Identification of Sources of Phoenix Aerosol by Positive Matrix Factorization

Abstract

Chemical composition data for fine and coarse particles collected in Phoenix, AZ, were analyzed using positive matrix factorization (PMF). The objective was to identify the possible aerosol sources at the sampling site. PMF uses estimates of the error in the data to provide optimum data point scaling and permits a better treatment of missing and below-detection-limit values. It also applies nonnegativity constraints to the factors. Two sets of fine particle samples were collected by different samplers. Each of the resulting fine particle data sets was analyzed separately. For each fine particle data set, eight factors were obtained, identified as (1) biomass burning characterized by high concentrations of organic carbon (OC), elemental carbon (EC), and K; (2) wood burning with high concentrations of Na, K, OC, and EC; (3) motor vehicles with high concentrations of OC and EC; (4) nonferrous smelting process characterized by Cu, Zn, As, and Pb; (5) heavyduty diesel characterized by high EC, OC, and Mn; (6) sea-salt factor dominated by Na and Cl; (7) soil with high values for Al, Si, Ca, Ti, and Fe; and (8) secondary aerosol with SO 4 -2 and OC that may represent coal-fired power plant emissions.For the coarse particle samples, a five-factor model gave source profiles that are attributed to be (1) sea salt, (2) soil, (3) Fe source/motor vehicle, (4) construction (high Ca), and (5) coal-fired power plant. Regression of the PM mass against the factor scores was performed to estimate the mass contributions of the resolved sources. The major sources for the fine particles were motor vehicles, vegetation burning factors (biomass and wood burning), and coal-fired power plants. These sources contributed most of the fine aerosol mass by emitting carbonaceous IMPLICATIONS A new form of factor analysis, positive matrix factorization, has been applied to PM 10 and two separate sets of PM 2.5 aerosol composition data derived from samples collected in Phoenix, AZ. The results for the two sets of samples were quite comparable, and the results overall show a good resolution of the major sources of atmospheric PM.

Evaluation of elemental carbon as a marker for diesel particulate matter

“…Also presented in Table 5 is the source apportionment conducted in Phoenix over the period of 1995-1998 using a positive matrix factorization (Ramadan et al, 2000). This technique did not completely separate diesel-and gasolinepowered vehicles but can be used to estimate the contribution of motor vehicles to EC concentration, which is found to be very consistent with other studies listed in Table 5.…”

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

“…The ratio of EC to OC for this factor was 0.43 which is much higher than typically reported for gasoline engine emissions. This factor was a dominant contributor of Mn (42%), consistent with the use of Mn-based additives to enhance engine performance (Lewis et al, 2003;Ramadan et al, 2000).…”

Receptor model source attributions for Utah's Salt Lake City airshed and the impacts of wintertime secondary ammonium nitrate and ammonium chloride aerosol

“…Previous work has also linked similar factors to gasoline vehicles (Hwang and Hopke, 2007;Kim et al, 2004;Maykut et al, 2003;Hopke, 2004, 2006;Kim and Hopke, 2006;Kim and Hopke, 2008b). For diesel emissions, the relatively high proportions of Mn and Fe along with OC and EC are similar to findings in other major urban areas (Kim et al, 2004;Maykut et al, 2003;Ramadan et al, 2000;Kim and Hopke, 2008a;Wu et al, 2007;Amato and Hopke 2011;Kim et al, 2010;Lewis et al, 2003). The diesel factor showed a statistically significant higher average mean contribution on weekdays versus weekend (Student t-test 90% confidence level).…”