Atmosphere doi: 10.3390/atmos17070630

Authors: Stephan F. J. De Wekker Alec J. D. Bateman Christopher M. Hocut Edward D. Creegan Robb M. Randall

The representativeness of point measurements in the atmospheric boundary layer is a fundamental challenge for interpreting observations and evaluating numerical models. In this study, we quantify the representativeness of near-surface wind measurements using a dense network of 13 meteorological towers from the Army Research Laboratory’s Meteorological Sensor Array. These towers are distributed over an approximately 3 × 3 km domain at the U.S. Department of Agriculture Jornada Experimental Range in southern New Mexico. The analyzed domain consists of relatively flat terrain within a broader region of more complex topography. Representativeness is assessed using pairwise differences between towers and deviations from the array mean. Spatial variability decreases with temporal averaging, with the largest reductions occurring between 1 and 10 min and diminishing improvements beyond 10–30 min. Wind measurements become progressively less similar with increasing separation distance, particularly at separations approaching 1 km. Representativeness errors are larger under unstable conditions due to enhanced turbulence and spatial variability, while stronger winds increase wind speed variability but enhance directional coherence. Deviations from domain-averaged conditions are comparable among towers, indicating that no single location is uniquely representative. These results quantify the extent to which temporal averaging, spatial separation, and atmospheric conditions influence representativeness, providing practical estimates of the associated spatial scales and residual errors. The results are useful for interpreting observations, evaluating models, and designing sampling strategies using fixed and mobile platforms, including Uncrewed Aircraft Systems.

Atmosphere doi: 10.3390/atmos17070629

Authors: Xiaolei Niu Rachel T. Pinker

A methodology for deriving high-resolution (5-km) surface shortwave radiative (SWR) fluxes over the Arctic was applied to observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) during the spring and summer melt season (March–September) of 2007, when the Arctic experienced a historically significant and well-documented decline in sea ice extent. The derived SWR fluxes were used to estimate solar heat input into the Arctic Ocean during the melt season, a task that had not previously been undertaken at such high spatial resolution. According to the National Snow and Ice Data Center (NSIDC), Arctic sea ice extent reached a record minimum of 4.13 million km2 on 16 September 2007, approximately 38% below the 1979–2000 climatological mean and 24% below the previous record minimum in 2005. This extreme reduction in sea ice resulted in several weeks of ice-free opening along portions of the ‘Northwest Passage’. Availability of high spatial resolution SWR fluxes in the Arctic is particularly important for improving estimates of solar heat input into the Arctic Ocean, especially within the highly heterogeneous marginal ice zone. To facilitate comparison with sea ice concentration products from NSIDC, the MODIS-derived 5-km SWR fluxes were aggregated to 0.25° equal-area grid cells (approximately 25 km resolution). Our results show that the abrupt increase in the open water fraction produced anomalies in solar heating to the upper ocean exceeding 300%, hereby enhancing the ice–albedo feedback mechanism and promoting further sea ice melt. The estimated monthly cumulative solar heat input to the ocean for a nominal 1° grid cell was 164.9 MJ m−2 in May. In contrast, the corresponding four 0.25° sub-grid cells, resolved using the high-resolution MODIS data, exhibited cumulative heat inputs of 58.0, 93.0, 189.3, and 296.4 MJ m−2, respectively. Although the average heat input for the 1° grid cell (165 MJ m−2 was similar to the average value obtained from the four 0.25° grid cells (159 MJ m−2 the substantial sub-grid variability is important because the oceanic and sea-ice responses to solar heating are highly nonlinear. Consequently, unresolved spatial variability can significantly affect the magnitude of derived quantities and associated feedback processes. These findings demonstrate the importance of high-spatial-resolution radiative flux information for accurately quantifying ocean heating and ice–ocean interactions in the Arctic.

Atmosphere doi: 10.3390/atmos17070628

Authors: Chen Chen Guanjie Jiao Jingyi Fan Sijia Lou

Ozone (O3) pollution is highly sensitive to meteorological variability and regional transport, particularly in coastal southeastern China. During April–May 2025, Xiamen experienced an atypical, persistent springtime O3 episode substantially exceeding the 2014–2024 baseline. Using surface observations and ERA5 reanalysis data, this study investigates the meteorological drivers and formation mechanisms. At Hongwen station, the MDA8 O3 > 160 μg m−3 exceedance frequency reached 11.5% (historical average: 0.1%). This anomaly was closely linked to an anomalous Western Pacific Subtropical High (WPSH) configuration, characterized by northward displacement and accompanying westward extension. Compared to historical high-pollution conditions, surface temperature and downward solar radiation increased by 2.32 °C and 51 W m−2, while wind speed and planetary boundary layer height decreased by 15.3% and 24.2%, favoring O3 production and precursor accumulation. Two distinct pollution periods were identified. Period 1 (29 April–1 May) featured local photochemical enhancement under stagnant conditions; regional mean NO2 increased by 31 μg m−3 before the peak, indicating substantial precursor accumulation. Simultaneously, the mean nighttime O3 concentration at the Huli site during Period 1 was 50.5 μg m−3 (43% lower than that at Hongwen) due to enhanced NO titration from port emissions. Period 2 (12–14 May) involved regional transport, where persistent 850-hPa southwesterly flow facilitated pollutant transport along the coastal corridor, increasing O3 and PM2.5 by 40 μg m−3 and 38 μg m−3. Thus, extreme springtime O3 over southeastern coastal China resulted from anomalous large-scale circulation, regional transport, and local photochemical processes.

Atmosphere doi: 10.3390/atmos17070627

Authors: Giorgia Proietti Pelliccia Erika Brattich Andrea Faggi Silvana Di Sabatino Tiziano Maestri

Recently, numerous studies have exploited satellite Aerosol Optical Depth (AOD) to estimate near-surface particulate matter (PM) concentrations, with the aim of overcoming the limited spatial and temporal coverage of ground-based air quality monitoring networks. Despite significant progress, the relationship between AOD and PM remains highly uncertain, mainly due to the inadequate representation of local aerosol microphysical properties and of hygroscopic growth effects. In particular, satellite AOD is retrieved at ambient relative humidity, whereas standard PM measurements are performed under dry conditions. This study proposes a physics-informed, semi-empirical approach that overcomes these limitations by directly relating satellite AOD to PM measured at ambient humidity. Co-located measurements, from a Light Optical Aerosol Counter (LOAC) in the urban area of Bologna (Po Valley, Italy) during 2023, are used. This study is designed as a pilot application to evaluate the physical consistency of the proposed framework under well-characterised observational conditions, including spatial co-location, temporal matching to satellite overpasses, and exclusion of precipitation and desert dust events. The LOAC provides particle number size distribution and particle-type classification, which are used to estimate key aerosol properties controlling the AOD–PM theoretical relationship, including the Effective Radius, Extinction Efficiency, and aerosol Mass Density. These quantities, together with Mixing Layer Height, are combined within a theoretical framework linking PM and AOD, allowing for the derivation of a physically based scaling coefficient without relying on empirical hygroscopic growth corrections. The results show that using ambient PM2.5 alone already yields a moderate linear correlation with AOD normalized by Mixing Layer Height (Pearson’s R = 0.56) whereas no meaningful correlation is found when using standard dry PM2.5. When aerosol microphysical properties derived from LOAC measurements are incorporated, the correlation substantially improves (R = 0.76), with regression slopes close to unity and reduced errors, independently of the season. These results demonstrate that explicitly accounting for aerosol size and optical properties enhances the physical consistency and robustness of satellite-based PM estimates. The proposed framework also provides a pathway to indirectly derive aerosol hygroscopic growth factors by coupling ambient PM estimates from satellite observations with conventional dry PM measurements. This opens new perspectives for characterizing aerosol–humidity interactions from space and for improving air quality monitoring in regions lacking of dense in situ networks.

Atmosphere doi: 10.3390/atmos17070626

Authors: Kuang Ao Yuzhu Liu

According to the “Technical Specification for Air Quality Testing in Archives Repositories,” air pollutants in archives can be categorized into exogenous and endogenous pollutants. Common exogenous pollutants include sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and hydrogen sulfide (H2S), while endogenous pollutants mainly consist of formaldehyde (HCHO) and acetic acid (CH3COOH). This study combines external electric field technology with density functional theory (DFT) and the B3LYP method to theoretically analyze the spectral characteristics and degradation mechanisms of these six pollutant gases. Molecular models of the six gases were constructed using Gaussian software. The configurations of five pollutant gas molecules (SO2, NO2, O3, H2S, and HCHO) were optimized using the B3LYP/6-31G(d) basis set, while the configuration of acetic acid was optimized using the B3LYP/3-21G basis set, yielding their stable structures and spectral information. The study found that characteristic peaks in the spectra shifted under the influence of an electric field. Additionally, by scanning the potential energy surfaces of selected molecular bonds under varying electric field strengths along specific directions, the required external electric field strengths for the degradation of the six common pollutant gases in archives were determined as follows: 0.1050 a.u. for SO2, 0.0975 a.u. for NO2, 0.0925 a.u. for O3, 0.1000 a.u. for H2S, 0.1500 a.u. for HCHO, and 0.0705 a.u. for CH3COOH. The results clarify the degradation thresholds of these six pollutant gases under an external electric field. The findings indicate that acetic acid (0.0705 a.u.) and ozone (0.0925 a.u.) are highly sensitive to electric fields, while formaldehyde requires the strongest electric field (0.1500 a.u.) for degradation. These results provide a reference and theoretical foundation for electric field-assisted degradation technology targeting pollutant gases in archives.

Atmosphere doi: 10.3390/atmos17070625

Authors: Ryo Kajiki Yasumichi Wakao Takahisa Kamikura Kanatomi Yoshihiko Chikako Kuroiwa Toshikazu Sugimoto Nakazawa Kazuma Yasuhiro Shoda

Tire and road wear particles (TRWPs) are major sources of non-exhaust traffic emissions. However, a limited understanding of their generation mechanisms and the lack of efficient collection methods under realistic driving conditions hinder accurate assessment. This study addresses these challenges by developing a vehicle-based methodology for the controlled recovery and characterization of TRWPs in the near-field region, rather than for direct quantification of real-world emissions. An autonomous electric vehicle was employed to ensure stable driving conditions and eliminate exhaust interference. Near-field distribution of TRWPs was visualized using a high-sensitivity optical scattering system. Based on this, a sealed tire enclosure with a high-power on-vehicle vacuum collection system was designed to enhance particle containment and recovery. Controlled circular driving tests were conducted on a dedicated outdoor test track under well-defined and repeatable conditions to enable system-level evaluation of TRWP generation and collection relative to measured tire wear. Particles were analyzed by thermogravimetric analysis, microscopy, scanning electron microscopy–energy-dispersive X-ray spectroscopy, and particle imaging. The results demonstrated stable, reproducible TRWP generation with ~60% collection efficiency relative to tire mass loss. These values are reported as system-dependent recovery indicators rather than precise emission estimates. Additional tests with an expanded recovery protocol indicated that collection efficiency can increase to ~81% (range: 73–91%), highlighting the influence of collection coverage. The collected TRWPs exhibited heterogeneous morphology, bimodal size distribution, and a mixed rubber–mineral composition in the 10–100 μm range. Spatial analysis revealed that TRWPs predominantly accumulated within a narrow zone around the driving lane. While the controlled experimental configuration enables reproducible particle generation and high-efficiency recovery, it represents a simplified driving scenario and may not fully capture the variability of real-world traffic conditions, including straight-line driving and transient maneuvers. Overall, this study demonstrates a technical framework for reproducible and comparative recovery of tire-associated particles under identical, well-defined conditions. The approach is intended to support controlled characterization studies while explicitly acknowledging limitations related to representativeness, particle origin attribution, and quantitative emission relevance, rather than to establish emission factors or mechanistic descriptions of TRWP generation.

Atmosphere doi: 10.3390/atmos17060624

Authors: Changyi Weng Zhenghua Shu Jueying Qian Jingwei Fan Xiaohu Luo

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.

Atmosphere doi: 10.3390/atmos17060623

Authors: George Katavoutas Konstantinos V. Varotsos Christos Giannakopoulos

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 (DIh) and lower (DIc) 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 (Tmin) to increase faster than maximum air temperatures (Tmax). 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 Tmax than in Tmin in most cities. DI-based thermal discomfort shows a clear contrast between upper (DIh) and lower (DIc) boundaries of daily variability, reflected in higher discomfort classes for DIh and lower classes for DIc. Higher DTR values are associated with higher DIh-based thermal discomfort, while the corresponding association between DTR and DIc is weak or absent. The positive association observed for the DIh-based conditions is largely governed by the shared contribution of Tmax to both DTR and the discomfort index, whereas the absent or weak association for DIc-based conditions may reflect the weaker association between DTR and Tmin as well as the relatively smaller variability of Tmin.

Atmosphere doi: 10.3390/atmos17060622

Authors: Jihong Zhang Xiaoqing Zuo Shipeng Guo Cheng Huang Xuefu Yue

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.

Atmosphere doi: 10.3390/atmos17060621

Authors: Filomena Loffredo Maria Quarto

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.

Atmosphere doi: 10.3390/atmos17060620

Authors: Ahmet Koç Murat Uçan Sülem Şenyiğit Doğan Mehmet Kaya Gökhan Şahin Erdal Akin

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.

Atmosphere doi: 10.3390/atmos17060619

Authors: Andualem Ayele Mengistu Andualem Mekonnen Hiruy Eyale Bayable Tegegne Marc N. Fiddler Solomon Bililign

Health impact assessments of ambient particulate matter remain far less developed in sub-Saharan African cities, despite fine particulate matter (PM2.5) being a significant contributor to premature mortality globally. This study quantified the public health burdens of adult mortality associated with long-term PM2.5 exposure in Addis Ababa, Ethiopia, under different counterfactual air quality scenarios. Hourly PM2.5 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, PM2.5 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.

Atmosphere doi: 10.3390/atmos17060618

Authors: Young Jong Han Mae Ja Han Seungbum Kim Jae-Won Oh Kyu Rang Kim

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.

Atmosphere doi: 10.3390/atmos17060617

Authors: Katarzyna Kauch Anna Mainka Ewa Brągoszewska

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.

Atmosphere doi: 10.3390/atmos17060616

Authors: Chen Song Quanbo Long Zhaobo Su Yanchao Jiang Li Wan Xiankun Zhang Tiantian Lv Wenhu Hao Zuxuan Shi

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 PM2.5 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 PM2.5 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 PM2.5 prediction, it effectively alleviates the error accumulation problem in long-sequence forecasting, demonstrating superior robustness and accuracy.

Atmosphere doi: 10.3390/atmos17060615

Authors: Almustafa AbdElkader Ayek Mohannad Ali Loho Nasser Ibrahem Afnan Abdullah Alturki Youssef M. Youssef Mayada Abdelkader Abdelaziz

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.

Atmosphere doi: 10.3390/atmos17060614

Authors: Bingbing Jiang Zhang Chen Yiyun Fu Zhibiao Wang

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.

Atmosphere doi: 10.3390/atmos17060613

Authors: Ye Yin Hantao Wang Hui Zhang Nanshan Zhao Cuihua Cheng Chenghua Xie

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.

Atmosphere doi: 10.3390/atmos17060612

Authors: Ruochen Jia Fang Liu Wennong Kuang Jinlei Zhu Yuan Liu Zhigang Wang Zhiming Xin Yuting Liu Chaoqun Ba Zhimin Liu

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.

Atmosphere doi: 10.3390/atmos17060611

Authors: Hüseyin Özdemir İbrahim Kaya Özkan Çapraz Hakan Çelikten Ilker Oruc Hacer Handan Demir Ali Deniz

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, PM2.5, 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%), PM2.5 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.

Atmosphere doi: 10.3390/atmos17060610

Authors: Lerato Shikwambana Moloko Sebake Moleboheng Molefe Henno Havenga Nkanyiso Mbatha

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 (PM2.5), 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. PM2.5 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.

Atmosphere doi: 10.3390/atmos17060609

Authors: Yi Liu Xiaobo Liu Siqing Xu Xiaoang Kong Binbin Zhao Xinmin Li Hui Yuan

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.

Atmosphere doi: 10.3390/atmos17060608

Authors: Jing Li Shilin Zhao Wenjie Chen

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.

Atmosphere doi: 10.3390/atmos17060607

Authors: Akiyoshi Wada

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.

Atmosphere doi: 10.3390/atmos17060606

Authors: Sebastiano Ettore Spoto

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.

Atmosphere doi: 10.3390/atmos17060605

Authors: Hyomin Kim Hee Jung Ko Jiyoung Jeong Hee-Jung Yoo Sangmin Oh

This study examined the chemical composition and quantitative source contributions of coarse (PM10–2.5) and fine (PM2.5) particles in ship-based PM10 and PM2.5 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.

Atmosphere doi: 10.3390/atmos17060604

Authors: Jianing Li Zhen Wang Jiuwei Zhao Leying Zhang Yue Li

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.

Atmosphere doi: 10.3390/atmos17060603

Authors: Zekai Guo Qingnian Deng Jingwei Liang Lina Yan Wei Liu Yufei Zhu Liang Zheng Yile Chen

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.

Atmosphere doi: 10.3390/atmos17060602

Authors: Miguel Ángel de Pablo Clara Bermejo Gabriel Goyanes Ariadna Sánchez

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.

Atmosphere doi: 10.3390/atmos17060601

Authors: Andrew Foster Armstrong Iza David Zabaneh Isabela Agi Maluli Paige Dafoe Angel Sheu Wade Swenson

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.

Atmosphere doi: 10.3390/atmos17060600

Authors: Yaoying Hong Guopang Chen Xiaofeng Li Qingxiang Li Xiao Xiao Siyi Zhong Yong Han

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.

Atmosphere doi: 10.3390/atmos17060598

Authors: Madhusmita Swain David Roy Fitzjarrald

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 (HIlow), 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-HIlow 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-HIlow framework and to identify how additional datasets that include more atmospheric variables can improve precipitation prediction skill.

Atmosphere doi: 10.3390/atmos17060599

Authors: Chenshuang Lin Zhitao Yan Shujie Miao

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.

Atmosphere doi: 10.3390/atmos17060597

Authors: Ahmet Reha Botsalı

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.

Atmosphere doi: 10.3390/atmos17060596

Authors: Simone Putzolu Rita Baraldi Luisa Neri Alessandro Zaldei Carolina Vagnoli Beniamino Gioli Adam Nawrocki Cinzia De Benedictis

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, PM2.5, 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.

Atmosphere doi: 10.3390/atmos17060595

Authors: Likang Xu Panjie Qiao Zaibo Zhao Tingting Xue Xu Li

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.

Atmosphere doi: 10.3390/atmos17060594

Authors: Lei Wang Jingyi Chen Xiaogang Li Hua Li Shifa Zhao Yaodong Guo Xiaocun Zhang

Mountain ecosystems are sensitive response units and critical ecological barriers to global climate change. Located in the mid-latitude climate transition zone, these ecosystems feature high ecological sensitivity and complex driving mechanisms, creating an urgent need to conduct long-sequence, high-precision dynamic assessments in order to support ecological conservation and climate adaptation decision-making. However, three key research gaps remain in the field: first, traditional assessments are dominated by static observation, lacking the capacity for long-sequence dynamic analysis and future projection; second, the coupled interaction mechanism among multiple ecological factors remains unclear, with insufficient quantitative and physical mechanism characterization; third, existing ecological zoning has not been validated for robustness, rendering it incapable of addressing climate disturbances and extreme scenarios. In order to study the regional atmospheric ecosystem, this study takes Shangluo in the eastern Qinling Mountains as the study area and constructs an integrated assessment framework integrating multi-dimensional diagnosis, simulation and projection, dynamic zoning and robustness validation based on long-sequence multi-factor data covering the years 1965–2024. The study aims to reveal the long-sequence evolution patterns and four-dimensional coupling mechanism of the Qinling Mountains atmospheric ecosystem, developing a reproducible and transferable dynamic assessment model. The results show that the study area exhibits the characteristic of elevation-dependent warming, and the correlation coefficients between elevation and air temperature, and between vegetation coverage and air quality reach −0.89 and −0.76, respectively.; ecological quality presents a spatial pattern of being high in the southwest and low in the northeast, with a coefficient of variation across the whole study area lower than 0.03. The results of 1000 Monte Carlo random disturbance validation runs show that even under intensified climate stress, the zoning pattern still maintains extremely strong disturbance resistance. This study reveals the steady-state multi-factor interaction mechanism in mountainous regions, addressing the defects of traditional static assessments that ignore ecosystem evolution and lag effects. The dynamic projection model constructed in this study can be transferred to similar mid-latitude mountainous regions worldwide, providing theoretical and technical support for regional ecological governance.

Atmosphere doi: 10.3390/atmos17060593

Authors: Paula Florencio Ramires Washington Luiz Félix Correia Filho Rodrigo de Lima Brum Flavio Manoel Rodrigues da Silva Júnior

Introduction: International studies have demonstrated a positive impact on air quality associated with the presence of green areas in urban conglomerates. However, in Brazil, studies addressing the impacts of urban green areas on air quality are still incipient and are predominantly focused on large urban centers. The objective of this study was to investigate the relationship between urban green areas, surface temperature (LST), and air quality across 15 medium-sized Brazilian cities. Methods: Concentrations of particulate matter fractions (PM1, PM2.5, and PM10) were monitored from January 2023 to May 2024 using second data from low-cost sensors. The NDVI and both daytime and nighttime LST profiles were extracted via Google Earth Engine within a 1 km buffer zone surrounding each station via the Sentinel-2 and MODIS 11A1 satellite data, respectively. Spatial–temporal co-variation patterns were explored using principal component analysis (PCA). To model these dynamics while controlling for spatial dependencies, a multi-criteria framework compared linear models (simple linear regression (LM) and linear mixed (LMM)) and generalized models (generalized additive (GAM) and generalized additive mixed (GAMM)). Results: The results revealed a positive relationship between NDVI and PM2.5 and PM10 fractions in specific regions, while surface temperatures showed a direct association with finer particles (PM1 and PM2.5). The regression coefficient showed the significant association of PM2.5 with NDVI and nighttime LST (β = 1.330; IC 95%: [0.397; 2.270]; p = 0.005). The GAMM was the best-fitting model for all particle fractions, demonstrating that incorporating monitoring stations as random intercepts successfully controls for unmeasured local heterogeneity, while penalized splines accurately capture non-linear environmental factors. Conclusions: Although many studies have shown that green areas in temperate regions typically act as consistent sinks for particulate matter, our study revealed localized and seasonal responses in tropical urban landscapes. It should be noted that our study is conducted on a national scale and that the use of low-cost sensors and remote sensing does not allow us to distinguish between the localized microclimatic benefits of vegetation and the long-range transport of regional pollutants.

Atmosphere doi: 10.3390/atmos17060592

Authors: Francesc Castellví Juan M. Sánchez Ramón López-Urrea

The La Mancha region (a semi-arid area of southeast Spain) hosts the world’s highest concentration of vineyards and is also one of the regions with the largest areas devoted to almond tree cultivation. Viticulture and nut fruit trees (mainly almonds) are one of the region’s principal sources of economic revenue. The Two-Source Energy Balance (TSEB) model can assist management of water resources. A simplified version of the TSEB approach (STSEB) was previously tested in a vineyard and almonds to estimate sensible heat (H) and latent heat (LE) fluxes using a parallel scheme method based on the Monin–Obukov similarity theory (MOST). This study introduces a method based on Surface Renewal (SR) theory to partition the sensible heat flux using low-frequency measurements as input. The latter was friendlier than the parallel MOST method under unstable conditions and than the series SR and MOST methods. The objective was to compare the MOST and SR models within a parallel scheme method. During the 2014 and 2015 growing season, measurements were collected in a 4 ha row crop drip-irrigated Tempranillo vineyard. Hourly sensible heat flux measured by an eddy covariance (EC) system and evapotranspiration (ET) registered by a 9 m2 monolithic large weighting lysimeter were used as a reference. ET estimates were obtained as a residual of the energy balance equation (known as the residual method) using three methods for estimating sensible heat flux, HSR, HMOST and HEC, yielding ETSR-RE, ETMOST-RE and ETEC-RE, respectively. For sensible heat flux, the index of agreement (IA expressed in %) for 2014 and 2015 was 93% and 83%, respectively, using SR, and 84% and 78%, respectively, for MOST. This represents a 6–10% improvement using SR. For evapotranspiration, the ETSR-RE and ETMOST-RE IA showed similar performance in both years (around 88%), while ETEC-RE yielded the best results (92% and 89% for 2014 and 2015, respectively). In addition, half-hourly EC fluxes, during the growing season of 2017, were used as a reference in an almond orchard. The SR sensible heat flux performed better (IA = 93%) than MOST (IA = 86%) in this case, whereas for the latent heat flux, the residual method performed the best, resulting in an IA of 81% for SR and of 78% for MOST. Overall, SR performed better than MOST, particularly under unstable conditions with wind speeds above 1 ms−1.

Atmosphere doi: 10.3390/atmos17060591

Authors: Enes Birinci Hüseyin Özdemir Ali Deniz

Urban air pollution results from complex interactions between vehicle emissions, meteorological conditions, and atmospheric chemistry. While machine learning models achieve high accuracy in air quality prediction, their limited transparency hinders policy adoption. We present an integrated (M-ETAQI) framework combining multiple XAI techniques, temporal decomposition, and causal inference to quantify traffic and meteorological contributions to PM10, PM2.5, NOX, and NO2 concentrations in the Istanbul FSM Bridge corridor (2022–2023 hourly data). Five machine learning models, including XGBoost, LightGBM, CatBoost, Random Forest, and CNN–LSTM–Attention, were trained with temporal cross-validation. SHAP, LIME, PDP, and ALE were applied for interpretability; STL decomposition isolated temporal components, and CCM tested causal links. Tree-based models achieved R2 > 0.80 for all pollutants, with CatBoost reaching PM2.5 R2 = 0.876. SHAP confirmed Lag1 as the dominant feature. Wind speed had a significant negative effect on NOX, while traffic contributed ~20% to NOX, twice that of other pollutants. STL showed the trend component dominated total variance; NO2 trend variance = 56.3%. CCM revealed wind speed as the strongest causal driver of NOX (ρ = 0.37) and confirmed direct traffic–NOX links. Knowledge distillation from CatBoost improved CNN–LSTM–Attention performance. The four XAI methods yielded consistent attributions, providing robust, cross-validated evidence for traffic management and air-quality policy.

Atmosphere doi: 10.3390/atmos17060589

Authors: Arda Eraslan David Topping Dudley E. Shallcross M. A. H. Khan Aşan Bacak

Urban air quality management has been playing a significant role due to its effects on public health and pollution characteristics of countries with constantly changing policies. Traditional approaches capture how much pollution is present but are unable to detect changes in the chemical character of the atmosphere, the relationships between co-emitted species, the balance of photochemical processing, and the combustion fingerprint of emission sources. This study introduces a framework that identifies and diagnoses such evolutions within the pollutants of the atmosphere. A chemistry-aware Variational Autoencoder is trained on 19 multivariate pollution features (7 raw concentrations, 5 chemical ratios, 7 temporal gradients) at London Marylebone Road (urban roadside) and North Kensington (urban background) from 2015 to 2019, and tested on 2022–2025. A four-method ensemble framework (VAE reconstruction error, reconstruction probability, Isolation Forest, and statistical Z-score) requires ≥3 agreement to identify high-confidence departed pollution states. Per-feature decomposition of the reconstruction probability diagnoses the chemical character of each departure. At the roadside site, 14.5% of post-COVID hours fall within departed states, dominated by the CO/NOx combustion ratio (513.2) and the photostationary state proxy (391.4), chemical relationships rather than individual concentrations. This indicates that at the point of emission, London’s fleet modernisation and Ultra Low Emission Zone (ULEZ) have changed the combustion fingerprint and photochemical equilibrium. The same structural indicators are carried over during the COVID-19 lockdown; however, O3 rises 3.2× during the pandemic period, reflecting suppressed NO titration. Conversely, at the urban background site, where the departures are driven by concentrations and boundary-layer trapping (r=−0.659), the combustion fingerprint of the atmosphere is invisible to detect (CO/NOx=−45.0). These findings indicate that London’s emission landscape has undergone fundamental transformations over the past decade, and the consequences of ULEZ and similar interventions or greater impacts of pandemic-related events are non-homogeneously distributed across the relevant region.

Atmosphere doi: 10.3390/atmos17060590

Authors: Lin Yang Min Wang Xiangzhao Feng Ling Zhu

Livestock production is a major source of agricultural methane (CH4) and nitrous oxide (N2O), making the synergistic mitigation of these two gases essential for meeting climate targets. Based on the EDGAR emission database from 2000 to 2024, this study employs international comparisons, spatial analysis, and STIRPAT-based scenario projections to characterize emissions from China’s animal husbandry and explore pathways for synergistic mitigation. The results reveal that China’s livestock CH4 emissions exhibited a trend of early-stage fluctuation followed by a late-stage rebound, while N2O emissions fluctuated sharply. The two gases are strongly synergistic yet driven by distinct mechanisms. China accounts for the largest share of global emissions and exhibits a distinctive emission structure—with comparable contributions from enteric fermentation and rice paddies—setting it apart from both pasture-based and intensive developed countries. High-emission areas are becoming increasingly concentrated in northern production regions. Under the baseline scenario, CH4 and N2O emissions are projected to peak in 2032 and 2030, respectively; under an ultra-low-carbon scenario, both gases peak around 2029, at substantially lower levels. Achieving synergistic mitigation calls for a regionally differentiated framework that combines top-down governance with bottom-up participation from farmers, integrating enteric fermentation control with optimized manure management to support a low-carbon transition.

Atmosphere doi: 10.3390/atmos17060588

Authors: Hanxing Zheng Yiman Chen

PM2.5 and near-surface O3 compound pollution is a major challenge for further air quality improvement in the Yangtze River Delta (YRD). Despite research on the chemical coupling mechanisms and concentration co-variation between PM2.5 and O3, the directional linkages of compound pollution events among cities and the network mechanisms underlying their formation remain unclear. Here, we identified PM2.5–O3 compound pollution events for 41 YRD cities from 2015 to 2024 using city-year-specific P80 dual-threshold criteria. We then constructed annual directed synchronization networks based on event-leading relationships and used temporal exponential random graph models to identify the formation mechanisms of significant leading ties. PM2.5–O3 compound pollution events in the YRD generally decreased during 2015–2024, with characteristics shifting from high frequency, persistence, and strong intercity linkage in the early stage to lower frequency, weaker intensity, and continued episodic fluctuations. Directed event networks exhibited a clear stage-dependent evolution: network density, total edge weight, reciprocity, and local closure were relatively high during 2015–2018, networks became markedly sparse during 2020–2022, and a partial rebound occurred after 2023. Spatial backbone analysis indicated reorganization of the dominant linkage structure, shifting from the Shanghai–southern Jiangsu–northern Zhejiang coastal core toward the northern Jiangsu, Anhui, and interprovincial corridors. Key node analysis further revealed a clear functional differentiation among cities, with some cities acting as potential leading sources, some as receiving nodes, and several non-traditional core cities serving as cross-regional bridges. Significant leading ties were jointly shaped by reciprocity, local closures, temporal memory, economic development, industrial structure, and digital governance. Therefore, as well as a problem of co-occurrence, PM2.5–O3 compound pollution in the YRD is a cross-city event-network process characterized by directionality, stage-dependent evolution, and differentiated urban roles. This study provides empirical evidence for dynamic joint prevention and control based on event linkages, urban roles, and cross-city coordination.

Atmosphere doi: 10.3390/atmos17060587

Authors: Sornkitja Boonprong Tunlawit Satapanajaru Anak Khantachawana Wangfei Zhang Pariwate Varnakovida Orrasa Rattana-amornpirom

Transboundary fine particulate matter (PM2.5) in Southeast Asia is commonly assessed using static source–receptor frameworks or descriptive associations that may not resolve how directional dependence changes through time under shifting meteorological conditions. This study examines regional PM2.5 as a time-varying, meteorology-adjusted directional coupling system using monthly data for 2003–2024 from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) meteorological covariates, climate controls, and administrative aggregation. Using a rolling-window directed network framework based on Peter and Clark Momentary Conditional Independence (PCMCI) causal discovery, we inferred lagged conditional-dependence networks from covariate-adjusted PM2.5 anomalies and summarized their structure at national and first-order administrative levels. The inferred network structure varies over time but retains measurable continuity across rolling windows. At the country level, cross-border links consistently account for a large share of the directed structure, indicating that PM2.5 variability within the study domain is strongly shaped by transboundary coupling rather than by country-contained dynamics alone. A recurrent backbone of country-level directional coupling corridors emerges, including persistent links among China, Indonesia, Myanmar, and Thailand. At the first administrative level, stable gateways and receptor basins become more evident, especially the bidirectional coupling corridor between Yunnan Province, China, and Shan State, Myanmar, which appears throughout the full window sequence. These results show that subnational structure can reveal transport-relevant coupling patterns that national summaries may conceal. The framework provides an interpretable basis for corridor-oriented monitoring and regime-aware early warning, while the inferred links should be interpreted as directional statistical dependence rather than direct emissions attribution or resolved physical transport pathways.

Atmosphere doi: 10.3390/atmos17060586

Authors: Yinxi Zhao Yanzai Wang Heng Wang Yang Wang

This study examines the multi-scale relationships between extreme climate indices and maize yield from a hydrothermal perspective, across both temporal (long-term trends, interannual anomalies, and abrupt changes) and spatial (regional and grid) scales in the Chengdu–Chongqing region, using long-term meteorological (1985–2025) and crop yield (1982–2015) datasets. Results reveal pronounced warming and drying trends, characterized by increasing warm-related temperature extremes and consecutive dry days, along with a decline in cold extremes. A shift toward drier conditions occurred around 2005, while temperature extremes have exhibited stepwise changes since the late 1990s. Maize yield shows a significant upward trend with an abrupt increase around 1997, closely linked to reduced cold stress. Scale-dependent analyses reveal that climate-yield relationships are primarily expressed through long-term hydrothermal changes rather than short-term variability, with maize yield showing positive responses to warm conditions and prolonged dry spell duration, and negative responses to cold extremes and excessive precipitation. In contrast, relationships based on interannual anomalies are weak and spatially inconsistent, suggesting limited sensitivity of yield to short-term climate variability due to system buffering and agricultural adaptation. Spatially, climate–yield relationships exhibit marked heterogeneity, with temperature constraints dominating in the western region and moisture-related effects being more pronounced in the central–eastern basin. Mechanistically, abrupt change analysis indicates two distinct controls: cold extremes act as threshold constraints associated with rapid yield shifts, whereas warming and drying exert gradual cumulative effects on productivity. Overall, maize yield dynamics are more strongly associated with long-term hydrothermal evolution than interannual variability, highlighting the importance of distinguishing temporal scales, hydrothermal regimes and long-term agricultural system evolution in climate–crop assessments under ongoing climate change.

Atmosphere doi: 10.3390/atmos17060585

Authors: Lili Wang Zhengwu Hu Zuhan Liu

Accurate prediction of PM2.5 concentration is essential for public health and environmental protection, and specifically crucial for the management of the availability of sufficient health personnel during adverse health episodes. However, its nonlinearity, variability, and complexity make this task challenging. This study proposes a long short-term memory (LSTM) weighted by K-nearest neighbor (KNN) algorithm (namely Weighted KNN-LSTM Model) that can effectively predict the PM2.5 concentration time series. Firstly, the K-nearest neighbors of each time point are sought based on the Euclidean distance within the data time range. Given that neighboring observations typically exert a more pronounced influence than distant ones in spatial processes, weights are accordingly assigned to these neighbors to quantitatively reflect their relative importance in the analysis. Subsequently, after the initial data is processed by the weighted KNN algorithm, it is reorganized and transformed into a reconstructed dataset with a size K times that of the original data. The data used for model training and the data used for evaluating the model’s prediction performance are completely independent, and the test dataset is never involved in the model training process to ensure the authenticity and reliability of the prediction performance evaluation. Then, the LSTM neural network model is trained on this new dataset to enhance its generalization ability. The experimental results show that the weighted KNN-LSTM model exhibits excellent predictive performance in predicting PM2.5 concentration. It is important to note that the dataset used to evaluate the model’s performance was strictly independent from the data used to train the model. This separation ensures that the reported accuracy reflects true predictive capability rather than mere fitting quality. The model provides a technical reference for hourly PM2.5 concentration prediction in Nanchang City, and the prediction results can be used as an auxiliary reference for regional air quality monitoring; the application of the model in heavy pollution warnings needs to be further optimized and verified by combining multi-source data such as meteorology, which provide reliable data support for the formulation of dynamic emission reduction policies.

Atmosphere doi: 10.3390/atmos17060584

Authors: Zhiwei Han Chenliang Tao Mengyuan Zhang Shuhuan Wang Ying Chen Hongliang Zhang

India faces severe PM2.5–O3 compound pollution, and the 2023 G20 Summit in New Delhi provided a valuable case for examining how short-term emission controls interact with unfavorable late-monsoon meteorology. In this study, the WRF-CMAQ modeling system was applied to quantify the relative contributions of meteorological variability and graded multisectoral emission controls to PM2.5 and ozone changes during the summit period. The results show that both pollutants exhibited clear stage-dependent variations, with lower concentrations during the summit and rapid rebound afterward. Relative to the 2022 meteorology sensitivity case, the 2023 meteorological background increased PM2.5 by 6.76 μg/m3 and MDA8 O3 by 4.37 ppb over New Delhi, indicating a distinct meteorological penalty during the monsoon withdrawal period. Under progressively strengthened control scenarios, PM2.5 declined from 79.01 to 66.35 μg/m3, while MDA8 O3 decreased from 81.19 to 77.67 ppb. The strongest control scenario reduced PM2.5 more than the meteorological penalty and substantially mitigated the ozone enhancement, although it did not fully offset the adverse meteorological effect on O3. These findings demonstrate that high-intensity coordinated controls can effectively alleviate PM2.5–O3 compound pollution even under unfavorable meteorological conditions.

Atmosphere doi: 10.3390/atmos17060583

Authors: Zhadyranova Aliya Baigazinov Zhanat Aliyev Nursultan Mukhamediyarov Nurlan Zhumadilov Kassym Polivkina Yelena Salmenbayev Sayan Aktayev Medet

Seasonal snow cover can serve as an informative single-season indicator of atmospheric deposition in industrial urban areas because it accumulates airborne contaminants during the winter period. A total of 55 snow samples were collected across the urban area, and the liquid phase was analyzed for major and trace elements using instrumental elemental analysis with defined detection limits and measurement uncertainty. Descriptive statistics, background comparisons, and integrated pollution indicators were used to characterize the spatial variability and intensity of contamination. The results showed that the median concentrations of most analyzed elements did not exceed the reference limits; however, aluminum and iron exhibited elevated levels, with aluminum reaching 1.1–27 times and iron 1.0–3 times the reference values. Median concentrations included 270 μg L−1 for Al, 118 μg L−1 for Fe, 30 μg L−1 for Zn, 11.5 μg L−1 for Ni, and 7.3 μg L−1 for Pb. The obtained data indicate a heterogeneous pollution pattern across Pavlodar and suggest the combined influence of mineral dust, urban-industrial emissions, road-dust resuspension, and natural inputs on snow chemistry. Because the study is based on one winter sampling campaign, the results should be interpreted as a single-season assessment of snow-cover contamination rather than as evidence of long-term temporal stability.

Atmosphere doi: 10.3390/atmos17060582

Authors: Xuemei Liu Xiufang Zhu Jianfeng Pang Xijun Ma

China’s pollutant discharge permit system mandates total-quantity emission control for industrial volatile organic compounds (VOCs), yet the actual utilization of permitted capacity remains poorly studied. This study developed an “emission idle rate” (IR = 1 − actual/permitted emissions) framework and applied it to 130 chemical enterprises across three cities in Jiangsu Province using 2020–2024 panel data. The mean idle rate reached 78.1%, with no significant inter-city differences (H = 0.96, p = 0.619), attributable to both production underutilization and systematic over-estimation of emission ceilings inherent in the design-capacity-based permit methodology. Ward hierarchical clustering revealed three emission behavioral patterns, Persistent Surplus (n = 74, IR = 0.95), Declining Surplus (n = 32, IR = 0.69), and Growing Surplus (n = 19, IR = 0.59), exhibiting distinct idle rate levels and temporal trajectories. Cluster differentiation was significantly associated only with production-side emission characteristics, while enterprise economic variables showed no significant effects. The estimated tradeable emission surplus reached 668.3 t/a, though its realization faces transaction cost barriers including the lack of standardized transfer mechanisms and formal VOC trading infrastructure. A quadrant-based strategy matrix integrating idle rate levels with temporal trends is proposed for differentiated permit management.

Atmosphere doi: 10.3390/atmos17060581

Authors: Jorge Samanes Ricardo Y. C. Cueva

Very low frequency (VLF) radio waves propagating within the Earth–ionosphere waveguide are highly sensitive to changes in lower ionospheric conditions, which are reflected in the amplitude of received transmitter signals. During the solar terminator passage, rapid changes in ionospheric conductivity modify propagation conditions and produce characteristic VLF amplitude minima associated with modal interference and mode conversion processes. In this study, we investigate the spectral characteristics of VLF amplitude variability during the sunrise transition, which spans extended time intervals along long west–east propagation paths, using signals from the NPM-PIU and NPM-PLO paths recorded in Peru under solar minimum conditions (2008–2010). One-hour intervals centered on amplitude minima are analyzed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) combined with the continuous wavelet transform. The analysis reveals recurrent wave-like fluctuations (WFs) with dominant periods between 2 and 6 min, whose amplitudes increase systematically within ±15 min around the amplitude minima. These fluctuations are better distinguished during the later-stage minima and exhibit enhanced occurrence during solstice months. The results indicate that the evolving modal structure of the waveguide during the sunrise transition may enhance the sensitivity of the VLF signals to small perturbations, enabling the detection of weak short-period ionospheric disturbances.

Atmosphere doi: 10.3390/atmos17060580

Authors: Duanjun Lu Loren D. White

The dryline is a sharp boundary between moist air from the Gulf of Mexico and dry air from the desert Southwest. In West Texas, this boundary often surges east during the day and retreats west at night. Understanding exactly why it moves back and forth is critical for predicting where severe thunderstorms will form. Yet the physical drivers of dryline life cycle remain poorly understood and frequently under-predicted. This study utilizes a variable-resolution Model for Prediction Across Scales (MPAS) configuration (3–60 km) with the YSU non-local planetary boundary layer (PBL) scheme to investigate a representative dryline event from April 2017. The control simulation was validated against NWS Surface Analysis, demonstrating a high spatial correlation in both synoptic-scale pressure distributions and mesoscale moisture gradients, successfully resolving a nocturnal retrogression of approximately 170 km, with the dryline retreating from its peak afternoon surge at 100.7° W to a recovery point of 102.5° W between 0000 UTC and 0600 UTC 10 April. This recovery occurred at an average speed of 28.3 km/h, consistently constrained beneath a resilient capping inversion. To decouple the environmental drivers of this motion, two targeted sensitivity experiments were conducted: (1) Mechanical Forcing: A 50% reduction in regional topography confirms that the West Texas sloping ramp acts as a “topographic pump.” Without this gradient, the hydrostatic pressure falls were insufficient to drive the nocturnal retreat, causing the boundary to stall eastward. (2) Thermodynamic Regulation: A 50% reduction in soil moisture revealed an “energy swap,” the near-total partitioning of net radiation into sensible heat drove the planetary boundary layer to a higher peak value—a 600 m increase over the control simulation. These results provide a comprehensive physical framework for dryline mobility, demonstrating that while terrain plays an important role in the extent of the diurnal oscillation, soil moisture governs the vertical structure and moisture gradient intensity. Our findings suggest that high-resolution vertical layering and accurate land-surface initialization are prerequisites for capturing the inversion layer dynamics essential for dryline forecasting. However, these findings are based on a single event and require validation across a broader range of dryline cases.

Atmosphere doi: 10.3390/atmos17060579

Authors: Gabriela Goudard Leila Limberger Camila Bertoletti Carpenedo Francisco Mendonça

The El Niño–Southern Oscillation (ENSO) is the main driver of interannual climate variability, strongly influencing precipitation, temperature, and extreme events worldwide. In South America, its impacts are well documented. However, studies examining different ENSO types—Eastern Pacific (EP), Central Pacific (CP), and Mixed (MX), defined according to the location of sea surface temperature (SST) anomalies in the tropical Pacific—remain limited, particularly for the Brazilian subtropical climate. This study investigates rainfall variability in the Brazilian subtropical region associated with different ENSO types. Composite analyses of precipitation, wind, and SST anomalies were performed, and monthly rainfall data from 703 stations were used to identify homogeneous regions. The results show the intensity and spatial coherence of rainfall signals vary according to El Niño type, with EP events favoring widespread wet conditions and CP events producing more heterogeneous or locally negative anomalies. For La Niña, the intensity and seasonal distribution of negative rainfall anomalies vary by ENSO type: stronger impacts occur in summer (EP), spring (MX), and autumn (CP). These findings improve the understanding of ENSO-related rainfall variability in the Brazilian subtropical region and provide valuable insights for the management of climate-related risks in an area frequently affected by rainfall extremes.

Atmosphere doi: 10.3390/atmos17060578

Authors: Yuwei Liu Yu Peng Yanjie Lu Yingjun Chen

Residential solid fuels are widely used for cooking and heating, but the atmospheric evolution of their particulate emissions remains insufficiently characterized. To address this gap, we constructed an integrated quartz-tube furnace–dilution–oxidation flow reactor (OFR) system for direct comparison of fresh and OFR-aged emissions across fuel types and combustion temperatures. Six biomass fuels and six coals were burned at 500 °C and 800 °C. Organic carbon (OC) subfractions and polycyclic aromatic compounds (PACs), including 16 parent polycyclic aromatic hydrocarbons (pPAHs) and 9 oxygenated polycyclic aromatic hydrocarbons (oPAHs), were quantified. In fresh emissions, increasing temperature reduced OC emission factors for both fuel types, whereas PAC emission factors increased for biomass but decreased for coal. OFR aging generally increased particulate OC and shifted OC toward less volatile or more thermally stable fractions. For coal burned at 500 °C, pPAHs decreased by 64%, whereas oPAHs increased by 127%. Although the overall quantitative structure–activity relationship (QSAR)-derived carcinogenicity indicator of PACs decreased by 46%, the oPAH contribution increased from 7% to 18%. These findings show that metrics based only on fresh emissions cannot fully capture the chemical evolution and toxicity-related implications of residential solid fuel emissions.

Atmosphere doi: 10.3390/atmos17060577

Authors: Margarita A. Tkachenko Eugene V. Rozanov

Future volcanic eruptions are largely omitted from CMIP6 simulations, thereby increasing the uncertainty in 21st-century climate projections. We performed an 80-year (2020–2100) 25-member stochastic ensemble simulation with the climate model SOCOL-MPIOM, driven by the SSP3-7.0 forcing scenario, and introduced five stochastically distributed tropical eruptions—three strong, one moderate, and one weak—for each ensemble member (hereafter, SV run). We analyse volcanic influence by comparing the SV run results against a single volcanic-free baseline simulation under the same anthropogenic forcing scenario. Five eruptions over the 80-year simulation period leave the trends of the major climate indicators statistically indistinguishable from those of the volcanic-free baseline at the global and annual mean scales. However, on local and seasonal scales, volcanic activity can substantially alter the results of the volcanic-free simulation. For example, over Northern Europe, volcanic eruptions produce winter temperature warming of up to 1.0 K (about 30% of the warming in the reference run) and an annual precipitation deficit of 36 mm yr−1. This emphasises the need to include volcanic eruptions for more accurate projections of future climate. Probabilistic analysis of the SV ensemble shows that the annual maximum daily temperature (TXx) exceeds +0.5 K over 16% of global land with more-likely-than-not probability, a perturbation absent in standard CMIP6 results. Since our scenario composition targets the upper bound of plausible 21st-century volcanic activity, these exceedance areas represent near-maximum rather than most-probable estimates.

Atmosphere doi: 10.3390/atmos17060576

Authors: Baruch Ziv Uri Dayan

This study assesses the effects of upper-level configuration on heavy rain episodes over the Levant. The dynamic upper-level forcings are attributed to ageostrophic effects. One is related to meandering jet ahead of troughs and the second to acceleration near both ends of straight jets. We collected 23 rainy episodes contained in four rainy months. The rain analysis was done on the eastern coast of the Mediterranean, and the synoptic analysis covers the domain 15–45° N, 12–45° E. The data were retrieved from ERA5 reanalysis, with 0.25° × 0.25° resolution. A subjective analysis revealed that the rain episodes are associated with three configurations, the two aforementioned and an additional, under upper trough, without upper-level divergence. In fourteen cases, the region was found ahead of trough and in only one at the end of a straight jet. In the remaining eight cases, upper trough was located over the region, implying an absence of upper-level support for rain formation. These configurations are exemplified by case studies and by composite maps. Most rain events occurred when an upper trough dominated the Levant, situated upstream (west) of a surface Cyprus Low (CL), with both contributing to rain formation. The CL persisted in the cases of the third type, in spite of an absence of upper-level support, due to surface-induced cyclogenesis. The two most frequent configurations are apparently similar but differ in the rain-producing factors. The one for the “ahead of” is upper-level dynamic, and for the “under trough”—lower level dynamic and enhanced static instability.

Atmosphere doi: 10.3390/atmos17060575

Authors: Long Cheng Yimei Wang Jialing Li Feng Xu Yi Fei Zhenyi Xu Chengrong Pan

To better understand the characteristics and causes of acid rain pollution in Huangshan City, China, in the context of reduced atmospheric pollutant emissions, this study systematically analyzes precipitation monitoring data from Huangshan City for the period 2013–2025. The analytical methods included volume-weighted mean, neutralization factor, and linear regression analysis. The results indicate that, with 2017 as a turning point, acid rain in Huangshan City transitioned from high-level fluctuations to a stabilization phase at medium-to-low levels. However, the annual mean pH remained below 5.6, indicating that the acid rain problem persists. Regarding pollutant emission reductions, sulfur dioxide (SO2) control has achieved significant results, but nitrogen oxide (NOx) pollution remains prominent due to factors such as a sharp increase in vehicle ownership. Analysis of the chemical composition of precipitation shows that the SO42−/NO3− ratio decreased from 4.09 to 0.92, and the acid rain type has shifted from sulfate-dominated to mixed sulfate-nitrate-dominated. In precipitation, highly specific ion pairings are observed: Ca2+ with SO42− (r = 0.989) and NH4+ with NO3− (r = 0.839). These two ion pairs together account for 81.4% of the total cations, forming two independent neutralization mechanisms—below-cloud and in-cloud—which explains the relative stability of precipitation pH despite a decline in total ion concentration. Furthermore, interannual variability in precipitation amount, particularly extreme wet events, is a key external factor driving fluctuations in acid rain frequency under stable emission conditions. The dominant driver of acid rain frequency variability has shifted from emission-dominated to precipitation-dominated.

Atmosphere doi: 10.3390/atmos17060574

Authors: Lin Wang Zichen Zhu Bojian Shi Pengxin Zuo Weixian Wang Weiqiang Gu Yuxi Yang Yuhan Shan

This study investigates the ionospheric response in the Southern and Northern Hemispheres over the period from 25 December 2024 to 7 January 2025. A major geomagnetic storm occurred on 1 January 2025, following the consecutive solar wind eruptions on 29–31 December 2024 and 1 January 2025. Global geomagnetic activity monitoring data showed that the Kp index surged to 8+, indicating the occurrence of this major geomagnetic storm. By analyzing the ionosonde, GNSS-TEC, and satellite in situ detection data from Learmonth, Australia (−21.8° N, 114.1° E), as well as Wake Island (19.29° N, 166.65° E), we found that the ionospheric anomalies in the two regions exhibited different patterns. The ionospheric parameters in Learmonth changed much more severely than those in Wake Island in the Pacific region. Relative to normal conditions, the disturbed ionosphere over Learmonth during 1–3 January 2025 exhibited a strong negative storm phase: foF2 decreased by 31.4%, TEC dropped by 27.17%, and M3000F2 declined by 41.2%, while hmF2 increased by 5.2%. This work provides an analysis of the differences in the ionosphere between the Northern and Southern Hemispheres affected by geomagnetic storms in late 2024. These findings highlight the need to incorporate hemispheric asymmetry into ionospheric dynamics models.

Atmosphere doi: 10.3390/atmos17060573

Authors: Qingyun Li Tao He Mingye Li Junfang Zhang Bing Lian Liye Liu Rui Qiu Junli Li

Large-scale atmospheric dispersion model for emergency response to nuclear accidents requires high computational efficiency and numerical reliability. A GPU-oriented Lagrangian particle dispersion model was developed within FLEXPART framework to address these demands. Core transport processes—including advection, turbulent diffusion, convective mixing, and dry/wet deposition—were restructured for GPU parallel execution. Further incorporation of fast arithmetic operators and multi-level parallelization strategies substantially improved overall computational performance while preserving physical accuracy. Additional MPI-based parallel meteorological data decoupling and preprocessing tool has been developed, which alleviates data-handling bottlenecks. Meanwhile, multi-GPU execution and a load-balancing strategy enable efficient scaling in heterogeneous computing environments. Using the first release of European Tracer Experiment (ETEX-I) as a benchmark, the GPU program’s accuracy and acceleration were rigorously evaluated. Results show that, while maintaining nearly comparable accuracy (with relative errors on the order of 10−2), the program achieves an overall speedup of approximately 40.45 on a single-GPU platform, which can be further increased to about 52.05 in high-performance application scenarios where meteorological background fields are reusable. Moreover, multi-GPU experiments reveal favorable parallel scalability across configurations ranging from one to four GPUs, and confirm that the proposed load-balancing strategy effectively enhances computational efficiency in heterogeneous GPU environments.

Atmosphere doi: 10.3390/atmos17060572

Authors: Fan Wang Yiming Liu Greg Michalski Wendell Walters Huan Fang

Nitrogen oxides (NOx), comprising nitric oxide (NO) and nitrogen dioxide (NO2), are key precursors of atmospheric nitrate, a major component of fine particulate matter (PM2.5) that critically affects air quality, human health, and ecosystems. Emission inventories provide detailed spatial and temporal information on NOx sources, while stable isotope techniques offer an additional constraint for source apportionment. Here, we incorporated stable nitrogen isotopes (14N, 15N) into the widely used Multi-resolution Emission Inventory for China (MEIC) over South China, with a focus on the Pearl River Delta (PRD) region, one of the most highly urbanized and industrialized regions in China, using an isotopic mass–balance model. The 2008 MEIC inventory indicated that NOx emissions across South China were spatially heterogeneous, dominated by transportation sources, and concentrated mainly in the PRD and other urban clusters. We then compared the simulated isotopic composition of emitted NOx with atmospheric measurements to assess the role of emission sources in controlling atmospheric nitrate (NO3−). The simulated δ15N(NOx) values were found to generally underestimate the observed δ15N(NO3−) values. This discrepancy highlights the need for future 15N-enabled air quality modeling to better represent both source contributions and atmospheric processing, thereby improving source apportionment, emission inventory evaluation, and our understanding of reactive nitrogen cycling.

Atmosphere doi: 10.3390/atmos17060571

Authors: Yihang Hou Hao Wang Jintai Li Hanxian Fang

Based on sodium lidar observations obtained at the Haikou station (20° N, 110.2° E) of the Chinese Meridian Project during 2012–2024, this study systematically investigates the climatology of sporadic sodium layers (SSLs) at low latitudes and their relationship with ionospheric sporadic E (Es) layers, and it further analyzes their morphological features and evolution through a case study. The results show that the occurrence rate of SSLs over Hainan exhibits significant interannual variability. In terms of the monthly occurrence rate for individual years, while there is no fixed intra-annual pattern, February and October repeatedly appear as months with high occurrence rates, with February appearing in 4 of the 12 analyzed years and October in 5 of the 12 analyzed years, indicating that SSLs have a certain seasonal preference for late winter and autumn. This feature seems to be related to meteoric injection. The monthly occurrence rate of SSLs averaged over 2012–2024 further shows pronounced maxima in February, June, and October. Comparison with the climatology of Es layers over the same period reveals that the Es occurrence rate reaches its annual maximum in June, while the SSL occurrence rate also shows a local peak in June, indicating that Es layers may play an important role in SSL formation. Nevertheless, the high SSL occurrence rates in February and October indicate that other physical and chemical processes also play important modulating roles. Statistical analysis of SSL over local time indicates that SSLs mostly occur between 21:00 and 01:00 LT, with both onset and peak times concentrated in this interval, durations mostly of the order of tens of minutes, and peak heights at 94–96 km. Overall, SSLs over Hainan exhibit significant interannual variability and a weak seasonal preference, and their formation is jointly influenced by direct meteoric injection, Es-related ionospheric processes, and neutral Na chemistry.

Atmosphere doi: 10.3390/atmos17060570

Authors: Lisi Kuang Lan Yang Chengliang Fan

Old residential communities, characterized by dense built environments, high levels of population aging, and insufficient public services, represent critical hotspots of urban heat risk. Taking old residential communities in the central urban area of Guangzhou as the study area, this study integrates multi-source data, including social, economic, and urban infrastructure information, to develop a heat risk assessment framework encompassing four dimensions: heat hazard, heat exposure, heat vulnerability, and heat adaptation. The model is validated using data from the China Health and Retirement Longitudinal Study (CHARLS). Results reveal a significant non-linear relationship between the comprehensive heat risk index and the prevalence of cardiovascular and cerebrovascular diseases (R2 = 0.739), indicating strong explanatory power for health risks. Spatially, heat risk exhibits a center–periphery gradient, with high-risk areas concentrated in older central districts. Heat adaptation is identified as a key regulatory factor that can either mitigate or amplify risk accumulation. Based on the spatial heterogeneity of different neighborhood types, targeted mitigation strategies are proposed, providing scientific support for urban renewal and heat resilience governance.

Atmosphere doi: 10.3390/atmos17060569

Authors: Rene Parra Cristian Caguana Claudia Espinoza

The last atmospheric emission inventory for Cuenca, a city located in the Andean region of southern Ecuador, was developed for the year 2021 (EI 2021) and encompasses both primary pollutants (NOx, CO, VOC, SO2, PM10, and PM2.5) and greenhouse gases (CO2, CH4, and N2O). We formally assessed the quality of this emission inventory by modeling air quality levels in October 2021 using the Weather Research and Forecasting with Chemistry (WRF-Chem 3.2) model at a high spatial resolution (1 km). Although we conducted simulations with different combinations of boundary conditions for chemical species and hourly profiles to disaggregate daily on-road traffic emissions, we selected two sets of numerical experiments to report. The results indicated that most of the assessed meteorological and air quality variables were modeled acceptably, suggesting that the EI 2021 emission inventory is a reasonable estimation of real emissions. The results also indicated the current capacity of WRF-Chem to model the atmosphere in a complex Andean city using the “one atmosphere” approach, highlighting the variables with good, fair, and poor modeling performance. We propose future research directions to improve emission inventories and the performance of atmospheric modeling in the Equatorial Andean region. Finally, the results and spatial distribution of the EI 2021 were compared to the emission data from the last version of the EDGAR Emissions Dataset (which has a spatial resolution of 11.1 km), one of the most used global emission datasets. We concluded that, for the Equatorial Andean region and for modeling purposes, the EDGAR Dataset results should be reviewed, both to account for the effects of height above sea level on the magnitude of primary pollutant emissions and their spatial configuration.

Atmosphere doi: 10.3390/atmos17060568

Authors: Basil E. Psiloglou Nikolas Gkinis Parina Machaira Christos Giannakopoulos

This study examines the evolution of thermal discomfort in Athens, Greece, using Thom’s Discomfort Index (TDI). The research commences from a historical reference period (1976–2005) and examines two future periods (2031–2060 and 2071–2100). TDI, which combines air temperature and relative humidity, was calculated based on three-hourly projections of five EURO-CORDEX regional climate models under the RCP4.5 and RCP8.5 emission scenarios. Model outputs were bias-corrected using observational data from the National Observatory of Athens for the reference period and subsequently applied to future projections. Results indicate a clear upward trend in high thermal discomfort days in the city center. Under RCP4.5, intense discomfort days increase by 21–39 days by mid-century and by approximately 1–2 months by the end of the century. Under the high-emission RCP8.5 scenario, the increase becomes dramatic, with intense discomfort conditions potentially extending by up to three months annually. Overall, projections reveal a clear deterioration of thermal conditions with a difference between RCP 4.5 and RCP 8.5, highlighting the critical importance of emission reduction strategies. The study of TDI shows that climate change does not merely raise temperatures, but drastically increases perceived discomfort and heat-related risk, transforming long parts of the year into thermally uncomfortable periods.

Atmosphere doi: 10.3390/atmos17060567

Authors: Chen Zhang Junkai Qian Qiang Wang

The Northeast China Cold Vortex (NECV) is a key circulation system affecting weather patterns over North China, frequently triggering thunderstorms, hail, and other severe convective weather. Accurate prediction of NECVs is therefore of great importance. However, substantial forecast errors still remain, largely due to uncertainties in the initial conditions. To improve NECV forecast skills, we investigate the optimal initial errors and targeted observation sensitive areas using a sampling-based approximation of the conditional nonlinear optimal perturbation (CNOP) method together with the Pangu-Weather deep learning model. We first evaluate the model’s performance over Northeast China and find that Pangu-Weather exhibits forecast skill generally comparable to the ECMWF Integrated Forecasting System (IFS) during the May–August 2022 period over Northeast China. Then the CNOP-based approach is used to capture the optimal initial errors with the greatest impact on NECV forecasts. The largest error amplitudes are primarily located upstream of the vortex and near upper-level jet-entrance regions, which are identified as the targeted observation sensitive areas. Perturbation kinetic-energy diagnostics further indicate that baroclinic conversion is the dominant mechanism for error growth. Observing system simulation experiments suggest that, under an idealized assumption of completely eliminating errors in a given region, targeted observations over the sensitive area can produce the largest forecast improvement, with an average error reduction of approximately 13% relative to other areas. This study contributes to a deeper understanding of NECV predictability and may help improve forecasting capability.

Atmosphere doi: 10.3390/atmos17060566

Authors: Jude Maduabuchi Anyanwu María Ángeles García Isidro A. Pérez

Heatwave conditions are increasingly being recognized as important drivers of urban air-quality variability in southern European cities, particularly in inland urban environments exposed to persistent summer warming and atmospheric stagnation. This study examines the long-term variability of O3, NO2, and PM2.5 concentrations in Valladolid, Spain, between 2006 and 2024, focusing particular attention on the occurrence and persistence of heatwave conditions. Ground-level ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM2.5) were analyzed to assess temporal variability, seasonal behavior, long-term trends, and exceedance characteristics. Results indicate an increasing persistence of heatwave episodes during the study period, particularly after 2015, with recent events exhibiting longer duration and broader regional extent. O3 concentrations showed stronger accumulation during warm-season conditions, which is consistent with enhanced photochemical activity under elevated temperatures, while NO2 concentrations generally declined over time. PM2.5 variability reflected both local emissions and episodic regional influences, including Saharan dust intrusions. These findings highlight the growing relevance of heatwave conditions in shaping urban air-quality variability in medium-sized inland cities of the Iberian Peninsula.

Atmosphere doi: 10.3390/atmos17060565

Authors: Qinqin Wu Ying Wu

Unsaturated hydrocarbons, including alkenes, alkynes, and aromatic hydrocarbons, are important components of atmospheric volatile organic compounds (VOCs) and serve as key precursors for ozone, a major photochemical pollutant. This study aimed to characterize the sources and ozone formation potential of 29 unsaturated hydrocarbon VOCs in Beihai, a coastal city in southern China, on the basis of continuous online monitoring conducted during the summer of 2022. Continuous monitoring of unsaturated hydrocarbon VOCs in the ambient air of Beihai city during summer was conducted using a rapid online monitoring system for atmospheric VOCs. The results revealed that the total daily average concentration of unsaturated hydrocarbon VOCs was 1.21 ppbv, with an average concentration of 0.026 ppbv. The order of abundance was alkenes > aromatic hydrocarbons > alkynes. Source apportionment using the positive matrix factorization (PMF) model revealed that vehicle exhaust emissions were the primary source of unsaturated hydrocarbon VOCs in the city of Beihai, contributing 36.02%. Secondary sources included combustion sources (26.15%), solvent usage (18.55%), fuel evaporation (10.18%), and biogenic sources (9.10%). The contribution of unsaturated hydrocarbon VOCs to ozone formation was estimated using the ozone formation potential (OFP). Aromatic hydrocarbons contributed the most (51.22%), followed by alkenes (41.8%). Analysis of the diurnal variation patterns of unsaturated hydrocarbons revealed that combustion sources occurred during the night (01:00–02:00), suggesting that enhanced supervision and control measures during nighttime hours are warranted.

Atmosphere doi: 10.3390/atmos17060564

Authors: Ming Wang Pengjie Sun Yang Cui Yang Xu

Accurate surface downward shortwave radiation (SDSR) is essential for solar resource assessment, photovoltaic applications, and land–atmosphere studies. Although ERA5 is widely used in radiation-related research, its SDSR estimates over China still show considerable uncertainties under complex topographic and climatic conditions. Using hourly observations from the 162-station China Meteorological Administration (CMA) radiation network during April 2024–March 2025, of which 160 stations were retained after quality control, this study systematically evaluated ERA5 SDSR and developed a physics-guided Light Gradient Boosting Machine (LightGBM) correction framework. Raw ERA5 exhibits a strong systematic positive bias (PBIAS = 57.40%, ME = 124.2 W/m2) together with a pronounced nonlinear structural bias, characterized by overestimation under low-radiation conditions and underestimation under high-radiation conditions. The largest errors occur in the Southern Monsoon region in summer and the Northwest Arid region in spring, indicating the combined effects of cloud extinction, aerosol attenuation, and terrain-related representativeness differences. To address these mechanisms, the correction model incorporates physically relevant predictors from ERA5 and Copernicus Atmosphere Monitoring Service (CAMS), including cloud microphysical variables, aerosol optical depth, solar geometry, and elevation. SHapley Additive exPlanations (SHAP) analysis shows that the learned correction behavior is broadly consistent with known radiative-transfer processes. On the independent station hold-out test set, the correction increases the Pearson correlation coefficient from 0.8680 to 0.8967 and reduces RMSE from 173.1 to 100.8 W/m2, while substantially suppressing the strong positive bias of raw ERA5. Additional robustness tests, including season-blocked validation, interpolation-sensitivity analysis, ablation experiments, and multi-model comparison, further support the stability of the framework. External benchmarking against FY-4B and Himawari also shows that the corrected ERA5 substantially narrows the gap relative to independent geostationary satellite products. Overall, the proposed framework provides an effective and physically interpretable approach for improving ERA5 SDSR over China.

Atmosphere doi: 10.3390/atmos17060563

Authors: Peng Gao Zhuan Liu Azmiah Abd Ghafar

Outdoor open spaces are essential for daily activities in ageing rural environments, yet the thermal effectiveness of vegetation under varying structural configurations remains unclear. Most existing Outdoor Thermal Comfort studies focus on dense urban canyons; the present study addresses this gap by examining a complexity threshold in vegetation cooling under high-SVF rural conditions and the radiation–ventilation trade-off that underlies it. An ENVI-met model was calibrated using field data from a typical village on the North China Plain and 17 vegetation scenarios were simulated. The findings reveal a non-linear relationship between vegetation complexity and cooling efficiency. A threshold of complexity was observed: the cooling performance declined with an increase in stratification from a double-layer (Scenario 12) to a triple-layer (Scenario 14) structure, with the change in mean radiant temperature (∆Tmrt) dropping from 23.16 °C to 21.10 °C. This is due to a radiation–ventilation trade-off, in which denser vegetation increases shading but reduces near-surface ventilation. Dense arrangements exhibit a heat trap effect, with the long-wave radiation flux changing from a cooling (−3.42 K/h) to a heating (+2.11 K/h) state. The results show a threshold effect in vegetation cooling and that thermal comfort is not necessarily enhanced by increased complexity. A shaded-canopy and permeable-understory structure is found to be optimal. The findings inform vegetation design in climate-adaptive rural settings.

Atmosphere doi: 10.3390/atmos17060561

Authors: Askarov Komiljon Jae-ho Choi

Urban construction activities are recognized as significant contributors to particulate matter (PM) emissions; however, the accurate real-time monitoring of size-resolved PM fractions presents a formidable challenge. Traditional low-cost PM sensors predominantly report cumulative concentrations, which obscures the distinct health and regulatory significance of PM1, PM2.5, and PM10. This study systematically evaluates the performance of two low-cost sensors—PMS5003 and Sniffer4D—utilizing non-cumulative measurements obtained under controlled laboratory conditions designed to simulate construction PM generated from concrete slab drilling. Sensor performance was rigorously analyzed using Pearson correlation coefficients, standard deviation, and mean percentage differences. Six correction models—linear regression, polynomial regression, Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and Kalman filter—were independently developed for each PM size fraction to enhance measurement precision. Findings reveal that RF and ANN consistently provided the most accurate corrections, particularly for PM1 and PM2.5, with RF achieving a coefficient of determination (R2) > 0.89 for PM1 and R2 > 0.87 for PM2.5 at the 50 s duration. This investigation introduces a size-resolved correction framework specifically designed for construction environments, thereby advancing the capability of low-cost sensors to enable accurate particle-specific exposure assessments.

Atmosphere doi: 10.3390/atmos17060562

Authors: Ioannis Faraslis Nicolas R. Dalezios Marios Spiliotopoulos Nikolaos Alpanakis Stavros Sakellariou Vagelis Brisimis Nicholas Dercas

There is a strong global increase in the installation of renewable energy power plants, due to increasing energy demand in the electricity generation sector and fast cost reduction. Recent studies indicate that the installation and operation of photovoltaic (PV) power plants have negligible microclimatic effects, although there are minor effects on night temperature in some cases, which, however, do not justify climate or environmental change. The development of solar energy and the installation and operation of PV power plants serve as a key solution for the energy transition to reduce carbon emissions and to address global warming. Despite the benefit of emission reduction, the deployment of solar energy through the installation of solar power plants causes land cover changes and may have minor effects on the surface energy balance by modifying roughness and albedo, biodiversity by disturbing habitats, and water resources by requiring water for cooling and cleaning. These changes may also lead to minor climatic, ecological, and social impacts. The objective of the paper consists of assessing the potential microclimatic effects of photovoltaic power plants based on satellite-based land surface temperature (LST) analyses. Specifically, the potential change in the land surface temperature, both under photovoltaic panels and on the panels, in relation to the temperature of the surrounding area is being examined in this study. The implementation is conducted in Mediterranean ecosystems, which are considered vulnerable agroecosystems due to increased climate variability. The final Landsat-based time series analysis further supports this synthesis, reporting that monthly LST differences between the PV Park and surrounding area are negligible and do not indicate a meaningful microclimate alteration attributable to PV operations. Accordingly, the evidence supports the core conclusion: utility-scale PV deployment does not constitute a driver of climate change, and the documented effects are best understood as localized surface–atmosphere energy-balance perturbations whose sign and magnitude depend on land cover, seasonality, and operation.

Atmosphere doi: 10.3390/atmos17060560

Authors: Asit Kumar Mishra

Energy retrofit interventions have become increasingly critical as building sectors worldwide pursue decarbonization targets and improved energy efficiency. However, establishing robust causal inference about retrofit impacts on indoor climate conditions remains challenging due to confounding variables including outdoor climate fluctuations and occupant behavior. This paper presents a methodological framework for analyzing pre- and post-retrofit indoor climate data using linear mixed effects (LME) models, which explicitly account for building-level variability while controlling for environmental and behavioral factors. The approach is demonstrated using a case study analyzing partial pressure of water vapor in Irish residential homes before and after energy retrofit interventions. The analysis incorporates standardized coefficients to assess relative importance of predictive factors and employs model parsimony through stepwise removal of non-significant terms. Complete R code is provided to facilitate adaptation by other researchers. Our results demonstrate that LME models provide unbiased estimates of retrofit effects while avoiding aggregation bias that plague simpler analyses. This paper serves as both methodological reference and practical guide for practitioners seeking to rigorously evaluate building retrofit effectiveness across diverse indoor climate parameters.

Atmosphere doi: 10.3390/atmos17060557

Authors: Siqi Xiong Zuhan Liu Yan Li Zonghao Nie

This study proposes a hybrid forecasting framework that integrates Seasonal-Trend decomposition using LOESS (the following abbreviations are referred to as STL) with two time series models, TimesNet and TimeXer. To capture the complex periodic characteristics of the PM2.5 series, the original data are first decomposed into trend, seasonal and residual components via STL. The trend and seasonal components are then predicted using TimesNet, which maps one-dimensional time series into a two-dimensional representation to better model multi-scale periodicities and temporal dependencies. In parallel, the residual component is forecast using TimeXer, which incorporates exogenous variables to improve the modeling of endogenous dynamics. The final PM2.5 prediction is obtained by aggregating the forecasts of the three components. Experimental results demonstrate that the proposed STL-TimesNet-TimeXer model achieves high predictive accuracy, with an R2 of 0.969, MAE of 2.834, MSE of 19.063, and MAPE of 6.435. Comparative analyses against single-model baselines further confirm that STL-based decomposition significantly enhances forecasting performance, indicating that STL provides an effective and interpretable approach for modeling PM2.5 time series.

Atmosphere doi: 10.3390/atmos17060559

Authors: Yevgenii Rastigejev Sergey A. Suslov Wenbin Dong

This study investigates the mechanical and thermodynamic effects of evaporating ocean spray on the structure and dynamics of a hurricane marine atmospheric boundary layer using Eulerian multifluid and mixture model approaches coupled with the E−ϵ turbulence closure. The multifluid framework treats air and spray as interpenetrating phases, enabling a physically consistent representation of air–droplet interactions governing momentum transfer, enthalpy exchange, and turbulence modulation. The mixture approach is based on a simplified description that captures only part of the underlying physics yet offers an advantage in its ability to yield analytical insight. Mechanically, spray produces competing effects: on one hand, droplet inertia causes wind deceleration, and on the other, spray-induced turbulence attenuation, primarily resulting from the air–droplet friction, leads to strengthening the wind. Analytical and numerical results show that the latter effect prevails for typical spray droplet sizes leading to wind acceleration and drag reduction at hurricane wind speeds. Thermodynamically, evaporating droplets redistribute total heat flux in favor of its latent component, with effects strongly dependent on the droplet size. Small droplets suppress turbulence and reduce the total enthalpy flux, whereas large ones enhance it. Furthermore, spray significantly increases the total enthalpy-to-drag coefficient ratio with wind speed, which agrees with field observations.

Atmosphere doi: 10.3390/atmos17060558

Authors: Wei Zhang Ming Hu Jialiang Feng Qingyan Fu Shunyao Wang

Nitrous acid (HONO) plays a vital role in atmospheric oxidation capacity (AOC) and ozone (O3) formation. Based on 2017–2021 observations at urban Pudong (PD) and suburban Qingpu (QP) in Shanghai, HONO concentrations ranged from 0.74 ± 0.45 to 1.38 ± 0.52 ppb in PD and 0.82 ± 0.50 to 1.19 ± 0.62 ppb in QP, with higher levels in summer and a typical morning peak at 8–9 a.m. HONO photolysis produced an average of 1.9 ppb h−1 of OH in summer, significantly elevating AOC. Under HONO constraints, summer O3 production rates via HO2 + NO and RO2 + NO increased by 16% and 20%, respectively. These results highlight the key contribution of HONO chemistry to photochemical pollution and provide implications for air quality control in the Yangtze River Delta.

Atmosphere doi: 10.3390/atmos17060556

Authors: Yi-Da Chung Hung-Hsun Chen Keh-Chin Chang

Horizontal measurement of global radiation on the rooftop of a weather station is generally hindered by the presence of obstructions surrounding the pyranometer. To investigate the sheltering effect, measured data from two weather stations in Taiwan, namely the Taitung (TWS) and Penghu (PWS) weather stations, were compared with corresponding in situ data measured under zero-shelter environments at nearby locations: the Taitung Center of National Open University (TCNOU) and the Penghu University of Science and Technology (PUST). The shelter view factor around the installed pyranometer was determined using a fisheye-lens image together with a calculation method based on a polar grid representation with sufficiently fine annuli. The shelter view factors for TWS and PWS were 11.8% and 5.0%, respectively. Comparisons of the monthly global radiation data measured at TWS and TCNOU and at PWS and PUST showed that underestimations of global radiation ranged from 1.8 to 9.1% (2016–2017) at TWS and from 1.3 to 4.2% (May 2015–December 2017) at PWS. These underestimations were primarily attributed to the magnitude of the shelter view factor for all obstructions around the pyranometer but were also dependent on the local pattern of global radiation (that is, beam and diffuse radiation), which is a climatological factor.

Atmosphere doi: 10.3390/atmos17060555

Authors: Miguel Ángel de Pablo Gabriel Goyanes

Near-surface air temperature is the main atmospheric forcing of frozen-ground systems in maritime Antarctica, yet most studies have emphasized mean annual or seasonal trends rather than thermal-regime evolution. This study analyzes hourly air-temperature records from eight PERMATHERMAL monitoring stations on Livingston and Deception Islands (South Shetland Islands, Antarctica) for 2000–2022 to evaluate changes in mean conditions, daily thermal regimes, degree-day forcing, and their implications for frozen ground. Hourly data were aggregated to daily, monthly, annual, and thermal-year scales, and valid days were classified into six thermal regimes (F1, F2, IS, FT, T2, and T1). FDD, TDD, annual degree-day balance (BDD), and freezing and thawing season duration were also calculated. Recent warming is expressed not only as higher mean annual air temperature, but also as a reorganization of the annual thermal regime, with fewer cold days, more thaw-related conditions, and less negative BDD values at most stations. These changes are consistent with previously reported GST evolution and indicate a shift in atmospheric forcing toward weaker freezing dominance and more thaw-favorable conditions. However, their implications for active-layer thickness and permafrost stability should be interpreted as climatic indications rather than as the direct evidence of ground thermal change.

Atmosphere doi: 10.3390/atmos17060554

Authors: Zewen Ju Zhiyuan Wang Wei Wang Dan Wang Ding Tong Jie Zhang

Understanding the mechanisms by which sand and dust particles detach from the land surface has always been one of the most fundamental and critical issues in aeolian physics and dust-storm forecasting. In this study, large-eddy simulation (LES) was employed to resolve the near-wall turbulence structures. Turbulent bursting events were identified using the second-quadrant method, and a force-balance equation for dust-particle entrainment was formulated at burst locations to numerically simulate the entrainment process of particles of different sizes under bursting conditions. By integrating the latest observational data on near-wall turbulent coherent structures during dust storms both the accuracy of flow-field simulations and the physical consistency of particle force analyses were enhanced. The results suggest that, within the present idealized force-balance framework, near-wall turbulent bursting can provide aerodynamic forcing that contributes to the entrainment of sand and dust particles over the simulated parameter range. Under the same friction velocity, the mean number of lifted particles first increases and then decreases with particle size, exhibiting a parabolic trend. For particles of the same size, the number of lifted particles increases significantly with friction velocity. Under identical incoming wind speeds, the number flux of lifted particles decreases nonlinearly with increasing particle size, whereas the mass flux continues to rise with both friction velocity and particle size. These findings further confirm the critical contribution of aerodynamic entrainment to aeolian transport and provide numerical support for refining the dual-mechanism theory of sand entrainment.

Atmosphere doi: 10.3390/atmos17060553

Authors: Logan T. Scamfer Elizabeth A. Silber Miro Ronac Gianonne Daniel C. Bowman Nora R. Wynn Michael Fleigle Justin LaPierre

Controlled spacecraft reentries from interplanetary trajectories provide rare, well-characterized hypersonic sources for advancing seismoacoustic observation techniques. Here we present seismic observations of the OSIRIS-REx sample return capsule (SRC) reentry on 24 September 2023, recorded by 16 three-component nodal seismometers deployed near Eureka, Nevada, at ground distances of 7–20 km from the capsule trajectory. Air-to-ground coupled signals are detected at all stations, exhibiting impulsive onsets consistent with ballistic shock arrivals from the descending Mach cone. We characterize the seismic wavefield through signal amplitude, period, waveform cross-correlation, and array processing. Signal periods decrease systematically with increasing distance from the trajectory within the airport array, indicating that higher-frequency content becomes more prominent at greater offsets, opposite to expectations from geometric spreading and atmospheric absorption. Seismic array processing identifies frequency-dependent back-azimuth variations whose origin remains unresolved; possible contributing factors include source geometry, scattering by fine-scale layered structure in the stratosphere, and near-surface effects. These observations document a spatially complex seismic wavefield from a well-characterized hypersonic line source and provide constraints for future modeling of atmospheric propagation and air-to-ground coupling.

Atmosphere doi: 10.3390/atmos17060552

Authors: Ruohan Li Xiaoyu Wang

Rapid Arctic warming has significantly altered sea ice–atmosphere boundary layer processes, which low-resolution models struggle to resolve accurately. This study evaluates the historical performance (1958–2014) of four high-resolution models from CMIP6 HighResMIP—EC-Earth3P-HR, CNRM-CM6-1-HR, HadGEM3-GC3.1-HH, and Fgoals-f3-H—against ORAS5 and CMEMS reanalysis datasets and examines their physical response to rapid warming under the SSP5-8.5 scenario (2015–2025). Results show substantial intermodel differences in simulating Arctic sea ice thickness, mixed layer depth, sea surface temperature and salinity, and deep convection. HadG-EM3-GC3.1-HH and CNRM-CM6-1-HR perform best overall, reliably reproducing trends in the two major deep convection regions, meridional temperature–salinity gradients, and long-term evolution with lower biases and higher correlations. Under decadal strong warming, models generally simulate shoaling mixed layers in deep convection zones and upper-water destabilization in the Canada Basin, but responses in sea ice, eddy kinetic energy, and transect temperature–salinity vary markedly. HadGEM3-GC3.1-HH and CNRM-CM6-1-HR better represent physical quantities and ocean stratification consistent with observed real-world responses. We conclude that these two models are more suitable for studies of Arctic sea ice–atmosphere boundary layer changes and deep convection, providing a basis for high-resolution model selection and Arctic climate projection.

Atmosphere doi: 10.3390/atmos17060551

Authors: Sijie Gai Jie Xu Qiaoqiao Jing Ruihang Ouyang Jinjian Li

With the rapid growth of tourism in Dayi County over the past decade, this study develops a meteorological disaster risk assessment framework for major tourist attractions in this region. Drawing upon daily precipitation and temperature records from 25 meteorological stations (2014–2023) alongside multi-source geospatial data, we evaluate six primary attractions: Xiling Snow Mountain, Huashuiwan, Anren Ancient Town, Xinchang Ancient Town, Tianfu Huaxigu Valley, and Shujiu Cultural Park. The evaluation model integrates four core dimensions: hazard, environmental sensitivity, asset vulnerability, and disaster mitigation capacity. Indicator weights are determined through the Analytic Hierarchy Process, and GIS-based spatial analysis is employed for risk zonation. Additionally, the 45-year ChinaMet dataset provides independent validation for the long-term stability of the hazard assessment. Results reveal a distinct west-low, east-high composite risk gradient. High-altitude mountainous regions in the west exhibit a lower overall risk. Despite frequent extreme weather events, extensive vegetation coverage and low visitor density effectively buffer the negative impacts of physical hazards. Conversely, tourist attractions on the eastern plains fall within high-risk zones. Concentrated visitor populations, dense built environments, and low-lying terrain collectively amplify exposure to severe rainstorms and extreme heatwaves. These findings demonstrate that meteorological disaster risk in tourism destinations fundamentally arises from the deep coupling of natural and human systems. Thus, this study provides a scientific basis for implementing differentiated disaster prevention, mitigation, and localized emergency management strategies.

Atmosphere doi: 10.3390/atmos17060550

Authors: Mateus Rebelo Michel Stefanello Daniel C. Santos Richard Lobato Tamires Zimmer Murilo Lopes Cinara E. da Rosa Alecsander Mergen Ernani de Lima Nascimento Gervasio Degrazia Debora Roberti Rafael Maroneze

Vento Norte (VNOR; Portuguese for North Wind) is a downslope windstorm that develops over modest terrain in the central region of Rio Grande do Sul (RS), southern Brazil. The regional topography is characterized by an abrupt terrain transition with elevation differences of approximately 400–500 m. This atmospheric flow typically occurs during the cold season and is characterized by strong wind gusts, rapid warming, and drying of the planetary boundary layer (PBL). In this study, the performance of different PBL parameterization schemes in the Weather Research and Forecasting (WRF) model is assessed for simulating a VNOR event that occurred between 19 and 20 August 2021 in Santa Maria (SMA), RS. Five high-resolution numerical simulations were conducted using the Yonsei University (YSU), Asymmetric Convective Model version 2 (ACM2), Mellor–Yamada–Nakanishi–Niino level 2.5 (MYNN2.5), Quasi-Normal Scale Elimination (QNSE), and Three-Dimensional Turbulent Kinetic Energy (3DTKE) PBL schemes. Model results were evaluated against observations from a flux tower providing turbulence measurements, twice-daily radiosoundings, and hourly surface meteorological observations. Statistical metrics indicate that the MYNN2.5 scheme provided the most accurate representation of the nighttime stable boundary layer preceding the VNOR, as well as its onset and subsequent evolution. Although this study analyzes a single VNOR event and the results may be case-dependent, the overall performance of the MYNN2.5 scheme suggests that it is a promising option for the operational forecasting of VNOR events. These findings provide new insights into the ability of different PBL schemes to reproduce the mean boundary-layer structure and turbulence characteristics associated with downslope windstorms over modest terrain, contributing to the understanding of these events.

Atmosphere doi: 10.3390/atmos17060549

Authors: Jie Xiao Xiaomei Chen Bao Wang Xishan Pan

Accurate sea surface wind forecasts are essential for marine disaster prevention, maritime economic activities, and renewable energy development. However, traditional numerical weather prediction (NWP) models often encounter limitations such as nonlinear error accumulation and systematic biases during long-lead-time integration. Consequently, this study develops a spatiotemporal deep learning post-processing framework based on state space mechanisms, utilizing ERA5 reanalysis data to correct errors in 0–72 h NWP 10 m wind speed forecasts over the Northwest Pacific and adjacent regions (0–90° N, 100–150° E). Evaluations against mainstream spatiotemporal deep learning models indicate that the proposed framework improves the forecast accuracy and spatial consistency of the NWP. Regarding overall error control, the post-processing model reduces the root mean square error (RMSE) of the raw NWP from 1.47 m/s to 1.10 m/s for 24 h forecasts. Meanwhile, during the 72 h long-lead-time integration, the pattern correlation coefficient (PCC) of the forecasted wind field is maintained at 0.86, and the overall systematic bias converges from −0.27 m/s to −0.02 m/s. Additionally, the framework effectively mitigates the over-prediction of gale-force winds, reducing the false alarm ratio (FAR) by 30–50% compared to the raw NWP. These results indicate that the proposed deep learning post-processing strategy effectively corrects underlying systematic biases in numerical models, thereby enhancing the accuracy and reliability of long-term wind field forecasts.

Atmosphere doi: 10.3390/atmos17060548

Authors: Man-Lok Chong Hiu-Fai Law Tsz-Ki Lau Ho-Yiu Fung Kai-Kwong Lai Pak-Wai Chan

A microscale vortex embedded in a cold front over the Pearl River Estuary was observed by weather radars in Hong Kong on the evening of 2 March 2026. This paper presents an observational and simulation study of this vortex. In addition to the reflectivity and Doppler velocity data, the three-dimensional wind field associated with this vortex was analyzed using two radar-based analysis methods. Updrafts were present within the vortex, and the formation of the vortex appears to be related to the horizontal wind shear within the frontal zone and vertical motion triggered by a mid-tropospheric wave. Three commercial aircraft flew across the vortex at low altitude southwest of Lantau Island. Flight data showed marked fluctuations in vertical velocity, including both upward and downward air motions, together with severe turbulence within the vortex. The vortex is therefore of both meteorological interest and operational significance for aviation safety. The event was also simulated using the Weather Research and Forecasting (WRF) model with 200 m resolution. The model reproduced the observed vertical motions and turbulence intensity reasonably well in comparison with aircraft observations. Sensitivity tests with varying sea surface temperature and local terrain over Hong Kong showed no significant impact on the formation of the vortex, confirming that the event was primarily driven by horizontal wind shear in the frontal zone and vertical motion triggered by mid-tropospheric waves.

Atmosphere doi: 10.3390/atmos17060547

Authors: Lankai Yang Chenglong Zhong

Marathon heat stress is an increasing public health concern under climate change, particularly for mass participation endurance events. Using 967,878 runners from 18 U.S. marathon events between 2011 and 2019, this study examined the nonlinear association between race-day thermal exposure and marathon performance. A two-way fixed effects framework was used to account for race- and year-specific heterogeneity, demographic characteristics, race-day maximum air temperature, relative humidity, their interaction, and non-stationary exposure proxies. The results identified a humidity-dependent thermal optimal zone (TOZ). At the sample mean humidity level of 77.7%, the estimated the TOZ based on the race-day maximum air temperature was 13.0 °C, with a low-penalty range of 8.5–17.4 °C for predicted losses below 60 s. In the main specification, the temperature–humidity interaction was positive, suggesting that humidity-related penalties may increase under warmer conditions; however, race-year-level sensitivity analyses indicated that this interaction should be interpreted cautiously. Under 28.0 °C and 80% relative humidity, the model predicted a finish-time penalty of approximately 737.5 s. Stratified analyses showed that mass participation runners experienced larger high-temperature penalties than elite runners, and male runners aged 35–49 years showed the highest estimated thermal sensitivity at 28.0 °C. The UTCI modestly improved model calibration but produced unstable optimum estimates, supporting its use as a complementary biometeorological benchmark rather than as the primary basis for defining a marathon-specific TOZ. These findings suggest that a marathon heat-risk assessment should jointly consider air temperature, humidity, integrated biometeorological exposure, and subgroup-specific vulnerability.

Atmosphere doi: 10.3390/atmos17060546

Authors: Lathiff Sesay Julius Mangisoni Innocent Panga-Panga Phiri Assa M. Maganga

An in-depth analysis of the drivers of agricultural emissions at the farm-gate level is crucial for achieving net-zero emissions by 2050. This study examines the short- and long-run effects of crop production on farm-gate emissions in the regional trading bloc (RTB) member states in Africa. Crop production was proxied by cereals, roots and tubers, vegetables, and fruits production, and emissions were split into methane (CH4) and nitrous oxide (N2O) emissions. Data on these variables were collected from 30 RTB member states from 1990 to 2022 and were analyzed using the cross-sectionally augmented autoregressive distributive lag approach. The pooled mean group was used as a robustness check, and a sensitivity analysis was conducted to ensure the reliability of the study findings. The results revealed that cereal production increases farm-gate CH4 and N2O emissions in the short and long run. The average increase ranges from 1.0021 to 1.0033 kilotons CO2–eq yr−1 for CH4, and from 1.0024 to 1.0035 kilotons CO2–eq yr−1 for N2O. In addition, fruit production increases farm-gate CH4 emissions by an average of 1.0023 kiloton CO2–eq yr−1 in the long run. Thus, cereal production has a more adverse effect on N2O than CH4 emissions, while the opposite is true for fruit production in the RTB member states’ Nationally Determined Contributions. With respect to mediation, cropland expansion (proxied by area harvested) plays a partial intermediary role in the impact of crop production on farm-gate CH4 and N2O emissions in the short run and CH4 emissions in the long run. However, it assumes a full mediation role in the long run and has an effect on crop production in farm-gate N2O emissions. Therefore, targeted use of nitrogen fertilizer and crop rotations to reduce cereal-related N2O and CH4 emissions, respectively, would be viable strategies. The use of a drip irrigation system in fruit production to reduce CH4 emissions and the scaling up of secured subsidies should also be explored. Finally, these recommendations should be incorporated into the Africa’s RTB member states’ Nationally Determined Contributions and the African Union’s Agenda 2063.

Atmosphere doi: 10.3390/atmos17060545

Authors: Yewen Teng Qianyu Tao Xuebei Chen Tiantian Feng Yijia Wang Bangchuan An Dingli Yan Rui Guo Yang Huang Siyang Liu Weicheng Zhou

Air and water pollution pose critical threats to public health and environmental stability, particularly in rapidly urbanizing developing nations. This study investigates synergistic interactions between air and water pollutants across 14 cities in Hunan Province, China (2020–2024), using multiparametric statistical approaches. The results show that the coefficient of variation (CV) of particulate matter (PM) with diameters less than 2.5 μm (PM2.5, CV = 46.9%) and turbidity (TU, CV = 47.4%) showed the highest variability among the air and water quality parameters, respectively. Annual trends revealed significant increases in ozone (O3) alongside decreases in carbon monoxide (CO) and nitrogen dioxide (NO2) concentrations. Concurrently, freshwater systems exhibited rising electrical conductivity (EC), water temperature (WT), and pH, paired with declining levels of ammonia nitrogen (NH3-N), total phosphorus (TP), and turbidity (TU). Principal component analysis (PCA) and Spearman correlation analyses showed significant positive correlations between PM and nitrogen species (TN, NH3-N), but negative correlations with TP, suggesting potential cross-media pollution interactions. Cross-correlation analysis revealed significant time-lagged relationships (1–5 months) between atmospheric pollutants and aquatic nutrients, suggesting that atmospheric deposition may serve as a contributing pathway for cross-media contamination. The study not only provides empirical evidence for integrated pollution control strategies in urbanizing watersheds, but also offers a transferable framework for addressing similar air–water quality interactions on a global scale.

Atmosphere doi: 10.3390/atmos17060544

Authors: Mark R. Jury Miryam Borbor

A study of agricultural yield sensitivity in Peru to climate variations is conducted from 1961 to 2024 to identify climate drivers and statistical tools for early warning and risk management. The statistical basis is year-on-year change in standardized crop yield rate (CYR) across the southeastern highlands of Peru 7–15° S, 70–77° W. Crops favoring La Nina include citrus, cotton, fruit, and sugar-cane. Based on temporal and spatial correlation and composite analysis, our findings indicate that (i) east Pacific and Caribbean sea temperatures and Atlantic upper winds provide advance warning signals of CYR fluctuations; (ii) during El Niño, the subtropical jet subsides over the Peruvian highlands, raising temperatures and lowering humidity; (iii) during La Niña, cooler temperatures conspire with rising motion and beneficial rains; and (iv) CYR fluctuations account for 26% of macro-economic variance, ~$66 B at the current value. Bringing technological information to agricultural decision making will improve resilience and help meet the twin challenges of a growing population and changeable climate. Adaptive measures are suggested to take advantage of Southern Oscillation’s influence on austral summer weather and subsequent annual crop yield.

Atmosphere doi: 10.3390/atmos17060543

Authors: Santigie Morlor Conteh Jianrong Pan Jie Jiang Chengguang Lai Xushu Wu Zhaoli Wang

Environmental changes driven by land use and climate variability profoundly affect basin water balance, yet their separate and combined effects remain poorly understood in data-scarce regions. This study investigates the individual and combined impacts of land use/land cover (LULC) and climate change on seasonal runoff in the Rokel-Seli River Basin (RSRB), Sierra Leone, over two periods (1965–1990 and 1991–2016). Using LULC maps derived from 1988 and 2013 Landsat imagery and the Soil and Water Assessment Tool (SWAT), we simulated hydrological responses under four scenario frameworks. The results reveal a marked expansion of urban, bare, and agricultural land at the expense of forest cover. The SWAT model satisfactorily captured streamflow dynamics during calibration and validation. Land use change alone increased wet-season runoff by 6.55% and decreased dry-season runoff by −13.15%, whereas climate change contributed changes of +24.87% and −31.43%, respectively. A double mass curve analysis and Budyko framework further revealed a regime shift toward higher runoff efficiency (runoff coefficient increased from 0.67 to 0.69), indicating a loss of basin retention capacity. Notably, land use change partially masked the full hydrological deficit induced by climate change, acting as a counter-buffering mechanism. This study provides critical evidence for water resource authorities and local stakeholders to develop adaptive land use and water conservation strategies in data-scarce tropical basins, emphasizing the need to consider both climatic and anthropogenic drivers in seasonal water availability assessments.

Atmosphere doi: 10.3390/atmos17060542

Authors: Fei-Yin Wang Wen-Kang Sui Peng-Tao Wang Mao Xu Hang Li

Sustainable aviation fuel (SAF) is widely recognized as a critical pathway for aviation decarbonization; however, its life-cycle carbon performance is highly sensitive to supply chain configurations. This study proposes a data-driven framework integrating life-cycle assessment (LCA) with a generative adversarial network (GAN) to model and optimize SAF supply chain pathways under structural constraints. A rule-constrained synthetic dataset comprising feasible pathways is constructed, incorporating feedstock sources, refinery locations, airport demand nodes, conversion technologies, transport modes, and distances. Each pathway is encoded into a numerical feature vector, and a GAN model is trained to learn the distribution of feasible configurations. Generated pathways are further validated through LCA-based post-processing to ensure physical feasibility and emission consistency. The results show that pathway-level carbon intensity varies significantly across configurations, with differences exceeding 30% under varying feedstock–transport combinations. The model successfully captures the multimodal distribution of carbon emissions and identifies structurally consistent low-carbon pathways. In particular, localized supply structures and reduced transport distances are found to play a dominant role in minimizing emissions. This study provides a scalable methodological framework for SAF pathway modeling and offers insights into supply chain design and spatial configuration for achieving aviation carbon reduction targets.

Atmosphere doi: 10.3390/atmos17060541

Authors: Tan Nguyen Tiep Phong Nguyen Duc

Precipitation variability in the VMD is a critical determinant of agricultural productivity, freshwater availability, and flood and drought dynamics in one of Southeast Asia’s most climate-vulnerable regions. Teleconnections between PPTA and three dominant climate modes (Niño 3.4, DMI and PDO) were quantified at ten meteorological stations from 1981 to 2025 using Pearson lag-correlation and WTC. ENSO is identified as the primary interannual driver, exhibiting a peak negative correlation at a lag of two months (r = −0.304, p < 0.001; 9.2% variance explained). The IOD exerts a secondary, delayed influence, peaking at lags of 11 to 12 months (r = 0.186, p < 0.001; 3.5% variance). The PDO functions as a persistent decadal modulator: positive phases suppress annual precipitation by 4.6%, while negative phases enhance it by 14.5% relative to the long-term mean (6.4% variance). WTC analysis reveals non-stationary coherence at 2–5 year (ENSO) and 8–16 year (PDO) periodicities. Compound El Niño and positive PDO events result in the most severe precipitation deficits, with non-linear responses during strong ENSO phases. These results establish a multi-index teleconnection framework that supports seasonal drought early warning and climate-adaptive water resource management in the VMD.

Atmosphere doi: 10.3390/atmos17060540

Authors: Hao Duan Yanqing Guo Haowei Xu Zhihui Zhao Tao Qin Hongkang Zhang

Actual evapotranspiration is a primary pathway for crop water consumption in irrigation districts, including the Shijin irrigation district, where understanding the impacts of irrigation is crucial for managing water resources in this large-scale, water-scarce region. However, existing studies on evapotranspiration driving mechanisms often overlook irrigation activities and lack an analysis of synergistic effects among different environmental factors, with such research remaining particularly limited for this area. This study investigates the synergistic impact mechanisms of multiple drivers on evapotranspiration. Using data from 2003 to 2017, a projection pursuit model was employed to quantitatively assess the contributions of meteorological factors, Leaf Area Index, and irrigation to evapotranspiration evolution. The results indicate a significant structural shift in evapotranspiration, and the reduction in soil evaporation plays an important role in driving the variation of total evapotranspiration. Among the various factors, Leaf Area Index and irrigation exhibited the highest contribution rates to evapotranspiration. Furthermore, irrigation primarily acts in synergy with crop growth to enhance evapotranspiration. This study provides critical scientific insights for evidence-based water resource management and policy optimization in the Shijin irrigation district.

Atmosphere doi: 10.3390/atmos17060539

Authors: Jie Song Yifan Yang Jianbin Xu

Fine particulate matter (PM2.5) exposure in public transport microenvironments has important implications for commuter health, yet concentration-based assessments may not adequately reflect the dose actually inhaled by passengers. This study quantified dynamic PM2.5 inhaled doses in Taiyuan, China, using 1 Hz portable monitoring and matched travel surveys across 19 bus and metro routes during summer and winter 2025. After data screening, 1103 valid commuter samples were retained. We combined dose estimation with DML, XGBoost-SHAP, SEM, and Random Forest analysis to examine adjusted associations, explore potential nonlinear patterns, and characterize behavioral responses. Trip-averaged PM2.5 concentrations exceeded the WHO 24 h guideline on most monitored routes when interpreted as a health-based reference benchmark for short commuting exposures rather than as a direct regulatory exceedance metric. More importantly, a clear concentration-dose decoupling pattern was observed: 6.6% of trips fell into a low-concentration but high-dose category, indicating that prolonged in-vehicle exposure could substantially elevate inhaled dose even when PM2.5 concentrations remained below the sample median. The mean inhaled dose in the longer observed-duration group (top 20% by observed in-vehicle duration) reached 612.26 ± 412.21 μg, which was 7.2 times that of the remaining trips (84.87 ± 115.71 μg). DML results showed that inhaled dose, rather than PM2.5 concentration alone, was significantly associated with psychological distress. SHAP analysis suggested an exploratory threshold-like pattern at approximately 300 μg per trip, above which health-risk attribution increased rapidly. SEM results indicated that inhaled dose was associated with higher self-reported somatic burden, whereas PM2.5 concentration mainly influenced health indirectly through risk perception. These findings suggest that public transport exposure assessment should move beyond static concentration metrics and incorporate dynamic inhaled dose to better identify high-risk commuting scenarios and support more targeted health-oriented transit management.

Atmosphere doi: 10.3390/atmos17060538

Authors: Victor Nijimbere Lucy J. Campbell

A time-dependent nonlinear model is presented to describe internal gravity waves propagating upwards in the F-region of the Earth’s ionosphere. The model is based on a configuration where the background neutral velocity is constant, the geomagnetic field is approximately constant, and the angular gyrofrequency of the ions is much larger than the ion-neutral collision frequency, which is in turn larger than the angular frequency of the gravity waves. For small-amplitude waves the equations are linearized, and a time-dependent analytical solution is obtained for the special case corresponding to the limit of zero vertical-to-horizontal aspect ratio. This analytical solution and the linear numerical results for general aspect ratio show that in the limit of infinite time the linear solution approaches a steady state in which the ion damps the wave amplitude in the vertical direction. For the more general configuration that includes larger amplitude waves, time-dependent nonlinear numerical simulations show that, in the presence of the ion drag, there are wave mean-flow interactions even in the absence of vertical shear in the background neutral flow. With time, the perturbation develops a zero-wavenumber component corresponding to a wave-induced mean flow acceleration, which depends on the dip angle of the geomagnetic field and on the aspect ratio.

Atmosphere doi: 10.3390/atmos17060537

Authors: Qing Feng Ruirui Zhang Qiqi Chen

In the context of increasing climate variability and carbon neutrality targets, understanding the relative roles of climate and human activities is essential for accurately assessing vegetation carbon sink dynamics. This study develops a climate-controlled attribution framework to disentangle human-induced effects from natural climatic variability in Guizhou Province, a representative karst region of Southwest China. Using multi-source remote sensing and climate data from 2004 to 2023, net ecosystem productivity (NEP) was estimated, and its spatiotemporal dynamics were analyzed. A two-step attribution approach was applied to isolate climate-driven variability and quantify the contribution of anthropogenic activities. Results indicate that mean NEP increased significantly from 273 gC·m−2·yr−1 in 2004 to 369 gC·m−2·yr−1 in 2023, with a provincial average of 318 gC·m−2·yr−1. Human activities are estimated to contribute a dominant share (approximately 60–75%), although uncertainties remain due to methodological limitations. Spatial analysis reveals pronounced heterogeneity, with stronger human-induced enhancement in eastern regions and mixed restoration–disturbance effects in ecologically fragile western areas. These findings suggest that ecological restoration policies in fragile karst ecosystems can generate amplified carbon sink responses beyond background climatic effects. These findings provide insights into understanding climate–carbon cycle interactions and improving region-specific climate mitigation strategies.

Atmosphere doi: 10.3390/atmos17060536

Authors: Haipeng Li Jun Liu Yan Liu Zelin Liu

Accurate forecasting of typhoon evolution from satellite cloud imagery is critical for disaster preparedness and mitigation, yet remains challenging due to the complex spatiotemporal dynamics of typhoon systems. While deep learning models have shown promise in spatiotemporal sequence prediction, existing approaches often struggle to balance the modeling of large-scale structural evolution with fine-grained local dynamics. In this paper, we propose FusionTyphoonPredictor, a novel dual-branch encoder–decoder framework designed for typhoon cloud image prediction. The model integrates a Global Fusion Module to capture multi-scale spatial interactions using large-kernel attention and multi-scale convolution, and an ST Recurrent Refiner to enhance temporal consistency and local detail through recurrent processing with ConvGRU and residual blocks. Extensive experiments on the Digital Typhoon dataset demonstrate that our approach achieves improved performance compared to existing methods (including PredFormer and PhyDNet) across most metrics and forecasting horizons. Specifically, FusionTyphoonPredictor shows consistent advantages in SSIM, MAE, and MSE, with particular strength in short-term forecasting. Comprehensive ablation studies validate the complementary design of the two branches and confirm the effectiveness of each proposed component. Our work advances typhoon forecasting and has potential for real-time operational deployment.

Atmosphere doi: 10.3390/atmos17060535

Authors: Pauline Teysseyre Carine Briand

A significant fraction of the HF waves is absorbed by the lowest ionospheric layer, the D-region. This region is perturbed by solar flares, which notably cause fast increases in the Sun’s X-ray flux. We present here a new chemistry model, the “Lower Ionosphere Region–Absorption and Chemistry Modelling” (LIR-ACheM), to study the D-region behaviour. It is based on the Mitra–Rowe scheme and takes into account four distinct sources (EUV, Lyman-α, X-rays and cosmic rays) and seven species (electrons, NO+, O2+, O4+, positive cluster ions, O2− and other negative ions). It thus offers a compromise between accuracy and computing time. The D-region’s sluggishness and its recovery time after a flare are analysed, highlighting the importance of detachment at low altitudes and soft X-ray fluxes above 80 km.

Atmosphere doi: 10.3390/atmos17060534

Authors: Kimberly Valle Kate DeMarsh Estrella Herrera Tim Tyner Derek Payton Stephanie Koch-Kumar Mayra Lemus Rangel Jermaine Reece Sandie Ha Sidra Goldman-Mellor Trevor P. Hirst Matt Holmes Adriana Espinosa Asa Bradman Alec M. Chan-Golston

Air quality in the San Joaquin Valley (SJV) ranks among the worst in the US. Exposures to traffic-related air pollutants have been associated with pediatric health complications, and few studies have investigated respiratory complications in relation to short-term exposures to PM less than 2.5 microns in diameter (PM2.5) in the SJV. We used Bayesian Poisson spatiotemporal conditional autoregressive models to analyze the association between PM2.5 and pediatric respiratory emergency department (ED) visits in Fresno County, California. Additional analyses stratified respiratory outcomes by sex and age group. Weekly ambient PM2.5 levels were estimated for each zip code using community science and regulatory air monitors. Weekly residential zip code counts of respiratory ED visits were provided by Fresno County Department of Public Health and Valley Children’s Hospital from 2 April 2022 to 31 December 2024. A ten-fold increase in PM2.5 was associated with increased asthma ED visits among females (Relative Risk (RR):1.15; 95% Credible Interval (CrI):1.01, 1.32) and children aged 0 to 4 (RR:1.18; 95% CrI:1.03, 1.34) and other chronic respiratory conditions among males (RR:1.93; 95% CrI:1.19, 3.16) and ages 10 to 14 (RR:2.90; 95% CrI:1.32, 6.30). Findings suggest that efforts to better assess and reduce pollution exposures will improve public health in the SJV.

Atmosphere doi: 10.3390/atmos17060532

Authors: Aliya Kalizhanova Murat Kunelbayev Anar Utegenova Ainur Kozbakova Serik Daruish

The relevance of this study is determined by the need for a scientifically grounded assessment of environmental risks associated with rocket launches and by the necessity of ensuring environmental safety in areas potentially affected by space activities. Comprehensive monitoring of rocket-stage impact zones and adjacent populated areas is especially important because pollutant distribution depends on natural, climatic, and spatial factors. This study assesses the environmental impact of the “Soyuz-2.1a” launch with the “Progress MS-29” cargo spacecraft in Kazakhstan using integrated field monitoring, laboratory analysis, and geoinformation methods. The work should be interpreted as a single-event environmental monitoring assessment, while historical monitoring data from 2020–2023 were used only as a retrospective comparative background for the U-25 impact area and were not included in the main BACI statistical analysis. The study covered the launch site, adjacent populated areas, and the U-25 stage impact zone. A before–after control-impact (BACI) design with distance stratification and consideration of wind direction was applied to identify post-launch changes. Measurements below the limit of detection and limit of quantification were processed using censored-data methods, including Regression on Order Statistics (ROS) and the Kaplan–Meier estimator. Spatial analysis was used to generate concentration fields, contour maps, and risk zones, revealing an anisotropic distribution of environmental stress in the downwind sector. An integrated hazard quotient (HQ) metric was applied to compare air, water, and soil conditions on a unified scale. The results indicate that the post-launch impact was localized and time-limited, with the greatest sensitivity observed in the soil component of the U-25 zone during the early post-launch period. Atmospheric air and water indicators remained within regulatory limits in populated areas. The proposed approach combines BACI monitoring, censored-data analysis, spatial modeling, and GIS-based visualization, providing a reproducible framework for the environmental assessment of rocket-stage impact areas. The practical recommendations include staged post-launch monitoring, temporary restriction of access to high-stress zones, primary reclamation of contaminated soil, and the use of WebGIS tools to support environmental decision-making.

Atmosphere doi: 10.3390/atmos17060533

Authors: Qian Li Zeming Shi Qingsong Wu Peng Yang Yanggang Zhao Zihong Liao

Remote alpine lakes on the Xizang Plateau are important archives for tracing the long-range atmospheric transport (LRAT) of persistent organic pollutants, yet historical records of polybrominated diphenyl ethers (PBDEs) from this region remain scarce. The main objective of this study was to reconstruct the historical record of PBDEs in Yamzho Yumco sediments and to evaluate whether this record reflects source evolution, atmospheric transport, deposition, and post-emission environmental fractionation in a remote alpine receptor system. To achieve this objective, 17 PBDE congeners were determined in a 210Pb- and 137Cs-dated sediment core spanning 1930–2023. Σ17PBDE concentrations ranged from 5.80 to 263.13 pg/g dw, and depositional fluxes ranged from 2.67 to 121.04 pg/cm2/yr, both showing a marked increase after the 1970s and remaining elevated after 2000. Lower-brominated congeners, especially BDE-47, dominated the core, whereas nona- and deca-BDEs appeared mainly in recent sediments, indicating progressive source evolution in recent decades. Tri- to penta-BDEs remained the dominant homologue fraction throughout the record, while elevated post-2000 BDE-47/BDE-99 ratios point to congener-selective environmental fractionation during atmospheric transport and deposition. Together, these results suggest that Yamzho Yumco sediments preserve not only the history of regional PBDE input, but also the coupled imprint of source evolution, transport-related fractionation, and delayed environmental response in a remote high-altitude receptor system. This study highlights the value of Xizang Plateau Lake sediments for process-based interpretation of POP fate in mountain environments.

Atmosphere doi: 10.3390/atmos17060530

Authors: Runying Wang Chao Wang Xiao Xiao

Using surface observations, ADTD lightning data, and radar reflectivity from April-September 2022–2024 in the Xi’an terminal area, this study classified severe convective events into four categories: ordinary thunderstorms, short-duration heavy precipitation, convective wind gust, and hail events. Their temporal variability, spatial distribution, life cycle characteristics, and propagation pathways were systematically analyzed. The results reveal significant differences among convective event types across multiple temporal and spatial scales. Convective wind gust events exhibited the strongest interannual variability, with a decrease of 44% from 2023 to 2024. Hail events occurred relatively infrequently, totaling only 16 cases from 2022 to 2024. Seasonally, convective wind gusts were concentrated in April-May, while ordinary thunderstorms and short-duration heavy precipitation events mainly occurred in July–August. Most events initiated during the afternoon and intensified toward evening, with short-duration heavy precipitation events showing a bimodal diurnal variation. Ordinary thunderstorms were dominated by short-lived events lasting 30–60 min, whereas heavy precipitation, convective wind gust, and hail events were primarily associated with long-lived convective systems exceeding 180 min. Spatially, severe convective weather generally initiated in the western part of the terminal area and propagated eastward. Lightning activity was more concentrated in the southeastern sector, indicating greater impacts on the SHX waypoint. Propagation paths were predominantly oriented toward the east-northeast.