 LST product across a wide range of environmental conditions. Nevertheless, a systematic warm bias of 1.75 °C was observed, indicating a tendency toward temperature overestimation. Model performance exhibited pronounced seasonal variability. The highest accuracy was achieved during winter conditions (RMSE = 2.17 °C; r = 0.99), whereas performance declined considerably during summer months (RMSE = 5.84 °C; r = 0.91). Analysis of atmospheric water vapor content revealed significant associations with retrieval errors at high-elevation and arid locations, particularly at FPK (r = 0.78) and DRA (r = 0.75), based on 106 matched observations. These relationships provide important insight into the atmospheric factors contributing to seasonal variations in retrieval accuracy. Temperature-dependent analyses further demonstrated that retrieval uncertainty increases with surface temperature. Performance progressively deteriorated from cooler to warmer thermal regimes, with RMSE values increasing from approximately 2.05 °C for temperatures below 20 °C to 5.71 °C for temperatures exceeding 40 °C. Spatial evaluation also revealed substantial differences among stations. Relatively homogeneous, low-elevation sites exhibited superior performance (GWN: RMSE = 2.60 °C; SXF: RMSE = 2.55 °C), whereas stations located in mountainous or topographically complex environments showed reduced accuracy (TBL: RMSE = 5.14 °C; FPK: RMSE = 5.62 °C). These outcomes emphasize the influence of terrain complexity and atmospheric heterogeneity on LST retrieval performance. Overall, this study establishes the first comprehensive benchmark for evaluating the reliability of Landsat-8 C2L2 LST products. The results provide valuable guidance for their application in climate research, precision agriculture, hydrological modeling, and environmental monitoring. Furthermore, the findings identify specific environmental conditions requiring enhanced validation efforts and suggest opportunities for future algorithm refinement through improved atmospheric correction procedures and more accurate surface emissivity characterization. Full article

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

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18 pages, 19610 KB

Open AccessArticle

Asymmetric Response of Summer Extreme Heat Events to CO₂ Removal Scenarios in Eastern Sichuan–Chongqing, China

by Bingbing Jiang, Zhang Chen, Yiyun Fu and Zhibiao Wang

Atmosphere 2026, 17(6), 614; https://doi.org/10.3390/atmos17060614 - 17 Jun 2026

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Abstract

In recent decades, summer extreme high-temperature (EHT) events in the Sichuan–Chongqing (SC) region of southwestern China have become increasingly frequent under global warming. Carbon dioxide removal (CDR) is considered a key strategy for achieving the temperature targets of the Paris Agreement; however, the [...] Read more.

In recent decades, summer extreme high-temperature (EHT) events in the Sichuan–Chongqing (SC) region of southwestern China have become increasingly frequent under global warming. Carbon dioxide removal (CDR) is considered a key strategy for achieving the temperature targets of the Paris Agreement; however, the response of regional EHT events to CDR remains poorly understood. Based on CN05.1 observations and idealized CO₂ ramp-up and ramp-down experiments from the CMIP6 Carbon Dioxide Removal Model Intercomparison Project (CDRMIP), this study investigates the historical characteristics of summer EHT events over eastern SC and their responses to CDR. The results show that historical EHT events have become more frequent, longer-lasting, and more intense, indicating an overall intensification of regional high-temperature risk. Under idealized CO₂ pathways, regional mean temperature and EHT frequency exhibit pronounced asymmetric and hysteretic responses, with positive anomalies persisting even after CO₂ returns to its initial level. This asymmetric response is closely associated with the enhanced slow oceanic response during the ramp-down period. Stronger El Niño-like and Indian Ocean Dipole-like SST warming intensifies the South Asian High and western Pacific subtropical high, favoring elevated summer temperatures and increased EHT events over eastern SC. Soil moisture also heats the atmosphere by altering the surface latent heat flux in the southwestern part of the study region during ramp-down period. These findings not only improve the understanding of regional extreme event responses in the SC region under carbon neutrality, but also confirm the positive effect of carbon neutrality targets on mitigating regional extreme climate change, thereby highlighting the urgent need to control CO₂ emissions. Full article

(This article belongs to the Special Issue Climate Change and Extreme Weather Disaster Risks (2nd Edition))

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19 pages, 3002 KB

Open AccessArticle

Evaluating and Merging Satellite and Reanalysis Precipitation Products with Station Observations Using XGBoost in the Jinsha River Basin, China

by Ye Yin, Hantao Wang, Hui Zhang, Nanshan Zhao, Cuihua Cheng and Chenghua Xie

Atmosphere 2026, 17(6), 613; https://doi.org/10.3390/atmos17060613 - 17 Jun 2026

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Abstract

The Jinsha River Basin constitutes the largest hydropower base in China. However, its complex terrain results in insufficient accurate data support for numerical forecasts, leading to low accuracy in precipitation predictions. To investigate the spatiotemporal distribution characteristics of precipitation in this basin with [...] Read more.

The Jinsha River Basin constitutes the largest hydropower base in China. However, its complex terrain results in insufficient accurate data support for numerical forecasts, leading to low accuracy in precipitation predictions. To investigate the spatiotemporal distribution characteristics of precipitation in this basin with high precision, we evaluated the applicability of several mainstream precipitation products—GSMAP (Global Satellite Mapping of Precipitation), GPM-IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement), CMORPH (Climate Prediction Center Morphing technique), and ERA5 (European Center for Medium-Range Weather Forecasts Reanalysis 5)—in the Jinsha River Basin. Based on the XGBoost algorithm, we developed a merging model that integrates satellite and reanalysis data with station observations for daily-scale applications. The results indicate that the GSMAP-Gauge precipitation product exhibits strong performance in both quantitative accuracy and precipitation event detection, with a better correlation coefficient (CC = 0.66), the lowest root mean square error (RMSE = 4.45), and higher probability of detection (POD = 0.88) and critical success index (CSI = 0.59). The ERA5 and GSMAP-Gauge products performed well in detecting light rain events (daily precipitation < 10 mm), with hit rates of 0.92 and 0.90, respectively. Meanwhile, the GPM-IMERG and CMORPH-BLD products showed higher hit rates for heavy rain events (daily precipitation > 25 mm) compared to the other two products. Specifically, the POD indices for GPM-IMERG and CMORPH-BLD were 0.45 and 0.60, respectively, while those for ERA5 and GSMAP-Gauge were below 0.4. Following the precipitation merging experiment, the multi-source precipitation merged product (MSP) substantially enhanced the accuracy of precipitation estimates, and the spatiotemporal distribution characteristics of the merged data aligned more closely with the station observations. This study analyzes the strengths and limitations of various precipitation products in the Jinsha River Basin and provides a feasible multi-source precipitation data merging scheme, offering a novel approach to constructing high-precision daily precipitation datasets in complex terrain regions. Full article

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

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23 pages, 6966 KB

Open AccessArticle

Differences in Dust Release, Near-Surface Transport Structure, and Static Settling Among Farmland Soils Under Wind Erosion

by Ruochen Jia, Fang Liu, Wennong Kuang, Jinlei Zhu, Yuan Liu, Zhigang Wang, Zhiming Xin, Yuting Liu, Chaoqun Ba and Zhimin Liu

Atmosphere 2026, 17(6), 612; https://doi.org/10.3390/atmos17060612 - 17 Jun 2026

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Abstract

Farmland wind erosion is usually assessed only by emission intensity, with limited understanding of how soil differences propagate through transport and post-wind settling. Here, seven typical farmland soils from west-central Inner Mongolia, northern China, were tested in a closed-circuit wind tunnel under five [...] Read more.

Farmland wind erosion is usually assessed only by emission intensity, with limited understanding of how soil differences propagate through transport and post-wind settling. Here, seven typical farmland soils from west-central Inner Mongolia, northern China, were tested in a closed-circuit wind tunnel under five wind speeds (8.0–14.0 m s⁻¹). Based on particle-size composition, dry aggregate fractions, and organic matter content, the soils were grouped into three particle–aggregate groups. The results showed that, at 14.0 m s⁻¹, differences in measured particle–aggregate properties among soils were first reflected in marked differences in steady dust release intensity and vertically integrated transport input, which ranged from 27.78 to 76.39 mg m⁻³ and from 14.52 to 135.32 g m⁻² 10 min⁻¹, respectively. These differences were then transmitted to the near-surface transport layer, where the soils exhibited contrasting patterns in upper-layer contribution, transport height, and vertical particle-size sorting. After wind cessation, the soils further diverged into early-concentrated, transitional, and sustained-accumulation settling types. Steady dust release intensity was positively correlated with transport input and also with early deposition load. These findings indicate that particle-size and aggregate properties influence not only dust release, but also the organization of transport processes and the post-wind fate of particles. Full article

(This article belongs to the Special Issue The Characterization and Evolution of Airborne Dust Particles)

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16 pages, 2934 KB

Open AccessArticle

Effects of Air Pollution Exposure on Hospital Admissions: A Time Series Study in Sivas, Türkiye

by Hüseyin Özdemir, İbrahim Kaya, Özkan Çapraz, Hakan Çelikten, Ilker Oruc, Hacer Handan Demir and Ali Deniz

Atmosphere 2026, 17(6), 611; https://doi.org/10.3390/atmos17060611 - 16 Jun 2026

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Abstract

The impact of air pollution on human health has been widely studied in recent decades. Recent findings show that even low levels of air pollution can be harmful to our health, causing disease and early death. However, these studies are very limited in [...] Read more.

The impact of air pollution on human health has been widely studied in recent decades. Recent findings show that even low levels of air pollution can be harmful to our health, causing disease and early death. However, these studies are very limited in the central region of Türkiye. Therefore, this study focused on the association between the daily variations in air pollutants (PM₁₀, PM_2.5, SO₂, and NO₂) and hospital admissions due to respiratory, cardiovascular, and total (non-accidental) causes in the Sivas province. Daily average concentrations of air pollutants were obtained from two air quality (AQ) monitoring stations, and daily meteorological (air temperature and relative humidity) data were obtained from one meteorological station in Sivas province to determine the effects of air pollution on hospital admissions. It was found to be a significant relationship between air pollution and respiratory hospital admissions in the province. The results of the study showed the relative magnitudes of the risks of cardiovascular diseases and hospital admissions related to air pollutants were as follows: The highest association of each pollutant with cardiovascular diseases was observed for PM₁₀ at lag 4 (ER = 1.74%; 95% CI = 0.95–3.19%), PM_2.5 at lag 2 (ER = 5.12%; 95% CI = 1.39–19.0%), NO₂ at lag 8 (ER = 4.89%; 95% CI = 0.08–288.8%) and SO₂ at lag 5 (ER = 1.21%; 95% CI = 1.10–1.32%). It was seen that short-term exposure to air pollution in Sivas between 2016 and 2019 was positively associated with increasing respiratory hospital admissions. As the first air pollution study to use the generalized linear model (GLM) method in hospital admissions in Sivas, these findings may have implications for local environmental policies and help to combat air pollution. Full article

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

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13 pages, 5773 KB

Open AccessArticle

Spatiotemporal Air Quality Forecasting in South Africa Using the LSTM Model

by Lerato Shikwambana, Moloko Sebake, Moleboheng Molefe, Henno Havenga and Nkanyiso Mbatha

Atmosphere 2026, 17(6), 610; https://doi.org/10.3390/atmos17060610 - 16 Jun 2026

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Abstract

This study applies a Long Short-Term Memory (LSTM) model to predict key air pollutants, i.e., sulphur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM_2.5), as well as the Air Quality Index (AQI) across South Africa using [...] Read more.

This study applies a Long Short-Term Memory (LSTM) model to predict key air pollutants, i.e., sulphur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM_2.5), as well as the Air Quality Index (AQI) across South Africa using satellite-derived observations. The analysis focuses on comparing original pollutant fields with model-generated predictions for two consecutive days, highlighting both spatial patterns and predictive performance. Results reveal a persistent and intense pollution hotspot over the Mpumalanga Highveld, driven by coal-fired power generation and industrial activities. Elevated pollutant concentrations in this region translate into AQI levels ranging from Unhealthy to Very Unhealthy, while most other parts of the country remain within the Good category. Spatial comparison between original and predicted fields shows strong agreement, with only minor deviations in areas characterized by steep emission gradients and localized plumes. Quantitative evaluation using RMSE (0.020390) and MSE (0.000416) confirms the high accuracy of the predictive model, with error values remaining extremely low across all pollutants and AQI outputs. PM_2.5 exhibits the smallest errors (MSE = 4.230169 × 10⁻⁶), while slightly higher values for SO₂ (MSE = 2.628 × 10⁻⁴) and NO₂ (MSE = 1.39541 × 10⁻⁴) reflect the difficulty of capturing sharp spatial transitions associated with point-source emissions. Despite these localized discrepancies, the model demonstrates robust skill in replicating both pollutant magnitudes and AQI classifications. Overall, the findings indicate that machine-learning approaches offer a reliable, high-resolution tool for air-quality prediction in South Africa and have strong potential for supporting operational forecasting, exposure assessment, and environmental policy development. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Clouds: Current Status and Emerging Challenges)

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18 pages, 112229 KB

Open AccessArticle

A Framework for High-Resolution Soil Moisture Mapping Using Sentinel-1/2 Predictors and a Stacking Ensemble

by Yi Liu, Xiaobo Liu, Siqing Xu, Xiaoang Kong, Binbin Zhao, Xinmin Li and Hui Yuan

Atmosphere 2026, 17(6), 609; https://doi.org/10.3390/atmos17060609 - 16 Jun 2026

Viewed by 342

Abstract

Soil moisture (SM) governs land–atmosphere exchanges and strongly influences agricultural management and hydrological assessment, yet high-resolution mapping remains challenging due to sensor-specific confounding effects and limited field observations. This study develops a practical workflow for point-scale SM estimation and wall-to-wall mapping by integrating [...] Read more.

Soil moisture (SM) governs land–atmosphere exchanges and strongly influences agricultural management and hydrological assessment, yet high-resolution mapping remains challenging due to sensor-specific confounding effects and limited field observations. This study develops a practical workflow for point-scale SM estimation and wall-to-wall mapping by integrating multi-sensor remote sensing predictors with ensemble learning. A compact predictor set was constructed from Sentinel-2 optical indices, Sentinel-1 SAR descriptors (σVV and the polarization ratio σVH/σVV), and topographic information, collocated with in situ SM measurements along a transect in the study area. Three tree-based regressors—Random Forest, XGBoost, and CatBoost—were trained under an identical feature configuration and evaluated using R², Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) together with predicted–observed diagnostics. A stacking ensemble was then implemented using leakage-controlled K-fold out-of-fold predictions to generate meta-features, with a Decision Tree as the meta-learner tuned via a grid search. Results show that base learners achieve comparable skill (R² ≈ 0.60–0.62; RMSE ≈ 0.038–0.039), while stacking improves test accuracy (RMSE = 0.0346) and provides a stable mapping-ready model. The trained framework was transferred to stacked raster predictors to produce spatially continuous SM maps, revealing coherent moisture heterogeneity across the region. Accordingly, the objective of this study is to develop a compact and application-oriented point-to-map workflow for high-resolution soil moisture estimation by integrating Sentinel-1/2-derived predictors with stacking-based model fusion, rather than to propose a new physically based retrieval model. Full article

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

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14 pages, 3489 KB

Open AccessArticle

Numerical Simulation-Based Study on the Mitigation of Carbon Dioxide Around Buildings by Spatial Morphology of Urban Road Greening

by Jing Li, Shilin Zhao and Wenjie Chen

Atmosphere 2026, 17(6), 608; https://doi.org/10.3390/atmos17060608 - 15 Jun 2026

Viewed by 224

Abstract

Rapid economic development has led to a growing reliance on private car commuting, making the mitigation of carbon dioxide (CO₂) pollution along road environments critical for the health of nearby residents. Road greening serves as an ecological barrier between traffic emissions [...] Read more.

Rapid economic development has led to a growing reliance on private car commuting, making the mitigation of carbon dioxide (CO₂) pollution along road environments critical for the health of nearby residents. Road greening serves as an ecological barrier between traffic emissions and adjacent residential areas, and its effectiveness in reducing local CO₂ pollution has been widely studied. However, the influence of different spatial morphologies of road greening on the distribution of CO₂ around buildings remains underexplored. In this study, we developed a numerical simulation model to investigate CO₂ dispersion on building surfaces under various road greening spatial configurations. Simulation results indicate that a “tree–shrub–grass” composite configuration significantly reduces CO₂ concentrations around buildings. These findings provide practical guidance for optimizing vegetation spatial layouts in high-density road networks and contribute to the global pursuit of carbon peak and carbon neutrality goals. Full article

(This article belongs to the Section Climatology)

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24 pages, 5219 KB

Open AccessArticle

A Diagnostic Framework for Phase-Dependent Synoptic Uncertainty in Tropical Cyclone Track Prediction Using Ensemble Space EOF Analysis: Application to Typhoon SHANSHAN (2024)

by Akiyoshi Wada

Atmosphere 2026, 17(6), 607; https://doi.org/10.3390/atmos17060607 - 13 Jun 2026

Viewed by 380

Abstract

This study investigates the forecast bust of Typhoon SHANSHAN (2024) characterized by large track errors using the four major interactive grand global operational ensemble data and the atmospheric reanalysis data. Ensemble space empirical orthogonal function (EOF) analysis is applied to 850, 500, and [...] Read more.

This study investigates the forecast bust of Typhoon SHANSHAN (2024) characterized by large track errors using the four major interactive grand global operational ensemble data and the atmospheric reanalysis data. Ensemble space empirical orthogonal function (EOF) analysis is applied to 850, 500, and 300 hPa geopotential heights at three target times to diagnose how synoptic-scale uncertainty contributed to the erroneous motions of SHANSHAN. We align the multi-level EOF bases to a reference-time basis via a weighted Procrustes rotation and evaluate similarity to the atmospheric reanalysis data in the aligned principal component (PC) space, enabling robust, distance-based conditioning of ensemble members. Results show that ensemble spread is consistently larger in the mid-latitudes, with relatively large uncertainty concentrated around the upper-tropospheric trough and lower-tropospheric structure near SHANSHAN. The dominant EOF modes differ by phase of SHANSHAN: lower-tropospheric modes govern the westward-moving stage, whereas mid- and upper-tropospheric modes dominate after recurvature. Selecting members whose EOF-based PC structures most closely match the atmospheric reanalysis effectively suppresses large-error outliers and yields improved conditional track predictions. These findings highlight phase-dependent synoptic controls and demonstrate that adaptive, reference-consistent conditioning can enhance the track guidance of tropical cyclones during difficult forecast situations. Full article

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

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32 pages, 1243 KB

Open AccessArticle

A Reduced-Order Regime Theory for Aerosol–Halogen–Dynamics Coupling in Volcanic Super-Eruptions

by Sebastiano Ettore Spoto

Atmosphere 2026, 17(6), 606; https://doi.org/10.3390/atmos17060606 - 13 Jun 2026

Viewed by 375

Abstract

Volcanic super-eruptions can perturb atmospheric composition and climate-relevant radiative properties in ways that are not captured by simple scaling from Pinatubo-like events. This study presents a reduced-order regime theory for the coupled evolution of stratospheric sulfur, sulfate aerosol burden, reactive halogens, ozone loss, [...] Read more.

Volcanic super-eruptions can perturb atmospheric composition and climate-relevant radiative properties in ways that are not captured by simple scaling from Pinatubo-like events. This study presents a reduced-order regime theory for the coupled evolution of stratospheric sulfur, sulfate aerosol burden, reactive halogens, ozone loss, stratospheric thermal adjustment, and aerosol residence time. The analysis is intended as an interpretive tool for organizing sulfur-rich volcanic scenarios, comparing literature-based benchmark classes, and designing chemistry–climate model experiments, rather than as an event-specific calibration or a substitute for three-dimensional models. Four control parameters structure the response: sulfur loading relative to microphysical saturation, effective halogen strength, ash-uptake efficiency, and dynamical lifetime sensitivity, with hemispheric asymmetry treated diagnostically. An external consistency check against published Pinatubo-like, idealized 10–40 teragrams of sulfur (Tg S), Toba-like, and Los Chocoyos-like responses is used to evaluate whether the reduced theory reproduces the expected rank ordering of aerosol saturation, forcing-efficiency decline, ozone-loss amplification, ash-driven sulfur suppression, and residence-time sensitivity. This comparison does not assign pointwise error margins against three-dimensional model output; it evaluates regime membership, sign of response, rank ordering, and broad magnitude behavior. The main conclusion is that volcanic super-eruption impacts are governed by interacting regime transitions rather than by sulfur mass alone. Microphysical saturation can limit forcing efficiency, halogens can shift the system toward chemically amplified ozone depletion, ash uptake can reduce the effective sulfur burden during the early phase, and dynamical state can control persistence and hemispheric expression. By separating these mechanisms, the study provides a compact basis for interpreting large volcanic perturbations to atmospheric chemistry and for designing targeted model experiments on extreme eruption scenarios. Full article

(This article belongs to the Section Aerosols)

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16 pages, 2634 KB

Open AccessArticle

Chemical Composition and Quantitative Source Apportionment of Aerosols over the Yellow Sea from 2020 to 2024

by Hyomin Kim, Hee Jung Ko, Jiyoung Jeong, Hee-Jung Yoo and Sangmin Oh

Atmosphere 2026, 17(6), 605; https://doi.org/10.3390/atmos17060605 - 12 Jun 2026

Viewed by 284

Abstract

This study examined the chemical composition and quantitative source contributions of coarse (PM_10–2.5) and fine (PM_2.5) particles in ship-based PM₁₀ and PM_2.5 filter samples from 2020 to 2024 across the Yellow Sea. The observations were primarily conducted [...] Read more.

This study examined the chemical composition and quantitative source contributions of coarse (PM_10–2.5) and fine (PM_2.5) particles in ship-based PM₁₀ and PM_2.5 filter samples from 2020 to 2024 across the Yellow Sea. The observations were primarily conducted during the spring season, when the influence of continental air masses from East Asia is pronounced, and detailed analyses of water-soluble ions and elemental species were performed. In coarse particles, sea salt components (e.g., Na⁺ and Cl⁻) and soil-derived species (e.g., nss-Ca²⁺ and CO₃²⁻) were predominant, whereas fine particles were dominated by secondary inorganic species such as nss-SO₄²⁻, NO₃₋, and NH₄⁺. Source contributions were estimated using Dispersion Normalized Positive Matrix Factorization (DN-PMF), and eight common factors were identified, including sea salt, soil, secondary nitrate, secondary sulfate, oil combustion, biomass burning, marine biogenic emissions, and plant growth. Additionally, an industry factor was uniquely resolved in coarse particles, whereas a mobile source factor was identified in fine particles. In coarse particles, sea salt (30.9%) and soil (15.1%) were the major contributing sources, whereas fine particles were dominated by secondary nitrate (48.6%) and secondary sulfate (15.6%). Potential Source Contribution Function (PSCF) analysis indicated that the sea salt and oil combustion factors in coarse particles were associated with coastal regions of the Yellow Sea and the East China Sea, while the soil factor corresponded spatially with inland regions of northern China. In contrast, the secondary nitrate, secondary sulfate, and biomass burning factors in fine particles showed strong associations with inland regions of eastern China. Using size-resolved DN-PMF and five years of repeated observations over the same marine region, this study provides the first quantitative source apportionment analysis of interannual atmospheric composition variability and long-range transport affecting air quality over the Yellow Sea. Full article

(This article belongs to the Special Issue Atmospheric Aerosol Characterization, Transport and Environmental Effects)

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21 pages, 4058 KB

Open AccessArticle

Intermember Simulation Uncertainty in North Pacific Tropical Cyclone Genesis Frequency Under the Influence of the Interdecadal Pacific Oscillation at Decadal-Scale

by Jianing Li, Zhen Wang, Jiuwei Zhao, Leying Zhang and Yue Li

Atmosphere 2026, 17(6), 604; https://doi.org/10.3390/atmos17060604 - 12 Jun 2026

Viewed by 197

Abstract

Substantial uncertainties remain in climate model simulations of tropical cyclones (TCs), particularly those associated with internal climate variability. While the influence of the El Niño–Southern Oscillation (ENSO) on interannual TC variability is well established, the contribution of the Interdecadal Pacific Oscillation (IPO) to [...] Read more.

Substantial uncertainties remain in climate model simulations of tropical cyclones (TCs), particularly those associated with internal climate variability. While the influence of the El Niño–Southern Oscillation (ENSO) on interannual TC variability is well established, the contribution of the Interdecadal Pacific Oscillation (IPO) to decadal-scale uncertainty is less well constrained. Although models generally reproduce IPO-related variations in tropical cyclone genesis frequency (TCGF) over the eastern North Pacific, large discrepancies persist across the broader North Pacific basin. Clarifying the role of IPO in modulating TCGF uncertainty is therefore essential for improving decadal TC projections. In this study, we analyzed a large ensemble of historical simulations from the MRI-AGCM within the d4PDF (Database for Policy Decision Making for Future Climate Change) framework. Empirical orthogonal function (EOF) analysis is applied to IPO-composited fields to identify the leading modes of intermember (100 members *60 y, 6000 times) simulation uncertainty on a decadal-scale. The results reveal that state-of-the-art models exhibit robust and spatially coherent uncertainty structures in TCGF under different IPO phases. Two leading modes are identified: (1) a South China Sea mode, closely associated with systematic precipitation biases, and (2) a zonal dipole mode between the eastern and western North Pacific, linked to the equatorward propagation of Arctic Oscillation (AO)-related variability. Misrepresentation of AO variability is found to contribute substantially to biases in simulated TCGF patterns. Comparisons with observational datasets further support the proposed mechanisms. These findings highlight the importance of improving the representation of precipitation processes and extratropical–tropical teleconnections in climate models, which is critical for enhancing the reliability of decadal predictions of North Pacific TC activity. Full article

(This article belongs to the Section Climatology)

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30 pages, 7931 KB

Open AccessArticle

Numerical Analysis on Shading-Based Pedestrian Environment Optimization for HOD: A UTCI-Based Comparison at Macau LRT Union Hospital Station

by Zekai Guo, Qingnian Deng, Jingwei Liang, Lina Yan, Wei Liu, Yufei Zhu, Liang Zheng and Yile Chen

Atmosphere 2026, 17(6), 603; https://doi.org/10.3390/atmos17060603 - 12 Jun 2026

Viewed by 374

Abstract

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) [...] Read more.

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

(This article belongs to the Special Issue Modelling of Indoor Air Quality and Thermal Comfort)

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23 pages, 24761 KB

Open AccessArticle

Topographic and Potential-Radiation Relationships with Ground-Surface Thermal Response During the Thawing Period in Maritime Antarctica

by Miguel Ángel de Pablo, Clara Bermejo, Gabriel Goyanes and Ariadna Sánchez

Atmosphere 2026, 17(6), 602; https://doi.org/10.3390/atmos17060602 - 11 Jun 2026

Viewed by 284

Abstract

Ground-surface temperature (GST) in maritime Antarctic ice-free areas is influenced by atmospheric forcing, snow cover, surface energy and topography. Previous PERMATHERMAL studies in Livingston and Deception Islands have shown changes in air and ground-surface thermal regimes, with fewer cold conditions, greater thawing influence [...] Read more.

Ground-surface temperature (GST) in maritime Antarctic ice-free areas is influenced by atmospheric forcing, snow cover, surface energy and topography. Previous PERMATHERMAL studies in Livingston and Deception Islands have shown changes in air and ground-surface thermal regimes, with fewer cold conditions, greater thawing influence and strong snow-cover modulation. However, the interval in which GST responds effectively to radiative and topographic forcing remains poorly explored. We characterize the station- and season-specific timing of the thermally effective GST thawing period and evaluate topographic and modeled potential controls on its thermal intensity and cumulative effect around the Spanish Antarctic Station Juan Carlos I, Hurd Peninsula, Livingston Island. Onset and end were objectively delimited by using three consecutive days with daily mean GST > 0.5 °C and daily thermal amplitude > 1.0 °C. Hourly GST records from six PERMATHERMAL stations were combined with potential radiation, potential insolation and topographic variables derived from a high-resolution UAV-based DEM. Accumulated thawing degree days were strongly influenced by period duration. Mean thermal intensity was primarily associated with elevation, while mean modeled potential radiation provided additional explanatory power only when combined with elevation. This UAV–GIS–GST approach provides a simple framework for assessing local surface–atmosphere coupling in remote Antarctic ice-free areas. Full article

(This article belongs to the Section Meteorology)

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23 pages, 1025 KB

Open AccessReview

Health Effects of Smoke Exposure in Wildland Firefighters

by Andrew Foster Armstrong, Iza David Zabaneh, Isabela Agi Maluli, Paige Dafoe, Angel Sheu and Wade Swenson

Atmosphere 2026, 17(6), 601; https://doi.org/10.3390/atmos17060601 - 11 Jun 2026

Viewed by 310

Abstract

Wildland firefighters play a critical role in protecting communities and natural resources, yet comparatively little research has examined the occupational health risks associated with repeated smoke exposure. This narrative review analyzed documented health effects, contributing exposure determinants, and mitigation strategies across 38 studies [...] Read more.

Wildland firefighters play a critical role in protecting communities and natural resources, yet comparatively little research has examined the occupational health risks associated with repeated smoke exposure. This narrative review analyzed documented health effects, contributing exposure determinants, and mitigation strategies across 38 studies meeting pre-specified inclusion criteria. Included studies were predominantly quantitative field investigations evaluating pulmonary, cardiovascular, metabolic, and chemical exposure outcomes. Consistent findings documented decreased lung function, elevated oxidative stress, increased carbon monoxide (CO) exposure, and cumulative cardiovascular risk. Wildland firefighters were associated with polycyclic aromatic hydrocarbon (PAH) levels 2.2–26.7 times higher than controls. Prescribed burns produced CO concentrations 233% higher than off-fire-line days. Cardiovascular disease accounts for approximately 45% of annual line-of-duty fatalities among U.S. firefighters. Contributing factors included career duration, fire type, and operational role. Altogether, these findings underscore the severe, multi-system health risks faced by wildland firefighters and highlight a pressing need for modern mitigation strategies and firefighter-specific protective technologies to safeguard long-term health. Full article

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

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22 pages, 26794 KB

Open AccessArticle

Comparative Study of Precipitation Characteristics and Causes of Similar Trajectories: Typhoons Chanthu and Mitag in the Western Pacific

by Yaoying Hong, Guopang Chen, Xiaofeng Li, Qingxiang Li, Xiao Xiao, Siyi Zhong and Yong Han

Atmosphere 2026, 17(6), 600; https://doi.org/10.3390/atmos17060600 - 11 Jun 2026

Viewed by 305

Abstract

Research on the differences and correlations of typhoon precipitation along similar trajectories, as well as their underlying causes, remains insufficient. Therefore, this study selects two typhoons with similar tracks but significantly different precipitation characteristics—Chanthu (2114) and Mitag (1918) in the Western Pacific—as research [...] Read more.

Research on the differences and correlations of typhoon precipitation along similar trajectories, as well as their underlying causes, remains insufficient. Therefore, this study selects two typhoons with similar tracks but significantly different precipitation characteristics—Chanthu (2114) and Mitag (1918) in the Western Pacific—as research cases. Using the China Meteorological Administration best-track dataset, ERA5 reanalysis data, surface station observations, and GPM IMERG precipitation products, their precipitation features and underlying mechanisms are analyzed. Results show that the area-averaged land precipitation associated with Chanthu (51.9 mm) was nearly twice that of Mitag (27.2 mm). Chanthu produced broader and more persistent rainfall, mainly distributed along the northern side of its track, whereas Mitag exhibited weaker and more scattered precipitation. These differences were primarily related to the combined effects of large-scale circulation, moisture transport, dynamical and thermodynamic structure, and convective instability. During Chanthu, the subtropical high remained stable and the upper-level trough stayed farther north, favoring the maintenance of an organized typhoon structure. Chanthu also featured stronger upper-level divergence, sustained dual-channel moisture transport, a deeper warm-core structure, stronger upward motion, and better-developed convective instability. In contrast, Mitag was affected by the southward extension of the upper-level trough and the eastward retreat of the subtropical high, together with weaker divergence, insufficient moisture supply, a shallower structure, and weaker instability. Overall, precipitation differences between similarly tracked typhoons result from the synergistic effects of multiple environmental and internal factors. These findings improve understanding of typhoon precipitation mechanisms and may provide guidance for forecasting. Full article

(This article belongs to the Section Meteorology)

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30 pages, 4355 KB

Open AccessArticle

Identifying Nonlinear Thresholds and Interaction Dominance of Meteorological Drivers on Rice Yield: A SHAP-Based Approach

by Chenshuang Lin, Zhitao Yan and Shujie Miao

Atmosphere 2026, 17(6), 599; https://doi.org/10.3390/atmos17060599 - 11 Jun 2026

Viewed by 241

Abstract

Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading [...] Read more.

Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading scale for interaction effects among factors is lacking. To explore the meteorological factor thresholds and interaction effect intensities affecting rice yield, rice unit yield and meteorological data from nine districts and counties in Ningbo City from 1995 to 2024 were utilized. Rice yield prediction models were constructed based on LASSO and six machine learning algorithms. Recursive Feature Elimination (RFE) based on the SHAP algorithm was conducted to screen out 11 core meteorological factors. Building upon this, two innovative methodological indicators were proposed. First, the Derivative Extrema Threshold (DET) was introduced as a supplement to the Zero-Crossing Threshold (ZCT). By locating the extremum points of the first derivative of the smoothed SHAP dependence plot curves, the critical positions where the effect intensity undergoes a qualitative change without a directional reversal were identified. Second, the Interaction Dominance Ratio (IDR) was proposed. This metric normalizes the interaction variability within a total effect framework and establishes a three-tier grading standard for strong, moderate, and weak interactions. It was observed that optimal performance was achieved by the LightGBM model after feature optimization (R² = 0.833). Direction reversal points with extremely narrow confidence intervals, such as an August cumulative precipitation of 210.6 mm and a June average temperature of 24.5 °C, were identified by the ZCT. Intensity mutation characteristics, such as the “weakening of the yield reduction effect” at a May cumulative precipitation of 64.9 mm, were further revealed by the DET. An Interaction Dominance Triangular Network, composed of the August–September average temperature, the June minimum temperature, and the August cumulative precipitation, was accurately characterized by the IDR analysis. This overcomes the constraints of traditional single-factor early warning systems. The “ZCT-DET-IDR” framework constructed in this study facilitates a methodological advancement from directional discrimination and intensity early warning to multi-factor synergistic analysis. This framework provides a quantifiable novel perspective for the refined early warning of regional agrometeorological disasters. Full article

(This article belongs to the Special Issue Recent Advances in Agrometeorological Techniques and Their Applications)

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20 pages, 26728 KB

Open AccessArticle

Land–Atmosphere Coupling Strength and Impact on Afternoon Precipitation over North America During April–September

by Madhusmita Swain and David Roy Fitzjarrald

Atmosphere 2026, 17(6), 598; https://doi.org/10.3390/atmos17060598 - 11 Jun 2026

Viewed by 649

Abstract

Precipitation is among the most uncertain and poorly predicted weather products in earth system science. Local convective precipitation is particularly sensitive to strong land–atmosphere coupling. Two indices derived from atmospheric thermodynamic vertical profiles, convective triggering potential (CTP), a measure of the temperature lapse [...] Read more.

Precipitation is among the most uncertain and poorly predicted weather products in earth system science. Local convective precipitation is particularly sensitive to strong land–atmosphere coupling. Two indices derived from atmospheric thermodynamic vertical profiles, convective triggering potential (CTP), a measure of the temperature lapse rate between approximately 1 and 3 km above the ground surface, and low-level humidity (HI_low), have become preferred measures of land–atmospheric coupling strength. To complement previous studies that primarily relied on limited station observations or regional analyses, this study provides a 20-year assessment of the CTP-HI_low framework for a wide area of the Continental United States (CONUS) using integrated satellite observations, reanalysis products, and surface datasets. The study further identifies important regional limitations in the framework’s predictive skill and demonstrates the influence of mid-level vertical wind shear on precipitation occurrence during both wet and dry soil advantage conditions. These findings provide new insight into why the framework performs inconsistently across different climate regions and suggest pathways for improving land–atmosphere coupling-based precipitation prediction. The objective is to determine the atmospheric and land-surface factors that control the regional performance of the CTP-HI_low framework and to identify how additional datasets that include more atmospheric variables can improve precipitation prediction skill. Full article

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

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25 pages, 7683 KB

Open AccessArticle

Solar Radiation and Climate Change Research: A Comprehensive Bibliometric Analysis (1991–2025)

by Ahmet Reha Botsalı

Atmosphere 2026, 17(6), 597; https://doi.org/10.3390/atmos17060597 - 11 Jun 2026

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Abstract

Solar radiation drives virtually every process in Earth’s climate system—from atmospheric circulation and the hydrological cycle to ecosystem carbon uptake and agricultural productivity. How this energy flux is changing under anthropogenic climate forcing and what the consequences might be have become central preoccupations [...] Read more.

Solar radiation drives virtually every process in Earth’s climate system—from atmospheric circulation and the hydrological cycle to ecosystem carbon uptake and agricultural productivity. How this energy flux is changing under anthropogenic climate forcing and what the consequences might be have become central preoccupations of modern Earth system science. Yet despite a rapidly growing literature spanning atmospheric physics, ecology, remote sensing, and energy engineering, no study has attempted to map the global scientific output on solar radiation and climate change as a unified research domain. This study addresses this gap through a large-scale bibliometric analysis of 8473 publications retrieved from the Web of Science Core Collection (1991–2025). Using the Bibliometrix R package (v5.0.1) and VOSviewer (v1.6.20), the study examined publication growth, country and institutional productivity, journal performance, co-authorship structures, keyword networks, thematic evolution, and emerging research fronts. The literature has grown at an annual rate of 14.87%, with China and the USA accounting for nearly half of all output—though American research shows markedly higher citation impact. Bradford’s Law identified 27 core journals, which accounted for roughly one-third of total publications; the Journal of Geophysical Research–Atmospheres ranked first. Consistent with Lotka’s Law, a large majority of authors (78.9%) appear only once in the dataset, pointing to a broad but peripherally engaged scientific community. Keyword co-occurrence mapping revealed five thematic clusters: ecological and biosphere impacts; climate dynamics and variability; atmospheric processes and data-driven methods; solar geoengineering; and energy and renewable applications. The most rapidly rising topics after 2020—machine learning, CMIP6, solar geoengineering, and heatwaves—suggest that the field is shifting toward data-driven methods and active climate intervention debates. These findings offer a structured overview of where the field stands and the most urgent knowledge gaps. Full article

(This article belongs to the Special Issue Solar Radiation and Its Influences on Climate Change)

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26 pages, 2669 KB

Open AccessArticle

Effects of Green Plants on the Indoor Environment: Real-Life Case Studies in Italian Schools and Office Spaces

by Simone Putzolu, Rita Baraldi, Luisa Neri, Alessandro Zaldei, Carolina Vagnoli, Beniamino Gioli, Adam Nawrocki and Cinzia De Benedictis

Atmosphere 2026, 17(6), 596; https://doi.org/10.3390/atmos17060596 - 10 Jun 2026

Viewed by 345

Abstract

Students and workers spend much of their day in school and office environments, where poor indoor air quality (IAQ) can negatively affect health and comfort. Indoor vegetation is increasingly proposed as a low-cost nature-based solution (NBS) to improve IAQ. This study evaluated the [...] Read more.

Students and workers spend much of their day in school and office environments, where poor indoor air quality (IAQ) can negatively affect health and comfort. Indoor vegetation is increasingly proposed as a low-cost nature-based solution (NBS) to improve IAQ. This study evaluated the effects of phytoremediation on IAQ and indoor microclimate in schools across different regions and educational levels, as well as in office environments, under real-world conditions. Several C₃ plants (e.g., Chamaedorea, Schefflera, Ficus, Epipremnum, Yucca, and Spathiphyllum) were used, with crassulacean acid metabolism (CAM) plants (Sansevieria) included in selected settings. Temperature, relative humidity, CO₂, PM_2.5, and PM₁₀ were continuously monitored using intercalibrated low-cost sensors in absence and presence of vegetation. A comparable plant configuration was implemented in offices to assess its effects on volatile organic compounds (VOC). Indoor greenery reduced particulate matter, especially PM10 (18–20%), and improved microclimatic conditions by lowering air temperature (1–2 °C) and increasing relative humidity (6–15%). However, CO₂ reductions were limited and context-dependent. In the tested office environments, plant introduction was associated with reduced total VOC concentrations (25–50%). Overall, our results further support that indoor vegetation constitutes a robust, cost-effective nature-based solution (NBS) capable of complementing conventional ventilation systems in both school and office environments. Full article

(This article belongs to the Special Issue Modelling of Indoor Air Quality and Thermal Comfort)

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Open AccessArticle

Influence of North Atlantic Sea Surface Temperature Anomalies on Tibetan Plateau Vortex Frequency Variability

by Likang Xu, Panjie Qiao, Zaibo Zhao, Tingting Xue and Xu Li

Atmosphere 2026, 17(6), 595; https://doi.org/10.3390/atmos17060595 - 10 Jun 2026

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Abstract

This study investigates the frequency of Tibetan Plateau vortices (TPVs) and their statistical relationship with global sea surface temperature (SST) anomalies. The results show that TPV frequency exhibits pronounced seasonal and interannual variability. Annual TPV frequency generally ranges from 50 to 70 events, [...] Read more.

This study investigates the frequency of Tibetan Plateau vortices (TPVs) and their statistical relationship with global sea surface temperature (SST) anomalies. The results show that TPV frequency exhibits pronounced seasonal and interannual variability. Annual TPV frequency generally ranges from 50 to 70 events, with short-lived TPVs, particularly those lasting two days, accounting for the majority of occurrences. TPV activity is most active during summer and relatively weak during autumn and winter. Lagged correlation analyses reveal that the North Atlantic exhibits the strongest statistical linkage with TPV frequency among all global ocean basins. After removing the linear trends, the maximum correlation occurs when North Atlantic SST anomalies lead TPV frequency anomalies by approximately two months, indicating a robust lagged relationship between the two variables. Further circulation analyses suggest that North Atlantic SST anomalies are closely associated with large-scale atmospheric circulation anomalies over the North Atlantic–Eurasian sector prior to TPV-active months. Anomalous geopotential height and wind fields at 500 hPa, together with upper-level wind anomalies at 200 hPa, indicate significant adjustments of the Eurasian midlatitude circulation and upper-level westerly jet associated with North Atlantic SST variability. During TPV-active months, enhanced upper-level divergence, strengthened upward motion, and intensified cyclonic anomalies emerge over the Tibetan Plateau, providing favorable dynamical conditions for TPV formation and development. Overall, the results reveal a statistically robust linkage between North Atlantic SST anomalies and TPV frequency variability and provide new insight into the associated large-scale circulation background over the Tibetan Plateau. Full article

(This article belongs to the Special Issue Simulation, Assessment, and Impacts of Extreme Hydroclimatic Events)

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Atmosphere, Volume 17, Issue 6 (June 2026) – 96 articles

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