The study aims at evaluating the groundwater vulnerability to contamination in the vicinity of a solid waste disposal site, Njelianparamba, a municipal dumping site in Kozhikode, Kerala, India, using DRASTIC model using Geographic Information System environment. Vulnerability maps are intended to show areas of most potential to groundwater contamination on the basis of hydrogeological conditions and human impacts. The DRASTIC model consists of seven hydrogeological parameters that affect groundwater quality. The ESRI GIS software, Arc Map 10.1 was used to create the groundwater vulnerability map by overlaying the seven layers. The resulting vulnerability map was then validated using chemical and bacteriological analysis of samples collected from nearby wells of the dumping site to assess the area which is of more potential risk to pollution. According to the vulnerability map, the study area was divided into three vulnerability classes ranging between a minimum value of 120 and a maximum value of 243. The vulnerability classes are moderate vulnerable, high vulnerable and very high vulnerable. The vulnerability map revealed that the eastern and south eastern portion of Njelianparamba dump site was very highly vulnerable to groundwater contamination. This is probably due to the lower sloped terrains towards the eastern portion which allows percolation of contaminants into the groundwater.

This study was conducted to measure the impact of a municipal solid waste landfill on groundwater quality around Njelianparamba, a solid waste dumping site in Kozhikode district, Kerala state, India. One of the major problems associated with dumping of municipal solid waste landfill is the release of leachate and its impact on surrounding groundwater. In this study, physico-chemical and bacteriological parameters of groundwater samples collected from the region surrounding the leachate area during the pre-and post-monsoon seasons were analysed. The majority of the groundwater samples contained contaminants at a level beyond the permissible limit set by the Bureau of Indian Standards for drinking water quality. The Geographic Information System software of the Environmental Systems Research Institute, (USA) ArcMap 10.1 was used to prepare spatial distribution maps of different parameters and Leachate Pollution Index and Water Quality Index in the study area were applied to assess the overall quality of groundwater. Characterisation of leachate and groundwater samples revealed that, water in the domestic wells has been deteriorated in response to the percolation of leachate. Additionally spatial and correlation analysis revealed that contamination was present maximum within 300 m radius around the landfill site.

Sediments play an important role in elemental cycling in the aquatic environment; it can be sensitive indicator for monitoring contaminants in aquatic environments. The heavy metal prominence, the amount and different forms of phosphorous present in the surface sediments of Kavvayi Wetland, which is in the south west coast of India was studied and reported in this paper. A total number of 10 surface sediment samples were taken from various regions of Kavvayi Lake and were subjected to heavy metal analysis and also phosphorous fractionation. Phosphorous forms in the sediment samples were determined by the modified sequential extraction procedure and among the inorganic phosphorous pool; Fe and Al bound phosphorous constituted the major portion while the Ca-bound phosphorous constituted the minor part only. Higher concentration of organic phosphorous was also detected in all the samples. The sediment samples were analyzed for heavy metals such as Fe, Mn, Cu, Pb, Cd, Ni, and Zn and the results showed comparatively higher concentration than the background values. The degree of contamination for each station was determined. Sediment pollution load index (PLI) values of the studied area ranged from 0.39 to 2.55 which indicated that the wetland sediments were polluted. Multivariate statistical techniques were applied to evaluate and characterize the analytical data. Spatial distribution maps of phosphorous fractions and heavy metals would help to identify the pollution sources and vulnerable sites.