GIS-based statistical model for the prediction of flood hazard susceptibility

At present, flood is the most significant environmental problem in the entire world. In this work, flood susceptibility (FS) analysis has been done in the Dwarkeswar River basin of Bengal basin, India. Fourteen flood causative factors extracted from different datasets like DEM, satellite images, geology, soil and rainfall data have been considered to predict FS. Three heuristic models and one statistical model fuzzy Logic (FL), frequency ratio (FR), multi-criteria decision analysis (MCDA) and logistic regression (LR) have been used. The validating datasets are used to validate these models. The result shows that 68.71%, 68.7%, 60.56% and 48.51% area of the basin is under the moderate to very high FS by the MCDA, FR, FL and LR, respectively. The ROC curve with AUC analysis has shown that the accuracy level of the LR model (AUC?=?0.916) is very much successful to predict the flood. The rest of the models like FL, MCDA and FR (AUC?=?0.893, 0.857 and 0.835, respectively) have lesser accuracy than the LR model. The elevation was the most dominating factor with coefficient value of 19.078 in preparation of the FS according to the LR model. The outcome of this study can be implemented by local and state authority to minimize the flood hazard.

     

Distribution of improved cook stoves: analysis of field experiments using strategic niche management theory

Close to three billion people globally and over 800 million in India are dependent on direct combustion of unprocessed solid biomass fuels in inefficient traditional mud stoves. Current cooking practices, besides causing serious health problems, are also being linked to emissions of climate change and pollution agents such as black carbon and ozone precursors. In India several initiatives have been taken up to tackle the problem but the present trajectory of limited technical and social change in cooking energy use is nonetheless persistent in rural areas. In order to develop and scale up alternative cooking technology options, we have analyzed, using the principles of strategic niche management, two projects implemented by The Energy and Resources Institute (TERI) in nine villages in India. The assessment, while highlighting reasons for stability of the current cooking regime, also points to triggers that can destabilize the regime. The focus is also on assessing the influence of protection in the form of subsidies on the process of transition. User preferences relating to social and technical aspects have been analyzed, pointing to forced draft cookstoves as the preferred option notwithstanding cost reductions to address affordability concerns. The assessment indicates that while it is critically important to understand and address the preferences of users and to improve the technology, scaling up will depend on stove cost reduction through further research. Creativity in effective financing schemes and support structures put in place by fostering public–private partnerships are also needed.     

Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin,eastern India

Environment, Development and Sustainability - The large-scale water-induced erosion is one of the most determining elements on land degradation in subtropical monsoon-dominated region. From this...     

Assessing the impact of ecological insecurity on ecosystem service value in stone quarrying and crushing dominated areas

The present study has tried to develop ecological insecurity model (EIM) in the growing stone quarrying and crushing dominated areas using robust machine learning techniques and attempted to link it with ecosystem service value (ESV). Satellite image-based landscape metrics have been used for developing machine learning-oriented EIM, and the global coefficient of Costanza et al. (Glob Environ Change 26:152–158, 2014) has been used for computing ESV. Field parameter-based ecological insecurity index (EII) has been developed for validating the EIMs along with the statistical methods. Applied Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) revealed that 21.88 to 60.79% area has predicted as highly ecologically insecure in all the selected four stone quarrying and crushing dominated clusters and this is has inflated from 2000 to 2020. All the applied models are acceptable in terms of their performances, but the RF model is found to be the best representative in relation to EII. It causes considerable loss of ESV which ranges from 160,845.18 US$ to 757,445.17 US$ in all the clusters from 2000 to 2020. The findings of the study are useful for ecological management in this area. It further recommends applying such an approach in such similar fields to establish the general finding and provides knowledge to the state of arts.

     

Fresh water blue green algae from three agro-climatic zones of Uttar Pradesh, India: distribution pattern with seasonal variation

The paper deals with 45 species of 21 genera of fresh water blue green algae (BGA) from three different agro-climatic zones of Uttar Pradesh. Samples were collected from different habitats varying in physico-chemical properties. Out of 45 species, 13 species belonged to order Chroococcales, 31 to order Nostocales, while only 1 species belonged to order Stigonimatales i.e. Fischerella mucicola. The physico-chemical parameters like pH, temperature, dissolved oxygen, electrical conductivity, nitrate, nitrite and rainfall play an important role in the periodicity of BGA. A positive correlation was found between dissolved oxygen (DO) of different ponds and species diversity, except in the case of western region of Uttar Pradesh (Farukhabad and Mahoba districts) where a positive correlation was found in electrical conductivity and total dissolved solids.     

Toxicity assessment of heavy metals with Nostoc muscorum L

Heavy metals in aquatic ecosystem (Mn, Ni, Zn, Cu) were tested for their toxicity against Nostoc muscorum L., a common fresh water phytoplankton, with respect to chlorophyll, protein, total carbohydrate and starch contents along with growth (OD). Mn showed stimulatory effect up to 1.5 ppm for all the observed parameters. However, Ni, Zn and Cu (0.1, 0.2, 0.3, 0.4 and 0.5 ppm) showed adverse effects even at 0.1 ppm. Chlorophyll was most sensitive parameter followed by carbohydrate, protein and starch. Ni was most toxic to N. muscorum followed by Cu and Zn. The IC50 for Ni was 0.1 ppm for growth, chlorophyll and protein, while total carbohydrate and starch showed IC50 at 0.3 and 0.4 ppm, respectively. The IC50 for Cu was recorded at 0.2 ppm for all the parameters. Zn showed IC50 at 0.3 ppm for growth, chlorophyll, protein and starch at 0.4 ppm for carbohydrate. This study can be applied as bioassay using cyanobacteria for toxicity assessment of various industrial wastes in aquatic ecosystem.     

Physico-chemical characteristics and pollution level of Lake Nainital (U.P., India): role of macrophytes and phytoplankton in biomonitoring and phytoremediation of toxic metal ions

Lake Nainital is the sole source of drinking water for the local people and even to majority of tourists. In background of lake utility and its importance at national level, such study is essential which is focused on toxic metal pollution and current nutrient status of the lake and their magnification by algae and macrophytes. Study has shown that lake water is rich in nutrients which supports growth of many aquatic macrophytes and algal blooms. Besides, water is contaminated with metals like Cr, Cu, Fe, Mn, Ni, Pb and Zn. Concentration of some of them like Fe, Pb and Ni were higher than the recommended maximum permissible limits. Concentration of these metals were also found high in lake sediments. The level of metals amongst various components of lake varied considerably in different season. Plants and algae growing therein accumulated appreciable amount of metals and water roots of Salix being more efficient than others. High metal removing potential of these plants may be significant for biomonitoring studies and could be a useful phytoremediation technology to restore water quality by harvesting submerged and floating biomass inhabiting littoral zone of the lake.     

Additional danger of arsenic exposure through inhalation from burning of cow dung cakes laced with arsenic as a fuel in arsenic affected villages in Ganga-Meghna-Brahmaputra plain

In arsenic contaminated areas of the Ganga-Meghna-Brahmaputra (GMB) plain (area 569,749 sq. km; population over 500 million) where traditionally cow dung cake is used as a fuel in unventilated ovens for cooking purposes, people are simply exposed to 1859.2 ng arsenic per day through direct inhalation, of which 464.8 ng could be absorbed in respiratory tract.