Evaluation and Development of Re-Aeration Equation Using Multivariate Linear Regression

To model and to allocate the waste load in a stream, it is necessary to estimate the re-aeration coefficient (k₂). Re-aeration rate coefficient as compared to loads, sources and sinks, cannot be measured under natural conditions. Indirect measurement with the calculation provide the efficient methodology for the estimation of re-aeration rate coefficient. Natural aeration system of river depending upon its assimilation capacity decomposes the organic matter which is mainly controlled by the flow rate and wastewater load. Currently, available k₂ when applied to different stream conditions as compared to their developed conditions produces poor estimate, because they were derived from small databases composed of potentially highly inaccurate measurements. In the present study, the re-aeration model for the different reaches of Yamuna river have been developed. Most commonly used predictive equation developed earlier have been tested and results obtained from these equation are compared with the field observation. A new predictive equation have been developed using Multivariate Linear Regression (MLR) with different flow conditions. The results of these studies are found with 95% level of significance with observed values and indicate that the modified re-aeration equation is a useful tool for measuring the atmospheric re-aeration rate coefficient.