Effects of elevated arsenic and nitrate concentrations on groundwater resources in deltaic region of Sundarban Ramsar site, Indo-Bangladesh region.

An attempt has been adopted to predict the As and NO3- concentration in groundwater (GW) in fast-growing coastal Ramsar region in eastern India. This study is focused to evaluate the As and NO3- vulnerable areas of coastal belts of the Indo-Bangladesh Ramsar site a hydro-geostrategic region of the world by using advanced ensemble ML techniques including NB-RF, NB-SVM and NB-Bagging. A total of 199 samples were collected from the entire study area for utilizing the 12 GWQ conditioning factors. The predicted results are certified that NB-Bagging the most suitable and preferable model in this current research. The vulnerability of As and NO3- concentration shows that most of the areas are highly vulnerable to As and low to moderately vulnerable to NO3. The reliable findings of this present study will help the management authorities and policymakers in taking preventive measures in reducing the vulnerability of water resources and corresponding health risks.

Effects of climate change and sea-level rise on coastal habitat: Vulnerability assessment, adaptation strategies and policy recommendations.

On a first-order basis, the global "sea level rise" induced by climate change magnifies coastal land subsidence. Various research related to this discipline is associated with estimated sea level vulnerability in various spatial scales. But the potential impact of climate change on sea level rise and its amalgamated vulnerability to the species remain undiscovered with appropriate procedures. So, in this perspective, our main objective of this research is to estimate the potential impact of climate change on sea level rise and it is associated with vulnerability to coastal habitat. From this research, it is established that the increasing tendency of sea level from the base period to the projected period. The major port city of India has been considered in this research. The qualitative "coastal vulnerability index (CVI)" is based on quantitative estimates to characterize the physical setting, including "geomorphology (G), sea level change (SLC), coastal slope (CS), relative sea-level change (RSLC), mean wave height (MWH), mean tide range (MTR), shoreline change rate (SCR), land use and human activities (LU), and population (P)". The projected sea level rise (SLR) is increasing at the highest rate under the higher RCP (Representative Concentrations Pathways) scenario. This information is very helpful to the decision maker for considering the most appropriate development strategies to maintain the sustainable development of coastal ecology in India.

Hydrogeochemical characterization based water resources vulnerability assessment in India's first Ramsar site of Chilka lake.

A limnological site is significantly characterized by rich biological, chemical, and physical properties of the environment and is also described as the epitome of a large aquatic ecosystem. During the last few decades, the Chilka lake Ramsar site has experienced substantial degradation of water quality with associated deterioration of aquatic biodiversity. Our study aims to quantify the VWRM of the Chilka lake Ramsar region using the most reliable MLAs, namely ANN and RF, with the help of seventeen hydro-chemical properties of lake water. The produced map is validated through six validating measures (ROC-AUC- 0.89, Sensitivity-0.90, Specificity-0.78, PPV-0.78, NPV-0.88, Taylor diagram (r)-0.94), which depict that ANN is the most reliable ML algorithm in assessing the VWRM of the concerned region followed by RF. The prepared map of our study revealed that the eastern part was remarkably high to very high vulnerable zone covered area with 22.41 % and 7.19 %, respectively.

Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation.

The problem of drought in India is a major issue in terms of various adverse impacts on livelihood of society. Drought Early Warning System (DEWS), a real-time drought-monitoring tool, has reported that over a fifth of India's geographical area (21.06 %) is suffering drought-like situations. This is 62 % larger than the drought-affected area during the same period last year, which was 7.86 %. Drought affects 21.06 %, with conditions ranging from unusually dry to extremely dry. While 1.63 % and 1.73 % of the area are experiencing 'extreme' or 'exceptional' dry conditions, 2.17 % is experiencing 'severe' dry conditions. Under 'moderate' dry circumstances, up to 8.15 % is possible. In this perspective groundwater vulnerability assessment in the overall country is needed for implementing the sustainable and long-term strategies for escaping from this type of hazardous situation. The main objective of this study is to estimate the drought vulnerability in changing climate which eventually influences the food security of India. The groundwater overdraft is one of the crucial elements in agricultural drought vulnerability. Various related parameters have been selected for estimating the drought vulnerability and its impact to food security in India. Here, MaxEnt (maximum entropy) and ANN (analytical neural network) has been considered in this perspective. The AUC values for the training datasets in the ANN and MaxEnt model are 0.891 and 0.921, respectively. The AUC values in ANN and MaxEnt model for the validation datasets are 0.876 and 0.904, respectively. Here MaxEnt model is most optimal than ANN considering predictive accuracy. From this study analysis it is established that western, south and middle portion of country is very much prone to drought vulnerability. So, special emphases in terms of the regional planning have to be taken into consideration for sustainable planning.

Application of data-mining technique and hydro-chemical data for evaluating vulnerability of groundwater in Indo-Gangetic Plain.

Vulnerability of groundwater is critical for the sustainable development of groundwater resources, especially in freshwater-limited coastal Indo-Gangetic plains. Here, we intend to develop an integrated novel approach for delineating groundwater vulnerability using hydro-chemical analysis and data-mining methods, i.e., Decision Tree (DT) and K-Nearest Neighbor (KNN) via k-fold cross-validation (CV) technique. A total of 110 of groundwater samples were obtained during the dry and wet seasons to generate an inventory map. Four K-fold CV approach was used to delineate the vulnerable region from sixteen vulnerability causal factors. The statistical error metrics i.e., receiver operating characteristic-area under the curve (AUC-ROC) and other advanced metrices were adopted to validate model outcomes. The results demonstrated the excellent ability of the proposed models to recognize the vulnerability of groundwater zones in the Indo-Gangetic plain. The DT model revealed higher performance (AUC = 0.97) followed by KNN model (AUC = 0.95). The north-central and north-eastern parts are more vulnerable due to high salinity, Nitrate (NO3-), Fluoride (F-) and Arsenic (As) concentrations. Policy-makers and groundwater managers can utilize the proposed integrated novel approach and the outcome of groundwater vulnerability maps to attain sustainable groundwater development and safeguard human-induced activities at the regional level.

COVID-19 strict lockdown impact on urban air quality and atmospheric temperature in four megacities of India.

COVID-19 pandemic has forced to lockdown entire India starting from 24th March 2020 to 14th April 2020 (first phase), extended up to 3rd May 2020 (second phase), and further extended up to 17th May 2020 (third phase) with limited relaxation in non-hotspot areas. This strict lockdown has severely curtailed human activity across India. Here, aerosol concentrations of particular matters (PM) i.e., PM10, PM2.5, carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), ammonia (NH3) and ozone (O3), and associated temperature fluctuation in four megacities (Delhi, Mumbai, Kolkata, and Chennai) from different regions of India were investigated. In this pandemic period, air temperature of Delhi, Kolkata, Mumbai and Chennai has decreased about 3 °C, 2.5 °C, 2 °C and 2 °C respectively. Compared to previous years and pre-lockdown period, air pollutants level and aerosol concentration (-41.91%, -37.13%, -54.94% and -46.79% respectively for Delhi, Mumbai, Kolkata and Chennai) in these four megacities has improved drastically during this lockdown period. Emission of PM2.5 has experienced the highest decrease in these megacities, which directly shows the positive impact of restricted vehicular movement. Restricted emissions produce encouraging results in terms of urban air quality and temperature, which may encourage policymakers to consider it in terms of environmental sustainability.

Threats of climate change and land use patterns enhance the susceptibility of future floods in India.

The main objective of this work is the future prediction of the floods in India due to climate and land change. Human activity and related carbon emissions are the primary cause of land use and climate change, which has a substantial impact on extreme weather conditions, such as floods. This study presents high-resolution flood susceptibility maps of different future periods (up to 2100) using a combination of remote sensing data and GIS modelling. To quantify the future flood susceptibility various flood causative factors, Global circulation model (GCM) rainfall and land use and land cover (LULC) data are envisaged. The present flood susceptibility model has been evaluated through receiver operating characteristic (ROC) curve, where area under curve (AUC) value shows the 91.57% accuracy of this flood susceptibility model and it can be used for future flood susceptibility modelling. Based on the projected LULC, rainfall and flood susceptibility, the results of the study indicating maximum monthly rainfall will increase by approximately 40-50 mm in 2100, while the conversion of natural vegetation to agricultural and built-up land is about 0.071 million sq. km. and the severe flood event area will increase by up to 122% (0.15 million sq. km) from now on.

Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements.

Water dominated gullies formation and associated land degradation are the foremost challenges among the planners for sustainability and optimization of land resources. This type of hazardous phenomenon is utmost vulnerable due to huge loss of surface soil in the sub-tropical developing countries like India. The present study has been carried out in rugged badland topography of Garhbeta-I Community Development (C.D.) Block in eastern India for assessing the gully erosion susceptibility (GES) mapping and optimization of land use planning. The GES mapping is the first and foremost steps towards minimization this adverse affect and attaining sustainable development. In this study we also describe the importance of plantation and alternation of ex-situ tree species with in-situ species for minimizes the erosional activity. To meet our research goal here we used two prediction based machine learning algorithm (MLA) namely random forest (RF) and boosted regression tree (BRT) and one optimization model of Ecogeography based optimization (EBO). The research study also carried out by using a total of 199, in which 139 (70%) and 60 (30%) gully head-cut points were used for training and validation purposes respectively and treated as dependent factors, and twenty gully erosion conditioning factors as independent variables. These models are validated through receiver operating characteristics-area under the curve (ROC-AUC), accuracy (ACC), precision (PRE) and Kappa coefficient index analysis. The validation result showed that EBO model with the highest values of AUC-0.954, ACC-0.85, PRE-0.877 and Kappa-0.646 is the most accurate model for GES followed by BRT and RF. The outcome results should help for the sustainable development of this rugged badland topography.

Improvement in ambient-air-quality reduced temperature during the COVID-19 lockdown period in India.

The COVID-19 pandemic forced India as a whole to lockdown from 24 March 2020 to 14 April 2020 (first phase), extended to 3 May 2020 (second phase) and further extended to 17 May 2020 (third phase) and 31 May 2020 (fourth phase) with only some limited relaxation in non-hot spot areas. This lockdown has strictly controlled human activities in the entire India. Although this long lockdown has had a serious impact on the social and economic fronts, it has many positive impacts on environment. During this lockdown phase, a drastic fall in emissions of major pollutants has been observed throughout all the parts of India. Therefore, in this research study we have tried to establish a relationship among the fall in emission of pollutants and their impact on reducing regional temperature. This analysis was tested through the application of Mann-Kendall and Sen's slope statistical index with air quality index and temperature data for several stations across the country, during the lockdown period. After the analysis, it has been observed that daily emissions of pollutants (PM10, PM2.5, CO, NO2, SO2 and NH3) decreased by - 1- - 2%, allowing to reduce the average daily temperature by 0.3 °C compared with the year of 2019. Moreover, this lockdown period reduces overall emissions of pollutants by - 51- - 72% on an average and hence decreases the average monthly temperature by 2 °C. The same findings have been found in the four megacities in India, i.e., Delhi, Kolkata, Mumbai and Chennai; the rate of temperature fall in the aforementioned megacities is close to 3 °C, 2.5 °C, 2 °C and 2 °C, respectively. It is a clear indicator that a major change occurs in air quality, and as a result it reduced lower atmospheric temperature due to the effect of lockdown. It is also a clear indicator that a major change in air quality and favorable temperature can be expected if the strict implementations of several pollution management measures have been implemented by the concern authority in the coming years.