You are encouraged to attend the defence. The details of the defence and attendance information is included below:

Date:  December 02, 2024
Time:  2:00 PM to 4:00 PM (PT)

Defence mode: Hybrid
In-Person Attendance: Senate Chambers, UNBC Prince George Campus
Virtual Attendance: via Zoom (additional Zoom details at bottom of message)

LINK TO JOIN:  Please contact the Office of Graduate Administration for information regarding remote attendance for online defences.

To ensure the defence proceeds with no interruptions, please mute your audio and video on entry and do not inadvertently share your screen. The meeting will be locked to entry 5 minutes after it begins: please ensure you are on time.

Thesis entitled:  TREATMENT WETLANDS FOR SECONDARY DOMESTIC WASTEWATER: AN EXPERIMENTAL AND MODELLING APPROACH

Abstract: This thesis explores the use of treatment wetlands (TWs) for domestic wastewater (WW), addressing the need for more experimental trials in secondary treatment scenarios and cold climates. Additionally, a data-driven model, the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, was employed for the first time to identify a nonlinear system of ordinary differential equations (ODEs) to describe pollutant removal rates in TWs. A 2x2x2 full factorial experiment was conducted to examine the effects of WW strength, temperature and vegetation on lab-scale horizontal subsurface flow TWs (HSSFW). High-resolution monitoring of biologically relevant parameters such as oxidation reduction potential (ORP), dissolved oxygen (DO), pH, and temperature was implemented. 

Experimental datasets were utilized for training and validating the chemical oxygen demand (COD), ammonia and phosphates reaction rate models. Results indicated no significant difference in COD removal between high and low WW strength, indicating TWs can effectively manage raw domestic WW. ODEs were identified for modelling pollutant removal, with simplified equations employing 3 to 6 polynomial basis functions in low-noise scenarios and up to 11 polynomial basis functions in high-noise situations. Key parameters such as COD*ammonia, ORP, and pH were found to have the most significant impact on the pollutant removal rates. 

Validation results demonstrated robust predictive capabilities for COD models, achieving R² values between 0.81 and 0.98, while ammonia and phosphate models showed varying accuracy levels. These results suggest the need for more datasets to be tested for model validation and highlight the importance of incorporating ORP and pH into TWs monitoring and modelling.

Defence Committee: 
Chair: Dr. Mauricio Dziedzic, University of Northern British Columbia 
Supervisor: Dr. Flor Y.  Garcia-Becerra, University of Northern British Columbia 
Co-Supervisor: Dr. Ron Thring, University of Northern British Columbia 
Committee Member: Dr. Deborah Roberts, University of Northern British Columbia 
Committee Member: Dr. Andy Wan, University of California, Merced
External Examiner: Dr. Siraj ul Islam, University of Northern British Columbia