Dataset for a wireless sensor network based drinking-water quality monitoring and notification system

This paper presents the collected experimental data for water quality monitoring which was conducted in ten experiments by using five different common sources of water contaminants namely soil, salt, washing powder, chlorine and vinegar and their combination. The data were collected indoors at room temperature during the day for several days using sensors that measure pH, turbidity, flow rate, and conductivity in water. The water consumption risk (CR) was calculated as deviation based on the water quality parameters standards proposed by the World Health Organisation (WHO) and the South African Department of Water Affairs (DWA), with respect to the sensor measurement readings obtained. While the error measurements were calculated based on the expected parameter measurement per conducted experiment and repeated for 26 measurements. Pure tap water was the benchmark of water safe for human consumption. The first five experiments were performed by introducing each contaminant into the water and thereafter, two contaminants in the sixth experiment and their additions until all different contaminants were experimented at once in the last experiment.


Data
The dataset is published online in the Mendeley data repository [1]. The presented data were collected for all ten experiments conducted with the first being data for pure tap water and the rest being data for contaminated water using different contaminants and their additions until all different contaminants were experimented at once. Table 1 present the benchmark WHO standards of water parameters for safe human consumable water. The graphs portray the trends of each experiment showing the change in parameter values due to introduction of contaminant(s) with scaling for pH, conductivity and LDR (Light Dependant Resistor) and real-time scaling as data was collected in realtime. The real-time measurement values are presented in Tables 2e11. Conductivity, pH and LDR resistance (representing turbidity) were measured for the first six experiments and only pH and LDR values were measured for the last four experiments because the values were beyond the conductivity meter rating of 0e1999 mS/cm. Fig. 1 portrays the trends for pure tap water and their range, which is the benchmark quality parameters used in comparison to WHO water quality standards. Furthermore, it is vital that the water quality ranges of the water used must be analysed and known in order to ensure the quality and validity of the results. Fig. 2 shows the trends for soil contaminated water. Fig. 3 shows the trends for chlorine contaminated water. Fig. 4 depicts salt contaminated water parameter trends. Fig. 5 shows washing powder contaminated water trends. Fig. 6 shows vinegar contaminated water trends. Trends for vinegar þ washing powder contaminated water are portrayed in Fig. 7. Fig. 8 shows the trends for vinegar þ washing powder þ chlorine contaminated water. Trends for vinegar þ washing powder þ chlorine þ salt contaminated water are presented in Fig. 9. Fig. 10 shows the trends for vinegar þ washing powder þ chlorine þ salt þ soil contaminated water.
Specifications Table   Subject Computer Networks and Communications, Engineering Specific subject area Application of computing network and Engineering in monitoring water quality safety, risk and reliability .  Type of data  Tables and Graphs  How data were acquired Data was captured using sensors and sent wirelessly with an HFY-FST radio transmitter module, then it was received with an HFY-J18 radio receiver module and analysed (graphically and tabularly) through MegunoLink interface tool. Both the transmitter and receiver were implemented with identical Arduino Uno R3 microcontroller boards. The data article is related to this [2] research article

Value of the Data
The dataset presented in this paper can be used for further experiments on water quality monitoring as leverage using data mining methods such as machine learning [3] and emerging technologies such as IoT and blockchain for water quality monitoring and management [4]. It can also be used to validate experimental data of the same nature as it is of significant quality and it was validated using international standards. Another value of this data is in water purification, as it contains ratios of contaminants which are very useful in water purification and can be used in the water industry.

Experimental design, materials, and methods
Fig . 11 depicts the water supply design set up by which the data was gathered and analysed. It also shows how each sensor was mounted on this subsystem. All the sensors were integrated into the water supply subsystem in a way that they can accurately gather measurements from the analysed water. The pH sensor was installed inside the pipe as it functioned accurately in that location. Two valves were used to monitor and control the flow rate of water inside the pipe. The LDR was mounted on the surface of the water tank as it depends on light and since there is no light but darkness inside the pipe (where the pH sensor was mounted), otherwise the LDR would not work properly but only produce the same results for changes in water colour. The water tank was wrapped in a white paper to confine and reduce the error in measurement of the LDR for changes in water colour and for usage in indoor environment. A one (1) litre water tank was selected, to enable mobility of the system, and also to save and converse water for the duration of the testing phase. The twenty (20) litre container was used to drain both the pure tap water and the contaminated tap water after each analysis and testing.

Methods
Pure tap water parameters were measured first to validate the water quality standards as well as the performance of the system developed. Then five contaminants were used namely; soil, chlorine, salt, vinegar and washing powder. The soil was chosen because water can be contaminated by the soil in events of leakages on the water supply and distribution system. Chlorine was chosen because water can be overtreated and distributed without proper analysis, this is a mistake that might happen in water industries. Salt was chosen because of its ability to dissolve in water, and also to test the LDR, pH response and the conductivity. Water with high dissolved substances is not healthy for consumption, so the system must be able to detect such effects. Washing powder is known to be soapy, thus alkaline. This contaminant was chosen to test the system's response to soapy substances present in water. Vinegar is known to be a sour substance, thus acidic. It was chosen to test the system's response to acidic substances present in water. This phenomenon occurs mostly in corrosive pipes, which produces an acidic substance. Later experiments are conducted by the addition of the above-mentioned contaminants one at a time and checking the system's response for combined contaminants in water.            11. Schematic diagram of the water supply system used for data gathering.