Carbon in southeastern U.S. aerosol particles: Empirical estimates of secondary organic aerosol formation

Atmospheric Environment 42 (2008) 6710–6720

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Carbon in southeastern U.S. aerosol particles: Empirical estimates of secondary organic aerosol formation Charles L. Blanchard a, *, George M. Hidy b, Shelley Tanenbaum a, Eric Edgerton c, Benjamin Hartsell d, John Jansen e a

Envair, 526 Cornell Avenue, Albany, CA 94706, USA Envair/Aerochem, 6 Evergreen Drive, Placitas, NM 87043, USA c Atmospheric Research and Analysis, 410 Midenhall Way, Cary, NC 27513, USA d Atmospheric Research and Analysis, 730 Avenue F Suite 220, Plano, TX 75074, USA e Southern Company Services, 600 North 18th Street, 14N-8195, Birmingham, AL 35203, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 27 October 2007 Received in revised form 27 March 2008 Accepted 2 April 2008

Fine particles in the southeastern United States are rich in carbon: Southeastern Aerosol Research and Characterization (SEARCH) network measurements from 2001 through 2004 indicate that fine particles less than 2.5 mm aerodynamic diameter (PM2.5) at two inland urban sites, Atlanta, GA and Birmingham, AL, contain 9 and 11% black carbon (BC) by mass, respectively, on average. For neighboring rural or urban Gulf Coast sites, the range is 4–7% BC. Organic carbon (OC) ranges from 25 to 27% in the inland cities, and 19–24% at the rural or Gulf Coast locations. Evidence in the literature suggests that a substantial fraction of the OC found in the Southeast is produced by atmospheric chemical reactions of volatile organic compounds (VOCs). Estimation of the fraction of OC from primary and secondary sources is difficult from first principles, because the chemistry is complex and incompletely understood, and the emission sources are both anthropogenic and natural. As an alternative, measurement-based models can be used to estimate empirically the primary and secondary source contributions. Three complementary empirical models are described and applied using the SEARCH database. The methods include (a) a multiple regression model employing markers for primary and secondary carbon using gas and particle data, (b) a carbon mass balance using carbon and CO data, along with certain assumptions about the OC/CO ratios in primary emissions for different urban and rural conditions, and (c) exploitation of isotopic measurements of carbon along with the BC and OC data. Secondary organic carbon (SOC) represents w20–60% of mean OC, depending upon location and season. The results are sensitive to estimates of emissions of primary OC and BC. Ó 2008 Elsevier Ltd. All rights reserved.

Keywords: Secondary organic aerosol Southeastern United States SEARCH network Organic carbon Black carbon

1. Introduction The ubiquitous presence of non-carbonate carbon in tropospheric aerosol particles has been recognized since the late 1960s (Mueller et al., 1972). The southeastern U.S. is of particular interest because observations suggest that

* Corresponding author. E-mail address: [email protected] (C.L. Blanchard). 1352-2310/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.04.011

the highest region-wide mass concentrations of fine particles (PM2.5) are generally found here, a substantial fraction of the particle composition is carbonaceous (IMPROVE, 2000), and relatively large emissions of volatile organic compounds (VOC) from vegetation occur and may be oxidized in the atmosphere to form particles. Black carbon (BC) is believed to be exclusively from primary sources, including transportation, industrial processes, and vegetation burning (McMurry et al., 2004). Organic carbon (OC) includes primary emissions from these

C.L. Blanchard et al. / Atmospheric Environment 42 (2008) 6710–6720

same sources, but OC also derives from secondary processes involving particle formation or growth from adsorption of organic vapors, or from VOC oxidation products of these vapors in the air, or from adsorption of VOCs followed by in-particle reactions, possibly involving acid catalysis. Primary carbon emissions may be semi-volatile, leading to low-volatility gas-phase species that, in turn, may be oxidized to condensed phase secondary OC (SOC) (Robinson et al., 2007). In the Southeast, a relatively large biogenic contribution to secondary OC has been hypothesized because of the dense vegetation present and the seasonal release of vapors, such as terpenes, which are readily oxidized to product species in the vapor and the condensed phases. The apportionment of particulate OC concentrations by primary source or by processes involving volatile organic compounds has been problematic (Turpin and Huntzicker, 1991, 1995). Spatial and temporal variations of OC and BC concentrations derive from a combination of source distributions and atmospheric processes, involving a large number of organic species, many of which have not been identified or quantified. Primary sources of OC have been estimated from application of chemical mass balance (CMB) receptor models using source profiles that include solvent-extractable organic compounds: Zheng et al. (2002, 2006, 2007) found that much of the OC at sites in the southeastern US could be accounted for using ‘‘fingerprints’’ from known source categories. According to these analyses, a large fraction (w50–90%) of the primary OC derives from motor vehicle exhaust and wood combustion. A component of the OC mass that is unaccounted for by the fitting procedure may be SOC associated with atmospheric chemical reactions; SOC production is likely to be strongest during periods of high photochemical activity (mainly in summer). Turpin and Huntzicker (1991, 1995) used a regression model with treatment of measurement error to estimate POC from measurements of BC, with SOC obtained as the difference OC–POC. Using the same approach, Lim and Turpin (2002) estimated SOC from BC using data from a four-week intensive study in Atlanta during August 1999; SOC contributed w46% of measured OC during the period. The empirical approaches can be refined using different assumptions and more extensive combined particle and gas data. Such refinements are introduced in this paper using observations from the Southeast Aerosol Research and Characterization (SEARCH) measurement network (Hansen et al., 2003; Edgerton et al., 2005, 2006) to estimate the secondary OC component of particulate carbon. 2. Measurements The SEARCH network (Hansen et al., 2003) consists of four paired urban–rural sites: Jefferson Street – Atlanta (ATL) and Yorkville (YRK) in Georgia, Birmingham (BHM) and Centreville (CTR) in Alabama, Gulfport (GFP) and Oak Grove in Mississippi, and Pensacola (PNS) and suburban Pensacola outlying landing field (OLF) in Florida (Fig. 1). The sites include instrumentation for filter-based PM10 and PM2.5 measurement, semi-continuous particulate OC and BC, meteorological parameters, and pollutant gases,

rural

6711

urban

suburban

Yorkville N. Birmingham Jefferson St. Centreville Oak Grove OLF Gulfport

Pensacola

Fig. 1. Locations of SEARCH sites.

including ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), oxides of nitrogen (NOx, NO þ NO2), oxidized nitrogen species (NOy), and nitric acid (HNO3) (Hansen et al., 2003). Occasional volatile organic compound (VOC) samples have been taken at some of the sites. Except Gulfport, the SEARCH sites have been taking observations since mid-1998, with new types of measurements incorporated into the monitoring program on an ongoing basis (Edgerton et al., 2005, 2006). The 24-h resolution fine particle carbon observations are from analysis of quartz filter samples (Edgerton et al., 2005). The SEARCH sampling protocol attempts to minimize both positive and negative carbon artifacts (Baumann et al., 2003). The quartz filter is preceded by an aluminum parallel-plate denuder with carbon paper coating the plates to remove gas-phase VOC. Negative artifacts, due to volatilization from the quartz filter, have not been completely quantified, but are minimized through the use of a backup-filter. The backup-filter OC averages about 10–15% of the front-filter OC concentrations. Carbon analysis was conducted by the Desert Research Institute (DRI) using the thermal optical reflectance (TOR) protocol to differentiate OC and BC (Chow et al., 2001). The separation of OC and BC is operational, and is a subject of debate (McMurry et al., 2004). Separate Teflon filter samples are analyzed for  water-soluble ions, including sulfate (SO2 4 ), nitrate (NO3 ), and ammonium (NHþ ), and for elements using conven4 tional procedures (Edgerton et al., 2005). Continuous measurements of fine particle mass at SEARCH sites complement the filter-based sampling. These include light scattering by nephelometry and mass by means of a dried, 30  C tapered element oscillating microbalance (TEOM) (Edgerton et al., 2006). Continuous measurements of major PM2.5 components consist of (1) þ  SO2 4 by reduction to SO2, (2) NH4 and NO3 by catalytic oxidation and reduction to NO, respectively, (3) BC by optical absorption, (4) total carbon (TC) by combustion to CO2 using an R&P model 5400 monitor, and (5) OC by difference of TC and BC (Edgerton et al., 2006). Ongoing continuous mass and composition measurements commenced in 2002 and 2003. The comparability of the continuous OC and BC measurements with the 24-h filter-based samples is described by Edgerton et al. (2006).

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3. Characterization of the carbon fraction The SEARCH network routinely reports organic mass (OM) by scaling the OC concentrations with a multiplicative factor of 1.4 (Edgerton et al., 2005). The value of the multiplicative factor is not known precisely, nor is it expected to be constant over time, but is thought to range from w1.3 to greater than 2. Thus, the analyses in this manuscript use unscaled OC concentrations. Urban sites show higher mean concentrations of both OC and BC, but especially of BC (Table 1). The mean OC/BC ratios vary among sites, with higher mean ratios occurring at each rural site compared with its urban counterpart (Table 1). The mean OC/ BC ratios were lowest at BHM (2.1–2.8 by day of week), followed by ATL (2.7–3.6). The mean OC/BC ratios were greatest at the rural sites: CTR (5.2–5.7), OAK (4.8–5.2), and YRK (4.2–5.2). 4. Empirical estimates of secondary carbon concentrations Lacking direct measurements of primary and secondary carbon species, secondary organic carbon concentrations must be estimated. Previous work has used hourly or multi-hour time-resolved measurements of BC as a tracer of POC (Turpin and Huntzicker, 1991, 1995; Lim and Turpin, 2002). A critical aspect of the BC tracer approach is the identification of days and times of day when little SOC is believed to be present, so that POC may be estimated from BC. The Turpin and Huntzicker (1991, 1995) model is POC ¼ a þ b*BC, where the intercept accounts for noncombustion primary OC; SOC is calculated as the difference OC–POC. Saylor et al. (2006) applied the BC tracer approach to SEARCH measurements made in 2002 at four sites. For consistency with the original work by Turpin and Huntzicker (1991, 1995), we applied the Deming regression coefficients obtained by Saylor et al. (2006) to SEARCH measurements made from 2001 through 2004. Results are summarized in Section 5, ‘‘Comparisons of Results.’’ Because both BC and OC are subject to measurement error, the assumption of an error-free independent variable (BC in this case) that underlies ordinary least squares (OLS) regression is not met, so Deming regression (Turpin and Huntzicker, 1991, 1995) or other statistical techniques (Saylor et al., 2006) are more appropriate. However, the

BC tracer method is also subject to other limitations. A positive intercept term in the regression of OC against BC is usually interpreted as non-combustion OC. Large positive intercept terms, though, can result in negative estimates of SOC for samples having low OC concentrations since the BC tracer model assumes that POC ¼ a þ b*BC and SOC ¼ OC  POC. This statistical artifact was especially prevalent for BHM, with approximately half the samples having estimated POC exceeding measured OC, and, hence, estimated SOC concentrations less than zero. For such samples, we set POC equal to OC and SOC equal to zero prior to computing the mean SOC. Tighter constraints may be placed on the actual SOC concentrations if several different estimation procedures are used. The proposed new estimation procedures are: 1. A multiple regression relating POC to BC and to CO as indicators of primary emissions, and SOC to O3, SO2 4 , and NO 3 as indicators of photochemical activity (Blanchard et al., 2002). 2. A partially constrained mass balance model utilizing hourly OC, BC, and CO data (Blanchard and Hidy, 2005). 3. A constrained model using empirically determined relationships for modern and fossil carbon based on measurements of carbon isotopes (14C and 12C). These methods are described in order of increasing complexity and demand for data. The results from the approaches are compared with each other, with the BC tracer method, with results reported by Lim and Turpin (2002) for Atlanta, and with CMB results reported by Zheng et al (2002, 2006, 2007). 4.1. Multiple regression method using tracers of primary emissions and photochemical activity The first method extends the widely used Turpin and Huntzicker (1991, 1995) approach. The principal advantage of the proposed method is to obtain SOC from the statistical model explicitly, rather than as the difference between measured OC and a statistical estimate of POC. We employed a multivariate statistical model that includes terms serving as tracers of POC (BC and CO) and additional terms as tracers of SOC. O3 is assumed to be an indicator for photochemically related oxidation of VOCs, and SO2 4 is assumed to be a marker for either additional

Table 1 Mean PM2.5 mass and major species concentrations at the SEARCH sites, based on 2001–2004 data (units are mg m3) Site

Location

N

Mass

SO2 4

NO 3

NHþ 4

BC

OC

MMOa

BHM ATL CTR YRK GFP PNS OAK OLF

Urban, inland Urban, inland Rural, inland Rural, inland Urban, Gulf Urban, Gulf Rural, Gulf Suburb, Gulf

451 1173 542 471 441 440 508 463

17.1 16.1 12.0 13.2 11.0 12.5 11.4 10.8

4.4 4.6 3.7 4.4 3.4 3.4 3.4 3.3

1.0 0.9 0.4 0.8 0.4 0.4 0.3 0.4

2.1 2.2 1.2 2.4 1.2 1.3 1.0 1.1

1.8 1.4 0.5 0.6 0.6 0.8 0.5 0.6

4.3 4.2 2.8 2.7 2.1 2.8 2.6 2.3

0.9 0.5 0.3 0.2 0.4 0.5 0.4 0.4

3 3 The standard errors of the mean concentrations are 0.01–0.07 mg m3 for MMO, BC, NHþ for OC and SO2 for PM2.5 4 , 0.06–0.15 mg m 4 , and 0.2–0.4 mg m mass. a Major metal oxides (Edgerton et al., 2005).

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oxidant activity, or potentially for acid-catalyzed SOC production (note, however, that convincing direct evidence for SOC formation from acid-catalyzed reactions has not been reported to date in the SEARCH region). For each site, the following regression equation was used: Predicted OC ¼ a þ bBC þ cCO þ dO3 þ elagðO3 Þ þ  f SO2 4 þ g NO3

(1)

where ‘‘a’’ through ‘‘g’’ are regression coefficients. Measurements of condensed phase species, gas-phase species, and meteorological parameters were obtained and converted to 24-h resolution averages to match the time resolution of the filter-based PM measurements. Both 24-h mean O3 and peak daily 1-h O3 mixing ratios were included in regressions and both were statistically significant as independent variables, but not simultaneously; the regression results were better using peak 1-h O3 mixing ratios. We included the previous-day peak 1-h O3 mixing ratios as a possible indicator of secondary species carryover, a potentially important consideration for multi-day pollution episodes. Meteorological parameters, including solar radiation and temperature, were some times significant if O3 mixing ratios were excluded, but the statistical fits were better using O3 than the meteorological variables. Stepwise multiple regression was employed to determine the statistically significant predictor variables at each site. We tested the sensitivity of the results by comparing them with two alternative regressions. In the first alternative, only BC and peak O3 were used as predictor variables. In the second, we used BC, CO, O3, and SO2 4 as predictor values and estimated the regression coefficients using a two-step approach in which we first identified, and then excluded, outliers based upon regression residuals. The regression coefficients varied among the three regressions, but the estimated POC and SOC concentrations were not unduly sensitive to the specific regression procedures employed. The results reinforce the well-known principle that correlation is not causality; covariances occur among predictor variables and some species may be correlated with POC or SOC as surrogates for actual causal factors. We use the range of SOC concentrations predicted by the alternative regressions as a measure of uncertainty. The intercept terms in Eq. (1) were negative at all sites; two (BHM and ATL) were not statistically significant at p < 0.05. A positive intercept plausibly indicates noncombustion POC, but negative intercepts have less physical meaning; they could imply the existence of non-combustion BC, but more likely indicate statistical overfitting. The regression coefficients were therefore re-calculated using zero-intercept regressions for all sites. The root-meansquare errors for the no-intercept cases exceeded those for the regressions with intercepts by 0.001–0.05 mg m3. As shown for the zero-intercept stepwise regressions, the regression coefficients were statistically significant for one or both primary species (BC and CO) and at least  one secondary species (O3, SO2 4 , and NO3 ) at all sites (Table 2) The coefficients relating OC to BC were lowest for the sites in the two largest metropolitan areas, 1.5

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(BHM) and 1.1 (ATL) (Table 2) and greatest (3.9) for two rural sites (CTR and OAK). The differences are consistent with literature values of OC/BC in primary emissions. Urban sites would be expected to exhibit substantial influence from motor vehicles, with typical OC/BC ranging from w0.3 for diesel exhaust to w1.5 for gasoline-engine exhaust (Zielinska et al., 1998; Watson et al., 2001; Chow et al., 2004). BHM is influenced by stationary emission sources, some of which exhibit OC/BC w1 (Blanchard et al., 2006). Many rural sites in the southeastern US are expected to exhibit strong influence from biomass combustion; reported values of OC/BC in biomass combustion are w4– 16 (Andreae and Merlet, 2001) and w5–9 at SEARCH sites when fires occurred nearby (w10 km) (Edgerton et al., 2001). The coefficients for O3 were significant at all sites, ranging from 0.011 to 0.024 mg m3 OC per ppbv O3. The same-day and previous-day peak ozone concentrations appear to be largely interchangeable as predictors, and no firm conclusions can be drawn about carryover. On average, there is an increase of approximately 0.1–0.2 mg m3 OC for each 10 ppbv increase in the daily maximum peak 1-h O3. In addition, statistically significant coefficients were  obtained for SO2 4 and NO3 at ATL and YRK. However, the SO2 coefficients were small at YRK and negative at ATL, 4 so the physical interpretation is unclear. The coefficients for NO 3 were significant and positive at both ATL and YRK, possibly suggesting the existence of emissions or processes that would lead to elevated concentrations of both SOC and NO 3 in that area. The negative coefficients for CO at CTR and for SO2 4 at ATL appear to be artifacts due to overfitting; negative coefficients for these variables would not otherwise be expected. Additional regressions were carried out separately for each day of the week at each site, since some sites exhibit day-of-week variations in the mean BC/OC. The RMS errors for the day-of-week specific regressions were not consistently better than those listed in Table 2, so we used the Table 2 coefficients to estimate POC and SOC. Secondary and primary OC were estimated as: Predicted POC ¼ b  BC þ c  CO

(2)

 Predicted SOC ¼ d  O3 þ e  lagðO3 Þ þ f  SO2 4 þ g  NO3

(3)

Eqs. (2) and (3) return positive values of both POC and SOC for all samples; however, the sum of the predicted POC and SOC is not constrained to reproduce the measured OC. We therefore rescaled the predicted POC and SOC by first calculating the residuals (residual ¼ predicted POC þ predicted SOC  measured OC). The rescaled POC and SOC were calculated as: Rescaled POC ¼ POC  ½POC=ðPOC þ SOCÞ  residual

(2b)

Rescaled SOC ¼ SOC  ½SOC=ðPOC þ SOCÞ  residual

(3b)

Rescaling in this way introduces the realistic constraint that the sum of POC and SOC is equal to the measured OC for each sample. Rescaling does not affect the mean SOC and POC concentrations. Results are discussed in Section 5, ‘‘Comparisons of Results.’’

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Table 2 Coefficients of multivariate regressions of daily-average OC against two primary and four secondary species. Regressions were forced through the origin (see text) Site

N

BHM CTR GFP ATL OAK OLF PNS YRK a b c

326 493 328 1236 457 410 261 447

Regression Coefficients (OC as mg C m3 per unit of predictor species) BC (mg m3)

CO (mg C m3)

Peak daily 1-h O3 (ppbv)

Previous-day peak daily 1-h O3 (ppbv)

3 SO2 4 (mg m )

3 NO 3 ( mg m )

1.48 3.85 2.59 1.12 3.86 2.75 2.88 2.05

4.23 3.73 N.S.c 6.15 N.S.c N.S.c N.S.c N.S.c

N.S.c 0.024 0.013 0.011 0.014 0.013 0.012 0.022

0.014 N.S.c N.S.c 0.010 N.S.c N.S.c N.S.c N.S.c

N.S.c N.S.c N.S.c 0.03 N.S.c N.S.c N.S.c 0.053

N.S.c N.S.c N.S.c 0.262 N.S.c N.S.c N.S.c 0.158

R2a

RMSb residual (mg m3)

0.81 0.75 0.76 0.74 0.75 0.66 0.84 0.66

1.3 0.97 0.71 1.2 1.0 0.77 0.80 0.83

R2 for regression with intercept. For regression without intercept, R2 > 0.9. Root mean square (mean prediction error). Not significant at p < 0.05.

4.2. Constrained mass balance method Our second method for calculating SOC utilizes hourly time-resolved measurements of OC, BC, and CO at SEARCH sites. By conservation of mass, OC ¼ POC þ SOC

(4)

Some of the variation that is related to fresh emissions, dilution, and atmospheric mixing can be removed by normalizing concentrations to those of a relatively inert primary species, such as CO or BC: OC=CO ¼ ðPOC=COÞ þ ðSOC=COÞ

(5)

We use CO for the denominator because its measurement errors are relatively smaller than those for hourly resolution BC concentrations (noting also that CO is reactive in the air on time scales typically longer than used here). In the following discussion, the units for ratios of OC/CO are mg C per g C (or, equivalently, mg C per mg C). During some times at urban sites, ambient concentrations are dominated by local emissions. We seek times when the following equality holds to a first approximation: ðOC=COÞmeasured ¼ ðOC=COÞprimary emissions

(6)

We assume that days with low-O3 mixing ratios potentially have low or perhaps negligible SOC concentrations (Saylor et al., 2006), approximating Eq. (6). We disaggregated the data from each site into days with low (less than 20th percentile), mid-range (20th to 80th percentiles), and high (greater than 80th percentile) peak 1-h O3 mixing ratios. The disaggregation was season-specific; e.g., summer mid-O3 days are the days during June through August having peak 1-h O3 levels ranging from the 20th to 80th percentiles during those months. We then regressed 24-h filter-based OC concentrations on low-O3 days against daily-average CO concentrations. For all sites and seasons combined, the result was: OCðmg m3 Þ ¼ 0:77 þ 11:65  COðmg C m3 Þ r 2 ¼ 0:68

(7)

A zero-intercept regression yielded a slope of 14.39  0.24. Seasonal variation was observed, ranging from

a winter (December–February) slope of 13.9  0.4 to a summer slope of 16.5  0.8. Though not a large variation, the winter minimum and summer maximum suggest the occurrence of a modest SOC component even on low-O3 days. Smaller slopes were observed at urban than at rural sites: BHM ¼ 13.2  0.6, ATL ¼ 14.3  0.3, PNS ¼ 15.8  1.4, CTR ¼ 19.0  0.9, YRK ¼ 17.4  1.1, and OAK ¼ 22.4  1.3. We use these site-specific regression slopes to represent the emissions ratio of OC/CO. We repeated all regressions using daily averages of the semi-continuous OC measurements; the resulting regression slopes did not change significantly. In a high-emissions region, the measured OC/CO ratio might be expected to approximate the ratio of OC/CO in emissions at night, when the nocturnal atmospheric inversion limits mixing, so that by early morning the pollutant mass from fresh emissions is potentially much larger than the carryover mass within the nocturnal boundary layer. The mean OC/CO ratios during the hours of midnight to 6 am were 17–37 mg C per g C (Table 3). These mean ratios exceed the values of the regression slopes. The difference is explained if non-negligible concentrations of SOC are present at night, e.g.: measured OC=CO ¼ ðPOC=COÞ þ ðSOC=COÞ

(8)

Recognizing potential carryover, Eq. (5) may be written: OC=CO ¼ ðPOC=COÞ þ ðSOCcarryover =COÞ þ ðSOCfresh =COÞ

(9)

where ‘‘SOCfresh’’ represents SOC produced from VOCs the same day the VOCs are emitted, and could involve, for example, condensation processes occurring at night as well as photochemically driven reactions during midday. Diurnal profiles and mean concentrations show that mean OC/CO ratios at the urban sites rise during the morning (mixing of fresh emissions with aged air masses aloft) to maxima during the afternoon hours (noon to 6 pm) (Table 3). During midday and afternoon, mixing occurs throughout a deepened atmospheric mixed layer, and maximum photochemically driven SOC formation is expected to occur in conjunction with maximum oxidant production.

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Table 3 Mean ratios of OC/CO at SEARCH sites, by averaging period and time of day (1 S.E. of the mean) Site

No. of days

Averaging period

OC/CO midnight–6 am

OC/CO 6 am–noon

OC/CO noon–6 pm

OC/CO 6 pm–midnight

BHM BHM CTR CTR ATL ATL OAK OAK PNS PNS YRK YRK

202 260 189 186 269 311 86 161 153 131 161 133

Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer

17  0.9 22  1 22  1.1 36  1.4 20  0.8 22  0.7 25  1.7 36  1.4 17  1.8 17  1.9 21  1 37  1.6

15  0.9 23  1.4 21  1.2 40  1.3 16  0.8 24  0.7 21  1.5 45  2.3 15  1.5 27  2.6 19  0.9 38  1.6

17  1.7 31  1.6 21  1.4 44  1.9 20  0.7 34  0.9 23  1.7 51  2.7 17  1.1 28  2.5 19  0.8 41  2

14  0.5 24  2.6 21  1 40  1.8 18  0.8 23  0.7 29  2 46  1.6 15  0.9 14  1.7 21  1 41  1.8

Data are from 2002 through 2004, except PNS and OAK (2002 only). Units are mg C per g C. Summer is defined as June through August and winter as December through February.

EPA’s 2001 national emission inventory estimates of CO and PM2.5 from mobile sources (on-road and non-road) plus fuel-combustion, which account for most of the CO and PM2.5 emissions within cities, were 104,094,000 tons CO and 1,771,000 tons PM2.5, yielding a ratio of 17 mg PM2.5 per g CO. Emissions of OC are not reported in the inventory. The mean emissions OC/CO would be w20 mg C per g C, for example, if 50% of the PM2.5 emissions from the sources in question consisted of carbon in the form of OC (with the remaining PM2.5 emissions mass comprised of noncarbonaceous material, BC, and atoms other than C associated with OC). The observed regression slopes of w13 to w22 mg C per g C in ratios of OC/CO are not unreasonable in relation to emissions from mobile and fuel-combustion sources. Primary and secondary OC at each location can be calculated as: site  specific POC=CO ¼ 13:2—22:4 mg C per g C

(10)

SOC=CO ¼ ðOC=COÞ — ðPOC=COÞ

(11)

Results are discussed in Section 5.

measurements from BHM and CTR, the analysis focuses on this pair of sites. The 2004 data included 37 dates having carbon isotope measurements for both CTR and BHM. The CTR data are assumed to represent regional OC and BC concentrations that affect BHM. On occasion, plumes from local biomass combustion events impact CTR (Edgerton et al., 2001, 2004), and PM2.5 mass and carbon concentrations at CTR are not necessarily regionally representative on such dates. We therefore excluded dates when CTR OC concentrations exceeded those at BHM. We also excluded dates when either modern or fossil TC at CTR exceeded the corresponding concentrations at BHM. After these exclusions, the data set included 28 dates with carbon isotope measurements from both sites. We developed a statistical model to partition fossil and modern BC and OC. By mass balance, TC is comprised of BC and OC only. Preliminary regression of the modern TC against BC and OC yielded equations with statistically nonsignificant intercepts and coefficients of multiple determination (R2) approaching unity, e.g.: CTR modern TC ¼ 0:077 þ 0:391  BC þ 0:965  OC R2 ¼ 0:989ð0:077  0:058; 0:391

4.3. Carbon isotope method Selected SEARCH samples have been analyzed for carbon isotopes (12C and 14C) (e.g., Zheng et al., 2006). The 12 C and 14C measurements partition total carbon (TC) into carbon derived from modern emissions and carbon derived from combustion of fossil fuels. For samples collected on nine dates between September 2003 and January 2004, the mean fractional fossil contributions to total carbon were 0.70 at BHM, 0.54 at ATL, 0.40 at PNS, and 0.28 at CTR (Zheng et al., 2006). SEARCH archives include carbon isotope analyses of daily-average samples collected from January through December of 2004 at BHM, CTR, PNS, and ATL, amounting to 39–48 samples per site. Application of the 12C and 14C data to estimation of SOC requires a procedure for extending the fossil-modern partitions of TC to fossil-modern partitions of BC and OC. The method discussed here requires paired urban–rural measurements. Since the 2004 12C and 14C data include

 0:202; 0:965  0:047Þ

(12)

Because the regression intercepts for both locations were not statistically significant and are not physically meaningful, we next used zero-intercept regressions: CTR modern TC ¼ 0:396  BC þ 0:938  OC R2 ¼ 0:998ð0:396  0:208; 0:938  0:043; RMS residual 0:13 mg m3 Þ

(13)

For a zero-intercept regression, the R2 value must be interpreted differently than for a regression with intercept, but good statistical fit is indicated by the small RMS residual. Mass-balance considerations provide a straightforward interpretation. Modern TC is composed of modern BC and modern OC; on average, the CTR modern BC is 0.396*BC and the modern OC is 0.938*OC. Individual samples may deviate from the average, but the deviations are usually not large, as indicated by the RMS residual.

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Fossil BC and fossil OC at CTR were obtained as:

urban  fossil TC ¼ BC þ 0:927  OC r 2 ¼ 0:973ð0:927

CTR fossil TC ¼ 0:604  BC þ 0:062  OC  0:043; RMS residual 0:13 mg m3 Þ

(14)

The mass-balance interpretation is that, on average, CTR fossil BC is 0.604*BC and fossil OC is 0.062*OC. Considering both Eqs. (13) and (14) indicates that, for Centreville, the mean modern/fossil split for BC is 0.396 modern and 0.604 fossil, averaged over all measurements. The mean CTR modern/fossil split for OC is 0.938 modern and 0.062 fossil. For BHM concentrations, the regression results are: BHM fossil TC ¼ 0:864  BC þ 0:529  OC R2 ¼ 0:976ð0:864  0:328; 0:529  0:143; RMS residual 0:77 mg m3 Þ

(15)

BHM modern TC ¼ 0:136  BC þ 0:471  OC R2 ¼ 0:934ð0:136  0:328; 0:471  0:143; RMS residual 0:77 mg m3 Þ

(16)

For BHM, the mean modern/fossil split for BC is 0.136 modern and 0.864 fossil. The BHM mean modern/fossil split for OC is 0.471 modern and 0.529 fossil. PM2.5 mass and carbon concentrations at BHM consist of contributions from both regional and local emissions sources (Blanchard et al., 2006). We determined the urban components of TC, BC, and OC as the differences between BHM and CTR concentrations on each date. We then determined the fossil and modern splits for the urban component by regressions similar to those for CTR and BHM: urban  fossil TC ¼ 1:007  BC þ 0:923  OC R2 ¼ 0:990ð1:007  0:137; 0:923  0:087; RMS residual 0:45 mg m3 Þ

(17)

urban  modern TC ¼ 0:007  BC þ 0:077  OC R2 ¼ 0:182ð0:007  0:137;0:077  0:087;RMS residual 0:45 mg m3 Þ

(19)

 0:030; RMS residual 0:44 mg m3 Þ

R2 ¼ 0:939ð0:604  0:208; 0:062

(18)

The negative coefficient for BC in Eq. (18) is physically meaningless and not statistically significant; similarly, the coefficient for BC in Eq. (17) is not physically meaningful because it is greater than unity. It is possible that a small bias exists due to under-representation of samples from January and February, when urban residential wood combustion might be more frequent than during warmer months (Zheng et al., 2006), but the measurements indicate that, within uncertainty limits, there was no urban-derived modern BC in the BHM samples. We impose constraints for physical realism: to remove the negative coefficient, the urban multivariate regressions were redetermined as univariate regressions, as follows:

urban  modern TC ¼ 0:073  OC r 2 ¼ 0:151ð0:073

(20)

 0:030;RMS residual 0:44 mg m3 Þ Although the coefficient of determination (r2) for Eq. (20) is low, the regression coefficient is statistically significant (p < 0.05), and mass-balance considerations require the coefficients for OC in Eqs. (19) and (20) to sum to unity. The computation of POC and SOC depends on supplementary information to assign OC/BC ratios in emissions from four source types: (1) rural-fossil, (2) rural-modern, (3) urban-fossil, and (4) urban-modern. Because the mean rural-fossil and urban-modern OC concentrations are small, uncertainties in the OC/BC ratios of those types of emissions do not affect the estimates of POC and SOC as much as uncertainties in the rural-modern and urban-fossil contributions. For urban-fossil OC affecting BHM, OC/BC ratios were obtained from the literature for mobile sources (Zielinska et al., 1998; Watson et al., 2001; Chow et al., 2004) and from samples collected close to emission sources at BHM (Blanchard et al., 2006). The choice for urban-fossil emissions (mean OC/BC ¼ 1), while supported by the data for BHM, may not be appropriate for other locations. Estimation of OC/BC in rural-modern emissions is subject to considerable uncertainty. Comprehensive literature review is available in Andreae and Merlet (2001). Ruralmodern carbon emissions affecting CTR largely derive from biomass combustion, including prescribed burns in managed forests, wildfires, and perhaps also residential fuel use. Laboratory and field studies of biomass burning show that more BC is produced during flaming than smoldering phases of biomass burning (Kuhlbusch and Crutzen, 1995, 1996; Cachier et al., 1996). The OC/BC ratio of biomass burning is lower for woody than non-woody biomass, and differs among different non-woody plant species (Kuhlbusch and Crutzen, 1995, 1996). Reported mean BC/ TC values (as percentages) include: 13.7 for residue from pine needles, needle litter, and experimental burns (Kuhlbusch and Crutzen, 1995, 1996); 8.4 for residue in experimental burn (Andreae et al., 1996); 8.7 for residue in experimental burns (Cachier et al., 1996); 6.9 for atmospheric samples downwind of three prescribed burns and one wildfire (Hobbs et al., 1996); 9.3 for atmospheric samples downwind of two prescribed burns and one wildfire (Martins et al., 1996). The mean of these reported results is ten percent BC, suggesting a mean OC/BC of about nine. Other measurements from biomass burning show mean ratios of OC/BC of 15.4 (Lee et al., 2005), 7.9 and 8.9 (Chow et al., 2004), and 14.5 (Watson et al., 2001). Averaged over all studies reported, the mean OC/BC is w10. Methods for measuring OC and BC varied among studies, potentially yielding different values of BC (Novakov and Corrigan, 1996) and affecting the reported OC/BC. The apportionment of

C.L. Blanchard et al. / Atmospheric Environment 42 (2008) 6710–6720

OC and BC in the SEARCH network follows the TOR methodology used by Chow et al. (2004) and Watson et al. (2001), which yields higher BC concentrations than do some other methods. Given the range of reported results for OC/BC in biomass combustion, the dependence of biomass burning OC/BC on specific conditions, and variations in reported BC concentrations due to differences in measurement methods, we determined the sensitivity of our calculations assuming five values (5, 7, 8, 9, and 12) of OC/BC for emissions from biomass burning. Our best estimate for mean biomass combustion OC/BC, as measured by SEARCH methods at CTR, is one of the mid-range values (OC/BC w7). Ratios of OC/BC in biomass combustion as observed at rural SEARCH sites are generally not as high as 10:1. For example, Edgerton et al. (2001, 2004) reported mean OC/BC of approximately 3:1 in samples collected at SEARCH sites on six occasions when prescribed burns or wildfires were observed at distances of 2–8 km from CTR, OAK, or OLF. In 2004 and 2005, carbon isotope measurements identified seven samples at CTR having a modern carbon fraction exceeding 95%, implying that the dominant source influence for these samples was biomass combustion; the mean OC/BC was 6 (range 4.3–8.2) (unpublished analyses of SEARCH data). These samples, as well as the full set of 2004 data, exhibited an approximately linear increase of OC/BC as a function of increasing fraction modern TC, with an expected value of OC/BC equal to w7 when TC is entirely modern. Carbon measurements made at OAK during periods of observed fire impact exhibited slopes of 5.8–9.1 for the regression of continuous OC against aethalometer measurements of BC (unpublished SEARCH data). We applied Eqs. (13)–(16), plus Eqs. 19 and 20, to the mean concentrations (28 days in 2004) of BC, OC, and TC at BHM, CTR, and to the urban component, BHM–CTR, to estimate the mean modern and fossil BC, OC, and TC. Application of the procedure to the 28 measurements made from March through December of 2004 is reasonable, since Eqs. (13)–(20) were derived from those same data. However, the representativeness of those 28 days is open to question. We therefore applied the same equations to all daily concentrations (401 days from 2001 through 2004) of BC, OC, and TC at BHM and CTR to re-estimate the mean modern and fossil BC, OC, and TC at each location (Table 4). The results in Table 4 agree with those calculated from the 28-day data set within uncertainty limits. As a measure of sensitivity, the results were recalculated for different values of OC/BC in emissions from modern carbon sources (biomass burning). Varying the assumed mean OC/BC in biomass combustion from 7:1 to 9:1 reduced the calculated SOC from 32 to 24% at BHM and from 40 to 26% at CTR. Use of OC/BC ¼ 5 for biomass burning increased the mean calculated SOC (all days) to 40% (1.82 mg m3) at BHM and 55% (1.36 mg m3) at CTR. Use of OC/BC ¼ 12 for biomass burning decreased the mean estimated SOC to 12% (0.54 mg m3) at BHM and 3% (0.08 mg m3) at CTR. The extreme values (5 and 12) of biomass combustion OC/BC are somewhat implausible for the SEARCH CTR data, however. The mean OC/BC in CTR measurements is 5.4 (Table 1), which must represent a mixture of fossil-fuel emissions having mean OC/BC of w1 and biomass emissions having

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a ratio of OC/BC exceeding 5.4. At the other extreme, only 12 of 595 (2%) of CTR samples collected from 2001 through 2004 exhibited OC/BC exceeding 12.

5. Comparisons of results Our three calculations of SOC are compared in Table 5, which also includes the results of the BC tracer method and of other studies. The three methods indicate that SOC represents w20–60% of OC. The summer estimates for ATL (35 and 57%) bound the estimated mean SOC contribution of 46% during August 1999 by Lim and Turpin (2002). In addition to the uncertainties inherent in all estimation methods, the time periods differ so that some differences in SOC estimates among investigators is not surprising. Our results are difficult to compare unambiguously with the CMB source apportionment by Zheng et al. (2002, 2006, 2007), which fits OC to primary emissions; the unfitted portion could represent SOC (Zheng et al., 2002) but could also include positive artifacts from gas-phase species (Zheng et al., 2006).

Table 4 Estimation of mean POC and SOC at BHM and CTR using daily-average BC and OC concentrations from 2001 through 2004 in conjunction with 12C and 14C determinations of fossil and modern total carbon Species

Locationa

Fossilb

Modernb

Totalc

TC

CTR Urban BHM CTR Urban BHM CTR Urban BHM CTR Urban BHMj CTR Urban BHM

0.43 3.40 4.08 0.28 1.47 1.67 0.15 1.93 2.41 0.20f 1.47h 1.66 0.04 0.46 0.75

2.50 0.15 2.41 0.18 0.00e 0.26 2.32 0.15 2.15 1.28g 0.15i 1.44 1.04 0.00 0.71

2.94 3.55 6.49 0.46 1.47 1.93 2.47 2.08 4.56 1.48 1.62 3.10 0.99 0.46 1.46

BC

OC

POC

SOCk

component

componentd

componentd

component

component

Units are mg m3 for all entries. Table entries are the means determined from 401 samples. Standard errors ranged from 0.01 to 0.16 mg m3. a BHM and CTR denote the concentrations at the monitoring sites. Urban component is the difference between BHM and CTR, except as noted. b BHM and CTR fossil and modern TC were determined from 12C and 14C measurements for 2004 data; fossil and modern BC and OC were calculated from regression equations as described in the text. c Total BC, OC, and TC are the means of the measured BC, OC, and sum of BC plus OC, respectively. The urban component value is the difference between BHM and CTR, computed for each sample, then averaged. d For each sample, the fossil and modern urban component BC and OC were estimated from the urban component TC (see text equations), and need not be equal to the difference between BHM and CTR. e Regression estimator is negative and not statistically significant. f POC ¼ 0.7 * BC. Assumes 2/3 diesel at OC/EC w0.3 and 1/3 auto at OC/ BC w1.5 (Zielinska et al., 1998; Watson et al., 2001; Chow et al., 2004). g POC ¼ 7* BC. Mid-range of reported biomass burning values (see text). h POC ¼ BC (data reported in Blanchard et al., 2006). i POC ¼ OC. Assigns OC to POC, assuming the principal source of modern urban OC is winter residential wood combustion with negligible secondary formation. j Sum of CTR and urban component POC. k Difference of OC less POC.

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Comparison of the methods’ mean estimated SOC concentrations by season reveals general consistency along with some important discrepancies (Fig. 2). The fraction of SOC for the different locations ranges from 15% (BHM) all days to 61% (YRK) in summer. Averaging across locations other than BHM, the multi-year mean SOC is w20–50% and the summer mean is w30–60%. The SOC concentrations tend to be minimum in winter and maximum in summer and fall. This seasonality is consistent with the expected influence of active photochemical oxidant with enhanced (biogenic) VOC emissions in summer months. Evidently, there is sufficient oxidant and VOC to produce SOC year-round in the Southeast. The multiple regression method yields lower SOC values at BHM than the other two approaches. Alternative multiple regression models yielded higher estimates of BHM mean SOC (18–24% of mean OC), but all were distinctly lower than the BHM mean SOC estimated using the carbon isotope and mass balance models (Table 5). For other sites, alternative regression models yielded mean SOC that deviated from the tabulated means by 0.02–0.4 mg m3. For YRK, two alternative regression models predicted mean SOC of 1.1 mg m3, compared with the tabulated mean of 1.5 mg m3. The mass balance model results are sensitive to the assumed estimates of primary OC/CO ratios. Variations of primary OC/CO evidently are related in turn to the presence of substantial regional carryover SOC during certain times of the year, which is difficult to characterize solely from the observations. In the mass balance model, the SOC fraction of OC is consistently high for all seasons, but decreases sharply in the winter at rural sites. This result suggests a seasonal and geographical difference in VOC emission rates, wherein the urban emission rates are more or less uniform through the year and biogenic emissions decrease sharply in the winter (non-growth) months. Uncertainties in the carbon isotope method may derive from the small number of isotope measurements taken in

BHM and CTR, but consistency when applying the isotope method to a small number of observations and to a broader data set without isotopic observations adds confidence to the findings. The results for the isotope method are sensitive to assumptions concerning the ratio of primary OC/ BC for major emission sources. This sensitivity has been estimated using various results for the ratio derived from the literature and from the SEARCH data themselves. The isotope method suggests the importance of modern carbon sources, implying biogenic VOC ‘‘dominance’’ at CTR. If these results are generally the case in the Southeast, then the relative influence of urban and rural sources will vary across the region by season. The seasonal dependence of the SOC fraction will depend on temperature as well as emission strengths. Cooler temperatures outside of summer will tend to enhance concentrations of semi-volatile species in the condensed phase, which offsets expected seasonal decreases in oxidation rates. Qualitatively, these features appear to be consistent with the seasonal results. 6. Conclusions The present work extends results reported in previous studies through the use of multiple methods. Although the SOC estimates are method dependent, the results confirm that SOC constitutes a significant portion of measured OC, w20–60%, and, hence of PM2.5 mass concentrations, in the southeastern U.S. The results suggest that both anthropogenic and biogenic VOC emissions are important for SOC production. Improved estimates are needed for the ratios of BC, OC, and CO in emissions, especially for emissions from biomass combustion, with quantification of source variability. Improved characterization of changes in the ratios of BC, OC, and CO as air masses age is also needed. Additional improvements in understanding of SOC formation could be facilitated with more detailed combined observations and analysis of speciated VOCs and particulate carbon data during day-night sequences by season. Ongoing identification

Table 5 Comparison of mean SOC fractions (as percent of OC) Method

Period

BHM

CTR

BC tracer (Deming)a

All days, 2001–2004 Summerb All Days, 2001–2004 Summerb All days, 2001–2004 Summerb All days, 2001–2004 Summerb Four monthsg Sep 03–Jan 04 Jan/July 2001 2002 Aug 1999

33  8 32  8 15  4 20  5 48  4 58  6 32  8 34  7 5 47

36  2 49  1 41  6 49  6 39  4 57  5 40  14 49  12 32 65

11–43

19–39

Multiple regressionc Diurnal OC/COd Carbon isotopee Zheng et al. (2002),f Zheng et al. (2006),f Zheng et al. (2007),f Saylor et al. (2006),h Lim and Turpin (2002) a b c d e f g h

GFP

ATL

29  5 36  5

34  1 34  1 27  3 35  2 45  2 57  3

29

0 59 27/72 15–37 46

OAK

OLF

PNS

YRK 51  4 57  4 56  8 61  7 40  5 57  6

26  9 32  10 36  5 56  7

27  1 33  1

22  2 30  3 30  8 43  11

2

50

1 49

Determined using coefficients from Saylor et al. (2006). Uncertainties are one-half the difference between Deming and York regressions. June through August (2001–2004). Uncertainties are one-half the range from alternative regressions. Uncertainties are by propagation of errors. Uncertainties are from sensitivity to assumed ratio of OC/BC in biomass combustion emissions. Fraction unexplained after fitting POC. April, July, October 1999 and January 2000. Range of values obtained using different statistical regression methods.

41

24–58

C.L. Blanchard et al. / Atmospheric Environment 42 (2008) 6710–6720

3

a

Multiple Regression Mass Balance Carbon Isotope BC Tracer

2.5

SOC (ug m-3)

6719

2 1.5 1 0.5 0

Fall

Winter

Spring

Summer

Spring

Summer

Spring

Summer

Spring

Summer

Season 3

SOC (ug m-3)

2.5

b

Multiple Regression Mass Balance Carbon Isotope BC Tracer

2 1.5 1 0.5 0 Fall

Winter

Season 3

c

Multiple Regression Mass Balance Carbon Isotope BC Tracer

SOC (ug m-3)

2.5 2 1.5 1 0.5 0 Fall

Winter

Season 3

SOC (ug m-3)

2.5

d

Multiple Regression Mass Balance Carbon Isotope BC Tracer

2 1.5 1 0.5 0

Fall

Winter Season

Fig. 2. Comparisons of mean estimated SOC concentrations at (a) BHM, (b) CTR, (c) ATL, and (d) YRK. The estimation methods are described in the text. Error bars are one standard error of the means.

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