Hydro-chemical Survey and Quantifying Spatial Variations in Groundwater Quality in Coastal Region of Chennai, Tamilnadu, India – a case study

The good quality of groundwater is important for the purpose of future planning and management. The present study has been undertaken to provide an overview on the status of groundwater quality through physicochemical parameters namely pH, alkalinity, total hardness, total dissolved solids, chloride, fluoride, nitrate, phosphate and iron through laboratory analysis. Samples have been collected from seven wells located in Chennai costal region of state Tamilnadu, India. The laboratory-based results shows that the mean value of pH is 7.29, alkalinity (308.57 mg/l), total hardness (285.71 mg/l), chloride (175.71 mg/l), iron (0.71 mg/l), nitrate (13.57 mg/l), phosphorus (2.71 mg/l) and total dissolved solids (924 mg/l), respectively. The geo-database water quality parameters were created in Geographical an Information System. Further, the kriging a geo-statistical method of interpolation is applied to know the health of groundwater in the then-sampled area. This interpolation method has been used to predict spatial distribution physicochemical parameters in the form of contour. Local planners and policy makers may utilize these results for efficient management of groundwater resources in this area

Assessment of wetland change dynamics of Chennai coast, Tamil Nadu, India, using satellite remote sensing

1258-1266The coastal wetlands of Chennai are increasingly being affected by anthropogenic factors, such as urbanization, residential, and industrial development. This study helps to monitor and map the dynamics of the coastal wetlands of Chennai using Landsat satellite images of 1988, 1996, 2006, and 2016 by following a supervised classification method. Post-classification wetland change detection was done in three temporal phases, that is, 1988 1996, 1996 2006, and 2006 2016. Change detection matrix analysis was performed to identify the from to changes. Ground truthing was carried out to validate the wetland classes. The overall accuracy of the classified image was 79.29% and the kappa coefficient was 0.7600. These results were imported into a GIS environment for further analysis. It was found that the wetlands have decreased to an alarming extent in the past 28 years from 23.14% in 1988 to 15.79% in 2016 of the total study area, owing to conversion of wetlands into industrial development, urban expansion, and other developmental activities