Developing a Low-Cost Wireless Device for Real-Time Monitoring of Lead Levels in Drinking Water

EPA Grant Number: SU839450
Title: Developing a Low-Cost Wireless Device for Real-Time Monitoring of Lead Levels in Drinking Water
Investigators: Tang, Xiaochao , Wajda, Alexander , Saha, Dipendu , Buchter, Gabrielle , Nordfors, Ian , Kienbaum, Madeleine , Wang, Po-Yen , Song, Xiaomu
Current Investigators: Tang, Xiaochao , Song, Xiaomu , Saha, Dipendu , Wang, Po-Yen , Buchter, Gabrielle , Nordfors, Ian , Kienbaum, Madeleine , Wajda, Alexander
Institution: Widener University - Main Campus
EPA Project Officer: Klieforth, Barbara I
Phase: I
Project Period: November 1, 2018 through October 31, 2019 (Extended to October 31, 2020)
Project Amount: $14,935
RFA: P3 Awards: A National Student Design Competition Focusing on People, Prosperity and the Planet (2018) RFA Text |  Recipients Lists
Research Category: P3 Awards , Sustainability , P3 Challenge Area - Safe and Sustainable Water Resources

Description:

By providing pervasive real-time monitoring of lead levels in drinking water, the proposed project will directly contribute to the prevention of lead poison and its adverse health effects on people, particularly on the more vulnerable young children and infants. This highly multidisciplinary project will provide educational opportunities to undergraduate students across the engineering programs at the proposal institution, especially by infusing sustainability into senior design projects in multiple departments.

Objective:

Lead contamination in drinking water is often a close-to-home contamination caused by corroded lead service pipes connecting households to main lines or lead-based pluming within the households. A water supplier's compliance with the lead and copper rule (LCR) offers no guarantee that lead-in-water levels at individual homes are not high or even extremely high. It is, therefore, necessary to monitor lead levels in drinking water at individual consumers' water taps. The objective of this project is twofold: 1) to design and develop a low-cost smart wireless water tap device for continuous and real-time monitoring of lead levels in drinking water at consumers' water taps; 2) to establish a decentralized wireless sensor network for pervasive monitoring of lead levels in drinking water for a community and enable dissemination of first-hand information.

Expected Results:

The expected output of this proposed project is a low-cost smart wireless water tap device that can be easily deployed to individual households and installed at the consumers' water taps to monitor lead levels in drinking water. With the widespread use of such a device and its spatiotemporal data transmitted onto the cloud server, a wireless sensor network can be formed to monitor a community and determine the source of potential lead contamination. The potential outcome is that the consumers are able to obtain first-hand and real-time information on lead levels in their drinking water and explore the mapped data within their community. More importantly, the development of the low-cost smart water tap device at consumers' water tap would enable a pervasive monitoring of lead levels and drastically reduce the potential possibility of reoccurrence of Flint's water crisis.

Contribution to Pollution Prevention or Control
The proposed project is directly in line with Section 1442 of the Safe Drinking Water Act (SDWA). This study will contribute to the prevention of toxic heavy metal, lead contamination in drinking water which has been known to have detrimental health effects, particularly to children and infants. The low-cost smart water tap devices will greatly enhance the current practices of monitoring lead levels in drinking water. The device will be able to identify and measure the lead contamination levels in drinking water in real time and issue alert to stakeholders.

Publications and Presentations:

Publications have been submitted on this project: View all 1 publications for this project

Supplemental Keywords:

drinking water, lead contamination, electrochemical analysis, sensors, open source, internet-of- things, machine learning

Progress and Final Reports:

  • 2019 Progress Report