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The groundwater is the main source of domestic and agricultural purposes in the arid and semi-arid regions where the surface water availability is limited. To protect and manage the groundwater system effectively, a thorough knowledge and understanding of groundwater quality and application of computational methods to simulate the complex and nonlinear groundwater system are paramount necessary. Generally, three types of models such as physically based model, conceptual models and Blackbox models are applied to study the interconnected processes in the subsurface media. In this study, Artificial Neural Network (ANN) (3 Models with 1, 2 and 3 outputs) was used to simulate and predict the concentration of groundwater quality parameters and Mamdani Fuzzy Inference System (MFIS) was used to simulate the water quality indices. Classification algorithms of NEUROSHELL and MATLAB were used to predict the class of items in a data set. The model was constructed using already-labelled items of similar data sets. The WQI of 29 samples was determined using weighted average method. Based on MFIS, 10 samples were classified as 'good', four samples as 'poor' and remaining samples as 'very poor'. The simulation model using the classification algorithm of ANN was used to predict the concentration of groundwater quality parameters and it was observed that three ANN models values and the actual data fit well with correlation coefficient varying from 0.93 to 0.99. When the soft computing techniques can be coupled with geospatial and geostatical method to map the spatial and temporal distribution of water quality parameters. [ABSTRACT FROM AUTHOR]

Abstract: A microwave-assisted persulfate oxidation method followed by ion chromatographic determination of nitrate was developed for total nitrogen determination in atmospheric wet and dry deposition samples. Various operating parameters such as oxidation reagent concentrations, microwave power, and extraction time were optimized to maximize the conversion of total nitrogen to nitrate for subsequent chemical analysis. Under optimized conditions, 0.012M K2S2O8 and 0.024M NaOH were found to be necessary for complete digestion of wet and dry deposition samples at 400W for 7min using microwave. The optimized extraction method was then validated by testing different forms of organic nitrogen loaded to pre-baked filter substrates and NIST SRM 1648 (urban particulate matter), and satisfactory results were obtained. In the case of wet deposition samples, standard addition experiments were performed. The suitability of the method for real-world application was assessed by analyzing a number of wet and dry deposition samples collected in Singapore during the period of March–April 2007. The organic nitrogen content was 15% (wet) and 30% (dry) of the total nitrogen. During the study period, the estimated wet fluxes for nitrate (NO3 −), ammonium (NH4 +), organic nitrogen (ON), and total nitrogen (TN) were 16.1±6.5kgha−1 year−1, 11.5±5.7kgha−1 year−1, 3.8±1.5kgha−1 year−1and 31.5±13.2kgha−1 year−1, respectively, while the dry fluxes were 2.5±0.8kgha−1 year−1, 1.4±0.9kgha−1 year−1, 2.3±1.4kgha−1 year−1 and 7.5±2.6kgha−1 year−1, respectively. [Copyright &y& Elsevier]