What we do

We build exposure models that account for where people spend their time, what they're doing, and how pollution varies across those environments. This goes beyond fixed-site monitoring to capture the exposures that matter for health.

- Local authorities assessing the health burden of air pollution on residents - Campaign organisations building the evidence base for cleaner air zones or school streets - Researchers who need exposure estimates for epidemiological studies - Foundations and funders evaluating the impact of air quality interventions

Every project starts with your question, not our methods. We work backwards from the decision you need to make, then design the analysis to answer it. We draw on published peer-reviewed methods and open data wherever possible, and we're transparent about uncertainty.

We help organisations plan how to rigorously assess their air quality interventions — and then carry out that evaluation. Low Emission Zones, school streets, traffic management schemes — all need credible evidence of impact, and naive before-after comparisons aren't good enough.

Local authorities evaluating LEZs or Clean Air Zones. Transport agencies assessing traffic interventions. Campaign organisations building evidence cases. Anyone who needs to prove that an investment in clean air made a measurable difference.

RHEA, our intervention analysis tool, applies causal inference methods from econometrics to air quality monitoring data. Deweathering isolates the intervention signal from weather noise. Diurnal fingerprinting reveals whether effects follow the time-of-day patterns you'd expect from the mechanism. The result is evidence that distinguishes real effects from coincidence.

We build the data plumbing that makes air quality work possible — from ingestion pipelines that pull data from monitoring networks worldwide, to dashboards that make complex data legible, to open-source libraries that other researchers can build on.

Research groups needing reliable data access. Organisations building internal air quality capacity. Anyone who needs to get data from point A to point B without losing quality or context along the way.

Aeolus, our open-source Python toolkit, connects to 13+ monitoring networks and returns clean, standardised DataFrames. For bespoke work, we build pipelines and dashboards tailored to your data sources and reporting needs. Everything is reproducible, documented, and designed to outlast the engagement.

We turn monitoring data and research evidence into clear, defensible arguments for policy action. Parliamentary submissions, campaign briefings, community engagement materials — all grounded in peer-reviewed science and presented for decision-makers, not journals.

Campaign groups building evidence for clean air advocacy. Lawyers preparing environmental cases. Local authorities making the case for investment. Communities who want to understand and communicate what the data says about their air.

We work at the intersection of science and policy — translating complex evidence into language that moves decisions. Every claim is traceable to published evidence. Every number comes with appropriate uncertainty. The goal is to be useful to the people making choices, not to impress other scientists.