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In addition to the focus on the Helmholtz International Laboratory aeroHEALTH (www.aeroHEALTH.eu), aeroHEALTH & Data Analysis seeks to address research objectives on the periphery of aeroHEALTH about analytical instrumentation development for aerosol characterisation, performing laboratory aerosol ageing, and supporting other aerosol research at CMA with advanced data analysis strategies and statistical modelling.

The German-Israeli Helmholtz International Laboratory aeroHEALTH strives to understand the biological and health effects of atmospheric aerosols mechanistically, combining information on primary emissions as well as secondary and ambient aerosols. Atmospheric processing (“ageing”) under atmospheric relevant conditions of biogenic and anthropogenic emissions are simulated on short and long-term scales to connect laboratory observations with the observed health impacts from field experiments. Comprehensive physical and chemical aerosol characterization is therefore a key contribution of TOPIC IX to unravel biological mechanisms in vitro and in vitro exposure studies.

The laboratory-ageing of aerosols is conducted at partner institutes, incl. University of Eastern Finland (Kuopio, Finland), Forschungszentrum Jülich (Germany) and Institute of Atmospheric Optics (Tomsk, Russia), with ageing simulation chambers (“smog chambers”) and oxidation flow reactors (OFR, “flowtubes”). In particular, the comparability of OFR-aged aerosol under harsh oxidation conditions and smog chambers as a more realistic atmospheric processing scenario is investigated by modelling approaches and validation by comprehensive chemical aerosol characterisation.

Aerosol analytical techniques are mainly based on, but not limited to, photoionisation time-of-flight mass spectrometry and hyphenated techniques. Special emphasis is put on the development of hyper-fast gas chromatography to enhance the chemical speciation in the online analysis of processes with dynamic emission profiles, such as solid fuel combustion emissions.

State-of-the-art analytical instrumentation for comprehensive aerosol analysis generates large data sets of “big p, little n”, i.e. many variables, but a low number of data points from individual samples/experiments. Therefore, the low number of degrees of freedom are inappropriate for many statistical techniques and require alternative approaches or extensive validation. Furthermore, aerosol composition often needs to be linked to associated effects, a total aerosol concentration or material properties, such as biological responses of cells after aerosol exposure, source apportionment or monitoring of industrial processes. Consequently, TOPIC IX intersects with all other TOPICs of CMA to develop solutions for problems in the fields of pattern recognition, prediction and classification models.