With respect to groundwater deterioration from human activities a unique situation of co-disposal of non-engineered Municipal Solid Waste (MSW) dumping and Secondary Wastewater (SWW) disposal on land prevails simultaneously within the same campus at Puducherry in India. Broadly the objective of the study is to apply and compare Artificial Neural Network (ANN) and Multi Linear Regression (MLR) models on groundwater quality applying Canadian Water Quality Index (CWQI). Totally, 1065 water samples from 68 bore wells were collected for two years on monthly basis and tested for 17 physio-chemical and bacteriological parameters. However the study was restricted to the pollution aspects of 10 physio-chemical parameters such as EC, TDS, TH,, Mg 2+ and K + . As there is wide spatial variation (2 to 3 km radius) with ground elevation (more than 45 m) among the bore wells it is appropriate to study the groundwater quality using Multivariate Statistical Analysis and ANN. The selected ten parameters were subjected to Hierarchical Cluster Analysis (HCA) and the clustering procedure generated three well defined clusters. Cluster wise important physiochemical attributes which were altered by MSW and SWW operations, are statistically assessed. The CWQI was evolved with the objective to deliver a mechanism for interpreting the water quality data for all three clusters. The ANOVA test results viz., F-statistic (F = 134.55) and p-value (p = 0.000 < 0.05) showed that there are significant changes in the average values of CWQI among the three clusters, thereby confirming the formation of clusters due to anthropogenic activities. The CWQI simulation was performed using MLR and ANN models for all three clusters. Totally, 1 MLR and 9 ANN models were considered for simulation. Further the performances of ten models were compared using R 2 , RMSE and MAE (quantitative indicators). The analyses of the results revealed that both MLR and ANN models were fairly good in pre- 100dicting the CWQI in Clusters 1 and 2 with high R 2 , low RMSE and MAE values but in Cluster 3 only ANN model fared well. Thus this study will be very useful to decision makers in solving water quality problems.

Starch manufacturing industrial units, such as sago mills, both at medium and large scale, suffer from inadequate treatment and disposal problems due to high concentration of suspended solid content present in the effluent. In order to investigate the viability of treatment of sago effluent, a laboratory scale study was conducted. The treatment of sago effluent was studied in a continuous flow anaerobic fluidized bed reactor. The start-up of the reactor was carried out using a mixture of digested supernatant sewage sludge and cow dung slurry in different proportions. The effect of operating variables such as COD of the effluent, bed expansion, minimum fluidization velocity on efficiency of treatment and recovery of biogas was investigated. The treated wastewater was analysed for recycling and reuse to ensure an alternative for sustainable water resourse management. The maximum efficiency of treatment was found to be 82% and the nitrogen enriched digested sludge was recommended for agricultural use.

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The removal efficiency of lead [Pb(II)], zinc [Zn(II)], nickel [Ni(II)] and chromium [Cr(VI)] from aqueous solutions by adsorption on non‐conventional materials (rice husk and sawdust) in its natural form and on their chemically modified form is presented. It is found that adsorption potential varies as a function of contact time, concentration, particle size, pH and flow rate. Of all the low cost adsorbents used in this study, sawdust is found to possess greater adsorption efficiency for all metals than rice husk under identical experimental conditions. Chemically activated sawdust could remove 95 percent of Pb(II), 93 percent of Zn(II), 80 percent of Ni(II) and 75 percent of Cr(VI) from the metal bearing industrial effluents.

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