Influence of anthropogenic contamination on groundwater chemistry in Mulbagal town, Kolar District, India

Geochemical studies are performed to examine the impact of leachate infiltration from on-site sewage disposal systems on the groundwater chemistry in Mulbagal town, Kolar District, Karnataka State, India. The leachate infiltration imposed nitrate concentrations ranging from 4 mg/L to 388 mg/L in the groundwater samples; it was observed that 79% of the samples exhibited nitrate concentrations in excess of drinking water permissible limit (45 mg/L). The average (of 43 measurements) E. coli levels in the groundwater samples corresponded to 189 MPN/100 mL and 55% of the samples tested exhibit pathogen contamination. Results also showed that the groundwater in the study area is characterized by acidic pH, large calcium + magnesium ion and Na/Cl ratios of < unity causing majority of the ground water samples to classify as Ca-Mg-Cl type and Na-Cl type. Saturation index (SI) computation using Visual MINTEQ program showed that the groundwater samples are under-saturated with respect to calcite. The theoretical SI values (of calcite) however suggested that the groundwater samples ought to be over-saturated with calcite. Under-saturation of the groundwater samples with calcite is attributed to increased dissolution of the mineral in the acidic environment of the groundwater.

Farmers' vulnerability to climate variability in Dimapur district of Nagaland, India

Certain parts of the State of Nagaland situated in the northeastern region of India have been experiencing rainfall deficit over the past few years leading to severe drought-like conditions, which is likely to be aggravated under a climate change scenario. The state has already incurred considerable losses in the agricultural sector. Regional vulnerability assessments need to be carried out in order to help policy makers and planners formulate and implement effective drought management strategies. The present study uses an 'index-based approach' to quantify the climate variability-induced vulnerability of farmers in five villages of Dimapur district, Nagaland. Indicators, which are reflective of the exposure, sensitivity and adaptive capacity of the farmers to drought, were quantified on the basis of primary data generated through household surveys and participatory rural appraisal supplemented by secondary data in order to calculate a composite vulnerability index. The composite vulnerability index of village New Showba was found to be the least, while Zutovi, the highest. The overall results reveal that biophysical characteristics contribute the most to overall vulnerability. Some potential adaptation strategies were also identified based on observations and discussions with the villagers.

A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: A case study of Tumkur district, India

This paper primarily intends to develop a GIS (geographical information system)-based data mining approach for optimally selecting the locations and determining installed capacities for setting up distributed biomass power generation systems in the context of decentralized energy planning for rural regions. The optimal locations within a cluster of villages are obtained by matching the installed capacity needed with the demand for power, minimizing the cost of transportation of biomass from dispersed sources to power generation system, and cost of distribution of electricity from the power generation system to demand centers or villages. The methodology was validated by using it for developing an optimal plan for implementing distributed biomass-based power systems for meeting the rural electricity needs of Tumkur district in India consisting of 2700 villages. The approach uses a k-medoid clustering algorithm to divide the total region into clusters of villages and locate biomass power generation systems at the medoids. The optimal value of k is determined iteratively by running the algorithm for the entire search space for different values of k along with demand-supply matching constraints. The optimal value of the k is chosen such that it minimizes the total cost of system installation, costs of transportation of biomass, and transmission and distribution. A smaller region, consisting of 293 villages was selected to study the sensitivity of the results to varying demand and supply parameters. The results of clustering are represented on a GIS map for the region.