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Abstract:
Probing sources of atmospheric pollution in complex environments often leads to the measurement and sampling of a mixture of different aerosols due to fluctuations of the emissions or the atmospheric transport situation. Here, we present the AERosol and TRACe gas Collector (AERTRACC), a system for sampling various aerosols independently on separate sampling media, controlled by parallel online measurements of particle, trace gas, and meteorological variables, like particle number or mass concentration, particle composition, trace gas concentration as well as wind direction and speed. AERTRACC is incorporated into our mobile laboratory (MoLa) which houses online instruments measuring various physical and chemical aerosol properties as well as trace gas concentrations. Based on preparatory online measurements with the whole MoLa setup, suitable parameters measured by these instruments are used to define individual sampling conditions for each targeted aerosol using a dedicated software interface. Through evaluation of continuously online measured data with regard to the sampling conditions, the sampler automatically switches between sampling and non-sampling for each of up to four samples, which can be collected in parallel. Particle and gas phase of each aerosol, e.g. source emissions and background, are sampled onto separate filters (PM1 and PM10) and thermal desorption tubes, respectively. Information on chemical compounds in the sampled aerosol is accomplished by thermal desorption chemical ionization mass spectrometry (TD-CIMS). The design, operation, and characterization of the sampler are presented. For in-field validation, wood-fired pizza oven emissions were sampled as targeted emissions separately from ambient background. Results show that the combination of well-chosen sampling conditions allows more efficient and effective separation of source-related aerosols from the background, as seen by increases of particle number and mass concentration and concentration of organic aerosol types, with minimized loss of sampling time, compared to alternative sampling strategies.
