Source Apportionment of Polycyclic Aromatic Hydrocarbons in the Urban Atmosphere:  A Comparison of Three Methods

Abstract

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants in urban atmospheres. Several PAHs are known carcinogens or are the precursors to carcinogenic daughter compounds. Understanding the contributions of the various emission sources is critical to appropriately managing PAH levels in the environment. The sources of PAHs to ambient air in Baltimore, MD, were determined by using three source apportionment methods, principal component analysis with multiple linear regression, UNMIX, and positive matrix factorization. Determining the source apportionment through multiple techniques mitigates weaknesses in individual methods and strengthens the overlapping conclusions. Overall source contributions compare well among methods. Vehicles, both diesel and gasoline, contribute on average 16-26%, coal 28-36%, oil 15-23%, and wood/other having the greatest disparity of 23-35% of the total (gas- plus particle-phase) PAHs. Seasonal trends were found for both coal and oil. Coal was the dominate PAH source during the summer while oil dominated during the winter. Positive matrix factorization was the only method to segregate diesel from gasoline sources. These methods indicate the number and relative strength of PAH sources to the ambient urban atmosphere. As with all source apportionment techniques, these methods require the user to objectively interpret the resulting source profiles.

Assessment of aquatic wastewater pollution in a highly industrialized zone with sediment linear alkylbenzenes

“…Several previous studies [30][31][32] have reported the utility of multivariate linear regression to apportion sources of organic pollutants in various environmental matrices. The framework (Fig.…”

Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM2.5 in Guangzhou, southern China, using positive matrix factorization (PMF)

“…Chemical mass balance (CMB) model has been widely used to estimate the source contributions to ambient pollutants (Larsen and Baker 2003;Lee et al 2008). However, there are certain limitations in this model such as requiring the input of unique profile for each major source (Robinson et al 2006;Shrivastava et al 2007;van Drooge and Ballesta 2009).…”

UNMIX modeling of ambient PM2.5 near an interstate highway in Cincinnati, OH, USA

“…Several factor-based approaches have been used, including principal component analysis (PCA) followed by multiple linear regression (MLR) (Larsen and Baker, 2003), positive matrix factorization (PMF) (Paatero and Tapper, 1994) and UNMIX (Henry, 2000). UNMIX has been recently utilized to establish ambient aerosol sources and was reported to provide good agreement with predictions of other multivariate receptor models (e.g.…”

“…UNMIX has been recently utilized to establish ambient aerosol sources and was reported to provide good agreement with predictions of other multivariate receptor models (e.g. PCA/MLR, PMF), especially in identifying the dominant source categories (Henry, 2000;Lewis et al, 2003;Larsen and Baker, 2003;Maykut et al, 2003;Mukerjee et al, 2004). A summary of various recent UNMIX applications are outlined in Table 1.…”