Article pubs.acs.org/est
Gas-Particle Partitioning of Primary Organic Aerosol Emissions: (2) Diesel Vehicles Andrew A. May, Albert A. Presto, Christopher J. Hennigan, Ngoc T. Nguyen, Timothy D. Gordon, and Allen L. Robinson* Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15289, United States S Supporting Information *
ABSTRACT: Experiments were performed to investigate the gas-particle partitioning of primary organic aerosol (POA) emissions from two medium-duty (MDDV) and three heavy-duty (HDDV) diesel vehicles. Each test was conducted on a chassis dynamometer with the entire exhaust sampled into a constant volume sampler (CVS). The vehicles were operated over a range of driving cycles (transient, high-speed, creep/idle) on different ultralow sulfur diesel fuels with varying aromatic content. Four independent yet complementary approaches were used to investigate POA gas-particle partitioning: artifact correction of quartz filter samples, dilution from the CVS into a portable environmental chamber, heating in a thermodenuder, and thermal desorption/gas chromatography/mass spectrometry (TD-GC-MS) analysis of quartz filter samples. During tests of vehicles not equipped with diesel particulate filters (DPF), POA concentrations inside the CVS were a factor of 10 greater than ambient levels, which created large and systematic partitioning biases in the emissions data. For low-emitting DPF-equipped vehicles, as much as 90% of the POA collected on a quartz filter from the CVS were adsorbed vapors. Although the POA emission factors varied by more than an order of magnitude across the set of test vehicles, the measured gas-particle partitioning of all emissions can be predicted using a single volatility distribution derived from TD-GC-MS analysis of quartz filters. This distribution is designed to be applied directly to quartz filter data that are the basis for existing emissions inventories and chemical transport models that have implemented the volatility basis set approach.
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INTRODUCTION Diesel vehicles are a substantial source of ambient fine particulate matter (PM), contributing to both atmospheric elemental carbon (EC) and primary organic aerosol (POA) concentrations. Many previous studies have measured the POA emissions from diesel vehicles due to their potentially harmful effects on the environment and human health.1−5 These data are often used to define the nonvolatile POA emission factors used by existing emissions inventories and chemical transport models. However, Robinson et al.6,7 hypothesize that the majority of the POA emissions collected on a quartz filter from diesel vehicles are semivolatile and that the gas-particle partitioning of these emissions varies widely with changing atmospheric conditions. Few studies have systematically investigated gas-particle partitioning of bulk POA emissions from diesel vehicles. Previous work indicated that a large fraction of the POA emissions from a small diesel generator was semivolatile.8,9 Chirico et al.10 attributed systematic changes in OA-to-black carbon ratios measured in a highway tunnel to changes in gasparticle partitioning of semivolatile organic vapors. Fujitani et al.11 reported that the organic carbon (OC) emission factors from a heavy-duty diesel engine decreased by a factor of 3−5 with dilution from near-tailpipe to near-road conditions. However, additional data are needed, especially from in-use vehicles operated over realistic driving cycles and equipped with © 2013 American Chemical Society
a broad range of after-treatment technologies, to ascertain whether the conclusions from these studies can be generalized. In addition, data are needed to quantitatively constrain the gasparticle partitioning of diesel POA. This manuscript presents results from emissions testing of a small diesel vehicle fleet operated on a chassis dynamometer. Quartz filter, dilution, thermodenuder, and composition measurements were performed to investigate the gas-particle partitioning of the POA emissions across a wide range of atmospheric conditions. There are two goals of this work: (1) to demonstrate that the majority of the POA emissions from diesel vehicles with different aftertreatment technologies are semivolatile, and (2) to derive a volatility distribution to predict gas-particle partitioning for use in emissions inventories and chemical transport models. A companion manuscript describes gas-particle partitioning of POA emissions from gasoline vehicles conducted as part of this project.12
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MATERIALS AND METHODS Vehicles, Fuels, and Test Cycles. The primary PM emissions were measured from two medium-duty diesel Received: Revised: Accepted: Published: 8288
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therefore, the chamber contents represented the emissions from a hypothetical trip. After filling, the exhaust in the chamber was a factor of ∼25−35 more dilute than the CVS and ∼250−700 times more dilute than at the tailpipe, depending on the driving cycle. There was high particle transmission efficiency (>90%) through the transfer line.15 POA concentrations inside the chamber were measured using a quadrupole aerosol mass spectrometer (AMS; Aerodyne Research, Inc.), and particle size distributions were measured using a scanning mobility particle sizer (SMPS; TSI, Inc.). To characterize the effects of temperature on gas-particle partitioning, these instruments were operated downstream of a thermodenuder and a bypass line maintained at 25 °C. The thermodenuder was based on the design of Huffman et al.16 and is described elsewhere.12 It was operated at temperatures of 40, 80, and 120 °C for MDDVs and 40, 80, and 100 °C for HDDVs with a centerline residence time (tres) of 27.9 s at ambient temperature. During the campaign, chamber temperature and relative humidity were 22.5 ± 3.4 °C and 19.9 ± 3.7%, respectively. Data Processing. POA data are reported as fuel-based emission factors (g kg-fuel−1), calculated using a mass balance on fuel carbon:
vehicles (MDDV) at the California Air Resources Board (ARB) Haagen-Smit Laboratory in El Monte, CA and three heavy-duty diesel vehicles (HDDV) at the ARB Heavy-Duty Engine Testing Laboratory in Los Angeles, CA. The two MDDVs were recruited from the California in-use vehicle fleet, whereas the three HDDV are owned by ARB. Two HDDVs were equipped with catalyzed diesel particulate filters (DPF); one of the MDDV was equipped with a diesel oxidation catalyst. Table S1 in the Supporting Information (SI) provides a description of the vehicle fleet, including model year, mileage, after-treatment, and engine displacement; SI Table S2 lists individual experiments. Although this is a relatively small test fleet, it includes both MDDVs and HDDVs with different aftertreatment technologies. The MDDVs were tested over a single cold-start unified driving cycle (UC) using commercial California ultralow sulfur diesel (ULSD). The HDDVs were tested over three driving cycles: the urban dynamometer driving schedule (2xUDDS), the heavy heavy-duty diesel truck (HHDDT) cruise mode (3xcruise), and the HHDDT creep mode with additional periods of engine idling (3xcreep/idle). The HDDV were tested with a nominal inertial load of 56 000 pounds. Additional details on the test cycles are provided in SI Table S3. The HDDV tests was performed with three different fuels: commercial California ULSD (12% aromatic), high aromatic ULSD (28% aromatic), and low aromatic ULSD (9% aromatic). Basic fuel composition data are presented in SI Table S4. Experimental Measurements. A schematic for the experimental setup is provided as SI Figure S1. Vehicles were operated on a chassis dynamometer, and the whole exhaust stream was drawn into a constant volume sampler (CVS). The exhaust was diluted inside the CVS with HEPA-filtered air. The average dilution factor in the CVS was ∼20 for the UC and 2xUDDS tests, ∼10 for the 3xcruise tests, and ∼100 for 3xcreep/idle tests. Dilute exhaust samples were drawn from the CVS onto bare-quartz (bare-Q) and quartz-behind-Teflon (QBT) filters. For the MDDV experiments, these sample trains were maintained at 47 °C following the procedures of CFR 86 protocol; for the HDDV experiments, these samples were not heated, yet the filter temperature was nearly always ∼47 °C (SI Table S2). Prior to sampling, the quartz filters were prebaked to remove any residual carbonaceous material. One set of quartz filter samples were analyzed for OC and EC using a DRI model 2001 OC/EC Analyzer (Desert Research Institute, Reno, NV) following the IMPROVE-A protocol.13 A second set of quartz filter samples were analyzed using thermal-desorption gaschromatography mass-spectrometry (TD-GC-MS) to derive volatility distributions following the protocol of Presto et al.,14 who have previously applied this approach to engine emissions. Filter handling blanks and dynamic blanks were also collected. Handling blanks always were less than the measurement detection limit. Dynamic blanks were collected while operating the CVS on dilution air but no exhaust. The average reported OC on bare-Q filters for dynamic blanks was ∼13.3 μg C m−3, similar to the QBT filters for dynamic blanks (∼13.6 μg C m−3), indicating that the dynamic blank was entirely organic vapors. Both handling blanks and dynamic blanks represented 10% of the total samples collected. Dilute exhaust from the CVS was also transferred to a portable ∼7 m3 Teflon environmental chamber.12 The exhaust was added to the chamber over the entire driving cycle;
EFOA =
POA fc ΔCO2
(1)
where POA is the measured organic aerosol concentration using quartz filters (CVS) or AMS (chamber), ΔCO2 is the background-corrected CO2 concentration (kg-carbon m−3), and f C is the measured carbon fraction in the fuel (0.85 kg carbon kg fuel−1). CO2 was a robust fuel tracer in these experiments; it accounted over 99% of the carbon emissions. Thermodenuder data are presented as the mass fraction remaining (MFR) in the particle phase: MFR =
C TD C bypass
(2)
where CTD and Cbypass are the organic aerosol concentrations measured using the AMS in the thermodenuder and unheated bypass line, respectively. CTD was corrected for particle number losses (mainly due to thermophoresis and diffusion).12 Losses in the thermodenuder were generally less than 10% for the particle size distributions and temperatures in this study. Partitioning Theory. A major goal of this study was to quantitatively predict the gas-particle partitioning behavior of the POA. In the atmosphere, gas-particle partitioning is thought to be an absorptive process in which semivolatile species form a solution with condensed phase organics.17 Using absorptive partitioning theory,18,19 the POA emission factor (EFOA) can be predicted using −1 ⎛ C *(T ) ⎞ ⎟ EFOA = EFtot ∑ f i⎜1 + i COA ⎠ ⎝ i
(3)
where EFtot is the emission factor of all lower-volatility organics (gas- plus particle-phase), f i is the mass fraction of species i, COA is the organic aerosol mass concentration, and Ci* is the effective saturation concentration (related to the subcooled liquid saturation vapor pressure) of species i. Since only a small fraction of the semivolatile emissions can be speciated,4,20 eq 3 is applied semiempirically using a surrogate set of compounds distributed across a one-dimensional volatility basis set of 8289
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logarithmically spaced Ci* bins.18 The mass fraction of these lower-volatility organics (set of f i) is the volatility distribution of the emissions. To calculate EFOA using eq 3, EFtot and f i must be known. For this work, data from the analysis of quartz filters were used to define these parameters. The m/z 57 chromatograms from the TD-GC-MS analysis were converted into volatility distributions (f i) using the approach of Presto et al.14 Briefly, m/z 57 (C4H9+) is a mass fragment produced from electron impact ionization of reduced hydrocarbons; it was the most abundant signal in the mass spectrum from the vehicle samples. Presto et al.14 developed a relationship between for Ci* and GC elution time using a suite of standard compounds. The mass fraction of the organics in each volatility bin was defined as the relative fraction of total m/z 57 signal in each volatility bin after correction for the n-alkane response factors. EFtot was defined as the OC measured during OC/EC analysis of a bare-Q filter, converted to organic mass by assuming an organic-mass-toorganic-carbon ratio of 1.2.21 Quartz filters do not capture all lower-volatility vapors so they underestimate EFtot,12 but this approach can be directly applied to update the existing quartzfilter-based POA emission factors. To indicate that quartz filters provide an imperfect estimate of the total emissions of lowervolatility organics we refer to it as EFQ (not EFtot). Equation 3 assumes absorptive partitioning. However, gasparticle partitioning at high EC loadings (e.g., during source testing) may be influenced by adsorption; this begins to occur when OC:EC < 2.22 The OC:EC in the CVS ranged from 0.21 to 55, with a median value of 0.46; this ratio depends strongly on driving cycle. Therefore, some of the data may be influenced by adsorption.
Figure 1. Quartz filter primary organic aerosol emissions data for diesel vehicles: (a) bare quartz (Bare-Q) and quartz-behind-Teflon filter (QBT) data for each vehicle, sorted from lowest to highest emissions. Total bar height is bare-Q mass; shaded regions is particlephase mass (bare-Q−QBT) while open regions represents sampling artifact mass (QBT). b) Data from panel (a) normalized to show the fraction of total mass that can be attributed to particle-phase mass. Empty space in the bars represents the contribution of sampling artifact.
EXPERIMENTAL RESULTS POA Emissions Measured with Quartz Filters. Figure 1a shows emission factors determined from the quartz filter samples collected from the CVS. The total bar height represents the bare-Q filter data (EFQ) corrected for handling but not dynamic blanks or sampling artifacts. Bare-Q filter data are traditionally used to define the nonvolatile POA emission factors used in current inventories and models. The POA emissions data in Figure 1 fall into three distinct regimes: ∼10 mg kg-fuel−1 for the DPF-equipped vehicles (D1 and D2); ∼100 mg kg-fuel−1 for the non-DPF-equipped vehicles operated over the UC/2xUDDS/3xcruise driving cycles; and >300 mg kg-fuel−1 for non-DPF vehicles operated over the 3xcreep/idle cycle. These emission factors are similar to previous work.5,23,24 The dynamic blank is also plotted in Figure 1; it is ∼3 mg kgfuel−1, based on the median ΔCO2 for all tests. This corresponds to ∼10−60% of the bare-quartz filter POA emissions factor for the DPF-equipped vehicles and 100 μg m−3 in the CVS versus 1−10 μg m−3 in the atmosphere). Diluting the non-DPF-equipped vehicle exhaust from the CVS to more atmospherically relevant conditions caused one-half to two-thirds of the diesel POA to evaporate. Therefore, models and inventories based on emission factors measured in a CVS-derived emission factors likely overestimate the contribution of non-DPF-equipped vehicles to ambient POA concentrations. Even at lower, atmospherically relevant concentrations, a significant fraction of the diesel POA emissions are semivolatile. For example, almost half of the diesel POA at 5 μg m−3 evaporated when it was heated in a thermodenuder. Therefore, this data set provides no evidence to support the widespread assumption that diesel POA emissions are nonvolatile at either source testing or atmospheric conditions. The data also indicate that positive sampling artifacts (adsorbed vapors) depend on the gas-particle partitioning. The relative contribution of artifacts increased for loweremitting vehicles. For very-low-emitting, DPF-equipped vehicles, essentially all of the POA emissions measured on a quartz filter collected from the CVS appear to be adsorbed vapors. Therefore, correcting for these artifacts is critical for developing robust POA emissions factors for low emitting vehicles. The clustering of the partitioning data suggests that the emissions from this small test fleet can be reasonably represented using a single volatility distribution. Although both the partitioning data and TD-GC-MS analysis suggest that there were some variations in partitioning between different vehicle/fuel/test-cycle combinations, these differences do not seem significant enough to justify using unique volatility distributions in emissions inventories and chemical transport models for each combination. The vehicles tested by this study were modern, low-emitting, low-mileage vehicles equipped with a range of aftertreatment technologies. Additional testing is needed to evaluate how well the proposed volatility distribution works for older, higher-emitting vehicles and for the light-duty diesel vehicles which are prevalent in Europe. Although the data presented here provide a largely consistent picture of gas-particle partitioning, some uncertainties remain, especially about the low-volatility tail of the emissions. The
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ASSOCIATED CONTENT
S Supporting Information *
Supporting Information including a schematic of experimental setup, vehicle information, relevant calculations, and experimental data. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We thank Matti Maricq at Ford and Hector Maldonado at the California Air Resource Board for helping to organize the Linking-Tailpipe-to-Ambient Project. This research would not have been possible without the hard work of the excellent and dedicated staff at the ARB Haagen-Smit Laboratory and HeavyDuty Engine Testing Laboratory. Funding was provided by the U.S. EPA NCER through the STAR program (R833748) and the Coordinating Research Council through A74/E96. The California ARB provided significant in-kind support, including 8294
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vehicle recruitment, vehicle testing, and sample analysis. The views, opinions, and/or findings contained in this paper are those of the authors and should not be construed as an official position of the funding agencies.
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