Development of a water quality prediction model through the utilization of solar-powered phytoremediation water treatment pool in improving the water quality of Pandurucan River, San Jose, Occidental Mindoro
Keywords:
analytical hierarchy process, effluent standards, geographical information system, water quality prediction model, water quality guidelines, water quality parametersAbstract
The Pandurucan River is vital for agriculture, fisheries, and domestic use in San Jose, Occidental Mindoro. However, it is currently suffering from severe water quality issues caused by pollution from surrounding agricultural and residential lands. To improve its water quality, Scatter Linear Regression, Analytic Hierarchy Process (AHP), and ArcGIS were utilized in this research to develop a water quality prediction model, analyzing the significant parameters such as temperature, pH, dissolved oxygen, chloride, TSS, nitrate, and phosphate. The Water Quality Guidelines and Effluent Standards of 2016 (DAO 2016-08) were followed during the water quality monitoring, and the Analytic Hierarchy Process (AHP) was used to determine parameter weights for map generation. This water quality assessment revealed high chloride and phosphate level contents, low dissolved oxygen, and saltwater intrusion, where all stations failed in the Water Quality Index (WQI), particularly in Brgy. San Roque. Further, the Solar-Powered Phytoremediation Water Treatment Pool efficiently improved all water quality parameters, as visualized in the ArcGIS-based model. This result was validated using the R-squared (R²) method, which ensured that the prediction model efficiently captured water quality variations, being a reliable tool for environmental management.
References
Ansari, A. A., Naeem, M., Gill, S. S., & AlZuaibr, F. M. (2020). Phytoremediation of contaminated waters: An eco-friendly technology based on aquatic macrophytes application. Egyptian Journal of Aquatic Research, 46(4), 371–376. https://doi.org/10.1016/j.ejar.2020.03.002
Booker, D. J., & Whitehead, A. L. (2021). River water temperatures are higher during lower flows after accounting for meteorological variability. River Research and Applications, 38(1), 3–22. https://doi.org/10.1002/rra.3870
DeepChand, N., Khan, N. A., Saxena, P., & Goyal, S. K. (2022). Assessment of supply water quality using GIS tool for selected locations in Delhi—A case study. Air Soil and Water Research, 15. https://doi.org/10.1177/11786221221111935
Enriquez, M. D., & Tanhueco, R. M. (2022). A basis water quality monitoring plan for rehabilitation and protection. Global Journal of Environmental Science and Management. 8(2), 227-238. https://doi.org/10.22034/gjesm.2022.02.07
Gorito, A. M., Ribeiro, A. R., Almeida, C., & Silva, A. M. (2017). A review on the application of constructed wetlands for the removal of priority substances and contaminants of emerging concern listed in recently launched EU legislation. Environmental Pollution, 227, 428–443. https://doi.org/10.1016/j.envpol.2017.04.060
Md Anawar, H. & Chowdhury, R. (2020). Remediation of Polluted River Water by Biological, Chemical, Ecological and Engineering Processes. Sustainability 2020, 12(17), 7017; https://doi.org/10.3390/su12177017
Mekonnen, M. M., & Hoekstra, A. Y. (2017). Global Anthropogenic phosphorus loads to freshwater and associated grey water footprints and water Pollution Levels: A High‐Resolution Global Study. Water Resources Research, 54(1), 345–358. https://doi.org/10.1002/2017wr020448
Murray, L. (2021). Globally, 3 billion people at health risk due to scarce data on water quality. United Nation Environment Programme. https://www.unep.org/news-and-stories/story/globally-3-billion-people-health-risk-due-scarce-data-water-quality
Muyot, N. (2022). Assessing surface water quality: Physico-chemical properties of the Pandurucan River, San Jose, Mindoro, Philippines. International Journal of All Research Education and Scientific Methods (IJARESM), 10(6). https://www.ijaresm.com/uploaded_files/document_file/Norma_B._Muyot,_FK69.pdf
Naubi, I., Zardari, N. H., Shirazi, S., Ibrahim, F., & Baloo, L. (2016). Effectiveness of Water Quality Index for monitoring Malaysian river water quality. Polish Journal of Environmental Studies, 25(1), 231–239. https://doi.org/10.15244/pjoes/60109
Padhye, L. P., Srivastava, P., Jasemizad, T., Bolan, S., Hou, D., Shaheen, S. M., Rinklebe, J., O’Connor, D., Lamb, D., Wang, H., Siddique, K. H., & Bolan, N. (2023). Contaminant containment for sustainable remediation of persistent contaminants in soil and groundwater. Journal of Hazardous Materials, 455, 131575. https://doi.org/10.1016/j.jhazmat.2023.131575
Pang, Y. L., Quek, Y. Y., Lim, S., & Shuit, S. H. (2023). Review on phytoremediation potential of floating aquatic plants for heavy metals: A promising approach. Sustainability, 15(2), 1290. https://doi.org/10.3390/su15021290
Philippine Institute for Development Studies. (2024). Quenching Policy Thirst: Reforming Water Governance in the Philippines. Socioeconomic research Portal for the Philippines. https://serp-p.pids.gov.ph/publication/public/view?slug=quenching-policy-thirst-reforming-water-governance-in-the-philippines
Priya, A. K., Muruganandam, M., Ali, S. S., & Kornaros, M. (2023). Clean-Up of Heavy Metals from Contaminated Soil by Phytoremediation: A Multidisciplinary and Eco-Friendly Approach. Toxics, 11(5), 422. https://doi.org/10.3390/toxics11050422
Republic Act No. 9275. (2004). The Philippine Clean Water Act of 2004. https://lawphil.net/statutes/repacts/ra2004/ra_9275_2004.html
U.S. Environmental Protection Agency. (2021). Basic Information about Lead in Drinking Water. US EPA. https://www.epa.gov/ground-water-and-drinking-water/basic-information-about-lead-drinking-water#:~:text=To%20address%20corrosion%20of%20lead,prevent%20lead%20in%20drinking%20water.
Verma, R. K., Sankhla, M. S., Jadhav, E. B., Parihar, K., & Awasthi, K. K. (2021). Phytoremediation of heavy metals extracted from soil and aquatic environments: Current advances as well as emerging trends. Biointerface Research in Applied Chemistry, 12(4), 5486–5509. https://doi.org/10.33263/BRIAC124.54865509
Wu, X., Zhang, Q., Wen, F., & Qi, Y. (2022). A water quality prediction model based on multi-task deep learning: A case study of the Yellow River, China. Water, 14(21), 3408. https://doi.org/10.3390/w14213408
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Copyright (c) 2025 Jaye Lord D. De Vera, Merelle P. Madredano, Trixie Anne G. Millaflores, Michelle D. Enriquez (Author)
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