1. INTRODUCTION

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Characterization of Natural and Affected Environments

Seasonally Varying Secondary Organic Aerosol Formation From In-Situ Oxidation of Near-Highway Air Provat K. Saha, Stephen M. Reece, and Andrew P. Grieshop Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 30 May 2018 Downloaded from http://pubs.acs.org on May 30, 2018

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Seasonally Varying Secondary Organic Aerosol Formation From In-Situ Oxidation of Near-Highway Air Provat K. Sahaa,b, Stephen M. Reecea, Andrew P. Grieshopa* a

Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, North Carolina, Raleigh, NC 27695, USA b now at: Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA Abstract: The extent to which motor vehicles contribute to ambient secondary organic aerosol (SOA) remains uncertain. Here, we present in-situ measurements of SOA formation at a near-highway site with substantial tree-cover 10 m from Interstate 40 near Durham, North Carolina. In July2015 (summer) and February-2016 (winter), we exposed ambient air to a range of oxidant (O3 and OH) concentrations in an Oxidation Flow Reactor (OFR), resulting in hours to weeks of equivalent atmospheric aging. We observed substantial seasonal variation in SOA formation upon OFR aging; diurnally varying OA enhancements of ~ 3-8 µg m-3 were observed in summer and significantly lower enhancements (~0.5-1 µg m-3) in winter. Measurements in both seasons showed consistent changes in bulk OA properties (chemical composition; volatility) with OFR aging. Mild increases in traffic-related SOA precursors during summer partly explains the seasonal variation. However, biogenic emissions, with sharp temperature dependence, appear to dominate summer OFR-SOA. Our analysis indicates that SOA observed in the OFR is similar (within a factor of 2) to that predicted to form from traffic and biogenic precursors using literature yields, especially in winter. This study highlights the utility of the OFR for studying the prevalence of SOA precursors in complex real-world settings.

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Photochemical age (days) *Address correspondence to: Andrew P. Grieshop, Department of Civil, Construction and Environmental Engineering, North Carolina State University, 431B Mann Hall, Raleigh, NC 27695-7908, USA. Email: [email protected]

1. INTRODUCTION Motor vehicle emissions are an important source of atmospheric fine particulate matter 1–3 that influences both air quality 4,5 and climate 6,7. Organic aerosol (OA) constitutes a significant 1 ACS Paragon Plus Environment


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fraction of the atmospheric fine particulate matter 8–10. Motor vehicles directly emit OA (primary OA; POA) and precursors for secondary OA (SOA). SOA forms chemically in the atmospheric through the oxidation of gas-phase organic emissions and/or heterogeneous condensed-phase reactions 11,12. Laboratory studies show that SOA production from motor vehicle emissions could be several times higher than POA 13–16. However, the relative role of vehicle emissions in ambient SOA burden remains unclear due to discrepancies between laboratory, field and modeling studies 1,17–21. Recent studies have shown that the abundance and properties of SOA from motor vehicles vary substantially with fuel type and formulation, vehicle type, operating conditions, exhaust aftertreatment technology, and between laboratory and real-world settings 22–26. Based on our current understanding of SOA precursors and yields, the relative role of gasoline and diesel vehicles to ambient SOA is unclear 1,27,281,27,28. For example, Gentner et al. 28 claimed that diesel exhaust is associated with substantially more SOA formation than gasoline exhaust, whereas Bahreini et al. 27 suggested that gasoline emissions dominate over diesel for SOA production in the US. Other studies have claimed that individual precursors or precursor classes in exhaust may disproportionally contribute to SOA formation 20,29–32. Traditionally, SOA production and yields from motor vehicle emissions have been investigated using smog chamber experiments with whole exhaust 22,23,33,34 or fuel/oil components 24. Smog chamber experiments are generally limited to a small number of vehicles tested on dynamometer driving cycles. The resulting SOA yields may be underestimated due to losses of precursors and oxidation products to the chamber walls 35,36, and the inability of chamber experiments to simulate multiple generations of oxidations (several days to weeks of equivalent atmospheric oxidation) 37. Recently, oxidation flow reactors (OFR) (a.k.a., Potential Aerosol Mass; PAM reactor) have been developed 38 enabling several hours to weeks of equivalent atmospheric oxidation. OFR systems have been applied to study the SOA formation in diverse laboratory and field settings, including individual precursors 39, concentrated emissions 26,40,41 and ambient air 42,43 . In particular, OFR portability allows the in-situ characterization of SOA formation from realworld fleets and driving conditions 26. An OFR deployment in a traffic tunnel by Tkacik et al. 26 showed that SOA formation from traffic emissions could be ~10 times higher than POA. OFR oxidation of ambient air at an urban site in southern California during the CalNex campaign 42 caused a factor of ~1.5-2 enhancement of OA (relative to ambient OA level). The tunnel measurements were conducted with using relatively concentrated emissions (NOx ~ 400-800 ppb; where NOx level is used as an indicator of traffic influence, not chemical regime), whereas those during CalNex were collected under typical urban background conditions (NOx ~20 ppb). NOx concentrations serve as an indicator for traffic influence at a site, and may also have a strong influence on SOA formed from vehicle exhaust 44. A near-road environment (NOx ~50-200 ppb) is an intermediate setting between tunnel and urban background conditions, thus providing an excellent location to investigate the SOA formation from traffic emissions under realistic conditions. For example, in-situ OFR experiments near a busy highway enables examination of the SOA production from real-world traffic fleets and driving conditions under realistic ambient conditions 1 and in the presence of other existing precursors, such as biogenic emissions. To our knowledge, no studies to date have quantified SOA formation from oxidation of road-side air. 2 ACS Paragon Plus Environment

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This experiment is critically important because while earlier work provides insight into the importance of traffic emissions as a potential source of ambient SOA, current estimates are uncertain 1. Further, such a setting provides an ideal opportunity to assess the relative role of traffic emissions and other precursor types (e.g. biogenic emissions) to ambient SOA loadings. The prevalence of ambient SOA precursor gases can vary substantially with seasonal variation in source strength and meteorological conditions (e.g., temperature). For example, since biogenic emissions increase strongly with increased temperature 45, one would expect that during summer, both traffic (local) and biogenic (regional) emissions would contribute to SOA formation in a near-road environment, whereas the contribution of biogenic emissions would be far lower during winter. Therefore, a systematic comparison of summer and winter SOA formation from in-situ oxidation of road-side air may provide insights into the relative contribution of SOA from traffic and biogenic sources to the ambient SOA burden. In this work, we perform in-situ SOA formation experiments at a site adjacent to Interstate 40, near Durham, North Carolina (8-lanes, with plentiful mixed forest nearby) to investigate summer- and winter-time SOA production from the oxidation of road-side air. We use an OFR system to simulate atmospheric oxidation over several hours to weeks of equivalent atmospheric aging. We compare summer and winter-time SOA production from the oxidation of road-side air, which provides insights into the potential contribution of traffic emissions to ambient SOA loading and its seasonal variation. 2. MATERIALS AND METHOD 2.1 Measurement Site Experiments were performed at a near-highway site near Durham, North Carolina (35.865°N, 78.820°W), located within 10 m from the edge of Interstate 40 (I-40). At the measurement location, I-40 is an eight-lane highway with an annual average daily traffic volume of 140 to 145 thousand vehicles per day, with approximately 95% of the fleet made up of light-duty vehicles (LDVs). Two measurement campaigns were conducted, in summer, 2015 and winter, 2016. The location and key measurements have been described in detail in two companion papers 46,47. Concentrations of vehicle-emitted primary pollutants (NOx, black carbon) are enhanced by factor of 3-10 at the measurement location relative to upwind background levels, as described in Saha et al. 46. The measurement periods for the OFR study were July 1-15, 2015 and February 5-25, 2016; hereafter referred to as summer and winter, respectively. Basic meteorological conditions (temperature, RH, wind) and traffic information during the measurement campaigns are summarized in Supporting Information (SI), Table S1. The campaign-average ambient temperatures during summer and winter were 26 ± 5°C and 6 ± 7°C, respectively. Overall traffic volume was ~ 17% higher in summer than winter.

2.2 OFR Experimental Setup The road-side air was sampled through an OFR system 38,48 and exposed to varying oxidant levels (OH radical and O3). A schematic of the experimental setup is given in Figure S1. The OFR used in our study is a stainless steel tube (39.4 cm long, 15.2 cm diameter, volume ~7 liters, 3 ACS Paragon Plus Environment


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plug-flow residence time ~70 s); the reactor and its evaluation are described elsewhere 49. The OFR contains two low-pressure mercury bulbs emitting UV radiation at 185 and 254 nm wavelengths within Teflon sleeves flushed with a sheath flow. The generation of oxidants (mainly OH and O3) occurs in the reactor via photolysis of ambient water vapor (H2O) and O2; NOx has a short lifetime under OFR conditions applied here and so participates minimally in the resulting chemistry50,51. Similar reactors have been applied in previous studies in diverse laboratory and field settings; details on reactor design, operation and oxidation conditions can be found elsewhere 26,37,38,40,42,43,48,50–52. The reactor was placed inside a road-side trailer (10 m from highway), with the inlet passing through the trailer wall towards the highway at ~2 m above ground. The reactor had a short and large-diameter inlet (18 inches long, 2-inch diameter). This inlet sampling was chosen to minimize possible losses of ‘sticky’ SOA precursors, such as semivolatile and intermediate volatility species (SVOC, IVOC) 42,43. The OFR inlet’s end was covered with a coarse-grid mesh screen to block insects and debris from entering the reactor and minimizing turbulence in the reactor during windy ambient condition 42. A suite of instruments alternately sampled OFR-processed and ambient air (bypass), switched using automated 3-way valves, with dump flows used to maintain constant flows in the OFR and bypass lines during both conditions (Figure S1). UV lamps intensities were stepped through 5-6 settings (e.g., 100, 80, 50, 30, 20, and 0% intensities; each ~20 min long) to moderate oxidant concentrations; the time to run through all settings for a full cycle was ~ 2 h. The time required to reach steady state oxidant conditions at each light setting was ~10 minutes. During this time, ambient air was directly sampled through a 6 mm diameter copper bypass line. Each OFR operation cycle included a lights-off (0% intensity) step. This step helps to estimate particle losses in the reactor from processes such as diffusional losses or re-partitioning of semi-volatile particles within the sampling system due to temperature changes (e.g., ambient vs trailer). Comparison of particle concentrations measured through the reactor with UV light off vs. through the bypass line (ambient) provided a composite correction factor. A time-varying correction factor was applied to the OFR measurements with lights on for each cycle. Real-time estimation of integrated OH exposure (OHexp) in the reactor was obtained from measured decay of carbon-monoxide (CO) 49,50, injected into the inlet using a mass-flow-controller, resulting in 5-8 ppm of un-aged CO). The estimated OHexp ranged from 6.5×1010 to 3.9×1012 molec cm-3 s, equivalent to 0.5 - 30 days of atmospheric aging assuming a 24 h average OH concentration of 1.5×106 molec cm-3 53. OFR bulbs and ballasts were changed to slightly higher output variants between summer and winter campaigns, which led to a slight increase in the range of OHexp in the latter. SOA production in the reactor is described in terms of OA mass enhancement (absolute: OFR-ambient; relative: OFR/ambient) as a function of OHexp. 2.3 Measurements and Instrumentation Submicron aerosol species mass concentrations, size distributions, and volatility were measured alternatively between the bypass (ambient) and the OFR-processed line at 10 min intervals. An Aerosol Chemical Speciation Monitor (ACSM; Aerodyne Research Inc.) measured submicron aerosol (75-650 nm) mass concentrations of non-refractory chemical species (organic, sulfate, nitrate, ammonium, and chloride) 54 with 10 ‘sample’ and 10 ‘filter’ scans at 200 ms per amu leading to scan time of ~10 minutes and an estimated error of 0.05 to 0.1 µg m-3. ACSM data were analyzed assuming a collection efficiency (CE) of 1 for both ambient and reactor measurements 43, an assumption supported by mass closure comparisons for this site described in 4 ACS Paragon Plus Environment

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a companion paper47. An example time series of ambient and OFR-processed OA mass concentrations is shown in Figure S2. A Scanning Mobility Particle Sizer (SMPS; TSI Inc.; Model 3081 DMA, 3010 CPC) measured particle size distributions (10-400 nm; 2.5 min scan time). Examples of particle number- and volume-weighted size distributions of ambient and OFR-processed aerosols are shown in Figure S3 and S4, respectively. All OFR data (measurements with UV lights on) were corrected for particle losses in the reactor, as described in Figs. S2-S4. Particle evaporation in a thermodenuder (TD) (an indicator of volatility) was measured at 60 ˚C and 120 ˚C using a TD/SMPS system described elsewhere47. To obtain measures of oxidant conditions inside the reactor, ozone (2B Technologies 205) and CO (Thermo 48i) were continuously sampled downstream of the reactor. Additional supporting measurements included the ambient concentration of black carbon (BC; 870 nm wavelength Photo-acoustic Extinctiometer; PAX, Droplet Measurement Technology), NOx/NO/NO2 (2B Technologies 401/410), CO (Thermo 48i), CO2 (LI-COR, Li-820), and real-time traffic and meteorological information.

3. RESULTS 3.1 SOA production from oxidation of road-side air Figure 1 summarizes the observed SOA production from oxidation of near-highway air during summer and winter. Figure 1a shows absolute OA enhancement during summer as a function of photochemical age. Data are separated into daytime (08:00-20:00 LT) and nighttime (20:0008:00 LT) periods. Enhancement was significantly higher at night than during the day, despite the higher daytime traffic volume. At all times, OA enhancement increased with OH exposure, with a peak in the range of 2-4 days of equivalent atmospheric oxidation, and then decreased with additional OH exposure, consistent with other recent OFR measurements in diverse settings 26,42,43 . OA enhancement is consistent with aging causing gas-phase precursors to become functionalized and condensing, while the decrease in OA mass at higher exposures is thought to be due to heterogeneous oxidation or fragmentation of semivolatile compounds 26,37,43. In this process, the amount of particle-phase carbon decreases due to fragmentation of carbon-carbon bonds and formation of smaller and higher volatility products that can escape to the gas phase 55. Net loss of OA mass was observed at very high exposures, typically after 10-12 eq. days of aging. The higher OA enhancement observed during the night than daytime during summer measurements (Fig. 1a) is consistent with other recent ambient studies using similar reactors 42,43. It is important to note that an in-situ/ambient OFR experiment does not necessarily reproduce true daytime and nighttime atmospheric chemistry, rather it provides a measure of SOA production from the oxidation of available precursor gases at those ambient conditions 43. Despite higher daytime traffic emissions, the ambient concentrations of overall SOA-forming precursor gases may be higher at night due to a larger contribution of terpenes (biogenic) emissions and the shallower nighttime boundary layer 42,43. Furthermore, the observed SOA formation in an OFR can be depleted during periods with active ambient photochemistry (daytime) due to their reaction with ambient oxidants (e.g., O3, OH) 42. We observed an inverse relationship between reactor OA mass enhancement and ambient Ox (O3+NO2) during summer (see Figure S6a). This influence of ambient oxidant concentrations on observed OFR enhancement is consistent with observations during CalNex 42. 5 ACS Paragon Plus Environment


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Figure 1b shows absolute OA enhancements during winter. Although the general trend in the observed OA enhancement profile during winter was similar to that during summer, a substantially lower enhancement (both absolute and relative) was observed in winter. The peak enhancement of OA mass concentration in winter (~0.5-1 µg m-3) was significantly lower than that observed in summer (~ 3-8 µg m-3). On a relative basis, a factor of 1.5 to 2.5 peak enhancement of OA mass concentrations was observed in summer versus ~ a factor of 1.3 during winter (Figure S5). Relative to summer observations, substantially less day-versus-night variation was observed for OA enhancements (both absolute and relative) in winter. However, the daytime enhancement was slightly higher than during nightime in winter. In winter, no consistent relationship was observed between OA mass enhancement and ambient Ox (Figure S6b). This observation could be related to weaker day-night variation of atmospheric boundary layer and/or ambient photochemistry during winter.

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Figure 1: The observed absolute OA enhancement (∆OA) from the oxidation of near-highway air as a function of photochemical age during (a) summer and (b) winter. Data are separated into daytime (08:00-20:00 LT) and nighttime (20:00-08:00 LT) and binned by photochemical age; symbol shows mean, and error bar represents ± 1 standard deviation. (c) Campaign-average ∆OA/∆CO vs. photochemical age measured in this study (Summer: July 2015; Winter: February 6 ACS Paragon Plus Environment

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2016) and comparison with OFR experiments in traffic tunnel 26 (May 2013) and urban air during CalNex 2010 42 (May-June 2010) using similar reactor. (d) Campaign-average ∆OA/∆CO vs. photochemical age measured in this study after accounting for low-volatility vapor loss corrections following the method of Palm et al. 43. Open symbols indicate LVOC fate corrected measurements, whereas closed symbols are the same as in panel c. Lines in all panels are to guide the eye. Varying dilution of emissions complicates direct comparison between measurements from two seasons and comparison with previous measurements in other settings 26,42. Therefore, Figure 1c presents the absolute OA enhancement (∆OA) normalized by background-corrected CO (∆OA/∆CO), where ∆CO accounts for the level of dilution, assuming ∆CO and SOA precursors are coming from same sources 8. The campaign average profiles are shown in Figure 1c. The comparison of ∆OA/∆CO profiles in Figure 1c indicates that after accounting for any dilution effects, SOA production in winter is still significantly lower than summer. However, our summer measurements are found to be broadly comparable with that measured in an urban environment in Southern California, also conducted during summer months 42. The ∆OA/∆CO values reported in the traffic tunnel OFR experiments 26 are higher than our observations (peak enhancement ~ 30% higher). The interpretation of SOA formation observed in flow reactors 42,43 and environmental chambers 56 can be influenced by losses of low-volatility vapors via different processes that may not be atmospherically relevant. In the atmosphere, the dominant fate of these low volatility vapors is condensation onto the aerosol surface (determined by condensational sink; CS) 43,57,58. However, in a flow reactor, these low volatility vapors may have other fates beside condensation onto aerosols, such as loss to the reactor walls, further reaction with OH to produce either condensable or non-condensable gas-phase products, or exiting in the gas-phase and condensing on sampling line walls 42,43. Furthermore, for a particular reactor setting (e.g., residence time, surface area to volume ratio, oxidant concentrations), a difference in available condensation sink may yield a difference in SOA formation in the reactor 39. In Figure 1d, dilution-corrected OA enhancements (∆OA/∆CO) are further corrected for losses of low-volatility vapors following the method of Palm et al. 43. This analysis is discussed in the SI and Figure S7. The condensation sink (CS) in the reactor as a function of OH oxidation was estimated using the average of the SMPS-measured particle size distributions before (ambient) and after OFR oxidation. This is because particle size distributions in the reactor evolve substantially due to new particle formation and growth and growth of the preexisting ambient particles (Figures S3, S4). At a particular OH exposure, the estimated CS during summer was ~10-20% higher than during winter (Figure S7), because substantially greater new particle formation was observed in the reactor in summer than in winter (Figures S3, S4). Therefore, vapor loss correction factors were higher in winter than in summer (Figure S7). Figure 1d shows that after accounting for dilution and vapor losses (open symbols), the observed OA enhancements in winter are still significantly lower than summer. 3.2 Evolution of OA properties with OFR oxidation Figure 2 shows a H:C vs. O:C plot of ambient and OFR-processed ACSM OA mass spectra from summer and winter campaigns. Atomic ratios of O:C and H:C in bulk OA were estimated using 7 ACS Paragon Plus Environment


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the empirical parameterization from Canagaratna et al.59, based on f43 and f44. f43 and f44 are fractional organic contribution at m/z 43 and 44, and are often used as tracers for fresh and aged OA, respectively 60,61. These parameterizations are approximations and ACSM f44 measurements have been shown to vary between instruments 62 and so our estimated H:C and O:C values should be regarded as estimates. However, we observed a reasonably good agreement with f44 values measured in other similar settings (Figure S9). ACSM spectra (Figure S8) show that OFR oxidation of near-highway air caused substantial increases in the m/z 44 signal (mostly ; associated with organic acids) and decreased signals at m/z 41 (. . , H ), 43 (. . , or H ), and 55 (. . , H ) in both seasons. The f44 vs. f43 plot (Figure S9) shows that f44 increases and f43 decreases with aging consistently in both seasons. As a result, Figure 2 shows that O:C increases and H:C decreases with aging. While there was a substantial seasonal difference in OA enhancement (as discussed in Sec. 3.1), changes in OA chemical composition upon aging in the reactor were relatively consistent across seasons. Furthermore, the range of f44 and f43 (Figure S9) and elemental ratios (O:C, H:C) (Figure 2) measured in the ambient air and reactor generally fall within the range observed in the atmosphere from multiple field campaigns 60 . This suggests that OFR oxidation is serving as a reasonable proxy for processes leading to increasingly oxidized aerosols in the atmosphere. 2.0

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Figure 2: van Krevelen plots showing changes in OA chemical composition (H:C vs. O:C, estimated from ACSM data) upon aging in the reactor. (a) summer and (b) winter. Green open symbols show ambient measurements and colored solid symbols show OFR data. OFR data are colored by the photochemical age in the reactor. Figure 3 shows the measured volume fraction remaining (VFR), a semi-quantitative description of aerosol volatility 63, at 60 and 120ºC as a function of photochemical age in the OFR. Although the observed VFR at a TD operating condition depends on aerosol loading, size and many other factors 64–66, the relative change in VFR with aging can provide a qualitative insights into the change in evaporation of bulk aerosol, and thus changes in their bulk volatility with aging 67. The observed change in VFR as a function of photochemical age was not dramatic (Figure 3). However, after 2-3 days of equivalent aging, bulk aerosol in the reactor appears as slightly more volatile (~15-30% more evaporates) than ambient aerosol, especially in summer. Further oxidation led to a decrease in evaporation (volatility) in both seasons. This observation is broadly consistent with the evolution of volatility of smog-chamber generated SOA during OH induced aging reported by Salo et al.67 . Salo et al.67 reported that bulk SOA appears more volatile immediately after adding a new precursor to an ongoing chamber aging experiment due to the 8 ACS Paragon Plus Environment

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formation of a new batch of semi-volatile material on top of aged aerosol. Further aging of the newly formed semi-volatile materials reduce the volatility of the overall SOA. Changes in VFR with aging were less evident in winter measurements. Part of the reason for this could be the amount of semi-volatile material that was formed in winter upon OFR-oxidation was substantially lower than summer, as demonstrated by the OA enhancements (Figure 1). To observe a significant change in VFR of bulk aerosol in the reactor (ambient+ newly formed via oxidation) likely requires a substantial formation of additional semi-volatile material to the ambient aerosols. Volume Fraction Remaining

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4. Comparison of measured and estimated SOA The measured OFR-SOA from the oxidation of road-side air is formed from a mixture of precursor gases from multiple sources. Here we conduct a simplified closure analysis to assess whether the trends observed in our OFR data can be explained based on source types expected to contribute to SOA at our site. At our site, adjacent to a large roadway in a forested area, we make the simplifying approximation that traffic and biogenic emissions are the major contributors to SOA-forming precursor gases. In this section, we compare the SOA mass concentrations observed in our OFR with SOA estimated from traffic and biogenic emissions at our site. The measured peak absolute OA enhancement is considered as the ‘potential’ SOA mass, which was observed with 2-4 days of equivalent atmospheric aging (Figure 1a and 1b). The LVOC-fateuncorrected OA mass enhancement data are used for this comparison, and so this should be considered a lower bound of potential SOA mass concentrations. The details on estimated SOA from traffic and biogenic emissions are given in SI, Table S2- S5 and are briefly discussed below. To estimate ‘expected’ SOA from vehicle emissions, we use OA enhancement factors observed in laboratory studies to scale vehicle-sourced POA estimated from our ambient observations. Vehicle-SOA (V-SOA) is estimated as: V-SOA = Vehicle POA Concentration × SOA enhancement factor; details are given in Table S2 and S3. In this analysis, the hydrocarbon-like OA (HOA) factor derived from a ‘coarse’ tracer m/z based factor analysis 68 of our ACSM mass spectra is taken to represent vehicular POA. SOA enhancement factors are adopted from recent traffic SOA studies: laboratory chamber SOA experiments with individual vehicles from Gordon et al. 13 (lower bound) and the traffic tunnel OFR results from Tkacik et al. 26 (upper bound). 9 ACS Paragon Plus Environment


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This is a reasonable approximation since our measured emission factors of traffic-related primary pollutants (NOx, BC, and POA) are found to be consistent with these studies (See Table S2 for details). An SOA enhancement factor of 7.5 (median) and range of 5-10 are considered in our analysis. Since traffic SOA measurements in these studies 13,26 were collected at 20-30 °C, their reported SOA enhancement factors are assumed to be representative for our summer calculation and thus, used for estimating our V-SOA in summer. In winter, colder temperatures shift partitioning of S/IVOC emissions, increasing POA emissions and reducing the associated vapors available for SOA production. This complicates the application of enhancement factors measured at higher temperatures. Therefore, the V-SOA in winter was estimated by applying several adjustments to summer V-SOA estimates (Eq. 1) to approximate the influence of several seasonally-varying factors that are expected to affect SOA formed from traffic emissions. These factors include seasonal variation in: a) road-side ‘source strength’, b) the emission factors of SOA precursors, and c) the temperature-driven shift in S/IVOC partitioning. V-SOA (winter) = V-SOA (summer) × f source strength× f precursor EF × f S/IVOC partitioning

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Eq.1

First, a seasonal difference in source strength driven by changes in traffic volume, fleet composition or dilution conditions between the roadway and our measurement site would influence the prevalence of SOA precursor gases from the traffic source. At our site, seasonal variation in fleet composition was not significant (HDV were 6± 3% of total traffic volume during both summer and winter campaigns). However, our traffic data (Figure S10, Table S1) indicate that campaign-average total traffic volume was 17% lower in winter than summer. Variation in road-to-OFR transport is more difficult to quantify, though mixing and dilution at the site were generally lower in the winter than summer 46. Neither fleet-average BC emission factor (Fig. S12) nor near-road BC concentration (Fig. 4) varied with season, which suggests that changes in the traffic volume and emission transport counteract each other. Therefore, a value of 1 is assumed for fsource strength, with an uncertainty in this value of ±0.2. Second, emission rates of SOA precursors from vehicles may vary with ambient temperature. For example, VOC emissions from motor vehicles, especially evaporative emissions, are sensitive to temperature 69. However, it is unclear whether SOA formation capacity would scale as these do. Ortega et al. 42 found a reasonably strong correlation between vehicle-related aromatic VOCs and SOA formation potential, indicating that a reduction in these will reduce SOA formation. While we did not perform near road VOC measurements, we explored potential temperature effects on organic gaseous emissions from our fleet using MOVES (MOtor Vehicle Emission Simulator) 70 including seasonal variation in fuel formulation. The results of this analysis are shown in Figure S11 and suggest that emissions of net (exhaust+ evaporative) VOCs are ~10% lower during winter than summer. Therefore, lacking information about the overlap between this total VOC estimate and the SOA precursors, a value of 0.9 was assumed for fprecursor_EF with an estimated uncertainty of ±0.1. In reality, this factor is not well constrained and the sensitivity of SOA production to vehicle operation temperature should be explored. While recent measurements of low temperature emissions and their SOA forming potential are available 71, these chamber experiments include both cold-start and running emissions and 10 ACS Paragon Plus Environment

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indicate that the former dominate VOC emissions and SOA formation potential. Therefore, they cannot be applied for this highway scenario, in which the vast majority of vehicles will be warmed up. Finally, there is strong evidence that SVOCs and IVOCs from motor vehicles are important SOA precursors 16,20. These emissions partition into both gas- and particle-phases depending on environmental conditions (e.g., dilution, temperature). In the gas-phase, they are SOA precursors for a road-side OFR experiment, while if they are in the condensed-phase, they are considered POA. Condensed SVOCs are less available for oxidation than vapors 72. As noted above, a larger fraction of these low-volatility vapors is expected to remain in the condensed phase at lower winter temperatures. That this is occurring is supported by our observed POA emission factors being a factor of ~ 2 higher in winter than summer (Figure S12). These emission factors are further discussed in a companion paper 46, which also discusses other observations of this temperature effect 73,74. An analysis of the influence of temperature on S/IVOC partitioning is shown in Figure S13. This analysis uses the volatility distribution of gasoline vehicle exhaust from May et al. 75 with enthalpies of vaporization (∆Hvap) from Ranjan et al.76. These calculations suggests a reduction of S/IVOCs in the gas-phase by ~ 25% in winter relative to summer. Therefore, in Eq. 1, a value of 0.75 was assumed for fS/IVOC partitioning with an assumed uncertainty of ±0.25. Combining the three factors, a composite seasonality factor for traffic-related SOA is estimated at 0.7 ± 0.3, indicating that we would expect ~ 30% less V-SOA in winter than summer. This approximated factor and the associated uncertainty are used to calculate the V-SOA value in pink in Fig. 4; details on calculations are given in Table S3.

8

Estimated SOA w/ Lit. yields/Enh

6 4 2 0

433 434 435 436 437 438

Observed OFR-SOA

BC POA V-SOA B-SOA V-SOA+B-SOA Observed OFR SOA

SOA

-3

Mass Conc (µg m )

10

Summer

SOA

Winter

S Figure 4: Comparison of potential SOA mass concentrations measured in the OFR aging experiments during our summer and winter campaigns with estimated SOA concentrations from traffic and biogenic emissions. Bar shows median of campaign measurements and estimates. The error bar on the observed OFR-SOA indicates the interquartile range (25th -75th percentile) of campaign measurement. The error bar on the estimated SOA indicates the uncertainty range in 11 ACS Paragon Plus Environment


439 440

estimates (see Table S5 for details). POA and BC concentrations are also shown to demonstrate seasonal variation of traffic-related primary pollutants.

441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484

The other major component of SOA in this setting is likely biogenic SOA (B-SOA), which was estimated as B-SOA (Biogenic-SOA) = Biogenic-VOC (BVOC) concentration × SOA yield; details are given in Table S4 in the SI. Only major B-SOA precursors, such as monoterpenes (as α-, β- pinene), isoprene, and sesquiterpenes (as β-caryophyllene), are considered. SOA yield of pinene and isoprene are adopted from OFR experiments of Lambe et al. 39, and sesquiterpenes (as β-caryophyllene) yield is taken from Tasoglou and Pandis 77 (See Table S4 for details). Since we did not measure VOCs during our field campaigns, BVOCs concentrations are adopted from Stroud et al. 78, which describes measurements collected in July 2003 (summer) at Duke Forest, North Carolina. The Duke Forest site is located about 25 km northwest of our near-highway site, and 2.4 km from I-40 further north. It is in a similar ecological setting to our sampling site, surrounded by mixed forest dominated by longleaf pines, as shown in Fig. S1 in the SI. While BVOC concentrations vary sharply with season, analyses of forested sites in the southeastern US, with varying tree density and proximity of forests, found relatively consistent summertime concentrations 79,80. For example, the mean of summer-season monoterpene concentrations measured at eight southeastern U.S. sites79 was within 15% of the mean at our site, and summer averages at all sites fall within our confidence intervals (Table S4). Therefore, we assume that seasonal-average BVOC concentrations in forested areas in this region are relatively consistent and that there have been no significant changes in BVOC emissions over the last decade. Wintertime BVOCs concentrations were not measured at the site but were estimated by applying temperature adjustment factors 45 to the measured summer-time concentrations (See Table S4 for details). Figure 4 compares our measured potential SOA mass concentrations in summer and winter with the SOA predicted to form from traffic and biogenic emissions. Consistent with measurements, our estimates also show a substantially lower SOA during winter than summer. Our estimates suggest that SOA from both traffic and biogenic emissions would be lower in winter than summer. Our estimation of lower V-SOA in winter is consistent with the laboratory measurements by Platt et al. 71, who reported lower SOA yields from gasoline car emissions at lower temperatures. Our analysis suggests that while traffic-sourced SOA in winter is ~30% lower, the seasonality in biogenic precursors dominates: B-SOA may be 10-15 times lower in winter than summer. Therefore, this indicates that while the observed seasonal variation in SOA production is influenced by temperature sensitivity of both vehicle- and biogenic VOC emissions, it is largely driven by the temperature dependence of BVOC emissions. Our analysis shows reasonable closure between the SOA observed in the OFR and that predicted to form from traffic and biogenic precursors using literature yields, especially during winter. The two sources included in our SOA estimate accounted for 50% of that measured in the OFR in summer. A number of factors could contribute to a potential low bias in estimated SOA in summer and to overall uncertainty in our comparison of estimated and measured SOA. For one, the simplified assumption that only two source-classes contribute to SOA precursors is clearly not correct. Other combustion (e.g. biomass burning) and non-combustion (e.g. cooking) sources contribute regional SOA precursors 11,81–83 that we are not accounting for. This will be true for both summer and winter, but our results may indicate that these ‘other’ precursor emissions (e.g. evaporative emissions of SVOCs/IVOCs)82 may make a larger contribution to SOA formation 12 ACS Paragon Plus Environment

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potential during summer. Another potential contributor to low-bias in our estimated SOA is if the laboratory measured SOA yields used in our analysis were underestimated, for example due to the un-corrected influence of vapor wall losses during chamber experiments. 1,56,65 Where possible, we used OFR-based yield measurements, but these are also subject to uncertainty due to vapor wall losses (Figure 1d). Our OFR-measured SOA is subject to this uncertainty as well; Figure 1d shows that potential SOA mass (accounting for the loss pathways for low-volatility vapors in the OFR) could be a factor of 2-3 higher than the values in this analysis. Finally, our assumed BVOC concentrations are a source of uncertainty; conditions near the roadway (e.g., solar radiation, mixing from the canopy edge, and density of tree cover) are distinct from those at Duke Forest and may vary from year to year. This could contribute to bias in either direction, though as noted above, studies have found concentrations to be regionally consistent and the Duke Forest concentrations are similar to regional measurements a decade earlier.

Despite uncertainties in our observations and estimates, they suggest that that available SOA precursors at this site are dominated by BVOCs in summertime, which was somewhat surprising considering that it immediately adjoins a busy interstate. The contribution of traffic SOA to ambient OA loading is still important, but biogenic emissions appear to make a substantially larger contribution in summer. While measurements and analysis in Figure 4 suggest that we capture the relative influence of traffic and biogenic emissions on ambient SOA loading and their seasonal variation, further measurements and modelling work are needed to better constrain the contribution of these and other emissions to ambient SOA burden. In particular, further work should emphasize measurement of diurnally and seasonally varying precursor concentrations to more strongly push towards closure of observed SOA. IMPLICATIONS The diurnal and seasonal patterns and strong seasonality in SOA production observed at our site point toward the dominance of BVOCs as SOA precursors in the southeastern US. The dominance and seasonality of B-SOA was observed in a nearby site using SOA tracer compounds by Kleindienst et al. 84 and has been long understood for the SE US 85, but their dominance at a near-road site, where concentrations of vehicle-emitted pollutants are enhanced by factor of 3-10 46, is especially telling. B-SOA is not directly controllable, though may be moderated by changes in other anthropogenic emissions 86. Vehicle emissions also appear to contribute substantially to SOA loadings at our site, consistent with laboratory predictions, and are controllable. Vehicle emission reductions appear to have already contributed considerably to OA reductions in the US over the past several decades 87, though our work shows that there is still an important contribution from vehicles to OA in near-road and regional contexts. However, there is evidence of strong dependence on chemical regime for SOA production from vehicle emissions 44, which was not captured by the operation of our OFR due to the accelerated chemistry and short NOx lifetime in the reactor 51. One effect captured by our study and possibly important for net vehicle emission impacts is the temperature effect on POA emissions and resulting SOA formation, likely driven by varying S/IVOC partitioning. Our results suggest a tradeoff between POA and SOA across seasons, which may have implications for control strategies and seasonally varying contributions from 13 ACS Paragon Plus Environment


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vehicles to regional air pollution and near-road exposures. Our measurement and modeling suggest that the influence of temperature on partitioning can be characterized using a volatility basis set-based representation of emissions 47. Other recent studies 71,88 have shown that coldstart emissions, not captured here, may make a dominant contribution to potential SOA from vehicles. Further work will be required to separate these effects and those of chemical regime on SOA formation from vehicle exhaust. These data may be useful for evaluation of SOA modeling approaches and demonstrates the utility of OFR systems in probing variability in SOA formation in diverse settings. Future work should couple this approach with more comprehensive measurements of VOC and OA composition, as has occurred at other sites 42,43 to give further insights into seasonal variation in SOA production in various settings. ACKNOWLEDGEMENTS This material is based upon work supported by the National Science Foundation under Grant No. CBET-13-51721 and the Health Effects Institute (HEI) under RFA 13-1. Contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of HEI, and no official endorsement should be inferred. We thank Sue Kimbrough, Rich Baldauf and Richard Snow of the US EPA Office of Research and Development, Research Triangle Park (RTP), NC for help and support with establishing the roadside trailer facility for this study and for sharing NOx, CO, O3 data. We thank Nagui Rouphail and his research group (NCSU) and NC-DOT for the traffic data used in this paper, Maryam Delavarrafiee for help with MOVES modeling, and Henry Ricca for help with the summer data collection. We thank Allen L. Robinson (Carnegie Mellon University) for helpful discussions. SUPPORTING INFORMATION AVAILABLE Details on site characteristics and instrumentation; comparisons of fleet-average emission factors; parameters used in estimating vehicle- and biogenic-SOA and comparison of estimates; example time series and particle size distributions of ambient and OFR-aged aerosol; campaignaverage OFR enhancement and relationship with ambient odd-oxygen; analysis of low-volatility organic fate in the OFR; evolution of OA mass-spectra with aging; information on traffic profile, VOC, HOA and BC emissions and SVOC partitioning estimated at site. This information is available free of charge via the Internet at http://pubs.acs.org.

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1. INTRODUCTION

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