SECONDARY ORGANIC AEROSOL FORMATION POTENTIAL IN
SOUTH GEORGIA
Venus Dookwah.
1
ABSTRACT
Organic aerosols comprise a significant fraction of the total atmospheric particle loading
and have strong correlations to climatic and health effects. Ambient aerosol is comprised
of both primary and secondary components. The fraction of secondary organic aerosol
was estimated for three cities in south Georgia by using ambient data collected and
estimates of background organic carbon/elemental carbon ratio. Nonparametric sign
correlations comparing estimated secondary organic carbon with another secondary
photo-oxidation product, ozone, supported this method of quantifying secondary organic
carbon. Secondary organic carbon is estimated to have contributed over 70% of total
organic mass on 50% of sampled days at Columbus, 83% of sampled days at Augusta and
100% of days sampled at Macon.
Estimates of the amount of secondary organic aerosol potentially contributed by species
found in mobile emissions in these cities were then determined using fractional aerosol
coefficients. The main contributor of secondary organic aerosol (SOA) production in
each city is toluene. It accounts for 48 % of the potential mobile emissions SOA loading.
These results are relevant to ozone and PM2.5 abatement strategies.
2
INTRODUCTION
Approximately 10-70 percent of the total dry fine atmospheric particulate matter, is
organic material [Turpin et al 2000]. PM2.5 is a US EPA regulated pollutant and the
current National Ambient Air Quality Standard for PM2.5 is :

Annual arithmetic mean of 15 g m-3

24 hour average of 65 g m-3
One of the main reasons that PM2.5 is regulated is because of its correlation to adverse
human health effects such as cardiopulmonary disease, morbidity and mortality [Pope et
al 1995]. PM2.5 bypasses our respiratory defenses, such as the ciliated mucous linings,
and is speculated to being easily absorbed into the lining of the respiratory pathway. The
organic constituent of PM2.5 is of particular significance in the mechanism of effecting
hazardous health effects since it is capable of reacting synergistically with trace metals
present on the same particle [Ron Wyzga EPA Supersite meeting]. The resulting
potentially harmful redox reactions are one of the main reasons that the organic
component of aerosols, which usually averages around 30-40 percent, requires study.
Another undesirous effect of PM2.5 relates to the possible effects of this pollutant on
agricultural production. Fine particles affect the flux of solar radiation passing through
the atmosphere by scattering and absorbing radiation. This can result in reduced
downward photosynthetically active radiation (PAR) resulting in reduced crop yield
[Chamedies et al, 1998]. For regions whose economies are strongly tied to agricultural
yields, such as China, this phenomenon can have serious implications.
3
Organics in aerosols can modify the thermodynamic and chemical properties of
atmospheric particles thereby, altering the role played by these particles in the
atmosphere. According to Saxena et al, 1995, particle phase organics can alter the
hygroscopic properties of the atmospheric particles. They reported that for non-urban
locations organics enhance water absorption whereas for urban locations, the presence of
organics inhibits water absorption of atmospheric particles.
Aerosols which serve as nuclei upon which water vapor condenses in the atmosphere are
called cloud condensation nuclei (CCN). As explained by solute effects, for small
particles, the higher the water solubility or wettability of an aerosol, the lower the
supersaturation at which it can serve as CCN [Wallace and Hobbs]. Hence, the
hygroscopic properties of organic aerosols are indeed important in this respect.
The optical and chemical properties of atmospheric particles and their ability to act as
cloud condensation nuclei (CCN) depend strongly upon their affinity for water [Saxena et
al 1995]. The albedo and radiative properties of clouds are determined largely by the
number density of cloud condensation nuclei. Novakov and Penner, 1993, reported that
organic aerosols accounted for a major part of both the total aerosol number
concentration and the CCN fraction and the role played by organic aerosols was at least
as important as sulphate aerosols in determining the climate effect of clouds.
Dickerson et al. 1997, reported that UV scattering by aerosols can have a substantial
positive impact on the production of ground level ozone. Aerosol scattering of UV
radiation was found to increase calculated boundary layer ozone mixing ratios by 20
ppbv or more and UV absorbing aerosol reduced calculated ozone mixing ratios by up to
24 ppbv.
4
In summary, organic aerosols are significant because:
-
they can contain toxins which can cause deleterious health effects, if inhaled
as the majority of fine aerosols are too small to be efficiently trapped in bronchial
passages and can reach the lungs and be absorbed into the mucous lining
-
visibility and climate forcing issues are strongly influenced by organic species
-
they play a role in cloud condensation nuclei, thereby affecting precipitation
patterns which affects the hydrological cycle
-
they contribute to photochemical reactions affecting tropospheric ozone formation
and removal of atmospheric oxidizing species such as OH, O3, and NO3.
Even though a significant fraction of atmospheric aerosols consists of organic substances,
little is known about source-reaction pathways and chemical composition of this organic
fraction. One main reason for this lack of knowledge is due to the fact that organic
particulate matter is really a complex aggregate of a wide variety of compounds which
have varying chemical and thermodynamic properties [Saxena and Hildermann, 1996].
Further complications are due to the presence of multiple phases of the organics, that is,
volatile, semi-volatile, and particle phases, which can interchange depending on the
prevailing ambient meteorological conditions and species concentrations. Also, no single
analytical technique can analyze the entire range of organics present in aerosols [Turpin
et al, 2000].
5
SOURCES OF ORGANIC AEROSOL
Primary organic aerosol particles are emitted directly into the atmosphere by a variety of
sources such as forest fires, biomass burning, oil refineries, chemical plants, pulp and
paper industries, vehicular emissions, producers and users of paints and solvents, meat
cooking and various agricultural activities, to name a few. Some primary aerosols are
emitted from many sources, for example, n-nonadecane (C19) can be emitted from
automobiles, road dust, vegetation, natural gas appliances, asphalt, boilers and wood
burning [Seinfeld & Pandis, 1998]. Some primary organics are emitted by one specific
type of activity and are, therefore, called tracer compounds or marker species for this
particular activity type, for example,
SOURCE
Meat cooking
Cigarette smoke
Biogenic sources
TRACER COMPOUND
Cholesterol
Anteisoalkanes
C27, C29, C31, C33, n-alkanes
REFERENCE
Rogge et al 1991
Rogge et al 1994
Mazurek & Simoneit 1984
Simoneit 1984
Rogge et al 1993
Secondary organic aerosols (SOA), like ozone, are formed as byproducts of gas-phase
photochemical oxidation of volatile organic compounds (VOCs), but whereas the
oxidation of most VOCs results in ozone formation, SOA is generally formed from the
oxidation of low vapor pressure VOCs, that is, those comprised of six or more carbon
atoms [Griffin et al. 1999 ; Grosjean and Seinfeld 1989]. Thus, for calculations of
secondary formation potential estimates, isoprene, benzene and all aliphatic compounds
with six or less carbon atoms, are not considered in this study.
6
Secondary organic aerosol is formed in the atmosphere by the oxidation of volatile
organic gases by oxidants such as OH radical, ozone and the nitrate radical. Oxidation
products which have low volatilities can condense onto existing particles in order to
establish equilibrium between the gas and aerosol phases, thereby forming secondary
organic aerosol via heterogeneous nucleation. Homogeneous nucleation is also a possible
SOA formation mechanism. For example, a stable reaction product of cyclohexene-ozone
oxidation is adipic acid. Assuming that for every 1 ppb of cyclohexene oxidation with
ozone, 0.01 ppb of adipic acid is formed. The saturation mixing ratio of adipic acid is
0.08 ppb, which, based on the previous assumption requires 8 ppb of cyclohexene to be
oxidized by ozone. When the adipic acid mixing ratio reaches saturation (0.08 ppb), then
further cyclohexene-ozone reaction will lead to supersaturation of the gas phase adipic
acid and the excess will condense onto any available aerosol particles or homogeneously
nucleate resulting in SOA production. SOA production, therefore, involves two stages:
1. gas phase oxidation of parent VOC, which is a chemical reaction and
2. partitioning of the oxidation product between gas and particulate phases, which is
a physicochemical process.
-
The chemical reaction pathways involved in stage 1 are complex and not fully
understood and the physicochemical processes leading to gas-to-particle
partitioning are also unclear but are speculated to involve absorption, adsorption
or some combination of these two processes.
Under peak photochemical smog conditions, when non-attainment of ozone and PM2.5
usually occurs, as much as eighty (80) percent of the observed organic particulate carbon
can be secondary in origin [Turpin & Huntzicker, 1995].
7
Organic particulate matter can be speciated using a number of analytical techniques such
as :







Gas Chromatography-Mass Spectroscopy [Rogge et al 1993]
Gas Chromatography-Flame Ionization Detector [Mazurek et al 1997]
Carbon isotope analysis [Johnson and Dawson, 1993; Kaplan and Gordon, 1994;
Hildemann et al., 1994]
Fourier Transform Infrared Spectroscopy (FTIR) [Mylonas et al., 1991; Pickle et
al., 1990]
High Pressure Liquid Chromatography-Ultraviolet/Visible detector [Gorzelska et
al., 1992]
MALDI – Matrix Assisted Laser Desorption/Ionization [Mansoori et al., 1996]
Thermal Desorption Particle Beam-Mass Spectroscopy [Ziemann and Tobias,
1999]
However, no analytical method by itself is able to distinguish between primary and
secondary organic material. This is due to the fact that some secondary products can also
be emitted by primary sources, for example, adipic acid is a by product of the
cyclohexene-ozone oxidation but is also emitted from meat cooking and wood burning
sources [Seinfeld and Pandis, 1998]. Hence, species can be identified but whether their
source is primary or secondary really cannot be determined by analytical methods only.
Additional assumptions must be used to make an estimate of the relative contribution of
primary and secondary organics to total PM2.5 mass.
Knowledge of the estimated secondary organic aerosol formation potential and the main
precursor species which contribute most to this fraction can lead to the institution of
better controls, especially during summertime periods when photochemical conditions are
ideal and exceedences are observed, and can mean the difference between attainment and
non-attainment.
8
Because of the complexity of SOA reaction pathways, the vast number of products
formed by photochemical oxidation of primary aerosol, and the costly analytical methods
required for speciation, indirect methods for quantitative assessment of SOA have
become very useful.
Literature review reveals three main empirical methods of estimating the secondary
organic aerosol (SOA) component of PM:

OC/EC ratios [Turpin and Huntzicker, 1991]

Fractional Aerosol Coefficient method (FAC) [Grosjean, 1992]

Gas/Particle Partitioning method [Pankow, 1994; Odum et al., 1996]
The first method will be used in this study to estimate the contribution of SOA to total
PM2.5 mass in metropolitan cities in south Georgia and the second method will be used
to estimate the relative species contribution of compounds found in mobile emissions of
these cities to SOA formation.
OC/EC Ratio Method
Elemental carbon, (EC), is predominantly formed through combustion processes and is
emitted into the atmosphere in particulate form. It is, therefore, a good tracer for primary
carbonaceous aerosol of combustion origin. Organic aerosol can be emitted directly in
particulate form (primary organic aerosol) or formed in the atmosphere from products of
photochemical oxidation of precursor reactive gases called Volatile Organic Carbon
(VOCs) or Reactive Organic Gases (ROGs) by various authors. The latter aerosol type is
called secondary organic aerosol (SOA).
9
This method is based on the observation that background OC/EC ratios are much smaller
than OC/EC ratios found during peak photochemical periods. This is expected since EC
is unaffected by photochemical oxidation reactions whereas primary OC is the precursor
of secondary OC. By participating in oxidation reactions, the OC fraction is increased
resulting in an increased OC/EC ratio.
In order for this method to be used for secondary OC estimation, an estimate of the
primary OC/EC ratio is first needed. OC/EC emissions vary from source to source and
hence the primary OC/EC ratio will be influenced by local sources, meteorology, as well
as diurnal and seasonal fluctuations in emissions. Therefore, it is only possible to
determine the range in which the primary ratio is likely to fall rather than using a specific
OC/EC ratio. This range will be determined by using the lowest evening/nightime
average OC/EC ratio observed for each period and location studied. The rationale for this
will be discussed later in this paper.
Experimental Procedure
The data used in this study were obtained during the “Fall Line Air Quality Study”
(FAQS) in summer 2000. The FAQS project was initiated in response to observed poor
air quality in Augusta, Macon and Columbus, which are metropolitan areas located south
of Georgia’s Fall Line. Table 1 provides details on the days during which poor air quality
was observed in these cities.
10
Table 1
Number of days with peak 8-hour averaged ozone concentrations exceeding 0.08 ppmv, 1997-1999.
Site
Augusta
Macon
Columbus – Airport
Columbus – Crime Lab
1997
5
12
1
2
1998
14
18
8
8
1999
8
18
9
13
Table 2
Site
Period of sampling
No. of OC/EC samples taken
Macon – Sandy Beach Park
Augusta – Ft.Gordon
Columbus – North Water Works
June 11-21
June 25-July 10
July 13-23
(11) 24 hr & (2) 12 hr
(13) 24 hr & (3) 12 hr
(10) 24 hr & (2) 12 hr
Facility
Location of sites
Sandy Beach Park, Macon – 10 miles West of downtown Macon.
Ft. Gordon, Augusta – 12 miles SW of downtown Augusta
Lakeside High School, Augusta – 12 miles NW of downtown Augusta
North Water Works, Columbus – 4 miles N of downtown Columbus
Oxbow Learning Center, Columbus – 5 miles S of downtown Columbus.
EXPERIMENTAL
Ambient VOC samples were collected four (4) times daily, at each of the sampling sites
during the sampling period, using evacuated canisters. The times selected for taking the
VOC samples were ~ 0:00, 08:00, 12:00 and 17:00. The VOC samples were analyzed by
The University of California, Irvine using gas chromatography / mass spectroscopy
(GC/MS).
The days during which sampling was conducted, and the number of samples taken at
each site is detailed in Table 2. OC/EC sampling was achieved using an insulated,
11
temperature controlled particle composition monitoring sampling box and pump. A
typical sampling setup can be seen in Figure 1.
In addition to the VOC samples and OC/EC samples, gas phase concentrations of NO,
NOy, CO and ozone were measured continuously over the entire sampling period at each
site. Meteorological parameters such as wind speed, wind direction, ambient temperature,
ambient pressure, solar irradiance and relative humidity were also measured continuously
at each location. The sampling setup used for OC/EC determination is illustrated in
Figure 1 below.
Figure 1
A cyclone separator was used at the sampler inlet to remove particles with aerodynamic
diameter of >2.5 micrometers. An XAD – coated glass denuder was plumbed
downstream of the cyclone head to remove volatile organic species from the sampled
12
aerosol. It is important to remove these volatile species from the aerosol sample since
they can be adsorbed onto the filter media resulting in an overestimation of organic
particulate mass (positive artifacts). Denuder techniques have been deployed for over a
decade by investigators such as Krieger and Hites, 1992; Gundel and Lane, 1998;
Eatough, 1999; Cui et al., 1997; Eatough et al., 1995. The removal of gas species from
the air stream, however, disturbs the delicate equilibrium which exists between the gas
and particle phases, and can result in volatilization of particle phase organics (negative
artifacts).
After being scrubbed for volatile organics, the particulate material is deposited onto a
quartz filter, and any gas phase organics which volatilize off of this first quartz filter is
captured by an XAD – coated quartz filter. The XAD resin increases the filter’s affinity
to organic gases. Thus, the mass of organic material measured on this backup XADcoated quartz filter is added to the OC mass found on the front (first) quartz filter in order
to correct for any negative artifacts that were created during sampling.
The system operated at an average flow rate of 16.7 litres per minute with a total sampled
volume of ~ 24 m-3 over a twenty-four hour period.
The Pallflex 2500 QAT-UP (47mm diameter) quartz filters are prepared for sampling by
pre-firing at 600 oC for 2 hours. The baked filters are then stored in Petri dishes at ~ - 10
degrees until they are fitted into filter packs to be used for sampling. Some of the baked
filters are coated with the XAD resin to be used as backup adsorbers. Following
collection, the filters were placed in air tight Petri dishes and stored at approximately – 10
degrees until analysis was conducted.
13
A thermal optical technique (TOT) was used to determine the organic carbon and
elemental carbon content of the samples. This technique has become very popular for
OC/EC analysis and is detailed in Birch and Cary, 1996.
Data Quality
Field blanks for each sample run were used. The blanks were handled and prepared
exactly like the actual sample. Any mass found on these blanks is, therefore,
representative of contamination due to handling, such as, storing, transporting, loading
and unloading of filters. From these blanks, the detection limit of OC/EC was determined
by using a two-tailed student’s t-distribution and an assumed 95% level of confidence.
The detection limit was calculated as follows:
DLn = cn,avg (B) + tN-1 . sn(B)
Where cn,avg (B) is the average blank concentration for species n, sn(B) is the standard
deviation of the blank distribution for species n, and tN-1 is the t-value for N-1 blanks
(N = total number of blanks) at 95% confidence level of a two-tailed student’s tdistribution.
a)
OC
EC
250
TOT Measurement ( g g-1)
200
y = 1.2829x
R2 = 0.9911
150
100
50
y = 0.3664x
R2 = 0.808
14
0
0
50
100
150
200
-1
NIST Standard (g g )
250
Accuracy estimates for EC (+9%) and OC(-10%) were obtained by comparison of
measurements obtined from TOT analysis of samples with that obtained from analysis by
National Institute for Standards and Technology (NIST).
RESULTS
In practice, (OC/EC)pri is defined as the ambient OC/EC ratio at times when the
formation of SOA is supposed to be negligible. This is the case when there is a lack of
direct sunlight and low oxidant concentrations (such as OH). Based on this definition, it
is reasonable to use the evening / nighttime samples OC/EC ratio as representative of a
background value since the major SOA formation pathway, via OH radical oxidation
[Grosjean and Seinfeld, 1989], stops at night. These evening OC/EC ratios are not ideal
background ratios since daytime and evening VOC sample analysis show similar ambient
concentrations of biogenic VOCs. Thus, even though anthropogenic nighttime VOC
concentrations were generally lower than daytime concentrations, it is still possible to
have little nighttime oxidation of VOCs by ozone and to a lesser extent nitrate radical
(NO3-). This possibility is probably negligible due to reduced nighttime temperatures,
reduced nighttime ozone concentrations and reactivity of VOCs with the nitrate radical is
4 to 5 orders of magnitude slower than with the OH radical [Grosjean and Seinfeld,
1989].
Another possibility which can account for nighttime SOA is condensation of volatile
VOC oxidation products produced during the daytime. The small drop in ambient
15
temperature observed during the nighttime could favor partitioning of gaseous SOA
products into the particle phase.
AUGUSTA
NO
(ppb)
DATE
OZONE
(ppb)
CO (ppb)
OC/EC
RATIO 24
hr
6/28/2000
0.42
33.22
136.8
6/29/2000
0.73
29.95
183.5
6/30/2000
0.62
53.63
284.43
14.20
7/1/2000
0.18
60.26
269.4
19.12
7/2/2000
0.23
55.49
266.1
7/3/2000
0.88
57.99
227.2
14.72
7/4/2000
0.12
50.5
207.5
21.56
7/5/2000
0.21
54.31
235.9
7/6/2000
0.38
49.64
219.6
17.80
7/7/2000
0.69
49.68
300.6
13.75
7/8/2000
0.43
48.47
251.8
12.12
7/9/2000
0.12
48.54
199.7
41.04
AVGE
0.42
49.31
231.88
STD DEV
0.26
9.11
46.38
OC/EC
RATIO 12
HR
DAYTIME
OC/EC
RATIO 12
HR
NIGHTTIME
9.05
7.42
56.12
59.33
25.97
8.02
3.94
MACON
NO
(ppb)
DATE
OZONE
(ppb)
CO (ppb)
OC/EC
RATIO 24
hr
6/11/2000
0.09
42.76
223.9
14.48
6/12/2000
0.23
29.64
214.3
11.64
6/13/2000
0.16
38.13
145.9
13.51
6/14/2000
0.48
28.11
153
6/15/2000
0.39
31.13
150.6
6/16/2000
0.49
25.46
148.6
6/17/2000
1.12
28.41
195.7
19.96
6/18/2000
0.83
26.71
212.9
74.57
6/19/2000
0.27
21.59
148.7
21.58
6/20/2000
0.21
26.02
165.9
14.77
6/21/2000
0.49
32.65
183.1
19.53
AVGE
0.43
30.06
176.60
STD DEV
0.31
6.01
30.39
OC/EC
RATIO 12
HR
DAYTIME
OC/EC
RATIO 12
HR
NIGHTTIME
3.17
25.62
13.31
5.63
15.46
16
COLUMBUS
NO
(ppb)
DATE
OZONE
(ppb)
CO (ppb)
OC/EC
RATIO 24
hr
7/17/2000
2.48
49.21
294.6
7/18/2000
3.43
56.87
319.1
7/19/2000
2.12
50.25
340.6
17.94
7/20/2000
0.85
59.6
280.9
26.81
7/21/2000
1.37
45.67
282.9
40.70
7/22/2000
0.89
40.89
299.5
18.26
7/23/2000
0.36
47.21
246.9
16.61
7/24/2000
0.97
39.98
268.3
7/25/2000
0.37
50.2
271.7
AVGE
1.43
48.88
289.39
STD DEV
1.04
6.51
28.09
OC/EC
RATIO 12
HR
DAYTIME
OC/EC
RATIO 12
HR
NIGHTTIME
19.56
8.51
7.74
9.72
28.42
30.06
Thus, though the nighttime OC/EC ratios are not ideal (OC/EC)pri ratios, they can still be
used as representative of the local primary ratios for these cities.
The secondary component of the organic aerosol can be calculated as follows:
Where
OCsec = OCtot - OCpri
eq 1
OCpri = EC(OC/EC)pri
eq 2
OCtot is measured OC, OCpri is primary OC, EC is measured EC, and (OC/EC)pri is the
primary ratio estimate.
The use of equations 1 & 2 would be supported if episodes identified with secondary
organic aerosol formation corresponded with elevated concentrations of other
photochemical reaction products. Ozone, like SOC, is a photochemically generated
secondary aerosol component. The chemical and dynamical processes involved in
secondary aerosol formation are quite complex (see appendix 1) and it is unlikely that a
linear regression analysis can adequately describe the relationships between SOC and
ozone.
17
Figure :
Macon Secondary OC (SNV) vs Ozone (SNV)
Secondary OC (SNV)
Secondary OC (SNV)
Augusta
Secondary OC (SNV) vs Ozone (SNV)
Ozone (SNV)
Ozone (SNV)
Secondary OC (SNV)
Columbus Secondary OC (SNV) vs Ozone
(SNV)
Ozone (SNV)
18
Therefore, a simple sign test might be more appropriate than linear regression techniques
for testing the relationship between SOC and ozone. In this test, variables are expressed
in terms of their standard normal variate (SNV) and correlations are sought between the
signs of those values [Turpin et al 1991].
The standard normal variate is calculated as follows:
SNV = ( X – Xav) / 
Where X is the concentration in gm-3, Xav is the average concentration at that site, and 
is the standard deviation of the concentrations at that site. A data set presented in the
form of standard normal variates has a mean of zero and a standard deviation of 1. The
purpose of a non-parametric sign test is to determine whether or not the signs of two
variables are positively or negatively correlated at a certain level of significance. For a
data set A, such that A = a1, a2, a3 etc. and a data set B, where B = b1, b2, b3 etc., if A and
B are positively correlated, then ai and bi are either both positive or both negative. If A
and B are negatively correlated, then when ai is positive, bi is negative and vice versa.
Thus, on the plots shown above, plotted points lying in the first and third quadrants
represent positive correlations whereas, points lying in quadrants two and four are
negatively correlated. No correlation exists if exactly 50 % of the data sets have the same
signs. The level of significance of a positive sign correlation is the probability of
observing a greater or equal fraction of positive products in a sample set that follows a
binomial distribution. Fractions greater than 0.71 are significant at 95% confidence
intervals.
19
From the plots above, the level of significance between the SNV of secondary organic
carbon and SNV of ozone are 0.73, 0.67 and 0.75 for Macon, Augusta and Columbus
respectively.
These results support the use of equations 1 & 2 for quantifying SOC. Positive
correlations between compounds emitted or formed in the same vicinity are usually
expected since they are equally affected by local meteorological factors, common
transport and common chemical processes leading to their formation. Thus, the observed
> 95% level of significance correlations found in Macon and Columbus. Although less
than 95% confidence level of correlation was observed in Augusta, the result of the above
analysis shows that there is still a positive correlation between SOC and ozone, though
not as strong as for Columbus and Macon.
Possible insight into the reason for this occurrence in Augusta could be that the bulk of
SOA is not formed at the same location, rate or time as ozone. Ozone photochemistry is
much more dynamic than SOA photochemistry. Ozone concentrations can rise and fall
but the concentration of SOA would continue to accumulate, the major loses of SOA
being deposition and dilution. Meteorological conditions can, therefore, greatly influence
the observed correlation between SOA and ozone especially over a 24 hour averaged
period.
Figure:
20
Graph of Calculated Secondary Organic Carbon as a
percentage of Total Organic Carbon and Total PM2.5
mass in Augusta
100
SOC %
90
80
70
60
SOC % of TOC
50
40
SOC % of Total
PM2.5 mass
30
20
6/
28
/2
6/ 000
29
/2
6/ 000
30
/2
0
7/ 00
1/
20
7/ 00
2/
20
7/ 00
3/
20
7/ 00
4/
20
7/ 00
5/
20
7/ 00
6/
20
7/ 00
7/
20
7/ 00
8/
20
7/ 00
9/
20
00
10
0
Date
Figure:
21
Graph of calculated Secondary Organic Carbon as
a percentage of Total Organic Carbon and of Total
PM2.5 mass in Columbus
90
80
SOC %
70
60
50
SOC % of TOC
40
SOC % OF Total PM
mass
30
20
10
7/
16
/2
7/ 000
17
/2
7/ 000
18
/2
7/ 000
19
/2
7/ 000
20
/2
7/ 000
21
/2
7/ 000
22
/2
7/ 000
23
/2
7/ 000
24
/2
7/ 000
25
/2
00
0
0
Date
Figure
Graph of Secondary Organic Carbon as a percentage
of Total Organic Carbon and Total PM2.5 mass in
Macon
120
SOC % of
TOC
80
60
SOC % of
Total PM2.5
mass
40
20
0
6/
11
/2
00
6/
12 0
/2
00
6/
13 0
/2
00
6/
14 0
/2
00
6/
15 0
/2
00
6/
16 0
/2
00
6/
17 0
/2
00
6/
18 0
/2
00
6/
19 0
/2
00
6/
20 0
/2
00
6/
21 0
/2
00
0
SOC %
100
Date
22
SOA accounted for greater than 70% of TOC for 50% the sampled days in Columbus,
83% of the sampled days in Augusta and 100% of the days sampled in Macon as
illustrated above. Using a primary OC/EC ratio of 3.94 for **********, well above the
literature range of 1.4 to 2.9 [Turpin et al 1992, Turpin and Huntzicker,1991], the daily
SOC contribution to total PM2.5 mass averaged around, 20% in Columbus, 18% in
Augusta and 23% in Macon. These are really lower estimates since the primary OC/EC
ratio used was a high estimate. Even using this conservative estimate of SOC, an average
20% contribution to total PM2.5 mass is high and significant.
A better understanding of the diurnal variation of the primary OC/EC ratio and more time
resolved OC/EC data is needed to determine the significance of secondary organic
aerosol formation to total PM2.5 mass variations.
Further analysis is conducted below to determine the major SOA precursor species.
FRACTIONAL AEROSOL COEFFICIENT (FAC)
The Fractional Aerosol Coefficient approach for determining secondary organic aerosol
yield is based on measurements of the total aerosol formed in smog chamber reactions of
a specific precursor species and a specific oxidant. Since the reaction mechanism is not
known, the kinetics and reaction rate constants are also not known. The smog chamber
data are, therefore, used to empirically derive the reaction stoichiometry, that is, to
determine the amount of condensed matter formed per gram of reactant. This quantity is
called the fractional aerosol coefficient or fractional aerosol yield. The aerosol yield can
be expressed on a molar, mass or carbon concentration basis [Grosjean and Seinfeld,
1989]. This dimensionless ratio of mass concentration is defined by Grosjean, 1992 as:
23
FAC = aerosol from VOC (gm-3) / initial VOC (gm-3)
With this definition, and knowing the VOC emission rate and the fraction of VOC that
has reacted in the atmosphere, the amount of aerosol formed from each VOC can be
calculated as:
Amount of aerosol produced = (amt. of VOC emitted) x (fraction of VOC reacted)
x (FAC)
For this study, the FAC values compiled by Grosjean and Seinfeld, 1989 were used.
Reactivity was calculated using VOC reaction with OH radical as the main oxidant
[Grosjean & Seinfeld, 1989], assuming [OH] = 1 x 106 molecules cm-3 and rate constants
were also taken from Grosjean and Seinfeld 1989.
The FAC is a very crude first order approximation to SOA formation. It summarizes the
complicated oxidation-condensation processes that govern SOA formation into one
constant for each precursor VOC species. It is, however, very useful since secondary
organic aerosol can be treated as primary emissions by applying the FAC method. It is
noteworthy to mention that aerosol formation varies with many factors such as oxidant
concentration, temperature, relative humidity, and existing aerosol concentration in the
ambient air. Thus, the results obtained from this study are estimates of secondary organic
aerosol formation potentials rather than quantification of SOA formation.
In this study, the FAC method was applied to aromatic species which were common to
both the VOC samples analyzed and the mobile emissions profile species. Such analysis
allows for the identification of those species present in mobile emissions which have the
greatest impact on atmospheric loading of SOA. Such information is useful as model
24
input data for the simulation of the effect of reducing the atmospheric concentration of
this species on overall PM2.5 mass.
Data input required for this analysis were daily county mobile VOC emissions which was
obtained from Georgia EPD, Air Protection Branch. Mobile source emissions are
currently determined by US EPA by using a processing software called SMOKE. This
program’s input data set includes county specifics such as road types, vehicle miles
traveled (VMT), gridded emissions of pollutants (VOC, NOx, CO), and emission factors
such as fuel volatility, fuel type, speeds, temperature, mode of operation of vehicle, to
name a few [www.epa.gov/ttn/chief/software/speciate]. From the VOC mobile emissions
data, a speciation profile [Sagebiel et al 1996] for gasoline and diesel fuel was applied to
determine the emission of the aromatic aerosol forming species from these two fuel types.
TABLE 3: VOC EMISSION RATES AND AMOUNT OF DAILY SOA PRODUCED IN EACH
COUNTY FOR 6 HOUR EPISODE BY PARENT VOC SPECIES.
SPECIES
1012 x
k(298K)
(cm3
molec-1
s-1)
Fraction
of
species
reacted
Daily
Emission
in kg
Fractional
aerosol
coefficient
Species
nonane
10.2
n-heptane
7.15
8.02E01
8.57E01
2-methylheptane
9.8
8.09E01
3-methylheptane
9.9
octane
8.68
toluene
5.96
8.07E01
8.29E01
8.79E01
m-xylene
23.6
8.58E01
6.01E01
p-xylene
14.3
7.34E-
ethylbenzene
7.1
Amt. of
aerosol
produced
(kg [6 hr
episode])
Muscogee
Amt. of aerosol
produced (kg [6 hr
episode])Richmond
Amt. of aerosol
produced (kg [6 hr
episode])Columbia
Amt. of
aerosol
produced
(kg [6 hr
episode])
Bibb
Muscogee
Richmond
Columbia
Bibb
7.70E-03
0.015
9.27E-05
1.21E-04
4.98E-05
1.11E-04
5.94E-02
0.0006
3.05E-05
3.00E-05
1.64E-05
3.68E-05
4.93E-02
0.005
2.00E-04
2.65E-04
1.29E-04
2.40E-04
4.39E-02
0.005
1.77E-04
2.35E-04
9.54E-05
2.13E-04
2.71E-02
0.0006
1.35E-05
1.79E-05
7.26E-06
1.62E-05
8.62E-01
0.054
4.09E-02
5.45E-02
2.21E-02
4.93E-02
1.70E-01
0.054
7.90E-03
1.05E-02
4.26E-03
9.52E-03
3.20E-01
0.047
9.02E-03
1.20E-02
4.86E-03
1.09E-02
3.20E-01
0.016
3.75E-03
5.00E-03
2.02E-03
4.52E-03
25
01
13.7
7.44E01
2.45E-01
0.05
9.11E-03
1.21E-02
4.91E-03
1.10E-02
3-ethyltoluene
19.2
6.61E01
7.71E-02
0.063
3.21E-03
4.27E-03
1.73E-03
3.86E-03
4-ethyltoluene
12.1
7.70E01
8.51E-02
0.025
1.64E-03
2.17E-03
8.82E-04
1.97E-03
57.5
2.89E01
1.02E-01
0.029
8.55E-04
1.14E-03
4.61E-04
1.03E-03
2-ethyltoluene
12.3
7.67E01
1.93E-01
0.026
3.85E-03
5.12E-03
2.07E-03
4.63E-03
124-TMBenzene
32.5
4.96E01
3.23E-01
0.017
2.72E-03
3.62E-03
1.47E-03
3.28E-03
32.7
4.93E01
8.59E-02
0.014
5.93E-04
7.88E-04
3.20E-04
7.14E-04
isopropylbenzene
6.6
8.67E01
2.98E-02
0.007
1.81E-04
2.41E-04
9.76E-05
2.18E-04
n-propylbenzene
5.8
8.82E01
5.04E-02
0.007
3.11E-04
4.14E-04
1.68E-04
3.75E-04
o-xylene
135-TMBenzene
123-TMBenzene
Rate constants were obtained from Grosjean and Seinfeld, 1989.
Fraction of species reacted was calculated using rate constants and an assumed [OH] = 1 x 106
molecules cm-3.
Daily emissions obtained from daily mobile emissions data generated for these counties by
Georgia EPD (using a SMOKE model). Emissions were given for eight vehicle types and twelve
road types giving a total of ninety-six categories. These were then divided into gasoline operated
and diesel operated vehicles and the respective species profile was then applied to determine the
species emissions.
Fractional Aerosol Coefficients for these species were obtained from Grosjean and Seinfeld,
1989.
FIGURE 5 : ESTIMATED DAILY AMOUNT OF AEROSOL PRODUCED DURING A 6 HOUR
EPISODE IN EACH COUNTY STUDIED
6.00E-02
5.00E-02
4.00E-02
M uscogee
3.00E-02
Richmond
2.00E-02
Columbia
1.00E-02
Bibb
oc
ta
ne
to
lu
en
et
e
hy
lb
en
ze
ne
m
-x
yl
en
e
pxy
le
ne
oxy
le
3ne
et
hy
lto
lu
4en
et
e
hy
lto
13
lu
en
5TM
e
Be
nz
en
2et
e
hy
lto
12
lu
en
4TM
e
Be
12
nz
3en
TM
e
Be
is
n
op
z
e
ro
ne
py
lb
en
nze
pr
ne
op
yl
be
nz
en
e
0.00E+00
no
na
ne
nhe
2pt
an
m
et
e
hy
lh
ep
3ta
m
ne
et
hy
lh
ep
ta
ne
Aerosol produced [kg per 6 hr episode]
Daily Am ount of Aerosol Produced (kg [6 hr episode]
Species
26
The Augusta metropolitan area lies midway between Columbia and Richmond counties
hence these two counties’ mobile emissions would impact on Augusta. These were,
therefore both considered in the calculations performed. As can be seen from the above
graph, toluene is estimated to be the major SOA precursor species in each county studied,
accounting for approximately 48% of the total SOA potential atmospheric loading.
This finding is important since toluene is a carcinogen and is a toxic/hazardous air
pollutant. Reducing the toluene content of gasoline would, therefore, have beneficial
health effects as well as potentially lower PM2.5 mass concentrations.
CONCLUSIONS
From this study, it is shown that secondary organic aerosol (SOA) contributes
significantly to the total PM2.5 mass, averaging around 30%, as a lower estimate, on
most of the days sampled. These metropolitan areas studied have significant vegetation
coverage and the SOA contribution to total organic carbon was estimated to exceed 70%
on almost all days. One possible explanation for this observation can be due to the
significant biogenic emissions found in these counties as compared to highly urbanized
metropolitan areas. The lack of mass transit facilities in Macon, Augusta and Columbus,
implies that commuting is achieved primarily via personal vehicles. The combined effect
of high aerosol forming biogenic VOCs with mobile NOx sources could create the right
conditions for ozone and SOA formation.
Of the mobile emissions studied, toluene is estimated to be the largest potential SOA
contributor. The details of SOA formation and its chemical composition are only partially
27
known but empirical data can be used to estimate the formation potential of precursor
gases, if their source species profile is available as well as their emissions data.
For quantitative estimates of ambient SOA concentrations, this empirical approach
neglects important variables such as timescales involved in SOA formation, transport
factors, relative humidity influences, competition between VOC species, synergistic
reactions of VOC species and other possibilities that exist in ambient gas mixtures that do
not exist in controlled chamber studies.
Despite these limitations, FACs can be used to compare the relative importance of VOC
sources for SOA formation. This study was limited to mobile sources of VOC since
species profiles were available. It would be extremely useful to conduct similar type
calculations for biogenic emissions, however, more research is needed to determine the
species profiles for the main vegetation types that exist and impact on these cities.
OPTIONS FOR REDUCING SOA
Despite the difficulties in quantification, some qualitative conclusions can be drawn from
this study:
1. The reduction of toluene content in gasoline is a possible means of reducing SOA.
Toluene is a toxic/hazardous air pollutant as well as a major SOA contributor and
it may well be more than worthwhile to investigate the possibilities of reducing
the toluene content of gasoline. However, detailed cost-effect analyses must first
28
be conducted to determine the feasibility of this option, similar to the studies that
lead to implementation of reformulated gasoline in Georgia in 1995.
2. The photochemistry of VOC is highly dependent on OH availability which is
coupled to NOx availability. The same is true for ozone photochemistry. OH and
NOx concentrations are dependent on VOCs with less than 6 carbons. Thus, it
seems that reducing ozone would result in a corresponding decrease in SOA.
Therefore, targeting selected VOC species might not be as important as it fist
seems. It is not yet possible to decide which of these two given options, reducing
selected VOC species versus reducing ozone, is more cost effective or whether
some combination of the two approaches is a better option. More research,
analysis and detailed modeling efforts are needed to draw further conclusions.
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Atkinson, R. Atmospheric Environment 24A, No 1, pp 1-41, 1990
Barthelmie, R.J; Pryor, S.C. Science of the Total Environment 205 (1997) 167-178
Baumann, K et al. ************
Castro, L.M. et al. Atmospheric Environment 33, (1999) 2771-2781
Chameides, W et al. **********
Griffin, R.J. et al. JGR, Vol 104, No D3, pp 3555-3567, 1999
Grosjean, D. Atmospheric Environment, 26A, No.6, pp 953-963, 1992
Grosjean, D; Seinfeld, J. Atmospheric Environment, Vol 23, No 8, pp 1733-1747, 1989
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Odum, J et al. ES&T 1996, 30, 2580-2585
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SMOKE Tool for Models-3 version 4.1 Structure and Operational Documentation, US EPA.
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Turpin, B.J; Huntzicker, J.J. Atmospheric Environment, 1991, 25, 1788-1793.
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30