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

Date: April 10, 2025 
Time: 11:00 AM to 1:00 PM (PT) 

Defence mode: Hybrid
In-Person Attendance: Small Lecture Theatre Agora (7-158) UNBC Prince George Campus  
Virtual Attendance: via Zoom 

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: OPTIMIZING COLD-CLIMATE TREATMENT WETLAND PERFORMANCE: INTEGRATING MACHINE LEARNING ANALYSIS WITH EXPERIMENTAL EVALUATION OF CAREX UTRICULATA'S FUNCTIONAL RESILIENCE

Abstract: Treatment wetlands experience significant seasonal temperature variations that affect biological treatment processes through complex interactions between plants, microorganisms, and environmental conditions. This thesis employs a dual methodological approach to optimize cold-climate treatment wetland design and operation. First, we developed interpretable machine learning models using data from 27 published studies and 118 treatment wetlands to predict effluent temperature (RMSE = 0.7°C) and ammonia (RMSE 2.9 – 9.3 mg/L) and organic matter (RMSE 11 – 44 mg/L) concentrations in different wetland configurations. Model interpretation revealed that influent temperature is the dominant predictor of effluent temperature followed by air temperature, with hydraulic loading rate modifying this relationship. For contaminant removal, we found that plant species' cold-temperature benefits are most pronounced in saturated systems, while their impact is marginal in unsaturated configurations.

Building on these insights, we conducted controlled microcosm experiments with Carex utriculata in batch-operated wetlands across temperature conditions (23°C and 5°C) and after harvesting. C. utriculata maintained superior organic matter removal in cold conditions (86±5% versus 73±9% for unplanted systems) and continued to outperform unplanted systems even after harvesting. Evapotranspiration-driven water level reduction in warm conditions improved porosity maintenance by 2-3% compared to unplanted systems. Our integrated approach demonstrates that C. utriculata provides resilient cold-temperature treatment benefits while offering operational advantages through reduced clogging potential and maintained performance post-harvest, addressing key challenges identified in machine learning analysis of published literature.

Defence Committee:  
Chair: Dr. Mauricio Dziedzic
Supervisor: Dr. June Garcia-Becerra
Co-Supervisor: Dr. Oliver Iorhemen
Committee Member: Dr. Theresa Adesanya
Committee Member: Dr. Cindy Smith
External Examiner: Dr. Andrea Gorrell