Journal of the Civil Engineering Forum (Mar 2024)

Water Quality Modelling with Industrial and Domestic Point Source Pollution : a Study Case of Cikakembang River, Majalaya District

  • Steven Kent,
  • Doddi Yudianto,
  • Cheng Gao,
  • Finna Fitriana,
  • Qian Wang

DOI
https://doi.org/10.22146/jcef.11807
Journal volume & issue
Vol. 10, no. 2

Abstract

Read online

Rapid industrial development is one of the leading causes of environmental degradation. The textile industries and the domestic activities in Majalaya District produce wastewater directly discharged into the Cikakembang River. As a result, the Cikakembang River’s water quality has decreased to the point that the water quality cannot be used for daily needs. This study modeled three main parameters in water quality modelling, namely Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD). Using MATLAB, the three-water quality governing equations originating from the Advection-Dispersion Equation were solved using the Runge Kutte-4 discretization scheme. The numerical modelling was carried out along 2.36 km of the Cikakembang River. All water quality coefficients, such as the DO Saturation (DOsat), the Reaeration Rate (ka), the Dispersion Coefficient (D), the Deoxygenation Rate (kd), and the Decomposition Rate (kc), for the Cikakembang River were estimated using equations developed by existing studies. The estimation of ka and D coefficients requires hydraulic parameters, which in this study were estimated using the HEC-RAS simulation. Meanwhile, kd and kc values were obtained from the calibration and verification process. The Relative Root Mean Square Error (RRMSE) objective function was used to evaluate the results of water quality modelling at three sampling points. In the calibration process, the results of water quality modelling produced RRMSE values for the DO, BOD, and COD parameters of 1.99%, 0.36% and 0.92%, respectively. Meanwhile, for the verification process, the RRMSE values for the DO, BOD, and COD parameters are 1.95%, 1.02% and 1.86%. All water quality parameters produce small RRMSE values in the calibration and verification processes. Hence, the water quality model created has good accuracy and stability.

Keywords