Application of PSCF and CPF to PMF-modeled sources of PM2.5 in Pittsburgh

Natalie Pekney, Cliff Davidson, Liming Zhou, Philip Hopke

Research output: Contribution to journalArticlepeer-review

134 Scopus citations


Ambient PM2.5 composition data in Pittsburgh, PA have been used with Positive Matrix Factorization (PMF) to determine the major sources of PM2.5 sampled. This paper describes the use of the potential source contribution function (PSCF) with the PMF-modeled source contributions to locate the sources in a grid of 0.1° × 0.1° cells. The domain extends from the Pittsburgh Supersite at 40.44°N, 79.94°W over the range 35°-50° north latitude and 75°-90° west longitude. Six-hour back trajectories have been obtained from HYSPLIT four times each day for the 13 months of the study for use with PSCF. Using the results, higher probability locations are compared with known locations of specific source types, based on information from the EPA Toxic Release Inventory (TRI) and the EPA AIRS Database. PSCF results for several sources are compared to the conditional probability function (CPF) analysis, which uses 15-minute wind direction data to determine the most probable direction of a source. Using PSCF and CPF together aids in interpretation of potential source regions. The selenium and sulfate factor source locations are regional, while the lead, cadmium, and specialty steel factor source locations are local. The gallium-rich and Fe, Mn, and Zn factor source locations are potentially both local and regional. The nitrate, vehicle emissions and road dust, wood combustion, vegetative detritus and cooking, and crustal material factor CPF and PSCF results were inconclusive as sources of these factors exist in all directions from the site and therefore one would not expect a clear probability field in any one direction.

Original languageEnglish (US)
Pages (from-to)952-961
Number of pages10
JournalAerosol Science and Technology
Issue number10
StatePublished - Oct 1 2006
Externally publishedYes

ASJC Scopus subject areas

  • Environmental Chemistry
  • General Materials Science
  • Pollution


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