Atmosphere

Rime accretion on transmission lines in mountainous terrain is strongly influenced by local airflow and the availability of supercooled liquid water. This study combined nested Weather Research and Forecasting (WRF) simulations, tower observations, and the Makkonen ice-accretion model to investigate a rime-icing event at Tower No. 117 in southern Hunan, China, during 1–3 January 2026. Regional analysis revealed persistent low-level cold-air advection, warm and moist airflow aloft, and a pronounced temperature inversion near the tower, indicating a cold and stably stratified environment favourable for rime formation. At the local scale, simulated airflow and model-derived liquid water content (LWC) showed patterns consistent with terrain channelling and windward-slope ascent. During both active ice-growth stages, wind speed, LWC, and a diagnostic liquid-water transport metric were higher at the tower than in a nearby reference area. During the subsequent weakening phase, air temperature remained below freezing and wind speed remained relatively high, whereas model-derived LWC and the transport metric decreased to 18.8% and 16.9% of their first-stage values, respectively. This contrast indicates that weakened rime growth was more closely associated with reduced liquid-water availability than with temperature or wind speed changes alone. The 300 m WRF configuration yielded end-of-stage ice thicknesses closer to observations than the 1.5 km configuration. Sensitivity experiments showed that quantitative ice-accretion estimates remained sensitive to model initialisation, meteorological forcing, and icing-model assumptions. Overall, the results highlight the importance of representing terrain-modulated airflow and liquid-water variability in tower-scale icing assessments over mountainous terrain.

This study assesses Land Use and Land Cover (LULC) and Land Surface Temperature (LST) variability in the semi-arid Aligarh District, Uttar Pradesh, India, using two Landsat 9 images acquired on 30 May 2022 (summer) and 24 December 2022 (winter). This research employs a semi-automated hybrid classification approach integrating Maximum Likelihood Classification and Object-Based Image Analysis with Landsat imagery to map seasonal LULC for summer and winter 2022. Five classes, namely, cropland, built-up areas, barren land, plantations, and water bodies, were identified with overall accuracies of 94.4% (Kappa = 0.93) in summer and 91.2% (Kappa = 0.89) in winter. On the summer date LST ranged from 26.48 °C to 46.40 °C and on the winter date from 13.11 °C to 23.85 °C. Inter-predictor Pearson correlation analysis revealed severe multicollinearity among NDVI, SAVI, NDBI, and NDWI, with inter-predictor correlations ranging from |r| = 0.855 to 0.994, forming a tightly redundant spectral cluster. NDVI and SAVI were consequently excluded from the final OLS model. The reduced model using NDBI, BSI, and NDWI explains 54.3% of LST variability in summer and 57.8% in winter. NDBaI was excluded from the regression models because it is computed from the thermal band that is also used to retrieve LST, whereas BSI uses reflective bands only. NDBI is the dominant predictor in both seasons, followed by BSI as a significant secondary predictor, confirming built-up surface intensity and bare soil exposure as the primary thermal drivers. In contrast, GWR substantially improved model fit (Summer: R² = 0.895, Winter: R² = 0.918) and reduced the residual spatial autocorrelation (Moran’s I from 0.404 to 0.028 in summer and from 0.299 to 0.033 in winter), showing its effectiveness in capturing spatial heterogeneity. LST patterns differed markedly between the two acquisition dates in association with LULC differences, confirming the utility of spatially adaptive modeling for climate-responsive planning. The findings provide evidence for the scientific basis of sustainable urban management and climate adaptation strategies, contributing to the implementation of Sustainable Development Goal 13 (Climate Action).

Hourly VOC observations collected in Shannan from September 2024 to September 2025 were analyzed using maximum incremental reactivity (MIR), positive matrix factorization (PMF), and backward trajectories. During the year-long analysis period, monthly median TVOC concentrations were lowest in May (52.65 µg m⁻³) and highest in October (208.52 µg m⁻³), whereas median ozone formation potential (OFP) was lowest in May (81.87 µg O₃ m⁻³) and highest in January (231.46 µg O₃ m⁻³). Isopentane had the highest median concentration, and acetaldehyde the highest species-level OFP. PMF of 8189 valid hours and 42 species resolved four factors accounting for 28.8%, 19.8%, 23.3%, and 28.1% of summed reconstructed mixing-ratio contributions. The factors were interpreted as traffic-related combustion (F1), mixed solvent-use and fuel emissions (F2), a halocarbon- and light-alkane-rich mixed factor (F3), and a fuel-related and OVOC-rich mixed factor (F4). During the study period, 7630 of 7949 PMF-valid hours had an assigned trajectory class; 319 remained unclassified. W trajectories comprised 356 h, 235 in winter. Season-adjusted W comparisons with pooled SSW and W/WNW hours showed positive conditional associations for F1 and F4, but not stable positive enhancement for F2 or F3. The 37-species PMF-reconstructed OFP showed weak in-sample agreement with observations (R² = 0.0283), limiting source-specific reactivity interpretation; the trajectory associations do not quantify regional emissions.