High ripening-stage temperatures strongly influence basmati rice yield, quality, and commercial value. This study investigated how different basmati varieties perform in yield and quality traits when exposed to varying temperatures by shifting the transplanting date. The response of four basmati varieties to early transplanting (5 July) and late transplanting (25 July) was evaluated under field conditions for two consecutive years. Across the varieties, basmati rice yield decreased for late transplanting (2.8 t /ha) as compared to early transplanting (3.5 t /ha), and the variety Pusa basmati-1509 (3.5 t /ha) produced higher grain yield than other tested varieties (2.9-3.1 t /ha). A wide variation in chalkiness (2–32%) was observed among the evaluated basmati rice varieties. Punjab basmati-3 and RYT-3390 exhibited markedly lower chalkiness than Pusa basmati-1121 and Pusa basmati-1509. Chalkiness showed a positive correlation with temperature during the ripening phase. Maximum temperatures ranged from 31.1 to 32.80C in the 5 July transplanting and 29.8 to 32.90C in the 25 July transplanting. Minimum temperature declined from 24.5 to 19.10C in the early transplanting and 23.9 to 16.7 0C in the late transplanting. The greatest reduction in temperature was observed under late transplanting during the later stages of grain filling, indicating a cooler thermal environment during grain development. Head rice recovery (HRR) was comparatively higher in Punjab basmati-3 (52–54%) and RYT-3390 (~57%) than in Pusa basmati-1121 (~49%) and Pusa basmati-1509(44–51%). In early-transplanted basmati, HRR was negatively associated with ripening-stage temperature. Varieties exhibiting high HRR together with low and stable chalkiness—such as Punjab basmati-3 and RYT-3390—may therefore serve as promising parental lines in breeding programs aimed at developing basmati varieties with improved grain appearance, quality, and HRR, particularly under warming climatic conditions.

This study optimized vermiwash production using Eisenia fetida from organic substrates rice straw + cow dung (1:1), wheat straw + cow dung (1:1), kitchen waste + cow dung (1:1), neem leaves + cow dung (1:1), and pure cow dung in a drip-irrigated earthen pot system at Punjab Agricultural University, Ludhiana. Kitchen waste vermiwash demonstrated superior nutrient enrichment (elevated NPK, micronutrients), microbiological (peak microbial populations, enzyme activities), and biochemical profiles (maximum proteins, pigments, sugars) across treatments (p<0.05). Kitchen waste's labile nutrients enhanced earthworm-microbial synergy over lignocellulosic straws and stabilized cow dung, positioning vermiwash as an optimal organic biostimulant for sustainable agriculture.

Myiasis, caused by dipteran larvae infesting living tissues, poses significant health and economic challenges in humans and livestock. The present study evaluates the larvicidal efficacy of crude leaf extracts of Ricinus communis L. (Euphorbiaceae) against third instar larvae of Sarcophaga ruficornis under laboratory conditions. Leaf extracts were prepared using suitable solvents and tested at different concentrations to determine their toxicity and growth-inhibitory effects. The treated larvae exhibited dose-dependent mortality, reduced feeding activity, developmental abnormalities, delayed pupation, and decreased adult emergence compared with untreated controls. Phytochemical constituents present in the extracts, including alkaloids, flavonoids, tannins, and phenolic compounds, are presumed to contribute to the observed bioactivity. The findings demonstrate that R. communis leaf extract possesses potent larvicidal properties and offers an environmentally safe, biodegradable, and sustainable alternative to synthetic insecticides. This ecofriendly botanical approach may serve as an effective strategy for controlling myiasis-causing flies and reducing their associated impacts.

The present study was aimed to document the diversity and conservation status of herpetofauna inhabiting the study region. A total of 38 species were recorded, representing 15 families of reptiles and amphibians. The annotated checklist provides species-specific details, including scientific and common names, conservation status under the Indian Wildlife (Protection) Act (IWPA), and global assessment through the IUCN Red List. Among the documented species, snakes formed a significant component with 16 species, reflecting the ecological richness and habitat heterogeneity of the area. The survey, however, did not record Telangana endemics such as Hemidactylus flavicaudus, H. xericolus, and H. aemulus, highlighting the need for further intensive exploration and long-term monitoring. The present checklist contributes to baseline information essential for regional biodiversity assessments and conservation planning. It serves as a reference for researchers, students, and conservation practitioners, while underlining the necessity of continued site-specific documentation to guide effective management strategies for herpetofauna diversity.

The present study provides the detailed spatio-temporal analysis and statistical assessment of three pertinent water quality parameters such as dissolved oxygen (DO), chemical oxygen demand (COD), and biochemical oxygen demand (BOD) in the urban reach of Gomti River at Lucknow for the period of 2023–2024. Four sites were selected based on the level of contamination due to combined anthropogenic, industrial and residential activities and monthly surface water samples were collected. The concentrations of the parameters were determined using standard analytical methods and the data were analyzed using statistical techniques such as multiple linear regression, descriptive analysis and Pearson correlation. The results revealed a high BOD (9.07 mg L⁻¹) and COD (39.86 mg L⁻¹) which indicates high extent of organic and chemical pollution and continuously low values of DO (mean: 3.15 mg L⁻¹) below the biological level required to support the aquatic organisms. During the summer months, dissolved oxygen was substantially reduced due to increased microbial activity, lower flow and warmer temperatures. BOD and COD were found to have a significant positive correlation (r = 0.734) which indicates their contribution to pollution severity. DO was found to have significant negative correlations with BOD (r = −0.404) and COD (r = −0.480). Regression analysis (R2 = 0.579, p < 0.001) shows that organic load has a significant influence on the COD variability, and BOD is the major determinant factor. Spatial variability revealed high levels of pollution due to untreated sewage and industrial effluents and the need for better treatment, regulation, monitoring and restoration.

Nanomaterials have emerged as promising materials for addressing major challenges in energy production, storage, environmental protection, and sustainable development. Their unique physicochemical properties, including high surface area, tunable optical, electrical, thermal, and catalytic characteristics, enable improved performance in a wide range of applications. This review highlights recent advances in nanomaterials for energy conversion and storage, including solar cells, batteries, supercapacitors, fuel cells, and hydrogen production. It also examines their environmental applications in wastewater treatment, air-pollution control, pollutant degradation, environmental sensing, and carbon capture. The review discusses the advantages, limitations, environmental risks, and future prospects of nanomaterial-based technologies, emphasizing their potential contribution to clean energy and sustainable environmental management.

Accurate estimation of potential evapotranspiration (PET) is essential for irrigation planning, agricultural water allocation, drought assessment, and sustainable water resources management. Conventional PET methods require several meteorological variables that may be incomplete or unavailable in data-scarce regions. This study evaluates Random Forest (RF) and Support Vector Machine (SVM) for monthly PET estimation in Supaul District, Bihar, India, using monthly meteorological data from 2001–2022. Seven predictors—specific humidity at 2 m (QV2M), relative humidity at 2 m (RH2M), wind speed at 2 m (WS2M), maximum and minimum air temperature (T2M_MAX and T2M_MIN), ultraviolet radiation (UVB), and sunshine duration (SDDN)—were used, while Penman-derived PET was the target. Model performance was evaluated using R², RMSE, MAE, MBE, NSE, KGE, Willmott’s index of agreement, residual diagnostics, Taylor diagrams, and Bland–Altman analysis. RF achieved R² = 0.976, RMSE = 6.30 mm month⁻¹, MAE = 4.30 mm month⁻¹, MBE = −0.21 mm month⁻¹, NSE = 0.976, KGE = 0.897, and d = 0.994. SVM achieved R² = 0.828, RMSE = 16.74 mm month⁻¹, MAE = 12.24 mm month⁻¹, and MBE = −1.94 mm month⁻¹. RF consistently showed closer agreement with the Penman-derived reference series and smaller residual dispersion. The results indicate that RF provides an efficient data-driven approach for reproducing monthly Penman-derived PET in the study region and may support irrigation and water-resource planning where complete operational calculations are inconvenient.

Artificial intelligence for surface water quality prediction has evolved rapidly from classical regression and ensemble learning toward deep sequence, graph-based, physics-informed, and representation-learning approaches, while a largely disconnected systems literature has matured around digital twins, edge intelligence, and federated learning. This critical review synthesizes thirteen primary manuscripts against expanded 2020–2026 literature to examine the relationship between predictive performance and operational deployability. The evidence reveals a persistent bifurcation between an accuracy-maximizing research track and a deployability-maximizing systems track, corroborated independently at classical-modeling, digital-twin, and edge-hardware scales: the corpus's largest single study reports 31,289 samples from 23 stations, whereas a systematic review of 147 water-sector digital-twin studies identifies only 8 cases achieving genuine bidirectional control, and the largest review of environmental federated learning (361 studies) finds only 12 addressing water quality, with predominantly simulated deployments. Ensemble tree methods show the most consistently replicated predictive performance, while near-perfect results reported by simpler models remain constrained by the absence of external or cross-site validation. Explainable AI remains concentrated in feature-attribution methods, convergent SHAP findings require a causal-versus-correlational caveat, and the integration of spatial, physical, and temporal-representational deep-learning strategies remains unexplored. Building on these findings, this review develops a twelve-category Research Gap Matrix and proposes an Integrated Water Intelligence Framework linking prediction, model compression, governance-compliant decision-making, and closed-loop actuation. The review concludes that future progress requires predictive accuracy to be evaluated jointly with validation, explainability, governance, and deployability, and closes with a staged, stakeholder-differentiated roadmap.

The Kallada River, a major west-flowing river in Kerala, serves as a critical source of drinking water, irrigation, fisheries, and ecological support. Increasing anthropogenic pressures, including sand mining, agricultural runoff, sewage discharge, pilgrimage activities, and riparian degradation, have adversely impacted its water quality. This study evaluated the physicochemical and bacteriological characteristics of the Kallada River at eight sampling stations during pre-monsoon, monsoon, and post-monsoon seasons from November 2022 to July 2023. Water samples were analyzed for temperature, pH, total dissolved solids (TDS), electrical conductivity, alkalinity, hardness, free carbon dioxide, nitrate, phosphate, sulfate, sodium, potassium, and total coliform counts using standard analytical procedures. Results indicated seasonal variations in water quality parameters, with most physicochemical parameters remaining within permissible limits established by Indian standards. However, elevated phosphate concentrations and exceptionally high total coliform counts reflected significant anthropogenic contamination. Site 1 consistently exhibited greater deterioration in water quality, primarily due to intensive human activities associated with the Sabarimala pilgrimage. Pearson correlation analysis revealed significant relationships among physicochemical parameters across seasons, underscoring the influence of nutrient enrichment, runoff, organic matter decomposition, and mineralization processes. Although the river water generally meets criteria for Class C surface water, it is unsuitable for direct consumption without treatment and disinfection. Continuous monitoring and implementation of effective management strategies are necessary to safeguard the ecological integrity and water quality of the Kallada River.

Curcumin, the primary bioactive curcuminoid from Curcuma longa, displays potent antioxidant activity yet is highly susceptible to oxidation, impacting its stability, bioavailability, and utility in environmental applications such as pollutant scavenging and natural preservative systems. This review comprehensively examines kinetic studies on curcumin oxidation up to 2025, encompassing oxidants including hydrogen peroxide, peroxydisulfate, hypochlorite, transition metal ions, reactive oxygen species (ROS), and autoxidation across diverse solvents (aqueous buffers, ethanol, DMSO, acetonitrile, and lipid media). Predominantly first-order or pseudo-first-order kinetics are observed, with rate constants spanning several orders of magnitude depending on pH, solvent polarity, temperature, and oxidant type. Organic protic solvents generally confer greater stability compared to alkaline aqueous media. The review incorporates data tables, comparative graphs, and Arrhenius insights derived from primary literature. Environmental implications for green chemistry and gaps in real-matrix studies are highlighted.

This study analyses spatial distribution and common places of abode for internally displaced persons (IDPs) in camp settings across Greater Yola, Adamawa State, Nigeria, from 2015 to 2019, amid Boko Haram insurgency displacement. Using Displacement Tracking Matrix (DTM) data from the International Organization for Migration (IOM), we mapped 15 camps across eight wards, revealing that 85-88% of IDPs (peaking at 29,000 in 2017) concentrated in peripheral fringes due to land availability and affordability, while core urban wards hosted only 12-15%. Descriptive spatial analysis and demographic surveys (n=269 household heads) highlight vulnerabilities, including 57% female respondents, 86% under age 50, and low education levels exacerbating livelihood challenges.Findings demonstrate non-uniform camp placement, with clustering in low-density outskirts (Namtari, Girei wards), driven by infrastructure constraints rather than security or services. This first ward-level DTM assessment in Adamawa underscores policy gaps in equitable displacement management. We recommend zoning reforms for balanced facility distribution to mitigate urban congestion and enhance resilience, informing scalable interventions amid Nigeria's >3 million IDPs.

Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, reshaping industries, enhancing productivity, and enabling solutions to complex global challenges. Yet its environmental footprint — encompassing energy consumption, carbon emissions, water usage, electronic waste (e-waste), and mineral extraction — remains largely invisible to end users and policymakers alike. This paper critically examines the environmental implications of AI development and large-scale deployment, with particular emphasis on large language models (LLMs) and generative AI systems. Drawing on the most current quantitative benchmarks (IEA, 2025; UN University, 2025; Global E-Waste Monitor, 2024), we situate AI's resource demands within the broader context of global industrial energy consumption, updating prior analyses with 2024–2026 data. Data centre electricity consumption reached 415 TWh globally in 2024, with AI-specific workloads surging 50% in 2025 alone; by 2030, the sector is projected to consume 945–1,300 TWh annually. We further evaluate mitigation strategies under the emerging paradigm of Green AI — encompassing algorithmic efficiency, renewable energy procurement, carbon-aware scheduling, and transparent reporting standards. The study concludes that while AI is not yet among the largest absolute environmental threats, its compound growth trajectory demands immediate, systemic sustainability interventions to ensure technological advancement and environmental stewardship remain aligned.

Lithium-ion batteries (LIBs) are rapidly gaining acceptance in electric vehicles, energy storage systems and portable electronics, and have brought the need for sustainable end-of-life management to a new level. Again, there is significant recovery value for spent batteries: NMC and NCA cathodes contain 8-15% cobalt and nickel and 1.3-1.9% lithium by mass and LFP cells contain over 43% iron/steel with virtually no cobalt content; therefore, recycling operations need to be chemistry-specific. Hydrometallurgical leaching with 1–4 M inorganic acid (H₂SO₄, HCl, HNO₃) and H₂O₂ as a reductant is able to recover 95–100% of Li, Co, Ni and Mn under moderate conditions (70-95°C and 60-240 min), although pyrometallurgical (smelting) process works above 1000C and tolerates feedstock but losses a substantial amount of lithium and manganese to the slag phase. A number of organic acids (citric, oxalic, lactic and malic acid), which enable comparable Li recovery of 80–100% at 60–100°C, and Co recovery of 80–98%, can also reduce the toxicity of the reagents, but the emergence of deep eutectic solvents (DESs) allows for lower generation of by-products and reactions at even milder conditions (60–90°C), while also presenting challenges in solvent regeneration and selectivity. The use of acidophilic microorganisms like Acidithiobacillus ferrooxidans and Aspergillus niger is an alternative technology for bioleaching, which can extract 85-95% of Li and Co at ambient conditions with a pH range of 1.2-3.5 and with slower processing time. Selective chemical precipitation is achieved in a controlled pH process, allowing for the ability to separate valuable metals from complex leachates in an economical and scalable fashion downstream. This review brings together the above process routes and compares their recovery efficiencies, consumptions of reagents/energy and scalability, and proposes a hybrid thermal-hydrometallurgical route as the most promising one to reach an economically viable, low carbon LIB recycling.

River morphology is very dynamic, and it continues to adapt to natural processes and human activities, with channel migration, bank erosion and deposition occurring at varying rates; these processes can create major difficulties in sustainable river management. The efficiency in monitoring such morphological changes over large spatial and temporal scales has been made possible due to advances in satellite remote sensing. The Digital Shoreline Analysis System (DSAS) is one of several approaches that have been widely used to measure rates of riverbank changes through statistical calculations, including Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR). The reliability of DSAS-based analysis, however, is very dependent on the accuracy of the bankline that is extracted from the satellite image. The recent advancement of machine learning techniques, especially those based on random forest (RF) and support vector machine (SVM), has shown better accuracy in land–water classification and feature extraction in challenging riverine environments. The combination of machine learning and DSAS provides increased robustness in the delineation of riverbanks and the evaluation of morphological changes that have occurred. The present paper provides a detailed review of remote sensing–based approaches for morphological assessment of natural streams with a particular emphasis on the simultaneous use of DSAS and machine learning techniques. The study reviews pertinent published sources, discusses their strengths and weaknesses and outlines current trends and gaps in the literature. A conceptual framework is suggested to combine the integration of DSAS, RF and SVM to support future studies in order to enhance the accuracy and applicability of river morphology monitoring in the context of sustainable management of river basins.

El Niño–Southern Oscillation (ENSO) has long shaped global weather patterns, but in an era of accelerating climate change, the risks it carries are growing sharper and more consequential. The 2023–2024 El Niño event is one of the five strongest on record, and 2024 was the warmest year in the 175-year observational record, with a global mean near-surface temperature of 1.55 ± 0.13°C above the 1850–1900 average (World Meteorological Organization. 2025), which pushed global temperatures to unprecedented levels, intensified droughts and floods across multiple continents, disrupted food systems, and exposed the deepening vulnerability of developing nations. This short communication examines how a warming world amplifies the downstream consequences of El Niño events, with a particular focus on the socioeconomic risks facing nations such as India. It highlights that developing countries face an annual adaptation-finance requirement of US$310–365 billion by 2035, compared with only US$26 billion in international public adaptation finance in 2023 (United Nations Environment Programme. 2025). It further explores the immediate need for anticipatory, transformational climate resilience spanning early warning systems, adaptive agriculture, climate finance reform, and integrated governance. The paper argues that El Niño is no longer a periodic natural disruption to be managed reactively, but a compounding threat that demands structural, forward-looking adaptation at every level.