Detection of endocrine disrupting compounds in water and sediment samples has gained much importance since the evidence of their effects was reported in aquatic ecosystems in the 1990s. The aim of this review is to highlight the advances made in the field of in vitro analysis for the detection of hormonally active compounds with estrogenic, androgenic and progestogenic effects in water and sediment samples. In vitro assays have been developed from yeast, mammalian and in a few cases from fish cells. These assays are based either on the hormone-mediated proliferation of sensitive cell lines or on the hormone-mediated expression of reporter genes. In vitro assays in combination with various sample enrichment methods have been used with limits of detection as low as 0.0027 ng L⁻¹ in water, and 0.0026 ng g⁻¹ in sediments for estrogenicity, 0.1 ng L⁻¹ in water, and 0.5 ng g⁻¹ in sediments for androgenicity, and 5 ng L⁻¹ in water for progestogenicity expressed as equivalent concentrations of standard reference compounds of 17β-estradiol, dihydrotestosterone and progesterone, respectively. The experimental results and limits of quantification, however, are influenced by the methods of sample collection, preparation, and individual laboratory practices.

Surface water is a primary source of drinking water in Bhutan. To ensure a reliable supply thatmeets both quality and quantity requirements, it is crucial to identify suitable sources. This studypresents an integrated approach using Geographic Information Systems (GIS) and machine learningto identify potential surface water sources. Satellite imagery from Landsat-8 and Sentinel-2 wasutilized to generate geospatial datasets. Five key variables influencing the spatio-temporal presenceof water—rainfall, temperature, soil type, Normalized Difference Vegetation Index (NDVI), andtopography were analyzed within a GIS environment. The Random Forest (RF) algorithm, knownfor its robustness in handling nonlinear and high-dimensional data, was employed to predictpotential water sources. Model outputs were validated through field surveys and spectral analysisusing the Normalized Difference Water Index (NDWI). The study identified 50 viable water sourcelocations situated above 450 meters in elevation. The model achieved an area under the curve (AUC)score of 0.99, indicating a strong correlation between predicted and actual water sources. Theseresults confirm that integrating machine learning with remote sensing and GIS is an effectiveapproach for surface water resource planning in Bhutan's hilly terrain.

Urban Heat Islands (UHIs) are urbanized areas that experience significantly higher temperatures than their surroundings, contributing to thermal discomfort, increased air pollution, heightened public health risks, and greater energy demand. In Bhutan, where urban expansion is concentrated within narrow valley systems, the formation and intensification of UHIs present emerging challenges for climate-resilient urban development. Thimphu, in particular, is experiencing rapid urban growth and densification, making it highly susceptible to UHI effects. Therefore, the aim of this study was to evaluate and simulate UHI conditions for Thimphu Thromde. We carried out the simulation using a GIS, multi-temporal Landsat imagery, and an Artificial Neural Network model. Land use and land cover classes were mapped through supervised classification in the GIS, and surface temperatures associated with each class were derived from thermal bands of Landsat data. These temperature values were normalized to identify existing UHI patterns. An Artificial Neural Network (ANN) model was then applied to simulate future UHI distribution under expected land use change scenarios. The results indicate that, by 2031, built-up areas in Thimphu Thromde are expected to increase to 72.82%, while vegetation cover is projected to decline to 23.52%. Correspondingly, both UHI and extreme UHI zones are projected to expand, accounting for approximately 14.26% and 6.08% of the total area, respectively. Existing hotspots, particularly dense residential areas, commercial centers, and major institutional or public spaces, are expected to intensify. In addition, new UHI zones are likely to develop along the urban fringe, where expansion is occurring around the current hotspots. These study findings will be useful for Thimphu Thromde authorities in deciding the mitigation measures and pre-emptive strategies required to reduce UHI effects.