The aims of this project are to develop models and techniques that will afford significantly improved monitoring and communication of the pollution level in cities without the need to significantly invest in monitoring equipment. It will explore how NO2 relate to PM10/2.5 and visa versa and how can other indicators in cities be used to evaluate air quality.

An Air Quality Sensor Network (deployed as part of a separate project, The Urban Flows Observatory) will be used to support this the project. The high quality fixed sensors and mobile sensing vehicle measure NO2, CO and SO2 together with PM2.5 & PM10.

These will be used to validate date from cheap sensors for NO2, CO and SO2 air pollution concentration measuring will be installed on mobile phones. This will allow the assessment of the cheap sensor based network to assess gas and particulate readings in the city. The project will comprise i) design, development and construction of a pollution analysis instrument ii) data analysis and visualisation, iii) development and validation of statistical models for detection and estimation and short term prediction of air pollution concentration and the inference of particulate levels, iv) energy efficiency of the proposed approaches, iv) integration in a decision making system.

Unlike previous models which encode only data related to spatial locations, in this project we will identify and incorporate other types of data provided by ‘social sensors’, e.g. people equipped with mobile wearable sensors. The project will develop methods both for people-centric and environment-centric applications.

PhD Candidate: Rohit Chakraborty

Supervisory Team: Martin Mayfield, Lyudmila Mihaylova, Anthony Ryan.

Project Status: Ongoing

Project Start Date: 1st October 2018

Keywords: Air Quality Monitoring, Air Pollution Modelling, Low Cost Sensor, Machine Learning, Wood Burner, School Run, Indoor Air Pollution, Sheffield:Air

Funding Scheme: Grantham Centre for Sustainable Future