The development of water pollution detector using conductivity and turbidity principles

Abstract

Water pollution has caused negative impacts on human health as humans depend solely on water for drinking, cooking, and cleaning. Even more worrying is that the number of polluted rivers seems to increase as time progresses. Due to no real-time monitoring device being implemented, the authorities are unaware of any given river’s real-time conditions. Therefore, this research aims to control the water pollution issue by designing and developing a low-cost device that can detect water pollutants and notifies the authorities if abnormalities occur. In this work, various water pollution sources in Malaysia have been identified: biochemical oxygen demand, ammoniacal nitrogen, and suspended solids. The general performance of the proposed device is also evaluated and analyzed. Water quality data is collected by the sensors and is sent to an IoT platform called ThingSpeak through a Wi-Fi module to be visualized and displayed. When the pollution is detected, the website will alert local authorities for their prompt actions. From the experiment conducted, the developed conductivity sensor managed to give readings with 6.84% and 6.35% error compared to the sensor in a benchmark paper and the ready-made sensor, respectively. Besides, the turbidity sensor also managed to give accurate readings according to various types of solution. The success of this research would help to reduce river pollution and provide positive outcomes to the environment.