01/09/2024
By Danielle Fretwell

The Francis College of Engineering, Department of Civil and Environmental Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Yigit Bozkurt on: Determination of Lead and Copper Spatial Distribution in Drinking Water Distribution Systems Using GIS-Assisted Models.

Candidate Name: Yigit Bozkurt
Degree: Doctoral
Defense Date: Friday, Jan. 12, 2024
Time: 11 a.m. to 12:30 p.m. EDT
Location: CEE Department Conference Room (Shah Hall Room 200Y, North Campus)

Advisor: Pradeep Kurup, Civil & Environmental Engineering, UMass Lowell

Committee Members:

  • Xiaoqi (Jackie) Zhang, Civil & Environmental Engineering, UMass Lowell
  • Weile Yan, Civil & Environmental Engineering, UMass Lowell
  • Mohammad Arif Ul Alam, Miner School of Computer & Information Sciences, UMass Lowell
  • Connor Sullivan, Civil & Environmental Engineering, UMass Lowell

Brief Abstract:

Drinking water is an essential resource for sustaining life. However, due to numerous sources of contamination, drinking water safety is not assured, even in developed countries. The issue of heavy metals, particularly lead and copper contamination, poses a formidable threat to drinking water. In the past, Newark, New Jersey; Flint, Michigan; Pittsburgh, Pennsylvania; and Washington, D.C., faced challenges related to lead and copper contamination in their drinking water. The U.S. Virgin Islands has recently had the same issue. The presence of these heavy metals in the water supply has emerged as a critical concern and drawn attention to the potential risks they pose to public health. To address this issue, the objectives of my doctoral research are (I) to locate high lead and copper-contaminated areas by analyzing existing data, newly generated data, and government records using spatial analysis (II) to determine the main features that cause lead and copper in drinking water distribution networks (III) to evaluate the performance of this methodology. I plan to focus my doctoral research on developing innovative methods for detecting lead and copper in drinking water using dynamic sampling with a citizen science approach and novel electrochemical techniques verified by standard analytical methods, artificial intelligence, and geographic information systems.