A novel ventilation-pollution nexus framework for offsetting carbon by smart indoor spaces

The considerable time people spend indoors has made indoor air quality (IAQ) more important than ever before. The biggest challenge is understanding the ventilation-pollution nexus as airtight buildings preserve energy but worsen the IAQ due to limited ventilation and vice-versa. This project aims to exploit low-cost sensors (LCSs), which can make rapid measurements and transmit data through the cloud, and to develop affordable tools which can be used to control the balance of ventilation for a safe IAQ as part of the government’s ambition to deliver carbon net zero.

The work would:

1. Build a framework for the ventilation-pollution nexus for indoor spaces by defining LCS deployment strategies that represent various types of building occupancy
2. Carry out real-world pilot studies by deploying a network of low-energy/low-cost sensors in indoor spaces, e.g. schools/offices/University buildings
3. Develop easy to adopt data processing techniques for end-users to build a ventilation/IAQ database for diverse spaces
4. Develop a predictive model to exploit the wider adoption of LCSs and inform decision making as part of green innovation.

The expected outputs would include a novel verified ventilation-pollution nexus framework in the form of a tool and 3-4 open-access research publications to support decision making in various aspects of ventilation/IAQ.

The PhD project will be supervised by Professor Prashant Kumar, who is a founding Director of the Global Centre for Clean Air Research (GCARE) and co-supervised by Dr Nick Martin (NPL). The candidate will have the opportunity to closely work with the diverse and multidisciplinary GCARE team and collaborate with various ongoing projects and beyond. They will work with the NPL and relevant GCARE collaborators, and will be an active member of the Guildford Living Lab.

This is a 4-year EPSRC i-CASE studentship project, commencing from 1 April 2023.

GCARE has a multidisciplinary team where researchers from all backgrounds are most welcome.

This project will be suitable for a UK or international student with a degree in any of the following:

• Science (atmospheric, physics, chemistry, mathematics, geoscience or material science)
• Engineering (environmental, civil, mechanical, chemical, computer)
• A closely related environmental/physical science discipline.

Some experience of physicochemical characterisation of particles and data analysis would be extremely useful.

English language requirement

IELTS minimum 6.5 overall with 6.0 in writing, or equivalent for international students.

GCARE is a multidisciplinary centre that was founded and directed by Professor Kumar. Its research is supported through a wide range of UKRI (e.g., NERC, EPSRC, ESRC, Innovate UK), H2020, industrial and international (e.g., FAPESP Brazil, QNRF Qatar) grants. With the mission to realise ‘clean air for all’, GCARE’s Air Quality Lab is well-equipped with a wide range of experimental facilities/equipment (e.g. ENVILUTION® chamber for sensor evaluation; portable PM and gaseous measurement equipment) and large body of research developed in the topic area to support the advertised project.

In addition, NPL has an advanced set of exposure chambers, besides field facilities, and leading expertise in the sensor area for evaluating the performance of the LCSs. These capabilities will be employed to support sensor validation measurements of CO₂, VOCs, relative, humidity, temperature and aerosols The facilities will be available to the student to characterise the performance of the sensor system being developed to enable limitations to be identified and addressed under controlled conditions. In addition, NPL’s technical contribution to expert committees such as Defra’s AQEG and the standardisation activities being carried out within CEN through the development of a Technical Specification for sensors, will enable the underpinning metrology to be applied to the system and methods being developed and to establish measurement uncertainty.