Air quality monitoring is an ever relevant field of study, with a large proportion of the global population living in areas with unsafe levels of pollution.

Increased monitoring is required to determine the health impacts on a local population, but traditional equipment is cost-prohibitive, requiring investment to operate and maintain.

This makes traditional monitors impractical to deploy in dense networks, rural areas or developing countries.

Low-cost alternatives are used as a way to supplement traditional measurement methods, but have issues in their measurement that need to be addressed prior to deployment.

This thesis aimed to recommend the best methodologies for an end-user of low-cost air quality monitors, with caveats.

This was done by first comparing the state-of-the-art within the literature to identify trends and gaps within published studies.

A suite of methodologies are then tested for both correcting off-the-shelf sensor system measurements and calibrating bespoke systems, using a range of locations and systems.

Finally, a network re-calibration methodology was developed to reinforce spatially disperse low-cost sensor system measurements against baseline drift, reducing downtime and re-location costs.

It is difficult to make conclusions based on the literature as the performance metrics used can vary with many variables other than the performance of a technique (length of study, average concentrations during training and testing, distribution of measured variables, etc).

Variance in results between different sensor systems and co-location sites is often more than methodologies when both correcting and calibrating low-cost sensor system measurements.

The network recalibration method reduced the Mean Absolute Error of a large proportion of systems tested and reduced the Mean Bias Error of most systems, but requires more work before being used with real-time measurements in a network.

Though no single technique proved most effective throughout, this thesis serves as a useful guide to end-users of low-cost air quality sensor systems and recommends a range of techniques to test prior to deployment.