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The main goal of this research paper is to apply a deep neural network model for time series forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are important issues for the reliable and efficient hydrological models and prediction of the spread of forest. Long Short Term Memory (LSTM) model for the time series forecasting of snow cover, temperature, and normalized difference vegetation index (NDVI) are studied in this research work. Artificial neural networks (ANN) are widely used for forecasting time series due to their adaptive computing nature. LSTM and Recurrent neural networks (RNN) are some of the several architectures provided in a class of ANN. LSTM is a kind of RNN that has the capability of learning long-term dependencies. We followed a coarse-to-fine strategy, providing reviews of various related research materials and supporting it with the LSTM analysis on the dataset of Himachal Pradesh, as gathered. Environmental factors of the Himachal Pradesh region are forecasted using the dataset, consisting of temperature, snow cover, and vegetation index as parameters from the year 2001–2017. Currently, available tools and techniques make the presented system more efficient to quickly assess, adjust, and improve the environment-related factors analysis. [ABSTRACT FROM AUTHOR]

Four yeast strains were isolated from the gut of stingless bee, collected in Churdhar, Himachal Pradesh, India. Physiological characterization, morphological examination, and sequence analysis of small subunit ribosomal RNA (18S rRNA) genes, internal transcribed spacer (ITS) region, and D1/D2 domain of the large subunit rRNA gene revealed that the four strains isolated from the gut of stingless bee belonged to the Debaryomyces clade. Strain CIG-23HT showed sequence divergence of 7.5% from Debaryomyces nepalensis JCM 2095T, 7.8% from Debaryomyces udenii JCM 7855T, and Debaryomyces coudertii JCM 2387T in the D1/D2 domain. In the ITS region sequences, strain CIG-23HT showed a 15% sequence divergence from Debaryomyces nepalensis JCM 2095T and Debaryomyces coudertii JCM 2387T. In 18S rRNA gene sequence, the strain CIG-23HT showed 1.14% sequence divergence from Debaryomyces nepalensis JCM 2095 and and Debaryomyces coudertii JCM 2387, and 0.83% sequence divergence from Debaryomyces hansenii NRRL Y-7426. Strain CIG-23HT can utilize more carbon sources than closely related species. The findings suggest that strain CIG-23HT is a novel species of the genus Debaryomyces, and we propose to name it as Debaryomyces apis f.a., sp. nov. The holotype is CBS 16297T, and the isotypes are MTCC 12914T and KCTC 37024T. The MycoBank number of Debaryomyces apis f.a., sp. nov. is MB836065. Additionally, a method using cresol red and Bromothymol blue pH indicator dyes was developed to screen for lipase producers, which is more sensitive and efficient than the currently used phenol red and rhodamine B dye-based screening methods, and avoids the problem of less differentiable zone of hydrolysis. [ABSTRACT FROM AUTHOR]

In recent times, waste management has emerged as a significant environmental challenge, and sewage is among the major contributors due to the rapidly increasing population. Despite sewage treatment plants (STPs) being the solution for the treatment of sewage, they have been identified as sources of greenhouse gas (GHG) emissions. This study aimed to estimate the contribution of STPs to GHG emissions in the state. This was achieved by visiting the sites, filling scientifically designed questionnaires, sample collection as well as computational methods by Intergovernmental Panel on Climate Change. The assessment of direct and indirect emissions from the STPs revealed that emissions were caused by the activated sludge process, electricity consumption, transportation, and sludge storage. Electricity consumption by STPs was responsible for the highest emissions, accounting for 43% of the total emissions, equivalent to 20,823 tCO2 eq. The activated sludge process contributed 31% (14,934 tCO2 eq) of the emissions, while storage of sludge in landfills accounted for 24% (11,359 tCO2 eq). Additionally, transportation contributed 2% (1121 tCO2 eq) of the emissions. In total, the STPs in Himachal Pradesh had the potential to contribute 48,237 tCO2 eq GHG emissions annually. Thus, the study suggests process-level modifications in STPs of Himachal Pradesh to mitigate GHG emissions. This research provides insight into the GHG emissions from STPs and highlights the need for their management to reduce environmental impacts. [ABSTRACT FROM AUTHOR]

The present research has been performed to analyze the chemical behavior of rainwater of the Shaune Garang catchment (32.19° N, 78.20° E) in the Baspa basin, located at a high elevation (4221 m above mean sea level) in the Himachal Himalaya, India. During the study period, sixteen rainwater samples were collected from the Shaune Garang catchment at five different sites. The volume-weighted mean (VWM) pH value of rainwater ranged between 4.59 and 6.73, with an average value of 5.47 ± 0.69, indicating the alkaline nature of rainfall. The total ionic strength in the rainwater ranged from 113.4 to 263.3 µeq/l with an average value of 169.1 ± 40.4 µeq/l. The major dominant cations were Ca2+ (43.10%) and Na+ (31.97%) and anions were Cl− (37.68%), SO42− (28.71%) and NO3− (23.85%) in rainwater. The ionic ratios were calculated among all the ions. The fraction of (NO3− +Cl−) with SO42− was measured as 2.3, which specifies sour faces of rainwater due to HNO3, H2SO4, and HCl. A multivariate statistical assessment of rainwater chemistry through Principal Component Analysis (PCA) shows the significance of four factors controlling 78.37% of the total variance, including four-component (PC1 explained 27.89%, PC2 explained 24.98%, PC3 explained 14.64%, PC4 explained 10.85%). However, the individual contribution of Factor 1(PC1) explains 27.89% of the total variance (78.37%) and displays a strong optimistic loading for Ca2+ and Cl−. Further, high loading of Ca2+ and NO3− and moderate loading of SO42− signify the contribution of burning fossil fuel and soil dust. Anthropogenic and natural pollutants influence the composition of rainwater in the pristine Himalayas due to local and long-distance transportation. The study area receives precipitation from the West and North-West, transporting dust and fossil fuel emissions from the Thar Desert and Northwestern countries. [ABSTRACT FROM AUTHOR]