CHEMO-PROFILING AND ASSESSMENT OF IN VITRO ANTIOXIDANT EFFICACY OF EIGHT FERNS OF DARJEELING HIMALAYAS, INDIA

AIM: The present study was performed to prole some phenolics and explore the antioxidant effect of eight locally available ferns collected from different places of Darjeeling Himalayas, India. Methods: The antioxidant activities of methanol (MeOH) extract was evaluated by DPPH free radical scavenging activity. Qualitative analysis of phenol was done using standard methods. Further, characterization of phenolics was done using High performance liquid chromatography. Result: The content of phenolics ranged from 6.77 to 60.066mg FAE/g dry weight. The DPPH antioxidant activity expressed as IC values 50 revealed Nephrolepis cordifolia and Microsorum punctatum to exhibit highest and lowest antioxidative activity respectively. Moderate correlation 2 (R =0.547) was observed between the total phenolics content and antioxidant activity. HPLC analysis of phenolics from all the investigated plants revealed the presence of caffeic acid, ferulic acid and salicylic acid while the other phenolics such as phloroglucinol, gallic acid, pyrogallol, 3,4- dihy droxybenzoic acid, catechol, catechin, chlorogenic acid, caffeine, vanillic acid and cinnamic acid were not uniformly present in all the plants. The phenolic contents values showed wide variation among themselves, as well as within different plants. These ferns with considerable amount of phenolics can be the potential source of natural antioxidants.

Socio-economic Development of Darjeeling Himalayas: Categorical Principal Component Analysis (CATPCA) and Ordinal Logistic Regression (OLR)

The measurement of regional development plays a crucial role in improving the quality of life of local communities. However, the process of analyzing the regional progress was challenging as regional development was presented as a multidimensional concept. Nonetheless, the study's primary objective was to understand the indicators that genuinely reflect the development process's various dimensions in the northernmost district of West Bengal, Darjeeling Himalayas. Seven dimensions of development, namely psychological well-being, health, education, governance, safety and crime, energy and environment and standard of living were identified for analyzing the socio-economic development of the Darjeeling Himalaya. A questionnaire was framed and circulated in the region for the collection of data. By applying Categorical Principal Component Analysis (CATPCA), the data collected was aggregated into the above mentioned seven dimensions of development and analyzed the relationship between these development indicators through the Ordinal Logistic Regression model (OLR). The results showed that education and governance indicators had a significant impact on the psychological wellbeing. Governance was affected by psychological wellbeing, while standard of living was affected by psychological wellbeing and health indicators in the region.

Structuring a Bayesian belief network using expert knowledge for landslide hazard assessment

<p>Landslide hazard assessment in India using historical data faces three challenges: (i) difficulty of obtaining systematic landslide occurrence data; (ii) under-representation of small-scale landslides; (iii) lack of recording of the physical/anthropogenic influences on landsliding. Here we show development of a Bayesian Belief Network (BBN) for a multi-hazard landslide assessment using experts’ judgements. Experts were chosen based on their experience on landslides and/or in Darjeeling Himalayas. A BBN produces a probability estimation of possible events and is a graph containing a set of variables (nodes) and conditional (in)dependencies between the nodes (arcs).</p><p>To better understand the relative weighting of potential causes of landslides in our case study area -Darjeeling Himalayas- we carried out four steps. (<strong>Step 1</strong>) We reviewed 29 peer- and grey-literature sources to list 13 physical/anthropogenic variables that might influence landsliding. (<strong>Step 2</strong>) We interviewed 11 experts about the importance of these 13 variables and asked for additional potential variables (resulting in 35 variables). (<strong>Step 3</strong>) We used interviews plus questionnaire to ask 16 experts to rate each of the 35 variables (scale 1-10) as to their potential to influence landsliding. The experts also added 7 more variables (resulting in 46 variables). (<strong>Step 4</strong>) Based on the ratings and interviews, we chose 35 out of 46 variables as our BBN nodes and from these the BBN arcs. Examples of these variables include rainfall, wildfires, geological weathering, planned infrastructure loading, cultivation (planned/unplanned), railway/road construction changing slope angle (planned), relief, slope, soil cohesion. Based on this study, we found that judgement of local people/academicians/technical experts can be of help whilst developing a BBN structure, allowing us to calculate probabilistic relationships between the nodes in a BBN. This process, therefore, can be utilised for landslide-based multi-hazard assessment in low data regions.</p>

Identifying morphological diversity and superior germplasm of Citrus reticulata Blanco (Mandarin) in Sikkim and Darjeeling Himalayas, India: an application of DIVA-GIS and cluster analysis

Citrus reticulata is an important cash crop for the farmers in the Sikkim and Darjeeling Himalayas, India. The lack of knowledge of its diversity has only resulted in the lack of uniformity in the fruit quality. It has become imperative to identify superior varieties that meet the demands of the market to improve the citrus industry. Hence, a total of 105 accessions of mandarin were collected randomly from different locations to assess the morphological diversity using cluster analysis and DIVA-GIS. The orchards were randomly visited in each district of the state of Sikkim and two hilly districts of the state of West Bengal (WB). A sample tree was selected from each orchard and the quantitative and qualitative characters of its leaves, flowers, and fruits were measured. A significant variation was observed in the quantitative characters with a positive correlation between fruit weight and length, fruit diameter and weight, total soluble solids (TSS) /acidity and fruit diameter. The first 6 components of Principal component analysis (PCA) exhibited 69.34% of the total variation. DIVA-GIS showed the highest diversity index for fruit weight, fruit diameter and TSS/acidity in East district, Sikkim. The highest coefficient variation for fruit diameter was observed in the East district and Darjeeling district, WB and TSS/acidity and fruit weight in East district. The dendrogram generated divided the accessions into two major clusters. The grid maps generated identified diverse accessions in the East district and Darjeeling district, which can be a source of superior germplasm

IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017–2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region.

Forecasting of Landslides Using Rainfall Severity and Soil Wetness: A Probabilistic Approach for Darjeeling Himalayas

Rainfall induced landslides are creating havoc in hilly areas and have become an important concern for the stakeholders and public. Many approaches have been proposed to derive rainfall thresholds to identify the critical conditions that can initiate landslides. Most of the empirical methods are defined in such a way that it does not depend upon any of the in situ conditions. Soil moisture plays a key role in the initiation of landslides as the pore pressure increase and loss in shear strength of soil result in sliding of soil mass, which in turn are termed as landslides. Hence this study focuses on a Bayesian analysis, to calculate the probability of occurrence of landslides, based on different combinations of severity of rainfall and antecedent soil moisture content. A hydrological model, called Système Hydrologique Européen Transport (SHETRAN) is used for the simulation of soil moisture during the study period and event rainfall-duration (ED) thresholds of various exceedance probabilities were used to characterize the severity of a rainfall event. The approach was used to define two-dimensional Bayesian probabilities for occurrence of landslides in Kalimpong (India), which is a highly landslide susceptible zone in the Darjeeling Himalayas. The study proves the applicability of SHETRAN model for simulating moisture conditions for the study area and delivers an effective approach to enhance the prediction capability of empirical thresholds defined for the region.