Assessment of surface water quality in eight major ponds of Coimbatore city and potential risk on ground water quality.

Water samples were collected from eight different water tanks located in the Coimbatore city during the study period of six months. Major surface water quality parameters such as turbidity, temperature, pH, electrical conductivity, total dissolved solids, chlorides, hardness, COD, BOD, DO, alkalinity, Calcium, Sodium, Sodium Absorption Ratio and Potassium were analysed. It was observed that the water quality gets deteriorated due to the continuous discharge / entry of domestic / industrial wastewater into these tanks. It was noted that the concentrations of all parameters follow a rising trend of variation with the time. Measures for the minimisation of organic load and suspended solids loading are to be made. It is evident that the discharges of domestic and industrial sewage into these ponds cause the deterioration in the water quality over the period.

Physico-chemical characteristics and correlation, and regression study on ground water of Coimbatore north zone.

Ground water samples were collected from 18 wards of Coimbatore City north zone, among which 2 samples were collected from 2 different locations from each ward, total 36 samples: Water quality assessment was carried out for the parameters like temperature, odour, taste, colour, turbidity, pH, electrical conductivity, total dissolved solids, chlorides, hardness, alkalinity, calcium, sodium and potassium. Correlation coefficients were determined to identify the highly correlated and interrelated water quality parameters. Regression equations relating these identified correlated parameters were formulated. Comparison of observed and estimated values of the different parameters reveals that the regression equations developed in the study can be very well used for making water quality monitoring by observing the above said parameters alone. This provides an easy and rapid method of monitoring of water quality.

Modelling of secondary clarifier using regression analysis and artificial neural networks.

Mathematical models for the surface area of secondary clarifier were developed for wastewater generated from a dairy industry and from domestic sources, by correlating the parameters namely, surface area per unit flow rate (A/Q), influent concentration (C(O)), underflow concentration (C(U)), recycling ratio (r) and Mean Cell Residence Time (theta C) using multiple regression analysis. There was found a good correlation between the measured data and the model results with regression coefficients of 0.9. Thickener area requirement of combined wastewater was comparehat obtained for dairy wastewater. Thickener area was found to decrease with increase in Mean Cell Residence Time and the area required for treating the combined wastewater was less, when compared with the requirement for dairy wastewater treatment. Neural network was trained with experimental data to 'acquire' knowledge about it. The Back Propagation Network technique was used in which the error was back propagated through the network. The results evolved from the neural network training were compared with the results of regression model and experimental data. Greater deviation was observed between the observed and predicted values of A/Q at high underflow concentrations, indicating that the limiting solids flux was reached. The output from Neural Network approach had greater consistency with the experimental data than the output from conventional regression analysis. Hence, Artificial Neural Network technique is highly adaptive and efficient in investigating input - output relationships.

Urban air pollution forecasting with respect to SPM using time series neural networks modelling approach--a case study in Coimbatore City.

The Coimbatore city due to its climatic conditions and industrial development is experiencing an exponential growth in the vehicular usage and fuel consumption. On the other hand, the existing weather pattern of the city is not favourable for the dispersion of pollutants. This paper presents the evolution of SPM forecasting models for the prediction of SPM one week in advance. Time series neural networks approach is used for modelling. The input variables are the meteorological parameters, the concentration of SPM one week before and the concentration of SPM on the monitoring day. The evolved models of SPM for selected monitoring stations will be useful for the effective functioning of the air quality management programme.