High Time Resolution Oxidative Potential Quantification

Exposure to air pollution is responsible for over 7 million premature deaths per year. Considering current World Health Organisation guideline limits, 99% of the world’s population are exposed to air that is damaging to their health. Particulate matter, microscopic particles suspended in the air, have been identified as the most damaging component of air pollution to health. Therefore, understanding and reducing the health impacts of these particles is a priority. Current policy aims to reduce the total mass concentration of these particles in the air. However, particles are a complex chemical mixture, emitted from a broad range of sources. When inhaled into the lung, these chemical constituents can engage in chemical reactions which cause harm. Therefore, if we can identify the particle components which drive this chemistry, and importantly their emission sources, we can develop more targeted and efficient polices to reduce their health impacts.

This project responds to this challenge by focusing on oxidative potential (OP), a biologically relevant measure of a particle’s capacity to drive oxidative stress, a key mechanism underpinning many cardiopulmonary and systemic health effects. OP offers a promising bridge between complex PM composition, emission sources, and adverse health outcomes, but current filter-based OP assays are low-throughput, low in time resolution and prone to artefacts, limiting their value for both research and policy.

To address this, we are developing and deploying the Online Oxidative Potential Ascorbic Acid Instrument, a next-generation platform for continuous, automated, high time resolution quantification of OP in real-world environments. This technology enables near real-time assessment of particle OP, opening new opportunities to understand how rapidly changing mixtures of particles from traffic, domestic heating, non-exhaust emissions and underground transport systems translate into health-relevant oxidative burden.

The project aims to:

• Advance OP measurement capability by rigorously characterising OOPAAI v3.0 with controlled, well-defined aerosols, to clarify how key PM components (e.g. secondary organic aerosol, redox-active metals, tyre/brake wear, underground particles) interact to generate OP.
• Characterise the spatiotemporal dynamics of OP by deploying OOPAAI v3.0 at urban supersites in London, combining high‑resolution OP data with detailed composition, source apportionment and machine learning to identify emission sources that disproportionately drive oxidative burden.
• Link OP to cellular responses using in vitro air–liquid interface exposures of lung epithelial cells to fresh and aged aerosols, relating oxidative stress, inflammation and cytotoxicity directly to concurrent OP measurements and specific chemical drivers.
• Assess the population-level health relevance of OP through time‑series epidemiological analyses of multi‑year OP datasets against short‑term mortality and morbidity, comparing OP with conventional mass-based metrics in multipollutant models to determine its value as a predictor of cardiovascular and respiratory risk.

By establishing a technically robust, biologically grounded framework for OP, this project aims to move beyond simple mass-based indicators towards a more nuanced understanding of PM toxicity. The resulting insights will enable the ranking of emission sources by health relevance, the identification of causal chemical drivers of harm, and the design of more targeted and cost-effective mitigation strategies to protect vulnerable populations and reduce the long-term health burden of air pollution.

PI Lead : Dr Steven Campbell