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This paper reveals the magnitude of heavy metal contamination of soil and groundwater in and around an unauthorized e-waste recycling site in Delhi. Though unsafe and unorganized and with the e-waste handling now legally banned in Delhi, the informal sector is still actively involved in dismantling, extracting and disposing of e-waste in certain places on a considerably large scale. The leachate produced by these recycling units contains a large amount of heavy metals which are likely to pollute the groundwater and soil adjoining the recycling sites. This study evaluates the e-waste contamination at such sites by monitoring the potential contaminants at a number of specific monitoring points in Krishna Vihar near Mandoli. The soil and underground water samples are tested for the presence of heavy metals around e-waste recycling and dumping sites using atomic absorption spectrometry. The standard values according to Central Ground Water Board are taken as reference values for water, and standards for agricultural soil in Britain as reference values for soil. The results show that the groundwater and soil in and around these sites have been contaminated by lead, copper, chromium and cadmium to a large extent. [ABSTRACT FROM AUTHOR]

The levels of heavy metals are measured at different dumpsites with different distances and directions under the jurisdiction of Greater Municipal Corporation of Hyderabad for ascertaining the soil and groundwater quality and forecasting as a part of integrated municipal solid waste (MSW) management study. The datasets indicate a steady decrease in the concentrations of ions and heavy metals in groundwater with distance from the MSW dumpsites. Similar trends are observed for the levels of heavy metals in soil at dump sites around the MSW dumpsite. In this study, we have used both linear and quadratic regression to predict water and soil constituents. As the datasets on the components of solid waste and groundwater are limited, the desired level of forecasting accuracy could not be achieved. However, for some components the results are promising. This study suggests that improvement can be achieved by removing the outliers from the dataset. If the errors are large for a component, it would mean that we need a better way of separation of this component from the waste. [ABSTRACT FROM AUTHOR]