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Comparison between sample-species specific uncertainties and estimated uncertainties for the source apportionment of the speciation trends network data
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Atmospheric Environment 41 (2007) 567–575 www.elsevier.com/locate/atmosenv
Comparison between sample-species specific uncertainties and estimated 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 Protection Agency (EPA) established the speciation trends network (STN) to characterize PM2.5 (particulate matter less than 2.5 mm in aerodynamic diameter) composition, to estimate long-term trends in conCorresponding author. Tel.: +1 315 268 3949; fax: +1 315 268 6654. E-mail address: [email protected] (E. Kim).
1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2006.08.023
stituents of PM2.5, and to support source apportionments for identification and quantification of sources impacting areas out of attainment of the PM2.5 national ambient air quality standards (Federal Register, 1997). The STN used multiple types of samples and multiple analytical laboratories to produce the data. There were also differences in the nature of the collected blanks and the treatment of the resulting data. The application of one of the widely used source apportionment methods, positive matrix factoriza-
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tion (PMF, Paatero, 1997), depends on uncertainties for each of the measured data values. The uncertainty estimation based on the analytical uncertainties and laboratory method detection limit (MDL) values provides a useful tool to decrease the weight of missing and below MDL data in PMF application as well as reduce the influence of noise in the measured data. Because the STN data were not accompanied by sample-species specific uncertainties (SSU) prior to July 2003, a comprehensive set of uncertainties was estimated (Kim et al., 2005). To develop a comprehensive set of uncertainties that could be used for PMF studies across the STN, general fractional uncertainties were estimated by comparing the available measured concentrations and their associated uncertainties and successfully applied to several STN data sets collected in midAtlantic US urban areas (Kim and Hopke, 2005). These fractional uncertainties were chosen to encompass most of the reported uncertainties and to provide the most reasonable PMF solution. The objective of this study is to examine the source apportionment results using SSU versus those obtained with the estimated fractional uncertainties (EFU). In the present study, the major sources of PM2.5 were identified and their contributions were estimated through the application of PMF with both SSU and EFU of the X-ray fluorescence (XRF) spectrometer elements for two selected STN sites: One of the thirteen STN sites considered in the previous uncertainty estimation study (Elizabeth, NJ) and one of the sites not considered in that study (Baltimore, MD). The identified source compositions and source contributions were compared for each site. 2. Experiment 2.1. Data collection STN PM2.5 samples were collected with Spiral Aerosol Speciation Samplers (Met One Instruments, Grants Pass, OR) at the monitoring sites located in Elizabeth, NJ and Baltimore, MD. The Elizabeth monitoring site (latitude: 40.6411, longitude: 74.2077) is located at a New Jersey Turnpike Interchange and about 6 km southwest of the Newark Liberty International Airport. The surrounding area is industrial. Interstate highways 278 and 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 an industrial area. Interstate highways 895 and 95 are located to the east of the site with Interstate highway 895 being adjacent to the site. There is a bus depot immediately west of the site. The toll booths of a major tunnel are located to the south of the site. STN PM2.5 samples were collected on Teflon, Nylon, and quartz filters. The Teflon filter was used for mass concentrations and for the elemental analysis via energy dispersive XRF spectrometers. The Nylon filter is analyzed via ion chromatography (IC) for sulfate (SO2 4 ), 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 for occupational safety and health/thermal optical transmittance (NIOSH/TOT) protocol (Birch and Cary, 1996). A limited set of the XRF analytical uncertainties and MDL values for Elizabeth and Baltimore sites for samples collected in 2002 were acquired from EPA. The comparisons between the reported SSU and EFU for Al, Fe, Si, and Zn are shown in Fig. 1. Subsequent discussions among the XRF laboratories and EPA have identified that the different laboratories are not using a uniform approach to estimating the uncertainties. In some cases only the statistical errors in the spectra are propagated into the uncertainties while in other cases, a more complete set of estimated errors are propagated into an overall uncertainty. It is anticipated that a harmonized approach to uncertainty estimation and reporting will be developed and implemented. Since the reported particulate OC concentrations were not blank corrected (RTI, 2004) and carbon denuders that minimize positive sampling artifact caused by adsorption of gaseous organic materials (Gundel et al. 1995; Pankow and Mader, 2001) were not used in the sampling line with the quartz filter, there appears to be a positive artifact in the OC concentrations measured by the STN samplers. The trip and field blank values were not reported in STN data. Therefore, the integrated OC blank concentrations including trip and field blank as well as OC positive artifact were estimated using the intercept of the regression of OC concentrations against PM2.5 (Tolocka et al., 2001; Kim et al., 2005; Kim and Hopke, 2005). The estimated OC blank values were 2.19 mg m3 at Elizabeth and 1.54 mg m3 at Baltimore, and these values were subtracted from the reported STN 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
PM2.5 OC EC S NH+ 4 NO 3 Na K Al Br Ca Cl Cr Cu Fe Mn Ni Si V Zn Zr a
Arithmetic mean (ng m3)
BDLa values (%)
S/N ratiob
Arithmetic mean (ng m3)
BDL values (%)
S/N ratio
16,666.2 3256.9 1784.1 1507.2 2037.3 1839.3 179.1 50.1 52.4 — 39.7 58.3 2.5 5.9 125.2 — 4.4 111.8 7.7 14.0 4.6
0.0 1.3 0.0 0.0 0.0 0.0 5.1 2.6 24.4 — 1.3 16.7 43.6 5.1 0.0 — 9.0 2.6 16.7 1.3 39.7
25.2 16.0 8.2 152.6 155.1 289.4 7.5 6.2 7.2 — 10.0 15.2 2.4 3.7 66.3 — 3.9 15.0 5.2 9.4 4.1
19,290.4 4362.4 1073.4 1915.5 2344.8 1889.0 204.5 133.7 44.0 4.6 76.5 54.2 — 5.4 138.2 4.2 2.8 129.8 4.5 25.7 —
0.0 0.0 0.0 0.0 0.0 0.0 2.7 0.0 16.4 16.4 0.0 21.2 — 13.0 0.0 28.1 32.9 0.0 31.5 0.0 —
28.7 21.5 4.8 197.2 166.3 272.9 9.3 10.4 5.1 2.3 17.7 18.5 — 3.3 72.1 2.1 2.3 14.0 2.5 21.2 —
Below method detection limit. Signal/noise ratio.
b
Baltimore, MD
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2.2. Source apportionment PMF is a multivariate receptor model providing source profiles and their contributions based on a weighted least square method that uses uncertainties for each measurements as the data point weights (Paatero, 1997). PMF uses non-negativity constraints on the factors to decrease the rotational ambiguity (Henry, 1987). Detailed explanations and equations are presented in previous publications (Kim et al., 2005, Kim and Hopke, 2005). Based on the reported SSU or EFU, the input data and associated uncertainty matrices were estimated. The measured concentrations below MDL values were replaced by half of the MDL values and their errors were set at 5/6 of the MDL values. Missing concentrations were replaced by the geometric mean of the concentrations and their accompanying errors were set at four times of this
geometric mean concentration (Polissar et al., 1998). In this study, samples for which PM2.5 or OC data were not available or below zero were excluded from the data sets. Samples on 7 July 2002 at Elizabeth and 8 July 2002 at Baltimore were affected by a Canadian wildfire in which PM2.5 and OC mass concentrations were unusually high. These samples were excluded from the source apportionment study. Overall, four samples (4.9%) at Elizabeth and one sample (0.7%) at Baltimore were excluded in this study. IC SO2 4 was excluded from the analyses to prevent double counting of mass concentrations since XRF S and IC SO2 showed good correlations (slope ¼ 4 3.170.05, r2 ¼ 0.98 for Elizabeth; slope ¼ 2.897 0.03, r2 ¼ 0.98 for Baltimore). The signal to noise (S/N) ratio was calculated for each chemical species according to Paatero and Hopke (2003). For the
Fig. 2. The comparison of the factor profiles deduced from PM2.5 samples measured at the Elizabeth site using SSU (black) and EFU (white) (prediction7standard deviation).
ARTICLE IN PRESS E. Kim, P.K. Hopke / Atmospheric Environment 41 (2007) 567–575
comparison studies, only chemical species that have S/N ratio above 2 (good variable) were used. Thus, a total of 77 samples and 19 species and 145 samples and 19 species including PM2.5 mass concentrations collected in 2002 were used for the Elizabeth and Baltimore sites, respectively. The PM2.5 mass concentration was included as an independent variable in the PMF modeling to provide direct mass apportionments (Kim et al., 2003). Summaries of PM2.5 speciation data for both sites are provided in Table 1.
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3. Results and discussion To determine the optimal solution, PMF was run with different numbers of factors. For the comparison of PMF solutions, both rotational control and species down-weighting to find the physically reasonable sources were not used in this study (Paatero et al., 2002; Kim et al., 2003). For the Elizabeth and Baltimore data, a seven-factor model and a nine-factor model provided the most interpretable factor profiles, respectively. The quality of
Fig. 3. The comparison of the factor profiles deduced from PM2.5 samples measured at the Baltimore site using SSU (black) and EFU (white) (prediction7standard deviation).
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the PMF solutions were evaluated by comparing the reconstructed PM2.5 mass contributions (sum of the contributions from PMF resolved factors) with measured PM2.5 mass concentrations. The regression slopes and coefficients showed that the resolved factors well reproduced the measured values and account for most of the variation in the measured PM2.5 concentrations (Elizabeth: slope ¼ 1.03, r2 ¼ 0.96 using either SSU or EFU; Baltimore: slope ¼ 0.97 and 0.99, r2 ¼ 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 in Figs. 2 and 3, respectively. As shown in the comparison plots, PMF resolved factor profiles from the data with EFU are similar to those from the SSU at both Elizabeth and Baltimore monitoring sites. However, there are minor but clear differences in Al (Factor 4 in Fig. 2, Factor 8 in Fig. 3), Ca (Factor 8 in Fig. 3), and Si (Factor 4 in Fig. 3), although these minor differences in
the factor profiles did not impact the factor separation. When the factor profiles were compared, EFU provided more realistic factor profiles (e.g., Al concentration was higher than Si concentration in Factor 8 profile from SSU at Baltimore). To further investigate, the normalized concentrations ( ¼ concentration/uncertainty) for Al, Si, and Ca were compared between using SSU and EFU. As shown in Fig. 4, similar to the example plots from the Interagency Monitoring of Protected Visual Environments (IMPROVE) data collected at Brigantine, NJ (Kim and Hopke, 2004), the normalized Al, Si, and Ca concentrations using EFU were well correlated. In contrast, those normalized concentrations using SSU did not show correlations indicating that the STN SSU were not estimated consistently. The differences in the factor profiles between using SSU and EFU were likely caused by the differences in SSU from the different laboratories.
Fig. 4. The comparisons of the normalized species concentrations (concentration/uncertainty) between using SSU and EFU. Data below the limit of detection were replaced by half of the reported detection limit values and their uncertainties were set at 5/6 of the detection limit values.
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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.
In Figs. 5 and 6, PMF resolved factor contributions are compared between using SSU and EFU for Elizabeth and Baltimore, respectively. Most of the factor contributions agree well between using the SSU and EFU except factors 5, 6, and 8 from Baltimore data in which regression slopes are 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 that their time-series contributions are well correlated. Both comparisons of factor profiles and contributions indicate that EFU provided similar source apportionment results to those using the existing SSU 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.
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4. Conclusions PMF was applied to two STN PM2.5 speciation data collected at the Elizabeth, NJ and Baltimore, MD monitoring sites to examine the impact to the source apportionment of using SSU versus EFU of the XRF spectrometer elements. The same number of factors were identified from the data with SSU and EFU: seven factors at the Elizabeth site and nine factors at the Baltimore site. PMF resolved factor profiles from the data with SSU and EFU were similar at two monitoring sites. The differences in the factor profiles were minor and the most of time-series contributions agreed well. The comparisons of normalized concentrations indicated that the SSU were not uniformly estimated and should probably not be used until revised estimates using a uniform approach to the uncertainty are available. This study shows that the EFU for the STN samples provide useful uncertainty structure for STN source apportionment studies. Acknowledgments We thank Dr. Shelly Eberly at US Environmental Protection Agency (EPA) for providing STN uncertainties and comments on this study. This research was supported in part by the New York Sate Energy Research and Development Authority under Agreement no. 7919 and by the US EPA through science to achieve to results (STAR) Grant number RD83107801. Although the research described in this article has been funded by the US EPA, the views expressed herein are solely those of the authors and do not represent the official policies or positions of the US EPA. References Birch, M.E., Cary, R.A., 1996. Elemental carbon-based method for monitoring occupational exposures to particulate diesel exhaust. Aerosol Science and Technology 25, 221–241.
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Federal Register, 1997. National Ambient Air Quality Standards for Particulate Matter. Federal Register 40 CFR Part 50 62 (138). Gundel, L.A., Lee, V.C., Mahanama, K.R.R., Stevens, R.K., Rau, J.A., 1995. Direct determination of the phase distributions of semi-volatile polycyclic aromatic hydrocarbons using annular denuders. Atmospheric Environment 29, 1719–1733. Henry, R.C., 1987. Current factor analysis models are ill-posed. Atmospheric Environment 21, 1815–1820. Kim, E., Hopke, P.K., 2004. Improving source identification of fine particles in a rural northeastern US area utilizing temperature resolved carbon fractions. Journal of Geophysical Research 109, D09204. Kim, E., Hopke, P.K., 2005. Identification of fine particle sources in mid-Atlantic US area. Water, Air, and Soil Pollution 168, 391–421. Kim, E., Hopke, P.K., Edgerton, E., 2003. Source identification of Atlanta aerosol by Positive Matrix Factorization. Journal of Air and Waste Management Association 53, 731–739. Kim, E., Hopke, P.K., Qin, Y., 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. Paatero, P., 1997. Least square formulation of robust nonnegative factor analysis. Chemometrics and Intelligent Laboratory Systems 37, 23–35. Paatero, P., Hopke, P.K., 2003. Discarding or downweighting high-noise variables in factor analytic models. Analytica Chimica Acta 490, 277–289. Paatero, P., Hopke, P.K., Song, X.H., Ramadan, Z., 2002. Understanding and controlling rotations in factor analytic models. Chemometrics and Intelligent Laboratory Systems 60, 253–264. Pankow, J.F., Mader, B.T., 2001. Gas/solid partitioning of semivolatile organic compounds (SOCs) to air filters. 3. An analysis of gas adsorption artifacts in measurements of atmospheric SOCs and organic carbon (OC) when using Teflon membrane filters and quartz fiber Filters. Environmental Science & Technology 35 (17), 3422–3432. Polissar, A.V., Hopke, P.K., Paatero, P., Malm, W.C., Sisler, J.F., 1998. Atmospheric aerosol over Alaska 2. Elemental composition and sources. Journal of Geophysical Research 103 (D15), 19045–19057. RTI, 2004. Quality assurance projects plan chemical speciation of PM2.5 filter samples /http://www.epa.gov/ttn/amtic/files/ ambient/pm25/spec/rtiqap.pdfS, Research Triangle Park, NC. Tolocka, M.P., Solomon, P.A., Mitchell, W., Norris, G.A., Gemmill, D.B., Wiener, R.W., Vanderpool, R.W., Homolya, J.B., Rice, J., 2001. East versus west in the US: chemical characteristics of PM2.5 during the winter of 1999. Aerosol Science and Technology 34, 88–96.
Atmospheric Environment 41 (2007) 567–575 www.elsevier.com/locate/atmosenv
Comparison between sample-species specific uncertainties and estimated 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 Protection Agency (EPA) established the speciation trends network (STN) to characterize PM2.5 (particulate matter less than 2.5 mm in aerodynamic diameter) composition, to estimate long-term trends in conCorresponding author. Tel.: +1 315 268 3949; fax: +1 315 268 6654. E-mail address: [email protected] (E. Kim).
1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2006.08.023
stituents of PM2.5, and to support source apportionments for identification and quantification of sources impacting areas out of attainment of the PM2.5 national ambient air quality standards (Federal Register, 1997). The STN used multiple types of samples and multiple analytical laboratories to produce the data. There were also differences in the nature of the collected blanks and the treatment of the resulting data. The application of one of the widely used source apportionment methods, positive matrix factoriza-
ARTICLE IN PRESS 568
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tion (PMF, Paatero, 1997), depends on uncertainties for each of the measured data values. The uncertainty estimation based on the analytical uncertainties and laboratory method detection limit (MDL) values provides a useful tool to decrease the weight of missing and below MDL data in PMF application as well as reduce the influence of noise in the measured data. Because the STN data were not accompanied by sample-species specific uncertainties (SSU) prior to July 2003, a comprehensive set of uncertainties was estimated (Kim et al., 2005). To develop a comprehensive set of uncertainties that could be used for PMF studies across the STN, general fractional uncertainties were estimated by comparing the available measured concentrations and their associated uncertainties and successfully applied to several STN data sets collected in midAtlantic US urban areas (Kim and Hopke, 2005). These fractional uncertainties were chosen to encompass most of the reported uncertainties and to provide the most reasonable PMF solution. The objective of this study is to examine the source apportionment results using SSU versus those obtained with the estimated fractional uncertainties (EFU). In the present study, the major sources of PM2.5 were identified and their contributions were estimated through the application of PMF with both SSU and EFU of the X-ray fluorescence (XRF) spectrometer elements for two selected STN sites: One of the thirteen STN sites considered in the previous uncertainty estimation study (Elizabeth, NJ) and one of the sites not considered in that study (Baltimore, MD). The identified source compositions and source contributions were compared for each site. 2. Experiment 2.1. Data collection STN PM2.5 samples were collected with Spiral Aerosol Speciation Samplers (Met One Instruments, Grants Pass, OR) at the monitoring sites located in Elizabeth, NJ and Baltimore, MD. The Elizabeth monitoring site (latitude: 40.6411, longitude: 74.2077) is located at a New Jersey Turnpike Interchange and about 6 km southwest of the Newark Liberty International Airport. The surrounding area is industrial. Interstate highways 278 and 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 an industrial area. Interstate highways 895 and 95 are located to the east of the site with Interstate highway 895 being adjacent to the site. There is a bus depot immediately west of the site. The toll booths of a major tunnel are located to the south of the site. STN PM2.5 samples were collected on Teflon, Nylon, and quartz filters. The Teflon filter was used for mass concentrations and for the elemental analysis via energy dispersive XRF spectrometers. The Nylon filter is analyzed via ion chromatography (IC) for sulfate (SO2 4 ), 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 for occupational safety and health/thermal optical transmittance (NIOSH/TOT) protocol (Birch and Cary, 1996). A limited set of the XRF analytical uncertainties and MDL values for Elizabeth and Baltimore sites for samples collected in 2002 were acquired from EPA. The comparisons between the reported SSU and EFU for Al, Fe, Si, and Zn are shown in Fig. 1. Subsequent discussions among the XRF laboratories and EPA have identified that the different laboratories are not using a uniform approach to estimating the uncertainties. In some cases only the statistical errors in the spectra are propagated into the uncertainties while in other cases, a more complete set of estimated errors are propagated into an overall uncertainty. It is anticipated that a harmonized approach to uncertainty estimation and reporting will be developed and implemented. Since the reported particulate OC concentrations were not blank corrected (RTI, 2004) and carbon denuders that minimize positive sampling artifact caused by adsorption of gaseous organic materials (Gundel et al. 1995; Pankow and Mader, 2001) were not used in the sampling line with the quartz filter, there appears to be a positive artifact in the OC concentrations measured by the STN samplers. The trip and field blank values were not reported in STN data. Therefore, the integrated OC blank concentrations including trip and field blank as well as OC positive artifact were estimated using the intercept of the regression of OC concentrations against PM2.5 (Tolocka et al., 2001; Kim et al., 2005; Kim and Hopke, 2005). The estimated OC blank values were 2.19 mg m3 at Elizabeth and 1.54 mg m3 at Baltimore, and these values were subtracted from the reported STN 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
PM2.5 OC EC S NH+ 4 NO 3 Na K Al Br Ca Cl Cr Cu Fe Mn Ni Si V Zn Zr a
Arithmetic mean (ng m3)
BDLa values (%)
S/N ratiob
Arithmetic mean (ng m3)
BDL values (%)
S/N ratio
16,666.2 3256.9 1784.1 1507.2 2037.3 1839.3 179.1 50.1 52.4 — 39.7 58.3 2.5 5.9 125.2 — 4.4 111.8 7.7 14.0 4.6
0.0 1.3 0.0 0.0 0.0 0.0 5.1 2.6 24.4 — 1.3 16.7 43.6 5.1 0.0 — 9.0 2.6 16.7 1.3 39.7
25.2 16.0 8.2 152.6 155.1 289.4 7.5 6.2 7.2 — 10.0 15.2 2.4 3.7 66.3 — 3.9 15.0 5.2 9.4 4.1
19,290.4 4362.4 1073.4 1915.5 2344.8 1889.0 204.5 133.7 44.0 4.6 76.5 54.2 — 5.4 138.2 4.2 2.8 129.8 4.5 25.7 —
0.0 0.0 0.0 0.0 0.0 0.0 2.7 0.0 16.4 16.4 0.0 21.2 — 13.0 0.0 28.1 32.9 0.0 31.5 0.0 —
28.7 21.5 4.8 197.2 166.3 272.9 9.3 10.4 5.1 2.3 17.7 18.5 — 3.3 72.1 2.1 2.3 14.0 2.5 21.2 —
Below method detection limit. Signal/noise ratio.
b
Baltimore, MD
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2.2. Source apportionment PMF is a multivariate receptor model providing source profiles and their contributions based on a weighted least square method that uses uncertainties for each measurements as the data point weights (Paatero, 1997). PMF uses non-negativity constraints on the factors to decrease the rotational ambiguity (Henry, 1987). Detailed explanations and equations are presented in previous publications (Kim et al., 2005, Kim and Hopke, 2005). Based on the reported SSU or EFU, the input data and associated uncertainty matrices were estimated. The measured concentrations below MDL values were replaced by half of the MDL values and their errors were set at 5/6 of the MDL values. Missing concentrations were replaced by the geometric mean of the concentrations and their accompanying errors were set at four times of this
geometric mean concentration (Polissar et al., 1998). In this study, samples for which PM2.5 or OC data were not available or below zero were excluded from the data sets. Samples on 7 July 2002 at Elizabeth and 8 July 2002 at Baltimore were affected by a Canadian wildfire in which PM2.5 and OC mass concentrations were unusually high. These samples were excluded from the source apportionment study. Overall, four samples (4.9%) at Elizabeth and one sample (0.7%) at Baltimore were excluded in this study. IC SO2 4 was excluded from the analyses to prevent double counting of mass concentrations since XRF S and IC SO2 showed good correlations (slope ¼ 4 3.170.05, r2 ¼ 0.98 for Elizabeth; slope ¼ 2.897 0.03, r2 ¼ 0.98 for Baltimore). The signal to noise (S/N) ratio was calculated for each chemical species according to Paatero and Hopke (2003). For the
Fig. 2. The comparison of the factor profiles deduced from PM2.5 samples measured at the Elizabeth site using SSU (black) and EFU (white) (prediction7standard deviation).
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comparison studies, only chemical species that have S/N ratio above 2 (good variable) were used. Thus, a total of 77 samples and 19 species and 145 samples and 19 species including PM2.5 mass concentrations collected in 2002 were used for the Elizabeth and Baltimore sites, respectively. The PM2.5 mass concentration was included as an independent variable in the PMF modeling to provide direct mass apportionments (Kim et al., 2003). Summaries of PM2.5 speciation data for both sites are provided in Table 1.
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3. Results and discussion To determine the optimal solution, PMF was run with different numbers of factors. For the comparison of PMF solutions, both rotational control and species down-weighting to find the physically reasonable sources were not used in this study (Paatero et al., 2002; Kim et al., 2003). For the Elizabeth and Baltimore data, a seven-factor model and a nine-factor model provided the most interpretable factor profiles, respectively. The quality of
Fig. 3. The comparison of the factor profiles deduced from PM2.5 samples measured at the Baltimore site using SSU (black) and EFU (white) (prediction7standard deviation).
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the PMF solutions were evaluated by comparing the reconstructed PM2.5 mass contributions (sum of the contributions from PMF resolved factors) with measured PM2.5 mass concentrations. The regression slopes and coefficients showed that the resolved factors well reproduced the measured values and account for most of the variation in the measured PM2.5 concentrations (Elizabeth: slope ¼ 1.03, r2 ¼ 0.96 using either SSU or EFU; Baltimore: slope ¼ 0.97 and 0.99, r2 ¼ 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 in Figs. 2 and 3, respectively. As shown in the comparison plots, PMF resolved factor profiles from the data with EFU are similar to those from the SSU at both Elizabeth and Baltimore monitoring sites. However, there are minor but clear differences in Al (Factor 4 in Fig. 2, Factor 8 in Fig. 3), Ca (Factor 8 in Fig. 3), and Si (Factor 4 in Fig. 3), although these minor differences in
the factor profiles did not impact the factor separation. When the factor profiles were compared, EFU provided more realistic factor profiles (e.g., Al concentration was higher than Si concentration in Factor 8 profile from SSU at Baltimore). To further investigate, the normalized concentrations ( ¼ concentration/uncertainty) for Al, Si, and Ca were compared between using SSU and EFU. As shown in Fig. 4, similar to the example plots from the Interagency Monitoring of Protected Visual Environments (IMPROVE) data collected at Brigantine, NJ (Kim and Hopke, 2004), the normalized Al, Si, and Ca concentrations using EFU were well correlated. In contrast, those normalized concentrations using SSU did not show correlations indicating that the STN SSU were not estimated consistently. The differences in the factor profiles between using SSU and EFU were likely caused by the differences in SSU from the different laboratories.
Fig. 4. The comparisons of the normalized species concentrations (concentration/uncertainty) between using SSU and EFU. Data below the limit of detection were replaced by half of the reported detection limit values and their uncertainties were set at 5/6 of the detection limit values.
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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.
In Figs. 5 and 6, PMF resolved factor contributions are compared between using SSU and EFU for Elizabeth and Baltimore, respectively. Most of the factor contributions agree well between using the SSU and EFU except factors 5, 6, and 8 from Baltimore data in which regression slopes are 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 that their time-series contributions are well correlated. Both comparisons of factor profiles and contributions indicate that EFU provided similar source apportionment results to those using the existing SSU 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.
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4. Conclusions PMF was applied to two STN PM2.5 speciation data collected at the Elizabeth, NJ and Baltimore, MD monitoring sites to examine the impact to the source apportionment of using SSU versus EFU of the XRF spectrometer elements. The same number of factors were identified from the data with SSU and EFU: seven factors at the Elizabeth site and nine factors at the Baltimore site. PMF resolved factor profiles from the data with SSU and EFU were similar at two monitoring sites. The differences in the factor profiles were minor and the most of time-series contributions agreed well. The comparisons of normalized concentrations indicated that the SSU were not uniformly estimated and should probably not be used until revised estimates using a uniform approach to the uncertainty are available. This study shows that the EFU for the STN samples provide useful uncertainty structure for STN source apportionment studies. Acknowledgments We thank Dr. Shelly Eberly at US Environmental Protection Agency (EPA) for providing STN uncertainties and comments on this study. This research was supported in part by the New York Sate Energy Research and Development Authority under Agreement no. 7919 and by the US EPA through science to achieve to results (STAR) Grant number RD83107801. Although the research described in this article has been funded by the US EPA, the views expressed herein are solely those of the authors and do not represent the official policies or positions of the US EPA. References Birch, M.E., Cary, R.A., 1996. Elemental carbon-based method for monitoring occupational exposures to particulate diesel exhaust. Aerosol Science and Technology 25, 221–241.
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