Identifying Pollution Source Locations Using Particle Back Trajectories and Filtered Kriging

• Activity Number: 132
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): William Christensen*+
• Companies: Brigham Young University
• Keywords: PSCF plot ; source apportionment ; Heterogeneous variance measurement-error-filtered kriging ; HFK ; spatial prediction

Abstract

For over 20 years, the Potential Source Contribution Function (PSCF) has been used by the aerosol research community to identify regions around an air-quality receptor location that are associated with high levels of a pollutant. PSCF uses particle back trajectories and measured levels of a pollutant in order to link high-measurement days with specific back trajectories. For a given rectangular area (s) on a map, the probability ?(s) that the area contains an important source of the pollutant of interest is estimated with p(s) = X(s) / n(s), where n(s) is the number of back trajectories that runs through that area and X(s) is the number of those back trajectories that were associated with a "high" day for the pollutant. Results are generally illustrated with a PSCF plot in which p(s) is plotted at each area (or pixel) on the map. In this talk, we use the basic notion of the PSCF plot and propose a modified potential source map that exploits both prior knowledge about ?(s) and a filtered kriging approach that accounts the heterogeneous measurement error variances. Results are illustrated using data from air quality data at the St. Louis Supersite.