# Human Health Impact Analysis of Contaminant in IoT-Enabled Water Distributed Networks
## 4 Results and Discussion
### Reducing the Impact of the Contaminant using Water Quality Sensors  
The main goal of deploying water quality sensors in WDS is to minimize the consequences associated with water contamination. The water quality sensor detects the contaminant based on the contaminant concentration that would change one or more parameters of water quality (i.e., chlorine, TOC, turbidity, and conductivity) enough that an event detection device (sensor) or a water utility operator can detect the change. For instance, if the residual chlorine decreased by 1 mg/L, the conductivity increased by 150 Sm/cm, or the TOC increased by 1 mg/L. In this section, the impact of water contamination on the lives of consumers is conducted, where the impact of the contaminated water used by people will be recorded before the water quality sensors are deployed. Then, the extent of the contaminant water impact  
Figure 8: Estimated impact for contaminant at one of the small isolated networks. Figure 9: Estimated infection, disease, and fatalities (contaminant at the isolated branch). is recorded after the water quality sensors have been deployed based on our proposed approach [30] reviewed in Section 2. Three different numerical experiments have been conducted in this section:  
1. The first experiment investigated the impact of the contaminant without using water quality sensors. 2. The second experiment shows the impact using the water quality sensors optimally deployed on WDS utilizing the volume of the contamination water (VCW) as an objective function for deploying the sensors. 3. The third experiment analyzes the impact using water quality sensors that are optimally deployed on WDS using time detection (TD) as an objective function to deploy the sensors. In the first experiment, the health impact was analyzed without using water quality sensors to detect the water contaminant. Table 3 shows the impact of water contaminant on the people and contains the essential properties to represent the impact of the contaminant, such as the contaminants' injections nodes, detection time, and the impact as infected people. In Table 3, we have to select thirty injected nodes (as samples) from more than one-thousand nodes included in the Madrid case study, and these samples will be used in all the numerical experiments. As we present this experiment without using deployed sensors (water quality sensors), the detection time is recorded as 1440 min, which means there is no detection until the end of the simulation (24 h). The impact is computed using the proposed model presented in Section 3.2, and it computes the number of people got affected. The impact of the contaminated water without sensors is represented clearly in Figure 10. The x-axis represents the water nodes that are infected with contamination, and the y-axis represents the impact of this contamination concerning the number of people that are affected by this contamination. From Figure 10, we notice that the results were significantly recorded in this experiment, as it was estimated 1 that 14,721 cases were recorded at injected node 17_No_1195. At the same time, the rest of the injected water nodes recorded significant numbers of people. In the second experiment, we have deployed ten sensors as monitoring stations to detect the water contaminant, the deployment of sensors used the volume of contaminant water (VCW) as its optimization objective [30]. Table 4 shows the impact of the contaminant after deploying the ten monitoring sensors, and the table contains the same properties presented in the previous experiment, while the detected stations represent the sensor station that detects the contaminant. The time detection column recorded the detection time of the contaminant by the monitoring stations. From Table 4, despite using monitoring stations (sensors), we still find that some injected nodes are not directly detected by any monitoring stations such as 17_No_1381, 17_No1532, 17_No_1702, and 17_No_1703. These  
Figure 10: Health impact without using water quality sensors.