| Project Supervisor | Kayla Schulte |
| Institution & Department | Imperial College London – Environmental Research Group |
| Research Area | RA 4: Environment and Sustainability |
| Project Start Date | End of June 2026 |
| Project Duration | 3 months |
| Application Deadline | 4th June 2026 |
| Working Pattern | Full-time (5 days per week over 3 months) |
| Working Arrangements | Hybrid |
| The student may work on-site at Imperial College London White City Campus or remotely. They will attend bi-weekly check-ins with partners and a final in-person meeting. | |
| How to Apply | View Guidance Here |
Project Description
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Every weekday, thousands of journeys to and from school affect the streets of London. This project asks a simple but important question: what does the school run mean for the air people are breathing across London?
Air pollution is a major environmental risk to health, contributing to respiratory and cardiovascular disease and reinforcing health inequalities. Children are particularly vulnerable due to their developing lungs and frequent exposure to roadside environments near schools. While urban traffic is a well-established source of pollution, the specific contribution of school-related travel remains underexplored.
Recent analysis suggests that school-run traffic may lead to short-term, local increases in air pollution. However, there is limited systematic evidence on how widespread this effect is across London, or how it varies by neighbourhood and school type.
This project addresses that gap through a data-driven analysis of air quality and traffic patterns across Greater London (using data from the Breathe London network and partnering with Solve the School Run). The LISS DTP student will work with high-resolution air quality data, alongside transport, meteorological, and socio-demographic datasets.
The project focuses on three key questions:
• Do pollution levels near schools increase during term time compared to school holidays?
• Are these increases larger in areas where car-based school travel is more common?
• Do these patterns differ across neighbourhoods with different levels of deprivation?
Internship Details
The project is collaborative and policy-relevant, involving external partners (Solve the School Run, The Social Innovation Partnership, etc). It offers a supportive and flexible working environment.
Internship Structure
Weeks 1–4: Literature review, data familiarisation, script preparation.
Weeks 5–8: Quantitative analysis and modelling.
Weeks 9–12: Interpretation, reporting, and presentation.
Data sources:
• Air quality data: Real-time sensor data from Breathe London Communities (BLC) network and GLA Breathe London sensors.
• Traffic data: Vehicle flow and bus movement data from Transport for London (TfL), including time-series data aligned with school term calendars.
• Meteorological data: Weather variables (e.g. wind speed, temperature, precipitation) from public datasets for adjustment in modelling.
• Sociodemographic and health data: Publicly available sources (e.g. Index of Multiple Deprivation, Local Super Output Area (LSOA) health statistics).
• School travel data: Derived from Solve the School Run, which highlights where school-run driving is likely to be most concentrated.
• The project has already received ethical approval through Imperial College London.
Analysis and modelling approach:
• Site selection: Identify sites with Breathe London sensors near schools across London, covering a spectrum of deprivation levels and school types.
• Temporal analysis: Compare pollution levels during term time and school holidays, adjusting for weather conditions and other confounders.
• Traffic correlation: Model the relationship between TfL traffic and bus data and pollutant concentrations at nearby sensors.
• Spatial equity analysis: Examine whether pollution changes differ by income level or school type using regression and spatial modelling techniques.
• There are existing programming scripts from which the student can work with.
• Student will be working alongside researchers who carry out this type of analysis regularly, and will have support from experienced colleagues throughout
Expected outputs include:
• Quantitative assessment of pollution increases linked to school-term traffic activity.
• Comparative analysis across school types and income levels.
• Data visualisations suitable for public communication via Breathe London’s community platforms.
• Serve as a foundation for an annual “School Run and Air Quality” monitoring framework, using Breathe London and partner networks.
Anticipated Benefits for the Student
The student will gain experience in quantitative analysis, working with real-world datasets, data visualisation, publishing and communicating findings. They will also develop project management and research communication skills.
Skills, Experience and Knowledge Requirements
Essential Requirements:
- Quantitative analysis experience, R/Python
- Data handling
- Strong communication skills.
Desirable Requirements:
- Interest in environmental or urban research, spatial data, regression methods