Low cost air quality sensor measurements in indoor environments

Time spent in homes and in transport micro-environments contribute significantly the exposure to air pollutants. Air quality is significantly impacted by the increased green house gas emissions and effects of climate change. However, concentrations in many of these are poorly quantified and challenging to monitor due to limited space, power and Wifi accessibility. This project will help to adapt, develop and test battery operated sensors from commercially available manufacturers to optimize their use in different environments. Work will also include the development of machine learning models to predict concentrations to establish the influence of different sources. The expected benefits is being able to support public health and environment of large residential cities by developing sensors that enable concentration prediction to better analyze air quality.

Faculty Supervisor:

Arthur Chan

Student:

Partner:

Imperial College London

Discipline:

Engineering

Sector:

Education

University:

University of Toronto

Program: