Civil Engineering


First Advisor

Yusong Li

Date of this Version



Liu, C. (2017). Numerical modeling to evaluate the performance of slow-release candles for groundwater remediation (master’s thesis). University of Nebraska-Lincoln, Lincoln, United States.


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Civil Engineering, Under the Supervision of Professor Yusong Li. Lincoln, Nebraska: November, 2017

Copyright (c) 2017 Chuyang Liu


Slow-release candles (SRC) have been developed as a cost-effective technology to treat groundwater contaminants by passively delivering oxidants into the subsurface over a long time. In this thesis, a numerical model has been developed to simulate oxidant release kinetics, transport, and reaction in a field scale. Parameters of the model were obtained from a field site with SRC installed. Modeling results showed that the radius of influence of oxidants was influenced by the relative contribution of reaction and solute transport, and the limited lateral spreading could be an issue to restrict the application of SRC.

Enhanced aeration could increase or decrease the radius of influence of a candle, dependent on the incoming contaminant concentration. Enhanced mixing due to aeration could reduce the concentration of persulfate adjacent to the candle. It can greatly improve the radius of influence when incoming contaminant concentration is relatively low. When incoming contaminant concentration is very high, it may lead to reduced radius of influence. In the slow-release system design, if extra supply of oxidant in a candle was considered and suitable aeration rates was designed, the demand of boring and labor work could be greatly reduced by using larger interval distances. In the meantime, the effective duration time could also be increased.

The model developed in this work can be adapted to simulate SRC remediation under various field scenarios. It can be a tool to help design and optimize the SRC for various oxidant and targeting contaminants.

Advisor: Yusong Li