A Simplified Model to Simulate pH and Alkalinity in the Mixing Zone Downstream of an Acidic Discharge

Abstract

A modelling methodology was developed that simulates the resulting pH and alkalinity in the mixing zone when acidic water is discharged into a river. The input to the model are the: pH, alkalinity, flow, and temperature of both the river water and the acidic discharge. Two different scenarios were simulated: (1) a change of pH in the acidic discharge, assuming constant flow; and (2) a change in the flow of the acidic discharge, assuming constant pH. The model incorporates the effect of carbonic acid and the modelled values agree well with the laboratory results. The model setup was subsequently used to predict the anticipated effect of contamination of the Zambezi River in Mozambique. The results indicate that the river will be impacted if the average pH of the water in the tributaries coming from the mining area is below 3. The model could be used by water managers to predict the potential impact of acidic discharges in poorly monitored rivers.

Use of Neural Networks to Estimate a Global Self-Purification Capacity Index for Mountain Rivers: A Case Study in Bogota River Basin

“…González et al (2014) evaluated the self-purification capacity by measuring 11 physicochemical parameters and applying principal component analysis. However, the generation of these models has a limited application to short stretches of water or stream of interest; in addition to that, the development of models elaborated for large hydrographic basins can be time-consuming and costly due to the number of samplings to be carried out, in addition to the challenges that arise during calibration when there is limited water quality data in the monitored stream (Azad et al 2017;Mishra et al 2017;Nhantumbo et al 2018).…”