Multi-Sensor Wireless Network for Integrated Aquatic Ecosystem Monitoring in Quirino Province, Philippines

Wilfredo B. Baniqued^{1, 2,} , Jordan C. Ursua^{1, 3}, Zyrel V. Santos^{1, 3}, Jaybert M. Cabañero^{1, 3}, Crista Souki D. Asuncion^{1, 4} and Romiro G. Bautista¹

¹Graduate School, University of La Salette, Inc., Santiago City, Philippines

²Engineering Department, Quirino State University-Cabarroguis Campus, Quirino, Philippines

³Schools Division Office-Quirino, Department of Education, Quirino, Philippines

⁴Schools Division Office-Isabela, Department of Education, Isabela, Philippines

Cite this paper:
Wilfredo B. Baniqued, Jordan C. Ursua, Zyrel V. Santos, Jaybert M. Cabañero, Crista Souki D. Asuncion and Romiro G. Bautista. Multi-Sensor Wireless Network for Integrated Aquatic Ecosystem Monitoring in Quirino Province, Philippines. American Journal of Environmental Protection. 2026; 14(1):16-21. doi: 10.12691/env-14-1-3

Abstract

This study deployed a multi-sensor wireless network to assess the health of selected freshwater bodies in Quirino Province by integrating chemical (dissolved oxygen, CO₂), physical (temperature, conductivity, pressure), and optical (turbidity, color) water quality parameters. Recognizing the sensitivity of tropical waters to warming, land-use change, and hydrological variability—and the limitations of low-frequency grab sampling in the Philippines—the study aimed to: (1) characterize spatial–temporal patterns in a midstream river, an agricultural stream, and a pond; (2) examine relationships between dissolved oxygen (DO) and key drivers such as temperature and CO₂; (3) identify hypoxia thresholds and risk periods; and (4) evaluate the effectiveness of wireless monitoring. Sensors for optical DO, NDIR CO₂, temperature, conductivity, pressure, turbidity, and color were installed at three sites and operated continuously for six months at 5-minute intervals. Periodic grab samples validated sensor readings. Data were analyzed using descriptive statistics, correlation, regression, and event-based analysis. Mean DO remained above the 5 mg/L guideline but declined from river (~7.3 mg/L) to stream (~6.4 mg/L) to pond (~5.9 mg/L). The pond exhibited the highest temperatures, often exceeding 30 °C, and showed pronounced nighttime DO minima. Temperature was the strongest inverse predictor of DO (r ≈ –0.66 to –0.78), with CO₂, turbidity, and color contributing additional negative effects. Hypoxic events were rare in the river but more frequent in the stream and pond, particularly after storms.Sensor validation showed strong agreement with grab samples, while continuous monitoring captured short-lived DO depressions missed by conventional methods. The study supports institutionalizing wireless monitoring and establishing site-specific early warning thresholds to mitigate oxygen stress under climate and land-use pressures.

Keywords:
dissolved oxygen wireless sensor network water quality tropical rivers CO₂ turbidity aquatic ecosystem health

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Casila, J. C. C., Nicolas, R. S. T., & co-authors. (2024). Assessing dissolved oxygen dynamics in Pasig River, Philippines: A HEC-RAS modeling approach during the COVID-19 pandemic. Water Practice and Technology, 19(4), 1365–1380.
[2]	van Vliet, M. T. H., Franssen, W., Yearsley, J. R., Ludwig, F., Haddeland, I., Lettenmaier, D. P., & Kabat, P. (2022). Impact of climate change on river water temperature and dissolved oxygen. Scientific Reports, 12, 12996.
[3]	Acad, J. P., Damiao, S. M. E., & co-authors. (2024). Water quality of Tago River. Cognizance Journal of Multidisciplinary Studies, 4(3), 216–220.
[4]	Zoleta, J. M. R., Almarez, D., Jr., & co-authors. (2023). Ambient water analysis of Cagayan de Oro River, Philippines. Liceo Journal of Higher Education Research, 19(1), 61–72.
[5]	Abrajano, M., & co-authors. (2024). IoT water quality monitoring in Philippine off-grid communities. arXiv preprint.
[6]	Lin, H.-Y., Chou, C.-F., & co-authors. (2021). An integrated wireless multi-sensor system for monitoring water quality in aquaculture tanks. Sensors, 21(24), 8265.
[7]	PASCO Scientific. (2018). Wireless optical dissolved oxygen sensor (PS3224) manual. https:// cdn.pasco.com/ product_ document/ Wireless-Optical-Dissolved-Oxygen-Sensor-Manual-PS-3224.pdf.
[8]	Demetillo, P. G., & co-authors. (2019). Implementing ZigBee-based wireless sensor network in water quality monitoring. International Journal of Recent Technology and Engineering, 8(3), 2431–2436.
[9]	Hassan, M. A., & co-authors. (2024). Advances in machine learning and IoT for water quality monitoring: A comprehensive review. Heliyon, 10(7), e28395.
[10]	Naval, R. B., Ignacio, H. R., Oppuer, E. R. (2022). Quality profile of QSU-farm pond irrigation system: Its suitability as irrigation waters. In Quirino State University Annual Report 2022 (pp. 45–47). Quirino State University. https://qsu.edu.ph/info/wp-content/uploads/2023/02/AnnualReport22.pdf.
[11]	Duka, M. A. (2016). Characterizing the water quality of the Cagayan River. Nueva Vizcaya State University Research Journal, 3(1), 15–25. http:// www.nvsu.edu.ph/ assets/ downloads/ journal/ vol3-1/NVSURJ_Vol.3_01_2016_2.pdf.
[12]	Reyes, R. A. R., Ramos, R. J., & co-authors. (2024). Water temperature modeling through regression and artificial neural network: A case study of the Marikina River, Philippines. Water Practice and Technology, 19(10), 4178–4192.
[13]	Dela Cruz, J., & co-authors. (2025). National-scale river water quality in the Philippines: Establishing baselines and identifying hotspots. Science of the Total Environment, 912, 174832.