Atmosphere doi: 10.3390/atmos17080800

Authors: Nan Yang Fei Qiu Dingqi Shi Yunjun Yao Lu Liu Jiahui Fan Qinghai Liu Jingya Qu Shengxiang Shi Siyuan He

Accurately estimating latent heat flux (LE) across water-limited grassland ecosystems is critically hampered by strong land-surface heterogeneity and pronounced intra-annual variability. Here, we proposed a ConvTransformer framework by integrating MODIS remote sensing products, China Meteorological Forcing Dataset (CMFD) data, and eddy covariance observations from six grassland sites to estimate daily LE across the Inner Mongolia grasslands. The model was evaluated using a leave-one-site-out cross-validation strategy and compared with three widely used machine learning models, including random forest (RF), gradient boosting regression trees (GBRT), and support vector regression (SVR). Across the six validation sites, the ConvTransformer achieved an average R2 of 0.69, an RMSE of 12.56 W m−2, a Bias of 0.67 W m−2, and an average KGE of 0.82. Although RF produced slightly higher R2 values at several individual sites, the ConvTransformer exhibited the highest overall KGE and the most stable performance, indicating superior cross-site generalization. Based on the trained model, a 1 km daily LE dataset for the Inner Mongolia grasslands during 2003–2018 was generated. The estimated LE revealed a distinct decreasing gradient from southeast to northwest and marked seasonality, with summer dominating the annual latent heat exchange. These results suggest that the ConvTransformer constitutes an effective framework for regional LE estimation, while also offering a valuable alternative for ecohydrological studies and regional water-resource assessment in water-limited grassland ecosystems.

Atmosphere doi: 10.3390/atmos17080799

Authors: Zhaosheng Wang Mei Huang

Quantifying the large-scale impact of combined ozone (O3) and nitrogen dioxide (NO2) pollution on terrestrial carbon sinks remains a major challenge. Here, we develop a parsimonious yet robust empirical framework that leverages high-resolution remote sensing datasets (CHAP O3/NO2 and MODIS NPP, 2008–2021) to characterize nonlinear threshold responses of terrestrial net primary productivity (NPP) across China’s diverse ecosystems. Our observational analysis identifies only associative temporal relationships between annual NPP variability and pollutant concentrations, with NPP positively correlated with O3 (Pearson’s r = 0.714, p < 0.01) and negatively correlated with NO2 (r = −0.599, p < 0.05). Notably, the ecosystem-specific threshold values (O3: 28,324–34,391 μg m−3 yr−1; NO2: 3646–4968 μg m−3 yr−1) are statistically derived from spatially aggregated pixel-level records across the full 14-year period, independent of the national annual time-series correlation analyses. Distinct from previous single-pollutant national evaluations, our study advances a novel analytical framework focusing on the interactive and combined impacts of O3 and NO2 co-exposure. The results demonstrate that NPP displays an increasing trend under low-level pollutant exposure but declines substantially once pollutant loads exceed the identified threshold ranges. Based on K-means clustering and segmented regression analyses, we estimate a national average NPP reduction of 17.4% per year (−0.68 Pg C yr−1), resulting in a cumulative carbon loss of −9.48 Pg C over the 14-year study period—equivalent to 2.45 years of China’s total terrestrial carbon uptake. Among all ecosystem types, forestlands experience the largest cumulative carbon loss (−4.22 Pg C), with prominent loss hotspots concentrated on the Tibetan Plateau and Northwest China. This refined national-scale assessment of dual-pollutant impacts provides observation-based evidence of substantial terrestrial carbon sink degradation, underscoring the necessity of combined air pollution mitigation strategies to sustain ecosystem stability and climate mitigation targets.

Atmosphere doi: 10.3390/atmos17080798

Authors: David Camarazo Aengus Ball Agnieszka Rorat Idriss Jairi Nathalie Pujol-Söhne Ludivine Canivet Hayfa Zgaya-Biau

Accurate air quality prediction is essential for environmental monitoring and public health protection. Among atmospheric pollutants, ground-level ozone remains particularly difficult to predict because of the complex and nonlinear interactions governing its formation. Although recent advances have achieved promising predictive performance using machine learning and deep learning, most existing approaches rely on black-box models whose explanations are provided only through post hoc explainability techniques. This work presents an alternative symbolic artificial intelligence framework based on association rule mining for intrinsically explainable ozone prediction. Hourly atmospheric observations collected from ground-level monitoring stations in the Hauts-de-France region (France) are preprocessed through cleaning, discretization, and class balancing before rule extraction. Two complementary symbolic AI approaches, Formal Concept Analysis (FCA) and a Genetic Algorithm (GA), are employed to automatically discover human-readable association rules linking meteorological and atmospheric variables to ozone concentration classes. The extracted rules provide transparent and directly interpretable decision mechanisms that can be readily validated by air quality experts. The experimental results show that both rule-mining approaches produce substantially more precise rule sets than decision trees, with average rule precisions of 0.76 for FCA and 0.79 for GA. Furthermore, the resulting rule-based classifier achieves an accuracy of approximately 0.79, outperforming the evaluated machine learning baselines while preserving intrinsic interpretability. These results demonstrate that symbolic AI constitutes a promising alternative for trustworthy air quality prediction and knowledge discovery.

Atmosphere doi: 10.3390/atmos17080797

Authors: Ronan Adler Tavella Fernando Rafael de Moura Alicia da Silva Bonifácio Rodrigo de Lima Brum Livia da Silva Freitas Juliana de Lima Rodrigues Elizabet Saes-Silva Rosália Garcia Neves Ronabson Cardoso Fernandes Ricardo Arend Machado Marla Rosana Pereira Melo Romina Buffarini Helotonio Carvalho Glauber Lopes Mariano Rodrigo Rodrigues Diana Francisca Adamatti Mariana Vieira Coronas Vera Maria Ferrão Vargas Gisela de Aragão Umbuzeiro Mariana Matera Veras Sandra de Souza Hacon Adriana Gioda Simone Andréa Pozza Edmilson Dias de Freitas Weeberb J. Requia Flavio Manoel Rodrigues da Silva Júnior

Brazil presents a distinctive convergence of continental-scale climatic diversity, extensive urbanization, large-scale biomass burning, rapid land-use change, persistent air-quality monitoring gaps, and deep social inequalities, producing highly heterogeneous and compound environmental health risks. In this context, treating air pollution and climate change as parallel environmental crises obscures their structural interconnections through shared emission sources, mutually reinforcing exposure pathways, and overlapping health and social consequences. In this narrative review, we critically synthesize scientific and institutional lines of evidence and argue that air pollution and climate risks can be more effectively addressed in Brazil through a single strategic agenda for science, public health, and governance. We first discuss why these challenges cannot be managed in isolation, emphasizing the effects of heat, drought, stagnation events, biomass burning, and extreme weather on pollutant formation, dispersion, and health burden. We then examine Brazil as a critical case where recent regulatory advances coexist with structural limitations in monitoring, data integration, and territorial coverage. Based on this diagnosis, we propose an integrated national agenda organized around five mutually reinforcing priorities: monitoring through hybrid networks; predictive science through climate-informed modeling and early warning; public health through the convergence of epidemiology, toxicology, and mechanistic research; equity-oriented research and action through the explicit incorporation of vulnerability, inequality, and climate justice; and policy appraisal through the assessment of disease burden, economic costs, mitigation co-benefits, and trade-offs. We further discuss the governance mechanisms needed to connect these priorities and translate evidence into coordinated action and adaptive public policies. We also argue that the Amazon should be approached not as an isolated ecological exception but as a central component of a broader Brazilian and Global South discussion on environmental health, land-use change, and climate justice. In this scenario, Brazil has the scientific capacity and regulatory momentum to become a reference in the integrated management of air pollution and climate risks, but this will depend on replacing fragmented approaches with a coordinated framework capable of linking exposure, mechanism, burden, inequality, and action.

Atmosphere doi: 10.3390/atmos17080796

Authors: Zhenqiang Xing

In the mining process of shallow-buried and close-distance coal seam groups in western China, the interconnected collapse fractures between the overlying goaf and the surface form large-area composite goafs, which aggravate surface air leakage and elevate oxygen levels within the goaf. This, in turn, leads to hazardous conditions such as CO over-limits and O2 deficiency at the working face’s return air corner, which seriously threatens the respiratory health of underground operators and the safe production of mines. Taking the 104 working face of a coal mine in Shenfu-Dongsheng Mining Area as the engineering background, this paper comprehensively adopts SF6 tracer gas test, fuzzy cluster analysis, and CFD numerical simulation methods to systematically study the distribution characteristics of three-dimensional air leakage channels in composite goafs and their influence mechanism on gas migration in goafs, and proposes a dynamic pressure equalization ventilation (PEV) regulation technology system. The research results show that a multi-dimensional three-dimensional air leakage channel of “surface-interlayer-own layer-roadway” exists in the research area, in which the surface fracture air leakage velocity is about 0.068 m/s, and the interlayer and internal goaf air leakage velocity is about 0.384 m/s. The atmospheric pressure difference between the working face and the surface is the main controlling factor inducing the O2 deficiency disaster of the working face. Every 100 Pa change in atmospheric pressure difference causes an O2 concentration fluctuation of about 0.30% at the return air corner, and the critical pressure difference for activating PEV is determined to be 300 Pa. Setting the PEV regulation point at the return air outlet of the working face and adopting the combined dynamic regulation system of fans and air windows can realize accurate pressure balance between the working face and the overlying composite goaf.

Atmosphere doi: 10.3390/atmos17080795

Authors: Vianni Giovanna Straccia Cepeda Elianny Bracho María B. Blanco Mariano Andrés Teruel

The degradation formed during the gas-phase reaction of amyl acetate, CH3COO(CH2)4CH3, initiated by chlorine atoms (●Cl), was investigated under atmospheric conditions using gas chromatography–mass spectrometry. The main products identified were acetic acid, formaldehyde, acetaldehyde, butyraldehyde, and propionaldehyde. Calibration curves were established for each identified product at different concentrations to enable their quantification by gas chromatography coupled with flame ionization detection. Product yields were subsequently determined from the calibration data, allowing a quantitative evaluation of the formation of the major oxidation products. The results obtained contribute to a better understanding of the atmospheric degradation pathways of amyl acetate and related ester compounds, providing useful information for assessing the atmospheric processing of ester-containing emissions, including those associated with biofuel applications.

Atmosphere doi: 10.3390/atmos17080794

Authors: Zhiyong Kong Xuelong Hu Yang Meng Haozhe Zhang Jie Wei Ziwei Wang Zhenghu Ge

Salt dust storms are a distinct and highly hazardous type of dust storm in arid and semi-arid regions. Salt crusts commonly develop on the surfaces of desiccated lake beds, and variations in their structure and properties directly influence dust release. To investigate how wetting–drying alternation affects the erodibility of sodium sulfate salt crusts with varying salt contents, four crust types with 0%, 1%, 3%, and 5% sodium sulfate were prepared under controlled laboratory conditions. A combination of wind-tunnel tests, direct shear tests, and surface morphology observations was employed to evaluate changes in mechanical properties and wind-erosion responses before and after wetting–drying treatment. The results showed that wetting–drying alternation induced pronounced cracking, salt crystallization, and the formation of a loose surface layer in salt-bearing crusts, with structural damage severity increasing with salt content. In contrast, the physical crust without added salt exhibited minimal surface deterioration. Direct shear tests revealed that after wetting–drying, the internal friction angle of salt-bearing crusts first decreased and then increased with salt content, while cohesion declined markedly; the 5% salt crust showed a 32.4% reduction in cohesion, indicating substantial structural degradation. Wind-tunnel tests further demonstrated that wind-erosion intensity increased significantly after wetting–drying treatment across all salt contents, with the largest relative increase observed in the 1% salt crust. Wind-erosion intensity also scaled approximately as a power function of salt content. These findings demonstrate that wetting–drying alternation is a critical trigger for the degradation of sodium sulfate salt crusts and for enhancing their erodibility. Post wetting–drying, salt crusts may evolve into highly erodible surfaces, becoming major potential sources of salt dust storms. This study provides a theoretical foundation for understanding salt dust release from desiccated lake beds and for improving early warning of ecological hazards in arid regions.

Atmosphere doi: 10.3390/atmos17080793

Authors: Despina Giannadaki Christina Oikonomou Nikolas Aristotelous Haris Haralambous

Global Navigation Satellite System (GNSS)-derived precipitable water vapor (PWV) has become an important source of atmospheric moisture information for severe weather monitoring. While previous studies have primarily relied on static GNSS-derived PWV metrics, comparatively little attention has been given to dynamic metrics capturing the temporal evolution of atmospheric moisture. This study evaluates the relationships of dynamic and static GNSS-derived PWV metrics with heavy rainfall characteristics in Cyprus using 30 events recorded between 2020 and 2026. GNSS-PWV observations from the CLOUDWATER network and collocated rainfall measurements from the Cyprus Department of Meteorology were analyzed. The pre-rainfall PWV growth rate (ΔPWV/Δt) and peak PWV were compared using correlation and regression analyses. All events exhibited a distinct increase in PWV before rainfall onset, with peak PWV typically occurring immediately before or shortly after precipitation began. The PWV growth rate showed a stronger relationship with peak rainfall intensity (R = 0.73) than peak PWV (R = 0.58) and remained the only significant predictor in multiple regression analysis. Neither metric was significantly related to total rainfall accumulation or rainfall timing. These findings show that the dynamic PWV metric provides a more informative characterization of heavy rainfall intensity than the static PWV metric alone.

Atmosphere doi: 10.3390/atmos17080792

Authors: Abdullah S. Alsubhi Tarek Sayad Khalafallah O. Kassem Eman F. El-Nobi

Fog is a major aviation hazard that reduces visibility and disrupts airport operations. This study presents the first integrated assessment of fog climatology, operational forecast evaluation, and meteorological controls at King Fahd International Airport, eastern Saudi Arabia, using METAR observations and Terminal Aerodrome Forecasts (TAFs) during 2001–2025. Fog variability, duration, intensity, and associated meteorological conditions were investigated, and TAF performance was evaluated against observed fog events. Fog occurred predominantly during autumn and winter, with the highest frequency in October, and reached a pronounced maximum near 0600 LT. Severe fog was the prevailing intensity type throughout most years. Although annual fog frequency exhibited slight decreasing trends, the annual longest fog duration increased marginally. TAFs successfully predicted only 49% of observed fog days during 2006–2025, highlighting the continuing difficulty of operational fog forecasting. Fog formation was favored by relatively high mean sea-level pressure (1010–1019 hPa), calm to low southerly winds (0–3 m/s), relative humidity of 87–100%, and dew point deficits of 0–2 °C, while wind speed exhibited the strongest statistical association with fog occurrence among the investigated meteorological variables. These findings provide a climatological reference and practical meteorological thresholds for improving aviation fog forecasting and airport operations in coastal arid environments.

Atmosphere doi: 10.3390/atmos17080791

Authors: Zhengyun Li Kui Chen Pengwu Zhao

Nitrogen dioxide (NO2) drives ozone and secondary aerosol formation and harms human health. Chongqing, a mountainous megacity of 32 million people, lacks a fine-scale satellite assessment of its NO2 evolution. We analyzed tropospheric NO2 vertical column density (VCD) over Chongqing for 2019–2024. The analysis used Sentinel-5P TROPOMI observations. We computed monthly, seasonal, and annual composites at 5.5 km resolution. Trends were quantified with the Theil–Sen slope and, at the pixel level, the Seasonal Mann–Kendall (SMK) test applied to the full 72-month series. A MODIS land-cover mask separated urban built-up from non-urban pixels. NO2 concentrated in the central districts and along the Yangtze valley. The core exceeded the mountainous counties by a factor of 3 to 4. TROPOMI resolved the Wanzhou and Yongchuan–Jiangjin hotspots as separate features. The record was divided into three phases. The 2020 lockdown produced the minimum, 31% below the prior February. Rebound emissions produced the 2021 maximum of 5.6 × 1015 molecules cm−2 under near-normal dispersion conditions, with ERA5 (the European Centre of Medium-range Weather Forecasts Reanalysis v.5) showing the January 2021 boundary layer 4.3% deeper than its climatological norm. Thereafter the regional mean stabilized: the area-weighted SMK slope was +0.042 × 1015 molecules cm−2 yr−1 and not significant (p = 0.14), because emission controls in the core and rising county emissions canceled in the average. Trends diverged sharply in space. The nine core districts declined (median urban Sen slope −0.11 × 1015 molecules cm−2 yr−1), whereas 41% of peripheral pixels rose significantly (p < 0.05). The urban-to-rural ratio narrowed from 3.0 in 2019 to 2.3 in 2024 (annual means). This convergence was robust to the built-up threshold (30–50%). Industrial relocation and county urbanization explain the peripheral rise. The results support extending vehicle and industrial emission standards from the core to the receiving counties.

Atmosphere doi: 10.3390/atmos17080790

Authors: Zhangsen Dong Xiao Li Ruixin Xu Shenbo Wang Fei Yu

Urban road transport policies must simultaneously address climate mitigation, local air quality, and the infrastructure requirements associated with vehicle electrification. However, these dimensions are rarely evaluated within a unified city-level framework. This study develops an integrated assessment framework that combines a bottom-up co-source inventory of CO2 and seven air pollutants, Long-range Energy Alternatives Planning (LEAP)-based scenario modeling, policy contribution analysis, elasticity-based co-benefit assessment, and electric vehicle charging demand estimation for Zhengzhou, China. In 2022, the road transport sector consumed 10,178 ktce of energy and emitted 27.8 Mt of CO2. Private cars contributed 66.7% of CO2 emissions, whereas heavy- and medium-duty trucks and light-duty trucks contributed 48.1% and 27.9% of NOx emissions, respectively, collectively accounting for 76.0% of the total. Under the existing policy scenario (EPS), CO2 emissions increase to 45 Mt in 2030 and 55 Mt in 2040. Under the dual carbon scenario (DCS), emissions peak at approximately 36 Mt in 2030 and decline to 32 Mt by 2040, representing reductions of 20% and 42% relative to the EPS, respectively. Electric vehicle promotion and green transport development contribute 42% and 32% of peak-year CO2 mitigation. Policy effectiveness differs across emission types. Electric vehicle promotion and green public transport are relatively more effective for CO2 mitigation, whereas old vehicle retirement, motorcycle phase-out, light-truck electrification, and tighter emission standards provide greater air pollutant reduction benefits. Supporting an electric vehicle stock of approximately 1.22 million in 2030 would require about 610,000 charging piles at a vehicle-to-charger ratio of 2:1. The principal contribution of this study is to demonstrate how complementary vehicle technology, transport structure, emission control, power sector, and infrastructure policies can be combined to support city-level carbon peaking and air pollution co-control.

Atmosphere doi: 10.3390/atmos17080789

Authors: Haoran Huo Qiang Liu Yunshan Zhang Zhaoyang Li Xiaozhen Yan Ran Liu Xiaodi Liu

Over the past decade, stringent emission control policies have been implemented in the North China Plain, fundamentally altering regional air pollution profiles. This study investigates the long-term temporal evolution (2015–2025) and chemical reconstruction of air pollutants in Jinan, China. Benefiting from rigorous emission controls, annual median concentrations of PM2.5, SO2, and CO decreased significantly by 64%, 81%, and 54%, respectively. Conversely, NO2 exhibited a slower decline (47%), and the maximum 8-h daily average (MDA8) ozone (O3) increased by 23%. Generalized additive model (GAM) analysis identified solar radiation, temperature, relative humidity, and NO2 as the primary factors strongly associated with O3 variations. The amplified atmospheric oxidation capacity driven by O3 has triggered a profound chemical reconstruction of secondary inorganic aerosols (i.e., SO42−, NO3−, and NH4+), with a significant increase in sulfur and nitrogen oxidation ratios (SOR and NOR), which may continue to intensify the combined pollution trend of ozone and PM2.5. These findings emphasize that coordinated reductions in nitrogen oxides and volatile organic compounds must be achieved in future air quality management while also taking into account the promoting effect of climate warming on ozone generation.

Atmosphere doi: 10.3390/atmos17080788

Authors: Chao Liu Qiuli Zhang Yiyuan Wei Chongxiang Zhang Haojun Chen Dewang Wang

Sea fog that lowers horizontal visibility below 1 km is a recurrent hazard to port operations and near-shore navigation, yet its objective, continuous short-range warning remains difficult. Using a microwave radiometer and a 35 GHz millimeter-wave cloud radar co-located at the Xiaoyangshan station near the Yangshan deep-water port, eastern China, supervised by a continuous minute-resolution visibility ground truth (about 0.65 million records, 2025–2026), we develop a calibrated probabilistic sea-fog nowcasting model for lead times of 0–3 h. Under strict date-grouped cross-validation—in which neither the input window nor any forecast label crosses a fold boundary—and at the operationally realistic fog base rate of ∼1.9%, the model attains a fog-state ROC-AUC of 0.95–0.96 across lead times and an onset-AUC of about 0.94 at the 3 h lead. After out-of-group isotonic calibration the output probabilities are reliable (expected calibration error 0.007), and a single-stage alarm reaches an event hit rate of 0.84 over the 2026 hold-out period—a figure that is unchanged under a strictly out-of-time protocol in which the model, the calibrator, and the alarm parameters are all frozen on data through 2025—with a median first alert about 3.1 h before fog onset. A compact near-surface scalar model already saturates discrimination; adding vertical profiles and radar microphysics through a mask-aware fusion network yields only a small, statistically non-significant gain, indicating that independent fog events, not model capacity, limit further improvement. The scheme is lightweight, locally deployable, and can be re-evaluated as observations accumulate.

Atmosphere doi: 10.3390/atmos17080787

Authors: Zhiyin Zou Zhe Wang Xueshun Chen Xu Zhou Wending Wang Huansheng Chen Zijian Jiang Zifa Wang

Accurately simulating the spatiotemporal distribution of global atmospheric CO2 remains challenging yet essential for reducing uncertainties in carbon source-sink inversions, quantifying the climate effects of heterogeneous CO2 fields, and supporting the development of CO2 observation networks. In this study, we simulated global atmospheric CO2 concentrations (2010–2019) at a horizontal spatial resolution of 1° × 1° using the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM) without data assimilation, with initial fields and flux data from the CarbonTracker CT2022 (CT2022) reanalysis product. The simulations were comprehensively evaluated against CT2022 and observations from ground-based (NOAA GML), airborne (ObsPack), and satellite (OCO-2) platforms. The results indicate that across all evaluated surface stations, CT2022 exhibits poorer overall statistical performance (R = 0.69, RMSE = 5.37 ppm, MB = 2.05 ppm) primarily due to noticeable overestimations at unassimilated ground stations, while IAP-AACM maintains robust performance across the surface network (R = 0.84, RMSE = 2.62 ppm, MB = 0.28 ppm). Vertically, airborne observations across eight global campaigns confirm that IAP-AACM accurately reproduces the vertical distribution of CO2, maintaining strong correlations (R = 0.72–1.00) and performance comparable to the CT2022 reanalysis (R = 0.86–1.00). In terms of total column CO2 concentrations (XCO2), IAP-AACM exhibits strong agreement with satellite retrievals annually (R = 0.97, RMSE = 1.08 ppm, MB = 0.26 ppm), with seasonal metrics remaining consistently robust across all four seasons (R = 0.96–0.97, RMSE = 0.98–1.20 ppm, MB = 0.13–0.37 ppm), demonstrating large-scale transport fidelity on par with the CT2022 reanalysis. Finally, across representative ObsPack land sites, unassimilated IAP-AACM achieves a high median correlation (R = 0.97), low error (RMSE = 2.01 ppm), and low mean bias (MB = −0.45 ppm), closely approaching the assimilated CT2022 reanalysis product (R = 0.98, RMSE = 1.40 ppm, MB = −0.07 ppm). Further analysis indicates that the optimized IAP-AACM exhibits robust performance under stable boundary layer conditions, where the revised diffusion scheme produces higher vertical diffusion coefficients that help mitigate excessive near-surface CO2 accumulation during nighttime. Overall, the optimized IAP-AACM effectively simulates the spatiotemporal distribution of global atmospheric CO2, serving as a reliable tool to support advanced research.

Atmosphere doi: 10.3390/atmos17080786

Authors: JaeWon Choi Sang-Pil Yoon Kyong-Hwan Seo

This study investigates the relationship between ground heat flux (GHF) over the Tibetan Plateau and heatwave days in Korea. A weak negative correlation was found during the pre-1998 period (1979 to 1997), whereas a strong positive correlation emerged during the post period (1998 to 2019). To further examine the mechanisms underlying this interdecadal change, seven positive and seven negative GHF years were selected for both the pre-1998 and post-1998 periods and composite analyses were conducted. During positive GHF years in the post-1998 period, the Tibetan Plateau experienced enhanced surface heating, reduced spring snow cover, and lower summer soil moisture. These conditions favored greater absorption and release of heat from the land surface. Concurrently, anomalous anticyclonic circulation strengthened throughout the troposphere, accompanied by enhanced subsidence over the latitude band containing the Korean Peninsula. In addition, both the western North Pacific high and the South Asian high expanded and intensified during positive GHF years, exerting a stronger influence on the middle and upper troposphere over Korea. The increased occurrence of heatwaves in Korea during the post-1998 period is partly attributable to enhanced thermal forcing over the Tibetan Plateau and the associated development of a circumglobal teleconnection-like wave train, which induced downstream anticyclonic circulation anomalies over East Asia. This upper-tropospheric forcing, together with a La Niña–like or negative Pacific Decadal Oscillation–like sea surface temperature pattern, created atmospheric conditions favorable for more frequent heatwave events over Korea. These results suggest that the influence of Tibetan Plateau thermal forcing on Korean heatwaves has strengthened since the late 1990s through its interaction with large-scale atmospheric circulation and oceanic background conditions.

Atmosphere doi: 10.3390/atmos17080785

Authors: Barouch Giechaskiel Anastasios Melas Amos Freri

Tyre wear is a major contributor to microplastic pollution. Heavy-duty vehicles, due to their higher weight and longer distances driven compared to passenger cars and vans, have the highest share of tyre wear from road transport. Heavy-duty vehicles cover a wide range of applications—from urban stop-and-go buses to long-distance lorries—and their maximum mass can vary from around 3.5 tonnes to more than 40 tonnes. However, experimental wear data on heavy-duty vehicles, and particularly on buses, for both original and retreaded tyres are scarce. Additionally, there is no standardised methodology for measuring their tyre wear. In this study, we measured the tread wear of regional buses operating around the city of Varese (Northern Italy) between 2019 and 2025. The tread depth reduction was 1.7–1.9 mm per 10,000 km for 22.5-inch tyres and 2.6–2.9 mm per 10,000 km for 19.5-inch tyres. Based on the annual distance driven (40,000 km for 22.5-inch tyres and 25,000 km for 19.5-inch tyres) and the tread characteristics, we estimated a mass loss of 550–800 mg/km per vehicle. These are some of the first wear emissions in the literature for new and retreaded tyres at both drive and steering axles.

Atmosphere doi: 10.3390/atmos17080784

Authors: Ruiqing Chen Yanli Wang Ming Yang Chanjuan Sun

To reveal the chemical composition and concentration distribution, spatial distribution characteristics, source composition, and health impacts of volatile organic compounds (VOCs) in the two multi-industry industrial parks in Tianjin, 57 VOC species were determined by using SUMMA canister sampling with preconcentration gas chromatography/mass spectrometry. Based on these measurements, the Positive Matrix Factorization (PMF) model was used for source analysis to achieve quantitative identification and contribution analysis of different source factors. The health risks of VOC concentrations were analyzed based on the assessment framework recommended by the United States Environmental Protection Agency (USEPA). The results showed that the average concentrations of TVOCs in A-1 (Industrial Park and Cluster A, 5 m), A-2 (Industrial Park and Cluster A, 10 m), B-1 (Industrial Park and Cluster B, 5 m), and B-2 (Industrial Park and Cluster B, 10 m) were 59.72 ppbv, 45.35 ppbv, 32.44 ppbv, and 21.65 ppbv, respectively. TVOC concentrations were higher at A-1 and B-1 than at A-2 and B-2, and were generally higher at site A than at site B. The VOC components were mainly alkanes (53.5%–71.4%), followed by aromatic hydrocarbons (15.6%–36.2%), alkenes (4.6%–9.9%), and alkynes (3.1%–7.6%). The proportion of aromatic hydrocarbons in A was higher (22.13% and 36.22%), while alkanes dominated absolutely in B (71.4% and 66.9%). n-Hexane was the key species driving the spatial differences (a typical source of VOCs in solvent usage). The concentration in A-1 was 3.1 times that of A-2, and B-1 was 3.3 times that of B-2. It was mainly controlled by local solvent unorganized emissions. The differences in species between the two points at site A for toluene, xylene, etc., were relatively gentle, while at site B, the concentration of the same aromatic hydrocarbons at B-1 was significantly higher than that at B-2, presenting a clearer spatial characteristic, indicating differences in the spatial distribution of organic solvent-related industrial activities in different sites. The source analysis showed that A-1 was dominated by solvent usage (38.64%) and natural gas/LPG sources (36.96%); A-2 by vehicle exhaust (40.20%) and natural gas/LPG sources (36.15%); B-1 by cleaning-agent usage (38.81%) and fuel combustion (30.33%); and B-2 by plastic and rubber production (36.90%) and fuel combustion (31.84%). The health risk assessment showed that benzene, n-hexane, and xylene dominated the non-carcinogenic risks, but the overall non-carcinogenic (HI < 1) and carcinogenic (CR < 1 × 10−6) risks were both below the threshold. This study reveals inter-site differences in VOC concentrations, compositions, and source contributions across two mixed industrial parks, providing a basis for site-specific VOC control priorities, source-targeted monitoring strategies, and risk management in mixed industrial areas.

Atmosphere doi: 10.3390/atmos17080783

Authors: Jianfeng Yao Lili Kang Kanghui Han Zhibin Tu

In order to improve the accuracy of near-ground temperature forecasting under winter rain, snow, and freezing weather conditions, three machine learning algorithms, namely neural network, random forest, and support vector machine, were used to train various ground and air elements in the rapidly updated assimilation model of Zhejiang Province based on multi-source observation data, reducing the error of temperature in the model field and forming hourly and 3 km horizontal resolution ground and air temperature datasets for two rainy, snowy, and frozen weather processes. After calibration using the backpropagation neural network algorithm, random forest algorithm, and support vector machine algorithm, the MAE of the simulated field temperature forecast decreased from 1.29 °C to 0.937 °C, 1.01 °C, and 0.988 °C, respectively. The backpropagation neural networks and support vector machine algorithms perform well, but support vector machine algorithms have relatively short computation times. Using 10 feature points for training achieves optimal performance; more points may not necessarily lead to better calibration results. Adding actual data at the initial time of the target point significantly improved the correction effect, and the improvement effect was even better when the forecast lead time was less than 10. The correction effect of the prediction field shows that when the forecast lead time is between 15 h and 24 h, it becomes unstable over time. The mean prediction accuracy of whether the temperature exceeds the 0 °C temperature threshold at 24 forecast moments before calibration is 0.928. After correction, it has been increased to 0.956.

Atmosphere doi: 10.3390/atmos17080782

Authors: Giuseppe Caso Fabio Marzaioli Mauro Rubino Miguel A. Hernández-Ceballos Francesca Barone Enikő Papp Zsófia Kertész Anikó Angyal

Atmospheric PM10 was investigated across Campania, southern Italy, during August 2024 to assess its elemental composition and probable sources. In total, 132 daily samples were collected at six ARPAC sites representing harbor, traffic, industrial, school, and regional-background conditions. PM10 concentrations ranged from 2 to 72 µg m−3, with the highest and lowest values recorded at the traffic and background sites, respectively. Elemental composition was determined by particle-induced X-ray emission and complemented by SEM–EDS. Elemental-based Positive Matrix Factorization (PMF) resolved six profiles, tentatively assigned to S-rich secondary aerosol, Cl-rich marine aerosol, mixed combustion/industrial emissions, Cu-rich traffic emissions, Ca–Sr-rich road dust, and Pb–Zn-rich waste combustion. At the industrial site, the three anthropogenic profiles together represented 74% of the apportioned mass. Traffic-related, marine, and S-rich secondary aerosol represented 49%, 65%, and 30% at the traffic, harbor, and background sites, respectively. SEM–EDS identified representative irregular S–K-rich and Ca-rich particles, crystalline Na–Cl-rich particles, and fine spherical metal-rich particles. Conditional probability function and trajectory analyses indicated local and regional influences, including a possible secondary sulfate contribution from the Mount Etna region. As the PMF analysis relied exclusively on elemental data, these source assignments should be regarded as indicative rather than definitive.

Atmosphere doi: 10.3390/atmos17080781

Authors: Yucheng Liu Ran Huo Bowen Zhu Lele Deng

Soil moisture is a key indicator of agricultural and hydrological drought, but its meteorological controls vary across temporal scales. Using ESA CCI soil moisture products from 2010 to 2022, this study investigated soil moisture variability in the Yangtze River Basin at weekly, monthly, and annual scales. The product was first validated using ground observations and ERA5 reanalysis data, and a generalized additive model (GAM) was then applied to quantify the relative contributions of precipitation, temperature, wind speed, and VPD under normal conditions and during three extreme drought events. The validation showed that ESA CCI soil moisture captured basin-scale variations well, with mean absolute deviations of 0.0460, 0.0434, and 0.0072 m3/m3 in the upper, middle, and lower reaches, respectively, and a basin-wide RMSE of 0.0127 m3/m3 against ERA5. The attribution results revealed a clear scale-dependent shift in soil moisture controls. At the weekly scale, VPD dominated soil moisture variability, with a basin-wide average contribution of 47.04% and a maximum contribution of 55.92% in the lower reaches, indicating that short-term soil drying is mainly driven by VPD. At the monthly scale, precipitation became the primary control, with a basin-wide average contribution of 36.62% and a maximum contribution of 44.65% in the upper reaches, reflecting the role of accumulated rainfall recharge in maintaining soil moisture storage. During extreme drought events, the monthly-scale dominance of precipitation weakened, and precipitation, VPD, temperature, and wind speed each contributed approximately 20–30%, suggesting that drought development results from the combined effects of reduced water input and enhanced atmospheric water loss. These findings indicate that precipitation-based drought monitoring may underestimate rapid soil drying risks, whereas incorporating atmospheric demand indicators such as VPD can improve drought early warning and water resource management under a warming climate.

Atmosphere doi: 10.3390/atmos17080780

Authors: Andrei Tudor Rusu Simona Elena Avram Tiberiu Rusu

Urban air pollution remains a major public health concern, with road traffic representing one of the dominant sources of particulate matter and volatile organic compounds in growing cities. This study evaluates the level of chemical air pollution in Alba-Iulia municipality (Romania) through two measurement campaigns (May 2025 and October 2025), carried out at 26 and 19 points, respectively, selected based on traffic intensity and population vulnerability criteria (schools, kindergartens, hospitals, and the food market). Measurements targeted PM2.5, PM10, total volatile organic compounds (TVOC) and formaldehyde (HCHO), as well as carbon dioxide (CO2), correlated with road traffic intensity. Statistical analysis revealed significant differences between the two seasons for PM2.5, PM10, and CO2 (p < 0.01), as well as a strong correlation between particulate matter concentrations and vehicle counts (r = 0.60–0.95), consistent with road traffic being an important local contributor to particulate matter, though correlation alone cannot establish source dominance in a strict causal sense. A multiple regression controlling for both traffic and season explained over 73% of the variance in PM2.5 and PM10 and showed that traffic and season each contribute independently to particulate levels. Both mean PM2.5 and PM10 exceeded WHO and EU limit values in both campaigns, and the low-cost sensor over-read absolute concentrations by roughly 2–4× relative to the official monitoring network, so absolute values should be treated as orientative. Despite this bias, the consistent spatial and seasonal patterns show that portable low-cost sensors can reliably rank exposure hotspots and support the targeting of traffic-mitigation measures, such as selective catalytic reduction (SCR) systems, in medium-sized cities that lack dense reference monitoring networks.

Atmosphere doi: 10.3390/atmos17080779

Authors: Suhrudh Chivukula Adrian J. Cortes Santos Ruben Delgado Dimuthu K. Arachchige Jordan A. Caraballo-Vega Mariel D. Friberg

Accurately predicting fine particulate matter (PM2.5) concentrations in regions with sparse monitoring networks remains a critical challenge for air quality management and public health. This study evaluates a machine learning (ML) data fusion approach that integrates daily federal regulatory observations, daily low-cost community sensor measurements, and monthly satellite-derived aerosol products (functioning as a regional background field) to improve PM2.5 prediction across under-monitored environments. Using a Long Short-Term Memory (LSTM) neural network architecture, the analysis examines how combining heterogeneous data sources influences predictions. Results show that pooled multi-source training was associated with higher holdout skill relative to some single-source configurations under this parsimonious baseline, though associations are city- and configuration-dependent and cannot be attributed solely to fusion because evaluation populations are not common. Comparisons against tree-based baselines (Random Forest, Gradient Boosting, XGBoost) indicate that overall predictive skill, not just the LSTM’s, is constrained by data availability, suggesting that data composition, rather than model choice, is the primary driver of the observed performance patterns. These findings highlight both the potential and the practical constraints of multi-source ML approaches for air quality prediction and exposure assessment, with implications for model design, monitoring strategy, and environmental equity. This study is intentionally scoped as an applied evaluation of data fusion performance rather than a comprehensive assessment of algorithmic optimality or operational forecasting readiness. The analysis focuses on daily PM2.5 prediction across a selected set of U.S. cities and does not address sub-daily variability, real-time deployment constraints, or event-specific model optimization. Model performance is therefore interpreted in the context of data availability, consistency, and representativeness, rather than as an upper bound on achievable predictive skill.

Atmosphere doi: 10.3390/atmos17080778

Authors: Xiaodie Pang Wei Song Jianqiang Zeng Zhuoyue Ren Xiao Tian Hongcheng Lu Xinming Wang

Oxygenated intermediate-volatility organic compounds (OIVOCs) represent an important but poorly constrained class of secondary organic aerosol (SOA) precursors due to analytical challenges in their molecular-level characterization. In this study, we conducted a systematic comparison of a non-polar gas chromatography column (HP-5MS) and a polar column (DB-Heavy WAX) for the measurement of atmospheric OIVOCs using thermal desorption–gas chromatography–mass spectrometry (TD–GC/MS). A set of 48 representative oxygenated standards together with urban ambient air samples was analyzed to evaluate chromatographic separation, compound identification, and quantitative responses. The developed analytical method demonstrated excellent linearity (R2 > 0.90) and precision, with relative standard deviations (RSDs) typically below 10%. The polar WAX column provided improved chromatographic resolution and peak shapes for 39 oxygenated standards (e.g., ether alcohols, esters, diesters, and nitrogen-containing compounds) driven by strong intermolecular hydrogen bonding and dipole interactions, whereas the HP-5MS column showed stronger selectivity toward non-polar species such as long-chain siloxanes and branched alkanes. Application to urban ambient samples revealed that the WAX column increased the number of identifiable OIVOC species by 14.6% relative to the HP-5MS column. Furthermore, total OIVOC concentrations derived from the WAX column were 7% higher than those obtained using the HP-5MS column, while OIVOC-to-alkane ratios were 2–4 times greater. These differences demonstrate that chromatographic selectivity can influence the characterization of oxygenated compounds within the IVOC volatility range. The potential implications of these analytical differences were further illustrated through secondary organic aerosol formation potential (SOAFP) estimation, although the limited availability of experimental SOA yield data remains a major uncertainty. Overall, this study demonstrates that appropriate chromatographic method selection is essential for reducing analytical uncertainty and improving the molecular characterization of OIVOCs in complex environmental samples.

Atmosphere doi: 10.3390/atmos17080777

Authors: Chutima Kranrod Chanis Rattanapongs Rattanawadee Tipkanya Nittaya Klakhaeng Nattharitha Sutthiprapa Supansa Khumkham Phachirarat Sola Shinji Tokonami

Hin Dad hot spring is a prominent natural geothermal facility in Thailand, widely frequented by domestic and international tourists seeking hydrotherapy. According to UNSCEAR, radon (222Rn), thoron (220Rn), and their short-lived decay products represent the primary sources of natural ionizing radiation exposure to humans. Geothermal waters inherently carry elevated levels of these radionuclides, potentially exposing visitors and facility staff to radiological hazards. This study evaluated 222Rn concentrations in water samples alongside ambient air concentrations of 222Rn, 220Rn, and their respective progeny within the Hin Dad hot spring area. The average 222Rn levels in water across six sampling sites ranged from 3 ± 1 to 24 ± 1 Bq L−1, remaining well below the World Health Organization (WHO) screening level for 222Rn in water (100 Bq L−1). Ambient air monitoring was conducted using both active and passive techniques. Active measurements via an AlphaGuard detector at three designated points yielded average 222Rn and 220Rn concentrations ranging from 12 to 41 Bq m−3 and 23 to 38 Bq m−3, respectively. As expected from the partitioning behavior of radon between water and air, atmospheric concentrations were markedly lower than the corresponding aqueous 222Rn concentrations. Furthermore, long-term passive monitoring over 103 days using RADUET detectors revealed maximum time-integrated average concentrations of 66 ± 1 Bq m−3 for 222Rn and 47 ± 4 Bq m−3 for 220Rn. All atmospheric concentrations complied with the reference levels established by both the WHO and the International Commission on Radiological Protection (ICRP) (100–300 Bq m−3); as this range is defined for indoor environments, it is applied here only as a conservative benchmark for the open and semi-outdoor monitoring stations. Regarding progeny exposure, the maximum Equilibrium Equivalent 222Rn Concentration (EERC) and Equilibrium Equivalent 220Rn Concentration (EETC) were determined to be 21.0 ± 2.5 Bq m−3 and 2.7 ± 0.3 Bq m−3, respectively. The measured concentrations and estimated doses indicate a low radiological concern under the conditions investigated, though the assessment is based on two seasonal sampling rounds and does not include personal dosimetry or wet-season monitoring.

Atmosphere doi: 10.3390/atmos17080776

Authors: Ponni Maya José A. Álvarez Antolínez Kai Parker Laura Cagigal Andrei V. Metrikine

This study presents a multivariate spatio-temporal clustering framework to characterise joint wind–wave regimes across European seas using the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5; 1979–2014). Seasonal and annual statistics of significant wave height, mean wave period, wind speed, and wave/wind direction were computed at 0.5° resolution. Principal component analysis was used to reduce dimensionality, retaining 30 components that captured 99% of the variance. K-means clustering was then used to identify nine coherent dynamical regimes with persistent spatio-temporal signatures. These regimes were grouped into open-ocean, transitional, and enclosed/semi-enclosed categories based on internal variability, directional spread, and geographic exposure. Open-Atlantic regimes are found to be energy-rich, exhibiting clear December–February maxima in significant wave height (Hs), mean wave period (T02), and 10 m wind speed (Ws10); enclosed and semi-enclosed basins show lower amplitudes and reduced variability, while transitional shelves and the southern Mediterranean display intermediate conditions, characterised by moderate T02 levels and seasonal rotation of wave and wind directions, reflecting a mixed influence of locally generated seas and remotely forced swell. Dispersion analysis highlights a clear Atlantic–Mediterranean partition, with transitional shelves forming a dynamical bridge between open-ocean and enclosed basins. Teleconnection analysis shows that the North Atlantic Oscillation and Arctic Oscillation dominate Atlantic regimes, while the Scandinavia, East Atlantic, and Polar/Eurasia patterns modulate variability and directional persistence in transitional and enclosed seas. The classification defines a climatological framework of European wind–wave conditions and establishes a practical basis for renewable energy assessment, engineering design, and long-term change analysis, with methods transferable to other basins.

Atmosphere doi: 10.3390/atmos17080775

Authors: Lorena Bermejo Santos Emma Gaitán Fernández F. J. Montalván Marisela Uzcategui-Salazar Alice Kimie Martins Morita F. Carreño

Climate change is altering global temperature and precipitation patterns, with particularly strong effects expected in Mediterranean regions, where reduced groundwater recharge and increased evapotranspiration may affect groundwater-dependent ecosystems. This study provides a preliminary, indicator-based assessment of the potential sensitivity of the Cabecera del Bornova Groundwater Body (Guadalajara, Spain), which sustains the Somolinos karst wetland, under natural conditions and protected as a Natural Groundwater Reserve and Natural Lacustrine Reserve. Empirical correlations were established between accumulated deviations of historical precipitation and observed piezometric levels in two monitoring piezometers using second-degree polynomial functions. The most informative relationships were obtained for piezometer ZE01, particularly at the daily scale, whereas the second piezometer showed weaker relationships. These functions were applied to regionalized climate projections generated with the FICLIMA methodology from ten CMIP6 models under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios from the IPCC Sixth Assessment Report. The results indicate a general decreasing tendency in empirical piezometric-level indicators throughout the 21st century, although the magnitude of the response is highly sensitive to the selected rainfall station, temporal resolution, climate model and scenario. Extreme projected declines are interpreted as extrapolation-sensitive outputs rather than deterministic predictions of aquifer drawdown or groundwater-reserve depletion. Direct impacts on lagoon level, spring discharge or wetland extent cannot be quantified with the dataset. The results highlight the need to expand piezometric monitoring, instrument the Manadero del Bornova spring, monitor lagoon water levels and develop physically based recharge and groundwater-flow models.

Atmosphere doi: 10.3390/atmos17080774

Authors: Shengwen Chen Yi Zhang Haoyao Gui Chuncheng Yu Xinke Wang

Coal dust carried by open-top freight trains can undergo complex transport and redistribution in confined railway tunnels, where train-induced airflow links in-tunnel particle motion to near-portal dispersion. However, how particle size and source position jointly influence transport across the train–tunnel–portal system remains insufficiently understood. A three-dimensional transient CFD–DPM model was developed for an open-top coal train traveling at 80 km/h through a 200 m local tunnel section and adjoining portal air domains. Four controlled cases combined two prescribed particle sources—a coal-surface source and a near-ground source—with representative diameters of 10 and 350 μm. In the simulated cases, the maximum air speed over the exposed coal surface increased from approximately 24 to 39 m/s during tunnel entry. The 350 μm particles exhibited stronger inertial settling and preferential migration toward the lower tunnel, whereas the 10 μm particles were more strongly coupled to the airflow and transported toward the portal by the train wake. Under the same prescribed source strength, the near-ground-source cases produced higher source-normalized concentration responses than the coal-surface-source cases, indicating a stronger suspended-transport response for particles introduced near the tunnel floor. In the 10 μm near-ground-source case, fine particles passed through the outlet portal and formed a transient elevated plume that spread downstream and laterally. Within the prescribed-input cases examined here, the simulations illustrate the joint influence of particle size and source position on cross-region coal-dust transport and organize the transport pathways into four particle-transport regions: the coal-surface, lower-tunnel, train-wake, and near-portal regions.

Atmosphere doi: 10.3390/atmos17080773

Authors: Chuancheng Zhao Shuxia Yao Tongyang Dao Jiaxin Zhou

Based on daily precipitation observations from six national meteorological stations in the Zhangye region from 1960 to 2023, this study examines the spatiotemporal evolution and possible driving factors of extreme precipitation using four ETCCDI-recommended indices (SDII, R10mm, R95p, and RX1day). Trends were evaluated using linear regression and the Mann–Kendall test, with Sen’s slope estimation. The results reveal significant (p < 0.05) increasing trends in both the frequency and intensity of extreme precipitation, with a shift from low-intensity, low-frequency to high-intensity, high-variability modes. Temporally, all indices exhibited a step-like surge around 2000, entering a period of high-level oscillation, with extreme characteristics amplified during strong El Niño years. Spatially, a distinct “higher in the south, lower in the north” pattern prevails among the six stations, with the southern Qilian Mountains being the primary contributor and the central plains showing a bimodal distribution, reflecting joint modulation by westerly troughs and local strong convection. The intensification is linked to enhanced atmospheric water vapor, northward penetration of the East Asian summer monsoon, and topographically forced lifting. While alleviating drought stress, this trend substantially elevates the risk of flash floods and debris flows in mountainous areas. It should be noted that the spatial patterns are derived from six stations and should be interpreted as inter-station comparisons rather than continuous spatial fields.

Atmosphere doi: 10.3390/atmos17080772

Authors: Rui Zhu Haiku Zhang Chuankai He Zhiding Wu Jun Dai Bin Chen Qian Li Lei Bai

This study quantifies same-grid-cell, same-date co-occurrence of low precipitation and low operational wind-power density (WPD) in Southwest China during 1979–2018. Daily precipitation and WRF-derived 10 m wind fields were analyzed on a 271 × 271 grid with 10-, 15-, 20-, and 30-day rolling windows. At each grid cell, rolling precipitation totals and rolling means of the daily operational-WPD index were ranked against a fixed seasonal distribution. Low conditions were defined by u≤0.1587, with sensitivity analyses at u≤0.10 and u≤0.20. Low precipitation represented a sub-monthly meteorological-drought condition. Domain compound frequency declined from 2.207% at 10 days to 1.704% at 30 days. Warm-season frequency was highest at every window, ranging from 2.624% to 2.028%. R3 had the highest compound frequency at the central threshold, whereas R5 had the lowest. Under the 1% regional active-area rule, median compound runs lasted three to five days. Spatial-block intervals changed with block size, so the 40-year record does not support a robust domain-wide trend conclusion. The results provide a 1979–2018 climatology of atmospheric precipitation–WPD co-occurrence.

Atmosphere doi: 10.3390/atmos17080771

Authors: Fan Zhang Siyuan Liu Xiyan Sun Yuanfa Ji Lu Zhang

Rainfall-induced landslides result from interactions between terrain predisposition and hydrometeorological forcing. In event-scale susceptibility modelling, uncertainty often arises from non-landslide controls. Locations without recorded failures may differ in rainfall history, storm exposure, or inventory completeness, which can bias models trained on static absence samples. This study develops a hydrometeorological control sampling strategy for rainfall induced landslide susceptibility modelling. The strategy defines each sample by grid cell and rainfall date, and constructs non landslide controls from storm related risk sets. A background predisposition prior is used to screen candidate controls. Regional same date controls, annular hard controls near failed slopes, and cross year rainy season background controls are then integrated to represent complementary hydrometeorological and terrain conditions. Design weights and density ratio calibration are applied to account for control reliability and reduce distribution mismatch between the training sample and the mapping domain. In the sample-level evaluation, the method was evaluated in Pubei County, Guangxi, China, using terrain, geology, land cover, daily and antecedent rainfall, and surface wetness. It outperformed Buffer, LowSlope, and IV Low across five classifiers. Relative to IV Low, mean AUC increased from 0.887 to 0.958, accuracy from 81.8% to 91.6%, and Kappa from 63.6% to 83.3%. Holdout validation of two July 2006 landslide clusters also showed greater concentration in top-ranked areas. Averaged over RF and GBDT, top 10% capture rose from 0.227 to 0.322, while the frequency ratio increased from 2.264 to 3.213. These findings suggest that, under the evaluated conditions, the strategy improves sample discrimination, increases landslide concentration in areas ranked as highly susceptible, and reduces uncertainty in the selection of nonlandslide controls.

Atmosphere doi: 10.3390/atmos17080770

Authors: Rong Zhao Peng Kang Jin Wang Changle Hu Wei Zhao Jian Zhang Leigang Tao

Methane is the second most important anthropogenic greenhouse gas after carbon dioxide, and its accurate measurement is essential for climate monitoring. In this work, a laser-locked cavity ring-down spectroscopy (LL-CRDS) system was developed for continuous atmospheric methane measurements. By locking the laser frequency to a longitudinal mode of the optical cavity, long-term wavelength stability was achieved, enabling highly stable methane measurements. Laboratory tests demonstrated a precision of 0.4 ppb at an integration time of 3.6 s, a minimum Allan deviation of 0.3 ppb at 15 s, and long-term stability better than 23 ppb over one month without recalibration. Field measurements with a commercial CRDS analyzer showed excellent agreement, with an average deviation of 1.4 ppb. These results demonstrate the suitability of LL-CRDS for long-term atmospheric methane monitoring requiring high precision and low maintenance.

Atmosphere doi: 10.3390/atmos17080769

Authors: José Villanueva-Díaz Arian Correa-Díaz Citlalli Cabral-Alemán José Manuel Zúñiga-Vásquez Jesús Valentin Gutiérrez-García David W. Stahle Matthew D. Therrell Aldo Rafael Martínez-Sifuentes

Assessing long-term hydroclimatic variability in central Mexico is essential to understand regional water availability and groundwater recharge for urban centers such as Querétaro. This study developed a multi-century winter–spring precipitation reconstruction for the Sierra Gorda Biosphere Reserve (SGBR) using ring width chronologies of Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco. Standard dendrochronological techniques were applied to develop a 284-year master chronology (1731–2015). Following the accepted Subsample Signal Strength criterion (SSS ≥ 0.85) for chronology reliability, the reconstruction was restricted to the 1744–2015 period, yielding a statistically robust 271-year December–April precipitation record. A bootstrapped ordinary least-squares regression model relating tree-ring indices to instrumental December–April precipitation was calibrated and validated using split-sample cross-validation, explaining 46% of the instrumental precipitation variance (R2 = 0.46) and yielding positive verification statistics (RE = 0.38–0.58; CE = 0.37–0.57). Spatial field correlations against gridded climate data (CRU TS4.08) confirmed a broad regional hydroclimatic signal centered over the Sierra Madre Oriental. Continuous wavelet transform (CWT), spectral analysis, superposed epoch analysis (SEA), and wavelet coherence (WTC) revealed significant interannual (2–8 years) and decadal (10–20 years) variability associated with large-scale ocean–atmosphere climate modes, including the El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and Tropical North Atlantic (TNA) index. The pronounced sensitivity of these conifer forests to pre-monsoonal moisture deficits highlights their vulnerability to projected warming and increasing spring evapotranspiration stress. Although the reconstruction is limited to pre-monsoonal (December–April) precipitation, it provides a robust centuries-long baseline for contextualizing regional hydroclimatic variability and supports water-resource management, groundwater conservation, and climate-adaptation strategies in central Mexico.

Atmosphere doi: 10.3390/atmos17080768

Authors: Ye Feng Jinglong Chen

Under the carbon neutrality context, underground coal gasification combined cycle (UGCC) power generation with carbon capture and storage (CCS) technology can effectively mitigate climate change and reduce pollutant emissions. However, due to the complexity of the UCG process and significant fluctuations in syngas composition, the overall power generation efficiency of the plant may be affected to some extent. Existing studies have predominantly focused on single-link energy efficiency analysis, with a lack of full life-cycle resource–environment synergistic evaluation based on the extended exergy analysis framework, and comparative sustainability research between UGCC and integrated gasification combined cycle (IGCC) systems remains inadequate. Accordingly, this study establishes an exergy Life-Cycle Assessment model for UGCC power plants based on Aspen Plus, systematically evaluates the resource utilization rate and environmental sustainability index, identifies key influencing factors, and conducts a comparative analysis with IGCC power plants. The results indicate that the comprehensive sustainability performance of UGCC power plants is significantly enhanced after CCS retrofitting, with exergy efficiency reaching 37.56% at an oxygen-to-coal ratio of 0.6 and a water-to-coal ratio of 0.1; compared with IGCC, UGCC demonstrates a superior resource utilization rate but relatively weaker environmental sustainability; and the underground gasification unit is the critical link affecting exergy efficiency. This study offers a new perspective for sustainability assessment of energy systems and provides theoretical support and technical reference for the construction of a low-carbon reliable supply system in the power industry, thereby facilitating the implementation and refinement of a novel sustainable energy system.

Atmosphere doi: 10.3390/atmos17080767

Authors: Guiting Song Muhsan Ali Kalhoro Veeranjaneyulu Chinta Senfeng Liu

Landfalling typhoons frequently cause severe flooding and socioeconomic losses along the eastern coast of China, yet the relative influence of oceanic and storm-related factors on precipitation variability remains insufficiently understood. This study investigates the relationships among sea surface temperature (SST), translation speed, typhoon intensity, and precipitation characteristics for 23 typhoons that made landfall in Zhejiang Province during 2000–2025. Typhoon track, intensity, precipitation, and SST data were analyzed to examine pre-landfall evolution, precipitation variability across intensity categories, and the environmental factors associated with typhoon-induced precipitation. Results showed that SST generally decreased as typhoon approached the coast, with mean values declining from 27.9 °C offshore to 24.5 °C at landfall. Translation speed exhibited a non-linear evolution during the 120 h preceding landfall, with an average speed of 9.8 km h−1 and a weak positive tendency toward the coast. Peak 24 h precipitation varied considerably among intensity categories. Severe typhoons (STY) produced the highest mean precipitation (120.9 ± 92.3 mm), whereas super typhoons (SuperTY) exhibited lower but more consistent rainfall totals. Correlation analysis revealed a weak relationship between translation speed and rainfall (r = −0.18), indicating that storm motion alone was insufficient to explain rainfall variability. SST showed a moderate negative correlation with rainfall (r = −0.53), highlighting the complex relationship between ocean thermal conditions and precipitation variability. These findings show that typhoon precipitation over Zhejiang is associated with the combined variability of storm intensity, ocean thermal conditions, atmospheric moisture availability, and coastal environmental conditions, providing scientific insights for understanding typhoon-related hazards under climate change.

Atmosphere doi: 10.3390/atmos17080766

Authors: Zhoulong Wang Wenjie Chen Yuan Niu Chen Wang Yancen Tao Jiahua Li Songtai Wu Guiting Song

Accurate regional lightning-occurrence prediction is important for operational weather-risk management, but its development is challenged by the severe class imbalance of grid-hour lightning samples. This study proposes a repeated random undersampling (RUS) stacking ensemble that combines heterogeneous machine-learning models and produces probabilistic lightning-occurrence forecasts using atmospheric variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5), together with spatial and temporal predictors. The effects of the undersampling ratio, ensemble size, and positive-class weighting were systematically evaluated, and a configuration with a RUS ratio of 1:15, 20 ensemble members, and a positive-class weight of 2 was selected. Using an operational threshold of 0.591 selected exclusively on the 2024 validation set, the final model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.945, an area under the precision–recall curve (PR-AUC) of 0.235, a probability of detection (POD) of 0.521, and an F1-score of 0.302 on the independent 2025 test set. Physical-variable-group ablation and aggregated TreeSHAP (tree-based Shapley additive explanations) analyses were further conducted to interpret the predictions. Removing the spatiotemporal predictors produced the largest reduction in performance, followed by removing cloud and microphysical variables. Both the Light Gradient Boosting Machine (LightGBM) and categorical boosting (CatBoost) models consistently identified longitude, latitude, total-column cloud ice water, and convective available potential energy as the leading predictors, while higher cloud-ice-water content and stronger convective-instability indices generally shifted model outputs towards lightning occurrence. Direct transfer of the ERA5-trained ensemble to corresponding ECMWF forecast fields without retraining retained useful predictive skill and substantially outperformed the operational ECMWF lightning product over the collocated evaluation samples, although performance degradation indicated a cross-dataset distribution shift. These results demonstrate the value of combining imbalance-aware ensemble learning with physically interpretable predictors for regional lightning-risk forecasting, while the strong influence of geographic variables indicates that external validation and local recalibration or retraining are required before application to other regions.

Atmosphere doi: 10.3390/atmos17080765

Authors: Bin Hu Haiming Fan Yushuai Wei Shangqing Zhang Hongyi Zhao

Reliable visibility forecasting is important for transportation safety and sustainable urban environmental management, particularly in the Sichuan Basin, where poor visibility remains a persistent concern. This study developed a comparative diagnostic framework for one-day-ahead prediction of continuous daily visibility in Chengdu using meteorological and air-quality observations from November 2021 to October 2024. Grey relational analysis and temporal diagnostics were used to characterize variable associations and temporal dependence. Seven methods—seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), CatBoost, long short-term memory (LSTM), Transformer, convolutional neural network–LSTM (CNN–LSTM), CNN–Transformer, and Transformer–LSTM—were evaluated under four input configurations, yielding 28 model–input combinations. SARIMAX achieved the best overall balance between predictive accuracy and generalization stability. CatBoost was the most accurate data-driven method and obtained the highest coefficient of determination (R2 = 0.644) under meteorological-only inputs. Meteorological-only inputs outperformed pollutant-only and full multivariate inputs for all models. Among the deep-learning models, LSTM performed better under univariate, meteorological-only, and pollutant-only inputs, whereas Transformer benefited more from the full multivariate input. CNN preprocessing improved LSTM mainly under the full multivariate and pollutant-only configurations. The moderate maximum R2 suggests that routine observations did not fully capture aerosol composition, particle number and size distributions, hygroscopic growth, aerosol–water interactions, and fog-related processes. These findings support model and input selection for daily visibility forecasting.

Atmosphere doi: 10.3390/atmos17080764

Authors: Hongchao Liu Qiaoke Duan Junjie Ma

The spatio-temporal variation of Arctic sea ice is one of the key indicators of polar climate change. This study used NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 5, and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data to quantitatively analyze the spatio-temporal patterns and primary drivers of changes in Arctic sea ice area (SIA), melt onset, and concentration from 1979 to 2025. The results show a significant decline in sea ice, with an annual reduction of 1.98 × 104 km2 in area and a decrease in concentration at a rate of 2.6%/decade. Edge concentrations fall below 25% from June to September. Spatial differences are pronounced: the Greenland and Barents Sea regions experience the most intense retreat, while the central Arctic remains relatively stable. The frequency of multi-year sea ice (MYI) cover has dropped from 6.7% to 3.5%, shifting the sea ice structure from ‘thick ice-dominated’ to ‘thin ice-dominated’. This research provides a scientific basis for early warning of Arctic climate change and the development of regional adaptation strategies.

Atmosphere doi: 10.3390/atmos17080763

Authors: Yanan Liu Miaojie Yu Hongjie Liu Yixian Zhang Anxiao Zhang Xianyun Cai

Respiratory infectious diseases pose substantial transmission risks in enclosed public-transport environments. Under a defined fully seated reference scenario, this study examines whether the same HVAC supply parameter governs two respiratory-particle control objectives in a subway carriage: whole-carriage particle removal and local seated breathing-zone exposure. Computational fluid dynamics (CFD) simulations, single-factor analyses, an L16 orthogonal design, analysis of variance (ANOVA), and grey relational analysis (GRA) were used to evaluate supply velocity, temperature, and angle. The results reveal an objective-dependent control hierarchy. Supply velocity is the leading factor for whole-carriage removal (contribution rate, 40.14%; p = 0.047), whereas supply angle contributes most to the seated breathing-zone particle proportion (59.30%), although the corresponding ANOVA result does not reach statistical significance at the 0.05 level (p = 0.056). The two single-objective optima share the same velocity and angle but differ in temperature, indicating a partial rather than complete mismatch. GRA identifies 2.0 m/s, 24 °C, and 0° as the best compromise among the investigated cases. This combination is specific to the modeled carriage, passenger arrangement, source location, and operating conditions. The more generalizable finding is that global clearance and local breathing-zone exposure are governed by different control variables.

Atmosphere doi: 10.3390/atmos17080762

Authors: Hao Wang Liping Zhao Kunqi Ding Fuyao Liu Rongrong Zhang Liuyan Chen Jingjing Qin Pengqiang Cao Shuying Wang

High-resolution precipitation fields are essential for flash-flood forecasting and hydrological risk management, especially in small and medium-sized basins, yet single-source precipitation products often show limited accuracy over complex terrain. This study develops a two-stage machine-learning framework for 1 km/1 h multi-source precipitation fusion over Shaoxing, China, during the 2025 flood season. In the first stage, a machine-learning classifier identifies precipitation occurrence and reduces zero-inflated noise; in the second stage, an optimized tree-based residual-regression model corrects precipitation estimates for rainy samples. A 61-dimensional feature set was constructed by integrating satellite precipitation estimates, weather-radar precipitation estimates from the Zhejiang radar network, temporal-lag and accumulation statistics, neighborhood descriptors, cyclic time variables, and terrain-derived interaction features, with gauge observations used as the training target. After quality control, the dataset comprised 41,458 hourly station samples from 72 rain gauges. The stations were divided at the station level into a 57-station development set and a fixed 15-station held-out spatial test set containing 8637 hourly samples. Station-blocked fivefold cross-validation within the development set was used for model selection, hyperparameter tuning, and probability-threshold selection, whereas the held-out stations were used only for final performance evaluation. On the fixed held-out test set, the occurrence classifier achieved an overall accuracy of 0.947, with a probability of detection of 0.806, a false alarm ratio of 0.158, a critical success index of 0.700, and an F1 score of 0.823. For quantitative estimation, the two-stage fusion product reduced root mean square error from 2.342 mm for satellite precipitation estimates to 1.189 mm, corresponding to a 49.22% reduction, and decreased mean absolute error from 0.712 mm to 0.262 mm, while increasing the coefficient of determination to 0.685. The fused precipitation product also improved the detection of intense rainfall events, with probability of detection and critical success index reaching 0.511 and 0.442, respectively, for events exceeding 10 mm/h, while reducing false weak precipitation and showing closer agreement with observed station-level spatial variability. By separating precipitation-occurrence identification from rainfall-intensity correction, the framework reduces zero-inflated bias, improves heavy-rainfall representation, and demonstrates predictive skill at gauges excluded from model development during the 2025 flood season.

Atmosphere doi: 10.3390/atmos17080761

Authors: Linyi Wang Kefeng Zhu Yuefei Zeng

The Desroziers diagnostic (D05) is widely used to estimate observation error covariances, but its behavior under different spatial correlation structures remains insufficiently understood. Through twin experiments with a modified shallow water model, this study evaluates D05 across Gaspari-Cohn, uncorrelated, SOAR-type, and mixed Gaspari-Cohn-SOAR spatial correlation regimes. D05 reproduces the prescribed correlation shape well in the Gaspari-Cohn and uncorrelated cases, while small variance biases remain when the assimilation system uses a diagonal observation-error covariance matrix. Under the SOAR-type correlation settings considered here, D05 produces longer diagnosed correlation scales than the prescribed curves, and this positive length-scale bias increases in the mixed Gaspari-Cohn-SOAR experiments. These results indicate that D05 is sensitive to the functional form of the prescribed spatial correlation and to covariance misspecification in the assimilation system. The findings provide guidance for interpreting D05-derived observation-error correlations in atmospheric data assimilation, especially when the true observation-error dependence structure differs from conventional compact-support correlation models.

Atmosphere doi: 10.3390/atmos17080760

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

Reactive nitrogen oxides (NOx = NO + NO2) are critical drivers of atmospheric chemistry, with far-reaching consequences for air quality, climate, and ecosystem health. Stable nitrogen isotopes provide a useful constraint for NOx source attribution, but source-derived isotopic signals can be substantially modified by atmospheric processes after emission. In this study, we used a previously developed 15N-enabled NOx emission dataset based on the 2008 Multi-resolution Emission Inventory for China (MEIC) as input to the Community Multiscale Air Quality modeling system (CMAQ) to simulate the spatiotemporal variation in δ15N(NOx) over South China, with a focus on the Pearl River Delta region. In the simulations, 14NOx and 15NOx were implemented as nonreactive tracers under three scenarios to isolate the effects of atmospheric physical processes, without explicitly simulating NOx oxidation chemistry, isotope fractionation, or nitrate formation. Under the “emission + transport + default deposition”, simulated δ15N(NOx) values generally ranged from approximately −2‰ to +6‰, with most areas showing values between +2‰ and +4‰. Compared with the corresponding “emission-only” baseline, atmospheric transport, mixing, and deposition tended to increase δ15N(NOx), especially in rural and low-emission regions, by redistributing anthropogenic NOx signals. Default deposition had only a minor effect on δ15N(NOx), whereas enhanced deposition, used to approximate effective near-source removal, produced spatially heterogeneous but mostly modest changes. Simulated δ15N(NOx) values were both systematically lower than measured δ15N(NO3−) at the rural Dinghushan and urban Guangzhou sites under all scenarios. This discrepancy indicates that atmospheric physical processes alone cannot fully explain observed nitrate isotope variability, and that isotope fractionation during NOx oxidation, source-endmember uncertainty, and emission inventory biases likely need to be considered. This study serves as a proof of concept and a necessary step toward fully 15N-enabled CMAQ simulations for evaluating atmospheric reactive nitrogen sources and improving NOx emission inventories.

Atmosphere doi: 10.3390/atmos17080759

Authors: László Lakatos

Accurate characterization of vineyard-scale climate is essential for understanding grapevine development, yield formation, and wine quality. However, climate analyses in viticulture are still largely based on interpolated macroclimate datasets, which may not adequately represent local conditions. This study evaluated the ability of commonly used interpolation methods to reproduce microclimatic conditions across vineyards, focusing on key meteorological variables and spatial resolutions. Daily values extracted from macroclimate grids (1/6° and 0.1°) and high-resolution (1 km) interpolated datasets were compared with in situ microclimate observations. The analysis covered temperature (minimum, mean, and maximum), vapor pressure deficit (VPD), and global radiation, using statistical metrics including correlation (r), root mean square error (RMSE), bias, and distribution-based error characteristics. The results reveal a strong variable-dependent performance of interpolation methods. Global radiation showed moderate to strong agreement between macro- and microclimate datasets, whereas temperature—particularly minimum temperature—exhibited substantial discrepancies. These differences are primarily attributed to local processes such as cold air pooling, topographic effects, and canopy-level interactions, which are not resolved by coarse-scale datasets. An exploratory Mean Topographic Association Score (MTAS) analysis further identified the Topographic Position Index (TPI) as the terrain descriptor showing the strongest overall association with interpolation errors across the investigated meteorological variables. Among the evaluated approaches, the 0.1° climate product generally provided the best overall agreement with vineyard observations for temperature-related variables, substantially reducing interpolation errors compared with the original 1/6° dataset. For example, the RMSE of Tmin decreased from 3.85 °C to 2.41 °C. Nevertheless, considerable residual errors remained, indicating persistent limitations of interpolation approaches in complex terrain. VPD deviations reflected the combined influence of temperature- and humidity-related uncertainties, highlighting the sensitivity of derived variables to local climatic conditions. The findings demonstrate that interpolated macroclimate datasets should be applied with caution in vineyard-scale analyses, especially for variables sensitive to local processes. For applications such as phenological modelling, climate suitability assessment, and precision viticulture, the integration of high-resolution data sources or direct microclimate measurements is essential. This study highlights the limitations of interpolated climate products in complex vineyard terrain and demonstrates that consideration of local terrain configuration, particularly relative topographic position, together with vineyard-scale observations and high-resolution climate information, is essential for reliable viticultural climate assessments.

Atmosphere doi: 10.3390/atmos17080757

Authors: Sadath Ismayil Adam Kristensson Jalisha Theanutti Kallingal Erik Swietlicki Axel C. Eriksson Erik Ahlberg Martin Ebert Konrad Kandler

Alternating marine inflow and continental outflow make southern Sweden a suitable region for investigating natural and anthropogenic contributions to background particulate matter (PM). However, interpreting dust and marine aerosol sources remains challenging because their source signatures often overlap during atmospheric transport. This study investigated aerosol sources at the Vavihill and Hyltemossa background stations using source apportionment, elemental analysis, transport modelling, reanalysis data, and particle-resolved microscopy. At Vavihill, filter samples provided elemental composition, while TEOM measurements provided PM10 and PM2.5 mass concentrations. The combined data were analysed using Positive Matrix Factorization (PMF). At Hyltemossa, online XACT elemental measurements were combined with FIDAS coarse PM observations to evaluate coarse PM source contributions. HYSPLIT backward trajectories, CAMS diagnostics, and SEM/EDX analysis supported the interpretation of selected dust-related episodes and particle mixing states. The analysis identified mineral- and marine-related aerosols, regional pollution, and mixed combustion particles as important components of background PM. Mineral-associated contributions increased markedly during spring, accounting for 39% of the measured coarse PM at Vavihill and 29% at Hyltemossa. In contrast, marine aerosol made its largest contribution to measured coarse PM during winter, accounting for 48% at Vavihill and 40% at Hyltemossa. The mineral-related factor reflected multiple source regions and transport pathways rather than a single recurring dust source. At both sites, mineral, marine, and anthropogenic components frequently co-occurred and underwent atmospheric processing and mixing. Together, these results highlight the chemically heterogeneous and seasonally variable nature of background PM in southern Sweden.

Atmosphere doi: 10.3390/atmos17080758

Authors: Xiangjin Shen

Terrestrial ecosystems are integral components of the Earth system, exerting significant influence on global carbon cycling, energy balance, and climate regulation [...]

Atmosphere doi: 10.3390/atmos17080756

Authors: Qiyu Liang Shuzhen Guo Shurong Huang Yebei Chen Yang Jiang Yue Zhao Chao He

Mitigating population exposure to fine particulate matter (PM2.5) is a core prerequisite for advancing Sustainable Development Goal 11.6.2 and global urban sustainability. This study integrated 0.1° × 0.1° gridded PM2.5 reanalysis data, WorldPop high-resolution population datasets, and official SDG 11.6.2 scoring records spanning 2000–2019, and adopted multi-scale spatial statistics and population-weighted exposure models to systematically explore the spatiotemporal differentiation of global PM2.5 concentrations and associated population exposure risks, as well as their coupling relationship with SDG 11.6.2 implementation progress. This study employs ArcGIS 10.6 software to harmonize the spatial scales of multi-source heterogeneous data through spatial statistics and resampling methods, and applies the SDSN (Sustainable Development Solutions Network) standardized scoring framework to quantify long-term progress across regions in meeting urban air quality targets. The results reveal significant latitudinal spatial heterogeneity in global PM2.5 concentrations, with values ranging from 0.95 to 262.15 μg/m3; severe pollution hotspots exceeding 35 μg/m3 were agglomerated across Asia, Africa and South America, while Canada, Greenland, the Tibetan Plateau and eastern Russia maintained ultra-low PM2.5 levels below 5 μg/m3. Global SDG 11.6.2 standardized scores rose steadily from 71.05 in 2000 to 76.64 in 2019, with Asia and Africa achieving the most remarkable score growth despite low initial baselines. Regional gaps in target realization remained stark: North America, Oceania and Northern Europe basically met the PM2.5 sustainability standards, whereas densely populated regions of Asia and Africa faced critical governance challenges. Among nine typical countries, only China and India recorded population-weighted PM2.5 concentrations above 25 μg/m3 in 2019; China’s air pollution control policies delivered continuous emission reductions after 2013, while India sustained extremely high exposure risks. From 2010 to 2019, the global range of urban population-weighted PM2.5 concentrations narrowed, yet Afghanistan, Tajikistan and North Korea remained the most severely exposed nations. This study verifies the severe cross-regional inequity of PM2.5 exposure risks under the SDG framework, and provides multi-scale empirical evidence for differentiated air quality governance and international collaborative interventions to accelerate the delivery of the 2030 sustainable development agenda.

Atmosphere doi: 10.3390/atmos17080755

Authors: Kristina Michl Michael Forsmann Iva Šunić Jelena Šarac Dubravka Havaš Auguštin Ivana Jakovljević Gordana Pehnec Morten Arendt Rasmussen Signe Kjeldgaard Jensen Klaus Bønnelykke Sune Rubak Susanne Halken Kristina Aagaard Tomislav Cernava Mario Lovrić

Poor indoor air quality is linked to respiratory symptoms and disease, but interactions between indoor pollutants and microbial communities remain unclear. Evidence suggests microorganisms can interact with polycyclic aromatic hydrocarbons (PAHs) through degradation or attachment, potentially shaping indoor microbiomes. In the COPSACsevere cohort, we analyzed bed dust from 84 children (58 asthma, 26 controls). PAHs were measured by HPLC-FLD and microbiota profiled by 16S rRNA and ITS2 sequencing. Questionnaires captured sociodemographic and lifestyle factors. Associations were tested using PERMANOVA, Spearman correlations, and MaAsLin2. Bacterial community composition varied with the host factors age, sex, and parental education, but showed no association with PAH concentrations. Fungal diversity and composition were significantly associated with PAHs, especially pyrene and fluoranthene, which explained 6.1–6.3% variance (p < 0.05) and were positively correlated with Shannon diversity and evenness. Several fungal taxa including Lecanoraceae, Helotiales, and Circinaria correlated positively with PAHs. Asthma status was not associated with bacterial or fungal alpha and beta diversity metrics. PAHs selectively influenced fungal but not bacterial communities in bed dust. Although no asthma associations were detected and no mechanisms can be inferred, these findings highlight potential chemical–fungal interactions in indoor environments and their relevance for future respiratory health research. These results suggest that chemical exposures may shape specific components of indoor microbiomes independent of host disease status, emphasizing the need to integrate chemical–biological interactions in indoor exposure assessments in future environmental health studies and interventions.

Atmosphere doi: 10.3390/atmos17080754

Authors: Xiaoyu Tan Kang Lin Lieyu Tian Yufang Tan Shidong Wang Yang Lv

Sentinel-3A and -3B satellite SAR altimeter wind speeds are routinely validated against moored buoys but increasingly benchmarked against ERA5 reanalysis when buoys are unavailable. We assess both reference choices over the Chinese nearshore seas using 14 months of matchups between satellite passes, moored meteorological buoys, and ERA5 wind fields. Against buoys, both altimeters show near-zero mean bias and a roughly flat error level across the wind-speed range. Against ERA5, the discrepancy grows strongly and monotonically with wind speed: the mean satellite-minus-ERA5 bias shifts from approximately −1.5 m/s in light wind to +1.4 m/s above Beaufort 6. Sentinel-3A tracks the buoy more closely than Sentinel-3B (r = 0.71 vs. 0.58), a gap that persists in the month-to-month RMSE record. Because the altimeter–buoy residual is flat, the wind-speed structure in the altimeter–ERA5 residual can be attributed to ERA5 rather than to the satellite retrieval. A compact quadratic fit to the altimeter–ERA5 residual, inverted to adjust ERA5 wind speed, removes essentially all of this wind-speed-dependent bias (per-bin mean bias reduced to within ±0.3 m/s), although the remaining scatter is set by representativeness differences that no bias model can remove. These results caution against uncorrected use of ERA5 as a stand-alone reference for altimeter evaluation in light-wind and extreme-wind regimes—over the semi-enclosed Chinese seas studied here, and more generally wherever sparse buoy coverage makes reanalysis the fallback reference for satellite wind validation.

Atmosphere doi: 10.3390/atmos17080752

Authors: Chongru Shi Yanhong Li Rui Zheng

Permafrost peatlands in high-altitude regions store substantial amounts of organic carbon, yet the biogeochemical consequences of thermokarst collapse remain poorly understood. Using a space-for-time substitution approach, we selected four habitats representing a thermokarst development sequence in the Altai Mountains peatlands—slightly collapsed peat mounds (P1), severely collapsed peat mounds (P2), thawed herbaceous peat (PB1), and thermokarst ponds (PB2)—and conducted in situ greenhouse gas flux monitoring, soil physicochemical analysis, enzyme activity assays, and structural equation modeling. We found that thermokarst development fundamentally altered the greenhouse gas source–sink balance through three interconnected mechanisms. First, CO2 fluxes shifted from net emission in P1 (684.1 mg m−2 h−1) to net uptake in PB2 (−25.6 mg m−2 h−1), driven primarily by the oxidative loss of mineral-associated organic carbon in the 40–60 cm layer (71.3% loss), whereas lateral dissolved organic carbon export accounted for only 12.3% of total carbon loss. Second, CH4 fluxes in PB2 (3.8 ± 0.7 mg m−2 h−1) reached approximately 43% of the theoretical maximum, with this suppression associated with phosphorus limitation (total phosphorus < 0.05 g kg−1) and a marked reduction in alkaline phosphatase activity. Third, N2O uptake increased along the thaw sequence to −28.6 μg m−2 h−1 in PB2, with the 40–80 cm layer contributing 42% more than the surface layer. This increase in N2O uptake occurred when the soil C/N ratio exceeded 300, a threshold that reflects the substantial stoichiometric imbalance between carbon and nitrogen following thermokarst development. These findings demonstrate that the transition from peat mounds to thermokarst ponds alters the net greenhouse gas source–sink balance through changes in MAOC stability, phosphorus availability, and carbon-to-nitrogen stoichiometry. Our results provide empirical constraints for evaluating carbon-climate feedbacks in cold-region peatlands.

Atmosphere doi: 10.3390/atmos17080753

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

To examine the fine-scale structure and evolution of the low-level wind field at a plateau valley airport under different weather conditions, this study analyzes a two-stage wind field event at Xining Caojiapu Airport on 8 April 2022. The analysis uses data from several scanning modes of a three-dimensional Doppler wind lidar (DWL), together with an automatic weather observation system (AWOS), sounding data, and the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The results show that: (1) From 13:25 to 13:45 BJT, downward momentum transport produced low-level wind shear. Under an upper-level jet, post-trough northwesterly flow, and stronger afternoon mixing in the boundary layer, west-northwesterly winds aloft descended and entered the runway area from west to east. Runway 11 responded about 1–2 min before Runway 29. The maximum wind vector difference between the runway ends was 9.42 m s−1, and the maximum wind speed component difference along the glide path was 9.02 m s−1. (2) From 20:15 to 20:35 BJT, the low-level wind field adjusted as a cold front moved into the airport. Strong easterly flow advanced westward from the eastern side as a shallow wedge, with local shear along its upper boundary. Runway 29 responded before Runway 11. The corresponding maximum differences at the runway ends and along the glide path were 6.44 and 4.01 m s−1. (3) The two stages differed in airflow direction, the evolution of the shear interface, and the order of response at the runway ends. Combining the DWL scanning modes with AWOS observations gave a clearer view of the descending strong wind layer, the advance of low-level airflow, local shear, and wind changes over the runway and approach path. This case provides a reference for low-level wind monitoring and operational risk assessment at plateau valley airports.

Atmosphere doi: 10.3390/atmos17080751

Authors: Laihua Shi Chongyao Wang Jianjian Kang Lan Li Xiaoliu Xu Di Wu Bing Liu Xin Wang

Heavy-duty methanol engines are regarded as a promising low-carbon solution for commercial vehicle decarbonization, yet the comprehensive coupled characteristics of fuel consumption, multi-dimensional exhaust emissions, and the corresponding aftertreatment adaptability between stoichiometric and lean-burn technical routes remain insufficiently quantified, restricting the optimized application of methanol powertrains for China-VI emission compliance. To address this research gap, this study systematically investigates two China-VI compliant heavy-duty methanol engines with stoichiometric and lean-burn combustion strategies under cold-start and hot-start Worldwide Harmonized Transient Cycle. And a comparative analysis is conducted to clarify the differences in the fuel consumption, raw exhaust emission (including regulated pollutants, particulate matters, greenhouse gases, and unregulated pollutants), and the catalytic performance of aftertreatment systems between two engines with stoichiometric and lean-burn strategy. Results demonstrate that the lean-burn strategy achieves a 6% reduction in methanol fuel consumption compared with stoichiometric combustion, delivering superior fuel economy. In terms of regulated gaseous pollutants, both combustion strategies satisfy China-VI emission limits for CO and NO, while lean-burn combustion effectively lowers raw CO and NO emissions and reduces the purification pressure of aftertreatment systems. Non-methane Hydrocarbon emission under cold-start condition is identified as the primary compliance challenge, requiring a minimum aftertreatment conversion efficiency of 95%. Although lean-burn increases raw exhaust NMHC emission under hot-start condition, the post-catalyst emission could still meet the regulation limits. For particulate pollutants, lean-burn strategy realizes substantial reductions in both PM and PN emissions, which can meet emission standards without the corresponding aftertreatment system. In contrast, the stoichiometric combustion faces a risk of PN emission exceeding the regulation limit under cold-start conditions even with aftertreatment system. Additionally, lean-burn strategy optimizes greenhouse gas emission performance by cutting CO and CH4 emissions. Regarding unregulated pollutants, lean-burn strategy increases raw exhaust unburned methanol and formaldehyde emissions, particularly under cold-start condition, but significantly inhibits NH3 emission. This study quantitatively clarifies the performance trade-offs and adaptation advantages of lean-burn and stoichiometric strategy for heavy-duty methanol engines, providing fundamental data support and technical guidance for the low-carbon and low-pollution optimization of heavy-duty methanol vehicles.

Atmosphere doi: 10.3390/atmos17080750

Authors: Zhen Li Baojun Liu Jing Zhang Wenjing Liu

GNSS ionospheric records are valuable measurements of earthquake-related ionospheric anomalies. The deep exploration of GNSS ionospheric data can help to better understand the seismic–ionospheric coupling effect. Thus, we used GNSS ionospheric data to detect and investigate the pre-earthquake ionospheric anomalies of the 7.6 magnitude earthquake, which occurred in Mexico on 19 September 2022. A 10.7 cm solar radio flux (F10.7), sunspot number (SSN), and geomagnetic activity indices (Dst and Hp30) are employed to reflect solar and geomagnetic activities, respectively. The result of the sliding quartile range method indicates that there was a ionospheric disturbance over the epicenter on the 10th day prior to the earthquake, and that there were no anomalies observed in solar activity and geomagnetic activity on that day. Meanwhile, the ionospheric anomaly over the epicenter on 15th day before the earthquake was the most significant, but the geomagnetic activity was abnormal on that day. To further distinguish whether the ionospheric disturbance on 15th day was caused by geomagnetic activity or the earthquake, this paper adopts the coherent wavelet to analyze the time-varying relationship between total electron content (TEC) and geomagnetic activity in the time-frequency space. The results show that the correlation and phase relationship between TEC, Dst, and Hp30 remained relatively stable on the 15th day before the earthquake, while the strong correlation between TEC, Dst, and Hp30 suddenly disappeared and the phase relationship sharply changed after the 10th day. This may be due to the influence of the seismic–ionospheric coupling effect, where the abnormal disturbances of TEC may disrupt the pre-existing stable relationship between TEC, Dst, and Hp30. Therefore, we conclude that the TEC anomaly on 10th day before the earthquake might be the precursor to the Mw7.6 Mexico earthquake.

Atmosphere doi: 10.3390/atmos17080749

Authors: Sushrit Kafle Eren Gultepe Sen Wang Byron Walter Blomquist Harindra J. S. Fernando O. Patrick Kreidl David J. Delene Ismail Gultepe

Reduced visibility due to fog poses significant safety and operational risks in marine environments, emphasizing the need for accurate short-term nowcasting. Typical machine learning forecasting models fail to capture temporal meteorological dependencies that influence fog dynamics, due to the reliance on data assimilated measurements for closed-loop time series prediction. This work introduces a novel multiple lookback window (MLW) architecture that utilizes the recurrent neural network model of Gated Recurrent Units (GRU) to nowcast dense fog visibility (Vis < 400 m) time series. This architecture facilitates learning the short- and long-term dependencies in meteorological data for non-data assimilation (open-loop) time series prediction. Prediction intervals were obtained using Bayesian approximation at 95% confidence interval. Vis time series nowcasts were obtained using autoregressive generation at increasing lead times, without relying on the assimilation of future observations and were evaluated across various fog Vis conditions characterized by its coefficient of variation. Marine fog Vis conditions and measurements were collected using instrumentation mounted on the Research Vessel Atlantic Condor from the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign in July 2022 in the Grand Banks and Sable Island areas of the North Atlantic region of Canada. The MLW-architecture with the GRU model outperformed the naïve persistence model nowcast for open-loop nowcasting dense fog Vis, with a mean RMSE of 11.829±1.260 (2.96% error at 400 m) and Skill Score of 0.073±0.061. The best nowcasting occurred at the 10 and 20 min lead times, where the mean Skill Score across both lead times was 0.132±0.082 and the RMSE was 7.013±1.105m (1.75% error at 400 m) and in high variability conditions with a mean SS of 0.387±0.037 and RMSE of 6.990±1.990 (1.75% error at 400 m). Thus, the proposed model is robust and practical for dense fog Vis nowcasting.

Atmosphere doi: 10.3390/atmos17080748

Authors: Benjamin Körner Volker Wulfmeyer Marcus Breil

This study explores the potential of Artificial Neural Networks (ANNs) for the calculation of momentum and sensible heat fluxes. The ANN is applied on idealized Large Eddy Simulation (LES) data. The LES test cases used to train the ANN correspond to convective conditions with partially low wind speeds and heterogeneous surfaces. To enable the ANN to learn the systematics of such conditions, the input variables for the ANN include the variables that are used in Monin Obukhov Similarity Theory (MOST) and an additional variable that accounts for the heterogeneity of the land surface. Simulation data is averaged over 30 min and different spatial scales. Our findings show that the modeling skill is generally higher for momentum flux than for sensible heat flux. Also, the performance increases with larger spatial-averaging scales. The ANN calculates momentum flux with a correlation of 0.81 and a normalized RMSE of 0.59 for a single grid point. On a spatial-averaging scale of 4000 m, the correlation changes to almost 1.00 and the normalized RMSE to 0.05. The importance of each input variable for model performance is determined with a feature importance weighting. Their relative importance depends strongly on the spatial-averaging scale. The importance of the variable that represents the influence of surface heterogeneity is low at smaller spatial-averaging scales, but increases at larger averaging scales. However, its contribution to the modeling skill is small. Reducing the number of input variables to two results in a substantial loss of performance. Although our results demonstrate the potential of this approach to improve the calculation of momentum and sensible heat fluxes, it is also clear that there are simplifications and limitations in the present setup that need to be overcome to assess general applicability. These include the height of analysis (40 m instead of 10 m or less), the exclusion of all latent heat processes, the exclusion of stable conditions, the data coverage of the required parameter space, and the usage of only surface roughness length to define surface heterogeneity.

Atmosphere doi: 10.3390/atmos17080747

Authors: Shimul Roy Yun Fat Lam Rezuana Afrin Nowara Tamanna Meghla Md. Mahbubul Hoque Mehnaz Abbasi Badhan

Air pollution is a growing environmental issue in rapidly urbanizing Asian cities, where meteorological conditions and seasonal variability strongly influence pollutant concentrations. This review synthesizes published evidence on the interactions between meteorology, seasonality, and urban air pollution across selected cities in East Asia, South Asia, and Southeast Asia. The literature was collected from major scientific databases and organized according to meteorological drivers, seasonal characteristics, and dominant emission sources. Particular emphasis was placed on particulate matter (PM2.5 and PM10), SO2, NO2, NOx, CO, and O3. The reviewed studies indicate that wind speed and direction, precipitation, temperature, atmospheric stability, and monsoon circulation strongly influence pollutant transport, dispersion, and removal. Pollutant concentrations generally reach their highest in winter due to stagnant conditions and higher human emissions. In contrast, the summer and monsoon seasons tend to have lower PM levels due to better atmospheric mixing and wet deposition. Regional differences in dominant sources were also observed, including coal combustion and heating in East Asia, traffic and biomass burning in South Asia, and biomass burning and transboundary transport in Southeast Asia. These findings highlight the importance of seasonally adaptive and region-specific air quality management strategies in Asian cities.

Atmosphere doi: 10.3390/atmos17080745

Authors: Demet Aydin

Real-world datasets may contain unusually large observations whose origin cannot always be clearly identified. When such observations are present, selecting an appropriate estimation method becomes particularly important because they may substantially influence distribution fitting and parameter estimation. In this study, daily maximum PM10 data recorded at the Erfelek Station in Sinop (Türkiye) during 2025 were analyzed, and several probability distributions were fitted. The lognormal (LN) distribution provided the best overall fit according to the goodness-of-fit criteria. To evaluate the sensitivity of estimation methods to unusually large observations, contamination scenarios were considered, and the performances of Weighted Least Absolute Deviation-1 (WLAD-1), Weighted Least Absolute Deviation-2 (WLAD-2), Least Absolute Deviation (LAD), Least Squares (LS), Least Median of Squares (LMS), and Maximum Likelihood (ML) were compared. The results indicate that WLAD-2 and LMS are the most robust estimators, remaining unaffected by outliers across all contamination levels. WLAD-1, LAD, and LS are also only negligibly affected, whereas the ML estimator becomes increasingly sensitive as the contamination level increases. Contamination also affects exceedance probabilities and return periods, with substantially greater changes observed for the ML estimator than for the remaining estimators.

Atmosphere doi: 10.3390/atmos17080746

Authors: Wei Zhao Weihua Gu Fenghua Bai Ying Xiao Hao Wang Fangyuan Liang

Terrestrial ecosystem carbon absorption capacity has become an important component of China’s strategy to enhance nature-based climate mitigation. However, existing studies on China’s terrestrial ecosystem carbon dynamics are still insufficient to support source-oriented control and unified supervision by national-level government departments, particularly in terms of temporal consistency, spatial completeness, and cross-ecosystem comparability. Here, we present a systematic assessment framework for NPP-based ecosystem carbon absorption capacity (NPP-based CAC) designed to support unified ecological and environmental supervision. In this study, NPP-based CAC is defined as the annual vegetation production-based carbon fixation capacity derived from net primary productivity. It represents carbon retained by vegetation after plant autotrophic respiration has been accounted for in the NPP estimation, and it should not be interpreted as gross ecosystem carbon uptake, NEP, NBP, net land–atmosphere carbon exchange, or a carbon sink for carbon neutrality accounting. The framework adopts year as the temporal unit and a 500 m × 500 m grid as the spatial unit, and integrates four core components: temporal series analysis, spatial distribution mapping, protection effectiveness evaluation, and enhancement potential estimation. Applying this framework to China from 2000 to 2022, we found that national NPP-based CAC increased from 0.673 Pg C yr−1 in 2000 to 0.868 Pg C yr−1 in 2022, with carbon absorption intensity rising from 98.47 to 121.37 t C km−2 yr−1. High-value areas covered 3,009,571 km2, accounting for 40.90% of the national area with NPP-based CAC, whereas areas showing degradation signals covered 2,075,286 km2, accounting for 29.01%. The national historical reference gap, termed enhancement potential in this framework, was estimated at 152.63 Tg C yr−1, equivalent to 17.6% of the 2022 national total; this value represents the difference between current NPP-based CAC and grid-level historical maxima, rather than a directly attainable restoration target. Because no formal attribution analysis was conducted, the observed temporal and spatial patterns are interpreted as diagnostic changes in NPP-based CAC rather than as direct evidence of policy effects or ecosystem degradation. This framework enables integrated assessment across temporal, spatial, and ecosystem-type dimensions and provides an operational diagnostic basis for national-level ecological supervision, spatial prioritization, and follow-up attribution or field verification.

Atmosphere doi: 10.3390/atmos17080744

Authors: Elizaveta Kruglova Stanislav Myslenkov

Despite numerous studies on Arctic wave climate changes associated with sea ice retreat, the spatiotemporal relationship between wind variability and significant wave heights in the Eurasian Arctic remains insufficiently understood. This study analyzes wave and wind variability in the Eurasian Arctic seas for 1979–2025 using the spectral wave model WAVEWATCH III. The quality of the modeling was verified against satellite altimetry and buoy data; the average correlation coefficient exceeded 0.9, and the RMSE was 0.368 m. An analysis of 3-h significant wave height (Hs) and wind speed data revealed positive trends in mean annual Hs across the Arctic, with the strongest statistically significant increases in the Barents and Kara Seas. Extreme wave conditions (95th percentile) showed the largest growth in the Kara, East Siberian, and Chukchi Seas. During ice-free periods, significant trends were weaker and more localized, although positive trends persisted near the Northern Sea Route. Mean wind speed trends were generally positive but mostly statistically insignificant. The results provide a spatially consistent characterization of long-term changes in wind and wave climate under evolving sea-ice conditions and may be used to inform assessments of navigation conditions and associated risks along the Northern Sea Route.

Atmosphere doi: 10.3390/atmos17080743

Authors: Carla Larissa Fonseca da Silva Maria Manuela Portela Luis Angel Espinosa José Pedro Matos

This study analyzes trends in 18 temperature- and precipitation-related indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) across mainland Portugal over the most recent 46-year complete period (1980–2025), using ERA5-Land reanalysis data. Prior to the computation of the indices, daily ERA5-Land extreme temperature and precipitation data were validated against ground-based observations, showing good agreement and confirming the suitability of the reanalysis dataset for assessing climate extremes at the national scale. The results reveal a consistent and statistically significant warming signal across the study area, with the strongest increases observed in southern and inland regions. The frequency of extreme heat events has intensified markedly since the early 2000s. In contrast, precipitation-related indices display high spatial variability and trends with limited statistical significance, reflecting both genuine climatic heterogeneity and the inherent challenges of reproducing rainfall extremes in complex terrain using reanalysis data. The combined interpretation highlights an emerging climatic asymmetry—robust and spatially coherent warming versus localized and uncertain precipitation trends—underscoring distinct regional vulnerabilities. The findings provide actionable insights for climate services, supporting the design of region-specific adaptation strategies in Portugal, especially the need to address increasing heat stress in southern regions and the growing exposure to short-duration heavy rainfall in northern areas.

Atmosphere doi: 10.3390/atmos17080742

Authors: Daniel Dębkowski Katarzyna Gładyszewska-Fiedoruk Marcin Krukowski Adam Kozioł Tomasz Teleszewski

This article discusses the results of measurements and surveys on indoor air quality in Warsaw secondary school classrooms. Measuring selected air parameters and basing them on subjective user assessments enables a comprehensive diagnosis that identifies key issues. Temperature, humidity, and carbon dioxide concentration were measured in all rooms on the second floor. Concurrently, a survey was conducted among high school students. The aim of the survey was to gather students’ perceptions of the comfort of their stay in selected rooms and their opinions on air quality. The next step in the analysis was to correlate indoor air quality measurements with student perceptions. This allowed for the observation of numerous correlations and several paradoxes. An analysis of carbon dioxide concentration versus perceived comfort showed a distinct correlation. However, no correlation was observed between carbon dioxide concentration and perceived discomfort. Unfortunately, indoor air quality in all rooms where measurements were taken in the studied school was poor. The air parameters tested did not meet any standards. Students reported symptoms such as drowsiness, difficulty concentrating, and headaches, which are strongly associated with elevated carbon dioxide levels and impair learning efficiency.

Atmosphere doi: 10.3390/atmos17080740

Authors: Yuxiang Ren Jianhe Huang Haipeng Duan Xiaoyun Liu Gulisumu Shayimu Ruifeng Li Ruming Chen

East Asian spring dust activity has generally weakened since the early 2000s (Theil-Sen trend −1.07 × 10−6 yr−1, significant over 62% of the domain), but severe events continue to occur when synoptic forcing, source-region dryness, and terrain-guided transport are favorably coupled. This study places the 19–23 March 2023 East Asian dust storm within this 2000–2024 background and provides an integrated three-dimensional analysis of its transport, vertical structure, and topographic controls. The event developed as a dual-source relay-convergence process: Taklamakan Desert dust was emitted first on 19 March and transported southeastward along the Hexi Corridor, while Mongolian Plateau dust intensified on 21 March and mainly affected North China. Independently calibrated, PM10-cross-validated FLEXPART-WRF trajectory arrays (R = 0.74–0.81) show Taklamakan contributed 100% of the calibrated near-surface dust mass at Lanzhou and Mongolian 98% at Beijing during each receptor’s event peak window. Four independent dynamical diagnostics quantify topographic control, showing the Helan Mountains attenuate westward-approaching Taklamakan dust by 23% across the range. TROPOMI AAI, CALIPSO, ground PM10, and CAMS EAC4 jointly corroborate multi-level cold-vortex/trough-frontal coupling and terrain blocking as the controlling mechanisms, demonstrating that extreme dust episodes can still occur under a weakening long-term background when dynamic lifting, dual-source activation, and topographic channeling act together.

Atmosphere doi: 10.3390/atmos17080741

Authors: Fan Li Guoqian Chen Mengfan Zhao Fei Li Yingcun Yan Liangdong Yan

Rational development and utilization of cli mate resources are essential for mitigating frost disasters, particularly in high-altitude regions. This study investigates the spatiotemporal characteristics of frost events in Qinghai Province to improve understanding of frost evolution under climate change. Daily minimum temperature data from 51 meteorological stations from 1961 to 2023 were analyzed to determine the first frost date (FFD), last frost date (LFD), and frost-free period (FFP), along with their trends and influencing factors. Results show that the mean FFD, LFD, and FFP were 5 October, 14 May, and 144 days, respectively. Over the past six decades, LFD advanced, whereas FFD was delayed, leading to a marked extension of FFP, with trends of 2.08, 2.87, and 4.68 days per decade, respectively. These changes became more pronounced after the 1990s. Spatially, lower-altitude regions exhibited later FFD, earlier LFD, and longer FFP, whereas higher-altitude regions showed the opposite pattern. Correlation analysis indicates that frost characteristics are most strongly influenced by altitude, followed by latitude and longitude. Overall, climate warming has shortened the frost season and extended the growing period in Qinghai Province. These findings provide a scientific basis for optimizing agricultural zoning, adjusting cropping systems, and improving climate resilience in cold, high-altitude regions.

Atmosphere doi: 10.3390/atmos17080739

Authors: Justina Kapilovaitė Egidijus Rimkus

Urban areas are increasingly exposed to heat stress under climate change due to rising temperatures, ageing populations, and increasing population density in cities. This study assesses current and future heat-stress risk in Vilnius, Lithuania, using the Humidex index and the CLIMADA risk modelling framework. Historical heat-stress conditions are analysed using observational meteorological data, while future conditions are evaluated using five CMIP6 climate models under SSP2-4.5 and SSP5-8.5 climate scenarios for the mid and late 21st century. The risk assessment combines high-resolution Humidex-based hazard data, gridded population exposure, and age-specific vulnerability functions. In addition to temporal Humidex trend analysis, CLIMADA is used to estimate spatially explicit Expected Annual Impact and population-normalised impact indicators. The frequency of heat-induced stress has increased significantly—the number of days when the Humidex index was ≥30 has risen by 5.6 days per decade. It is projected that by the end of the century, the proportion of such warm-season days (April–October) will increase from 13.1% to 25.8% under the SSP2-4.5 scenario and to 41.6% under the SSP5-8.5 scenario. Spatial impact maps identify areas with elevated total and population-normalised heat-stress burden, while age-specific results show higher impacts among residents aged ≥ 65 years. Cumulative heat-stress burden, expressed as Expected Annual Impact, could increase by approximately 1.8–11.4 times. The framework provides a spatially consistent and age-sensitive approach for assessing urban heat-stress risk and supporting local climate adaptation planning.

Atmosphere doi: 10.3390/atmos17080738

Authors: Jamaan A. Turki Fawaz Madah

This study aims to estimate latent heat flux (LHF) over the central Red Sea during the summer months (July and August) of 2000–2020, corresponding with the occurrence of the Tokar Gap (TG) wind jets. Sea surface temperature (SST) was derived from the SeaDAS-based multi-sensor ESA CCI/C3S satellite product (AVHRR-SST), and wind data were obtained from the scatterometer product. Both datasets were re-gridded to a uniform spatial resolution of 0.05°. The AVHRR-SST and scatterometer wind speed were compared with the corresponding ERA5 reanalysis fields. Because both the satellite-derived estimates and ERA5 are model-based products rather than independent in situ observations, this comparison constitutes an intercomparison between two approaches rather than a validation. The AVHRR-SST and scatterometer wind speed showed good agreement with ERA5 fields (correlation coefficients CC = 0.66 and 0.93, respectively). The spatial distribution of the estimated LHF reproduced the ERA5 LHF patterns but with relatively lower magnitudes. Time series analysis near the TG region showed that the estimated LHF underestimated ERA5 values, with a correlation coefficient of 0.77 and a root mean square error (RMSE) of 25.8 W/m2. The results represent an intercomparison between two model- and satellite-based approaches and are limited by the absence of independent in situ buoy observations for direct validation.

Atmosphere doi: 10.3390/atmos17080737

Authors: Alexandru Doicu Dmitry S. Efremenko Dirk Giggenbach Adrian Doicu

This paper investigates the feasibility of retrieving stratospheric aerosol extinction fields from GEO–LEO limb measurements using a dedicated inversion framework. The retrieval problem is severely ill-posed and involves a fundamentally three-dimensional observation geometry. To obtain stable solutions, variations of the extinction field in the cross-track direction are neglected, reducing the problem to the retrieval of radial and weakly horizontally varying aerosol distributions within a quasi-planar GEO–LEO geometry. Two simplified retrieval strategies are considered. In the first strategy, the radial extinction profile and the horizontal extent of the aerosol field are retrieved using external aerosol optical thickness observations as additional constraints. In the second strategy, the extinction field is represented by a parametric model and the corresponding model parameters are retrieved. To reduce the computational complexity, a simplified single-scattering forward model is adopted. The inversion problem is formulated as the minimization of a regularized Tikhonov function and is solved using a multistart optimization framework combining global random sampling, validation and selection of admissible starting points, local bounded optimization, discrepancy-principle filtering, and clustering of candidate solutions. Numerical simulations for a broad range of synthetic aerosol scenarios show that the proposed methodology is capable of reproducing the dominant aerosol structures with good accuracy. Although the retrieval problem remains strongly ill-conditioned, the effective degrees of freedom indicate that GEO–LEO limb measurements contain substantial independent information about the aerosol field and provide a promising basis for retrieving both radial aerosol extinction profiles and aspects of their horizontal structure.

Atmosphere doi: 10.3390/atmos17080736

Authors: Vidmantas Vaičiulis Gabrielė Domkutė Anna Papadima Ričardas Radišauskas Ivar Annus Katrin Kaur Gintarė Kalinienė

Climate change is progressing most rapidly in the high-latitude regions of the Northern Hemisphere, where boreal and Arctic ecosystems are experiencing significant environmental changes and transformations. These ecosystems are being altered by rising temperatures, changing precipitation patterns characterized by more intense rainfall events, decreasing snow and ice cover, and increasing floods, heat waves and fires. These changes pose significant risks to ecosystems, human health, and animal health, especially in the boreal zone. A narrative literature review was conducted across major scientific databases, including Scopus, Web of Science, PubMed, and Google Scholar, covering publications up to 2026. The evidence was combined thematically across key climate hazards and health domains, including infectious and non-communicable diseases, and mental health outcomes. The review shows that accelerating warming in northern regions is changing species distribution, increasing the risk of zoonoses and vector-borne diseases, and increasing cardiovascular, respiratory, and mental health issues. Extreme heat and cold events are important drivers of morbidity and mortality, while floods and wildfires contribute to long-term psychological distress. Vulnerable populations, including elderly adults and socioeconomically disadvantaged groups, are mostly affected. This review identifies key knowledge gaps that could be filled by developing evidence-based public health adaptation plans in high-latitude regions.

Atmosphere doi: 10.3390/atmos17080735

Authors: Ji-Won Jeon Sung-Won Park Hyo-Won Lee Soo-Jin Jeong Pyung-Rae Kim Young-Ji Han Sang-Deok Lee

Atmospheric ammonia (NH3) is an important alkaline precursor of secondary inorganic aerosols, but its variability in livestock- and agriculture-influenced peri-urban environments remains poorly constrained. In this study, atmospheric NH3 was measured at a peri-urban site in Chuncheon, South Korea and its variability was examined in relation to micro-meteorology, source direction, and surface–atmosphere exchange. Mean NH3 concentrations were 200.8 ± 91.1 ppb during the April campaign, 83.0 ± 34.4 ppb during the May campaign, and 27.9 ± 16.2 ppb during the December campaign, indicating higher NH3 levels during the April and May campaigns than during the December campaign. Campaign-specific correlation analyses showed that the relationships between NH3 and micrometeorological variables differed among the observation periods, with robust associations observed in April and May but not in December. Moreover, the higher NH3 concentration in the April campaign than in the May campaign, despite the lower mean temperature, indicates that the observed variability was not controlled by temperature alone. Conditional probability function analysis showed that elevated NH3 concentrations in the April and May campaigns were mainly associated with southwesterly winds, suggesting the influence of nearby livestock and agricultural sources. The Penman–Monteith-derived latent heat flux further showed that daytime NH3 enhancement coincided with evaporative surface-exchange conditions potentially favorable for volatilization, although it did not directly quantify manure-derived NH3 emissions. In contrast, the December campaign showed lower NH3 concentrations, weaker source-directional patterns, and limited latent heat flux influence, suggesting suppressed volatilization and intermittent local accumulation under stable conditions. These results indicate that the conditions associated with short-term NH3 variability differed among the selected campaigns, reflecting complementary influences of source direction and campaign-specific micrometeorological and surface-exchange conditions, highlighting the need for concurrent gas- and particle-phase measurements to assess potential implications for PM2.5 formation.

Atmosphere doi: 10.3390/atmos17080734

Authors: Ilias Petrou Pavlos Kassomenos

Climate change is intensifying heatwaves, wildfire activity, and dust transport in the Mediterranean region, yet their combined impacts on mortality remain poorly characterized. Associations between heatwaves, ambient air pollution, source-specific particulate matter, and cardiovascular and respiratory mortality were investigated across Greece at the NUTS2 regional level. Monthly mortality counts for 2014–2022 were analyzed using generalized linear Poisson regression models adjusted for region, year, age group, sex, relative humidity, and population size. Analyses included conventional pollutants (PM2.5, PM10, and O3), wildfire-related particulate matter, and Saharan dust-related particulate matter, while multiplicative interaction terms were used to evaluate modification of pollution effects during heatwave conditions. All major pollutants were positively associated with cardiovascular and respiratory mortality, with the strongest and most consistent effects observed for wildfire-related particulate matter. Heatwave–pollution interaction terms were generally weak, indicating that the combined effects of heat and air pollution were predominantly additive rather than strongly synergistic at the national scale. Spatial analyses revealed substantial regional heterogeneity, with stronger associations identified in parts of northern mainland Greece and selected urbanized regions. These findings highlight the importance of considering wildfire smoke and desert dust separately from conventional air pollutants and support the development of integrated climate-health adaptation strategies in Greece and other climate-vulnerable Mediterranean regions.

Atmosphere doi: 10.3390/atmos17080733

Authors: Fayuan Chen Lizhen Huang Huayang Li Xinzhi Wang

Specific humidity profile retrievals from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard Fengyun-4B (FY-4B) are often degraded by cloud contamination, while strict quality control flags further limit data usability. To address these issues, an optimization framework for FY-4B GIIRS specific humidity profiles was developed using ERA5 reanalysis data spanning January 2023 to January 2024. Guangxi and the Beibu Gulf were selected as the study domain. Independent ERA5 datasets, not involved in model construction, were used as a benchmark to evaluate performance. The results indicate that the original FY-4B GIIRS specific humidity profiles tend to underestimate moisture relative to ERA5. For profiles with quality flags of 0 and 1, the Bias, Root Mean Square Error (RMSE), and Mean Relative Error (MRE) range of −3~0 g/kg, 0~4 g/kg, and 17~53%, respectively. After optimization, the bias is effectively reduced to 0 g/kg. RMSE shows an average reduction of 15% within the 700~300 hPa layer, while the most notable improvement in MRE occurs between 1000 and 920 hPa. For lower-quality data (Flags 2–3), the bias, RMSE, and MRE span −12~0 g/kg, 0~13 g/kg, and 48~140%, respectively. Following optimization, the bias range narrows to −6~0 g/kg. RMSE decreases by 20~40% from the near-surface layer up to 400 hPa, and MRE is reduced by 40% below 300 hPa. A case study of Typhoon “Peipah” further demonstrates the model’s effectiveness. At stations experiencing intense rainfall, the optimized specific humidity profiles show markedly improved accuracy, and the Mean Absolute Errors (MAEs) of derived forecast-related physical variables are substantially reduced. Overall, the proposed optimization model significantly enhances both the accuracy and practical usability of FY-4B GIIRS specific humidity profiles, providing more reliable data support for monitoring severe weather events such as typhoons and heavy rainfall.

Atmosphere doi: 10.3390/atmos17080732

Authors: Jiawei Hao Er Lu Dian Yuan Juqing Tu Zhuoyuan Li Xuehan Zhao Hao Long

Seasonal prediction of summer rainfall over the middle and lower reaches of the Yangtze River Basin (MLYRB) remains challenging because the linkage between preceding climate signals and regional precipitation anomalies involves complex dynamic and thermodynamic processes. This study examines March precipitation over the eastern Mediterranean–West Asia region, hereafter referred to as PE, as an upstream spring signal associated with July rainfall anomalies over the MLYRB. The central objective is to determine, through a relative humidity decomposition framework, whether the humidification accompanying PE-related July rainfall anomalies is dominated by moisture changes or by temperature-related saturation effects. The results indicate that high-PE years are associated with a significant increase in July rainfall over the MLYRB. This rainfall enhancement is accompanied by anomalous circulation patterns favourable for moisture transport and convergence over eastern China. Meanwhile, positive relative humidity anomalies extend from the lower to the upper troposphere, with the 400 and 300 hPa levels showing a particularly close spatial correspondence with the significant rainfall anomalies over the rainfall region. Although absolute water vapour content decreases with height, the coherent upper-tropospheric relative humidity response indicates the presence of a deep moist layer, which is favourable for sustained condensation, reduced dry-air entrainment, and persistent monsoon rainfall. A further moisture–temperature decomposition shows that the PE-related relative humidity response is jointly controlled by changes in atmospheric moisture content and saturation vapour pressure. Over the MLYRB, the increase in relative humidity is primarily associated with enhanced moisture content, whereas temperature-induced changes in saturation conditions are more evident in regions with more coherent temperature anomalies. These findings suggest that the PE-related July rainfall anomaly is supported by a combination of dynamic moisture supply and thermodynamic humidification of the atmospheric column. The study provides a physically consistent explanation for the potential precursor relevance of the PE signal and emphasizes the importance of vertical humidity structure in understanding and predicting summer rainfall anomalies over eastern China.

Atmosphere doi: 10.3390/atmos17080731

Authors: Laleh Jafari Ben Jarihani Jack Koci Ioan Vasile Sanislav Stephanie Duce Dipak Paudyal

Accurate digital elevation models (DEMs) are essential for hydrological modelling and floodplain analysis, particularly in low-relief floodplains where small elevation errors can significantly affect flow routing and inundation extent. This study evaluated the vertical accuracy of six freely available global DEMs across the Flinders River catchment, North Queensland, Australia, using 30,916,100 quality-filtered ICESat-2 ATL06 elevation points for regression-based bias correction and airborne LiDAR datasets from five benchmark regions for independent validation. The evaluated DEMs included TANDEM-X, Copernicus DEM, ALOS AW3D30, SRTM, ASTER GDEM, and the Hydrological DEM. Vertical accuracy was assessed using mean error (ME), root mean square error (RMSE), and residual dispersion before and after calibration. Results showed substantial pre-calibration bias in the Hydrological DEM (ME = −2.93 m) and SRTM (ME = −2.66 m), whereas Copernicus DEM showed minimal initial bias (ME = −0.01 m). Regression-based correction reduced mean errors to within ±0.13 m across all DEMs. SRTM showed the largest improvement, with RMSE decreasing from 3.20 m to 0.55 m, while TANDEM-X achieved the highest post-calibration accuracy (RMSE = 0.14 m). Independent LiDAR validation confirmed improved vertical accuracy while preserving terrain morphology and river gradients.

Atmosphere doi: 10.3390/atmos17080730

Authors: Munkhtuya Lkhamsuren Ganbaatar Gunsen Tsolmonbaatar Danjkhuu Ariunbayar Samdantsoodol Naranbaatar Erdenesuren

Urban intersections are important hot spots of traffic–related air pollution, particularly in places where signal delays and idling increase vehicle emissions. This study evaluates near–road carbon monoxide (CO) concentrations at a signalized four–leg intersection in Ulaanbaatar, Mongolia, by combining field measurements with CALINE4 dispersion modeling. CO concentrations were measured at four receptor locations during two observation periods, 9:00–10:00 and 15:00–16:00. Hourly traffic volume, fleet composition, microclimatic conditions, and receptor geometry were used as model inputs. A fleet-weighted CO emission factor of 12.5 g veh−1 mile−1, adjusted for vehicle age and local stop-and–go traffic conditions, was applied as the base case, while 11.5 and 13.5 g veh−1 mile−1 were tested as sensitivity scenarios. The base case CALINE4 results reproduced the measured spatial pattern reasonably well, with overall performance indicators of r2 = 0.759, FB = −0.006, NMSE = 0.010, RMSE = 0.400 ppm, and MAE = 0.325 ppm. Sensitivity analysis showed that higher emission factors increased predicted CO concentrations, although differences among scenarios were limited. The results suggest that the MOVES–CALINE4 modeling chain is suitable for preliminary screening level CO hot–spot assessment at congested intersections in Ulaanbaatar.

Atmosphere doi: 10.3390/atmos17080729

Authors: Zhiyi Wang Tong Zhou Xun Li Wenxia Xie Lingyu Li

Biogenic volatile organic compounds released from soil (SBVOCs) are an important component of the material exchange and information transmission between terrestrial ecosystems and the atmosphere. Soil ecosystems act as both critical sources and frequently overlooked sinks of BVOCs. SBVOC emissions are mainly regulated by the temperature, moisture, and pH of the soil. Climate warming may enhance volatilization and microbial production in the short term. However, its long-term effects depend on drought, vegetation composition, substrate availability, permafrost thaw, and microbial acclimation. SBVOCs also influence microbial activity, nutrient cycling, plant–microbe interactions, plant defence, and below ground trophic interactions, although the strength of evidence differs among these functions. Ecologically, SBVOCs promote carbon cycling, modulate plant-microbe interactions, and influence atmospheric chemistry. This review further synthesizes SBVOC emission and uptake patterns across different climatic zones. Several challenges remain, particularly the scarcity of long-term quantitative measurements and difficulties in distinguishing multiple emission sources. Our understanding of rhizosphere interactions and climate-change feedback is also limited. It is essential to enhance long-term observational studies and optimize models to deepen our understanding of the role of SBVOCs in the global carbon cycle and air quality.

Atmosphere doi: 10.3390/atmos17080728

Authors: Valérian Authelet Sébastien Dandrifosse Valéry Michaud Jean Pierre Huart Viviane Planchon Damien Rosillon

Weather forecasts are crucial in agricultural decision-making. The first objective was to assess the performance of eight weather forecast models for predicting five key meteorological variables in agriculture: air temperature, humidity, wind speed, global radiation, and precipitation. The tested models were ICON-D2, AROME, HARMONIE, MAR, GFS, and three models from the ECMWF: the deterministic model (HRES), the ensemble model (ENS), and the deep learning-based model (AIFS). The forecasts were compared over a six-month period with observations from 22 weather stations located in Wallonia (Belgium). AIFS achieved the lowest RMSE for predicting global radiation. For lead times up to 48 h, ICON-D2 had the lowest RMSE for wind speed; ICON-D2 and AIFS showed the lowest RMSE for air temperature and humidity, and AIFS and HRES performed best at predicting rainy versus non-rainy days. For lead times beyond 48 h, AIFS had the lowest RMSE for predicting air temperature and humidity, and ENS had the lowest RMSE for predicting wind speed. The second objective was to evaluate the suitability of these forecasts to feed a simple agricultural decision-support tool, helping farmers identify the optimal time windows for spraying plant protection products. ICON-D2 and AIFS yielded the most reliable recommendations.

Atmosphere doi: 10.3390/atmos17080727

Authors: Yunus Tektaş Nizar Polat

Precipitation records from meteorological stations frequently contain gaps caused by sensor, power, or transmission failures, creating uncertainty in hydrological, agricultural, and water-resources applications. This study compared two simple baselines (station-specific monthly climatological mean and temporal linear interpolation), deterministic spatial interpolation, direct reanalysis-based replacement, and machine-learning methods for daily precipitation imputation. Daily precipitation from 14 stations in Eastern and Southeastern Türkiye during 1985–2014 was evaluated using an independent final-test set formed by stratified random masking of 15% of complete observations; the remaining 85% was used for calibration, SHapley Additive exPlanations (SHAP) analysis, cross-validation, and hyperparameter optimization. ERA5-Land variables were transferred to the stations, precipitation was calibrated by Empirical Quantile Mapping, and leakage-controlled Kriging estimates were incorporated as predictors in XGBoost, LightGBM, Random Forest, Support Vector Regression, and Multilayer Perceptron models. The station-month climatological mean (RMSE = 5.4820 mm; NSE = 0.0527) and temporal linear interpolation (RMSE = 5.7059 mm; NSE = −0.0262) performed substantially worse than optimized Kriging and IDW. The full-hybrid LightGBM model achieved the best performance (RMSE = 3.2001 mm; MAE = 0.9814 mm; Pearson r = 0.8317; NSE = 0.6772), whereas direct ERA5-EQM replacement was less accurate (RMSE = 5.2252 mm; NSE = 0.1394). Combining local observations, spatial information, and ERA5-Land covariates therefore improved daily precipitation imputation in the study region.

Atmosphere doi: 10.3390/atmos17080726

Authors: Lulu Zhang Wenwen Yan Yan Wang Pengchu Bai So Fukaya Huiqin Zhang Kewu Pi Keisuke Fukushi Seiya Nagao Ning Tang

Trace elements bound to PM2.5 constitute crucial factors increasing human health risks. To optimize health-oriented air quality management strategies for industrial cities, this study monitored major trace elements in PM2.5 in Wuhan, China, from June 2023 to May 2024. During the study period, the mean concentration of trace elements was 3290 ± 2270 ng/m3, comprising 93.9% crustal elements and 6.1% heavy metals. The positive matrix factorization model identified fugitive dust as the primary source of trace elements, with a mean contribution of 35.6%, followed by traffic emissions (21.8%), industrial emissions (19.1%), biomass and waste incineration (12.3%), and coal combustion (11.1%). Health risk assessment revealed that the cumulative non-carcinogenic and carcinogenic risks of trace elements via inhalation exposure exceeded acceptable levels for both local children and adults, with Mn, As, and Cr as key risk factors. Although coal combustion contributed less to trace element concentrations than other sources, it was the largest contributor to non-carcinogenic and carcinogenic risks, followed by fugitive dust and traffic emissions. These results suggest that trace element pollution control in Wuhan should prioritize reducing coal combustion emissions, while emphasizing the critical importance of sustained traffic regulation and fugitive dust suppression to improve air quality and protect public health.

Atmosphere doi: 10.3390/atmos17080725

Authors: Wan Feng Wei Wang Xueying Zhou

FY-4B/GIIRS temperature-profile products provide an important source of atmospheric sounding information for the radiosonde-sparse Tibetan Plateau, but their error structure and correctability over complex terrain remain insufficiently understood. In this study, the FY-4B/GIIRS temperature profiles over the central-eastern Tibetan Plateau were systematically evaluated using collocated satellite and radiosonde observations from 2023 to 2025. In addition to conventional validation metrics, an error-structure diagnosis was conducted to distinguish the relative contributions of systematic and random components of the satellite–radiosonde differences across 12 cloud–season–layer scenarios. The results show that product quality varies markedly with cloud condition and season. Clear-sky samples are more frequent during the cold season, whereas cloudy samples increase during the warm season, and the proportion of high-quality retrievals decreases substantially under cloudy conditions. Retrieval errors also exhibit clear vertical dependence, with the highest accuracy in the middle layer and larger errors in the lower and upper layers. The error-structure diagnosis further reveals pronounced scenario dependence: random errors dominate most clear-sky conditions, whereas systematic biases are particularly evident in the clear-sky lower layer in winter, the clear-sky upper layer in summer, and most cloudy scenarios. A scenario-based LightGBM framework effectively reduces these systematic differences across the 12 cloud–season–layer scenarios. Test-set R2 ranges from 0.43 to 0.82, while mean Bias decreases from −1.66 to 0.01 K and mean RMSE from 3.35 to 1.78 K, corresponding to an average reduction of 45.56%. In contrast, the residual-error magnitude is less predictable, with STD-model R2 values of 0.09–0.37. These findings indicate that error-structure diagnosis can help identify correctable retrieval-error components and that scenario-based correction provides an effective approach for improving the reliability of FY-4B/GIIRS temperature profiles over complex plateau terrain.

Atmosphere doi: 10.3390/atmos17080724

Authors: Simon M. Berkowicz Bert G. Heusinkveld

Increasing global insecurity for potable water has led to atmospheric water harvesting as a viable supplementary source. Passive dew water harvesting is simple to carry out but atmospheric conditions determine the frequency and amount of dew that can be collected, and up to 0.5 L/m2/night can be considered as an upper ceiling. Thus, active condensers using refrigeration and cooling systems have been developed to increase collection totals, requiring an electrical or solar power supply. In the last decade, adsorption/absorption techniques of water vapor have been studied to maximize collection, with the potential for low costs, portability, and high volumes, and they are operational even in arid regions with low humidity, islands, and remote regions. This could become a gamechanger in securing affordable potable water. Citizen Science is suggested for dew observation and collection data to increase observation points that could be used to improve the resolution/accuracy of local, regional, or global dew modelling. It would promote environmental and water literacy by engaging participants ranging from primary school communities to senior individuals. Teleconferencing now provides access to a worldwide audience and the inclusion of participants no matter their location.

Atmosphere doi: 10.3390/atmos17080723

Authors: Wenbo Liu Zong Tan Fang Wang Han Wang Fuquan Ma Xiaojian Song Xiaomei Lu Wenkai Guo

Volatile organic compounds (VOCs) are the main precursors of ozone (O3) and secondary organic aerosols (SOAs), posing a significant threat to the environment and human health. In this study, the characteristics, potential for the generation of O3 and SOAs, sources and health risks of volatile organic compounds were investigated based on annual monitoring data for 2021 in Dezhou, which is located in the northwest of Shandong Province, China, and adjacent to the Beijing–Tianjin–Hebei region. The results showed that the ambient VOC concentrations were lower in spring and summer and higher in autumn and winter. The species contributing to the O3 formation potential (OFP) and SOA formation potential (SOAFP) varied by season, but those with high contributions were all olefins and aromatic hydrocarbons. O3 pollution occurred frequently in summer, with a proportion of 40.22%. Focusing on summer, six sources were identified through the positive matrix factorization (PMF) model, with the petrochemical industry (26.1%) and combustion (25.6%) being the primary sources. The non-carcinogenic risk values of the involved 18 toxic VOCs were all within the safety threshold, while the carcinogenic risk of benzene was regarded as low-probability under long-term exposure. This study provides significant support for targeted control of air pollutant emissions and improvement in regional air quality.

Atmosphere doi: 10.3390/atmos17080722

Authors: Myeong-Ju Kim Ui-Jae Lee Yong-Pyo Kim Ji-Yi Lee Young-Ji Han Na-Rae Choi Yong-Jae Lim Seung-Ha Lee In-Ho Song Sang-Deok Lee

Volatile organic compounds (VOCs) are key precursors of ozone and secondary pollutants, yet transport-related changes in their composition between upwind and downwind regions remain insufficiently characterized. Simultaneous high-time-resolution measurements were conducted at an urban site in the Seoul Metropolitan Area (SMA) and a suburban downwind site in Chuncheon, South Korea, from 3 to 18 June 2024. Thirteen VOC species were measured using proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) and evaluated using backward trajectory clustering, concentration-weighted trajectory analysis, ozone formation potential (OFP), and a toluene-to-benzene ratio-based photochemical aging proxy. The summed concentration of the 13 measured VOC species was higher in SMA (40.24 ± 19.41 ppb) than in Chuncheon (18.28 ± 6.39 ppb), while oxygenated VOCs accounted for approximately 80% at both sites. In Chuncheon, VOC composition and OFP varied descriptively among air-mass clusters, with westerly and southwesterly pathways passing through or near SMA associated with relatively elevated aromatic VOC concentrations and OFP. Equivalent photochemical age overlapped substantially among clusters and was not directly proportional to VOC concentration or OFP. These results indicate that downwind VOC characteristics reflect the combined effects of source composition, transport, dilution, atmospheric mixing, and chemical processing.

Atmosphere doi: 10.3390/atmos17080721

Authors: Dario Hourngir Massimiliano Burlando

A real downburst event that occurred in the city of Genoa, Italy, on 14 August 2018, is simulated using the full-cloud model CM1. The initiation of the thunderstorm cell is triggered through the warm bubble technique, which allows simulating the whole development of the cumulonimbus cloud in a spontaneous way. Following the release of initial instability into the lower troposphere, a sustained updraft generates an almost 13-km tall cloud. This, in turn, produces a strong downdraft that accelerates to around −20 m/s within the lowest 3 km near the ground. It is shown that, despite the downdraft’s shape is almost linear during the mature stage rather than circular, the downburst maintains to a large extent a cylindrical axisymmetry while spreading out, similar to impinging jet (IJ) downburst-like models. Like IJ models, the downburst develops multiple vortex rings at the ground, whose physical origin is mostly related to subsequent downdraft discharges. Thunderstorm outflows are validated using LiDAR wind measurements, and the simulated wind fields are made available to the scientific community as an open-access dataset in a public repository.

Atmosphere doi: 10.3390/atmos17080720

Authors: Stuart N. Riddick

Offshore methane emissions are increasingly quantified using a range of observational approaches, yet reported estimates often show substantial variability between methods. This study examines whether disagreement can arise from atmospheric transport processes alone by investigating how the marine boundary layer influences the relationship between measured concentrations and inferred emission rates. An idealised dispersion framework was used to compare three commonly applied approaches, Gaussian plume inversion, aircraft mass balance, and satellite-based methods, across representative offshore atmospheric regimes. By holding emissions constant and varying only atmospheric structure, the framework isolates the influence of transport processes on inferred emissions. The results show that each method exhibits regime-dependent bias arising from different physical mechanisms. Gaussian methods are highly sensitive to plume alignment and may fail under lateral displacement. Aircraft mass balance remains robust when the plume is fully sampled but becomes unreliable when the sampling volume does not intercept the plume. Satellite methods consistently detect the plume but exhibit systematic bias when plume transport is decoupled from assumed wind speeds. These results demonstrate that disagreement between offshore methane emission estimates can arise solely from changes in atmospheric regime under the conditions represented by the model. Under conditions where standard transport assumptions are not satisfied, differences between estimates reflect the influence of atmospheric structure on plume behaviour and sampling, rather than measurement error alone. Emission estimates are therefore only reliable where the underlying transport assumptions are satisfied, and convergence between methods cannot be universally assumed under realistic offshore conditions.

Atmosphere doi: 10.3390/atmos17080719

Authors: Shaohua Zhai Feng Wang Mengyu Zhai Hongkuan Zang Yupeng Fu Mengmeng Hu

Drought is a major climatic hazard affecting agricultural production and water resource management. This study investigates the spatiotemporal evolution of summer drought across nine major agricultural regions in China and quantifies associated uncertainties under historical (1981–2010) and future periods (2041–2070 and 2071–2100). Using multi-model simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four SSP scenarios (SSP126, SSP245, SSP370, and SSP585), drought variations were characterized using the Standardized Precipitation Index at a 3-month timescale (SPI-3). Sen’s slope estimator combined with the Mann–Kendall test was applied to detect drought trends, and factorial analysis was used to quantify uncertainty contributions. Results show pronounced regional differences in historical drought conditions, with northern arid and semi-arid regions exhibiting stronger precipitation deficits (mean SPI-3 = −0.435), while the Yangtze River Plain and Northeast China Plain show wetter conditions. Historical trends indicate increasing drought conditions in South China, whereas humidification trends dominate several northern and eastern regions. Under future scenarios, most regions show overall wetting trends, but drying tendencies persist in South China. Factorial analysis reveals that regional differences (58.34%) and climate model uncertainty (31.15%) are the dominant contributors to drought variability. Overall, future summer drought in China is characterized by a general wetting tendency with localized intensification of drought conditions.

Atmosphere doi: 10.3390/atmos17080718

Authors: Miodrag Rancic

The accurate representation of frontal zones—characterized by sharp scalar gradients—remains a critical challenge in regional objective analysis and data assimilation, particularly when utilizing sparse or stochastically distributed observations. This study evaluates the efficacy of Multiquadric Radial Basis Functions (RBFs) as a high-order alternative to standard spatial mapping operators frequently used in machine learning atmospheric emulators. We contrast the performance of the regularized, C∞-continuous RBF approach against nearest neighbor and linear mesh interpolation schemes using both synthetic baroclinic wave profiles and an operational case study of the intense extratropical cyclone that impacted the East Coast of North America in mid-March 1993. To mitigate characteristic boundary artifacts and geometric clipping in bounded regional domains, we implement a targeted numerical stabilization framework combining localized boundary mirroring with four-corner domain anchoring. Our quantitative results demonstrate that the optimized RBF framework substantially improves gradient fidelity and reduces Root Mean Square Error across a wide range of observation densities. Furthermore, we evaluate the computational scalability of RBFs on high-performance computing architectures, demonstrating how Algebraic Multigrid solvers and Graphics Processing Unit acceleration mitigate the foundational O(N3) computational bottleneck. We conclude that RBF interpolation provides a physically consistent, analytically differentiable manifold that addresses the derivative discontinuities of traditional linear methods, offering a stable pre-processing framework for high-resolution meteorological analysis and machine learning optimization.

Atmosphere doi: 10.3390/atmos17080717

Authors: Xiaowei Wang Tao Gao Yimeng Cui Guanzhang He Tengteng Li Lin Zhang Mingda Wang Ye Liu

Hardware-in-the-loop (HIL) technology combines physical hardware with virtual models to achieve efficient closed-loop simulation of automotive systems, demonstrating significant advantages in the development of automotive emissions and energy consumption. This paper reviews the current applications of three HIL technologies, including powertrain-in-the-Loop (PIL), engine-in-the-Loop (EIL), and Virtual Test Bed (VTB). It also explores their role in addressing the increasingly stringent regulations on emissions and energy consumption. Research indicates that PIL technology can significantly improve the efficiency with which hybrid powertrain control strategies are verified by integrating real powertrains with virtual environments. EIL technology enables the high-precision simulation of real-world emissions at low hardware cost. VTB technology, meanwhile, significantly reduces the calibration period by leveraging high-precision models and intelligent algorithms. These three technologies form a comprehensive development and verification chain, covering everything from components to vehicles. However, HIL technology still faces challenges relating to model accuracy, system complexity and cost. Therefore, the most suitable technology should be selected based on development objectives, timeframe, and budget.

Atmosphere doi: 10.3390/atmos17080716

Authors: Qin Huang Moyan Liu Yeongbin Kwon Upmanu Lall

Artificial intelligence (AI) weather models achieve forecast skill comparable to numerical weather prediction at far lower computational cost, yet their reliability for high-impact extremes remains largely uncharacterized. We present an event-based diagnostic evaluation of Aurora, a deterministic AI model, across 16 case studies chosen for physical diversity rather than statistical representativeness, spanning tropical cyclones (TCs), freezes, heatwaves, atmospheric rivers (ARs), and extreme precipitation at lead times from 1 to 21 days. Aurora showed strong short-range (1–7 day) skill: TC track and landfall positions were accurate for well-behaved systems, temperature extremes achieved high spatial agreement, and the atmospheric river structure was reproduced faithfully. This study’s central finding is a pattern–amplitude divergence: beyond 7 days, large-scale circulation patterns remained moderately skillful even as surface amplitudes weakened toward climatological values. Event-specific failures include a severe recurvature forecast failure for Hinnamnor, TC intensity biases, and a pronounced in-sample versus out-of-sample precipitation skill gap that is substantially confounded by event-type differences (large-scale monsoon vs. mesoscale-convective/cutoff-low regimes), so the gap cannot be attributed to training-period recency alone. Across the events examined here, Aurora provides reliable deterministic guidance within 7 days. We recommend deploying Aurora as a rapid ensemble generation and regime-identification tool alongside physics-based numerical weather prediction at short-to-medium range, with its directional intensity and amplitude biases corrected through post-processing before standalone use in operational warnings.

Atmosphere doi: 10.3390/atmos17080714

Authors: Aikaterini Pappa John Kalogiros Maria Tombrou Anastasios Papadopoulos Petros Katsafados

Accurate short-term precipitation nowcasting remains challenging in complex terrain regions, where storm displacement, evolution, and orographic enhancement strongly affect precipitation distribution. This study evaluates three precipitation nowcasting schemes initialized from LAPS analysis fields: first-order advection (Control), advection–diffusion (AD), and advection–diffusion coupled with the linear theory of orographic precipitation (ADLOP). The schemes are tested over the Attica region of Greece using three high-impact precipitation events representing different synoptic weather regimes and verified against high-resolution weather radar observations. Forecast performance is assessed using continuous, categorical, and neighborhood-based spatial verification metrics. Results show that the Control performs competitively for light precipitation and at larger neighborhood sizes in localized events. The inclusion of diffusion in the AD scheme generally reduces random errors. In this limited three-case sample, aggregated results show that ADLOP reduces systematic bias, with reductions reaching approximately 33% at longer lead times and showing higher detection scores. However, its added value is strongly dependent on the terrain-influenced precipitation regime and may be accompanied by increased error at longer lead times. Overall, the benefits of incorporating diffusion and simplified linear orographic forcing depend on precipitation regime, lead time, and verification metric; therefore, the results should be interpreted as diagnostic case-study evidence rather than as a general assessment of ADLOP performance.

Atmosphere doi: 10.3390/atmos17080715

Authors: Tingting Zhao Le Thi Nguyet Yadong Li Yuanyuan Meng Maowei Chen

Maritime transport emits a range of atmospheric pollutants, including sulfur oxides (SOx), nitrogen oxides (NOx), particulate matter (PM), and volatile organic compounds (VOCs), which contribute to air pollution and are associated with adverse environmental and public health impacts. To mitigate these impacts, the International Maritime Organization (IMO), London, UK introduced the global sulfur cap (IMO 2020), which entered into force on 1 January 2020, limiting the sulfur content of marine fuels to 0.50% m/m. This study systematically reviews the environmental and public health impacts of shipping emissions following the implementation of IMO 2020. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines using Scopus, Web of Science, PubMed, and supplementary sources. Following a structured screening process, 67 studies published between 2020 and 2025 were included and analyzed through descriptive, bibliometric, and thematic synthesis approaches. The reviewed studies consistently reported substantial reductions in sulfur dioxide (SO2) emissions, sulfate aerosols, and shipping-related particulate matter following IMO 2020. These reductions were associated with improved air quality in major maritime and port regions and reduced population exposure to harmful pollutants. However, the reviewed evidence also identified ongoing challenges, including emissions of ultrafine particles and volatile organic compounds, secondary pollutant formation, contamination associated with scrubber washwater discharge, and reduced sulfate aerosols contributing to positive radiative forcing. Overall, the reviewed evidence suggests that sulfur-related air pollution generally declined following the entry into force of IMO 2020, although these observations should be interpreted alongside other concurrent developments that influenced global shipping activities during the study period. This review synthesizes current evidence on the environmental and public health impacts of IMO 2020, identifies emerging knowledge gaps, and provides an evidence base to support future shipping emission policies and research.

Atmosphere doi: 10.3390/atmos17080713

Authors: Guangli Huang Zhen Wang Tengfei Xu

Coal mine gas is one of the primary hazards encountered in mining operations. The continuous emission of gas in the heading face during tunneling poses potential dangers, with a significant likelihood and magnitude of gas-related accidents. Tunnel ventilation not only dilutes the concentration of pollutants but also ensures underground air quality, safeguarding the physical and mental health of workers. Therefore, underground tunnel ventilation is particularly crucial. Analyzing the airflow field in single-heading tunnels and optimizing ventilation layout are of paramount practical significance for ensuring mining safety. Initially, based on engineering realities, on-site experimental measurements were conducted in the simulated area of the single-heading tunnel. The essential parameters for ventilation simulation were obtained, and a numerical three-dimensional model of the ventilation in the single-heading tunnel was established using Fluent software. Subsequently, through numerical simulations, the airflow field and gas concentration distribution patterns in the single-heading tunnel were obtained. It was verified that the tunnel airflow field exhibits distinct layering and uneven distribution. Gas concentration on the return air side is higher than that on the intake air side, with elevated gas concentrations in the upper corner and the lower right corner on the return air side. Further, four main factors influencing gas concentration were identified: duct diameter, duct exit velocity, distance from the duct exit to the heading face, and the suspension position of the duct. Finally, an orthogonal design approach was employed, and numerical simulations were conducted to study the sensitivity of these factors to gas concentration. The optimal ventilation layout was determined and validated: a duct diameter of 0.7 m, an airspeed of 13 m/s (without considering a fixed air volume), a distance of 7 m from the duct exit to the heading face, and the duct suspended in the lower part of the tunnel. The stabilized gas concentration under these conditions was determined to be 0.1378%.

Atmosphere doi: 10.3390/atmos17080712

Authors: Ivica Djalović Dejan B. Stojanović Rastislav Stojsavljević Mladjen Jovanović Dalibor Nikolić

The Western Balkans (Serbia, Croatia, Bosnia and Herzegovina, and Montenegro) occupy a transitional climatic position between the Mediterranean hotspot and the continental Balkan interior within Southeast Europe, yet multi-country, multi-model, station-resolved assessments of regional aridification remain scarce. We combined quality-controlled monthly temperature and precipitation records from 100 stations across Serbia, Croatia, Bosnia and Herzegovina, and Montenegro (1961–2020) with bias-corrected projections from a five-member CMIP6 ensemble (EC-Earth3, MPI-ESM1-2-HR, CNRM-CM6-1, MRI-ESM2-0, IPSL-CM6A-LR) under four SSP scenarios to 2100 and benchmarked these projections against five short-term forecasting baselines on a held-out 2019–2020 period. Aridity was quantified using the Ellenberg Climate Quotient (EQ) and De Martonne Index. Results: Ninety-eight of 100 stations showed significant warming (1961–2020, p < 0.05), and 26 showed significant aridification. Ensemble mean end-of-century EQ change ranged from −6.9% (SSP1-2.6) to +44.6% (SSP5-8.5, Montenegro), with the largest absolute increases in the Pannonian lowlands; inter-model uncertainty exceeded inter-scenario uncertainty by roughly a factor of two. Deep learning forecasters (TFT, N-HiTS) outperformed bias-corrected CMIP6 output for short-term, station-scale temperature forecasting, while a simple climatological baseline remained competitive for precipitation. CMIP6 projections and AI forecasting are complementary: multi-model ensembles remain indispensable for long-term, scenario-conditioned planning, while AI offers superior near-term predictive skill for operational decisions.

Atmosphere doi: 10.3390/atmos17080711

Authors: Yuanfa Ji Shifeng Liu Xiyan Sun Wenjie Sun Wenbin Liang Kamarul Hawari bin Ghazali Qiang Fu Yihua Huang

Low-latitude ionospheric irregularities cause rapid fluctuations in GNSS signal amplitude and phase, threatening receiver tracking and services. We propose a dual-channel XGBoost–Transformer–LSTM fusion model to address rare events, missed-detection costs, and the difficulty of combining physics-informed and profile-structure information. Data from the HKOH station and geospace products spanning 1 January 2020–21 March 2024 were used. Space-weather variables, temporal factors, previous-day scintillation status, and a TEC120 latitude–UT grid at 06:00–11:00 UT were used to predict GPS L1 amplitude scintillation during 11:00–14:00 UT. The XGBoost channel used the raw TEC grid, first-order latitudinal and hourly TEC differences, and background variables, whereas the Transformer–LSTM channel extracted within-day structure. Channel probabilities were combined by soft voting, with the weight and threshold selected on the validation set. Chronological test results were recall = 0.8788, F1-score = 0.5949, ROC-AUC = 0.8263, PR-AUC = 0.6097, CSI = 0.4234, TSS = 0.4511, and MCC = 0.4096. Fusion raised recall from 0.8030 to 0.8788 but reduced precision from 0.4609 to 0.4496. Paired bootstrap analysis supported a stable improvement only in recall. The model therefore suits warning applications prioritizing detection over minimizing false alarms.

Atmosphere doi: 10.3390/atmos17080710

Authors: Muzaffer Göztaş Nida Oruç Ünal Doğan Yıldız Dursun Yıldız

In this study, changes in the surface area of Lake Burdur during the 2015–2025 period and the spatial direction of the associated shrinkage were examined using Sentinel-2 Level-2A satellite images. A total of 111 satellite images, each representing a monthly period, were analyzed using a fixed study window and a lake vicinity mask; a three-cluster unsupervised K-means segmentation method was applied to separate the water surface from bare/drained areas and vegetation classes. The resulting binary water masks were used to convert the lake surface area to km2 on a pixel-by-pixel basis, and the geometric center of the lake mass was calculated for each observation date. The unique aspect of this study is that it evaluates lake shrinkage not only through a decrease in surface area but also as a directional spatial process via the movement of the centroid center. In this context, the cumulative displacement was decomposed into X/West and Y/South components using the initial centroid point as a reference; OLS-based linear and logarithmic trend models were established for both directions. Model performances were compared using a 15-fold Monte Carlo cross-validation approach with R2, adjusted R2, NSE, KGE, MAE, MAPE, MSE, and RMSE metrics; additionally, the statistical significance of model differences was assessed using the Wilcoxon signed-rank test. The findings indicate that the logarithmic model yields more balanced and reliable results in the X/West direction, while the linear model does so in the Y/South direction. Based on this model structure, spatial projections were generated for the 2026–2035 period, and it was observed that the projection bands remained stable despite Monte Carlo-based coefficient uncertainty. In conclusion, the study demonstrates that lake drawdowns in semi-arid closed basins can be monitored in a more interpretable and statistically robust manner using Sentinel-2-based segmentation, centroid kinematics, and cross-validated trend modeling.

Atmosphere doi: 10.3390/atmos17080709

Authors: Godiraone A. Nkoni Kgakgamatso M. Mphale Nicholas C. Mbangiwa Sydney. H. Samuel

The monthly CHIRPS ver. 2 gridded rainfall dataset from 1981 to 2016 was employed to analyze distinct precipitation variability patterns and regimes in semi-arid Botswana. An S-mode eigen analysis was performed on the correlation matrix of the rainfall data to extract principal components. The principal component scores (pc-scores) were further rotated using the Varimax eigen analysis method to yield unique precipitation patterns. The rotated pc-scores indicated three separate sub-regions displaying varying precipitation patterns over time. The application of non-hierarchal clustering (K-means) on the pc-scores identified four distinct zones characterized by unique rainfall patterns. A regional frequency study of rainfall in the sub-regions was performed using L-moments. Probabilistic analysis was utilized to model annual rainfall using six common regional frequency analysis probability distribution functions (pdfs): Pearson Type 3 (PE III); three-parameter Weibull; generalized; extreme value (GEV), normal (GNO), logistic (GLO), and Pareto (GPA). The pdfs that demonstrated the optimal correspondence were determined by the goodness-of-fit test, utilizing the Z-statistic. Each cluster displayed unique pdfs and goodness-of-fit pdfs, with the GLO, GEV, GNO, and Weibull offering the most precise representations.

Atmosphere doi: 10.3390/atmos17080707

Authors: Amaechi E. Innocent Kevin P. Wyche Balendra V. S. Chauhan

Urban air pollution poses significant risks to human health, ecosystems, and the environment, highlighting the need for accurate monitoring of atmospheric pollutants. This study investigated the spatial and temporal variability of key tropospheric pollutants, including nitrogen dioxide (NO2), sulfur dioxide (SO2), nitrous acid (HONO), formaldehyde (HCHO), and ozone (O3), in Brighton, UK, using an integrated approach that combined ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), Long-Path Differential Optical Absorption Spectroscopy (LP-DOAS), and Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) observations. Ground-based measurements comprised four MAX-DOAS campaigns conducted between 2021 and 2024 and a long-term LP-DOAS dataset spanning 2017–2023, complemented by coincident TROPOMI observations. The datasets were spatially co-located, temporally aligned, quality-controlled, and analysed using statistical methods, time-series analysis, and polar plot techniques to assess pollutant variability, identify emission sources, and evaluate the agreement between satellite and ground-based observations. The results revealed clear seasonal and diurnal variations in pollutant levels, with elevated NO2 during winter and enhanced O3 during summer, reflecting the influence of anthropogenic emissions and photochemical processes. Polar plot analysis further identified distinct wind-dependent pollutant patterns, indicating the importance of local emission sources. Comparisons between ground-based and satellite observations showed that TROPOMI successfully captured the temporal variability of NO2 measured by means of LP-DOAS, with a moderate positive correlation (rs = 0.55), but underestimated NO2 relative to MAX-DOAS observations (rs = 0.38), reflecting differences in measurement geometry, spatial resolution, and retrieval sensitivity. The overall findings demonstrate that integrating ground-based and satellite observations provides a more comprehensive understanding of urban air quality than either approach alone. This combined monitoring framework improves confidence in satellite-derived atmospheric products and supports more effective air quality assessment and management in Brighton and similar urban environments.

Atmosphere doi: 10.3390/atmos17070708

Authors: Juan P. Romero-Astudillo Luis H. Álvarez-Játiva Paúl Tafur-Escanta Juan Guamán-Tabango

Understanding microclimatic variability at the land–atmosphere interface is essential for improving knowledge of atmospheric moisture dynamics in heterogeneous mountainous ecosystems. This study analyzes atmospheric relative humidity and soil moisture variability in an experimental Juglans neotropica Diels plantation located in the Ecuadorian Andes under real field conditions. An autonomous photovoltaic-powered monitoring system equipped with low-cost environmental sensors was deployed continuously for 60 days, generating more than 86,000 environmental measurements of atmospheric relative humidity above and below the canopy, together with soil moisture observations. The results revealed persistent vertical humidity stratification associated with canopy structure, characterized by systematically higher atmospheric humidity beneath the canopy compared to the upper atmospheric layer (ΔRH ≈ −36%). Strong intersensor coherence was observed between canopy levels (r = 0.8535), indicating stable temporal consistency in atmospheric variability patterns throughout the monitoring period. Soil moisture exhibited comparatively more stable temporal dynamics than atmospheric humidity, suggesting partial microclimatic decoupling between atmospheric and edaphic layers. The observed humidity gradients remained temporally stable during both daytime and nighttime conditions, supporting the interpretation of canopy-mediated atmospheric buffering processes within the plantation environment. From an ecological perspective, the results indicate that vegetation structure contributes to localized moisture retention, attenuation of short-term atmospheric fluctuations, and regulation of near-surface microclimatic conditions under heterogeneous Andean environmental conditions. Rather than focusing on instrumentation performance, the study provides empirical evidence of persistent canopy-related atmospheric regulation and moisture stratification in a native Andean forest species under continuous field monitoring conditions. These findings contribute to the understanding of land–atmosphere interactions, ecohydrological dynamics, and vegetation-mediated microclimatic regulation in mountainous ecosystems.

Atmosphere doi: 10.3390/atmos17070706

Authors: Andrey Tsapalov Chutima Kranrod

Although radon measurements have been carried out in most countries for several decades, a rigorous methodology for assessing the reliability (as well as the effectiveness) of indoor radon testing between short-term and long-term measurements has not yet been established at either the national or international level. National regulators, lacking scientific justification, arbitrarily set the duration of indoor radon testing (from a few minutes to several months) preferring long-term measurements simply because it “seems more reliable”. To solve this fundamental regulation problem, an original (innovative) methodology is proposed based on the algorithms of the previously published Rational Method for indoor radon testing, which ensures 95% decision-making reliability within modern metrological concepts. A comparison of the reliability (and effectiveness) between short-term and long-term measurements can be made by taking into account the radon situation at both national and global levels. The obtained dependence of the mean (among 26 countries) percentage of definite (reliable) decisions vs. the duration of measurements indicates: (i) the greater effectiveness of using short-term measurements compared to long-term ones, as well as (ii) the excessive focus of national and international regulators only on instrumental uncertainty within QA/QC to the detriment of temporal (key) uncertainty in decision-making. Key conclusion: The established practice of radon regulation on both global and national levels contains a number of underlying myths, and therefore clearly needs to be rethought and updated within a rational (scientific) strategy. A vital necessity are specific experimental (field) studies to verify (clarify) the national values of the temporal (key) uncertainty of indoor radon (and thoron EEC), as well as the creation of simple and effective (inexpensive but reliable) IoT tools for large-scale measurements of indoor radon through the involvement (and at the expense) of the population.

Atmosphere doi: 10.3390/atmos17070705

Authors: Fiona A Symon Katie Eminson Jack Satchwell Leah Cuthbertson Anna L. Hansell

Asthma and seasonal allergic rhinitis are common conditions affecting health, wellbeing, and work place productivity. Current pollen monitoring requires experienced technicians and has a lag of at least 24 h, which limits usefulness for patient management. We evaluated agreement of daily pollen concentrations from an automated real-time sampler, the Swisens Poleno Jupiter, with a traditional Hirst-type trap using technician counting over a single monitoring season in England. We used regression models to assess the impact of weather covariates on differences. Total pollen showed a strong correlation (r = 0.74) but moderate Intraclass Correlation (ICC = 0.52) between devices, due to concentrations (total and taxa level) being higher for the manual than the automated sampler. For individual pollen taxa, strong correlation and good agreement was observed for Pinus, Alnus and Fraxinus throughout the monitoring period. Strong correlation but low/moderate agreement was observed for Poaceae (grass), Betula, Quercus and Taxus/Cupressus. No correlation or agreement was observed for Corylus and Platanus. Temperature explained most of the variability between samplers. We conclude that daily total pollen can be tracked using an automated Poleno, as can grass within its flowering season, but Poleno recognition algorithms developed for mainland Europe need further refinements for UK species. Improving agreement in quantification is of particular importance given that current allergenic thresholds are based on manual readings.

Atmosphere doi: 10.3390/atmos17070704

Authors: Yuval Yoav Levi Pavel Khain David M. Broday

Disentangling the relative roles of emissions and atmospheric processes in controlling air-pollutant concentrations remains a central challenge in air-quality management. Although machine-learning (ML) models can accurately predict pollutant concentrations, the temporal evolution of the importance of individual drivers is often difficult to interpret. Here, we introduce a framework for reconstructing time-resolved feature importance (FI) in ML air-quality models. Hourly NO2 and PM2.5 concentrations were simulated across clusters of observations, defined along concentration trajectories in a state–space spanned by source intensity and meteorological variables. Within each cluster, predictor importance is quantified and mapped back onto the corresponding time points, yielding continuous FI time series for all predictors. The framework is demonstrated using observations from the nationwide air-quality network in Israel, together with traffic-related source indicators and meteorological parameters. The dominant drivers differ markedly between the two pollutants: NO2 variability is primarily associated with local emissions, mechanical transport, and turbulent mixing, whereas PM2.5 variability reflects predictors that are related to nucleation, coagulation, hygroscopic growth, long-range transport, and chemical transformation. The feature importance exhibits pronounced seasonal, regional, and diurnal variability, including modulation around traffic rush hours. These results demonstrate the value of time-resolved interpretability for diagnosing drivers of air-pollutant variability and improving the representation of processes in statistical air-quality models.

Atmosphere doi: 10.3390/atmos17070703

Authors: Jie Tang Tao Feng Zhou Jian Lei Wang Li Li

Understanding the physical mechanisms of compound hot-drought events (CHDEs) over the Yangtze River Basin (YRB) is essential for improving climate predictability. Using high-resolution observations and ERA5 reanalysis, this study conducted a three-dimensional comparative diagnosis of two spatially distinct CHDEs (August 2022 and 2019), deconstructing their circulation dynamics and thermodynamic budgets. Specifically, the 2022 basin-wide event was highly associated with La Niña and a negative Indian Ocean Dipole (NIOD). Anomalous latent heating in the tropical eastern Indian Ocean favored an extensive meridional Hadley circulation and a deep high-pressure belt, blocking southwest moisture transport. Thermodynamically, intense downward vertical motions produced severe descending adiabatic warming, which offset longwave radiational cooling and sustained anomalous heat accumulation throughout the deep troposphere. Conversely, the 2019 localized event occurred under a weak Central Pacific El Niño and a positive IOD. The anomalous tropical cooling weakened systematic subsidence and provided a favorable background for a Rossby wave train, featuring an offshore cyclone over the Western North Pacific that severed moisture pathways. Characterized by shallower vertical subsidence, the lack of a penetrative adiabatic heat source merely maintained a lower-level thermodynamic balance, confining extreme heat to the central–eastern YRB. Although superficially similar at the surface, these CHDEs are sustained by two distinct ocean–atmosphere interaction paradigms. Clarifying these dual paradigms provides a critical physical basis for improving sub-seasonal prediction.

Atmosphere doi: 10.3390/atmos17070702

Authors: Valentina Cannelli Gianfranco Galli Antonio Piersanti Gaia Soldati Massimiliano Ascani

This study presents a combined analysis of radon and meteorological time series acquired over several years in a building regularly occupied by workers and occasionally by visitors. The building is founded directly on basaltic tuff in an area characterized by elevated radon levels. A multilevel monitoring system with high spatial and temporal resolution was deployed, consisting of 14 low-cost detectors measuring radon and indoor meteorological parameters; an outdoor weather station was employed for environmental and soil monitoring, and a RAD8 instrument was used to identify the main radon entry points. The monitoring system allowed us to characterize, on daily and seasonal timescales, the variability of radon concentration throughout the building and its dependence on meteorological and anthropogenic factors. Cluster analysis combined with a principal component analysis revealed three distinct meteorological regimes (warm, cold, stormy). Indoor radon concentration centroids in the cold and stormy regimes were associated with up to about 3.9 kBq/m3 at RDP2exp, over an order of magnitude above the EU reference level of 300 Bq/m3, while warm, dry conditions yielded significantly lower levels. Horizontal radon diffusion times ranged from 30 to 90 min, and vertical diffusion times ranged from 90 to 180 min across floors. A 24 h exposure risk assessment shows that occupancy during working hours (08:00–18:00) coincides with the daily radon minimum, but baseline concentrations remain above 300 Bq/m3 even during these periods, necessitating mitigation strategies.

Atmosphere doi: 10.3390/atmos17070701

Authors: Felipe Puff Dapper Rafael Battisti Elvis Felipe Elli Maxuel Fellipe Nunes Xavier Alisson Neves Harmyans Moreira Rilner Alves Flores Henrique Fonseca Elias de Oliveira Marcos Vinícius da Silva Jeffer Andrey Quintero Garcia Maria Lucia Jaimel Verjel Camilla de Souza Correia Marcio Mesquita

Crop management strategies are essential to achieve maximum crop yields. In this context, crop models can be used to understand yield variability. The aim of this study was to evaluate historical yields based on the sowing date and plant density. Field experiments were conducted during two growing seasons at two locations using a maturity group 7.6 cultivar, with distinct sowing dates and plant densities (10 to 50 plants m−2). After calibration, the model was applied to historical weather data (1990–2022) for nine locations across the Cerrado biome. The CSM-CROPGRO-Soybean model simulated soybean development with biases for physiological maturity of +8 and −7 days for calibration and evaluation, respectively. The relative root mean square error for yield was below 14%. Similar trends were observed for the leaf area index (LAI) across plant densities, while biomass partitioning proved to be a limiting component, mainly for stem biomass. The optimal plant population ranged from 20 to 30 plants m−2, whereas a low density (10 plants m−2) resulted in higher yield losses under late sowing. Therefore, the use of crop simulation models is an effective tool to understand and optimize sowing dates and plant densities under historical climatic conditions in the Cerrado.