scholarly journals River Ganga pollution: Causes and failed management plans (correspondence on Dwivedi et al. 2018. Ganga water pollution: A potential health threat to inhabitants of Ganga basin. Environment International 117, 327–338)

2019 ◽  
Vol 126 ◽  
pp. 202-206 ◽  
Author(s):  
Meenakshi Chaudhary ◽  
Tony R. Walker
2018 ◽  
Vol 117 ◽  
pp. 327-338 ◽  
Author(s):  
Sanjay Dwivedi ◽  
Seema Mishra ◽  
Rudra Deo Tripathi

Author(s):  
Vani Sharma ◽  
Padma Singh

Ganga is the largest river in India and has both religious and economical importance to our country. Ganga water has very important reverence in various religious ceremonies as holy water along it had been used for drinking and irrigation purpose. In developing cites such as Haridwar Ganga start facing water pollution problems but still its water quality was maintain, it was may be due to its microbial community which may have an adorable capability to clean the Ganga river, so in present study we had focused on the isolation of river Ganga Fungal community during different season at four different sites, Haridwar, India. The isolated strains were morphological identified as Aspergillus, Talaromyces, Fusarium, Curvularia, etc. All these strains had showed highest heavy metal tolerance against As, Cu, Fe (200- 1000 mg/L) followed by Cr, Ni, Cd (200-800 mg/L) and least against Hg (200-400 mg/L), along with this these strains are mostly sensitive to different antifungal such as Nystatin, Amphotherecin, Fluconazole and Ketomycin.


2019 ◽  
pp. 1225-1241 ◽  
Author(s):  
Rabindra K. Barik ◽  
Rojalina Priyadarshini ◽  
Harishchandra Dubey ◽  
Vinay Kumar ◽  
Kunal Mankodiya

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This article discusses the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This article proposes and develops a fog computing-based framework, i.e. FogLearn. This is for the application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. The proposed architecture employs machine learning on a deep learning framework for the analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results show that fog computing holds an immense promise for the analysis of medical and geospatial big data.


2019 ◽  
pp. 278-297 ◽  
Author(s):  
Rabindra K. Barik

The present research paper proposes and develops a Cloud computing based Spatial Data Infrastructure (SDI) Model named as CloudGanga for sharing, analysis and processing of geospatial data particularly in River Ganga Basin management in India. The main purpose of the CloudGanga is to integrate all the geospatial information such as dam location, well location, irrigation project, hydro power project, canal network and central Water Commission gauge stations locations related to River Ganga. CloudGanga can help the decision maker/ planner or common users to get enough information for their further research and studies. The open source software (Quantum GIS) has been used for the development of geospatial database. QGIS Plugin has been linked with Quantum GIS for invoking cloud computing environment. It has also discussed about the various overlay analysis in CloudGanga environment. In the present research, machine learning approaches are also used in a R tool for well locations which are associated with the basin of River Ganga.


2019 ◽  
Vol 193 (2) ◽  
pp. 536-547 ◽  
Author(s):  
Manoj Kumar ◽  
Neelima Gupta ◽  
Arun Ratn ◽  
Yashika Awasthi ◽  
Rajesh Prasad ◽  
...  

2014 ◽  
Vol 29 (4) ◽  
Author(s):  
Ellen Webb ◽  
Sheila Bushkin-Bedient ◽  
Amanda Cheng ◽  
Christopher D. Kassotis ◽  
Victoria Balise ◽  
...  

AbstractUnconventional oil and gas (UOG) operations have the potential to increase air and water pollution in communities located near UOG operations. Every stage of UOG operation from well construction to extraction, operations, transportation, and distribution can lead to air and water contamination. Hundreds of chemicals are associated with the process of unconventional oil and natural gas production. In this work, we review the scientific literature providing evidence that adult and early life exposure to chemicals associated with UOG operations can result in adverse reproductive health and developmental effects in humans. Volatile organic compounds (VOCs) [including benzene, toluene, ethyl benzene, and xylene (BTEX) and formaldehyde] and heavy metals (including arsenic, cadmium and lead) are just a few of the known contributors to reduced air and water quality that pose a threat to human developmental and reproductive health. The developing fetus is particularly sensitive to environmental factors, which include air and water pollution. Research shows that there are critical windows of vulnerability during prenatal and early postnatal development, during which chemical exposures can cause potentially permanent damage to the growing embryo and fetus. Many of the air and water pollutants found near UOG operation sites are recognized as being developmental and reproductive toxicants; therefore there is a compelling need to increase our knowledge of the potential health consequences for adults, infants, and children from these chemicals through rapid and thorough health research investigation.


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