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Atmospheric Environment 41 (2007) 567–575

www.elsevier.com/locate/atmosenv

Comparison between sample-species specific uncertainties andestimated uncertainties for the source apportionment of the

speciation trends network data

Eugene Kim�, Philip K. Hopke

Center for Air Resource Engineering and Science, Clarkson University, Potsdam, NY 13699-5708, USA

Received 7 February 2006; received in revised form 6 July 2006; accepted 17 August 2006

Abstract

In order to use the US Environmental Protection Agency’s speciation trends networks (STN) data in source

apportionment studies with positive matrix factorization (PMF), uncertainties for each of the measured data points are

required. Since STN data were not accompanied by sample-species specific uncertainties (SSU) prior to July 2003, a

comprehensive set of fractional uncertainties was estimated by Kim et al. [2005. Estimation of organic carbon blank values

and error structures of the speciation trends network data for source apportionments. Journal of Air and Waste

Management Association 55, 1190–1199]. The objective of this study is to compare the use of the estimated fractional

uncertainties (EFU) for the source apportionment of PM2.5 (particulate matter less than 2.5 mm in aerodynamic diameter)

measured at the STN monitoring sites with the results obtained using SSU. Thus, the source apportionment of STN PM2.5

data were performed and their contributions were estimated through the application of PMF for two selected STN sites,

Elizabeth, NJ and Baltimore, MD with both SSU and EFU for the elements measured by X-ray fluorescence. The PMF

resolved factor profiles and contributions using EFU were similar to those using SSU at both monitoring sites. The

comparisons of normalized concentrations indicated that the STN SSU were not well estimated. This study supports the

use of EFU for the STN samples to provide useful error structure for the source apportionment studies of the STN data.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Uncertainty; Speciation trends network; Source apportionment; PM2.5

1. Introduction

Beginning in 2000, the US Environmental Protec-tion Agency (EPA) established the speciation trendsnetwork (STN) to characterize PM2.5 (particulatematter less than 2.5 mm in aerodynamic diameter)composition, to estimate long-term trends in con-

e front matter r 2006 Elsevier Ltd. All rights reserved

mosenv.2006.08.023

ing author. Tel.: +1315 268 3949;

6654.

ess: ugene@clarkson.edu (E. Kim).

stituents of PM2.5, and to support source apportion-ments for identification and quantification ofsources impacting areas out of attainment of thePM2.5 national ambient air quality standards(Federal Register, 1997). The STN used multipletypes of samples and multiple analytical labora-tories to produce the data. There were alsodifferences in the nature of the collected blanksand the treatment of the resulting data.

The application of one of the widely used sourceapportionment methods, positive matrix factoriza-

.

ARTICLE IN PRESSE. Kim, P.K. Hopke / Atmospheric Environment 41 (2007) 567–575568

tion (PMF, Paatero, 1997), depends on uncertain-ties for each of the measured data values. Theuncertainty estimation based on the analyticaluncertainties and laboratory method detection limit(MDL) values provides a useful tool to decrease theweight of missing and below MDL data in PMFapplication as well as reduce the influence of noisein the measured data. Because the STN data werenot accompanied by sample-species specific uncer-tainties (SSU) prior to July 2003, a comprehensiveset of uncertainties was estimated (Kim et al., 2005).To develop a comprehensive set of uncertaintiesthat could be used for PMF studies across the STN,general fractional uncertainties were estimated bycomparing the available measured concentrationsand their associated uncertainties and successfullyapplied to several STN data sets collected in mid-Atlantic US urban areas (Kim and Hopke, 2005).These fractional uncertainties were chosen toencompass most of the reported uncertainties andto provide the most reasonable PMF solution.

The objective of this study is to examine thesource apportionment results using SSU versusthose obtained with the estimated fractional un-certainties (EFU). In the present study, the majorsources of PM2.5 were identified and their contribu-tions were estimated through the application ofPMF with both SSU and EFU of the X-rayfluorescence (XRF) spectrometer elements for twoselected STN sites: One of the thirteen STN sitesconsidered in the previous uncertainty estimationstudy (Elizabeth, NJ) and one of the sites notconsidered in that study (Baltimore, MD). Theidentified source compositions and source contribu-tions were compared for each site.

2. Experiment

2.1. Data collection

STN PM2.5 samples were collected with SpiralAerosol Speciation Samplers (Met One Instruments,Grants Pass, OR) at the monitoring sites located inElizabeth, NJ and Baltimore, MD. The Elizabethmonitoring site (latitude: 40.6411, longitude:�74.2077) is located at a New Jersey TurnpikeInterchange and about 6 km southwest of theNewark Liberty International Airport. The sur-rounding area is industrial. Interstate highways 278and 95 are closely located to the north, northeast,and east of the site. The Baltimore monitoring site(latitude: 39.2889, longitude: �76.5544) is situated

at the Ponca St. site of the Baltimore Supersite in anindustrial area. Interstate highways 895 and 95 arelocated to the east of the site with Interstatehighway 895 being adjacent to the site. There is abus depot immediately west of the site. The tollbooths of a major tunnel are located to the south ofthe site.

STN PM2.5 samples were collected on Teflon,Nylon, and quartz filters. The Teflon filter was usedfor mass concentrations and for the elementalanalysis via energy dispersive XRF spectrometers.The Nylon filter is analyzed via ion chromatography(IC) for sulfate (SO4

2�), nitrate (NO3�), ammonium

(NH4+), sodium (Na+), and potassium (K+). The

quartz filter was analyzed for organic carbon (OC)and elemental carbon (EC) via national institute foroccupational safety and health/thermal opticaltransmittance (NIOSH/TOT) protocol (Birch andCary, 1996). A limited set of the XRF analyticaluncertainties and MDL values for Elizabeth andBaltimore sites for samples collected in 2002 wereacquired from EPA. The comparisons between thereported SSU and EFU for Al, Fe, Si, and Zn areshown in Fig. 1.

Subsequent discussions among the XRF labora-tories and EPA have identified that the differentlaboratories are not using a uniform approach toestimating the uncertainties. In some cases only thestatistical errors in the spectra are propagated intothe uncertainties while in other cases, a morecomplete set of estimated errors are propagatedinto an overall uncertainty. It is anticipated that aharmonized approach to uncertainty estimation andreporting will be developed and implemented.

Since the reported particulate OC concentrationswere not blank corrected (RTI, 2004) and carbondenuders that minimize positive sampling artifactcaused by adsorption of gaseous organic materials(Gundel et al. 1995; Pankow and Mader, 2001) werenot used in the sampling line with the quartz filter,there appears to be a positive artifact in the OCconcentrations measured by the STN samplers. Thetrip and field blank values were not reported in STNdata. Therefore, the integrated OC blank concentra-tions including trip and field blank as well as OCpositive artifact were estimated using the intercept ofthe regression of OC concentrations against PM2.5

(Tolocka et al., 2001; Kim et al., 2005; Kim andHopke, 2005). The estimated OC blank values were2.19mgm�3 at Elizabeth and 1.54mgm�3 at Baltimore,and these values were subtracted from the reportedSTN OC concentrations before further analyses.

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Fig. 1. The comparison between measured concentrations and associated analytical uncertainties. Solid lines indicate EFU.

Table 1

Summary of PM2.5 species mass concentrations

Elizabeth, NJ Baltimore, MD

Arithmetic mean (ngm�3) BDLa values (%) S/N ratiob Arithmetic mean (ngm�3) BDL values (%) S/N ratio

PM2.5 16,666.2 0.0 25.2 19,290.4 0.0 28.7

OC 3256.9 1.3 16.0 4362.4 0.0 21.5

EC 1784.1 0.0 8.2 1073.4 0.0 4.8

S 1507.2 0.0 152.6 1915.5 0.0 197.2

NH4+ 2037.3 0.0 155.1 2344.8 0.0 166.3

NO3� 1839.3 0.0 289.4 1889.0 0.0 272.9

Na 179.1 5.1 7.5 204.5 2.7 9.3

K 50.1 2.6 6.2 133.7 0.0 10.4

Al 52.4 24.4 7.2 44.0 16.4 5.1

Br — — — 4.6 16.4 2.3

Ca 39.7 1.3 10.0 76.5 0.0 17.7

Cl 58.3 16.7 15.2 54.2 21.2 18.5

Cr 2.5 43.6 2.4 — — —

Cu 5.9 5.1 3.7 5.4 13.0 3.3

Fe 125.2 0.0 66.3 138.2 0.0 72.1

Mn — — — 4.2 28.1 2.1

Ni 4.4 9.0 3.9 2.8 32.9 2.3

Si 111.8 2.6 15.0 129.8 0.0 14.0

V 7.7 16.7 5.2 4.5 31.5 2.5

Zn 14.0 1.3 9.4 25.7 0.0 21.2

Zr 4.6 39.7 4.1 — — —

aBelow method detection limit.bSignal/noise ratio.

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2.2. Source apportionment

PMF is a multivariate receptor model providingsource profiles and their contributions based on aweighted least square method that uses uncertaintiesfor each measurements as the data point weights(Paatero, 1997). PMF uses non-negativity con-straints on the factors to decrease the rotationalambiguity (Henry, 1987). Detailed explanations andequations are presented in previous publications(Kim et al., 2005, Kim and Hopke, 2005).

Based on the reported SSU or EFU, the inputdata and associated uncertainty matrices wereestimated. The measured concentrations belowMDL values were replaced by half of the MDLvalues and their errors were set at 5/6 of the MDLvalues. Missing concentrations were replaced by thegeometric mean of the concentrations and theiraccompanying errors were set at four times of this

Fig. 2. The comparison of the factor profiles deduced from PM2.5 sam

(white) (prediction7standard deviation).

geometric mean concentration (Polissar et al.,1998).

In this study, samples for which PM2.5 or OCdata were not available or below zero were excludedfrom the data sets. Samples on 7 July 2002 atElizabeth and 8 July 2002 at Baltimore wereaffected by a Canadian wildfire in which PM2.5

and OC mass concentrations were unusually high.These samples were excluded from the sourceapportionment study. Overall, four samples(4.9%) at Elizabeth and one sample (0.7%) atBaltimore were excluded in this study. IC SO4

2� wasexcluded from the analyses to prevent doublecounting of mass concentrations since XRF S andIC SO4

2� showed good correlations (slope ¼

3.170.05, r2¼ 0.98 for Elizabeth; slope ¼ 2.897

0.03, r2¼ 0.98 for Baltimore). The signal to noise

(S/N) ratio was calculated for each chemical speciesaccording to Paatero and Hopke (2003). For the

ples measured at the Elizabeth site using SSU (black) and EFU

ARTICLE IN PRESSE. Kim, P.K. Hopke / Atmospheric Environment 41 (2007) 567–575 571

comparison studies, only chemical species that haveS/N ratio above 2 (good variable) were used. Thus,a total of 77 samples and 19 species and 145 samplesand 19 species including PM2.5 mass concentrationscollected in 2002 were used for the Elizabeth andBaltimore sites, respectively. The PM2.5 massconcentration was included as an independentvariable in the PMF modeling to provide directmass apportionments (Kim et al., 2003). Summariesof PM2.5 speciation data for both sites are providedin Table 1.

Fig. 3. The comparison of the factor profiles deduced from PM2.5 sam

(white) (prediction7standard deviation).

3. Results and discussion

To determine the optimal solution, PMF was runwith different numbers of factors. For the compar-ison of PMF solutions, both rotational control andspecies down-weighting to find the physicallyreasonable sources were not used in this study(Paatero et al., 2002; Kim et al., 2003). For theElizabeth and Baltimore data, a seven-factor modeland a nine-factor model provided the most inter-pretable factor profiles, respectively. The quality of

ples measured at the Baltimore site using SSU (black) and EFU

ARTICLE IN PRESSE. Kim, P.K. Hopke / Atmospheric Environment 41 (2007) 567–575572

the PMF solutions were evaluated by comparing thereconstructed PM2.5 mass contributions (sum of thecontributions from PMF resolved factors) withmeasured PM2.5 mass concentrations. The regres-sion slopes and coefficients showed that the resolvedfactors well reproduced the measured values andaccount for most of the variation in the measuredPM2.5 concentrations (Elizabeth: slope ¼ 1.03,r2¼ 0.96 using either SSU or EFU; Baltimore:

slope ¼ 0.97 and 0.99, r2¼ 0.94 and 0.95 using SSU

and EFU, respectively).The PMF deduced factor profiles using SSU and

EFU for Elizabeth and Baltimore are compared inFigs. 2 and 3, respectively. As shown in thecomparison plots, PMF resolved factor profilesfrom the data with EFU are similar to thosefrom the SSU at both Elizabeth and Baltimoremonitoring sites. However, there are minor but cleardifferences in Al (Factor 4 in Fig. 2, Factor 8 inFig. 3), Ca (Factor 8 in Fig. 3), and Si (Factor 4in Fig. 3), although these minor differences in

Fig. 4. The comparisons of the normalized species concentrations (conc

the limit of detection were replaced by half of the reported detection li

limit values.

the factor profiles did not impact the factorseparation. When the factor profiles were com-pared, EFU provided more realistic factorprofiles (e.g., Al concentration was higher than Siconcentration in Factor 8 profile from SSU atBaltimore).

To further investigate, the normalized concentra-tions ( ¼ concentration/uncertainty) for Al, Si, andCa were compared between using SSU and EFU. Asshown in Fig. 4, similar to the example plots fromthe Interagency Monitoring of Protected VisualEnvironments (IMPROVE) data collected at Bri-gantine, NJ (Kim and Hopke, 2004), the normalizedAl, Si, and Ca concentrations using EFU were wellcorrelated. In contrast, those normalized concentra-tions using SSU did not show correlations indicat-ing that the STN SSU were not estimatedconsistently. The differences in the factor profilesbetween using SSU and EFU were likely caused bythe differences in SSU from the different labora-tories.

entration/uncertainty) between using SSU and EFU. Data below

mit values and their uncertainties were set at 5/6 of the detection

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Fig. 5. The comparisons of factor contributions deduced from PM2.5 samples measured at the Elizabeth site using SSU and EFU.

E. Kim, P.K. Hopke / Atmospheric Environment 41 (2007) 567–575 573

In Figs. 5 and 6, PMF resolved factor contribu-tions are compared between using SSU and EFUfor Elizabeth and Baltimore, respectively. Most ofthe factor contributions agree well between usingthe SSU and EFU except factors 5, 6, and 8from Baltimore data in which regression slopesare 1.41, 0.73, and 1.51, respectively. The Pearson

correlation coefficients of the factors 5, 6, and 8(r ¼ 0.89, 1.00, and 0.82, respectively) indicate thattheir time-series contributions are well correlated.Both comparisons of factor profiles and contribu-tions indicate that EFU provided similar sourceapportionment results to those using the existingSSU for most of the factors at two monitoring sites.

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Fig. 6. The comparisons of factor contributions deduced from PM2.5 samples measured at the Baltimore site using SSU and EFU.

E. Kim, P.K. Hopke / Atmospheric Environment 41 (2007) 567–575574

ARTICLE IN PRESSE. Kim, P.K. Hopke / Atmospheric Environment 41 (2007) 567–575 575

4. Conclusions

PMF was applied to two STN PM2.5 speciationdata collected at the Elizabeth, NJ and Baltimore,MD monitoring sites to examine the impact to thesource apportionment of using SSU versus EFU ofthe XRF spectrometer elements. The same numberof factors were identified from the data with SSUand EFU: seven factors at the Elizabeth site andnine factors at the Baltimore site.

PMF resolved factor profiles from the data withSSU and EFU were similar at two monitoring sites.The differences in the factor profiles were minor andthe most of time-series contributions agreed well.The comparisons of normalized concentrationsindicated that the SSU were not uniformly esti-mated and should probably not be used until revisedestimates using a uniform approach to the un-certainty are available. This study shows that theEFU for the STN samples provide useful uncer-tainty structure for STN source apportionmentstudies.

Acknowledgments

We thank Dr. Shelly Eberly at US EnvironmentalProtection Agency (EPA) for providing STNuncertainties and comments on this study. Thisresearch was supported in part by the New YorkSate Energy Research and Development Authorityunder Agreement no. 7919 and by the US EPAthrough science to achieve to results (STAR) Grantnumber RD83107801. Although the research de-scribed in this article has been funded by the USEPA, the views expressed herein are solely those ofthe authors and do not represent the official policiesor positions of the US EPA.

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