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Landslide causes damage to property and life in the Himalayan region as well as Sikkim Himalaya. Landslide susceptibility assessment is very important to mark out the landslide susceptibility area, and researchers take some plans for the future. Landslide susceptibility map has become essential to identify the landslide-prone zones and to find out the probable causes of a landslide in an area. The main objectives of this study are to produce landslide susceptibility mapping by frequency ratio method and to find out the dominant parameters which are responsible for the occurrences of frequent landslide in Lachung River basin, the main tributary of Teesta River in Sikkim Himalaya. The study utilized different types of data which include geological data, advanced spaceborne thermal emission and reflection radiometer-based digital elevation model, Sentinel-2A sensor data, published thematic map and precipitation data, and all data have been processed with the help of remote sensing and GIS tools. Ten influential causative factors of landslide occurrence are used for the susceptibility assessment, and they are slope angle, slope aspect, elevation, profile curvature, land use/land cover, normalized differences vegetation index, drainage density, road density, geology and rainfall. The GIS-based landslide susceptibility analysis has been discussed with ten dominant factors by using frequency ratio model. Finally, the landslide susceptibility map was classified into very high (0.591%), high (1.867%), moderately high (5.172%), moderate (19.682%), moderately low (25.685%), low (29.816%) and very low (17.187%). The map was compared with the validation of landslide location, and the model was verified by the receiver operating characteristic curve. The results revealed 88.9% prediction rate and 92.3% success rate, which means this model is validated with landslide susceptibility analysis in the study area.

The outbreak of COVID-19 has now created the largest pandemic and the World health organization (WHO) has declared social distancing as the key precaution to confront such type of infections. Most of the countries have taken protective measures by the nationwide lockdown. The purpose of this study is to understand the effect of lockdown on air pollutants and to analyze pre-monsoon (April and May) cloud-to-ground and inter-cloud lightning activity in relation to air pollutants i.e. suspended Particulate matter (PM 10 ), Nitrogen dioxides (NO 2 ) Sulfur dioxide (SO 2 ), Ozone (O 3 ) and Aerosol concentration (AC) in a polluted tropical urban megacities like Kolkata. After the strict lockdown the pollutants rate has reduced by more than 40% from the pre-lockdown period in the Kolkata megacity. So, decreases of PM 10 , NO 2 , SO 2 , O 3 and AC have a greater effect on cloud lightning flashes in the pre-monsoon period. In the previous year (2019), the pre-monsoon average result shows a strong positive relation between the lightning and air pollutants; PM 10 (R 2 = 0.63), NO 2 (R 2 = 0.63), SO 2 (R 2 = 0.76), O 3 (R 2 = 0.68) and AC (R 2 = 0.83). The association was relatively low during the lock-down period (pre-monsoon 2020) and the R 2 values were 0.62, 0.60, 0.71, 0.64 and 0.80 respectively. Another thing is that the pre-monsoon (2020) lightning strikes decreased by 49.16% compared to the average of previous years (2010 to 2019). The overall study shows that the reduction of surface pollution in the thunderstorm environment is strongly related to the reduction of lightning activity where PM 10 and AC are the key pollutants in the Kolkata megacity.

Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.