PREDICTION OF RE-AERATION COEFFICIENT OF RIVERS FROM STREAM FLOW CHARACTERISTIC

Abstract

Stream re-aeration is the physical absorption of atmospheric oxygen by flowing in water, and this is primarily how the stream replenishes the depleted oxygen that is consumed in the decomposition of organic matter. One important parameter is the re-aeration rate, which allows for the evaluation of the quality and self-purification rate of water bodies. The re-aeration rate can be determined using a variety of methods, such as empirical and semi-empirical equations that can rapidly produce estimates based on hydraulic and hydrodynamic variables. However, depending on the variables, these equations often produce very different results and can lead to underestimated or overestimated values. In this study, a new re-aeration model was developed, designated as the N-Model, and its results were compared with some selected existing models to evaluate its suitability for use without vigorous laboratory work. The selected models include O'Connor, Parkhurst, Churchill, Krenkel, Thackston, and Owen. The re-aeration coefficient was computed using data collected from four selected rivers, namely: Otammiri, Kaduna, Adada,and Oshika Lake. The re-aeration coefficient for the Otammiri River was 0.075; O'Connor's model provided a very accurate result of 0.0753, with a correlation coefficient of 99.2%. N-Model predicted 0.076, giving an accuracy of 98.4%, and Parkhurst gave 0.078, unlike the others, Churchill, Krenkel, Thackston, and Owen, with percentage error values of 99.95%, 92.6%, 89.3%, and 73.17%, respectively. In summary, N-Model produced an average 98.9% correlation coefficient with less than 4% standard error, while O'Connor gave 84.6% for all three rivers except Oshika Lake, Parkhurst gave 46.8%, and others produced a significantly low correlation of between 13.222.4%. The standard error values for the re-aeration models ranged from 4% to 2208.35%. The order of performance of the various re-aeration models used was based on their proximity based on the value to past laboratory work done on the rivers.