Low-cost IoT based system for lake water quality monitoring

Abstract

Water quality monitoring is a critical process in maintaining the well-being of aquatic ecosystems and ensuring growth of the surrounding environment. Clean water supports and maintains the health, livelihoods, and ecological balance of the ecosystem as a whole. Regular assessment of water quality is essential to ensure clean and reliable water is available to everyone. This requires regular measurement of pollutants or contaminants in water that can be monitored in real-time. Hence, this research showcases a system that consists of low-cost sensors used to measure five basic parameters of water quality that are: turbidity, total dissolved solids, temperature, pH, and dissolved oxygen. The system incorporates electronics and IoT technology that are powered by a solar charged lead acid battery. The data gathered from the sensors was stored locally on a micro-SD card with live updates that could be viewed on a mobile device when in proximity to the system. Data was gathered from three different bodies of water over a span of three weeks, precisely during the seasonal transition from autumn to winter. We adopted a water sampling technique since our low-cost sensors were not designed for continuous submersion. The results show that the temperature drops gradually during this period and an inversely proportional relationship between pH and temperature could be observed. The concentration of total dissolved solids decreased during rainy periods with a variation in turbidity. The deployed system was robust and autonomous that effectively monitored the quality of water in real-time with scope of adding more sensors and employing Industry 4.0 paradigm to predict variations in water quality.

Fig 1. Physical, chemical, nutrient and microbial indicators of water quality.

Fig 2. The labelled areas highlight three testing sites.

1. Lucas Creek, 2. Lake Albany, 3. Lake Pupuke.

Fig 3. Overview of the hardware design.

Fig 4. Illustration of the water quality monitoring system on a float boat.

Fig 5. The flow chart illustrates the process of water sampling, sensor reading, and datalogging.

Fig 6. Initial testing of the system in pool the of water-jet cutter (Top); the system’s water sampling container and the electrodes (Bottom).

Fig 7. Results for Lucas creek.

Fig 8. Results for Lake Albany.

Fig 9. Results for Lake Pupuke.

Fig 10. (left) sediment in water sampling tank, (right) small amount of dirt on inlet pump.

Fig 11. An extension of current work with more sensors and a few water quality systems distributed in water body, enabled with long range communications.