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Biochemical Oxygen Demand (DO-BOD) System Dynamics Model of Pasig River, Philippines Using STELLA

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Biochemical Oxygen Demand (DO-BOD) System Dynamics Model of Pasig River, Philippines Using STELLA Development of a Dissolved Oxygen - Biochemical Oxygen Demand (DO-BOD) System Dynamics Model of Pasig River, Philippines using STELLA Engr. Eduardo Bornilla Jr. Engr. Tolentino Moya, Ph.D. Merliza Bonga Brisbane, Australia | 20-24 October 2019 What’s important with DO and BOD? Dissolved Oxygen Biochemical Oxygen Demand - an essential element supporting - a measure of organic pollutants the aquatic life in the water - provides the maximum - amount of dissolved oxygen information about water quality needed to break down organic conditions (Ji, 2008) matter in water Polluted aquatic systems lead to reduction of dissolved oxygen. Further reduction to 2 – 3 mg/L DO concentration leads to a hypoxic condition or worse to anoxic condition (0 mg/L DO) that results to altered breathing patterns or fish kills. Brisbane, Australia | 20-24 October 2019 The Case of Pasig River System • Coastal estuarine • Drains the Philippine National Capital Region • Connects the Laguna Lake and Manila Bay • Declared biologically dead in Manila Bay early 2000s (Gorme, et al. 2010) Laguna Lake Brisbane, Australia | 20-24 October 2019 Source: Pasig River Rehabilitation Commission (PRRC) Annual Water Quality Data Water Quality Standard • BOD ≤ 7 mg/L • DO ≥ 5 mg/L Brisbane, Australia | 20-24 October 2019 Source: Pasig River Rehabilitation Commission (PRRC) Rationale behind the Pasig River Model Situationer Potential Applications • This model was an academic research • reconstruct pollution transport and project in partnership with PRRC for identify the hypoxia hot spots along purposes of additional environmental the river channel management tool. • estimate the BOD load contribution of the Pasig River watershed to Main Objective Manila Bay • develop a system dynamic model to • estimate a daily allowable BOD load understand the non-linear behavior of from Pasig River tributaries to attain surface water dissolved oxygen (DO) Class C water and biochemical oxygen demand (BOD) over time as water moves downstream Brisbane, Australia | 20-24 October 2019 The River Water Quality Model Streeter-Phelps Model • pioneering river water quality model • dissolved oxygen is consumed to degrade BOD in water • mathematically describes Source: nptel.ac.in the decrease in the DO concentration by Aquatic hypoxia is a result of complex interactions of biogeochemical degradation of BOD and physical processes. The water quality is a set of parameters that along a stream are mutually interrelated (Khalil et al. 2010) and these processes can be coupled (Jolankai 1997) based on interrelations. Brisbane, Australia | 20-24 October 2019 Modeling Equations • BOD decay • K1 temperature correction • Oxygen deficit equation • Saturation equation of dissolved oxygen • Reaeration coefficient • Denitrification (Jolankai, 1997) The equations can explain the non - linear DO and BOD interaction as water moves at a distance or downstream. Brisbane, Australia | 20-24 October 2019 Overall STELLA Model Setup Brisbane, Australia | 20-24 October 2019 STELLA User Interface • run and stop buttons • f value and temp sliders • background DO and BOD knobs • graph and table Brisbane, Australia | 20-24 October 2019 Results: Sensitivity Analysis • to identify the important parameters that affect the model results (Chapra, 1997) for DO and BOD • done by varying each of the parameters by a set of percentage • STELLA modeling platform has a built-in parameter perturbation technique for sensitivity analysis. Brisbane, Australia | 20-24 October 2019 Results: Sensitivity Analysis The parameters were set to: DO sensitivity to Temperature (20 to 32°C) • 7 mg/L dissolved oxygen • 15 mg/L BOD Sensitive where at higher temperatures can reach • 2.5 f hypoxia and anoxia. • 28°C water temperature The model was run 5 times for 10 days DO sensitivity to Depth (1 to 12 m) at different parameter with fixed Sensitive where at shallower depth, the faster the increment. oxygen regeneration. DO sensitivity to Velocity (0.1 to 1.5 m/s) Highly sensitive where a slight increase in velocity delays the oxygen regeneration BOD sensitivity to Temp (20 to 32°C) A slight increase in water temperature does not affect much the rate of BOD decay. Brisbane, Australia | 20-24 October 2019 Results: Preliminary Scenario Building Dry Season (from Napindan Station) Wet Season (from Napindan Station) • 32 °C, 1.2 f, 28 mg/L BOD, 5.1 • 23 °C, 2.3 f, 14 mg/L BOD, 6.8 mg/L DO mg/L DO Anoxic condition is most probable during dry season at high temperature and slow river velocity. Dissolved oxygen can regenerate faster during wet season. Brisbane, Australia | 20-24 October 2019 Further Development: Segmentation North Bank Tributary Distance (km) South Bank Tributary Distance (km) Laguna Lake 0 Laguna Lake -0.15 Ilugin Creek 3.86729 Daang Paa Creek 5.18223 Marikina River 6.69536 Pateros-Taguig River 6.64688 Pineda Creek 8.39455 Guadalupe Nuevo Creek 9.27288 Buayang Bato Creek 9.11592 Balisampan Creek 10.02217 San Juan River 16.7557 Estero de Santa Clara 13.01583 Estero de Valencia 18.77561 Estero de Pandacan B 16.61422 Estero de Sampaloc 19.24824 Estero de Pandacan A 18.63983 Esterdo de Uli Uli 19.51631 Estero de Santibanez 20.00717 Estero de San Miguel 21.41272 Estero de Paco 20.21842 Estero de la Reina 21.89083 Estero de Tanque 20.31066 Estero de Binondo 22.47864 Estero de Balete 20.6049 Manila Bay 25.90958 Manila Bay 25.10755 The boundary conditions will be based on the distance of each confluence point from Laguna Lake (0 km). Brisbane, Australia | 20-24 October 2019 Further Development • Initial BOD and DO condition: 0 km at Laguna Lake • After transport of x1 km (t1), simulated BOD and DO will be the new boundary conditions • General dilution equations apply at 21 confluence points until the water mass reaches Manila Bay Brisbane, Australia | 20-24 October 2019 Future Works: Calibration and Validation • Calibration is required to “tune” the model to fit a data set. Sample Output • Which data set from the water quality data? • 2016 and 2017 monthly water quality data will be used for calibration • For validation, the comparison of simulation results to 2018 actual water quality data. Brisbane, Australia | 20-24 October 2019 Challenges, Opportunities and Conclusion Modeling Challenges Modeling Opportunities • Pasig River is tidally-influenced • Can be a science-based tool for (Tamura, et al. 2000) managing the Pasig River • Consistency and availability of • Replicable to other river systems water quality data • Limited hydrologic data; latest data is dated September 2009 Conclusion • • It is possible to develop a simple Technical skills of modeler and model to help us understand the model users dynamics of a river system. Brisbane, Australia | 20-24 October 2019 Major References and Acknowledgement Chapra, S. C. (1997). Surface water quality modeling. In Surface water quality modeling. WCB/McGraw-Hill. Ford, F. A. (1999). Modeling the environment: an introduction to system dynamics models of • University of the Philippines environmental systems. Island Press. Gorme, J. B., Maniquiz, M. C., Song, P., & Kim, L. H. (2010). The water quality of the Pasig • The Marine Science Institute River in the City of Manila, Philippines: current status, management and future recovery. Environmental Engineering Research, 15(3), 173-179. • Institute of Environmental Jacinto, G. S., Sotto, L. P. A., Senal, M. I. S., San Diego-McGlone, M. L., Escobar, M. T. L., Science and Meteorology Amano, A., & Miller, T. W. (2011). Hypoxia in Manila Bay, Philippines during the northeast monsoon. Marine pollution bulletin, 63(5), 243-248. • Ji, Z. G. (2008). Hydrodynamics and water quality: modeling rivers, lakes, and estuaries. John Pasig River Rehabilitation Wiley & Sons. Commission Jolankai, G. (1997). Basic river water quality models: Computer aided Learning (CAL) programme on water quality modelling (WQMCAL versión 1.1). In Technical documents in hydrology (No. 13). Unesco. • Philippine Young Water Khalil, B., Ouarda, T. B. M. J., St-Hilaire, A., & Chebana, F. (2010). A statistical approach for the rationalization of water quality indicators in surface water quality monitoring Professionals networks. Journal of Hydrology, 386(1), 173-185. Tamura, H., K. Nadaoka, E.C. Paringit, F.P. Siringan, G..Q. Tabios, C.L. Villanoy, A.C. Blanco, J. • International RiverFoundation Kubota and H.Yagi (2003). Field survey on hydrodynamics and water quality in Manila Bay and Laguna Lake. Proc. Sympo. Environmental Issues Related to Infrastructure Development, JSPS Core Univ. Program on Env. Eng., 81-93. Brisbane, Australia | 20-24 October 2019 Brisbane, Australia | 20-24 October 2019.
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