Biography: Dr. Mohsen Asadi is a Senior Research Scientist at NCASI Inc. for the Canadian Mill Environment Program. He holds a Ph.D. in Civil and Environmental Engineering from the University of Saskatchewan and is a licensed Professional Engineer (P.Eng.) with the Association of Professional Engineers and Geoscientists of Saskatchewan (APEGS). His research focuses on developing innovative nature- and AI-driven solutions to optimize environmental systems given climate change and emerging pollutants. Dr. Asadi was an instrumental member of a multidisciplinary team of scientists and engineers that partnered with the Public Health Agency of Canada (PHAC), Saskatchewan Health Authority (SHA), and communities within Saskatchewan to develop an early-warning system for the viral disease and COVID-19 spread using wastewater surveillance.
Abstract: Quantification and minimization of greenhouse gas (GHG) and odour emissions from municipal wastewater treatment plants (MWTPs) have drawn marked interest from various stakeholders worldwide in response to air quality and global decarbonization goals. MWTPs typically include open-to-air treatment units with large surface areas, which make the accurate quantification of gas emissions challenging. Unmanned aerial vehicles (UAVs), floating chambers, and process-based models have been used and developed to quantify gas emissions and optimize wastewater treatment processes. However, these techniques face significant challenges in Canadian cold-region facilities, where high variability in influent wastewater characteristics and environmental conditions further complicate accurate emission quantification. This presentation will introduce a hybrid approach that integrates novel machine learning models with experimental data to simulate clarifier and biological treatment processes under varying seasonal conditions to enable more accurate quantification of GHG (including carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) and odour (including ammonia, NH3; and hydrogen sulphide, H2S) emissions while identifying optimal operating parameters for emission reduction. This work provides practical tools for facility operators to balance treatment efficiency with environmental performance in challenging climatic conditions.