Application of PSCF and CPF to PMF-Modeled Sources of PM2.5 in Pittsburgh

• Published in: Aerosol science and technology Vol. 40; no. 10; pp. 952 - 961
• Main Authors: Pekney, Natalie J., Davidson, Cliff I., Zhou, Liming, Hopke, Philip K.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 01.10.2006
• ISSN: 0278-6826; 1521-7388
• DOI: 10.1080/02786820500543324

Ambient PM 2.5 composition data in Pittsburgh, PA have been used with Positive Matrix Factorization (PMF) to determine the major sources of PM 2.5 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.