Developing predictive models for air pollution and heat exposures in South Africa and Canada

The overall objective of the Mitacs Globalink Project is to develop and assess the validity of models of exposure to air pollutants and temperature variability, using data extracted from a variety of available sources in South Africa and Canada. This objective will be achieved through a series of specific aims.

The first of these aims will be (1) the identification of the sources of available datasets, using established networks in South Africa and Canada to locate, access and extract the relevant data from these sources. Ground station data in both countries is generally available through publicly accessible portals.

Aim (2) will require subjecting these datasets to rigorous and systematic evaluation to determine their quality. This will require using established methods of data checking, and a review of the quality control methods used in data collection. Having determined the quality of the data, the next step is ensuring validity and ground truthing. This will require a variety of methods of data triangulation.

The data will then be used to achieve Aim (3), to use the validated datasets to develop predictive models using machine learning (ML) methods for air pollution and heat exposures using satellite earth observations and ground-level monitoring stations.

Faculty Supervisor:

Paul Peters

Student:

Partner:

University of KwaZulu Natal

Discipline:

Life Sciences

Sector:

Education; Environmental Science and Technology; Health and Related Sciences & Technology

University:

Carleton University

Program: