Grants and Contributions

In this proposal, we will combine satellite measurements and a high-resolution model to address two questions about Canadian air quality and greenhouse gas emissions: 1. How well can satellite measurements of carbon monoxide (CO) constrain Boreal forest fire emissions and improve the current state-of-the-art Boreal fire emissions models? 2. How well can satellite measurements of methane (CH4) reduce uncertainty on Canada's anthropogenic methane emissions?

New developments in this project will be directly transferrable to the ECCC Prediction System.

Recently, we developed a novel evaporation model that characterizes land-atmosphere feedback processes. In this project, we will adapt this model to satellite land dryness observations, allowing us to estimate evapotranspiration across Canada and globally, and investigate physical mechanisms inducing drier climate.

The project develops a new Earth system analytical model “CAN-TG” which utilises satellite data from a number of Canadian sensors or missions to predict carbon and moisture dynamics of key Canadian terrestrial ecosystems, including wetlands and at the northern tree line. Key strengths of new model is its fine spatial and temporal scale and its verification using existing Canadian datasets. This project has been developed as a collaboration between academia, the Canadian Forest Service and Environment and Climate Change Canada.

The purpose of this agreement is the delivery of capacity funding for Indigenous groups to engagement with the department of Natural Resources Canada.

This Project seeks to develop an Artificial Intelligence (AI) enabled new multimodal microscope, based on coherent nonlinear optics and having the ability to learn from previous measurements to selectively choose subsequent imaging techniques.

While Canada spearheads the plant-based diets, it is essential to establish environmental and economic implications and trade-offs of current routes to their production. This Project will provide a detailed technical, economic, and environmental assessment of current processes for both protein and starch production. By combining experimental data and process simulations, the Project will provide optimization pathways that will enhance sustainability assessments using plant protein based on technical, economics and environmental criteria.

This Project will develop new machine learning algorithms for automating aspects of physical chemistry by discovering new pathways for creating materials with desired properties. The goal is to use the framework of reinforcement learning as a means of discovering, controlling, and optimizing the production of new materials. This Project will explore a novel new direction to perform decision making associated with established but highly complex processes used to transform one chemical compound into another.

To mitigate cost and space issues, the Project will develop an on-chip adaptive optics (AO) phase shifting system using the integrated silicon photonic analogue of a deformable mirror.

This Project is a collaboration between NRC and the University of Victoria (UVic) to develop Transfer Learning methods that utilize a large dataset for healthy patients with the aim of developing a model for the analysis of smaller patient populations. In this way, it will provide a base model for early personalized-diagnosis that is accurate across different patient subpopulations. The Project will focus on two neurodegenerative diseases - Alzheimer's and Parkinson's disease.