Integration of sensors for dam water quality analysis - a prototype.

Water consumed is stored in several water bodies in and around us, out of which dams accommodate a major portion of water. The quantity and quality monitoring of water in dams is troublesome due to their large surface area and high depths. Although groundwater resources are the primary water source in India, dams plays a vital role in water distribution and storage network. The Central Water Commission in India has identified more than 5,000 dams of which a major portion is persistently consumed by the rural and urban populations for drinking and irrigation. The water quality of these reservoirs is of serious concern as it would not only affect the socio-economic status of the nation but the aquatic systems as well. Water quality control and management are vital for a delivering clean water supply to the general society. Because of their size, collecting, assessing, and managing a vast volume of water quality data are critical. Water quality data are primarily obtained through manual field sampling; however, real-time sensor monitoring is increasingly being used for more efficient data collection. The literature depicts that the methods involving remote sensing and image processing of water quality analysis consume time, and require sample collection at various depths, analysis of collected samples, and manual interpretations. The objective of this study was to propose a novel cost-effective method to monitor water quality devoid of considerable human intervention. Sensor-based online monitoring aids in assessing the sample with limited technology, at various depths of water in the dam to analyze turbidity which gives the major indication of pure water. The quality analysis of the dam water is suitable if the water is assessed at the distribution end before consumption. Hence, to enhance the water management system, other quality parameters like pH, conductivity, temperature are sensed and monitored in the distribution pipeline. An unstable pH can alter the chemical and microbiological aspects of water, resulting in a variation of other water quality parameters. Temperature variations affect the amount of dissolved oxygen in the water bodies and results in unstable quality parameters. The change in dissolved solvents and the ionic concentration alters the electrical conductivity of the water and the increased concentration of salts also results in turbidity. The data from all the sensors are processed by the microcontroller, transmitted, and displayed in a mobile application comprehensible to the layman.

Polyaza functionalized graphene oxide nanomaterial based sensor for Escherichia coli detection in water matrices.

Water quality is widely discussed owing to its significance in public health due to the inability to access clean water. Waterborne diseases account for the presence of pathogens like Escherichia coli (E. coli) in drinking water in the environmental community. Owing to the rapid increase of such bacterial microorganisms, a cost-effective sensor setup has been developed. Herein, we demonstrate the amine-functionalized graphene oxide (fGO) based 2D nanomaterial used to graft E. coli on its surface. The comparative analysis of the deposition of nanosheets on the glass substrate and PDMS was executed. The impedance variations of GO-based nanosensor at various concentrations of E. coli were performed and their potential difference was recorded. It was observed that the impedance changes inversely with the bacterial concentrations and was fed to the Arduino microcontroller. The experimental setup was standardized for the range of 0.01 Hz to 100 kHz. The obtained analog data was programmed with a microcontroller and the bacterial concentration in colony-forming units was displayed. The real-time analysis showsthe low-level detection of E. coli in aquatic environments. Experiments were conducted using the developed nanosensor to test the efficiency in complex water matrices and whose behavior changes with various physical, chemical, and environmental factors.