工业和生活点源污染水质建模:Majalaya 区 Cikakembang 河研究案例

Steven Kent, D. Yudianto, Cheng Gao, Finna Fitriana, Qian Wang
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引用次数: 0

摘要

工业的快速发展是环境恶化的主要原因之一。马惹拉雅区的纺织工业和家庭活动产生的废水直接排入 Cikakembang 河。因此,Cikakembang 河的水质已经下降到无法满足日常需要的地步。本研究模拟了水质建模中的三个主要参数,即溶解氧 (DO)、生物需氧量 (BOD) 和化学需氧量 (COD)。利用 MATLAB,采用 Runge Kutte-4 离散方案求解了源于平流-分散方程的三个水质治理方程。数值建模沿着 2.36 公里长的 Cikakembang 河进行。Cikakembang 河的所有水质系数,如溶解氧饱和度 (DOsat)、反应速率 (ka)、分散系数 (D)、脱氧速率 (kd) 和分解速率 (kc),均使用现有研究中开发的方程进行估算。ka 和 D 系数的估算需要水力参数,在本研究中使用 HEC-RAS 模拟估算。同时,kd 和 kc 值是在校准和验证过程中获得的。采用相对均方根误差(RRMSE)目标函数来评价三个采样点的水质模拟结果。在校准过程中,水质模拟结果产生的溶解氧、生化需氧量和化学需氧量参数的相对均方根误差值分别为 1.99%、0.36% 和 0.92%。同时,在验证过程中,溶解氧、生化需氧量和化学需氧量参数的 RRMSE 值分别为 1.95%、1.02% 和 1.86%。在校准和验证过程中,所有水质参数的 RRMSE 值都很小。因此,所创建的水质模型具有良好的准确性和稳定性。
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Water Quality Modelling with Industrial and Domestic Point Source Pollution : a Study Case of Cikakembang River, Majalaya District
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 resultsof 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.
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