Acquisition and Representation of Knowledge for Atmospheric New Particle Formation

Sensors are used in environmental science to monitor an increasingly large multitude of properties of real world phenomena. An important scientific aim of such monitoring is more accurate and more complete understanding of phenomena, with respect to, e.g., their formation, development, or interactions. Properties and phenomena may be, for instance, mass or concentration and particulate matter or eutrophication, respectively. Typically, measurement data must undergo considerable processing in order to become useful to a scientific aim. We outline the architecture and implementation of an ontology-based environmental software system for the automated representation of knowledge for real world situations acquired from measurement data. We evaluate and discuss the system for the automated representation of knowledge for situations of atmospheric new particle formation. Such knowledge is acquired from measurement data for the particle size distribution of a polydisperse aerosol, as measured by a differential mobility particle sizer.