Design of a mobile aerosol research laboratory and data processing tools for effective stationary and mobile field measurements - F. Drewnick; T. Böttger; S.-L. von der Weiden-Reinmüller; S. R. Zorn; T. Klimach; J. Schneider; S. Borrmann (Atmospheric Measurement Techniques, 2012)
A compact mobile aerosol research laboratory (MoLa) for stationary and mobile measurements of aerosol and trace gas characteristics was developed at the Max Planck Institute for Chemistry (MPIC) in Mainz, Germany. Major efforts were made to design an aerosol inlet system which is optimized and characterised for both, stationary and mobile measurements using a particle loss modelling approach. The instrumentation on board allows the determination of a multitude of physical and chemical aerosol parameters, for example particle number and mass concentration (PM1/2.5/10), particle size distributions in the diameter range 6 nm up to 32 μm, and chemical composition of the sub-micron aerosol. Furthermore, trace gas concentrations of O3, SO2, CO, CO2, NO, NO2 and water vapour as well as meteorological parameters like temperature, relative humidity, pressure, wind, solar radiation and precipitation are measured together with various housekeeping parameters. All instruments collect data with high time resolution in the second to minute-range. The measurement platform, as well as data acquisition and handling tools, are optimized for efficient application to various measurement settings. The mobile laboratory is designed to be used for mobile investigation of anthropogenically influenced environments. Possible applications include pollutant mapping, chasing of mobile sources or Lagrangian-type measurements in emission plumes, but also stationary measurements with possible frequent position changes and a well-characterised instrument setup. In addition to the design and features of the mobile laboratory, its inlet system and instrumentation as well as examples of applications of this platform are presented. Challenges associated with such measurements and approaches to extract the desired information from the mobile datasets are discussed.