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This paper examines the Linguistic Survey of India (LSI), a monumental exercise supervised by George Grierson to survey and classify the languages of colonial India. It considers why the LSI developed into an atypical scheme that corresponded with a multiethnic and multinational network of officials and scholars to survey India's languages. It makes the case that the networked practice of surveying was reciprocated at different scales, from localised linguistic surveys in districts and princely states to gather information and specimens, to a loosely governed transnational exercise involving Indians and Europeans to edit, review and publish results. The paper argues that the LSI's scalar geographies were negotiated by Grierson and, more importantly, his assistant Gauri Kant Roy and demonstrates that scale, as an analytic or process, was not an abstraction or predetermined for those entangled in the LSI's survey of India's languages. • Examines the Linguistic Survey of India from a historical geography perspective. • Highlights the LSI's uniqueness as a colonial Indian survey. • Draws on underutilised materials from National Archives of India. • Proposes a scalar and networked approach to the history of science and surveying. • Emphasises the significance of Gauri Kant Roy and various Indian surveyors in shaping India's linguistic geography. [ABSTRACT FROM AUTHOR]

Bamboo spp. are a significant source of raw materials for the paper and pulp industries worldwide. Therefore, it is crucial to identify pathogens accurately since they can pose a significant threat to bamboo in nurseries and plantations. In January 2021, symptoms of leaf spot and blight disease were observed on Bambusa polymorpha in the central nursery of Forest Research Institute, Dehradun, India. After examining the morphological characteristics and conducting a phylogenetic analysis of the internal transcribed spacer (ITS) region of ribosomal DNA, Nigrospora sphaerica was identified as the causative agent. In pathogenicity experiments, N. sphaerica induced disease symptoms and was successfully re-isolated from the inoculated leaves, thereby satisfying Koch's postulates. This study marks the first report of N. sphaerica as a foliar pathogen of B. polymorpha in India. Further research should focus on developing strategies to manage this disease in bamboo nurseries. [Display omitted] • This study reports Nigrospora sphaerica as a foliar pathogen of Bambusa polymorpha for the first time in India. • The pathogen was identified based on microscopic features, ITS sequence analysis and pathogenicity tests. • This is a new taxonomic addition to the diversity of pathogenic bambusicolous fungi. • This study provides directions to study the host range and develop control methods for N. sphaerica and other bamboo pathogens. [ABSTRACT FROM AUTHOR]

Phenolic compounds are one of the main contributors to water source contamination worldwide. In this review, the data collected on Elsevier, Scopus, and Pubmed, considering papers published between 2000 and 2023, showed more than 60 different phenols have been identified in water matrix (<0.065–179,000,000 ng L−1). The highest concentration reported was in surface water canals in India. The most recurrent and studied compound was bisphenol A (n = 93) in concentrations ranging from 0.45 to 2,970,000 ng L−1. The solid phase extraction (HBL Oasis cartridge) and methanol as solvent was the method of pre-concentration most used followed by gas chromatography for the determination of phenols in water samples. The importance of drinking water guidelines incorporating more phenolic compounds was emphasized given the variety of these compounds quantified in water matrix. The human health risk assessment (HRA) was performed for the min-max concentrations of the pollutants reported in the literature. High HRA even at the lowest concentrations for 2-nitrophenol, 2,6-dichlorophenol, 3,4,5-trichlorophenol, 2,3,4,6-tetrachlorophenol, and 2,4-dinitrophenol was recognized. The cancer risk estimated was considered possible for 3-methylphenol, 2,4-dimethylphenol, 2,4,6-trichlorophenol, pentachlorophenol, and 2,4-dinitrophenol in the highest concentrations. The in-depth discussion of mechanisms, advantages, challenges, and carbon footprint of membrane technologies in water treatment and phenols retention demonstrated the great potential and trends for the production of safe drinking water, highlighting reverse osmosis, as a mature technology, and membrane distillation, as an emergent technology. [Display omitted] • More than 60 phenolic compounds were identified in water sources. • The highest concentration in surface water was reported as 1.79 E8 ng L−1 in India. • Water guidelines address, in general, only 14 phenols. • High risk to human health and the possibility of cancer were estimated. • Membrane technology can effectively remove phenols, especially reverse osmosis. [ABSTRACT FROM AUTHOR]

South Asia's coastlines are the most densely inhabited and economically active ecosystems have already begun to shift due to climate change. Over the past century, climate change has contributed to a gradual and considerable rise in sea level, which has eroded shorelines and increased storm-related coastal flooding. The differences in estuary water quality over time, both seasonally and annually, have been efficiently controlled by changes in stream flow. Assessment requires digitized analytical platforms to lower the risk of catastrophes associated with climate change in coastal towns. To predict future changes in an area's vulnerability and waste planning decisions, a prospective investigation requires qualitative and quantitative scenarios. The paper concentrates on the development of a forecasting platform to evaluate the climate change and waste water impacts on the south coastal region of India. Due to the enhancement of Digitization, a multi-model ensemble combined with manifold learning is implemented on the multi-case models influencing the uncertainty probability rate of 23% and can be ignored with desired precaution on the coastal environmental. Because Manifold Learning Analysis results cannot be utilized directly in wastewater management studies because of their inherent biases, a statistical bias correction and meta-feature estimation have been implemented. Within the climate-hydrology modeling chain, the results demonstrate a wide range of expected changes in water resources in some places. Experimental statistics reveal that the forecasted rate of 91.45% will be the better choice to reduce the uncertainty of climatic change and wastewater management. • Development of Multi-Objective Neighborhood Embedding Ensemble Strategy (NEES) to predict waste water and climate change impacts. • Manifold learning with metafeatures estimator model to forecast the temperature index and oxygen depletion. • Enhanced Coastal Management system with multi-parameter reliability model for South Asia Coastal region to safeguard the environmental eco-stability. • Manifold predictor models for waste water impact and climatic influence with maximum data samples with optimized cost function. [ABSTRACT FROM AUTHOR]