A trajectory-clustering-correlation methodology for examining the long-range transport of air pollutants

Abstract

We present a robust methodology for examining the relationship between synoptic-scale atmospheric transport patterns and pollutant concentration levels observed at a site. Our approach entails calculating a large number of back-trajectories from the observational site over a long period of time and subjecting them to cluster analysis. The short-term component (weather-related variations) of the pollutant concentration time-series data is segregated according to the back-trajectory clusters. Non-parametric statistics are then used to test for “significant” differences in the chemical composition of pollutant data associated with each cluster. Additional information about the spatial and temporal scales of pollutant transport is obtained from the time-lagged inter-site correlation analysis of ozone for a specific cluster. To illustrate the application of this methodology, we examined 5 yr long time-series data of ozone concentrations measured at Whiteface Mountain, NY, Cliffside Park, NJ, and Quabbin Summit, MA. The results provide evidence of ozone transport to these sites, revealing the spatial and temporal scales involved in the transport.