Rohit Chakraborty - Research Student
Research Group: Resources, Infrastructure Systems and Built Environments
Research Project: Low Cost Internet Of Things Based Sensor Networks For Air Quality In CitiesThe World Health Organisation (WHO) estimated that air pollution in 2012 was responsible for 1 in 8 of the total number of deaths worldwide.
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.
This would afford redirecting people to areas with a reduced concentration of pollution and afford the passengers to select their best routes.
The project will test the model in Sheffield, working with local electronics company Pimoroni to develop the analytical equipment, and then test the low cost IoT network in a city in a developing country (Dar es Salaam) through contacts held by the supervisors.
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 and algorithms 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. The sensor node can be on a mobile app, such as a cell phone but can be also on a vehicle platform.