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The latest Coronavirus (COVID-19) has become an infectious disease that causes millions of people to infect. Effective short-term prediction models are designed to estimate the number of possible events. The data obtained from 30th January to 26 April, 2020 and from 27th April 2020 to 11th May 2020 as modelling and forecasting samples, respectively. Spatial distribution of disease risk analysis is carried out using weighted overlay analysis in GIS platform. The epidemiologic pattern in the prevalence and incidence of COVID-2019 is forecasted with the Autoregressive Integrated Moving Average (ARIMA). We assessed cumulative confirmation cases COVID-19 in Indian states with a high daily incidence in the task of time-series forecasting. Such efficiency metrics such as an index of increasing results, mean absolute error (MAE), and a root mean square error (RMSE) are the out-of-samples for the prediction precision of model. Results shows west and south of Indian district are highly vulnerable for COVID-2019. The accuracy of ARIMA models in forecasting future epidemic of COVID-2019 proved the effectiveness in epidemiological surveillance. For more in-depth studies, our analysis may serve as a guide for understanding risk attitudes and social media interactions across countries.

The Sundarban mangrove or tidal influenced natural ecosystem is extremely productive and providing multiple goods and services to millions of people. In the last few decades, the quality and quantity of mangrove ecosystem are being deteriorated. The main objectives of this current research are (i) to investigate the ecosystem service values (ESVs) using a time series satellite data (1975, 2000 and 2020) and different unit values (ii) to analyze the dynamic pattern of carbon sequestration through InVEST model and (iii) determination of ESVs change hotspots by Getis-Ord Gi * method. Here, mangrove forest has the highest ecosystem service value and highest carbon sinker. The total loss of ESVs was estimated 3310.79 million USD during last 45 years in Sundarban Biosphere Reserve (SBR) due to high natural and anthropogenic adversities. InVEST model also revealed that the total static carbon storage over the study area was 48.87, 46.65 and 43.33 Tg for the year 1975, 2000 and 2020 respectively. Total 6313944 mg/6.31Tg loss of carbon has been observed in the case of mangrove forest during the overall study period (1975–2020). So, illegal human encroachment should be strictly (through law and regulations) restricted within Sundarban mangrove ecosystem for the benefits of people.

Increased consumption of water resource due to rapid growth of population has certainly reduced the groundwater storage beneath the earth which leads certain challenges to human being in recent time. For optimal management of this vital resource, exploration of groundwater potential zone (GWPZ) has become essential. We have applied Analytical Hierarchy Process (AHP), Frequency Ratio (FR) and two machine learning techniques specifically Random Forest (RF) and Naïve Bayes (NB) here to delineate GWPZ in Gandheswari River Basin in Chota Nagpur Plateau, India. To achieve the goal of the study, twelve factors that determine occurrence of groundwater have been selected for inter-thematic correlations and overlaid with location of wells. These factors include elevation, drainage density, slope, lithology, geomorphology, topographical wetness index (TWI), distance from the river, rainfall, lineament density, Normalized Difference Vegetation Index (NDVI), soil, and Land use and Land cover (LULC). A total 170 points including 85 in well site and 85 in non-well site have been selected randomly and allocated into two parts: training and testing at the share of 70:30. The implemented methods have significantly provided five GWPZs specifically Very Good (VG), Good (G), Moderate (M), Poor (P) and Very Poor (VP) with high and acceptable accuracy. The study also finds that geomorphology, slope, rainfall and elevation have greater importance in shaping GWPZs than LULC, NDVI, etc. Model performance has been tested with receiver operator characteristics (ROC), Accuracy (ACC), Kappa Coefficient, MAE, RMSE, etc., methods. Area under curve (AUC) in ROC curve has revealed that accuracy level of AHP, FR, RF and NB is 78.8%, 81%, 85.3% and 85.5, respectively. The machine learning techniques coupled with AHP and FR unveil effective delineation of groundwater potential area in said river basin which by genetically offers low primary porosity due to lithological constrains. Therefore, the study can be helpful in watershed management and identifying appropriate location wells in future.

The global economic activities were completely stopped during COVID-19 lockdown and continuous lockdown partially brought some positive effects for the health of the total environment. The multiple industries, cities, towns and rural people are completely depending on large tropical river Damodar (India) but in the last few decades the quality of the river water is being significantly deteriorated. The present study attempts to investigate the river water quality (RWQ) particularly for pre- lockdown, lockdown and unlock period. We considered 20 variables per sample of RWQ data and it was analyzed using novel Modified Water Quality Index (MWQI), Trophic State Index (TSI), Heavy Metal Index (HMI) and Potential Ecological Risk Index (RI). Principal component analysis (PCA) and Pearson’s correlation (r) analysis are applied to determine the influencing variables and relationship among the river pollutants. The results show that during lockdown 54.54% samples were brought significantly positive changes applying MWQI. During lockdown, HMI ranged from 33.96 to 117.33 with 27.27% good water quality which shows the low ecological risk of aquatic ecosystem due to low mixing of toxic metals in the river water. Lockdown effects brought river water to oligotrophic/meso-eutrophic condition from eutrophic/hyper-eutrophic stage. Rejuvenation of river health during lockdown offers ample scope to policymakers, administrators and environmentalists for restoration of river health from huge anthropogenic stress.

Shortage of potable water is a global problem, and this problem can be met by searching new areas where groundwater is available. GIS is an effective and necessary tool to identify groundwater potential zones in an area. In the present study, groundwater potential zones (GWPZs) were identified in the Kangsabati River basin of east India having an area of about 6488 km2 using multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP). The criteria like geology, geomorphology, elevation, slope, drainage, lineament, curvature, topographic wetness, land use/land cover, and soil were extracted from satellite data and the weights for each parameter and its sub-parameters were assigned through analytical hierarchy process based on their respective relevance as influencing factors for groundwater recharge. Very low, low, moderate, high, and very high groundwater potentiality represent 28.93%, 30.56%, 19.75%, 14.62%, and 6.11% area, respectively. The low-lying flat plains of the southeastern section, as well as the centrally located dam, are ideal for groundwater recharge, while the upland plain of the northwestern part, with its hard rock terrain, is less so. This outcome has been verified using pre-monsoon and post-monsoon groundwater depth data, indicating that the strategy is most appropriate for this region. Thus, the groundwater potential zone maps remain very useful for conducting extensive ground-based hydrogeological studies that facilitate the identification of suitable bore well/dug well sites.

Scarcity of groundwater is a severe problem in this region due to over exploitation of groundwater from unconfined hard rock aquifers. The main objectives of this study are to analyse the spatiotemporal variability and fluctuation of groundwater table and to predict the location of groundwater depression pockets. Total 21 consecutive years (1996–2017) groundwater monitoring well data (pre- and post-monsoon) have been collected from CGWB, Government of India. The nonparametric Mann–Kendall trend analysis and standardized precipitation index (SPI) have been applied to detect the trend of groundwater level and rainfall variability, respectively. Exponential smoothing has also been fitted for future prediction. The pre- and post-monsoon results (1996–2017) showed that around 77% (22 stations) and 78% (23) monitoring stations were indicating declining trend of groundwater table at the rate of −0.006 to −0.205 m/year and −0.005 to −0.192 m/year, respectively. Similarly, future (2040) groundwater depression result predicted that around 75% (21) stations, the groundwater table will be depleted above 5 m during pre-monsoon while about 53% (16 stations) monitoring wells, the groundwater table will be fallen above 5 m during post-monsoon. Consequently, around 52% (15) and 50% (14) stations are being faced groundwater drought in the recurrent interval of above 2 years during pre-monsoon and post-monsoon, respectively. Driving factors of water table depletion are huge withdrawal of groundwater for dry farming and reduction of recharge areas due to rapid land use modification. The uniqueness of this study exhibits the nature of declining trend of groundwater table and identification of depression pockets.

Groundwater is a vital and purest form of natural resource. In the recent years, various anthropogenic causes threat its natural quality. Therefore, its suitability for drinking, irrigation and other purposes make doubtful conditions of human well-being, especially in developing countries. In this present study, groundwater quality was evaluated for drinking, irrigation and human health hazard purposes particularly in Chotanagpur plateau fringe of India. In total, 58 water samples were collected from different locations in pre-monsoon (February–March 2020) and post-monsoon (October–November 2020) seasons to delineate seasonal variation of groundwater quality according to as reported by WHO (WHO guidelines for drinking-water quality, World Health Organization, Geneva, 2011) guidelines. Groundwater Quality Index (GWQI) and Heavy metal Pollution Index (HPI) have been applied to assess the suitability of drinking purposes. Irrigation parameters (SAR, SSP, MAR, PI, KR) showed the significant deterioration of water quality in pre-monsoon than post-monsoon period. Major cations (such as sodium, calcium) and major anions (such as bicarbonate, nitrate and fluoride) exceeded their standard limit in both the seasons. Non-carcinogenic health risk is found due to heavy metal contamination through drinking water. The health risk index was higher for children in comparison with adults. This research finding can definitely help to planners and administrators for immediate decision making regarding public health (for groundwater quality improvement).

The fatal novel coronavirus (COVID-19) pandemic disease smashes the normal tempo of global socio-economic and cultural livelihood. Most of the countries impose a lockdown system with social distancing measures to arrest the rapid transmission of this virus into the human body. The objective of this study is to examine the status of air quality during and pre-COVID-19 lockdown and to recommend some long-term sustainable environmental management plan. The pollution data like PM 10 , PM 2.5 , O 3 , SO 2 , NO 2 and CO have been obtained from State Pollution Control Board under Govt. of West Bengal. Similarly, various land surface temperature (LST) maps have been prepared using LANDSAT-8 OLI and LANDSAT-7 ETM + images of USGS. The maps of NO 2 and aerosol concentration over Indian subcontinent have been taken from ESA and NASA. The digital thematic maps and diagrams have been depicted by Grapher 13 and Arc GIS 10.3 platforms. The result shows that the pollutants like CO, NO 2 and SO 2 are significantly decreased, while the average level of O 3 has been slightly increased in 2020 during the lockdown due to close-down of all industrial and transport activities. Meanwhile, around 17.5% was the mean reduction of PM 10 and PM 2.5 during lockdown compared with previous years owing to complete stop of vehicles movement, burning of biomass and dust particles from the construction works. This study recommends some air pollution-tolerant plant species (in urban vacant spaces and roof tops) for long-term cohabitation among environment, society and development.

In several areas of Chhattisgarh, the disintegration of the content of groundwater by the anthropogenic activities is rising at an alarming pace. However, there is still limited information regarding groundwater quality. The present study develops a geostatistical-based water quality index (WQI) and analyzes the spatial variation of contamination zone within the study area. Water samples were collected and tested for different parameters from 55 different locations based on random sampling method. The physio-chemicals properties of water like pH, conductivity, total dissolved solids, calcium, magnesium, bicarbonate, carbonate, sulfate, chloride, nitrate, and fluoride were used for analysis. Pearson Correlation coefficient analysis showed strong and positive correlation between EC, HCO3−, and Cl−. Empirical Bayesian kriging (EBK) is used to interpolate the physio-chemical variables. In EBK interpolation, the lowest Root-Mean-Square Error (RMSE) and the standard error values were calculated for EC, HCO3−, SO4−, NO3−, Ca2+, Mg2+, and F− using K-Bessel model. The lowest RMSE value was calculated for pH (0.952), Cl−(1.016), Na+(0.905), and TH (0.979) using Whittle model of EBK interpolation technique. Results showed that north and north-east of the district are characterized by low groundwater quality and the north-west and central part of the district have very good groundwater quality. The very good WQI is covered by 1989.65 sq km (12.64%) of the study area.

The floodplain wetland habitat in the lower Gangetic plains of West Bengal played a significant role in protecting from environmental degradation like pollution, lowering groundwater table, natural hazards, and others as well as supports for human wellbeing. Thus, it is needed to investigate the health status of wetlands and suggest restoration strategies to protect the livelihood patterns dependent on wetlands. This paper presents the health of the wetland ecosystem by comprising the wetland ecosystem health index (WHI) in 2011 and 2018 at the block level of Malda district, as a part of the lower Gangetic flood plain using the pressure–state–response model (PSR model) and AHP method. A total number of six Landsat satellite images and statistical census data were used to determine the wetland health. Wetlands are classified as very healthy (2.81–3.33), healthy (2.41–2.80), sub-healthy (2.01–2.40), unhealthy (1.61–2.00), and sick (0–1.60) category on the basis of the wetland ecosystem health index score. The results of this study showed that the wetlands located surrounding English Bazar, Manikchak, Ratua-II, and Kaliachak-II blocks have a sub-healthy to very healthy condition in 2011 but changed to unhealthy to sick category in 2018 due to the increase of rapid urbanization, population density, and development activities. These areas have belonged to the sub-healthy to sick category in the year 2011 as well as 2018 due to high wetland pressure. Our observation reveals that the ecosystem service value provided by wetlands decreased by 62.51% and 20.46 in the observed period. Management of WEH should emphasize on large (>100 ha) and medium (51–100 ha) sizes of wetlands in the Diara region of West Bengal. Developing local-level institutions and setting restoration goals are useful strategies to manage wetland resources, and protecting biodiversity should be guided by the Government organization and NGOs.