Search Results (327)

Understanding spatio-temporal rainfall variability is critical for water resource management, especially for climate-sensitive river basins. This study examines rainfall trends and variability in the Baitarani River Basin (eastern India) using high-resolution gridded data for 1979–2020. Rainfall trends were investigated using non-parametric Mann–Kendall test (MK test) and Sen’s slope estimator (SSE). The shift point was detected using multiple homogeneity tests [Pettitt test, Standard Normal Homogeneity Test (SNHT), and Buishand test], while rainfall variability was quantified using an entropy-based Marginal Disorder Index (MDI). The analyses were performed at annual and seasonal scales. MK Z-statistic indicates the increasing or decreasing nature of a series, whereas Sen’s β slope provides the rate of change in that particular series. The MK test and SSE were applied again to examine trends before and after the identified change point. Finally, maps illustrating spatial trends and percentage changes were produced using ArcGIS 10.6. Over the 42-year period, the MK test revealed significant increasing annual trends in both districts, Keonjhar (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +21.8%, and Mayurbhunj (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +19.2%. During 1979–2020 post-monsoon rainfall showed the highest increase (62–70%) while, post 2001, monsoon rainfall declined substantially (1.7–3.3 mm/year) across all districts, with Balasore showing the largest decrease (−3.3 mm/year). The earlier period (1979–2001) had stable monsoon rainfall but greater variability in retreating monsoon, especially in northern regions. Entropy-based variability analysis indicated the Bhadrak and Balasore districts as having maximum variability with an MDI value of 1.44 and 1.35, respectively, for monsoon and annual rainfall series. These findings underscore the importance of incorporating changing seasonal dynamics into water-resource planning and flood-risk management for the Baitarani River Basin in the context of climate change. Full article

Road Traffic Accidents (RTAs) represent a significant public safety issue in rapidly urbanising nations, resulting in considerable fatalities, injuries, and economic losses. This research investigates the spatio-temporal distribution and hotspot dynamics of RTAs in Siliguri City, India, a principal transnational transport corridor connecting northeastern India with adjacent countries. A geocoded dataset comprising RTA incidents from 2021 to 2023 was analysed using integrated GIS-based machine learning and statistical methods. Temporal clusters were identified through Kulldorff’s purely temporal scan statistics, while Kernel Density Estimation (KDE) quantified accident density during morning peak, midday/off-peak, evening peak, and lean/night-time intervals. Spatial clustering was further assessed using LISA-Moran’s I, purely spatial scan statistics, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Emerging Hotspot Analysis (EHA) was employed to detect evolving hotspot patterns over time. The findings indicate that major accident hotspots are concentrated at key intersections and transport corridors, such as Hill Cart Road, Darjeeling More, Sevoke Road, Eastern Bypass, and Burdwan Road. Moran’s I (0.157; p = 0.007) demonstrates significant but moderate spatial autocorrelation, and spatial scan statistics identified three principal high-risk zones. HDBSCAN classified 81.90% of incidents within clustered areas. Lean/night-time periods exhibited the highest accident densities, reaching 14.21 accidents/km² at critical intersections. These results underscore the utility of integrating GIS and machine learning techniques for urban traffic safety planning and hotspot-focused intervention strategies. Full article

To assess the potential risk of expansion of the sweet potato weevil (Cylas formicarius) in China under climate change, we combined principal component analysis in environmental space (PCA-env) with a Biomod2 ensemble model, using 173 occurrence records from its native range in India and its invaded range in China. We quantified the dynamics of the climatic niche between the native and invaded ranges and projected both current and future climatically suitable areas in China. Precipitation during the wettest month (Bio13), mean temperature during the driest quarter (Bio9), and isothermality (Bio3) were the key climatic predictors. Niche overlap between India and China was low (Schoener’s D = 0.107). The invaded niche was characterized by high stability (0.991) with very limited expansion (0.009), indicating strong niche conservatism. However, a relatively high unfilling value (0.633) suggests that the species has not yet occupied all potentially suitable climatic space in China. The current suitable area was estimated at 37.55 × 10⁴ km², primarily concentrated in South China and the southeastern coastal region. Under future climate scenarios, suitable habitat is projected to expand overall, extending into Central, Eastern, and Southwestern China. This study provides a climate-informed forecasting framework for assessing the potential spread of C. formicarius in China and offers practical support for quarantine surveillance and region-specific management. Full article

This study examines the multidimensional nature of poverty and its underlying local determinants within the Suri Sadar Sub-Division of Birbhum District, Eastern India, an area marked by sharp ecological and socio-economic contrasts. Adopting a mixed-method approach, the research integrates primary household survey data (2024-25) with secondary spatial datasets to construct a comprehensive analytical framework. The extent and intensity of multidimensional poverty were measured using the Alkire–Foster (AF) method, while the determinants were identified through a Binary Logistic Regression model. Findings reveal that multidimensional poverty in the region is deeply rooted in the intersection of human, environmental, and spatial factors rather than mere income deprivation. Approximately 26.8 per cent of households were found to be multidimensionally poor, with the western plateau blocks, i.e., Rajnagar, Khoyrasole, and Md. Bazar, showing the highest deprivation levels. Spatial poverty drivers include education, agriculture, and gender equality improvements. Policy implications emphasise the need for geographically tailored, multi-sectoral interventions that focus on human capability, investing in infrastructure, and promoting gender-inclusive development. By elucidating the localized dynamics of poverty, this research contributes to the broader discourse on spatial inequality and sustainable development in rural Eastern India, offering actionable insights for evidence-based regional planning and targeted poverty alleviation. Full article

Based on leaf area index (LAI) and enhanced vegetation index (EVI) datasets, this study systematically analyzes the spatial distribution and temporal variation characteristics of vegetation index trends at the global scale, clarifying the overall pattern of global greening and the seasonal differences in vegetation greening between eastern China and India. Regions with significant greening in China and India were selected as sensitivity zones, and a coupled land–atmosphere model was used to simulate seasonal differences in the climate response to greening. The findings reveal that: (1) Vegetation greening in eastern China is most pronounced in summer, whereas in India, the greening effect is most prominent in autumn; (2) The synergistic greening of both regions induces a year-round cooling effect in southeastern China, whereas northeastern China experiences summer warming and cooling in the other seasons. Furthermore, spring greening in China and India leads to a pronounced and widespread cooling across the mid-to-high latitudes of Eurasia. (3) In terms of precipitation, southwestern China shows an increasing trend in summer rainfall, while southeastern China shows a decreasing trend. In India, synergistic greening leads to spring and summer warming and autumn and winter cooling, with the cooling and increased precipitation effects being most significant in autumn. Full article

Fermented foods hold a significant position in global culinary traditions, particularly within ethnic and traditional diets. They are widely consumed for their distinctive flavors, textures, and health-promoting attributes. Although extensive research exists on fermentation processes, comprehensive insights into the nutraceutical potential and mechanistic health benefits of these foods remain limited. This review highlights key fermented products traditionally consumed in the north-eastern region of India including Hawaijar, Soibum, Ngari, alongside global counterparts such as Natto, Chongkukjang, Miso, Kefir, Tempeh, Kimchi, Kombucha, and Sauerkraut. These foods are rich in bioactive compounds (phenolics, peptides, organic acids, and exopolysaccharides), probiotic microorganisms, and essential nutrients that collectively contribute to their antioxidant, anti-inflammatory, antidiabetic, and cardioprotective effects. Recent in vitro and in vivo studies demonstrate that regular consumption of such foods may support the prevention and management of chronic conditions, including diabetes, cardiovascular diseases, obesity, gastrointestinal disorders, and neurodegenerative diseases. However, mechanistic studies remain insufficient to fully elucidate the synergistic interactions between microbial metabolites, host metabolism, and gut microbiota modulation. The review therefore emphasizes the biochemical and therapeutic mechanisms underlying ethnic fermented foods, advocating for advanced metabolomic and molecular approaches to validate their health-promoting efficacy. This review provides a timely and integrative perspective by critically evaluating preclinical and clinical evidence, highlighting mechanistic insights, translational gaps, and future research priorities. These insights will support the development of functional food formulations and reinforce the integration of traditional fermented foods into modern dietary strategies for disease prevention and overall well-being. Full article

Childhood stunting remains a major public health challenge in India and is influenced by multiple socioeconomic and environmental factors. This ecological study examined district-level correlates of childhood stunting, including Crimes Against Women (CAW), the Multidimensional Poverty Index (MPI), and drought severity, using data from NFHS-5, the National Crime Records Bureau, NITI Aayog’s MPI reports, and the Drought Atlas of India. Spatial autocorrelation and Spatial regression models were applied alongside machine learning approaches and SHAP-based Explainable AI (XAI) interpretation. Childhood stunting exhibited significant spatial clustering (Moran’s I = 0.520, p < 0.001), with hotspots in northern, central, and eastern India. Higher stunting was associated with higher birth order, low maternal BMI, child anaemia, and MPI, and negative associations with iodised salt usage, electricity access, and timely postnatal care. A significant spatial lag parameter (ρ = 0.348) indicated substantial spillover effects. Machine learning models consistently identified MPI, drought severity, and CAW as key predictors. The integrated spatial and machine learning framework identifies key correlates and spatial dependencies of childhood stunting, highlighting the need for region-specific, multisectoral interventions. Full article

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article

Southern India and Sri Lanka (SISL) rainfall during January–April (JFMA) exhibits strong interannual variability and is influenced by the El Niño–Southern Oscillation (ENSO), yet the long-term evolution of this relationship and its physical drivers remain unclear. Based on multiple precipitation datasets and atmospheric reanalysis products for 1950–2024, this study reveals a pronounced strengthening of the negative correlation between JFMA-mean SISL rainfall and the Niño 3.4 index, shifting from a statistically insignificant relationship prior to the late 1970s to a more coherent association after the 1980s. This transition is accompanied by intensified ENSO-related circulation anomalies. The strengthened and westward-extended Northwest Pacific Anticyclone (NWPAC) plays a dominant role, whereas an enhanced cross-equatorial temperature gradient in the Indian Ocean contributes to a lesser extent. Composite analyses further indicate that, on average, Eastern Pacific (EP) ENSO events tend to produce stronger rainfall anomalies over SISL than Central Pacific (CP) events; however, the differences between EP and CP composites are not statistically significant, reflecting pronounced event-to-event variability, especially for CP events. These results highlight the complexity of ENSO–SISL teleconnections and underscore the importance of NWPAC as a key bridge linking Pacific SST variability to regional rainfall responses. Full article

The saw-scaled viper Echis carinatus, one of the “Big Four” causes of snakebites in India, is found from Sri Lanka to eastern Iraq. To investigate clinical reports regarding the limited efficacy of Indian polyvalent antivenom (IPAV) against envenomation in Echis carinatus sochureki (ECS) in northwestern India, we obtained 22 snakes from three locations in Rajasthan and identified 148–174 toxin isoforms belonging to 21–25 toxin families in their venom using a bottom-up proteomics approach. All samples showed a high abundance of snake venom metalloproteinases (SVMPs), particularly SVMP class III. Other major components were phospholipases A₂, L-amino-acid oxidases, snake venom serine proteases and snaclecs (C-type lectins). Variation in venom composition among locations in Rajasthan, compared to E. c. carinatus (ECC) from southern India, was primarily due to differences in the relative abundance of these toxin families. Recognition of all venom components by IPAV was poor at lower antivenom concentrations. Notably, SVMP classes II and III were poorly recognized at all venom-to-antivenom ratios in all ECS venoms, and a plasma clotting assay revealed poor neutralization of procoagulant activity. This collaborative study highlights the need for the development of regional antivenoms to effectively treat snakebites in northwestern India. Full article

The Paleoproterozoic Aravalli Supergroup in northwest India hosts one of the oldest phosphorite deposits on Earth, located in the Jhamarkotra Formation, which was deposited after ca. 1762 Ma. Secondary enrichment is identified in the eastern region, resulting in upgradation of phosphate content, while primary stromatolitic columns are well-preserved in the western area of the Jhamarkotra mines. In this study, drill-core samples were collected from the unaltered western Block B and the upgraded eastern Block E to understand the alteration process. Petrographic studies reveal evidence of structural deformation and alteration. Elemental mapping of petrographic thin sections, employing SEM-EDS, indicates that dolomite has been leached out, resulting in phosphorite upgrading in the E-block. The major element oxide data support the leaching of dolomite. In the upgraded E-block, the weighted average P₂O₅ content nearly doubled (from 21% to 38%), while the MgO content decreased from 21% to 4% compared to the B-block. REE+Y contents in Block E are increased with minor Ce and Eu anomalies developed compared to the B Block. The U and Sr concentrations are also increased in Block E phosphorites. The petrographic and geochemical studies indicate that phosphorite enrichment was driven by structurally controlled, low-temperature hydrothermal alteration in the Jhamarkotra mines. Full article

Unmitigated climate change, coupled with habitat loss, has made the grassland and shrubland bird communities particularly vulnerable to extinction. Climate change-induced drought reduces net primary productivity, food availability, habitat quality, and alters vegetation structure. These factors collectively increase mortality in grassland and shrubland birds. However, limited data on habitat use by tropical birds hampers the development of effective management plans for drought-affected landscapes. We examined the foraging sites of 18 shrubland bird species, including two endemic and four declining species, across three shrubland forest sites in the Eastern Ghats of India during the dry season. We recorded microhabitat features within an 11 m radius of observed foraging points and compared them with random plots. Additionally, we examined the association between bird species and plant species where a bird was observed foraging. Foraging sites differed significantly from random plots, indicating active selection of microhabitats by shrubland birds. Using linear discriminant analysis, we found that the microhabitat features important for the bird species were presence of ground cover, shrub density, vegetational height, and vertical foliage stratification. Our results show that diet guild and foraging strata influence the foraging microhabitat selection of a species. Microhabitat attributes selected by shrubland specialist species differed from those of generalist shrubland users. Thirteen out of 18 focal species showed a significant association with at least one plant species. Birds were often associated with plants that were green during the dry season. Based on habitat selection and plant associations, we identified several habitat attributes that can be actively managed. Despite being classified as wastelands, the heavily degraded shrub forests can be rehabilitated through strategic and selective harvesting of forest products, targeting invasive species, and a spatially and temporally controlled livestock grazing regime. Full article

Ecosystems worldwide are being influenced by climate change in complex ways, leading to profound impacts on biodiversity. While these shifts may constrain the adaptive capacity of some species, they may simultaneously promote the expansion of others, including certain rodents. The Funambulus pennantii (five-striped northern palm squirrel) and Funambulus palmarum (three-striped palm squirrel) demonstrate exceptional adaptability across diverse habitats from rural plantations to densely populated urban areas. Their close association with humans ranges from causing crop damage to becoming urban nuisances, leading to their broad classification as agricultural pests. Hence, this study utilizes ensemble species distribution models to identify the climatic niches of these two species and delineate overlapping suitable habitats within agricultural zones in current and different future climate scenarios. The ensemble model identified 215,748 km² of suitable habitat for F. pennantii and 39,578 km² for F. palmarum under current climatic conditions in India. The future projections indicate habitat increases of about 20.6% for F. pennantii and 48.1% for F. palmarum. The agricultural overlap is also projected to rise by up to 45% and 48%, respectively, compared with present overlaps of 215,595 km² and 39,073 km². The most substantial expansions are projected in the Gangetic Plains, Central Highlands, and parts of the Western and Eastern Ghats and Deccan Plateau. These findings suggest that both species possess high resilience to environmental change and may pose increasing risks to agriculture in a warming climate. The study underscores the need for proactive, ecologically sustainable management strategies to mitigate potential human–squirrel interactions and agricultural losses, offering a foundation for targeted regional interventions. Full article

The spatial relationships of the widely distributed latest Jurassic–Early Cretaceous igneous rocks in the Tethyan Himalaya, northeastern India, and southeastern Australia are crucial for understanding the breakup of eastern Gondwana. However, available paleomagnetic datasets from the central Tethyan Himalaya are limited. To better constrain the distribution of these igneous rocks, new paleomagnetic data were obtained from the Weimei Formation volcanic rocks in the central Tethyan Himalaya. The tilt-corrected site-mean directions for 11 sites is Ds = 315.4°, Is = −58.4°, ks = 17.2, α₉₅ = 11.3°, corresponding to a paleopole at 9.6° N, 297.3° E with dp/dm = 16.7°/12.4° and a paleolatitude of ~39.1 ± 12.4° S for the study area (29.7° N, 83.7° E). A positive regional fold test and the occurrence of dual polarity suggest that the characteristic remanent magnetization is of pre-fold origin. These new results, together with those from the eastern Tethyan Himalaya, northern India, and southwestern Australia, show that the ~147–130 Ma circum-eastern Gondwana large igneous province spans a latitudinal range of ~40.7–58.1° S, centered at ~49.4° S, which is consistent with the present Kerguelen hotspot (~49.0° S). Integrating other evidence, we consider that the Kerguelen mantle plume played a key role in the breakup of eastern Gondwana. Full article

The lower Mahanadi basin in eastern India is experiencing significant land and soil transformations that directly influence agricultural sustainability and ecosystem resilience. In this study, we used geospatial techniques to analyze the spatial-temporal variability of soil quality and land cover between 2011 and 2020 in the lower Mahanadi basin. The results revealed that the cropland decreased from 39,493.2 to 37,495.9 km², while forest cover increased from 12,401.2 to 13,822.2 km², enhancing soil organic carbon (>290 g/kg) and improving fertility. Grassland recovered from 4826.3 to 5432.1 km², wastelands declined from 133.3 to 93.2 km², and water bodies expanded from 184.3 to 191.4 km², reflecting positive land–soil interactions. Soil quality was evaluated using the Simple Additive Soil Quality Index (SQI), with core indicators bulk density, organic carbon, and nitrogen, selected to represent physical, chemical, and biological components of soil. These indicators were chosen as they represent the essential physical, chemical, and biological components influencing soil functionality and fertility. The SQI revealed spatial variability in texture, organic carbon, nitrogen, and bulk density at different depths. SQI values indicated high soil quality (SQI > 0.65) in northern and northwestern zones, supported by neutral to slightly alkaline pH (6.2–7.4), nitrogen exceeding 5.29 g/kg, and higher organic carbon stocks (>48.8 t/ha). In contrast, central and southwestern regions recorded low SQI (0.15–0.35) due to compaction (bulk density up to 1.79 g/cm³) and fertility loss. Clay-rich soils (>490 g/kg) enhanced nutrient retention, whereas sandy soils (>320 g/kg) in the south increased leaching risks. Integration of LULC with soil quality confirms forest expansion as a driver of resilience, while agricultural intensification contributed to localized degradation. These findings emphasize the need for depth-specific soil management and integrated land-use planning to ensure food security and ecological sustainability. Full article