Groundwater is one of the main sources of drinking water in Ranchi district and hence its vulnerability assessment to delineate areas that are more susceptible to contamination is very important. In the present study, GISbased fuzzy pattern recognition model was demonstrated for groundwater vulnerability to pollution assessment. The model considers the seven hydrogeological factors [depth to water table (D), net recharge (R), aquifer media (A), soil media (S), topography (T), impact of vadose zone (I), and hydraulic conductivity (C)] that affect and control the groundwater contamination. The model was applied for groundwater vulnerability assessment in Ranchi district, Jharkhand, India and validated by the observed nitrate concentrations in groundwater in the study area. The performance of the developed model is compared to the standard DRASTIC model. It was observed that GIS-based fuzzy pattern recognition model have better performance than the standard DRASTIC model. Aquifer vulnerability maps produced in the present study can be used for environmental planning and predictive groundwater management. Further, a sensitivity analysis has been performed to evaluate the influence of single parameters on aquifer vulnerability index.

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Groundwater is the most important natural resource for drinking water to many people around the world, especially in rural areas where the supply of treated water is not available. Drinking water resources cannot be optimally used and sustained unless the quality of water is properly assessed. To this end, an attempt has been made to develop a suitable methodology for the assessment of drinking water quality on the basis of 11 physico-chemical parameters. The present study aims to select the fuzzy aggregation approach for estimation of the water quality index of a sample to check the suitability for drinking purposes. Based on expert's opinion and author's judgement, 11 water quality (pollutant) variables (Alkalinity, Dissolved Solids (DS), Hardness, pH, Ca, Mg, Fe, Fluoride, As, Sulphate, Nitrates) are selected for the quality assessment. The output results of proposed methodology are compared with the output obtained from widely used deterministic method (weighted arithmetic mean aggregation) for the suitability of the developed methodology.

Sequencing batch reactor (SBR) was assessed for direct co-treatment of old landfill leachate and municipal wastewater for chemical oxygen demand (COD), nutrients and turbidity removal. Nitrogen removal was achieved by sequential nitrification and denitrification under post-anoxic conditions. Initially, SBR operating conditions were optimized by varying hydraulic retention time (HRT) at 20% (v/v) landfill leachate concentration, and results showed that 6 d HRT was suitable for co-treatment. SBR performance was assessed in terms of COD, ammonia, nitrate, phosphate, and turbidity removal efficiency. pH, mixed liquor suspended solids, mixed liquor volatile suspended solids (MLVSS), and sludge volume index were monitored to evaluate stability of SBR. MLVSS indicated that biomass was able to grow even at higher concentrations of old landfill leachate. Ammonia and nitrate removal efficiency was more than 93% and 83%, respectively, whereas COD reduction was in the range of 60–70%. Phosphate and turbidity removal efficiency was 80% and 83%, respectively. Microbial growth kinetic parameters indicated that there was no inhibition of biomass growth up to 20% landfill leachate. The results highlighted that SBR can be used as an initial step for direct co-treatment of landfill leachate and municipal wastewater.

Groundwater pollution due to anthropogenic activities is one of the major environmental problems in urban and industrial areas. The present study demonstrates the integrated approach with GIS and DRASTIC model to derive a groundwater vulnerability to pollution map. The model considers the seven hydrogeological factors [Depth to water table (D), net recharge (R), aquifer media (A), soil media (S), topography or slope (T), impact of vadose zone (I) and hydraulic Conductivity(C)] for generating the groundwater vulnerability to pollution map. The model was applied for assessing the groundwater vulnerability to pollution in Ranchi district, Jharkhand, India. The model was validated by comparing the model output (vulnerability indices) with the observed nitrate concentrations in groundwater in the study area. The reason behind the selection of nitrate is that the major sources of nitrate in groundwater are anthropogenic in nature. Groundwater samples were collected from 30 wells/tube wells distributed in the study area. The samples were analyzed in the laboratory for measuring the nitrate concentrations in groundwater. A sensitivity analysis of the integrated model was performed to evaluate the influence of single parameters on groundwater vulnerability index. New weights were computed for each input parameters to understand the influence of individual hydrogeological factors in vulnerability indices in the study area. Aquifer vulnerability maps generated in this study can be used for environmental planning and groundwater management.