Understanding the anthropogenic factors driving climate and ecosystem changes in the Western Arctic will require careful consideration of the sources and long term trends for anthropogenic pollutants. In this regard the database from the NOAA-CMDL Barrow Observatory is the longest and most complete record of direct pollutant measurements in the Arctic. The database includes observations of CO2, CH4, CO, O3 aerosol back scatter and number density, optical depth and black carbon. In some cases the data records go back more than 20 years. However to date relatively few analysis tools have been applied to this dataset. In this project we propose to use a variety of "data mining" strategies to gain new insight into the Barrow record. The techniques to be employed include a variety of statistical methods including simple 2 component correlations, trend analysis, and multi variate statistical tools such as principal component and factor analysis. The statistical results will be combined with isentropic back trajectories clustered on the basis of transport pathway. This will give important new insights into the contributions associated with various pollutant source regions. In addition we can use the clustered trajectories to evaluate the trends in pollutant concentrations associated with each individual source region, thus providing an independent check on the reported emission trends in these regions. The results of this study will likely prove to be valuable in future policy decisions concerning arctic pollution.