Daniel Draycott
Pronouns: He/Him/His
About
My research project
Novel Technologies in Aerosol Sensing for Air Quality AssessmentCurrent low-cost sensing methods allow measurement of mass concentration of harmful aerosols, but face
limitations such as inability to provide size-resolved measurements, sensitivity to harmless natural aerosols,
and interference from seasonal and climatic variations. The project aim is to explore enhancements of current
low-cost methodologies to produce a sensing array which discriminates between particle types. To begin with,
a literature review will be carried out to ascertain demand for aerosol identification and which common
aerosols are most valuable to distinguish. A comprehensive study of low-cost sensors will take place, to
identify suitable candidates. The selected technology, along with the enhancements will be integrated into a
sensing array. This array will be calibrated using instruments in the Enflo Meteorological Wind Tunnel at the
University of Surrey, to assess accuracy and reliability. Then, a machine-learning sensor-fusion approach will
combine data outputs and address complex interdependencies between sensor measurements. The final stage
will be to produce a compact and deployable prototype, which will enable data collection via field testing on
the University of Surrey grounds.
Supervisors
Current low-cost sensing methods allow measurement of mass concentration of harmful aerosols, but face
limitations such as inability to provide size-resolved measurements, sensitivity to harmless natural aerosols,
and interference from seasonal and climatic variations. The project aim is to explore enhancements of current
low-cost methodologies to produce a sensing array which discriminates between particle types. To begin with,
a literature review will be carried out to ascertain demand for aerosol identification and which common
aerosols are most valuable to distinguish. A comprehensive study of low-cost sensors will take place, to
identify suitable candidates. The selected technology, along with the enhancements will be integrated into a
sensing array. This array will be calibrated using instruments in the Enflo Meteorological Wind Tunnel at the
University of Surrey, to assess accuracy and reliability. Then, a machine-learning sensor-fusion approach will
combine data outputs and address complex interdependencies between sensor measurements. The final stage
will be to produce a compact and deployable prototype, which will enable data collection via field testing on
the University of Surrey grounds.