Comparison of Source Apportionments of Fine Particulate Matter at Two San Jose Speciation Trends Network Sites

Abstract

In this study, the chemical composition of fine particulate matter samples collected at U.S. Environmental Protection Agency Speciation Trends Network sites in San Jose, CA, from February 2000 to February 2005 were analyzed. A San Jose site was initially established at 4th Street and then subsequently moved to Jackson Street in mid-2002. These sites are ϳ1 km apart. There were no known major changes in the nature of the sources in the area over this period. The study used positive matrix factorization model to extract the source profiles and their mass contributions and to compare the results for the congruence of the source apportionments between these two nearby sites. In the case of the 4th Street site, the average mass was apportioned to wood combustion (32.1 Ϯ 2.5%), secondary nitrate (22.3 Ϯ 2%), secondary sulfate (10.7 Ϯ 0.6%), fresh sea salt (7.7 Ϯ 0.9%), gasoline vehicles (7.3 Ϯ 0.5%), aged sea salt (6.8 Ϯ 0.4%), road dust (6.7 Ϯ 0.7%), diesel emissions (3.9 Ϯ 0.3%), and a Ni-related industrial source (2.5 Ϯ 0.4%). At the Jackson Street site, the average mass was apportioned to wood combustion (33.6 Ϯ 2.6%), secondary nitrate (20.3 Ϯ 1.9%), secondary sulfate (13.9 Ϯ 0.9%), aged sea salt (12.4 Ϯ 0.7%), gasoline vehicle (8.3 Ϯ 0.6%), fresh sea salt (5.3 Ϯ 0.5%), diesel emission (3.2 Ϯ 0.3%), road dust (1.9 Ϯ 0.1%), and Ni-related industrial source (1.3 Ϯ 0.1%). Conditional probability function analysis was used to help identify local sources. These results suggested that moving the sampling site a short distance had little effect on the nature of the resolved source types although some differences in their quantitative impacts were obtained in the positive matrix factorization analyses.

Fine Particulate Matter Source Apportionment for the Chemical Speciation Trends Network Site at Birmingham, Alabama, Using Positive Matrix Factorization

“…This problem has been recently addressed with mixed success in numerous applications to study urban air quality and human exposure issues. 13,22,45,[53][54][55][56][57][58][59][60] Three fundamental assumptions underlie all the receptor models: (1) PM compounds are present in different proportions in different source emissions; (2) these proportions remain relatively constant for each source type over the evaluated measurement period; and (3) changes in these proportions between source and receptor, i.e., the chemical transformation during atmospheric transport and dispersion of emissions, are negligible or can be approximated. Here, we employed EPA version 1.1 of the PMF receptor model, solving the multilinear regressions using constrained, weighted, least-squares.…”

Comparison of Source Apportionment of PM2.5 Using PMF2 and EPA PMF Version 2

“…Although the main species in sea salt are known to be Na, Cl, SO 4 2-, K and Ca (Hopke, 1985), only Na showed a high contribution in 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 Sampling date association with SO 4 2-and NO 3 -. The Cl was depleted because NaCl was converted into Na 2 SO 4 and NaNO 3 as a result of reactions of NaCl with gaseous H 2 SO 4 and gaseous HNO 3 , respectively (Hwang and Hopke, 2006). The higher contributions of the aged sea salt are presumably from the Atlantic Ocean.…”

Source Identification and Estimation of Source Apportionment for Ambient PM10 in Seoul, Korea

“…In this study, the measured concentrations below the method detection limit (MDL) values were replaced by half of the MDL values and their uncertainties were set at five sixths of the MDL values. Missing concentrations were replaced by the geometric mean of the concentrations and their accompanying uncertainties were set at four times the geometric mean concentration (Hwang et al, 2008;Hwang and Hopke, 2006;Polissar et al, 1998).…”