Research Assistant in Wearable Device Data Processing and Analysis

Project: Measuring Personal Air Quality Exposure During Active Travel in Sheffield

Air quality monitoring in urban environments relies predominantly on static, fixed location sensors, which are usually sparsely distributed across cities. It remains unclear whether air pollutant data from these sensors accurately reflect people’s exposure during their everyday activities such as walking and wheeling in urban spaces. 

This project will: (1) collect primary air quality (PM2.5) exposure data using wearable air quality monitoring devices carried by participants over a four week period during their active travel commutes (walking, running or wheeling); (2) analyse of personal exposure levels measured during movement; and (3) generating a proof-of-concept Clean Air Map.

In this role, you will be responsible for pre-processing including cleaning and validating raw sensor output, as well as analysis and visualisation of the data. You will work closely with Dr Harry Kai-Ho Chan.

Job Description:

• Pre-process and quality-control raw data collected from wearable air quality sensors, applying correction and cleaning to ensure accuracy and validity
• Conduct exploratory and statistical analysis of personal PM2.5 exposure data collected
• Develop clear, compelling data visualisations and map to communicate exposure patterns 
• Maintain well-documented data workflow and contribute to project reporting

Person Specification:

• Hands-on experience with data from portable or wearable air quality monitoring devices, such as HabitatMap AirBeam, Plume Labs Flow or equivalent
• Proficiency in data processing using Python or R, including working with sensor or environmental datasets
• Familiarity with data formats such as CSV/JSON, and data wrangling workflows
• Currently enrolled as a PhD student
• Strong written and verbal communication skills

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