Identification of anthropogenic marker compounds through LC/HRMS analysis of atmospheric organic aerosol from the ACROSS dataset: Development and method validation

Comprehensive untargeted analysis of atmospheric organic aerosol filter samples can provide detailed insight into the history of air masses and atmospheric conditions. Due to the large amount of data, automatic analysis must be used in order to interpret and connect individual datapoints across multiple measurements in a given dataset.

This poster presents results of untargeted atmospheric organic aerosol analysis with LC/HRMS. The samples were acquired during the ACROSS campaign in summer of 2022 in the Rambouillet forest in central France (south-west of Paris). Through automated analysis, several individual compounds and compound classes could be identified as tracers or markers for certain sources and events.

In order to identify anthropogenic marker compounds, the influence of wind direction has been investigated in this work. Samples were divided into two different subsets. Samples where the primary wind direction was coming over central Paris and the Seine river were in subset A (higher anthropogenic influence estimated), and samples where the primary wind direction was coming over rural France were in subset B (lower anthropogenic influence estimated). By applying K-Means clustering, a total of 30 individual compounds were found to be indicative of samples from subset A. As expected, the compounds were mainly nitrophenols and a whole compound class containing one sulphur atom (CnH2nSO5). Those compounds have also been found in earlier studies in anthropogenically influenced aerosol samples (Wang et al. 2021). Here, no information about the structure could be provided, so in this study, an inhouse developed algorithm was used to identify fragments of the individual compounds from FullMS/AIF measurements to yield structural information. That information indicated that the compounds do not possess a sulphate or sulfonate group but are rather a thiol. The identified compounds can be used as tracers for anthropogenic influences in future studies.

The influence of the diurnal cycle on the concentration of individual compounds was also studied, and by applying the same methods, compounds specific for night and day could be identified. Note that other factors (e.g. the diurnal cycle) can just as easily be investigated, completely depending on the desired information.

The presented analysis methods hugely benefit from the availability of large, continuous and high-quality datasets with accurate and detailed metadata. This ensures that smaller contributing factors can be identified with statistical significance. Although the provided dataset that was acquired during the ACROSS campaign is comparatively large, datasets acquired at different locations, during different seasons and with an increased time resolution might be beneficial for identifying more contributing factors with a higher statistical significance. We therefore aim to set up an easy-to-operate and low maintenance aerosol measurement station. This station will also be used to allow students to get hands on experience in atmospheric aerosol analysis and analytical techniques in general.

This poster presents the results and findings of the analysis of the ACROSS samples regarding aerosol marker classes for specific sources and atmospheric events. We thank Vincent Michoud and Chris Cantrellas co-organizers of the ACROSS 2022 campaign.