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 PM2.5 measurements in a given location. As such, it does 

not rely on a priori knowledge concerning chemical composition 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 actual sources. Since these methods are based on 

statistical patterns of correlations, rather than empirical chemical source profiles, naming the 

factors as specific sources is somewhat subjective. However, based on data on typical sourcecompositions for major sources, as well as researcher experience, very useful information on 

PM2.5 contributions can be derived from PMF. These include both total mass contributions for 

given factors/sources, as well as temporal patterns in such contributions, allowing one to 

investigate the seasonal and weekly patterns, as well as specific days of interest. 

GA EPD applied the EPA version of Positive Matrix Factorization (EPA-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 contributions throughout the state, avoiding potential issues, such as 

breakdown of OC between SOA and 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 breakdown between primary and secondary OC). Such an approach would 

be valid based on the rationale that many of the factors affecting PM2.5 levels are regional in 

nature, such as sulfate, nitrate, SOA and biomass-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 between the combined analysis and site-specific 

ones show fairly minor differences between the two approaches (the main differences are related 

to breakdown of OC mass between primary and secondary factors, as expected, and emphasize 

the advantage of the combined analysis approach). 

Here, we will focus on the four STN sites within 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 (June 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 average 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), however, 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 were blank-corrected using the intercept point of 

the regression between PM2.5 and OC, for each site individually. Blank values, however, were 

applied to PMF results post analysis, since when blank corrected OC values were analyzed in 

PMF directly, all carbon was apportioned to an “OC” factor, and was not broken into individual 

categories (mobile source, biomass burning etc.). 

Table 1: Species included in the PMF analysis, along with average concentrations (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 entire dataset used in the combined analysis). 

Species Atlanta avg. 

(g/m3) 

Floyd co. 

avg. (g/m3)

Macon avg. 

(g/m3) % 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 

PM2.5 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 number of factors were 

investigated. A combination of twelve 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 correlation with the ambient concentrations of trace 
metals, carbon and major ions (Table 2). Determination of the total PM2.5 mass associated with 

each factor was carried out using several methods, such as regression of the factors against total 

PM2.5 mass, inclusion of PM2.5 as a weak variable in PMF, or by summation of the various 

components in the obtained factors (using an OM/OC ratios and metal oxides mass for crustal 

elements). While each of these approached is valid, 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 related to the choice of the OM/OC ratio. One can 

chose the commonly used ratio of 1.4, however recent evidence suggest this ratio to be low, 

especially for biogenic aerosol. We used an optimization mechanism to minimize the error 

between factor-reconstructed and measured PM2.5 levels, by changing the OM/OC ratio for the 

three carbon containing factors: mobile-sources, biomass burning, 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

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Biomass Burning Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Soil --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Lime/Minerals --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Na-rich --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Ti

V

Zn

Oil burning

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Ti

V

Zn

Resuspended road dust

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Cu-rich Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Zn-rich --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Sec. Sulfate --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Sec. Nitrate --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Ti

V

Zn

Sec. Organic Aerosol

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Ti

V

Zn

Mobile sources

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Biomass Burning Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Soil --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Lime/Minerals --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Na-rich --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Ti

V

Zn

Oil burning

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Ti

V

Zn

Resuspended road dust

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Cu-rich Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Zn-rich --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Sec. Sulfate --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4

Sec. Nitrate --

Ti

V

Zn

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Ti

V

Zn

Sec. Organic Aerosol

0.00001

0.0001

0.001

0.01

0.1

1

Al

NH4+

Br

Ca

EC

OC

Cu

Fe

Pb

Mn

Ni

NO3-

K

Se

Si

NA+

SO4--

Ti

V

Zn

Figure 1: Compositions (factor loadings, g/m3

) of the twelve factors identified by PMF 
Table 2: Correlations (R) between factors 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 

PM2.5 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 loadings, and correlations with EC, OC, Fe and Zn (lubricating 

oil additive). Contributions are generally substantial, ranging from 1.6-2.8 g/m3

 on 

average. 

2. Biomass burning - loading and correlations with OC and K. Contributions are generally 

substantial, ranging from 1.1-1.5 g/m3 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/m3 on average. 

4. Lime/minerals operations/processing– loading and correlation with Ca. Contributions are 

generally negligible on average in Atlanta (0.05 g/m3), but are higher at Rome (0.2 

g/m3) and Macon (0.6 g/m3). 

5. Na-rich – based on loadings and correlation with sodium ions. Could represent a mixture 

of sea-salt (elevated at the coastal Savannah site), and/or pulp/paper industry emissions. 

Contributions are relatively small, ranging from 0.3-0.4 g/m3 on average. 

6. Oil burning – based on correlations and loadings of Ni and V. Contributions are generally 

negligible (<0.05 g/m3) on average. 
7. Resuspended road dust – based on correlation with crustal elements, as well and EC and 

OC. Contributions are relatively small (0.1 g/m3) on average. 

8. Cu-rich factor – based on correlations and loading of Cu. Could be an indicator of metal 

smelting operations. Contributions are relatively small (<0.7 g/m3

) on average. 

9. Zn-rich – based on loading and correlation with Zn. Could be an indicator for industrial 

operations or “smoking” vehicles (Zn is a lubricating oil additive). Contributions are 

generally negligible (<0.05 g/m3

) on average. 

10. Secondary sulfate factor – based on correlations and loadings of sulfate and ammonium, 

as well as peak contributions occurring during summertime. Contributions are very 

substantial, ranging from 4.7-5.1 g/m3

 on average. 

11. Secondary nitrate – based on the nitrate loading and correlation, as well as peak 

contributions occurring during wintertime. Contributions are fairly substantial, ranging 

from 0.7-1.0 g/m3 on average. 

12. Secondary organic aerosols – based on the high OC loading, accompanied by a smaller 

sulfate content, as well as peak contributions occurring during summertime. 

Contributions are very substantial, ranging from 1.8-3.0 g/m3 on average. 

For consistency across sites, presented here are source-apportionment results for 2003-2004 

(Figure 2 and Table 3). These once again show 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, SOA, mobile sources and biomass burning. The 

Atlanta area, as expected, has higher levels of mobile-source PM2.5, compared to Floyd county 

and Macon. On the other hand, these latter two sites exhibit higher levels of soil dust, and the 

Macon site exhibits a relatively high contribution of Lime/Minerals 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-sulfate factor, it is evident that controlling SO2 

emissions (mainly from coal-burning EGUs) would reduce PM2.5 levels throughout the state. 

Controlling emissions of SOA precursors would 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 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 insignificant. 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 Macon

ug/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 PM2.5 (g/m3) 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 for the Hamilton county STN monitor (47-065-4002) 

using data from 2002-2006. Table 4 provides a summary of species used in the analysis. 

Table 4: Species included in the PMF analysis, along with average concentrations, % 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 

Species Atlanta avg. 

(g/m3) % BDL S/N Classification 

SO4

-2 5.0E+00 14.1 0.0 Strong 

NO3

- 1.1E+00 13.5 0.0 Strong 

NH4

+ 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 

PM2.5 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 (classified 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 ten 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 correlation with the ambient concentrations of trace 

metals, carbon and major ions (Table 5). Determination of the total PM2.5 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

As

Al

Br

Ca

Cr

Cu

Cl

Fe

Pb

Mn

Ni

Ti

Si

Zn

K

NH4

Na+

K+

OC

NO3

EC

OC1

OC2

OC3

OC4

OP

SO4

Biomass burning

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

Na+

K+

OC

NO3

EC

OC1

OC2

OC3

OC4

OP

Soil SO4

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

Na+

K+

OC

NO3

EC

OC1

OC2

OC3

OC4

OP

SO4

Zn-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

Mn

Ni

Ti

Si

Zn

K

NH4

Na+

K+

OC

NO3

EC

OC1

OC2

OC3

OC4

OP

Fe-rich SO4

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

Na+

K+

OC

NO3

EC

OC1

OC2

OC3

OC4

OP

SO4

Na-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

Mn

Ni

Ti

Si

Zn

K

NH4

Na+

K+

OC

NO3

EC

OC1

OC2

OC3

OC4

OP

Ca-rich SO4

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

Na+

K+

OC

NO3

EC

OC1

OC2

OC3

OC4

OP

SO4

Sec. sulfate

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+00

1.00E+01

As

Al

Br

Ca

Cr

Cu

Cl

Fe

Pb

Mn

Ni

Ti

Si

Zn

K

NH4

Na+

K+

OC

NO3

EC

OC1

OC2

OC3

OC4

OP

Sec. nitrate SO4

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

Na+

K+

OC

NO3

EC

OC1

OC2

OC3

OC4

OP

SO4

SOA

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+00

1.00E+01

As

Al

Br

Ca

Cr

Cu

Cl

Fe

Pb

Mn

Ni

Ti

Si

Zn

K

NH4

Na+

K+

OC

NO3

EC

OC1

OC2

OC3

OC4

OP

SO4

Figure 3: Compositions (factor loadings, g/m3) of the twelve factors identified by PMF 

Table 5: Correlations (R) between factors and species 

Specie

s 

Mobil

esource

s 

Bioma

ssburnin

g 

Soil Znrich Ferich Narich Carich Sec. 

Sulfat

e 

Sec. 

Nitrate 

SOA 

SO4

-2 -0.06 0.02 0.06 -0.10 0.11 0.02 0.10 0.97 -0.13 0.49 

NO3

- 0.36 0.30 -0.08 0.40 0.12 0.14 -0.05 -0.13 0.98 -0.07 

NH4

+ 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 

PM2.5 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 loadings, and correlations with EC, OC, Fe and Zn (lubricating 

oil additive). The average contribution is 1.1 g/m3

. 

2. Biomass burning - loading and correlations with OC and K. The average contribution is 

0.7 g/m3. 

3. Soil – correlations and loadings of crustal elements such as Al, Si, Fe, Ti. The average 

contribution is 0.5 g/m3

. 

4. Zn-rich – based on loading and correlation with Zn. Could be an indicator for industrial 

operations (metals processing etc.) or “smoking” vehicles (Zn is a lubricating oil 

additive). This factor is also rich in carbon. The average contribution is 0.6 g/m3. 

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/m3. 

6. Na-rich – based on loadings and correlation with sodium ions. Could be an indicator of 

pulp/paper industry emissions. The average contribution is 0.2 g/m3. 

7. Lime/minerals operations/processing – loading and correlation with Ca. The average 

contribution is 0.2 g/m3. 

8. Secondary sulfate factor – based on correlations and loadings of sulfate and ammonium. 

Contributions are very substantial, 6.3 g/m3 on average. 

9. Secondary nitrate – based on the nitrate loading and correlation. The average contribution 

is 0.2 g/m3. 

10. Secondary organic aerosols – based on the high OC loading, accompanied by a smaller 

sulfate content, as well as peak contributions occurring during summertime. 

Contributions are very substantial, 4.4 g/m3

 on average. 

These results are useful for a preliminary assessment of control strategies. Given the large 

contributions to PM2.5 from the secondary-sulfate factor, 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 would 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 regulations on mobilesource emissions and biomass-burning. The efficiency of NOx controls on PM2.5 was addressed 

via a photochemical modeling study, as was found to be insignificant. Contributions from all 

other source-categories were 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 PM2.5 (g/m3) for 2002-06 at the Chattanooga STN 

site using PMF 

FIRE-STATION #8 ANALYSIS 

PM2.5 levels at the Fire-Station #8 (FS#8) site are substantially higher than any of the other 

Atlanta NAA sites. Higher PM2.5 levels have been observed consistently ever since such 

measurements have become available (Figure 5). Compared to all other Atlanta PM2.5 NAA sites 

for the 1999-2006 period, FS#8 PM2.5 levels are 2.4 g/m3

 higher on average. Of interest, two 

sites are located within 3 miles 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 PM2.5 levels at FS#8 

are substantially higher than at these two sites as well, by an average increment of 2.1 g/m3 for 

the 1999-2006 period. This is an indication that the PM2.5 increment at FS#8 is likely due to a 

local source. To further investigate the nature of the local PM2.5 increment, PM2.5 concentrations 

at FS#8, E. Rivers and JST were plotted against wind-direction (measured at JST, the only site of 

the three at which such measurement were available) (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 can serve as an additional method 

for estimating the urban increment in PM2.5 levels, comparing PM2.5 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/m3 (for these three sites), which is fairly consistent with the estimate based on 

spatial patterns within the Atlanta nonattainment area. Further analyzing Figure 7, a peak in 

PM2.5 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 located southwest of FS#8, which may be contributing 

to the local increment in PM2.5 levels. 
12.0

14.0

16.0

18.0

20.0

22.0

24.0

1999 2000 2001 2002 2003 2004 2005 2006

ug/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 PM2.5 at Atlanta NAA sites for 1999-2006 

F.S.#8

E.Rivers

Jeff. St.

2.2mile

2.9mile

SouthDeKalb

Doraville 

Health

F.S.#8

E.Rivers

Jeff. St.

2.2mile

2.9mile

SouthDeKalb

Doraville 

Health

Figure 6: Location of the FS#8 monitor compared 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: PM2.5 concentrations at FS#8, E.Rivers and Jeff. 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 possible sources of the local PM2.5 increment at FS#8, we used the 

PMF model. Since the FS#8 site is not a STN site, no speciation data were readily available. 
However, selected collected PM2.5 filters from 2002-2004 were analyzed by X-ray fluorescence 

(XRF) to quantify ambient trace metals concentrations. However, ions and carbon data were not 

available. Hence PMF was run using metals data 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 average signal-to-noise ratio (S/N, defined here as 

the average of concentration-to-uncertainty ratios per species) was greater than 2.0 were included 

in the analysis (Table 6). The metals were classified as “Strong” or “Weak” in PMF, with the 

“Weak” species being down-weighted in the PMF optimization process, hence giving more 

emphasis to the “Strong” species. Species classified as “Bad” were excluded from the analysis. 

The classification was based on the S/N values. However, user judgment was used for specific 

species: V was classified as “strong” and Ni kept in 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 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. 

Species Avg. Conc. 

(g/m3

) 

MDL 

(g/m3) % 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 “Bad”), due to the majority of samples being below the MDL 

Using the species in Table 6, several solutions based on a varying number of factors were 

investigated. A combination of six 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 correlation with the ambient concentrations of trace 

metals (Table 7). The factors were identified as representing the following source categories: 

 Factor A: Soil/fugitive dust, due to the high loading 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 representing 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 lieu of EC and OC data, these are not included 

in this factor, though would typically be. 

 Factor F: Sulfur rich aerosol, likely 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: Sulfate

Factor 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: Sulfate

Factor loading (ug/m3)

Figure 9: Compositions of the six factors identified by PMF for FS#8 
Table 7: Correlations (R) between factors 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 sources contributing to the local 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 factors. The average contribution of each factor to 

the local PM2.5 increment (Table 8) was represented 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 increment at FS#8 are the steel and Zn-rich factors, contributing 

1.2 and 0.6 g/m3 respectively (together explaining 60% of the increment). These two factors 

were also the only ones found to be statistically significant in the regression process, with pvalues of 5*10-8 and 0.025, respectively. The daily contributions 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/m3

 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/m3

), as sulfate is a secondary, regional pollutant. Of note, it was not possible to 

characterize all sources affecting local PM2.5 levels, as ions and carbon data were not available 

for the analysis. 
Table 8: Contribution of each factor to the local PM2.5 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 source of steel particles represented by the steel factor, we estimate 

the source to be related to activity at the adjacent rail-yard. This is supported by a study 

conducted in the New-York city subway (Chilrud et al, 2004), in which Fe, Mn, Cr levels at the 

subway were 100 times greater than those in the outside ambient air. In comparison, Fe, Mn, and 

Cu levels at FS#8 are more than twice of those 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 trains. 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 leading 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, these 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 correlated with the local PM2.5 increment at FS#8. The Zn-rich 

factor may be an indicator of local traffic on Marietta Boulevard (which has a high percentage of 

diesel traffic), idling fire trucks at the fire station, or idling locomotives at the nearby rail-yard. 
-5.0

-2.5

0.0

2.5

5.0

7.5

10.0

12.5

15.0

Jan-02

Mar-02

May-02

Jul-02

Sep-02

Nov-02

Jan-03

Mar-03

May-03

Jul-03

Sep-03

Nov-03

Jan-04

Mar-04

May-04

Jul-04

Sep-04

Nov-04

ug/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.