PUBLICATIONS. Journal of Geophysical Research: Atmospheres

Transcription

1 PUBLICATIONS RESEARCH ARTICLE Key Points: Emitted aerosol was observed to partially evaporate downwind of highway Nonvolatile aerosol formation was observed downwind of highway Current gas/particle partitioning mechanisms cannot explain aerosol formation Supporting Information: Sections on denuder efficiency, meteorological back trajectories and analysis of aerosol mass spectrum Readme Correspondence to: C. A. Stroud, Citation: Stroud, C. A., et al. (2014), Rapid organic aerosol formation downwind of a highway: Measured and model results from the FEVER study, J. Geophys. Res. Atmos., 119, , doi: / 2013JD Received 16 MAY 2013 Accepted 10 JAN 2014 Accepted article online 14 JAN 2014 Published online 12 FEB 2014 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Rapid organic aerosol formation downwind of a highway: Measured and model results from the FEVER study Craig A. Stroud 1, John Liggio 1, Jie Zhang 1, Mark Gordon 1, Ralf M. Staebler 1, Paul A. Makar 1, Junhua Zhang 1, Shao-Meng Li 1, Cristian Mihele 1, Gang Lu 1, Daniel K. Wang 1, Jeremy Wentzell 1, Jeffrey R. Brook 1, and Greg J. Evans 2 1 Air Quality Research Division, Science and Technology Branch, Environment Canada, Toronto, Ontario, Canada, 2 Department of Chemical Engineering, University of Toronto, Toronto, Ontario, Canada Abstract The Fast Evolution of Vehicle Emissions from Roadway (FEVER) study was undertaken to strategically measure pollutant gradients perpendicular to a major highway north of Toronto, Canada. A case study period was analyzed when there was an average perpendicular wind direction. Two independent, fast response measurements were used to infer rapid organic aerosol (OA) growth on a spatial scale from 34 m to 285 m at the same time as a decrease was observed in the mixing ratio of primary emitted species, such as CO 2 and NO x. An integrated organic gas and particle sampler also showed that near the highway, the aerosol had a larger semivolatile fraction than lower volatile fraction, but over a relatively short distance downwind of the highway, the aerosol transformed to being more low volatile with the change being driven by both evaporation of semivolatile and production of lower volatile organic aerosol. A new 1-D column Lagrangian atmospheric chemistry model was developed to help interpret the measured increase in the ΔOA/ΔCO 2 curve from 34 m to 285 m downwind of highway, where the Δ refers to background-corrected concentrations. The model was sensitive to the assumptions for semivolatile organic compounds (SVOCs). Different combinations of SVOC emissions and background mixing ratios were able to yield similar model curves and reproduce the observations. Future measurements of total gas-phase SVOC in equilibrium with aerosol both upwind and downwind of the highway would be helpful to constrain the model. 1. Introduction On-road mobile emissions of reactive trace gases and particles are important sources of air pollution, especially in urban air sheds [Bhave et al., 2007; Ying et al., 2007]. The advancement in rapid response and precise instrumental methods to sample and analyze trace gases and aerosol (i.e., Aerodyne aerosol mass spectrometer) enables much improved characterization of near-roadway gradients in pollutants. Vehicle-emitted organic gases can be classified into their effective saturation vapor pressure ranges as volatile, intermediate volatile, and semivolatile organic carbon species [Robinson et al., 2010]. Vehicle-emitted carbonaceous particle components include elemental carbon, nonvolatile organic carbon, and semivolatile organic carbon. The oxidation of organic gases to form secondary gases, which subsequently partition to the aerosol phase, is the traditional definition of secondary organic aerosol (SOA) formation [Hallquist et al., 2009; Donahue et al., 2009; Seinfeld and Pankow, 2003]. Miracolo et al. [2010] observed rapid SOA production in a smog chamber, within 10 min of irradiation, for the injection of motor vehicle emission surrogates, i.e., n-pentacosane (C 25 H 52 ) representing a semivolatile organic component. Direct emissions from the tailpipe (termed primary emissions) include both particles and precursor gases that have the potential to form SOA downwind. A growing number of health studies have associated traffic-related air pollution with adverse outcomes such as reduced lung function, respiratory symptoms, asthma, heart failure, cancer, and mortality [e.g., Brunekreef et al., 1997; Line et al., 2002; Hoek et al., 2002; Maynard et al., 2007; McCreanor et al., 2007; Jerrett et al., 2009; Crouse et al., 2009]. Several studies have found the greatest health risk associated with living within 300 m of a highway [Van Vliet et al., 1997; Venn et al., 2001] or even closer, such as within 50 m [Hoffmann et al., 2007]. The causative pollutants in this near-traffic mixture have yet to be fully elucidated [Polidori et al., 2010]. In addition to specific primary pollutants emitted from vehicles in the particle and/or gas phase being responsible for the effects, there is the potential for these pollutants to interact with each other and with STROUD ET AL The Authors. 1663

2 background pollutants with or without sunshine to change their physical and chemical characteristics while in the near-roadway environment, thus altering toxicity. Characterizing rapid SOA production in this nearroad environment is thus important because it can contribute to the higher concentrations of particle-bound pollutants observed within distances of 100 to 500 m downwind of roadways. Developing improved mobile emissions inventories and numerically efficient mechanisms for SOA formation in air quality models is necessary because model output is used to provide forecast guidance in predicting national air quality health indices [Smyth et al., 2009; Eder et al., 2010]. Furthermore, recent air quality modeling studies have suggested that using finer-scale numerical grids increases predictions of SOA in urban air sheds due to improved spatial resolution of precursor emissions and due to a better characterization of nonlinearities in gasphase chemistry and the SOA formation mechanisms [Stroud et al., 2011]. Therefore, characterizing mechanisms for rapid SOA formation becomes more important as air quality models use shorter time steps, on the order of a minute, with horizontal grid cell dimensions that can resolve the rapid particle growth (less than 1km 2 ). Recent work has hypothesized that low-volatility organic vapors may be important precursors to SOA production downwind of urban areas, as semivolatile organics (SVOCs) evaporate from primary emitted exhaust particles, undergo gas-phase oxidation, followed by gas-phase products condensing as SOA [Robinson et al., 2010]. Unaccounted for intermediate-volatile organic compounds (IVOCs) were also shown to contribute to rapid SOA formation; however, their exhaust emissions are poorly constrained experimentally. Water soluble organic gases (WSOGs) from gasoline combustion have also been observed to rapidly partition to aqueous sulfate particles [Li et al., 2011] in a flow tube downstream from a gasoline engine. The emissions inventories for IVOCs, SVOCs, and WSOGs remain highly uncertain. Water soluble species are also formed from the gasphase oxidation of precursor gases (e.g., glyoxal from aromatic gases). After partitioning to the aqueous or cloud phase, water soluble species can be further oxidized to produce low-volatility products [Ervens et al., 2011; Lim et al., 2010; Gong et al., 2011]. The FEVER (Fast Evolution of Vehicle Emissions from Roadways) study took place in summer 2010 along Hwy 400, north of Toronto, Canada [Liggio et al., 2012]. Mobile measurements of pollutants from CRUISER (Canadian Regional and Urban Investigation System for Environmental Research) were strategically made on a minor side road perpendicular to the highway [Gordon et al., 2012a]. In this paper, rapid aerosol growth was observed by several independent instruments along a perpendicular distance to the highway under crosswind conditions. Understanding the source of this rapid aerosol growth provides the rational for our development of a novel 1-D Lagrangian model, which integrates current emission estimates and an observationally constrained vertical mixing scheme with a state-of-the-science gas-phase chemistry mechanism, primary organic aerosol (POA) volatility distribution and SOA scheme [Pye and Seinfeld, 2010; May et al., 2013]. The sensitivity of the modeled SOA to the uncertain traffic-related emission inputs (SVOCs, IVOCs, and HONO) and assumptions for the partitioning behavior of the background aerosol will be assessed. 2. Experimental Methods 2.1. FEVER Measurements For the FEVER project, a six-lane highway site north of Toronto, Ontario, Canada ( N, W), with negligible surrounding local pollution sources was selected so that motor vehicles are the dominant source of fresh emissions. The surrounding land use was largely agricultural (soy bean) or barren so that biogenic emissions were smaller than if the countryside would have been forested. A case study period from 13 to 15 September 2010 and a sample interval from 15:00 to 16:00 EDT were used for averaging. This period was selected because the average wind direction over this period was very close to perpendicular to the highway. CRUISER drove slowly toward the highway (on a perpendicular side road) and quickly away. Only data from the approach to the highway was used. Data filters to exclude questionable data, based upon the net wind speed difference between CRUISER and the oncoming wind, were used. These measures ensured that data considered here was not contaminated by the CRUISER exhaust. The CRUISER mobile laboratory housed instrumentation to measure particle size distributions (Fast Mobility Particle Sizer (FMPS), TSI Inc.), PM 1 OA (organic aerosol) mass concentrations (unit resolution aerosol mass spectrometry (AMS), Aerodyne Research, 20% uncertainty), NO and NO 2 (TECO 43C, Thermo Electron Co.), CO 2 (LI-6200, LI-COR, 10% uncertainty), O 3 (Thermo Scientific/TECO 49), and CO (Thermo Scientific/TECO 48). STROUD ET AL The Authors. 1664

3 Figure 1. Background-corrected ratio of particle OA mass concentration or PM 2.5 mass concentration relative to CO 2 plotted as a function of air parcel transport time from the center of the highway. Median values and 25th and 75th percentile ranges are plotted. In addition to these instruments, the mobile lab was outfitted with a Global Positioning System and a 3-D sonic anemometer. A stationary traffic camera (Miovision) was used to count highway traffic, with counts separated as passenger cars, medium sized vehicles, and heavy-duty trucks. In addition to the mobile laboratory, gas and particle instrumentation were operated: 110 m west (upwind) of the highway (Site A), 34 m east (downwind) of highway (Site B), and 285 m east of highway (Site C). An air quality and meteorology monitoring system (Airpointer, Recordum) was located at Sites A, B, and C to measure wind speed, wind direction, temperature, and humidity. A radiometer was mounted on a 3 m tower positioned 10 m from highway to measure incoming solar radiation. At Sites B and C, an integrated organic gas and particle sampler (IOGAPS; URG Corporation), i.e., an upstream gasphase denuder followed by a quartz particle filter followed by two sorbent (XAD) impregnated filters (SIF) were used to collect ~22 h integrated samples for laboratory analysis. The quartz and SIF filters were analyzed by thermal-optical transmission analysis (Sunset Labs) to determine particle-phase organic carbon (OC), elemental carbon, and semivolatile organic carbon (SVOC) as described in Fan et al. [2003]. The denuders were spiked with recovery standards and then extracted after each sample period for subsequent gas chromatography mass spectrometry to quantify a suite of polycyclic aromatic hydrocarbons (PAHs), alkanes, and petroleum biomarkers (i.e., hopanes and steranes). Naphthalene was one of the PAHs measured and was used in this study as a representative of a gas-phase SVOC species. A second mobile laboratory operated by the University of Toronto was positioned upwind of the highway during the case study period and housed an High Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-TOF AMS) to measure PM 1 organic aerosol concentrations. The two mobile AMS instruments were cross calibrated during coincident sampling periods. A positive matrix factorization (PMF) analysis was performed on the HR-TOF AMS instrument mass spectra (as in McGuire et al. [2011]) to derive upwind hydrocarbon-like organic aerosol (HOA) factors. The average HOA fraction from this instrument was used to estimate the model background primary organic aerosol (POA) mass concentration. The morphology of the background aerosol is unknown with regards to the internal mixing of the HOA with other aerosol components (e.g., aqueous phase, viscous layers, and black carbon core). The morphology most likely impacts the partitioning behavior of the HOA. Background organic aerosol concentrations measured upwind of the highway were subtracted from the mobile organic aerosol concentration measurements to characterize concentration changes only associated with emissions, chemistry, and vertical mixing downwind of the highway (ΔOA). Organic aerosol concentrations are composed of primary organic and secondary components. To isolate changes in organic aerosol concentrations due to primary emission and vertical mixing from SOA formation, a ratio of ΔOA relative to ΔCO 2 was calculated where the Δ refers to differences relative to the upwind background concentrations. A plot of ΔOA/ΔCO 2 against perpendicular distance from the highway that is a horizontal constant line would describe a condition where primary organic aerosol is being diluted and deposited like CO 2, and there is no SOA production. CO 2 is a good choice for a mobile emission and dilution surrogate because it is conserved chemically on the time scale of the air masses sampled and can be measured with good precision (better than CO) and time resolution. STROUD ET AL The Authors. 1665

4 Table 1. Chemical Species Mass Emission Rates (g/km Driven) by Fuel Type Emission Gasoline (g/km) Gasoline Combustion Reference Diesel (g/km) Diesel Combustion Reference CO MGEM, Liggio et al. [2012] 715 a MGEM, Liggio et al. [2012] NO x Gordon et al. [2012] 1.13 Gordon et al. [2012a] VOC MOBILE6.2C MOBILE6.2C HONO 1.17E-3 Kirchstetter et al. [1996], 1% truck fraction; Kurtenbach et al. [2001], 25% truck fraction 1.59E-3 Kirchstetter et al. [1996], 1% truck fraction; Kurtenbach et al. [2001], 25% truck fraction CO 3.91 MOBILE6.2C MOBILE6.2C NAPH 4.92E-4 SPECIATE4.2, # E-4 SPECIATE4.2, #4674 SVOC Pye and Seinfeld [2010] Pye and Seinfeld [2010] IVOC Pye and Seinfeld [2010] Pye and Seinfeld [2010] POA MOBILE6.2C a MOBILE6.2C a Average value for heavy-duty diesel vehicle classes HDDV3 to HDDV7. During the case study period, the observed ΔOA/ΔCO 2 ratio increases with distance from the highway which suggests there is some OA growth occurring and countering the mixing, evaporation, and dilution of vehicleemitted POA. Figure 1 illustrates the observed median Δ ratios from the two fast time response instruments (AMS PM 1 OA and FMPS PM 2.5 assuming unit particle density). The two instruments give a Δ ratio with similar shape with the exception of one AMS point at ~70 s transport time. The 75th and 25th percentiles of the AMS Δ ratio are also included and show a general widening of the data range around the median with distance from the highway. Because the ratios become more uncertain with distance from the highway, we focus on the 30 m to 150 m distance for our analysis and subsequent model comparison. An analysis of the m/z ratio 44 of the AMS spectrum was also performed as a function of distance from the highway (see supporting information) to study changes in aerosol oxygenation. While there is large variability in the bins, an increasing trend in m/z 44 relative to total OA with distance from the highway is observed. To help understand the underlying rapid chemical and mixing processes occurring near the highway, a new 1-D column Lagrangian model was developed One-Dimensional Column Lagrangian Atmospheric Chemistry Model A 1-D numerical model was developed with a Lagrangian framework to simulate the vertical mixing and chemistry of emissions as a column of air moves perpendicular away from a highway. A grid spacing of 1 m was used in the vertical with a model top of 500 m. The following processes were considered in the 1-D model Emissions Mobile emissions are needed in our analysis for NO, NO 2, HONO, total gasoline VOC, speciated VOCs, CO, CO 2, PM 2.5 POA, and naphthalene specifically. In our model, naphthalene has been used as a surrogate to calculate the IVOC and SVOC-lumped emission species (based on Pye and Seinfeld [2010, Table 2]). In the Results section 3.5, a comparison was performed by calculating IVOC and SVOC emissions scaled from POA emissions [Robinson et al., 2007]. Similarly, total gasoline VOC has been used as a surrogate to calculate gasoline water soluble organic gaseous (WSOG) emissions [Li et al., 2011]. The traffic camera data collected during FEVER were used in the calculation of the model emissions. The average total vehicle count rate for the afternoon period was 117 vehicles per minute with a heavy-duty truck fraction of 3.4%. All cars and trucks were assumed to burn gasoline and ultralow sulfur diesel, respectively. Table 1 lists the estimated chemical species mass emission rates by fuel type. The Canadian Mobile Greenhouse Gas Emission Model (MGEM) was used to generate the mass emission rate for CO 2 per kilometer driven [Liggio et al., 2012]. The U.S. Environmental Protection Agency (EPA) MOBILE6.2C model was used to calculate mass emission rates for total VOC, CO, and PM 2.5 POA per kilometer driven. The NO x (sum of NO and NO 2 ) mass emission rate per kilometer driven was calculated from roadside data collected during FEVER and was deemed more accurate due to its generation for the specific highway location and on-road vehicle fleet [Gordon et al., 2012a]. MOBILE6.2C emission ratios of NO to NO 2 were used to speciate the NO x emission. MOBILE6.2C gasoline and diesel split factors were applied to the observationally derived NO x emission rates to generate the gasoline and diesel specific emission rates in Table 1. The HONO to NO x ratios reported in Kirchstetter et al. [1996] and Kurtenbach et al. [2002] were used to calculate HONO mass emissions. A linear interpolation of the HONO to NO x ratio was used as a function of heavy-duty truck fraction to generate gasoline and diesel specific emission rates in Table 1. The U.S. EPA SPECIATE4.3 VOC profiles were used to speciate the total VOC mass emission rate into STROUD ET AL The Authors. 1666

5 Table 2. Organic Gases and Their Gas-to-Particle Partitioning Coefficients Species and Dominant Reaction α1, α2 ISOP + OH , OLE1 + OH , OLE2 + OH ALK5 + OH ALK4 + OH K1, K2 (m 3 /μg) (300K) individual VOCs (#1313 for gasoline and #4674 for diesel). This method was used to generate the naphthalene mass emission rates in Table 1. The low-volatility organic mass emissions were generated using the ratios relative to naphthalene. A mass emission ratio for SVOCs and IVOCs relative to naphthalene of 38 and 64, respectively, was calculated from Table 2 in Pye and Seinfeld [2010] for anthropogenic sources (assumed same for both gasoline and diesel). For the base case, the POA emissions from gasoline combustion were estimated from the U.S. EPA SPECIATE3 database as a percentage of the PM 2.5 emissions (profile #92122, 58% POA in PM 2.5 ). Sensitivity calculations were also done to calculate SVOC and IVOC emissions and volatility using factors calculated from Shrivastava et al. [2008] and May et al. [2013] (section 4). For the sensitivity runs, the POA emissions were already assumed to be included in the SVOC emissions, thus removing the possibility of double-counting POA in the base case run. The gasoline exhaust mass emissions entered the grid cell at 1 m aboveground to simulate a car tailpipe emission injection height. The diesel exhaust mass emissions entered the grid cell at 5 m above ground to simulate a truck tailpipe emission injection height. To calculate the initial vehicle-emitted pollutant concentration for the 1 m 1 m 1 m grid cell at the center of the highway at 1 m and 5 m aboveground, the perpendicular wind speed (1.87 m/s) was used to calculate the time available for emissions to accumulate into the box (0.535 s). The numbers of cars and trucks emitting into the boxes were calculated by multiplying the observed car or truck counts per minute and the time available while the boxes were centered over road (0.535 s) Vertical Turbulent Diffusion Vertical transfers due to turbulent air motion are parameterized in the form of a vertical diffusion equation (based on Côté et al. [1998]) ψ t ¼ SOA Yield at 10 μg/m 3 (300K) Reference , % Based on Lane et al. [2008], fit to two-product model , % Based on Lane et al. [2008], fit to two-product model, values for OLE % Based on Lane et al. [2008], fit to one-product model, assume ALK4 OA yield same as ALK5 ARO1 + OH , , % Donahue et al. [2009], fit to twoproduct model ARO2 + OH 0.031, (298K), % Ng et al. [2007] BENZ + OH 0.072, (298K), % Ng et al. [2007] SESQ + O , , % Based on Lane et al. [2008], fit to two-product model APINE + O , (298K), 7.5% Pathak et al. [2007] BPINE + OH 0.13, (313K), 8.5% Griffin etal. [1999] LIMO + O , , % Based on Zhang et al. [2006], fit to two-product IVOC + OH 0.21, (299K), % Pye and Seinfeld [2010], Chan et al. [2009] SVOC + OH 0.49*1.5 a, 0.51* , % Pye and Seinfeld [2010], Grieshop et al. [2009] a Factor of 1.5 is derived from Pye and Seinfeld [2010, Table 1] and represents the mass increase of the SVOC through functionalization. K z 2 ψ z 2 (1) where ψ is a species concentration, K z is the vertical diffusion coefficient, and z is the vertical coordinate. The Crank-Nicholson numerical scheme was used to solve 1 [Crank and Nicolson, 1947]. The K z coefficients are expressed as STROUD ET AL The Authors. 1667

6 Figure 2. Vertical profile of the diffusivity coefficient for highway conditions and 285 m downwind of highway. K z ¼ c λ pffiffi e (2) φ where c = constant = 0.516, λ is the mixing length = min(k(z + z o, λ e )), λ e is a limiting value of 200 m, k = 0.4, z o is the surface roughness, and φ is the stability function for fair weather, high-pressure surface synoptic conditions = (1 16(z r /L)) 1/4. The z r /L term can be calculated as follows: z r L ¼ z r k g w T u 3 T (3) where z r =3 m sample height, k = constant = 0.4, g =constant=9.8 m/s 2, T = temperature, w T is the heat flux term, and u* is the friction velocity. The turbulent kinetic energy, e, near the highway is calculated from the parameterization developed in Gordon et al. [2012b] for FEVER conditions e U 2 ¼ 1:94 x 0:53; h ðx < 24hÞ (4) where U is the perpendicular wind speed to the highway, x is the distance from the highway, and h is the 3 m CRUISER measurement height. Equation (4) accounts for the additional vertical mixing created by vehicle-induced turbulence (VIT). Below 5 m altitude, the above equations were calculated with measurements from CRUISER. Above 5 m, the vertical diffusion coefficients were (1) extracted from the Canadian operational weather forecast model (GEM) [Côté et al., 1998] output which had been run for the exact days and times of the FEVER study, (2) averaged, and (3) vertically interpolated to the 1-D model grid spacing. Figure 2 illustrates the 1-D model vertical diffusion coefficients constrained by measurements near the surface and interpolated from the model above 5 m. The measurement-constrained near-surface values were calculated as a function of distance from the highway to take into account the near-highway vehicle-induced turbulence. The line in Figure 2 at model time zero (center of highway conditions) has higher diffusivity coefficients than the line calculated 285 m from the highway, i.e., close to background conditions. The column assumption used in the Lagrangian model is a simplification of conditions with low vertical wind shear. It is likely that increasing wind speeds with height would allow more vertical mixing of cleaner air down to the surface. Thus, the column assumption likely results in an upper limit impact of mobile emissions of downwind concentrations at the surface. The good agreement for CO 2 and NO x downwind of the highway suggest that this treatment is a reasonable approximation Gas-Phase Chemistry The detailed SAPRC-07 mechanism was used to represent the VOC/NO x gas-phase chemistry [Carter, 2010] which includes 466 reactions and 207 species (no chlorine chemistry included here). This version was derived by Carter to represent a 122 individual component mixture of emitted VOCs found typically in the atmosphere. The list of chemical reactions was processed with a kinetic preprocessor which produces Fortran90 code using the Rosenbrock numerical method [Sandu and Sander, 2006]. Clear-sky photolysis rates were calculated using National Center for Atmospheric Research s tropospheric ultraviolet visible (TUV) radiation model [Palancar et al., 2011]. TUV requires several model inputs including date, time, latitude, longitude, altitude, ozone optical depth, aerosol optical depth, and surface albedo. An ozone optical depth of 314 DU was obtained from the Ozone Monitoring Instrument (OMI) satellite product for 14 September 2010 over the site location. The standard continental aerosol optical depth recommended by TUV was assumed (0.328 at 340 nm). A surface albedo of 0.15 was used based on the medium value of the data range reported for soy bean crops. Cloud correction factors were calculated and applied to the clear-sky photolysis rates. The cloud correction factors were calculated using the near-uv radiometer measurements. A clear-sky day was chosen in September STROUD ET AL The Authors. 1668

7 Figure 3. Three meter height NO x mixing ratio as a function of air parcel time from the center of highway. Measurement data is binned with respect to time, and a median NO x mixing ratio is plotted. from the measurement data set. Next, the diurnal radiation profile for 13 September 2010 was compared to the clear-sky data profile. The 13 September data profile was slightly adjusted with a vertical offset so that clear-sky periods matched (accounting for day-to-day variations in ozone and aerosol optical depth). The ratio of the clear-sky observed radiation to 13 September observed radiation was calculated for the 3 4 P.M. afternoon study period to calculate the cloud correction factor. The same procedure was performed for 14 and 15 September, and an average case study cloud correction factor was calculated. The average case study cloud correction factor was then applied to each species-specific TUV-calculated photolysis rate (so-called J-values). However, this did not include all the species in the detailed SAPRC-07 mechanism. The SAPRC-07 mechanism is documented with typical midlatitude photolysis rates, and the ratio of the site-specifictuv-calculatedno 2 J-value to the documented NO 2 J-value was calculated; this correction factor was applied to other species in the SAPRC-07 mechanism that were not modeled in TUV Background Organic Gases and Aerosol Background mixing ratios of VOCs were taken from the National Air Pollutant Surveillance (NAPS) VOC measurements at the nearby rural Egbert site [Stroud et al., 2008]. The upwind AMS-measured OA mass concentration for the case study period was 1.37 μg/m 3. The average fraction of the HOA to total OA was observationally derived from a PMF analysis of the AMS mass spectra and applied to the average upwind OA mass concentration of 1.37 μg/m 3 during the case study period to derive a model background POA mass concentration of 0.2 μg/m 3 (14%) and a background SOA concentration of 1.17 μg/m 3 (86%). Several assumptions were tested for the partitioning behavior of the background-modeled POA. First, the base case simulation assumed that the POA was nonvolatile. The base case also assumed a separate SVOC-lumped species with a gas-phase mixing ratios in the background air (upwind of the highway) equal to 10 parts per thousand by volume (pptv). Several sensitivity runs were also performed assuming that the modeled POA was semivolatile. The sensitivity runs assumed there was a semivolatile gas phase (termed SVG1 and SVG2) in equilibrium with the modeled POA (termed semivolatile aerosol, SVA1, and SVA2). A two-product volatility representation (SVG1, SVA1, SVG2, and SVA2) from Pye and Seinfeld [2010] and a three-product volatility representation (SVG1, SVA1, SVG2, SVA2, SVG3, and SVA3) based on data in May et al. [2013] are considered in a series of sensitivity runs. The sensitivity runs assumed that the POA was included in the SVOC, thus removing the possibility of double counting in the base case simulation. Figure 4. Three meter height CO 2 mixing ratio as a function of air parcel time from center of highway. Measurement data is binned with respect to time, and a median CO 2 mixing ratio is plotted Organic Particle Growth Two parameterizations were included for organic particle growth. First, SOA formation was calculated from the gas-phase oxidation of VOCs, IVOCs, SVOCs, and partitioning of products to the existing organic particle phase. In our base case, SOA growth onto the organic phase of existing particles was modeled using an SOA aerosol yield STROUD ET AL The Authors. 1669

8 Figure 5. Vertical profile of CO 2 mixing ratio at 34 m and 285 m from center of highway. approach [Odum et al., 1996]. The SOA yields for the higher volatility organic gases are empirically derived based on fits to smog chamber data. The VOC precursor gases considered in the SAPRC-07 speciation include isoprene (ISOP), monoterpenes (TERP), alkanes at varying OH reactivity (ALK4 and ALK5), aromatics of varying OH reactivity (ARO1 and ARO2), alkenes at varying OH reactivity (OLE1 and OLE2), benzene (BENZ), and sesquiterpenes (SESQ). Gas-phase loss reactions for intermediate-volatile organic compounds (IVOCs) and semivolatile organic compounds (SVOCs) with OH were also considered with a rate coefficient of cm 3 / molecules/s [Robinson et al., 2007]. Table 2 lists the α i, K i partitioning coefficients for the organic gases forming SOA under high NO x conditions (representative for roadside conditions). For SVOCs, the volatility of SVG1 and SVG2 emitted species were the same as Pye and Seinfeld [2010] (based on Shrivastava et al. [2006]). The SVG1 and SVG2 SOA yields were calculated based on data in Grieshop et al. [2009] (same as in Pye and Seinfeld [2010, Table 1] and shown here in Table 2). For the sensitivity run with two SVOC species and one NVOC species (nonvolatile), the volatility partitioning was based on May et al. [2013]. An organic particle density of g/cm 3 was used in the literature references for the conversion of laboratory particle volume measurements to mass. An enthalpy of vaporization of 40 kj/mol was used for converting the equilibrium partitioning coefficient, K i, at the reference temperature to the model temperature. The SAPRC-07 TERP model species was assumed to be composed equally of 3 monoterpenes (α-pinene, β-pinene, and limonene) for the calculation of SOA yields. Second, the rapid uptake of gasoline exhaust water soluble organic gases (WSOGs) to the ambient particle aqueous-phase (sulfate, water) was measured by Li et al. [2011] based on flow tube experiments downwind of a gasoline engine and constant volume sampler. An effective Henry s law coefficient was parameterized by Li et al. [2011] as a function of total gasoline VOC and the sulfate aerosol. The water soluble species in the total VOC likely includes aldehydes, possibly alkenes, although the parameterization is based on the total VOC and not the partitioning of individual species. Acid-base neutralization reactions [Liggio et al., 2011] and rapid accretion reactions, such as aldol condensation equilibria [Jang et al., 2002; Kroll et al., 2005; Nozière and Córdova, 2008], are likely chemical mechanisms for the fast uptake. 3. Results In this section, the model will be evaluated based on available measurements. A base case and sensitivity runs were also performed with varying emission-scale factors and assumptions for the partitioning of SVOCs. Figure 6. Three meter altitude ozone mixing ratio as a function of air parcel time from center of highway. Measurement data is binned with respect to time, and a median O 3 mixing ratio is plotted Decay of NO x and CO 2 Mixing Ratio With Perpendicular Distance From Road Predictions of longer-lived emitted species, NO x and CO 2, were used to evaluate the traffic activity, pollutant emission factors, and vertical mixing in the 1-D model. Here traffic activity refers to the counts per minute of vehicles and the fractions of vehicles that are classified as heavy-duty trucks and STROUD ET AL The Authors. 1670

9 Figure 7. Three meter altitude ratio of NO 2 /NO as a function of time from center of highway. Measurement data is binned with respect to time, and a median ratio is plotted. passenger cars. Figure 3 shows the model decay in the mixing ratio of NO x with air parcel time from the center of the highway. Figure 3 also includes the observed median NO x mixing ratio for transects perpendicular to the highway. The model decay is slightly sharper than the observations for intermediate transport times. Overall, the comparison is good especially for the first and last measurement point. Figure 4 is the corresponding plot for the CO 2 mixing ratio decay. Again, the first few and last measurement points are predicted well, but at intermediate transport times, the model tends to overpredict CO 2. The initial rise in modeled CO 2 and NO x is a result of the several seconds it takes for the tailpipe emissions, injected at 1 m and 5 m, to reach the 3 m measurement and model output height. In summary, we conclude that the traffic activity and mixing are modeled sufficiently well to assess organic aerosol growth processes Vertical Profile of a Long-Lived Emitted Tracer, CO 2 Figure 5 illustrates the vertical profiles of CO 2 for transport times of 18 s and 160 s which correspond to horizontal perpendicular distances of 34 m and 285 m. The two curves illustrate the importance of vertical mixing in shaping the vertical profile with distance from the highway. By 285 m, the CO 2 profile is almost uniformly mixed in the vertical Evaluation of O 3, NO, and NO 2 Photochemical Cycling O 3,NO,andNO 2 molecules cycle rapidly during daylight hours with peroxy radicals perturbing this cycling by converting NO to NO 2 and thus forming net ozone. The emission of NO from traffic combustion shifts the socalled photostationary state from O 3 to NO 2 by the titration reaction of O 3 with NO to form NO 2.Thisresultsin the dip in O 3 mixing ratio in Figure 6 at short transport times from the highway. Figure 7 is the corresponding increase in the NO 2 to NO ratio with transport time from the highway. Overall, the model predicts the O 3 dip and the NO 2 /NO increase reasonably well. The rapid cycling between O 3,NO,andNO 2 also shifts with time because of the vertical mixing of background levels of these trace gases. This results in the recovery of the O 3 and the leveling off of the NO 2 /NO ratio at longer transport times. There is considerable uncertainty in the observed ratio of NO 2 /NO at longer transport times (>160 s) due to mixing ratios dropping to background levels where mixing ratios can be more influenced by shifts in wind direction and other NO x emission sources IOGAPS Measurement and Model Analysis Twenty-four hour IOGAP samples were collected at 34 m and 285 m from the highway on two consecutive days during the modeled case study period. The IOGAP samples from each day are averaged here. The IOGAP Table 3. Closure of Total PM 2.5 OC for Model and Measurements PM 2.5 OC on Denuded Quartz Filter, 14 Sep 2010 Sample Low Volatile in Nature SVOC on Backup SIFs Total PM 2.5 PM 2.5 Black Carbon Measurements 34 m 0.62 μgc/m = 0.87 μg/m 3a 1.01 μgc/m μgc/m μg/m = 1.41 μg/m 3a 2.28 μg/m 3a Measurements 285 m 0.72 μgc/m = 1.0 μg/m 3a 0.24 μgc/m μgc/m μg/m = 0.34 μg/m 3a 1.34 μg/m 3a Measurement Change Factor 1.2 Increase Factor 4.1 Decrease Factor 1.7 Decrease Factor 5.9 Decrease Model SOA SVA1 + SVA2 Total PM 2.5 OC BC not modeled 34 m, 18 s May et al., 2013 Partitioning Run 1.22 μg/m μg/m μg/m m, 152 s May et al., 2013 Partitioning Run 1.20 μg/m μg/m μg/m 3 a Factor of 1.4 used to scale PM 2.5 OC to PM 2.5 OM. STROUD ET AL The Authors. 1671

10 Table 4. Ratio of Background-Corrected Organic Aerosol Relative to Background-Corrected CO 2, ΔOA/ΔCO 2, at Two Transport Times From the Highway a Ratio ΔOA/ΔCO 2 55 s (103 m) 130 s (243 m) Difference in Ratio From 130 to 55 s b Median Observed Model: Base , 79% Model: no IVOC, no SVOC Model: no WSOG (water soluble gases) Model: 100 times HONO emissions , 66% Model: 100 times IVOC emissions , 52% Model: 100 times SVOC emissions , +46% Model: 50 times SVOC emissions , 0% Model: Base Emis, PS2010 Partitioning , +120% Model: Base Emis, May2013 Partitioning , 9% a A transport time period from 55 to 130 s is used for the sensitivity analysis. b Percentages included in last column are model percent errors relative to the median-observed value. samples included in series: a cyclone to provide a 2.5 μm diameter size cut, a gas-phase denuder (walls coated with XAD), quartz particle filter followed by two XAD-impregnated SIF filters. The denuder collects the mass concentrations of semivolatile organic carbon in the gas phase. The quartz filter collects the organic carbon particles. It is hypothesized that the two backup XAD SIFs collect semivolatile organics that evaporate from the particles while on the quartz filter; however, a positive artifact from VOCs that pass through the denuder and collect on the SIFs has been noted in the literature [Fan et al., 2003]. Denuder breakthrough occurs once the denuder reaches its capacity. However, testing of our denuder with naphthalene, fluorene, and phananthene at both low and high concentrations showed no breakthrough for these semivolatiles (see supporting information for more details and references). Naphthalene OH-initiated oxidation was modeled explicitly in the gas-phase chemistry model based on adding its reactions from the SAPRC-07 detailed chemistry mechanism. Naphthalene (NAPH) is used in this study to parameterize the IVOC and SVOC emissions, as tabulated by Pye and Seinfeld [2010]. It is informative to evaluate the model emissions, mixing, and chemistry for naphthalene since it is used in the emissions parameterization. The IOGAPS measured NAPH in the gas-phase denuder extract. For the case study period, at 34 m from the highway, the NAPH in the denuded extract was analyzed and corresponded to 24.1 ng/m 3 in the gas phase. The model NAPH at 34 m from the highway, corresponding to the measurement sample location, was 26.5 ng/m 3. This very good agreement between model and measurement suggests that NAPH may be a good choice for scaling up IVOC and SVOCs emissions. The scaling factors used to estimate IVOC and SVOC emissions from NAPH emissions are the most uncertain parameters and least constrained in our analysis (see section later). It should also be noted that the black carbon aerosol (a nonvolatile component) was measured on both the denuded and a separate undenuded filter and the measurement yielded similar mass loading (0.66 μg/m 3 undenuded versus 0.59 μg/m 3 denuded) providing confidence in the filter sampling procedure. Table 3 lists the IOGAP sample results at locations B and C. At location B, the sum of the SVOCs on the two backup SIFs (1.01 μgc/m 3) is larger than the denuded PM 2.5 OC (0.62 μgc/m 3 ), which suggests that a considerable fraction of the PM 2.5 OC under ambient conditions is semivolatile in nature. The denuded PM 2.5 OC increased in mass concentration from 0.62 to 0.72 μgc/m 3 in moving from location B to C (factor of 1.2 increase), at the same time as the PM 2.5 BC decreased from 0.65 to 0.11 μgc/m 3 (factor of 5.9 decrease). Thus, as the freshly emitted particles were being diluted, the less volatile portion of PM 2.5 OC still increased. The increase in less volatile OC is likely related to chemical transformations, either gas-phase oxidation followed by gas-to-particle transfer or rapid particle-phase chemistry (e.g., oligomerization and acid/base neutralization). The SVOC mass concentration on the SIFs also decreased sharply from 1.01 to 0.24 μgc/m 3 in moving from location B to C (factor of 4.1 decrease). This change suggests that the semivolatile material evaporates from the particles as it dilutes, and a fraction of this is converted to less volatile particle OC while the air mass is transported from location B to C. Only a select number of gas-phase compounds were speciated from the denuder extract, but there is evidence for SVOC evaporation. For example, NAPH only decreased in mass concentration by a factor of 1.19 in moving from location B to C, while the PM 2.5 BC decreased by a factor of 5.9. As shown in Table 3, combining the SVOCs on the SIFs to the less volatile portion yields total PM 2.5 OC. STROUD ET AL The Authors. 1672

11 The total PM 2.5 OC decreased from 2.28 to 1.34 μg/m 3 (using a factor of 1.4 to convert from OC to OM) in moving from location B to C (factor of 1.7 decrease). It should be noted that the IOGAP samples are 24 h samples while the AMS observations and model setup was for midafternoon conditions. Figure 8. Background-corrected ratio of particle OA mass concentration or particle volume concentration relative to CO 2 plotted as a function of air parcel transport time from the center of the highway. Also included are model results from the base case and emission scenarios. COA is the condensed organic gasoline vapor onto the sulfate aerosol. Figure 9. Background-corrected ratio of particle OA mass concentration or PM 2.5 mass concentration relative to CO 2 plotted as a function of air parcel transport time from the center of the highway. Also included are model results from sensitivity runs with different assumptions for the POA partitioning and background SVOCs Background-Corrected OA Mass Concentration Relative to the Background-Corrected CO 2 : Model Results and Sensitivity Simulations Figure 1 illustrates the observed and modeled ΔOA/ΔCO 2 ratio for points at different distances from the highway. The points at transport time 55 s and 130 s have the least variability associated with them and best represent the change in ratio to be modeled. Table 4 illustrates the change in the ratio from the observations and model results at these times Base Case Figure 8 shows that the base case model curve only increases slightly over the 55 s to 130 s time frame. The base case assumes a nonvolatile POA, SOA production from VOC, IVOC, and SVOC, background concentrations as described in section 2.2.4, base case emissions as described in section and gas-phase total hydrocarbon (GTHC) uptake to form COA (condensable organic aerosol). GTHC is a proxy for the water soluble organic gas (WSOG) in gasoline exhaust as described in Li et al. [2011]. Figure 8 also includes the growth curve for WSOG uptake to sulfate aerosol assuming no background for WSOG. A small increase in COA relative to background-corrected CO 2 was observed over the 55 s to 130 s time frame. Several sensitivity runs with increasing concentrations assumed for the background COA (and associated background gas-phase WSOG in equilibrium) were performed and resulted in increases in the model ratio near the tailpipe but could not increase the growth rate over the 55 s to 130 s period. For our base case, a COA background of μg/m 3 was assumed throughout the column, which corresponds to a ~3% assumption for gas-phase WSOG in the background column relative to the maximum WSOG concentration at time zero in the grid box of gasoline emission. This yielded a reasonable fit for the modeled ratio compared to data closest to the highway. We acknowledge that the background WSOG is unconstrained in the model and that developing measurement-based STROUD ET AL The Authors. 1673

12 receptor modeling methods to estimate the gasoline combustion contribution to water soluble organic carbon would be helpful to constrain the background model atmosphere. Gas-phase oxidation chemistry forms water soluble products (e.g., glyoxal from aromatics), but this chemistry is slower than the minute time scale needed to produce the observed trend. Also, particle-phase oxidation chemistry likely has a longer time scale than the minute time scale needed to reproduce the modeled ratio. These oxidation processes contribute to the chemistry of the background aerosol Emission Sensitivity Analysis The sensitivity of the base case model ΔOA/ΔCO 2 ratio to emission scenarios are examined in this section. The results are illustrated in Figure 8 and listed in Table The sensitivity of the model ΔOA/ΔCO 2 ratio to removing the IVOC and SVOC emissions is significant. The ratio is almost constant over the transport time period (53 s to 137 s). This suggests that IVOC and SVOC are the most important SOA precursors on this time scale (Figure 9). 2. The sensitivity of the model ΔOA/ΔCO 2 ratio to a hundredfold increase in HONO emissions was examined. This represents an upper limit scenario for the influence of heterogeneous reactions forming HONO. Once HONO is formed during the day, the HONO can photolyze to produce OH which can further oxidize organic gases forming SOA. Figure 10 shows the increase in the HONO mixing ratio for the factor of 100 increase in HONO emissions. The HONO mixing ratio almost scales linearly. Figure 10 also shows that when HONO emissions were zeroed out, the concentrations dropped below 10 pptv suggesting that for the near-highway environment the HONO budget was dominated by traffic emissions. Figure 11 summarizes the OH model time series for the HONO scenarios. At 100 s, the base case OH mixing ratio is molecules/cm 3, which is only slightly larger than the case with no HONO traffic emissions. At a hundredfold increase in HONO emissions, the OH mixing ratio is molecules/cm 3 at 100 s. However, Figure 8 that illustrates the model ratio over the transport time period was only slightly sensitive to the large increase in HONO mixing ratios, suggesting that uncertainties in the HONO chemistry are not likely impacting the OA growth significantly. 3. The sensitivity of the model ΔOA/ΔCO 2 ratio to a hundredfold increase in IVOC emissions resulted in a 52% underprediction in the ratio difference over the transport time period, as compared to a 79% underprediction for the base case (see Table 4). The sensitivity of the model ΔOA/ΔCO 2 ratio to a hundredfold increase in SVOCs resulted in a 46% overprediction in the ratio difference over the case study transport time period. Clearly, the model is most sensitive to the SVOC emissions. As shown in Figure 8, a fiftyfold increase in SVOC emissions matched the observations the best of any emission scenario. A fiftyfold increase in total SVOC emissions increased the SVOC mass concentrations in the model at 34 m from 1 μg/m 3 to 50 μg/m 3. Unfortunately, there were no direct measurements of the total IVOC and SVOC mass concentrations during FEVER to better constrain the model. In the next section, we will explore the uncertainties in the literature for the IVOC and SVOC emission factors. Figure 10. Three meter altitude model-derived HONO mixing ratio as a function of time from the center of the highway. Three additional HONO emission scenario results are plotted Uncertainties in IVOC and SVOC Emission Factors Scaling From Naphthalene Emissions Mobile emission factors for IVOCs and SVOCs are poorly characterized for input into air quality models. In our base case run, IVOCs and SVOCs are scaled up from NAPH using scaling factors derived from Pye and Seinfeld [2010, Table 2] global modeling study. Naphthalene is an important SVOC from many sources including vehicle exhaust and wood combustion [Chan et al., 2009; Schaueretal., 2001, 2002]. In the work of Pye and Seinfeld [2010], the annual naphthalene emissions were taken from the global EDGAR2 inventory and annual IVOC emissions were calculated from scaling up from annual POA emissions. STROUD ET AL The Authors. 1674

13 Figure 11. Three meter altitude model-derived OH mixing ratio as a function of air parcel time from the center of the highway. Three additional HONO emission scenario results are also plotted. However, there is considerable uncertainty in the global POA emissions of Pye and Seinfeld [2010, Table 2], based on the global inventory of Bond et al. [2004]. Also,aratioofIVOCtoPOAof2.1was used, the same as derived in the work of Shrivastava et al. [2008] for gasoline combustion using polyurethane foam samplers. In Pye and Seinfeld [2010, Table 2], SVOC emissions were calculated by scaling up POA emissions using a scalingfactorof1.27,basedonthe work of Schauer et al. [2001] for woodburning experiments. Including the unresolved organic mass in Schauer et al. [2001] increases the scaling factor from 1.27 to Scaling From POA Emissions We performed additional calculations of IVOC and SVOC emissions scaled up from POA emissions (POA from MOBILE6.2C; see Table 1) instead of naphthalene emissions. For an SVOC/POA mass ratio of 1.27, we calculate an SVOC gasoline exhaust emission factor of g/km which is a factor of 7 lower than derived from scaling up from naphthalene emissions. For IVOC/POA mass ratio of 2.1 (from Shrivastava et al. [2006] using diesel engine generator), we calculate an IVOC gasoline exhaust emission factor of g/km which is also a factor of 7 lower than derived from scaling up from naphthalene emissions. Thus, scaling up from naphthalene, as done in our base case, yields larger initial IVOC and SVOC concentrations at the highway. Given our good agreement to the naphthalene roadside measurements and the likelihood that IVOC and SVOC emissions are underestimated, we selected naphthalene as the scaling species. POA is also more difficult technically to measure because of its wide range of semivolatile constituents, as its concentrations depend on the measurement conditions such as temperature and existing organic aerosol loading. We investigated the recent literature for more up-to-date gasoline emission factors for POA. May et al. [2013] measured kilogram POA per kilogram fuel burned for an extensive set of vehicles of various ages. Median emission factors for LEV-I vehicles (ages ) and LEV-II vehicles (ages ) were 7 and 4 mg POA per kilogram fuel burned, respectively (particle fractions were ~0.5 at the temperature and organic aerosol loading conditions; see May et al. [2013, Figure 2]). Using the CO 2 and POA emission factors of 208 and g/km from Table 1, we can combine and calculate a factor of /208 = or 9.6 mg POA/kg fuel burned from MOBILE6.2C, which is similar to that derived recently from May et al. [2013]. The calculation suggests that the recently derived POA emission factors are less but still close to the MOBILE6.2C database. Using the recently derived POA emission factors would yield lower IVOC and SVOC emissions in our model compared to our base case Other Model Uncertainties There is also the possibility that unaccounted for reactive biogenic organic gases may be contributing to the observed ΔOA/ΔCO 2 trend. The surrounding landscape was farmland and barren ground. A nearby VOC measurement site (Egbert, ON, in the NAPS network) characterized the monoterpenes and isoprene mixing ratios, and these measurements were used to assign upwind background mixing ratios. However, sesquiterpenes were not measured by the NAPS network. A prior regional air quality model simulation [Stroud et al., 2011] for summertime conditions characterized biogenic SOA formation in Ontario including SOA from sesquiterpenes. Sesquiterpene emissions were parameterized from monoterpene emissions based on Helmig et al. [2007]. Our regional air quality model-derived total sesquiterpene mixing ratios for the afternoon were typically in the range pptv for the highway location with a maximum value of ~10 pptv. We have assumed 1 pptv of sesquiterpenes in our base case simulation and performed a sensitivity simulation at STROUD ET AL The Authors. 1675