Environment and Climate Change

Overview

Environmental features and industrial activities such as weather patterns and seasonal bitumen production influence methane biogenesis rates from Oil Sands Tailings Ponds (OSTP) and End Pit Lakes (EPL). This project leverages data mining, satellite imagery, and AI-driven analysis to uncover correlations promoting methane emissions. Real-time monitoring using IoT devices enhances our predictive capabilities and informs mitigation strategies.

Air Quality Monitoring (AIr System)

AIr is a robust air quality monitoring and prediction system combining low-cost IoT network architecture with AI-powered data analysis. It aims to:

  • Democratize access to air quality insights in both developed and developing countries
  • Support public health decisions through timely and scalable air pollution forecasts

Objective

Design and deploy AI, data science, and mathematical technologies to measure and forecast GHG emissions from OSTP and EPL.

Expected Outcomes

  • Improved detection and quantification of methane emissions
  • Reduced uncertainty in emission measurements
  • Standardization of multi-scale emission data monitoring
  • Actionable insights for climate change mitigation policies

Subproject 1: Smart Air Quality Monitoring Device

Development of a device capable of monitoring pollutants and environmental factors including methane, particulate matter, CO, NO2, SO2, VOCs, temperature, humidity, noise, and radiation.

Subproject 2: Methane Emission Detection Platform

Targeting methane emissions from Alberta’s oil sands, this platform integrates real-time AI, mathematical modeling, and field data to inform emissions limits and land reclamation strategies.

Aquatic Ecosystem Dynamics

Objective

Utilize AI and mathematical models to explore how climate and anthropogenic disturbances impact aquatic systems such as fish populations and phytoplankton dynamics.

Subproject 1: Fish and Fishing Community Adaptation

Investigate interactions between climate change, fishing, and marine sustainability. Key questions:

  • How do climate and fishing impact marine species abundance and distribution?
  • How do communities adapt to these changes?
  • What governance structures promote sustainable practices?

Subproject 2: Phytoplankton Dynamics Modeling

Study environmental factors such as light, nutrient levels, and water column depth that affect phytoplankton growth and distribution, using AI and mathematical tools.

📚 Selected Refereed Journal Publications

Kong, J. D., Wang, H., Siddique, T., Foght, J., Semple, K., Burkus, Z., & Lewis, M. A. (2019). Second-generation stoichiometric mathematical model to predict methane emissions from oil sands tailings. Science of the total environment. Science of the total environment, 694, 133645. .

Kong, J. D., Salceanu, P., & Wang, H. (2018). A stoichiometric organic matter decomposition model in a chemostat culture. Journal of Mathematical Biolog, 11, 76, 609-644. .