# SOURCE APPORTIONMENT OF PM2.5 IN GEORGIA
Receptor models are useful tools for identifying the source contributing to measured levels of pollutants. One of the most recently used source -apportionment models in recent years is the Positive Matrix Factorization (PMF) model. PMF is a factor analytic method that distinguishes correlation patterns among speciated PM 2.5 

# measurements in a given location. As such, it does not rely on a prioriknowledge concerning chemical compos ition of sources to generate source contribution estimates. A limitation of using factor analysis methods is the inability to directly link observed factors in the analysis with actua l sources. Since these methods are based on statistical patterns of correlations, rather thanempirical chemical source profiles, naming the factors as specific sources is somewhat subjectiv e. However, based on data on typical sourcecompositions for major sources, as well as resear cher experience, very useful information on PM 2.5 

# contributions can be derived from PMF. Th ese include both total mass contributions for given factors/sources, as well as temporal patterns in such contributions, allowing one to investigate the seasonal and w eekly patterns, as well as specific days of interest.
GA EPD applied the EPA version of Positive Ma trix Factorization (E PA-PMF1.1) in a novel way, using all available STN data for Georgia in one analysis. This allows for a more accurate spatial comparison of source contri butions throughout the state, avoi ding potential issues, such as breakdown of OC between SOA an d primary OC sources, leading, for example, to erroneous conclusions about spatial patterns biomass burning impacts (the inclusion of more data in the analysis assists in the breakdow n between primary and secondary OC). Such an approach would be valid based on the rationale that many of th e factors affecting PM2.5 levels are regional in nature, such as sulfate, nitrate, SOA and biom ass-burning (hence, all sites are affected by the same sources), and for other sources, such as mobile sources, we do not expect a major difference in composition between different sites. However, some bias may be introduced for unique local sources of primary PM2.5, and these may require further investigation using sitespecific PMF analyses. However, a comparison betw een the combined analysis and site-specific ones show fairly minor differences between the tw o approaches (the main differences are related to breakdown of OC mass between primary and s econdary factors, as expected, and emphasize the advantage of the combined analysis approach).

Here, we will focus on the four STN sites w ithin the GA PM2.5 NAA: Atlanta, Chattanooga, Rome (Floyd county) and Macon. Unfortunately, the Chattanooga STN site (on the TN side, 47- 065-4002) was not included in the combined spatial PMF analysis, and results for that site are presented for the stand-alone analysis. By the time of the analyses, 468 samples were available for Atlanta (March 01 – May 05); 269 for Chattanooga (January 02 – December 06), 167 for Rome (May 02 – May 05) and 162 for Macon (J une 02 – May 05). Sample uncertainties were developed following the approach outline in Kim et al., (2004, JAWMA 2003), incorporating both analytical uncertainties and detection limits for each species. Samples to be included in the analysis, and which species should be given more weight in the optimization process within PMF were determined based on the aver age signal-to-noise ratio (average of ratios of concentrationto-uncertainty for each species), and the percent of samples below detection limit (Table 1). In general, species with relatively high S/N ratios were chosen as “Strong” species is PMF (given more weight compared to “Weak” species), howe ver, user judgment was used for species that serve as unique traces (such as Ni, V for oil burning) or species that were relatively elevated at

# specific site/s (such as Na + 

# , Ni, V and the coastal Savannah site, not shown here; Cu at the Coffee county site, not shown here). OC levels we re blank-corrected using the intercept point of the regression between PM 2.5 

# and OC, for each site individuall y. Blank values, however, were applied to PMF results post analys is, since when blank corrected OC values were analyzed in PMF directly, all carbon was apportioned to an “OC” factor, a nd was not broken into individual categories (mobile source, biomass burning etc.).

Table 1 : Species included in the PMF analysis, along with average concentra tions (for each site separately), % of samples below detection limit (BDL ), signal to noise ratio (S/N, defined here as the average of concentration-to-uncertainty ratios per species), and classification within PMF (%BDL, S/N and classification reported for the enti re dataset used in the combined analysis). Species Atlanta avg. (  g/m 3 

# ) Floyd co. avg. (  g/m 3 

# ) Macon avg. (  g/m 3 

# ) % BDL S/N Classification


# SO4 -2 

# 4.5E+00 4.9E+00 4.6E+00 0.0 14.1 Strong NO3 - 

# 8.3E-01 8.9E-01 6.6E-01 0.0 13.2 Strong NH4 + 

# 1.3E+00 1.5E+00 1.3E+00 0.4 13.2 Strong

EC 9.6E-01 5.1E-01 6.7E-01 2.0 7.63 Strong OC 5.1E+00 5.0E+00 5.5E+00 0.1 12.9 Strong Al 2.3E-02 5.1E-02 7.2E-02 41 3.72 Weak As 1.4E-03 1.4E-03 1.1E-03 45 2.38 Bad Br 3.3E-03 3.5E-03 3.6E-03 4.5 7.59 Weak Ca 3.2E-02 1.3E-01 3.2E-01 0.8 7.12 Strong Cu 4.6E-03 2.1E-03 1.5E-03 29 5.41 Strong Fe 8.1E-02 8.8E-02 8.1E-02 0.1 17.7 Strong K 5.8E-02 7.5E-02 8.0E-02 0.3 8.14 Strong K + 

# 3.5E-02 3.5E-02 3.7E-02 60 3.40 Weak Mn 1.4E-03 1.9E-03 1.8E-03 37 3.53 Weak Na 4.8E-02 6.9E-02 6.1E-02 54 2.48 Bad Na + 

# 1.6E-01 1.5E-01 1.2E-01 16 6.02 Strong Ni 4.2E-04 4.0E-04 5.4E-04 80 1.50 Strong Pb 2.7E-03 2.6E-03 2.9E-03 50 2.67 Weak Se 1.2E-03 1.4E-03 1.3E-03 60 1.94 Weak Si 9.8E-02 2.0E-01 2.2E-01 2.3 7.75 Strong Ti 5.2E-03 9.6E-03 9.1E-03 16 6.15 Weak V 1.0E-03 1.2E-03 1.2E-03 59 2.46 Strong Zn 8.9E-03 8.8E-03 9.3E-03 1.5 10.3 Strong PM 2.5 

# 1.55E+01 1.70E+01 1.64E+01 0.1 12.8 Weak * - Ag, Au, Ba, Cd, Ce, Cl, Co, Cr, Cs, Eu, Ga, Hf, Hg, In, Ir, La, Mo, Nb, P, Rb, Sb, Sc, Sm, Sn, Sr, Ta, Tb, W, Y, and Zr were excluded from the analysis (classified as “Bad”), due to the majority of samples being below the MDL



# Using the species in Table 1, several solutions based on a varying numb er of factors were investigated. A combination of twel ve factors was chosen, as these factors seem to best represent identifiable source categories. The identification of sources was conducted based both on the composition of factors (Figure 1) and their correl ation with the ambient concentrations of trace

# metals, carbon and major ions (Table 2). Determination of the total PM 2.5 

# mass associated with each factor was carried out using several methods, such as regressi on of the factors against total PM2.5 mass, inclusion of PM2.5 as a weak variab le in PMF, or by summation of the various components in the obtained fact ors (using an OM/OC ratios a nd metal oxides mass for crustal elements). While each of these approached is va lid, we focused on the “summation” approach, as it seemed most physically meaningful in terms of mass conservation (the other two approaches may assign high amounts of mass to factors containing only trace elements/mass). The drawback of the summation approach is the uncertainty re lated to the choice of the OM/OC ratio. One can chose the commonly used ratio of 1.4, however recent evidence sugg est this ratio to be low, especially for biogenic aerosol. We used an optimization mechanism to minimize the error between factor-reconstructed a nd measured PM2.5 levels, by changing the OM/OC ratio for the three carbon containing factors: mobile-sources, biomass burni ng, and SOA. A combination of 1.2, 2.1 and 1.4, respectively, yielded the optimal solution.

Mobile sources

0.00001 0.0001 0.001 0.01 0.1 1

Al NH 4 + B r Ca EC O C Cu F e Pb Mn Ni N O 3- K Se Si N A+ SO4-- Ti V Zn Biomass Burning

0.00001 0.0001 0.001 0.01 0.1 1

Al NH4+ B r Ca EC OC Cu Fe Pb M n Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Soil

0.00001 0.0001 0.001 0.01 0.1 1

Al N H 4+ B r Ca EC OC Cu Fe Pb M n Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Lime/Minerals

0.00001 0.0001 0.001 0.01 0.1 1

Al N H 4+ B r Ca EC OC Cu Fe Pb M n Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Na-rich

0.00001 0.0001 0.001 0.01 0.1 1

A

l NH4+ Br Ca E C O

C Cu Fe Pb Mn N i N

O 3 - K Se S i NA+ S O 4- - Ti V Z n Oil burning

0.00001 0.0001 0.001 0.01 0.1 1

Al NH 4 + B r Ca EC OC Cu Fe Pb Mn Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Resuspended road dust

0.00001 0.0001 0.001 0.01 0.1 1

Al N H4 + Br Ca EC O C Cu F e Pb M n Ni N O 3- K Se Si N A+ SO4-- Ti V Zn Cu-rich

0.00001 0.0001 0.001 0.01 0.1 1

Al N H 4+ Br Ca EC OC Cu Fe Pb Mn Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Zn-rich

0.00001 0.0001 0.001 0.01 0.1 1

Al NH4+ Br Ca EC OC Cu Fe Pb Mn Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Sec. Sulfate

0.00001 0.0001 0.001 0.01 0.1 1

Al NH4+ Br Ca EC OC Cu Fe Pb Mn Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Sec. Nitrate

0.00001 0.0001 0.001 0.01 0.1 1

A

l N

H 4+ B r Ca E C O

C Cu Fe Pb M n N

i N

O 3 - K Se S i NA+ S O 4- - Ti V Z n Sec. Organic Aerosol

0.00001 0.0001 0.001 0.01 0.1 1

Al N H4 + Br Ca EC OC Cu Fe Pb M n Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Mobile sources

0.00001 0.0001 0.001 0.01 0.1 1

Al NH 4 + B r Ca EC O C Cu F e Pb Mn Ni N O 3- K Se Si N A+ SO4-- Ti V Zn Biomass Burning

0.00001 0.0001 0.001 0.01 0.1 1

Al NH4+ B r Ca EC OC Cu Fe Pb M n Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Soil

0.00001 0.0001 0.001 0.01 0.1 1

Al N H 4+ B r Ca EC OC Cu Fe Pb M n Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Lime/Minerals

0.00001 0.0001 0.001 0.01 0.1 1

Al N H 4+ B r Ca EC OC Cu Fe Pb M n Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Na-rich

0.00001 0.0001 0.001 0.01 0.1 1

A

l NH4+ Br Ca E C O

C Cu Fe Pb Mn N i N

O 3 - K Se S i NA+ S O 4- - Ti V Z n Oil burning

0.00001 0.0001 0.001 0.01 0.1 1

Al NH 4 + B r Ca EC OC Cu Fe Pb Mn Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Resuspended road dust

0.00001 0.0001 0.001 0.01 0.1 1

Al N H4 + Br Ca EC O C Cu F e Pb M n Ni N O 3- K Se Si N A+ SO4-- Ti V Zn Cu-rich

0.00001 0.0001 0.001 0.01 0.1 1

Al N H 4+ Br Ca EC OC Cu Fe Pb Mn Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Zn-rich

0.00001 0.0001 0.001 0.01 0.1 1

Al NH4+ Br Ca EC OC Cu Fe Pb Mn Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Sec. Sulfate

0.00001 0.0001 0.001 0.01 0.1 1

Al NH4+ Br Ca EC OC Cu Fe Pb Mn Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn Sec. Nitrate

0.00001 0.0001 0.001 0.01 0.1 1

A

l N

H 4+ B r Ca E C O

C Cu Fe Pb M n N

i N

O 3 - K Se S i NA+ S O 4- - Ti V Z n Sec. Organic Aerosol

0.00001 0.0001 0.001 0.01 0.1 1

Al N H4 + Br Ca EC OC Cu Fe Pb M n Ni N O 3 - K Se Si NA+ SO 4 -- Ti V Zn 

# Figure 1 : Compositions (factor loadings,  g/m 3 

# ) of the twelve factors identified by PMF

# Table 2 : Correlations (R) betweenfactors and species

Speci

es Mobil

esourc
es Biomas

s burning Soil Lime/ Minera ls Na-rich Oil burning Roaddust Cu-rich Zn-rich Sec. Sulfat

e Sec. Nitr ate SOA

SO4 -2 

## 0.07 -0.01 0.11 0.03 0.03 0.05 0.14 0.03 0.13 0.98 - 0.07 0.49

NO3 - 

## 0.13 0.24 -0.11 -0.02 0.10 -0.04 0.02 -0.08 0.20 -0.04 0.99 -0.10 NH4 + 

## 0.13 0.06 0.03 -0.01 -0.05 -0.03 0.13 0.01 0.21 0.95 0.26 0.40

EC 0.99 0.25 -0.01 0.08 0.04 -0.01 0.56 0.13 0.47 0.11 0.12 -0.10 OC 0.64 0.49 0.20 0.14 0.11 0.01 0.51 0.06 0.43 0.33 0.07 0.64 Al -0.02 0.09 0.85 0.20 0.05 0.06 0.55 -0.05 0.04 0.00 - 0.11 0.21

As 0.28 0.13 0.00 -0.01 -0.06 -0.03 0.21 0.09 0.27 0.20 0.07 0.09 Br 0.32 0.50 0.01 0.09 0.17 0.00 0.23 0.05 0.34 0.20 0.41 0.10 Ca 0.01 0.22 0.20 1.00 0.03 0.01 0.12 -0.07 0.02 0.00 - 0.04 0.12

Cu 0.16 -0.02 -0.03 -0.07 -0.07 -0.07 0.08 1.00 0.05 0.03 - 0.06 -0.04

Fe 0.52 0.25 0.70 0.18 0.04 0.05 0.99 0.08 0.40 0.12 - 0.02 0.16

K 0.30 0.90 0.48 0.27 0.12 0.06 0.57 0.00 0.37 0.09 0.14 0.21 K + 

## 0.30 0.62 0.05 0.09 0.25 -0.01 0.22 0.02 0.24 0.00 0.17 0.06 Mn 0.22 0.30 0.32 0.17 0.03 0.01 0.53 -0.05 0.28 0.10 0.11 0.09 Na -0.01 0.22 0.14 0.07 0.36 0.13 0.05 -0.02 -0.04 -0.05 - 0.04 0.02

Na + 

## 0.06 0.15 0.04 0.03 0.99 0.10 0.06 -0.07 0.01 -0.06 0.01 0.07 Ni -0.04 0.04 0.06 0.00 0.14 0.91 0.05 -0.06 0.00 0.00 - 0.03 0.06

Pb 0.25 0.14 0.03 0.03 -0.03 -0.04 0.19 0.09 0.36 0.05 0.09 0.03 Se 0.16 0.06 0.02 0.00 -0.04 -0.03 0.15 -0.02 0.22 0.38 0.30 0.09 Si -0.01 0.12 0.99 0.24 0.05 0.07 0.65 -0.03 0.08 0.07 - 0.14 0.28

Ti 0.06 0.13 0.72 0.25 0.11 0.18 0.59 -0.06 0.11 0.08 - 0.08 0.21

V -0.03 0.05 0.08 0.02 0.11 0.99 0.07 -0.06 0.00 -0.01 - 0.06 0.06

Zn 0.52 0.30 0.08 0.07 -0.02 0.00 0.42 0.03 1.00 0.14 0.24 0.07 PM 2.5 

## 0.37 0.33 0.28 0.20 0.04 0.02 0.46 0.06 0.38 0.77 0.06 0.58



# The following factors were identified based on: 1. Mobile-sources - carbon loadin gs, and correlations with EC , OC, Fe and Zn (lubricating oil additive). Contributions are gene rally substantial, ranging from 1.6-2.8  g/m 3 

# on average. 2. Biomass burning - loading and correlations w ith OC and K. Contri butions are generally substantial, ranging from 1.1-1.5  g/m 3 

# on average. 3. Soil – correlations and loadings of crustal elements such as Al, Si, Fe, Ti. Contributions are relatively small, ranging from 0.3-0.7  g/m 3 

# on average. 4. Lime/minerals operations/processing– loading and correlation with Ca . Contributions are generally negligible on average in Atlanta (0.05  g/m 3 

# ), but are higher at Rome (0.2  g/m 3 

# ) and Macon (0.6  g/m 3 

# ). 5. Na-rich – based on loadings and correlation w ith sodium ions. Could represent a mixture of sea-salt (elevated at the coastal Savannah site), and/or pulp/pape r industry emissions. Contributions are relatively small, ranging from 0.3-0.4  g/m 3 

# on average. 6. Oil burning – based on correlations and loadings of Ni and V. Contri butions are generally negligible (<0.05  g/m 3 

# ) on average.

# 7. Resuspended road dust – based on correlation w ith crustal elements, as well and EC and OC. Contributions are relatively small (0.1  g/m 3 

# ) on average. 8. Cu-rich factor – based on correlations and lo ading of Cu. Could be an indicator of metal smelting operations. Contributions are relatively small (<0.7  g/m 3 

# ) on average. 9. Zn-rich – based on loading and correlation withZn. Could be an indicator for industrial operations or “smoking” vehicles (Zn is a lubricating oil additive). Contributions are generally negligible (<0.05  g/m 3 

# ) on average. 10. Secondary sulfate factor – ba sed on correlations and loadin gs of sulfate and ammonium, as well as peak contributions occurring du ring summertime. Contributions are very substantial, ranging from 4.7-5.1  g/m 3 

# on average. 11. Secondary nitrate – based on the nitrate lo ading and correlation, as well as peak contributions occurring during wintertime. Contributions are fairly substantial, ranging from 0.7-1.0  g/m 3 

# on average. 12. Secondary organic aerosols – based on th e high OC loading, accompanied by a smaller sulfate content, as well as peak contributions occurring during summertime. Contributions are very subs tantial, ranging from 1.8-3.0  g/m 3 

# on average.
For consistency across sites, presented here are source-apportionment results for 2003-2004 (Figure 2 and Table 3). These once again sh ow the regional nature of PM2.5, given the similarities between the PMF results for the three sites. The major impacting factors at all sites are secondary sulfates, secondary nitrates, SO A, mobile sources and biomass burning. The Atlanta area, as expected, has higher levels of mobile-sour ce PM2.5, compared to Floyd county and Macon. On the other hand, thes e latter two sites exhi bit higher levels of soil dust, and the Macon site exhibits a relatively high contribution of Lime/Min erals PM2.5. This may be due to local sources in the industrial area where the site is located.
These results are also useful for a preliminary assessment of control strategies. Given the large contributions to PM2.5 from the secondary-sulfatefactor, it is evident that controlling SO2 emissions (mainly from coal-burning EGUs) w ould reduce PM2.5 levels throughout the state. Controlling emissions of SOA precursors would al so be useful, unfortunately these are not yet well understood, and limited available knowledge s uggest biogenic sources as the primary SOA precursors. Reducing primary PM2.5 may be be st achieved by regulations on mobile-source emissions and biomass-burning. The efficiency of NOx controls on PM2.5 was addressed via a photochemical modeling study, as was found to be in significant. Contributions from all other source-categories were small in general, though the Macon and Rome sites seem to be affected by local sources related to lime/minerals processing and soil dust resuspension.

## 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0



# Atlanta Floyd county Maconug/m3 

## Unspec. SOA Sec. Nitrate Sec. Sulfate Zn-rich Cu-rich Road-dust Oil burning Na-rich Lime/Minerals Soil Biomass burning Mobile-sources



# Figure 2 : Average factor contributions for 2003-04 at the Atlanta, Floyd county, and Macon STN sites using PMF

Table 3 : Average factor contributions to PM 2.5 

# (  g/m 3 

# ) for 2003-04 at the Atlanta, Floyd county, and Macon STN sites using PMF Factor Atlanta Floyd county Macon Mobile-source 2.78 1.61 2.12 Biomass burning 1.06 1.31 1.50 Soil 0.29 0.61 0.67 Lime/Minerals 0.05 0.24 0.59 Na-rich 0.43 0.42 0.33 Oil 0.03 0.04 0.04 Road-dust 0.12 0.13 0.11 Cu-rich 0.07 0.03 0.02 Zn-rich 0.03 0.03 0.03 Sec. Sulfate 4.82 5.10 4.67 Sec. Nitrate 0.99 1.05 0.74 SOA 2.55 3.46 3.81 Unspecified 2.32 2.97 1.81

# Chattanooga PMF analysis A standalone PMF analysis was conducted fo r the Hamilton county STN monitor (47-065-4002) using data from 2002-2006. Table 4 provides a summary of speci es used in the analysis.

Table 4 : Species included in the PMF analysis, along with average concentr ations, % of samples below detection limit (BDL), signal to noise ratio (S/N, defined here as the average of concentration-to-uncertainty ratios per sp ecies), and classifi cation within PMF Species Atlanta avg. (  g/m 3 

# ) % BDL S/N Classification


# SO 4 -2 

# 5.0E+00 14.1 0.0 Strong NO 3 - 

# 1.1E+00 13.5 0.0 Strong NH 4 + 

# 1.7E+00 13.5 0.0 Strong

EC 6.9E-01 8.0 0.0 Strong OC 5.0E+00 12.9 0.0 Strong OC1 1.2E+00 7.0 0.0 Weak OC2 1.4E+00 7.4 0.0 Weak OC3 1.0E+00 6.7 0.0 Weak OC4 1.2E+00 6.9 0.0 Weak OP 1.5E-01 2.4 0.0 Weak Al 3.1E-02 2.1 61.3 Weak As 1.8E-03 1.6 65.1 Weak Ba 2.1E-02 1.5 77.7 Bad Br 3.5E-03 4.3 26.4 Weak Ca 6.7E-02 5.9 2.2 Strong Cl 1.3E-02 1.7 71.4 Weak Cr 2.8E-03 2.4 61.3 Weak Cu 4.1E-03 3.7 37.5 Weak Fe 1.1E-01 16.7 0.0 Strong K 6.2E-02 6.5 0.4 Strong K + 

# 4.1E-02 3.7 55.0 Weak Mn 4.2E-03 4.0 34.9 Weak Na 6.8E-02 1.4 76.2 Bad Na + 

# 1.0E-01 4.7 24.5 Strong Ni 1.5E-03 1.7 70.3 Weak Pb 4.3E-03 2.0 64.3 Weak Se 1.7E-03 1.1 81.8 Bad Si 1.1E-01 5.6 11.2 Strong Ti 5.5E-03 2.4 53.5 Weak V 1.9E-03 1.0 84.0 Bad Zn 1.8E-02 9.3 3.0 Strong PM 2.5 

# 1.53E+01 13.3 0.0 Weak * - Ag, Au, Cd, Ce, Co, Cs, Eu, Ga, Hf, Hg, In, Ir, La, Mo, Nb, P, Rb, Sb, Sc, Sm, Sn, Sr, Ta, Tb, W, Y, and Zr were excluded from the analysis (cla ssified as “Bad”), due to the majority of samples being below the MDL

# Using the species in Table 4, several solutions based on a varying number of factors were investigated. A combination of te n factors was chosen, as these factors seem to best represent identifiable source categories. The identification of sources was conducted based both on the composition of factors (Figure 3) and their correl ation with the ambient concentrations of trace metals, carbon and major ions (Table 5). Determination of the total PM 2.5 

# mass associated with each factor was carried out by including PM2.5 as a “weak” variable in the PMF analysis.

Mobile-sources

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

A s Al Br Ca C r Cu Cl Fe Pb M n Ni Ti Si Zn K NH4

Na

+ K+ OC NO

3 EC

OC1

OC2

O C3

OC4 O P S O4 Biomass burning

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

A s Al B r C

a C

r Cu C l Fe Pb M n Ni Ti Si Zn K NH

4 Na

+ K+ O C N O 3 EC

O C 1 O C2

OC3

O C4 OP SO4 Soil

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

As Al Br Ca Cr Cu Cl Fe Pb Mn Ni Ti Si Zn K NH4

N a + K+ O C NO

3 EC

OC1

OC2

O C3

OC4 OP S O4 Zn-rich

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

As Al B r C

a Cr Cu C l Fe Pb Mn Ni Ti Si Zn K NH

4 N a + K+ OC N O 3 EC

O C 1 O C2

OC3

O C4 O

P SO4 Fe-rich

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

As Al Br Ca Cr Cu Cl Fe Pb M n Ni Ti Si Zn K N H 4 Na+ K+ OC N O3 EC OC1

OC2

O C3

OC4 O P S O4 Na-rich

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

As Al B r C

a Cr Cu C l Fe Pb M n Ni Ti Si Zn K N H4

Na+ K+ O C NO3 EC O C 1 O C2

OC3

O C4 OP SO4 Ca-rich

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

A s Al Br Ca C r Cu Cl Fe Pb M n Ni Ti Si Zn K NH4

Na

+ K+ OC NO

3 EC

OC1

OC2

O C3

OC4 O P S O4 Sec. sulfate

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00 1.00E+01

A s Al B r C

a C

r Cu C l Fe Pb M n Ni Ti Si Zn K NH

4 Na

+ K+ O C N O 3 EC

O C 1 O C2

OC3

O C4 OP SO4 Sec. nitrate

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

As Al Br Ca Cr Cu Cl Fe Pb M n Ni Ti Si Zn K N H 4 Na+ K+ OC N O3 EC OC1

OC2

O C3

OC4 O P S O4 SOA

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00 1.00E+01

As Al B r C

a Cr Cu C l Fe Pb M n Ni Ti Si Zn K N H4

Na+ K+ O C NO3 EC O C 1 O C2

OC3

O C4 OP SO4 

# Figure 3 : Compositions (factor loadings,  g/m 3 

# ) of the twelve factors identified by PMF

Table 5 : Correlations (R) betweenfactors and species Specie

s Mobil

esource
s Bioma

ssburnin g Soil Znrich Ferich Narich Carich Sec. Sulfat

e Sec. Nitrate SOA

SO 4 -2 

# -0.06 0.02 0.06 -0.10 0.11 0.02 0.10 0.97 -0.13 0.49 NO 3 - 

# 0.36 0.30 -0.08 0.40 0.12 0.14 -0.05 -0.13 0.98 -0.07 NH 4 + 

# 0.08 0.14 0.01 0.06 0.16 -0.01 0.06 0.91 0.25 0.45 EC 0.99 0.37 0.03 0.61 0.54 0.04 0.04 -0.01 0.20 0.44 OC 0.68 0.47 0.13 0.44 0.44 0.10 0.15 0.25 0.14 0.83 OC1 0.70 0.35 0.04 0.48 0.41 0.04 0.04 0.17 0.11 0.60 OC2 0.59 0.36 0.09 0.34 0.34 0.10 0.04 0.18 -0.01 0.63 OC3 0.58 0.35 0.09 0.33 0.35 0.07 0.05 0.23 -0.03 0.60 OC4 0.55 0.37 0.04 0.31 0.27 0.05 0.03 0.24 0.11 0.66 OP 0.08 0.11 -0.03 0.05 0.08 0.04 0.00 -0.02 0.10 0.15 Al -0.02 0.07 0.90 -0.07 0.23 0.03 0.23 -0.12 -0.10 -0.07 As 0.36 0.17 0.10 0.34 0.22 0.05 -0.03 -0.06 0.24 0.10 Br 0.55 0.51 -0.01 0.43 0.26 0.17 -0.03 0.04 0.35 0.32 Ca -0.03 -0.01 0.43 -0.01 0.04 0.03 1.00 -0.06 -0.07 -0.05 Cl 0.29 0.36 0.00 0.29 0.03 0.13 0.00 -0.02 0.30 0.13 Cr -0.03 -0.04 0.05 0.00 0.32 -0.02 0.13 0.08 -0.02 -0.14 Cu 0.48 0.07 0.11 0.40 0.51 0.01 0.14 -0.03 0.13 0.18 Fe 0.51 0.23 0.67 0.43 0.87 0.01 0.28 -0.02 0.04 0.15 K 0.33 0.92 0.46 0.30 0.33 0.18 0.09 0.05 0.18 0.23

# K +

# 0.31 0.72 0.13 0.32 0.22 0.26 0.08 0.00 0.27 0.14 Mn 0.53 0.19 0.23 0.53 0.81 0.05 0.04 0.01 0.06 0.12 Na +

# -0.02 0.23 0.16 -0.02 0.02 0.99 0.07 -0.04 0.05 -0.08 Ni 0.22 -0.04 -0.06 0.20 0.44 0.05 -0.01 -0.04 0.05 -0.14 Pb 0.33 0.11 0.01 0.29 0.24 -0.09 0.05 0.08 0.09 0.25 Si -0.02 0.11 0.98 -0.06 0.25 0.05 0.55 -0.08 -0.10 -0.02 Ti 0.03 0.15 0.88 0.03 0.38 0.04 0.27 -0.02 -0.06 -0.01 Zn 0.64 0.30 0.00 0.99 0.53 -0.02 0.03 -0.07 0.29 0.14 PM 2.5 

## 0.34 0.27 0.15 0.21 0.32 0.03 0.15 0.75 0.05 0.71


# The following factors were identified based on: 1. Mobile-sources - carbon loadin gs, and correlations with EC , OC, Fe and Zn (lubricating oil additive). The average contribution is 1.1  g/m 3



# . 2. Biomass burning - loading and correlations withOC and K. The average contribution is 0.7  g/m 3



# . 3. Soil – correlations and loadings of crustal el ements such as Al, Si, Fe, Ti. The average contribution is 0.5  g/m 3



# . 4. Zn-rich – based on loading and correlation withZn. Could be an indicator for industrial operations (metals processing etc.) or “sm oking” vehicles (Zn is a lubricating oil additive). This factor is also rich in carbon. The average contribution is 0.6  g/m 3



# . 5. Fe-rich – based on correlation with Fe, as well as Mn and carbon fractions. Likely an indicator for resuspended soil. The average contribution is 0.5  g/m 3



# . 6. Na-rich – based on loadings and correlation withsodium ions. Could be an indicator of pulp/paper industry emissions. Th e average contribution is 0.2  g/m 3



# . 7. Lime/minerals operations/processing – load ing and correlation with Ca. The average contribution is 0.2  g/m 3



# . 8. Secondary sulfate factor – ba sed on correlations and loadin gs of sulfate and ammonium. Contributions are very substantial, 6.3  g/m 3 

# on average. 9. Secondary nitrate – based on th e nitrate loading and correlat ion. The average contribution is 0.2  g/m 3



# . 10. Secondary organic aerosols – based on th e high OC loading, accompanied by a smaller sulfate content, as well as peak contributions occurring during summertime. Contributions are very substantial, 4.4  g/m 3 

# on average.
These results are useful for a preliminary asse ssment of control strategies. Given the large contributions to PM2.5 from the secondary-sulfatefactor, it is evident that controlling SO2 emissions (mainly from coal-burning EGUs) would reduce PM2.5 levels in the Chattanooga MSA. Controlling emissions of SOA precursors woul d also be useful, unfortunately these are not yet well understood, and limited available knowledge suggest biogenic sources as the primary SOA precursors. Reducing primary PM2.5 may be best achieved by re gulations on mobilesource emissions and biomass-burning. The effici ency of NOx controls on PM2.5 was addressed via a photochemical modeling study, as was found to be insignifi cant. Contributions from all other source-categories we re small in general.

# 1.11 0.65
0.46
0.64

0.48
0.21
0.24

6.27 0.20 4.39 0.59 

# Mobile-sources Biomass-burning Soil Zn-rich Fe-rich Na-rich Ca-rich Sec. Sulfate Sec. Nitrate SOA Unspecified



# Figure 4 : Average factor contributions to PM 2.5 

# (  g/m 3 

# ) for 2002-06 at the Chattanooga STN site using PMF

FIRE-STATION #8 ANALYSIS PM 2.5 

# levels at the Fire-Station #8 (FS#8) site ar e substantially higher than any of the other Atlanta NAA sites. Higher PM 2.5 

# levels have been observed consistently ever since such measurements have become available (Fi gure 5). Compared to all other Atlanta PM 2.5 

# NAA sites for the 1999-2006 period, FS#8 PM 2.5 

# levels are 2.4  g/m 3 

# higher on average. Of interest, two sites are located within 3 mile s of FS#8, the E. Rivers sites (2.9 miles NE of FS#8) and the Jefferson Street (JST) SEARCH site (2.2 miles SE of FS#8) (Figure 6), yet PM 2.5 

# levels at FS#8 are substantially higher than at these two si tes as well, by an average increment of 2.1  g/m 3 

# for the 1999-2006 period. This is an indication that the PM 2.5 

# increment at FS#8 is likely due to a local source. To further investigate the nature of the local PM 2.5 

# increment, PM 2.5 

# concentrations at FS#8, E. Rivers and JST were plotted against wi nd-direction (measured at JST, the only site of the three at which such measurement were availabl e) (Figure 7). These data clearly indicate that PM2.5 levels are the highest during southern winds at all sites, which is expected due to their location with respect to downtown Atlanta. In fact, these data canserve as an additional method for estimating the urban increment in PM 2.5 

# levels, comparing PM 2.5 

# levels from the SW and SE quadrants to those from the NE and NW quadrants . Such an analysis yields an urban increment of 2.2-3.0  g/m 3 

# (for these three sites), which is fairly consistent with the estimate based on spatial patterns within the A tlanta nonattainment area. Further analyzing Figure 7, a peak in PM 2.5 

# is observed on days of southwestern winds at the FS#8 site, but not at the other two sites. As shown in Figure 8, a large rail-yard is locate d southwest of FS#8, wh ich may be contributing to the local increment in PM 2.5 

# levels.

# 12.0 14.0 16.0 18.0 20.0 22.0 24.0

1999 2000 2001 2002 2003 2004 2005 2006ug/m3 

## Forest Park (13-063-0091) Kennesaw (13-067-0003) South-DeKalb (13-089-0002) Doraville Health (13-089-2001) E. Rivers (13-121-0032) Fire-Station 8 (13-121-0039) Lawrenceville (13-135-0002) Yorkville (13-223-0003) Gainesville (13-139-0003) Jefferson St. (SEARCH)



# Figure 5 : Annual averages of PM 2.5 

# at Atlanta NAA sites for 1999-2006



# F.S.#8 E.Rivers

Jeff. St. 2

. 2 m

i l e 

## 2 .

9

m

i l e



## SouthDeKalb Doraville Health



# F.S.#8 E.Rivers

Jeff. St. 2

. 2 m

i l e 

## 2 .

9

m

i l e



## SouthDeKalb Doraville Health



# Figure 6 : Location of the FS#8 monitor comp ared to other Atlanta monitors

# 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0



# F.S.#8 E.Rivers Jeff. St.PM2.5 (ug/m3) 

# NE (0-90) SE (90-180) SW (180-270) NW (270-360)



# Figure 7 : PM 2.5 

# concentrations at FS#8, E.Rivers and Je ff. St. as a function of wind-direction (measured at the Jeff. St. site)



# Fire Station 8

Figure 8 : Location of the FS#8 monitor compared to the adjacent rail-yard and Marietta Boulevard
To further investigate the po ssible sources of the local PM 2.5 

# increment at FS#8, we used the PMF model. Since the FS#8 site is not a STN s ite, no speciation data were readily available.

# However, selected collected PM 2.5 

# filters from 2002-2004 were analyzed by X-ray fluorescence (XRF) to quantify ambient trace me tals concentrations. However, ions and carbon data were not available. Hence PMF was run using metals da ta only. 118 samples for January 2002- January 2005 were available for the analysis. Only metals for which the majority of samples were above the minimum detection limit (MDL), and the averag e signal-to-noise ratio (S/N, defined here as the average of concentration-to-uncertainty rati os per species) was greater than 2.0 were included in the analysis (Table 6). The metals were cla ssified as “Strong” or “W eak” in PMF, with the “Weak” species being down-weighted in the PM F optimization process, hence giving more emphasis to the “Strong” species. Species classified as “Bad” were excluded from the analysis. The classification was based on th e S/N values. However, user j udgment was used for specific species: V was classified as “strong” and Ni keptin the analysis, to attempt to identify oil combustion particles; Cu downgraded to “weak” as it worsened the overall fit, and Br and Pb excluded for the same reason.

Table 6 : Species included in the PMF analysis, along with average concentrations, minimum detection limits (MDL), % of samples below det ection limit (BDL), signal to noise ratio (S/N, defined here as the average of concentration-to-uncertainty ratio s per species), and classification within PMF. Species Avg. Conc. (  g/m 3 

# ) MDL (  g/m 3 

# ) % BDL S/N Classification Ca 4.75E-02 1.79E-03 0.0 32.5 Strong Cl 2.29E-02 2.12E-03 7.6 13.7 Strong Fe 1.28E-01 4.42E-04 0.0 118 Strong K 5.84E-02 1.48E-03 0.0 45.7 Strong S 1.58E+00 2.82E-03 0.0 259 Strong Si 9.79E-02 5.21E-03 0.8 17.7 Strong V 2.94E-03 1.05E-03 11.8 3.8 Strong Zn 1.05E-02 5.73E-04 0.0 22.4 Strong Al 4.47E-02 1.10E-02 12.6 4.4 Weak As 1.29E-03 6.59E-04 31.9 2.3 Weak Cu 4.16E-03 4.68E-04 0.8 10.7 Weak Mg 1.20E-02 4.52E-03 34.5 3.0 Weak Mn 2.69E-03 6.15E-04 7.6 5.2 Weak Na 6.60E-02 1.62E-02 5.9 4.9 Weak Ni 6.25E-04 4.10E-04 47.9 2.1 Weak Se 1.62E-03 7.09E-04 19.3 2.9 Weak Br 3.26E-03 6.40E-04 2.5 6.0 Bad Pb 3.59E-03 1.72E-03 31.1 2.5 Bad * - Ag, Au, Ba, Cd, Ce, Co, Cr, Cs, Eu, Ga, Hf, Hg, In, Ir, La, Mo, Nb, P, Rb, Sb, Sc, Sm, Sn, Sr, Ta, Tb, Ti, W, Y, and Zr were excluded from the analysis (classified as “B ad”), due to the majority of samples being below the MDL



# Using the species in Table 6, several solutions based on a varying numb er of factors were investigated. A combination of si x factors was chosen, as these factors seem to best represent identifiable source categories. The identification of sources was conducted based both on the

# composition of factors (Figure 9) and their correl ation with the ambient concentrations of trace metals (Table 7). The factors were identified as representing the followi ng source categories:

 Factor A: Soil/fugitive dust, due to the high lo ading of crustal elements (Si, Al, and Fe),

and high correlations with Al and Si.

 Factor B: Cement operations (kiln, crushing), due to the high Ca loading, and correlations

with Ca and V (an additive to tires, often used as fuel in cement kilns).

 Factor C: Biomass burning, due to the high loading and correlation with K (OC was not

available for the analysis, but would typically account for most of the mass is a biomass burning factor).

 Factor D: Steel particles, due to the high Fe loading and correlations with steel components

such as Fe, Cu, Mn, Ni and Zn.

 Factor E: Zn-rich aerosol, possibly represen ting mobile sources. Zn is an additive to

lubricating oil used in mobile sources, and in lieu of industrial sources of Zn in Atlanta, may serve as a tracer for mobile sources. In lie u of EC and OC data, these are not included in this factor, though would typically be.

 Factor F: Sulfur rich aerosol, lik ely representing secondary sulfate.

0.00001 0.0001 0.001 0.01 0.1

1

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn 0.00001 0.0001 0.001 0.01 0.1

1

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn

0.00001 0.0001 0.001 0.01 0.1

1

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn 0.00001 0.0001 0.001 0.01 0.1

1

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn

0.00001 0.0001 0.001 0.01 0.1

1

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn 0.00001 0.0001 0.001 0.01 0.1

1 10

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn 

# a: Soil

b: Cement

c: Biomass d: Steel

e: Zn-rich/Mobile

f: SulfateFactor loading (ug/m3) 0.00001 0.0001 0.001 0.01 0.1

1

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn 0.00001 0.0001 0.001 0.01 0.1

1

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn

0.00001 0.0001 0.001 0.01 0.1

1

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn 0.00001 0.0001 0.001 0.01 0.1

1

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn

0.00001 0.0001 0.001 0.01 0.1

1

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn 0.00001 0.0001 0.001 0.01 0.1

1 10

Al As Ca Cl Cu Fe K Mg Mn Na Ni S Se Si V Zn 

# a: Soil

b: Cement

c: Biomass d: Steel

e: Zn-rich/Mobile

f: SulfateFactor loading (ug/m3)



# Figure 9 : Compositions of the six fact ors identified by PMF for FS#8

# Table 7 : Correlations (R) between fa ctors and species at FS#8 Species Soil Cement Biomass Sulfate Steel Zn-rich Al 0.96 0.11 0.31 0.23 0.09 -0.01 Ca 0.34 0.95 0.07 -0.04 0.27 0.41 Cl 0.15 0.04 0.26 0.94 0.13 0.04 Cr 0.06 0.14 0.20 -0.02 0.38 0.33 Cu 0.09 0.16 0.26 -0.10 0.93 0.38 Fe 0.34 0.27 0.35 -0.02 0.96 0.47 K 0.30 0.14 0.95 0.10 0.41 0.28 Mn 0.26 0.39 0.19 -0.02 0.66 0.56 Na 0.29 -0.15 0.68 0.32 0.19 -0.11 Ni 0.11 0.08 0.25 -0.08 0.79 0.28 S 0.15 -0.02 0.14 0.99 0.00 -0.08 Si 0.99 0.18 0.32 0.09 0.17 0.08 V 0.37 0.88 0.24 -0.04 0.37 0.44 Zn 0.14 0.39 0.18 0.01 0.71 0.92

To further investigate the s ources contributing to the loca l PM2.5 increment at FS#8, these factors were regressed against the difference in PM2.5 levels at FS#8 and the average of the JST and E. Rivers sites (to quantify and represent the local increment). We used a multiple linear regression model, assuming the local FS#8 PM2.5 increment can be explained by the sum of increments associated with each of these six fact ors. The average contribu tion of each factor to the local PM2.5 increment (Table 8) was repres ented by the regression slope. The correlations between the reconstructed (modeled) increment and the observed one was R=0.76, and the average ratio of modeled to observed concentrations was 0.97. The two factors with the highest contribution to the local PM2.5 in crement at FS#8 are the steel a nd Zn-rich factors, contributing 1.2 and 0.6  g/m 3 

# respectively (together explaining 60% of the increment). These two factors were also the only ones found to be statistically significant in th e regression process, with pvalues of 5*10 -8 

# and 0.025, respectively. The daily contributi ons of these two factors to the local PM2.5 increment at FS#8 are shown in Figure 10. Average contributions from the soil, cement and biomass burning were in the range of 0.21-0.25  g/m 3 

# each, but none were statistically significant. As expected, the contribution of the sulfate factor to the local PM2.5 increment was low (0.08  g/m 3 

# ), as sulfate is a secondary, regional po llutant. Of note, it was not possible to characterize all sources affecting local PM2.5 leve ls, as ions and carbon da ta were not available for the analysis.

# Table 8 : Contribution of each factor to the local PM 2.5 

# increment at FS#8 Factor Avg. contribution (  g/m3) p-value Soil 0.25  0.17 0.138 Cement 0.21  0.19 0.279 Biomass burning 0.23  0.20 0.247 Secondary sulfate 0.08  0.18 0.646 Steel 1.18  0.18 5E-08 Zn-rich / Mobile sources 0.60  0.26 0.025

Investigating further into the sour ce of steel particles represente d by the steel factor, we estimate the source to be related to activity at the adj acent rail-yard. This is supported by a study conducted in the New-York city subway (Chilrud et al, 2004), in wh ich Fe, Mn, Cr levels at the subway were 100 times greater thanthose in the outside ambient air. In comparison, Fe, Mn, and Cu levels at FS#8 are more than twice of th ose at JST. The difference in magnitude (>100 compared to >2) is due to the subway being an enclosed “micro” environment, as opposed to two ambient measurements at JST and FS#8. However, an increment in the levels of metals associated with steel is evident. The increment in Fe, Mn and Cr at the subway was associated with the friction between the rails and the subway tr ains. It is reasonable to assume the same for the rail-yard adjacent to FS#8. It is, however, important to note that the steel particles themselves do not account for most of the mass emissions l eading to the local increment in PM2.5 levels. Most mass associated with locomotive emissions are in the form of engine-generated carbon (mainly EC, or “soot”) emissions. However, th ese EC emissions are accompanied by frictiongenerated steel particles from movement of the locomotives on the rail. These co-emitted metals were identified by PMF as a factor (as carbon data were not available for the analysis). Hence, the metals associated with steel serve, in this case, as a tracer or proxy for activity at the railyard, and were found highly corre lated with the local PM2.5 in crement at FS#8. The Zn-rich factor may be an indicator of lo cal traffic on Marietta Boulevard (which has a high percentage of diesel traffic), idling fire trucks at the fire sta tion, or idling locomotives at the nearby rail-yard.

# -5.0 -2.5 0.0 2.5 5.0 7.5 10.0 12.5 15.0Jan-02Mar-02May-02Jul-02Sep-02Nov-02Jan-03Mar-03May-03Jul-03Sep-03Nov-03Jan-04Mar-04May-04Jul-04Sep-04Nov-04ug/m3



# Observed PM2.5 increment Contribution of steel factor to PM2.5 increment Contribution of mobile-sources factor to PM2.5 increment 

# Figure 10 : Daily contributions of the “steel” and “Zn -rich/Mobile” factors to the observed local PM2.5 increment at FS#8.