A waterspout was sighted in the offshore waters of Hong Kong in mid-October 2025, the second-latest occurrence of this weather phenomenon in a single year since 1959. Due to the close proximity of the phenomenon to Lamma Island in Hong Kong, detailed sighting information and photographs of the waterspout are available for analysis. This paper investigates the meteorological background of the event, the stability of the atmosphere, and weather radar images from two dual-polarization weather radar stations within the territory to determine the type and intensity of the observed waterspout and its formation mechanism. At that time, the atmosphere was rather unstable, with high values for CAPE and bulk Richardson number, along with an upper-level divergence area that provided updraft momentum for convective development. Detailed observations from these weather radar images showed that the waterspout was a rather weak system with relatively low radar reflectivity and generally weak Doppler velocities, although the velocity signatures, such as Doppler velocity couplets, and azimuthal shear were quite clear. The potential for an operational 2-kilometer ensemble prediction system (EPS) from the Hong Kong Observatory to indicate a favorable environment for waterspout development was also investigated. While the EPS cannot be expected to resolve the waterspout problem or reproduce its exact location and timing, it can capture weak low-level cyclonic anomalies and convergences near Lamma Island that would provide favorable conditions for the formation of waterspouts and are broadly consistent with the observed mesoscale environment.

The conventional treatment of high-concentration volatile organic compounds (VOCs) relies on energy-intensive dilution to avoid explosion risks. This study proposes an efficient catalytic combustion process treating VOCs directly within the explosive range while recovering reaction heat using Pt/γ-Al2O3-based catalysts promoted with La and Ce. Catalysts (0.05–0.5 wt% Pt) were synthesized via impregnation and characterized using FE-SEM, BET, and XRD. Catalytic combustion experiments at VOC concentrations up to 13,000 ppm showed combustion initiation below 200 °C, achieving 83–99% conversions at 300 °C with complete oxidation to CO2. Although 5 vol.% moisture significantly inhibited low-temperature activity through competitive adsorption, La and Ce promoters (10 wt%) effectively overcame this limitation by increasing surface area (up to 194.93 m2/g) and oxygen mobility. The Ce-promoted catalyst demonstrated superior water tolerance, achieving complete conversion at 200–210 °C due to its high Oxygen Storage Capacity (OSC). Bench-scale validation using a 1 Nm3/h system confirmed industrial feasibility. Operating at 220 °C with 13,000 ppm toluene for 100 h, the catalyst maintained >99.98% conversion with negligible deactivation and THC emissions below 2 ppm. The double-jacket heat exchanger effectively managed reaction heat (limiting temperature rise to ~20 °C) and recovered it as steam. Compared to Regenerative Thermal Oxidation, this Regenerative Catalytic Oxidation approach reduced emissions and energy consumption. This work demonstrates a robust “combustion-with-recovery” strategy for high-concentration VOC treatment, offering a sustainable alternative with high efficiency, stability, and safe energy-integrated operation.

With the increasing frequency extreme weather events associated with climate change, real-time monitoring of rainfall intensity is critical for water resource management, disaster warning, and other applications. Traditional methods, such as ground-based rain gauges, radar, and satellites, face challenges like high costs, low resolution, and monitoring gaps. This study proposes a novel real-time rainfall intensity monitoring method based on deep learning and audio signal processing, using acoustic features from rainfall to predict intensity. Conducted in the semi-arid region of Northwest China, the study employed a custom-designed sound collection device to capture acoustic signals from raindrop-surface interactions. The method, combining multi-feature extraction and regression modeling, accurately predicted rainfall intensity. Experimental results revealed a strong linear relationship between sound pressure and rainfall intensity (r = 0.916, R2 = 0.838), with clear nonlinear enhancement of acoustic energy during heavy rainfall. Compared to traditional methods like CML and radio link techniques, the acoustic approach offers advantages in cost, high-density deployment, and adaptability to complex terrain. Despite some limitations, including regional and seasonal biases, the study lays the foundation for future improvements, such as expanding sample coverage, optimizing sensor design, and incorporating multi-source data. This method holds significant potential for applications in urban drainage, agricultural irrigation, and disaster early warning.

Tree pollen allergies are influenced by regional atmospheric pollen concentrations and flora distribution. Climate change and urban landscaping have altered airborne pollen profiles in Ukraine, potentially affecting sensitization patterns. We examined 7518 patients (57.63% children) sensitized to at least one of 26 molecular components from 19 tree species using ALEX testing (2020–2022). Atmospheric pollen data from Ukrainian aerobiology stations were integrated with clinical data. Regional sensitization was mapped using the Geographic Information System, and Bayesian network modeling determined hierarchical relationships. Sensitization to Cry j 1 (46.01%), Bet v 1 (41.67%), and Fag s 1 (34.38%) dominated across age groups. High Fagales sensitization correlated with elevated atmospheric Betula, Alnus, and Corylus pollen concentrations, confirming environmental exposure-sensitization relationships. Bayesian modeling identified Bet v 1 as the root allergen (89.43% accuracy) driving cascading sensitization to other Fagales and non-Fagales allergens. Unexpectedly high Cry j 1 sensitization despite minimal atmospheric Cryptomeria presence suggests Thuja and Ambrosia cross-reactivity. Fagales sensitization dominated 10 of 17 regions, correlating with forest geography and urban landscaping. This study validates aerobiological monitoring’s clinical relevance. Diagnostic protocols should prioritize Bet v 1 while interpreting Cry j 1 positivity as potential cross-reactivity. Climate-driven shifts in atmospheric pollen patterns require ongoing coordinated aerobiological and clinical surveillance.

Validation of ICON model configurations optimized over a limited domain is essential before accepting new semi-empirical parameters that influence the behavior of subgrid-scale schemes. Because such parameters can modify the dynamics of a numerical weather prediction (NWP) model in highly nonlinear ways, we analyze one season of forecasts (December 2022, January and February 2023) generated with the NWP ICON-LAM through the lens of machine learning–based diagnostics as a complement to traditional evaluation metrics. The goal is to extract physically interpretable information on the model behavior induced by the optimized parameters. This work represents the first part of a wider study exploring machine learning tools for model validation, focusing on two specific approaches: Empirical Orthogonal Functions (EOFs), which are widely used in meteorology and climate science, and autoencoders, which are increasingly adopted for their nonlinear feature extraction capability. In this first part, EOF analysis is used as the primary tool to decompose weather fields from observed reanalysis and forecast datasets. Hourly 2-m temperature forecasts for winter 2022–2023 from multiple regional ICON configurations are compared against downscaled ERA5 data and in situ observations from ground station. EOF analyses revealed that the optimized configurations demonstrate a high skill in predicting surface temperature. From the signal error decomposition, the fourth EOF mode is effective particularly during night-time hours, and contributes to enhancing the performance of ICON. Analyses based on autoencoders will be presented in a companion paper (Part II).

The subject of this research is the exploration of the potential of remote sensing and Geographic Information Systems (GIS) for basin-scale spatio-temporal monitoring of drought and its impacts in the Ukrina River Basin, Bosnia and Herzegovina (BH), during the last decade (2015–2024). The aim is to integrate meteorological, hydrological, agricultural, and socio-economic drought signals and to delineate areas of long-term drought exposure. Meteorological drought was evaluated using CHIRPS precipitation and the Standardized Precipitation Index (SPI) calculated at 1-, 3-, 6-, and 12- month accumulation scales using Gamma fitting and a fixed long term reference period; hydrological drought was examined using available water-level records complemented by the Standardized Water Level Index (SWLI) and supported by correspondence with standardized ERA5-Land runoff anomalies; agricultural drought was mapped using remote sensing indices—the Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI)—calculated from MODIS satellite data; and socio-economic effects were assessed using municipal crop-production statistics (2015–2019). The results indicate that drought conditions were most pronounced in 2015, 2017, 2021, and especially 2022, showing consistent agreement between precipitation deficits, hydrological responses, and vegetation stress, while 2016, 2018–2020, 2023, and 2024 were generally more favorable. As a key novelty, a persistent drought-prone zone was delineated by intersecting drought-affected areas across major episodes, providing a basin-scale identification of chronic drought hotspots for a river basin in BH. The persistent zone covers 40.02% of the basin and spans nine cities and municipalities, with >93% located in Prnjavor, Derventa, Stanari, and Teslić. Hotspots are concentrated mainly in lowlands below 400 m a.s.l., with a statistically significant concentration across lower elevation classes, indicating higher long-term exposure in the central and northern valley sectors, and land use overlay further highlights high relative exposure of productive land. Overall, the integrated remote sensing and GIS framework strengthens drought monitoring by providing spatially explicit and repeatable evidence to support targeted adaptation planning and drought-risk management.

High-resolution air quality models (AQMs) are critical for real-time air quality forecasting and exposure assessment, although their computational costs increase cubically with resolution. Quantifying model sensitivity to resolution is therefore crucial for developing effective forecasting systems. This study conducts a systematic model intercomparison of three widely used AQMs (CAMx, CMAQ, NAQPMS) under identical input conditions at 45, 15, and 5 km resolutions to forecast PM2.5 and O3 in the North China Plain during 2021. Results indicate distinct, model-dependent responses to grid refinement. NAQPMS achieves the optimal PM2.5 forecasting performance at 5 km, with improvements in nearly all evaluated statistics. CMAQ excels in O3 prediction at 5 km resolution, with RMSE reducing 6.48 μg/m3 relative to the coarsest grids. We also found that terrain complexity significantly influences these resolution-dependent biases, leading to a substantial 19.51% reduction in NMB in the CAMx PM2.5 simulation over mountain areas. Moreover, the evaluation of 10-day forecasting accuracy suggests that a high-resolution setting is recommended for NAQPMS and CMAQ, whereas a coarser resolution is sufficient for CAMx. These findings underscore that optimizing real-time forecasting strategies requires a critical investigation of inter-model physicochemical discrepancies rather than universally pursuing higher resolution.

Rapid urbanization and anthropogenic activities have led to a significant deterioration of air quality, adversely affecting human health and ecosystems. The study of transboundary river basins, where air pollution is exacerbated by political and socio-economic factors, is of particular relevance. This paper presents the results of an analysis of the spatiotemporal distribution of pollutants (Aerosol Index (AI), Methane (CH4), Carbon Monoxide (CO), Formaldehyde (HCHO), Nitrogen Dioxide (NO2), Ozone (O3), Sulfur Dioxide (SO2)) in the ambient air within the Orontes River basin across Lebanon, Syria, and Turkey for the period 2019–2024. The research is based on satellite monitoring data (Copernicus Sentinel-5P), processed using the Google Earth Engine (GEE) cloud-based platform and GIS technologies (ArcGIS 10.8). The dynamics of population density (LandScan) and the impact of military operations in Syria on air quality were additionally analyzed using media content analysis. The results showed that the highest concentrations of pollutants were recorded in Syria, which is associated with the destruction of infrastructure, military operations, and unregulated emissions. The main sources of pollution were: explosions, fires, and destruction during the conflict (aerosols, CO, NO2, SO2); methane (CH4) leaks from damaged oil and gas facilities; the use of low-quality fuels and waste burning. Atmospheric circulation contributed to the eastward transport of pollutants, minimizing their spread into Lebanon. Population density dynamics are related to changes in concentrations of pollutants (e.g., nitrogen dioxide). The results of the study highlight the need for international cooperation to monitor and reduce air pollution in transboundary regions, especially in the context of armed conflicts. The obtained data can be used to develop measures to improve the environmental situation and protect public health.

With the rapid advancement of clean energy, wind farm planning and construction are expanding worldwide, increasing the demand for accurate resource assessments. This study investigates the influence of vertical wind characteristics on wind farm siting and energy production, using measured meteorological data from the Hangjinqi wind farm. Results show that both mean wind speed increase substantially with altitude, indicating that upper layers provide richer and more stable wind resources. The estimated annual energy production of the site reaches 23,500 MWh, with capacity factors ranging from 35% to 42%, well above the national average. Seasonal and diurnal variations are evident: wind speeds strengthen during winter and spring, particularly at night, while turbulence intensity peaks in the daytime and decreases with height. Carbon dioxide (CO2) reduction also increases with hub height, with the most pronounced seasonal reductions in spring (3367.6–5041.1 tCO2, +49.7%) and winter (3215.7–5380.0 tCO2, +67.4%), equivalent to several thousand tons of standard coal per turbine annually. Optimal performance is observed at 100–140 m, demonstrating efficient utilization of mid- to high-altitude resources. Nevertheless, discrepancies in turbine performance at different hub heights suggest untapped potential at higher elevations. These findings highlight the importance of incorporating vertical wind characteristics into wind farm siting decisions, and support the deployment of turbines with tower heights ≥140 m alongside intelligent scheduling and forecasting strategies to maximize energy yield and economic benefits.

Most studies have focused on extending the applicability of the Ångström–Prescott equation and improving its accuracy in estimating solar irradiation. Only a limited number of studies have addressed atmospheric climatology using the Ångström–Prescott equation. In contrast, this study reformulates the Ångström–Prescott equation to explore its potential for extracting long-term atmospheric transparency information from radiometric measurements. It introduces a new annual formulation of the Ångström–Prescott equation derived from its common monthly version. While the formal structure is preserved, the equation shifts from its usual role, as a solar irradiation estimator, to a new role, as a predictor of long-term atmospheric transparency. The revised equation naturally defines an annual effective sunshine duration, which assigns greater weight to relative sunshine during summer months than during winter months. To enable prediction, the revised Ångström–Prescott equation is combined with Gaussian process regression. The equation provides the historical annual time series, while Gaussian process regression predicts future values and quantifies their associated uncertainty. To demonstrate the predictive capability of the method, it is applied to the analysis and prediction of four annual parameters characterizing atmospheric transparency: mean clear-sky atmospheric transparency, mean cloud transmittance, mean atmospheric transparency, and effective relative sunshine duration. The analysis is conducted using radiometric data collected at 14 stations distributed across Europe. Predictions for the upcoming decade (2024–2033) indicate that, at most stations, mean atmospheric transparency is expected to remain stable or change within approximate margins of −5% to +10%.