SECONDARY ORGANIC AEROSOL FORMATION POTENTIAL IN
SOUTH GEORGIA
Comprehensive Exam Paper
Submitted by
Venus Dookwah.
March 3, 2003. 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 calculated for three cities in south Georgia by using ambient data collected and estimates of background organic carbon/elemental carbon ratio. Nonparametric sign correlations comparing calculated secondary organic carbon with another secondary photo-oxidation product, ozone, supported this method of quantifying secondary organic carbon. Secondary organic carbon appears 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. IMPORTANCE OF ORGANIC COMPONENT OF PM2.5
Approximately 10-70 percent of the total dry fine atmospheric particulate matter [mass], is organic material [Turpin et al 2000 this could be narrowed down further for SE vs W
US; see eg ASSE99, SEARCH, ASACA, FAQS, etc.]. 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 ug m-3
24 hour average of 65 ug m-3
One of the main reasons that PM2.5 is regulated is because of its high correlation to adverse human health effects. PM2.5 bypasses our respiratory defenses, such as the ciliated mucous linings, and is 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. 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 [of what; how is this number related to the above 10-70 %?], 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 and by acting as cloud condensation nuclei, thereby influencing the properties of clouds. This can result 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. 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 either enhance or inhibit deliquescence of atmospheric particles which has associated climatic impacts.
In summary, organic aerosols are significant because:
- they can contain toxins which can cause deleterious health effects, if inhaled
- 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 due to optically active organic species
- they play a role in cloud condensation nuclei, thereby affecting precipitation
patterns
- they contribute to photochemical reactions affecting tropospheric ozone formation
and removal of atmospheric oxidizing species such as OH, O3, and NO3.
Even though a dominant 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. Also, no single analytical technique can analyze the entire range of organics present in aerosols [Turpin et al, 2000].
SOURCES OF PRIMARY AND SECONDARY ORGANICS
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 and other agricultural activities, to name a few.
Secondary organic aerosols are formed as byproducts of gas-phase photochemical reactions of volatile organic compounds (VOCs), ozone and nitrogen oxides. Secondary organic products which have low vapor pressure can then condense onto existing aerosol particles (heterogeneous nucleation) in order to establish equilibrium in the gas phase or if their molar concentrations are sufficiently high, they can homogeneously nucleate.
There are thousands of reactions that occur between various oxidants and VOCs. The reaction pathways and products of these many reactions are not well understood.
However, for clarity, a specific example will be used for illustration purposes. A stable reaction product of VOC oxidation is adipic acid which is formed from the reaction of cyclohexene with ozone. Adipic acid has a saturation vapor pressure of 0.08 x 10-9 atm.
With sufficient molar yields, low vapor pressure hydrocarbons can significantly impact the concentration of organic aerosols. When the vapor pressure of the hydrocarbon reaches its saturation vapor pressure, condensation onto existing particles begins or it can homogeneously nucleate thereby forming new particles [slightly redundant to the above better stick to your example with adipic acid here]. The hydrocarbon can then exist in both aerosol and gas phases until the partial pressure is lowered and evaporation from the particles occurs. Lowering of the concentration is achieved by:
- dilution due to atmospheric transport (winds, convection)
- lack of oxidants available to produce more product[??]
- decreased emission of VOC precursor
- other gas/particle reactions which can deplete product concentration.
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]no more recent ref available?
Cabada’s latest and Turpin’s overview papers and literature cited therein may give more recent numbers.
Organic particulate matter can be speciated using a number of analytical techniques such as :
GC-MS GC-FID Carbon isotope analysis Fourier Transform Infrared Spectroscopy (FTIR), Raman, NMR[nuclear magnetic resonance], X-ray adsorption and other spectroscopic methods HPLC-MS, LC-MS MALDI – Matrix Assisted Laser Desorption/Ionization
However, no analytical method by itself is able to distinguish between primary and secondary organic material. 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, and can mean the difference between attainment and non-attainment.
Literature review reveals three main methods of estimating the secondary organic aerosol
(SOA) component of PM:
OC/EC ratios [also called EC tracer method] [Turpin and Huntzicker, 1991]
Fractional Aerosol Coefficient method (FAC) [Grosjean, 1992]
Gas/Particle Partitioning method [Pankow, 1994]
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).
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. One of these indirect methods is Turpin and Huntzicker’s OC/EC ratio method.
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 [to some extent] 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]delete or rewrite based on below. 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. See Appendix 1.
Number of days with peak 8-hour averaged ozone concentrations exceeding 0.08 ppmv, 1997-1999.
Site 1997 1998 1999 Augusta 5 14 8 Macon 12 18 18 Columbus – Airport 1 8 9 Columbus – Crime Lab 2 8 13
Site Period of sampling No. of OC/EC samples taken Macon – Sandy Beach Park June 11-21 (11) 24 hr & (2) 12 hr Augusta – Ft.Gordon June 25-July 10 (13) 24 hr & (3) 12 hr Columbus – North Water Works July 13-23 (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 [not listed]
North Water Works, Columbus – 4 miles N of downtown Columbus Oxbow Learning Center, Columbus – 5 miles S of downtown Columbus [not listed].
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). See Appendix 3 for list of chemical compounds identified during VOC sampling. OC/EC sampling was achieved using a particle composition monitor (PCM) as described in Baumann et al., [2002]ing sampling box and pump. A typical sampling setup can be seen below [note, that the set-up you show there was not used during FAQS 2000!]:
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 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
PM2.5. The removal of these gas species, 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 XAD- coated 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. Note that due to shortage in sample media, we were not able to consequently sample with XAD-denuded channels, and also operated difference methods; see FAQS_KBpcm.doc in “Reports” folder on w://karsten.
The system operated at an average flow rate of 16.7 litres per minute with a total sampled volume of ~ 24 m-3.
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. A thermal optical technique (TOT) was used to determine the organic carbon and elemental carbon content of the samples. A precision tool was used to punch out a 1.5 cm2 section from the filter sample for TOT analysis.
Analysis of the quartz filters involves 2 stages. During the first stage the sample is heated in a pure helium atmosphere as the temperature is gradually stepped from 50 oC to 220oC, then to 400 oC, 550 oC and 890 oC within 4.5 minutes. This allows for the volatilization of the organic and carbonate carbon content of the sample. The evolved carbon is
o catalytically oxidized to CO2 using MnO2 at ~ 890 C, reduced to CH4 (in a Ni/firebrick
o methanator at ~500 C), and then quantified as CH4 by a flame ionization detector (FID).
In the second stage elemental carbon is measured. The oven temperature is lowered, an
o o o O2/He mixture is introduced and the temperature is raised from 480 C, 550 C, 600 C,
o o o 670 C, 750 C, to 890 C within another period of approximately 4 minutes. As O2 enters the oven, the elemental carbon generated by heat is oxidized to CO2 and the filter’s transmittance increases concurrently, as the darkened carbon (soot) is driven off. A He-
Ne laser light is passed through the filter to monitor transmittance as temperature and atmosphere are controlled. The point at which the filter transmittance reaches its initial value is referred to as the “split” between organic and elemental carbon. Carbon evolved prior to the split is considered “organic” (including carbonate), and carbon volatilized after the split is considered “elemental”.
For the XAD coated quartz analysis, in stage 1, condensed gas phase of organic carbon is catalytically oxidized to CO2 in a pure He atmosphere as the temperature is stepped up to
176oC from ~ 50oC within 1 minute and held at 176oC for a period of 3 minutes. Then
CO2 is reduced to CH4 and quantified by the flame ionization detector. No O2 is introduced into the oven and no elemental carbon is generated and measured. The split point is manually set before the internal calibration is initiated in order to limit the FID signal integration to the actual sample.
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 t- distribution.
a) OC EC
250
200 ) 1 - y = 1.2829x
g 2 R = 0.9911 g 150 (
t n e m e r u
s 100 a e M
T O T 50 y = 0.3664x R2 = 0.808
0 0 50 100 150 200 250 NIST Standard (g g-1) b) OC EC
20
y = 0.9035x R2 = 0.9661
15 ) 3 - m
g
( 10
T I G
5
y = 1.09x + 0.34 R2 = 0.87
0 0 5 10 15 20 25
NIST (g m -3)
The “b)” plot from the ASSE Artifact paper Fig. 2 should be used here, which is the comparison of same samples with NIST. 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. The in situ formation may stop, correct, but that does not mean that air masses are impacting the site that have substantial fractions of
SOA. O3 is not zero in the evenings yet, most evenings show >50 ppb O3 well above the regional background. Early morning is when O3 is typically below reg backgrd in urban environments, ie titrated away from primary emissions, and that seems the more plausible time to estimate primary ratio, and also rather use the early morning VOC sample. 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-) don’t quite follow your reasoning here??. 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]. The main reason for nighttime O3 titration is due to [mainly vehicular] NOx and to a lesser extent due to reactive VOCs; eg olefins, ketones, which might no be abundant enough in the nocturnal surface layer…
Another possibility which can account for nighttime SOA is condensation of volatile
VOC oxidation products produced during the daytime. The small drop in ambient temperature observed during the nighttime could favor partitioning of gaseous SOA products into the particle phase. Site Location Date evening Daytime avg Evening avg Temperature sample taken temp /oC Temp /oC drop Augusta July 5th 2000 31.74 28.96 2.78 Columbus July 18th 2000 32.37 26.13 6.24 Macon June 16th 2000 26.19 21.82 4.37
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:
OCsec = OCtot - OCpri eq 1
Where OCpri = EC(OC/EC)pri -b eq 2
OCtot is measured OC, OCpri is primary OC, EC is measured EC, and (OC/EC)pri is the
primary ratio estimate, and b is the non-combustion contributor to SOC.
Augus ta C o l u m b u s S e c o n d a r y O C ( S N V ) v s O z o n e ( S N V ) Se condary OC (SNV) vs Ozone (SNV)
y = 0.5668x - 0.8386 R2 = 1 ) V N S (
C O
y r a d n o c e S
Ozone (SNV) O z o n e ( S N V ) Macon Secondary OC (SNV) vs Ozone (SNV) ) V N S (
C O
y r a d n o c e S
Ozone (SNV)
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 SOCC (secondary organic carbon), is a photochemically generated secondary aerosol(?) component. The chemical and dynamical processes involved in secondary aerosol formation are quite complex (see appendix 2) and it is unlikely that a linear regression analysis can adequately describe the relationships between SOC and ozone.
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) / -3 Where X is the concentration in gm , 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.
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 relatively quickly due to faster chemical production and loss mechanisms relative to
SOA, butwhile the concentration of SOA would continue to accumulate, the major losses of SOA being the relatively slow surface deposition [dilution due to physical mixing could be considered the same for ozone and PM2.5 in this conceptual view here]and dilution. Meteorological conditions can, therefore, greatly influence the observed correlation between SOA and ozone especially over a 24 hour averaged period. More time resolved SOA and ozone data would probably give more insight into the correlation between these two photo-oxidation products. More insight yes, may be, but you would still need to autocorrelate them, ie need to know what the rate-difference is between
P(O3) and P(SOA) and the subsequent time-delay in observed concentration peaks!!
SOC as a % of Total PM 2.5 m ass and TOC in Colum bus
100
90
80
70
60 % SOC % of total mass C 50 O SOC % of TOC S 40
30
20
10
0 7/16/00 7/17/00 7/18/00 7/19/00 7/20/00 7/21/00 7/22/00 7/23/00 7/24/00 7/25/00 Date SOC as a % of Total PM2.5 m ass and TOC in Augusta
100
90
80
70
60 % SOC % of total mass C 50
O SOC % of TOC S 40
30
20
10
0 6/28/00 6/29/00 6/30/00 7/1/00 7/2/00 7/3/00 7/4/00 7/5/00 7/6/00 7/7/00 7/8/00 7/9/00 Date
SOC as a % of Total PM2.5 m ass and TOC in Macon
100
90
80
70
60 %
C SOC % of total mass 50 O SOC % of TOC S 40
30
20
10
0
Date
SOA accounted for greatermore than 70% of TOC for 50% of 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 Augusta, 7.74 for
Columbus and 3.17 for Macon, well above the literature range of 1.4 to 2.9 [Turpin et al
1992, Turpin and Huntzicker,1991][these might be so high because the intercept “b” was not separated out; also, Cabada’s data set ranged bewteen 0.9 and 3.1, a bit wider range than B Turpin’s], the daily SOC contribution to total PM2.5 mass averaged around, 20% in Columbus, 18% in Augusta and 23% in Macon [add +- STDs]. 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)
Based on chamber studies, the fraction of VOC converted into aerosol, called aerosol yield, can be expressed on a molar, mass or carbon concentration basis [Grosjean and
Seinfeld, 1989]. The dimensionless ratio of mass concentration is called the fractional aerosol coefficient [Grosjean 1992] and is defined as:
FAC = aerosol from VOC (ugm-3) / initial VOC (ugm-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 (see Appendix 2?). 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 Did the ratios of those species compare well also? If not, you would have an indication of potentially significant influence from other sources, either within each community or imported from outside!!. 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 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)[they do not use actual fuel consumed?, 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[how about vehicle fleet composition, ie fraction of old vs new vehicles? [www.epa.gov]. From the mobile emissions lumped VOC data, a speciation profile [Sagebiel et al 1996] was used to determine the emission of the aromatic aerosol forming species identified in our experimental VOC sampling.
TABLE OF DAILY SOA FORMATION POTENTIAL FOR 6 HOUR EPISODE
Amt. of Amt. of 1012 x aerosol aerosol k(298K) Fractio produced produced (cm3 n of Fractional (kg [6 hr Amt. of aerosol Amt. of aerosol (kg [6 hr molec-1 species Daily aerosol episode]) produced (kg [6 hr produced (kg [6 hr episode]) SPECIES s-1) reacted Emission coefficient Muscogee episode])Richmond episode])Columbia Bibb Species Muscogee Richmond Columbia Bibb 8.02E- nonane 10.2 01 7.70E-03 0.015 9.27E-05 1.21E-04 4.98E-05 1.11E-04 8.57E- n-heptane 7.15 01 5.94E-02 0.0006 3.05E-05 3.00E-05 1.64E-05 3.68E-05
8.09E- 2-methylheptane 9.8 01 4.93E-02 0.005 2.00E-04 2.65E-04 1.29E-04 2.40E-04
8.07E- 3-methylheptane 9.9 01 4.39E-02 0.005 1.77E-04 2.35E-04 9.54E-05 2.13E-04 8.29E- octane 8.68 01 2.71E-02 0.0006 1.35E-05 1.79E-05 7.26E-06 1.62E-05 8.79E- toluene 5.96 01 8.62E-01 0.054 4.09E-02 5.45E-02 2.21E-02 4.93E-02 8.58E- ethylbenzene 7.1 01 1.70E-01 0.054 7.90E-03 1.05E-02 4.26E-03 9.52E-03 6.01E- m-xylene 23.6 01 3.20E-01 0.047 9.02E-03 1.20E-02 4.86E-03 1.09E-02 7.34E- p-xylene 14.3 01 3.20E-01 0.016 3.75E-03 5.00E-03 2.02E-03 4.52E-03 7.44E- o-xylene 13.7 01 2.45E-01 0.05 9.11E-03 1.21E-02 4.91E-03 1.10E-02 6.61E- 3-ethyltoluene 19.2 01 7.71E-02 0.063 3.21E-03 4.27E-03 1.73E-03 3.86E-03 7.70E- 4-ethyltoluene 12.1 01 8.51E-02 0.025 1.64E-03 2.17E-03 8.82E-04 1.97E-03 2.89E- 135-TMBenzene 57.5 01 1.02E-01 0.029 8.55E-04 1.14E-03 4.61E-04 1.03E-03 7.67E- 2-ethyltoluene 12.3 01 1.93E-01 0.026 3.85E-03 5.12E-03 2.07E-03 4.63E-03 4.96E- 124-TMBenzene 32.5 01 3.23E-01 0.017 2.72E-03 3.62E-03 1.47E-03 3.28E-03 4.93E- 123-TMBenzene 32.7 01 8.59E-02 0.014 5.93E-04 7.88E-04 3.20E-04 7.14E-04 8.67E- isopropylbenzene 6.6 01 2.98E-02 0.007 1.81E-04 2.41E-04 9.76E-05 2.18E-04
8.82E- n-propylbenzene 5.8 01 5.04E-02 0.007 3.11E-04 4.14E-04 1.68E-04 3.75E-04
Daily Am ount of Aerosol Produced (kg [6 hr episode]
6.00E-02 ] e
d 5.00E-02 o s i p e 4.00E-02 r
h M u s c o g e e
6
r
e 3.00E-02 p
R i c h m o n d g k [
d 2.00E-02 e C o l u m b i a c u d
o 1.00E-02 r B i b b p
l o s 0.00E+00 o r e A
Species
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 the major SOA precursor species in each county studied, accounting for approximately 48% of the total SOA potential atmospheric loading. This finding is quite significant since toluene is a carcinogen and is a toxic/hazardous air pollutant. Reducing the toluene content of gasoline would, therefore, have beneficial effects in terms of lower PM2.5 and ozone as well as beneficial health effects.
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 seen 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 [Careful: this is highly speculative, since you don’t know how much of the vehicle emissions signature is home-made and how much is actually transported into each of the communities; note that you have Atlanta metro area with 2 orders of magnitude higher VMTs as a potential “precursor”; ie a lot of the SOA can be produced from Atlanta primary emissions on their way to these cities…].
Of the mobile emissions studied, toluene is the largest potential SOA contributor [how about monoterpenes?]. The details of SOA formation and its chemical composition are only partially 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 drawbacks, 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[ok, answers my question about monoterpenes above, however, biogenic emissions in the SE-
US are well-modelled, see BEIS model and inventory; will dig out reference from Alex
Guenther et al., JGR.. 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
be conducted to determine the feasibility of this option, similar to the studies that
lead to implementation of reformulated gasoline in Georgia in 1995. Ted Russell
might be able to help answer this question!
The photochemistry of VOC is highly dependent on OH availability which is coupled to NOx availability [you mean the termination of NO-NO2 cycling via NO2+OH to HNO3]. The same is true for ozone photochemistry. Ambient concentrations [or abundance] of 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 first 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 [you are getting into the topic of NOx vs VOC limitation for P(O3) here, and from our SCISSAP program we can conclude that over most of the SE- US, ozone formation is NOx-limited, though not always in urban areas where there can be a greater sensitivity to VOC emissions. Outside of primary emissions of particulate matter, SOx appears to be the most sensitive precursor for PM formation since it also captures ammonia and water. Sulfate appears to be formed primarily via gas phase oxidation, though aqueous phase reactions are important. Organic PM appears to be split between primary emissions and oxidation of biogenic emissions. Nitrate is formed from oxidation of NO2, which takes place both during the day and at night, followed by reaction with ammonia. Ammonia acts as a neutralizing agent for sulfate and nitrate. The nitrate is highest, at least during the summer, in the early morning hours when the air is cooler and more humid, promoting condensation. We found that elevated NOx sources are less efficient at forming ozone than ground level sources, as has been found from aircraft studies as well. Increased emissions, while increasing ozone, can decrease the “ozone production efficiency” (OPE). We saw a much more linear response in SO2 emissions. Strategies to reduce NOx and SO2 simultaneously will be effective in reducing ozone and PM at the same time. For example, using new, combined cycle gas turbines (or coal gasification), could lower both pollutants effectively. On the other hand, one could envision controls that only go after one of the precursors alone. We did not do an economic optimization to find which would be best. Also of importance, both ozone and PM share a largely uncontrollable source, biogenics such as trees, which will limit the effectiveness of controls. For example, there will be a limit on how low PM levels can go since the biogenic fraction appears to be substantial on stagnant and hot days. Further, in the Southeast, VOC controls primarily will be effective only in and around urban areas, at least on high ozone days.
Our model results show (and as indicated by the measurements) that, at times, reducing SO2 emissions, and hence PM sulfate, can be offset by increased nitrate aerosol as ammonium is no longer tied up neutralizing the sulfuric acid. The extent of this was quite varied over the region. In some cases, this led to a very small impact, though at other times and locations upwards of about 50% of the reduction in sulfate could be lost by an increase in ammonium nitrate. It was also found that this result will change in the future as SO2 emissions are reduced due to acid rain controls and ammonia emissions may increase due to increased agricultural operations. In such cases, the effect of reduced sulfate leading to increased nitrate becomes more significant. We also found that there is a seasonal dependence. As part of a separate project, using URM-1ATM, we found that over a synthetic year that the replacement phenomena led to a relatively small reduction in the overall benefits of SO2 control, on the order of 10%.
All taken from our final report to the sponsor of SCISSAP, which I’ll send you, FYI]. 2. .. More research, analysis and detailed modeling efforts are needed to draw
further conclusions.
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APPENDIX 1
This photo shows the location of the sites studied during the FAQS 2000 project.
APPENDIX 2 This figure illustrates the complexity involved in SOA formation. APPENDIX 3
Chemical groups of compounds analyzed from VOC canister samples collected in Macon: 11-21st June, Augusta: 29th June – 10th July and Columbus: 17th – 29th July 2000 are listed below.
Halo- Alkanes Alkenes & alkynes Aromatics Biogenics organics F-12 MeONO2 Ethene Benzene Isoprene CH3Cl EtONO2 Ethyne Toluene Limonene F-114 n-PrONO2 Propene Ethylbenzene Alpha-pinene H-1211 ethane 1,3-butadiene m-xylene MeBr propane t-2-butene p-xylene F-11 i-butane cis-2-butene o-xylene F-113 n-butane 1-butene 1,2,3-trimethylbenzene CH2Cl2 i-pentane 3-methyl-1-butene isopropylbenzene CHCl3 n-pentane 1-pentene propylbenzene MeCCl3 hexane 2-methyl-1-butene 3-ethyltoluene CCl4 heptane t-2-pentene 4-ethyltoluene C2Cl4 octane c-2-pentene 2-ethyltoluene MeI 2-methylpentane 2-methyl-2-butene 1,3,5-trimethylbenzene I-PrONO2 3-methylpentane t-2-pentene 1,2,4-trimethylbenzene C2HCl3 2,2-dimethylbutane c-2-pentene 1,3-diethylbenzene CH2Br2 2,3-dimethylbutane 2-methyl-2-butene 1,4-diethylbenzene CHBr3 2,4-dimethylpentane 4-methylpentene 1,2-diethylbenzene 2,3-dimethylpentane 2-methyl-1-pentene 2-methylhexane 3-methylhexane n-heptane 2,5-dimethylhexane 2,3,4-trimethylpentane 2-methylheptane 3-methylheptane nonane 2,2,4-trimethylpentane