The positive relationship between temperature and surface ozone is a cornerstone of climate air-quality thinking, yet its year-to-year stability has rarely been tested in a tropical megacity using more than one or two seasons of data. Using hourly ozone, NOx, volatile organic compound (VOC), temperature, and solar-radiation observations at three Central Pollution Control Board (CPCB) Continuous Ambient Air Quality Monitoring Stations (CAAQMS) in Delhi (DMS, NSIT, and IHBAS), we analysed four consecutive pre-monsoon seasons (March–May 2023–2026). Across all four years without exception, Delhi remained persistently VOC-limited (VOC/NOx = 0.005–0.05), and measured VOC declined with temperature, which in principle creates a latent thermal optimum, a point beyond which ozone production should decline every single year. Whether that latent optimum was actually reached, however, differed sharply by year and depended on NOx. In three of the four years (2023, 2024, and 2026), NOx remained adequately available even at the highest temperatures, so ozone kept rising with temperature in a broadly monotonic or moderately saturating manner at NSIT and IHBAS (Pearson r = 0.19–0.73, p < 0.001) despite the ongoing VOC decline. Only in 2025 did NOx itself fall together with VOC at the highest temperatures; in that one year alone, ozone rose to a moderate optimum near 30–35°C and then declined sharply beyond it. DMS, the most traffic- and industry-influenced site, remained titration-suppressed in all four years regardless of this NOx pattern. These results indicate that the ozone–temperature relationship in Delhi is not fixed, and that 2025 represents an atypical, NOx-limited precursor-availability year rather than a stable annual ‘switch’; year-to-year NOx availability acting on a persistently VOC-limited background present in all four years is the primary control on whether ozone tracks temperature or decouples from it at extreme heat, with implications for ozone management and heat-health advisories in Delhi.
The present study investigates subsidence issue at the Pakyong Greenfield Airport in the Sikkim Himalaya, India, in two phases. The study aims to quantify subsidence rates and identify causes. It also evaluates the effectiveness of ground and space-based monitoring techniques. In Phase-1 (2015–2018), installed Total Station and inclinometer instruments tracked movement along the Reinforced Earth wall on the valley side of the runway, revealing significant displacement. Due to damaged monitoring posts, vegetation growth and clogged inclinometers, Phase-2 (2019–2021) monitoring was conducted from the runway level. In this phase, 25 monitoring posts were installed, and only Total Station measurements were collected. The Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique was also applied, and its results were compared with Phase-2 Total Station data. Despite some scatter in areas of high deformation, both methods showed good agreement. SBAS-InSAR analysis indicated eastward downslope movement, with deformation rates ranging from 1.05 to 43.67 cm/y. Field investigations identified causes such as damaged drainage culverts, internal seepage, soil piping and high overburden pressure on weak strata. Continuous monitoring and appropriate mitigation measures are therefore essential to ensure the long-term safety of this airport.
We quantify ERA5 longwave cloud radiative effect (LWCRE) discrepancies against Clouds and the Earth’s Radiant Energy System (CERES) over Iraq (2001−2024), using a fully quality-controlled 24-year record that resolves a 34-month gap in the ERA5 archive that had previously been filled with a constant placeholder value. ERA5 shows a negative bias of −6.5 W m–2 (RMSE: 7.0 W m–2) relative to CERES, masking strong dependence on cloud regime, season, and cloud-top temperature (CTT). Splitting the record using an objective threshold (domain-mean monthly CTT of 260 K), both weak/warm and strong/cold clouds carry a negative bias, but the magnitude differs sharply: −5.8 W m–2 for weak/warm clouds versus −9.3 W m–2 for strong/cold clouds (t = 7.75, p < 0.0001). Bias peaks in spring (MAM: −10.0 W m–2) and is smallest in summer (JJA: −4.6 W m–2), driven by the cold-cloud subcomponent. This CTT dependence is seasonal rather than inter-annual: The monthly bias correlates significantly with CTT (r = 0.56, R2 = 0.31, p < 0.001) and reverses sharply near −39°C, whereas the annual-mean relationship is not significant (r = −0.05, p = 0.82). MODIS retrievals show cloud fraction and optical thickness highest over northern Iraq, while the coldest cloud tops occur in the southwest; thick-cloud occurrence peaks in winter (31.4
The water-based infrastructures such as dams, reservoirs, and many other hydraulic structures are at risk due to the problems arising out of environmental degradation, structural weakness, sedimentation, seepage anomalies, and vegetational encroachment. In the present study, an approach for analysing structural and environmental changes in the Vaigai sub-basin using earth observation datasets through satellite remote sensing datasets and Google Earth Engine (GEE) is proposed. Sentinel-1 SAR data, Sentinel-2 optical images, and SRTM DEM data were combined to create 26 thematic layers including vegetation, moisture, sedimentation, terrain features, and structural stress indicators. Environmental metrics such as normalised difference vegetation index (NDVI), enhanced vegetation index (EVI), SAVI, NDWI, modified NDWI (MNDWI), normalised difference moisture index (NDMI), BSI, Synthetic Aperture Radar (SAR) backscattering values in the VV/VH band ratio, slope, and flow accumulation were computed. A structural health index (SHI) and environmental stress index were computed through the process of integrating spectral, hydrological, and terrain-based features. In addition, a Random Forest (RF) model was applied for risk categorisation. From the study’s results, it can be observed that high-risk zones and structurally stressed areas are mostly found in the vicinity of the margins of the reservoir, sediment deposition areas, and vegetation intrusion areas. From the SHI analysis, it was discovered that 528.93 and 391.84 km2 of the basin lie within high and very high structural stress categories, respectively. From the risk classification analysis, 704.18 km2 of the basin has very high-risk conditions, while 45.59 km2 of the area falls under the very low risk zones. On the environmental stress aspect, the study has shown that there are 428.35 and 262.15 km2 of high and very high stress, respectively. The machine learning algorithm gave high prediction accuracy and stability in making decisions. It is therefore concluded that the model can integrate remote sensing, topography analysis, SAR imagery, and machine learning algorithms to assess the health status of the structures and environmental stress condition of water infrastructures in the semi-arid environment.
The present study investigates the role of vegetation and material composition in enhancing the mechanical stability of external waste dumps at the Gare Palma III Colliery, Raigarh, India. The overburden dump material, predominantly composed of sandstone and shale, is loosely compacted and prone to erosion and shallow slope failures under monsoonal conditions. A re-vegetation program utilising locally adapted species with strong root-binding capacity was implemented, and its effect on slope stabilisation was assessed. Direct shear tests were conducted on dump samples before and after vegetation establishment to quantify changes in shear strength parameters. The results show a marked improvement in both cohesion and internal friction angle after vegetation, indicating the reinforcing influence of root networks on particle interlocking. Additionally, a sensitivity analysis was performed by mixing shale with sandstone at varying proportions (5–25
