Using the complexity of secondary organic aerosols to understand their formation, ageing and transformation in three contrasting megacities

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Exposure to poor air quality is the top environmental risk factor of premature mortality globally. By far the most damaging air pollutant to health is particulate matter, with the greatest effects associated with particles less than 2.5 microns in diameter (PM2.5). In megacities, with large numbers of inhabitants/emissions sources, PM2.5 can often exceed recommended guideline values. The World Health Organization recommend an annual mean concentration of less than 10 micrograms/m3, as current evidence suggests lower health risks below this value. However, over 90 % of the worlds population live in regions where this value is exceeded, with London, Beijing and Delhi having values in 2016 ~ 1.5, 8 and 15 times higher. Secondary organic aerosol (SOA) can make up a significant fraction of PM2.5 in urban areas, and this may increase as many counties act to reduce emissions of ammonia and NOx. Current analytical approaches fail to provide sufficient chemical speciation to routinely apportion the contributing sources of SOA, limiting the opportunities to develop more targeted PM abatement strategies. High complexity approaches have revolutionised biomedicine, however uptake within the environmental community has been slower. In this project, we will embrace the atmosphere's complexity to make a step change in our understanding of the sources and transformation of SOA in urban atmospheres. This will be achieved through the combination of two state of the art research areas; high resolution mass spectrometry (MS) and machine learning. We will develop new tools to allow high throughput screening and quantification of SOA tracers in atmospheric aerosol samples. We will develop a mass spectral database of SOA tracer species built using a novel aerosol flow reactor designed at the University of York and supplemented with samples from 6 world-leading simulation chambers. The key here is to identify unique source specific tracer molecules that allow a direct link between the gas phase organic molecule that is emitted to the atmosphere and it's specific oxidation products that can be measured in ambient particles. The MS uses electrospray ionization, one of the most common approaches used in analytical labs throughout the world. This method is ideally suited to many SOA tracer molecules, however the ionization efficiency is strongly dependent on the chemical structure. We will carry out a systematic evaluation of the ionization efficiencies of a wide range of molecules with different functionalities to build a regression model to predict instrument response as a function of a molecular "fingerprint". We will combine these tools to carry out the most comprehensive quantification of SOA tracers in ambient aerosol and use machine learning methods to determine the factors that impact SOA concentration and estimate the relative strength of biogenic and anthropogenic sources of SOA to PM2.5. Our project will provide the first demonstration of such methods; the lack of sufficient chemical speciation and low time resolution in previous studies has so far restricted our proposed analysis. The timing of this project allows us to exploit recent investment in the NERC Air Pollution and Human Health program, providing access to an archive of PM2.5 samples and a wealth of co-located air quality data collected by leading groups from the UK, China and India. To communicate our results we will produce city specific policy reports, highlighting the main conclusions for each city, for use by government and regulatory agencies. This will be aided by a two month knowledge transfer placement in the Air Quality policy group at the Department for Environment, Food and Rural Affairs in London. This project will provide evidence of the key factors that control the amount of SOA in cities, using London, Beijing and Dehli as test cases. However, the methodology could be applied in cities across the globe to develop abatement policies that would target SOA reduction.