The EDAM project is a collaborative effort between computer scientists and 
environmental chemists at Carleton College and UW-Madison. The goal is to 
develop data mining techniques for advancing the state of the art in analyzing
 atmospheric aerosol datasets. The traditional approach for particle 
measurement, which is the collection of bulk samples of particulates on 
filters, is not adequate for studying particle dynamics and real-time 
correlations. This has led to the development of a new generation of real-time
 instruments that provide continuous or semi-continuous streams of data about 
certain aerosol properties. However, these instruments have added a significant
 level of complexity to atmospheric aerosol data, and dramatically increased 
the amounts of data to be collected, managed, and analyzed. We are 
investigating techniques for automatically labeling mass spectra from 
different kinds of aerosol mass spectrometers, and then analyzing and 
exploring the rich spatiotemporal information collected from multiple 
geographically distributed instruments. In this talk, I will present an 
overview of some novel data mining problems, describe some of the techniques 
we are developing to address them, and discuss the broader applicability of 
these techniques to problems from other domains.