This study investigated an intense and unusual summer transboundary dust storm event that occurred between 21 and 23 June 2024. By integrating remote sensing observations, reanalysis data, WRF-Chem simulations, and LAGRANTO trajectory tracking, we systematically revealed the dust emission, transport, deposition, and formation mechanisms of this event. The dust primarily originated from the Gobi region of southern Mongolia, where concentrations exceeded 10,000 µg m−3, and decayed exponentially as the Mongolian cyclone moved southeastward. Post border-crossing into China, the event transitioned to blowing and floating dust, with concentrations decreasing significantly. During transport, dry deposition dominated the source area and the frontal part of the transport path in the early stages, while wet deposition was associated with the precipitation system of the Mongolian cyclone and concentrated north and east of the cyclone’s track. On 21 June 2024, the average wind speed in the source region reached 11.35 ms−1, the highest recorded in the past 45 years. This was attributed to surface anomalies, including reduced soil moisture, poor vegetation cover, higher temperatures, and decreased precipitation relative to the multi-year average. The comprehensive application of multi-source data and models in this work elucidates the full lifecycle of this rare summer dust event, providing scientific insights into the atmospheric processes governing extreme dust events and their transboundary impacts.

Understanding the upper-ocean thermal response during and between typhoons is critical for accurate prediction of typhoon intensity and for evaluating air–sea interactions. Previous studies have primarily focused on ocean cooling induced by individual typhoons and sea surface temperature (SST) recovery after that, yet oceanic thermal rejuvenation within the typhoon Inactivity Duration and its influence on the subsequent typhoon remains insufficiently explored. Using 42 years of typhoon best-track data, satellite observations and reanalysis data, we provide the first systematic quantification of the physical link between Inactivity Duration and subsequent typhoon intensification. Here we found that the intensity of the subsequent typhoon increased with typhoon Inactivity Duration. The subsequent typhoon is 6.34 kt and 7.69 hPa stronger than the previous typhoon for every 10 days of increase in typhoon Inactivity Duration. Upper-ocean thermal condition rejuvenated with time and contributed to subsequent typhoon development, and both SST and ocean heat content (OHC) exhibited significant phase changes from negative after the preceding typhoon to positive prior to the subsequent one, accompanied by a notable shoaling of the mixed layer depth (MLD) and sustained high levels of atmospheric instability. These coordinated environmental changes provide enhanced energy reserves and more favorable thermodynamic conditions for typhoon development after the inactivity period. These findings highlight the importance of considering ocean thermal rejuvenation in forecasting typhoon intensity and provide a quantitative framework for assessing sequential typhoon interactions with the upper ocean, offering theoretical support for improved intensity forecasting.

For the appropriate evaluation of environmental odours, it is necessary to develop a reliable odour measurement scale. Odour intensity is one of the main odour characterisation parameters and a remarkably common and important sensory indicator of environmental odours. In this study, the odour intensity level between two and four of the six-point odour intensity scale was focused on, and odour intensity reference solutions of representative odorants for environmental odour evaluation were developed. As a result, propionic acid, propylamine, ethyl acetate, and isobutyraldehyde were selected as representative odorants, and three concentration steps of each odorant were determined to cover the odour intensity of two, three, and four of the six-point odour intensity scale. These reference odour solutions will be applicable to the training of inexperienced panel members and reliable on-site investigations of environmental odours.

Gridded meteorological data sources, such as reanalysis datasets, are increasingly used to estimate evapotranspiration, a key variable for surface water-budget analyses at regional and national scales and for assessing plant water requirements for irrigation. This study, conducted over mainland Portugal for the 44-year reference period from 1980 to 2023, first presents a comprehensive comparative analysis of the spatial patterns of potential (Ep) and reference (Eto) evapotranspiration at a 0.1° spatial resolution using daily data. Estimates derived from two high-resolution datasets (GLEAM and ERA5-Land) are compared with those obtained from the Thornthwaite, Hargreaves–Samani, and Penman–Monteith models. Secondly, trend analyses of Eto magnitudes on a monthly and annual basis in a gridded format were conducted. The resulting spatial distributions of Ep and Eto show higher values in milder and flatter southern Portugal and lower values in the cooler and more mountainous northern regions, in agreement with existing knowledge. The Penman–Monteith model exhibited the highest reliability, while the Thornthwaite model generally underestimated evapotranspiration across the country, and the Hargreaves–Samani model showed underestimation in coastal areas. Trend analysis of Eto indicates an overall increase in atmospheric evaporative demand over the full study period, with a more pronounced rise during the recent 22-year period (2002–2023) compared with the earlier period (1980–2001). These increases are statistically significant in August and October and may reflect a climate shift towards a progressively longer dry season. Understanding how changes in evapotranspiration affect hydrological processes—including surface water availability, river discharge, reservoir performance, and crop requirement—is critical. This study aims to contribute to addressing these emerging challenges.

Meteorological drought is a major natural hazard in semi-arid regions, where high climate variability and strong dependence on precipitation intensify pressure on water resources and socio-economic systems. This study examined the spatiotemporal characteristics of meteorological drought in the Wadi Sly basin (northwestern Algeria) over the period 1967–2022, using long-term monthly precipitation records from seven meteorological stations. The Standardized Precipitation Index (SPI) was calculated at multiple time scales (1-, 3-, 6-, 9-, and 12-month) to characterize drought onset, severity, persistence, and temporal variability. In addition, drought severity probability and frequency analyses were conducted to evaluate the likelihood and recurrence of different drought classes. The results indicate pronounced inter-annual and decadal variability in drought conditions, with severe and prolonged drought episodes occurring during the mid-1980s, early-to-mid-1990s, and late 2010s. During these periods, SPI values frequently fell below −2.0, signifying extreme drought conditions. Spatial analysis reveals strong basin-wide synchronicity of drought events, suggesting the influence of large-scale atmospheric drivers, although localized variations in drought intensity remain evident. Overall, near-normal conditions dominate the record (accounting for approximately 60–70% of observations), while moderately dry conditions occur more frequently than moderately wet conditions at several stations. Drought characteristics exhibit strong scale dependence, with short-term droughts prevailing at shorter SPI time scales, while longer time scales emphasize drought persistence and accumulation. Overall, the findings indicate an increasing prominence of long-duration drought conditions in recent decades, as evidenced by recurrent low SPI values at longer aggregation scales. Such conditions may pose heightened risks to groundwater recharge processes and long-term water resource availability. Despite the limitations inherent in precipitation-based indices, this study provides a robust statistical framework for drought characterization and contributes valuable insights for improved drought monitoring, early warning systems, and climate-resilient water resource management in semi-arid basins.

This study investigated the effects of a high-fat diet (HFD), particulate matter (PM) exposure, and resistance exercise training on circulating lipid profiles, adipokines, inflammatory responses, neurotrophic factors, and blood–brain barrier (BBB) permeability. Forty-eight 10-week-old male C57BL/6 mice were randomly assigned to four groups (n = 12 per group): normal diet (ND), HFD, HFD with PM exposure (HFD + PM), and HFD with PM exposure plus exercise training (HFD + PM + EX). ND and HFD were administered for 16 weeks, whereas PM exposure and exercise training interventions were initiated after 8 weeks of dietary treatment and continued for an additional 8 weeks. PM was administered via tail vein injection three times per week, and resistance exercise training consisted of a ladder-climbing exercise performed five times per week. The results indicated that body weight, total cholesterol (TC), triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C), leptin, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), S100 calcium-binding protein B (S100B), and neuron-specific enolase (NSE) levels were significantly higher in the HFD group than in the ND group (p < 0.05), whereas adiponectin and brain-derived neurotrophic factor (BDNF) levels were significantly lower (p < 0.05). In addition, the HFD + PM group exhibited significantly lower BDNF and vascular endothelial growth factor (VEGF) levels (p < 0.05) and significantly higher S100B and NSE levels (p < 0.05) than the HFD group. In contrast, the HFD + PM + EX group showed significantly lower TG, LDL-C, leptin, and IL-6 levels than the HFD group (p < 0.05). Moreover, compared with the HFD + PM group, the HFD + PM + EX group demonstrated significantly lower TG, LDL-C, leptin, S100B, and NSE levels (p < 0.05) and significantly higher high-density lipoprotein cholesterol (HDL-C), adiponectin, BDNF, and VEGF levels (p < 0.05). Collectively, these findings suggest that an HFD may contribute to dyslipidemia, heightened inflammatory responses, downregulation of neurotrophic factors, and increased BBB permeability and that concurrent PM exposure under HFD conditions may exacerbate adverse alterations in neurotrophic factors and BBB permeability. The results indicate that an HFD induces metabolic and neurovascular alterations, whereas concurrent PM exposure under HFD conditions is associated with additional changes in neurotrophic factors and BBB-related markers. Resistance exercise training attenuated these changes.

This work systematically evaluates the combustion and emission characteristics of n-butanol–diesel blends to clarify load-dependent trade-offs. A single-cylinder diesel engine was operated under low (25%)- and high (75%)-load conditions using commercial diesel and n-butanol blends (5–15 vol%). The results indicate that n-butanol addition tends to improve brake thermal efficiency (BTE) and reduce brake-specific energy consumption (BSEC), particularly at high loads, likely due to enhanced premixed combustion and fuel oxygenation. Emission trends exhibited distinct load-dependent behaviors: nitrogen oxides (NOx) emissions decreased at low loads, ostensibly because the charge-cooling effect of n-butanol’s high latent heat dominated, whereas they increased at high loads driven by elevated temperatures and oxygen availability. Smoke opacity, carbon monoxide (CO), and carbon dioxide (CO2) emissions were consistently reduced across all operating conditions, suggesting benefits from improved oxidation and the lower carbon content. In contrast, unburned hydrocarbon (HC) emissions increased significantly, which is primarily attributed to prolonged ignition delay and local quenching arising from the fuel’s low cetane number and high latent heat. These findings demonstrate n-butanol’s potential to enhance efficiency and mitigate smoke, CO, and CO2 emissions, though the trade-offs with HC and high-load NOx necessitate optimized control strategies.

Under the context of global warming, accelerated glacier melting poses a severe threat to regional water security, necessitating systematic quantification of the spatiotemporal evolution of glacier mass balance (GMB) and its impacts on runoff. This study employed the Spatial Processes in Hydrology (SPHY) distributed hydrological model, integrated with remote sensing data, meteorological observations, and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios, to reconstruct the spatiotemporal evolution of glacier mass balance in the Manas River Basin on the northern slope of Tianshan Mountains from 2000 to 2014, quantify the coupling relationships between glacier mass balance and climate factors as well as glacier meltwater runoff, and project future trends from 2015 to 2045. Results showed that glaciers in the basin experienced persistent negative mass balance during the study period, with a 15-year mean glacier mass balance of −0.87 m w.e.·a−1, cumulative loss of 12.16 m w.e., and glacier area shrinkage of 11.9%. Glacier mass balance exhibited significant spatiotemporal heterogeneity, with the most severe mass loss occurring in steep south-facing slopes, and glacier thickness change displayed a “single-peak” altitudinal dependence with the ablation peak elevation stabilized at approximately 4400 m. Glacier mass balance showed a significant negative correlation with melt-season positive accumulated temperature (r = −0.9, p < 0.01), with a temperature sensitivity coefficient of 55.17 %·°C−1. The contribution rate of glacier meltwater runoff increased from 19.93% to 29.50%, showing a significant negative correlation with glacier mass balance (r = −0.73, p < 0.01), revealing the phenomenon of “compensatory runoff increase”. Under three future scenarios, glacier mass balance loss exhibited an intensifying trend, with the most severe loss in high-altitude areas, and glacier meltwater runoff continued to increase but demonstrated unsustainability. This study provides a scientific basis for predicting “peak water” timing and adaptive water resource management in high mountain glacierized basins under climate change.

Topography plays a crucial role in shaping local urban microclimates and can drive the formation of cold-air pools in valley bottoms. This study examines the Eiras Valley (Coimbra, Portugal), a rapidly growing peri-urban area, to identify the conditions under which cold-air pools form and to characterize their spatial and vertical dynamics. Field measurements were carried out using Tinytag Plus 2 data loggers at the surface (≈1.5 m above ground) and mounted on an unmanned aerial vehicle (UAV) for vertical profiles, complemented by high-resolution thermal mapping through Empirical Bayesian Kriging. The results show that a nocturnal cold-air pool develops within the valley under clear, anticyclonic winter conditions, persisting into the early morning hours and dissipating after sunrise due to solar heating. In contrast, under overcast or summer conditions, no cold-air pooling was observed. The temperature inversion capping the cold-air pool was found at approximately 275 m altitude, inhibiting vertical mixing and trapping pollutants near the ground. These findings underscore the importance of topoclimatology in urban and regional planning, with implications for thermal comfort, air quality, and public health. The study contributes to urban climate research by highlighting how local topography and seasonal atmospheric stability govern cold-air pool formation in valley environments, supporting the development of mitigation strategies aligned with urban sustainability goals.

Accurate humidity forecasts are crucial for environmental and operational applications, yet Numerical Weather Prediction systems frequently exhibit systematic and random errors. To address this problem, this study introduces a modified hybrid post-processing approach that extends a previously developed methodology, enabling a direct comparison of computational efficiency and predictive capacity. The proposed framework integrates a quadratic Kalman Filter with a Radial Basis Function Neural Network trained via the Orthogonal Least Squares algorithm and updated online through Recursive Least Squares. This modified method was evaluated via a time-window process, using forecasts from the Weather Research and Forecasting model and recorded observations from stations in northern Greece. The results show substantial improvements in forecast accuracy, as the Bias was reduced by over 85%, and the MAE and RMSE decreased by approximately 65% and 58%, respectively, compared with the baseline model. Furthermore, the proposed framework also demonstrates enhanced computational efficiency, reducing processing time by more than 95% relative to the initial methodology.