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

  • Steven Kent Civil Engineering Department, Faculty of Engineering, Parahyangan Catholic University, Ciumbuleuit Street, No. 94, 40141, INDONESIA
  • Doddi Yudianto Civil Engineering Department, Faculty of Engineering, Parahyangan Catholic University, Ciumbuleuit Street, No. 94, 40141, INDONESIA
  • Cheng Gao Institute of Water Science and Technology, Hohai University, Nanjing, 210098, CHINA
  • Finna Fitriana Civil Engineering Department, Faculty of Engineering, Parahyangan Catholic University, Ciumbuleuit Street, No. 94, 40141, INDONESIA
  • Qian Wang Institute of Water Science and Technology, Hohai University, Nanjing, 210098, CHINA
Keywords: Advection-Dispersion Equation, MATLAB, Point-Source Pollution, Water Quality Coefficients, Water Quality Modelling

Abstract

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.

References

Benedini, M. and Tsakiris, G. (2013), Water Quality Modelling for Rivers and Streams, Springer Dordrecht, s.l. URL: https://doi.org/10.1007/978-94-007-5378-3

Deng, T., Chau, K.-w. and Duan, H. (2021), ‘Machine learning based marine water quality prediction for coastal hydro-environment management’, Journal of Environmental Management 284, 112051. URL: https://doi.org/10.1016/j.jenvman.2021.112051

Despotovic, M., Nedic, V., Despotovic, D. and Cve tanovic, S. (2016), ‘Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation’, Renewable and Sustainable Energy Reviews pp. 246–260. URL: https://doi.org/10.1016/j.rser.2016.11.064

Dey, S. and Islam, A. S. (2015), ‘A review on textile wastewater characterization in bangladesh’, Resources and Environment 5(1), 15–44. URL: https://doi.org/10.5923/j.re.20150501.03

Fitriana, F. et al. (2023), ‘The assessment of citarum river water quality in majalaya district, bandung regency’, Rekayasa Sipil 17(1), 37–46. URL: https://doi.org/10.14710/teknik.v17i1.45029

Haider, H., Ali, W. and Haydar, S. (2013), ‘Evaluation of various relationships of reaeration rate coefficient for modelling dissolved oxygen in a river with extreme flow variations in pakistan’, Hydrological Processes pp. 3949–3963. URL: https://doi.org/10.1002/hyp.9501

Iqbal, M. M., Shoaib, M., Farid, H. U. and Lee, J. L. (2018), ‘Assessment of water quality profile using numerical modeling approach in major climate classes of asia’, International Journal of Environmental Research and Public Health 15(2258). URL: https://doi.org/10.3390/ijerph15102258

Iwasa, Y. and Aya, S. (1991), ‘Transverse mixing in a river with complicated channel geometry’, Bulletin of the Disaster Prevention Research Institute 41 (3), 129–175. URL: https://doi.org/10.11408/suirikagaku.41.129

Jha, R., Ojha, C. S. P. and Bhatia, K. K. S. (2001), ‘Refinement a predictive reaeration equations for a typical indian river’, Hydrological Processes pp. 1047–1060. URL: https://doi.org/10.1002/hyp.122

Jha, R., Ojha, C. S. P. and Bhatia, K. K. S. (2004),‘A supplementary approach for estimating reaeration rate coefficients’, Hydrological Processes 18(1), 65–79. URL: https://doi.org/10.1002/hyp.1289

Khalish, M., Utami, A., Lukito, H. and Herlambang, S (2022), ‘Evaluation of textile industry waste water treatment as an effort to control river water pollution in central java’, KnE Life Sciences pp. 48–61.URL: https://doi.org/10.18502/kls.v8i1.10565

Lin, L., Yang, H. and Xu ,X.(2022),‘Effects of water pollution on human health and disease heterogeneity: A review’, Frontiers in Environmental Science 10. URL: https://doi.org/10.3389/fenvs.2022.846520

Liyanage, C. P. and Yamada, K.(2017),‘Impact of population growth on the water quality of natural waterbodies’, Sustainability 9(8), 1405. URL: https://doi.org/10.3390/su9081405

Menendez, A., Lecertúa, E., Badano, N. and García, P. (2016), ‘Numerical modeling to define remediation actions for water quality in streams’, Journal of Applied Water Engineering and Research pp. 67–81. URL: https://doi.org/10.1016/j.jaer.2016.03.004

Nasrollahi, Z., Hashemi, M., Bameri, S. and Taghvaee, V. M. (2020), ‘Environmental pollution, economic growth, population, industrialization, and technology in weak and strong sustainability: using stirpat model’, Environment, Development and Sustainability 22, 1105–1122. URL: https://doi.org/10.1007/s10668-019-00352-0

Nogare, M. A. and Bauer, B. O. (2022), ‘A field-based evaluation of the reliability of empirical formulae for quantifying the longitudinal dispersion coefficient in small channels’, geosciences 12(281). URL: https://doi.org/10.3390/geosciences12120281

Polisar, A. (2023), ‘Study of the impacts of domestic and textile industry wastewater discharge in cikakembang river, majalaya, bandung regency’, Parahyangan Catholic University. URL: https://doi.org/10.13140/RG.2.2.10236.33924

Popescu, I. (2014), Computational Hydraulics Numerical Methods and Modelling, IWA Publishing, London. URL: https://doi.org/10.2166/9781780404996

Schnoor, J. L. (1996), Environmental modeling: Fate and transport of pollutants in water, air, and soil, Wiley, Iowa. URL: https://doi.org/10.1002/9781119116469

Srinivas, T. A. S. et al. (2023), ‘Unlocking the power of matlab: A comprehensive survey’, IJARSCT 3(1), 20–31. URL: https://doi.org/10.47595/IJARSCT.2023.3710

Sun, L. et al. (2020), ‘A review of applications of fractional advection–dispersion equations for anomalous solute transport in surface and subsurface water’, WIREs Water. URL: https://doi.org/10.1002/wat2.1448

Tabari, H. (2020), ‘Climate change impact on food and extreme precipitation increases with water availability’, Scientific Reports 10, 13768. URL: https://doi.org/10.1038/s41598-020-70895-w

Tang, W. et al. (2022), ‘Twenty years of china’s water pollution control: Experiences and challenges’, Chemosphere p. 133875. URL: https://doi.org/10.1016/j.chemosphere.2022.133875

Wang, X., Jiang, J. and Gao, W. (2022), ‘Reviewing textile waste water produced by industries: characteristics environmental impacts, and treatment strategies’, Water Sci Technol pp. 2076–2096. URL: https://doi.org/10.2166/wst.2022.098

Wikiandy, N., Rosidah and Herawati, T. (2013), ‘The impact of textile industry waste pollution on damage to the structural organs of fish living in the upper section of the citarum river flow (das)’, Journal of Fisheries and Marine Affairs (Jurnal Perikanan dan Kelautan) pp. 215–225. URL: https://doi.org/10.14710/jpk.5.3.215-225

Worldwide, C. (2019), ‘Cdp global water report: Are companies responding to the risks and opportunities’, CDPWorldwide. URL: https://doi.org/10.46755/cdp.2019.011

Yan, B., Yu, F., Xiao, X. and Wang, X. (2019), ‘Ground water quality evaluation using a classification model: a case study of jilin city, china’, Natural Hazards 99 (2), 735–751. URL: https://doi.org/10.1007/s11069-019-03654-3

Yu, X., Shen, J. and Du, J. (2020), ‘A machine–learning based model for water quality in coastal waters, taking dissolved oxygen and hypoxia in chesapeake bay as an example’, Water Resources Research p. 56. URL: https://doi.org/10.1029/2020WR027227,

Zeng, Y. H. and Huai, W. X. (2014),‘Estimation of longitudinal dispersion coefficient in rivers’, Journal of Hydro-environment Research pp. 2–8. URL: https://doi.org/10.1016/j.jher.2014.05.001

Published
2024-03-28
How to Cite
Kent, S., Yudianto, D., Gao, C., Fitriana, F., & Wang, Q. (2024). Water Quality Modelling with Industrial and Domestic Point Source Pollution: a Study Case of Cikakembang River, Majalaya District. Journal of the Civil Engineering Forum, 10(2), 151-162. https://doi.org/10.22146/jcef.11807
Section
Articles