Restaurant Impacts on Outdoor Air Quality: Elevated Organic Aerosol

Article Cite This: Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Restaurant Impacts on Outdoor Air Quality: Elevated Organic Aerosol Mass from Restaurant Cooking with Neighborhood-Scale Plume Extents Ellis Shipley Robinson,†,‡ Peishi Gu,†,‡ Qing Ye,§,∥,‡ Hugh Z. Li,†,‡ Rishabh Urvesh Shah,†,‡ Joshua Schulz Apte,⊥ Allen L. Robinson,†,‡,∥ and Albert A. Presto*,‡,† †

Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States § Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States ∥ Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States ⊥ Department of Civil, Architectural & Environmental Engineering, University of Texas at Austin, Austin, Texas 78705, United States

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S Supporting Information *

ABSTRACT: Organic aerosol (OA) is a major component of fine particulate matter (PM2.5) in urban environments. We performed in-motion ambient sampling from a mobile platform with an aerosol mass spectrometer (AMS) to investigate the spatial variability and sources of OA concentrations in Pittsburgh, Pennsylvania, a midsize, largely postindustrial American city. To characterize the relative importance of cooking and traffic sources, we sampled in some of the most populated areas (∼18 km2) in and around Pittsburgh during afternoon rush hour and evening mealtime, including congested highways, major local roads, areas with high densities of restaurants, and urban background locations. We found greatly elevated OA concentrations (10s of μg m−3) in the vicinity of numerous individual restaurants and commercial districts containing multiple restaurants. The AMS mass spectral information indicates that majority of the high concentration plumes (71%) were from cooking sources. Areas containing both busy roads and restaurants had systematically higher OA concentrations than areas with only busy roads and urban background locations. Elevated OA concentrations were measured hundreds of meters downwind of some restaurants, indicating that these sources can influence air quality on neighborhood scales. Approximately 20% of the population (∼250 000 people) in the Pittsburgh area lives within 200 m of a restaurant; therefore, restaurant emissions are potentially an important source of outdoor PM exposures for this large population.

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modeling7,8 and land-use regression (LUR) modeling9 are used to describe spatial variability at scales of tens to hundreds of meters in urban environments. LUR requires measurements for model construction and validation, and so measurement strategies that resolve pollutant variation at high spatial resolution have been employed. Sampling from mobile platforms (e.g., measurements on-board a motor vehicle10 or bicycle11) or distributed networks of sensors12 are both commonly used to characterize spatial variation of pollutant concentrations. Distributed sampling can be cost- and laborintensive, and therefore typically focuses on a small number of pollutants (e.g., PM2.5 and NO2), often using passive samplers13 or integrated filter samples.14 More recently, lowcost sensor networks have also been deployed to quantify spatial variations of these pollutants in real time.15 An

INTRODUCTION Exposure to particulate matter with diameters less than 2.5 μm (PM2.5) is a global risk factor for death and other negative health effects.1,2 Organic aerosol (OA) is a major component to PM2.5 mass, especially in urban locations. Important sources of primary OA are vehicles and food-cooking. Despite the consequences of PM2.5 exposure for human health, there remain important gaps in our understanding of its spatial distribution, especially in complex urban areas.3 Even in the United States, where there has been extensive air quality monitoring for the past half-century,4 estimates using data from fixed monitors can fail to capture the variability of pollutants in source-rich environments, and thus fail to accurately assess exposure.5 Recent work has shown that for some pollutants, for example various traffic-related pollutants (e.g., nitric oxide, ultrafine particles), intraurban variability in concentrations may be much greater than interurban differences measured at urban background monitors.6 There have been substantial efforts to better understand the spatial patterns of air pollution at fine scales. Dispersion © XXXX American Chemical Society

Received: May 21, 2018 Revised: July 13, 2018 Accepted: July 20, 2018

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DOI: 10.1021/acs.est.8b02654 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology

Figure 1. Map of sampling domain showing all grid cells containing mobile data (map created using ArcMap software v.10.4, ESRI, Inc.). Grid cells are 100 m × 100 m, and are colored according to the land-use classifications in the Methods section. The inset box shows the sampling area for the time series discussed in Figure 2. The CMU campus background site is marked with a yellow star.

different sources, we conducted significant sampling in urban background locations, on busy roads, and near commercial districts with substantial numbers of restaurants. Source attribution was performed using both spatial and mass spectral information. We quantified the prevalence of OA plumes from different source categories, as well as the spatial scale for which primary emissions from restaurants are elevated downwind of the source. These measurements illustrate how restaurants affect local concentrations of OA, and thus human exposure to outdoor PM2.5.

advantage of mobile sampling is the potential use of sophisticated and high time resolution instrumentation, which has led to increased understanding of the spatial scales affecting pollutant concentrations3 and can offer insight into the sources driving observed spatial patterns.16 The spatial patterns of traffic-related air pollution have been extensively studied.6,17 Species such as CO, NO, and particulate black carbon (BC) are directly emitted from onroad vehicles. Concentrations of these pollutants are elevated at the road edge and fall to background levels within a few hundred meters downwind of the roadway. Little work has been done to establish the spatial patterns of PM related to food cooking or other urban sources. This is surprising given that in many locations the organic fraction of PM (organic aerosol, OA) attributable to cooking is comparable to or greater than the contribution from fresh traffic emissions.18−22 A few studies have examined the spatial distribution of cooking-related PM in the context of outdoor air quality. Elser et al.23 used mobile sampling to show that cooking OA is enhanced in the city center of Talinn, Estonia relative to the city’s outskirts. They attributed the enhancement to restaurants. Similarly, Vert et al.24 found that particle number concentrations are elevated in front of restaurants in Utrecht compared to nonrestaurant locations. Abernethy et al.25 found restaurant density to be a significant predictor of ultrafine particle concentrations in Vancouver, Canada. These studies show that restaurants contribute to particulate air pollution. Relative to traffic-related pollutants though, our understanding of how restaurant cooking-related PM concentrations and exposures are spatially distributed remains low. In this work, we systematically investigated the spatial distribution of OA from food cooking and traffic in an urban environment (Pittsburgh, PA) by combining a spatially stratified sampling strategy with chemically resolved measurements. Our measurements of OA, conducted on a mobile platform with an Aerosol Mass Spectrometer (AMS), target different sources and land-use types, such as commercial areas containing restaurants, areas with high traffic, and urban background sites. In order to understand the contribution of

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METHODS Mobile Sampling Strategy. We conducted mobile sampling to quantify the spatial variation in submicron mass concentration and composition of OA as part of the Center for Air, Climate, and Energy Solutions (CACES). Our sampling was stratified across the following land-use categories in Pittsburgh, PA and adjacent boroughs in Allegheny County: urban background locations, areas containing restaurants but not busy roads, areas containing busy roads but not restaurants, and areas containing both restaurants and busy roads. The sampling domain (18 km2, total land area of Pittsburgh ∼151 km2) and the distribution of these land-use categories is shown in Figure 1, and how we performed this categorization is discussed below. By sampling across these land-use types, we aimed to measure OA concentrations in locations influenced by both traffic and cooking activities, and quantify how cooking OA is spatially distributed in Pittsburgh. Because our focus is the spatial distribution of cooking-related OA, we sampled largely during evening mealtimes when cooking emissions from restaurants are likely at their peak. Roads were defined as “busy” if they were in the upper two quintiles of annual average daily traffic (AADT) counts of state-managed roads (AADT > 7058 vehicles/day). The source of road data and traffic volumes came from Pennsylvania Spatial Data Access (PASDA) database (http://www.pasda. psu.edu/uci/SearchResults.aspx?Shortcut=transportation). Restaurant locations were determined using a list of restaurants in Allegheny County from the Allegheny County Health B


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source attribution can be decoupled from location because of the chemical information provided by the AMS. Semistationary Sampling to Quantify the Spatial Extent of Plumes. For a limited number of periods, we used low-speed saturation sampling to quantify the spatial extent of elevated OA concentrations around small clusters of restaurants (containing 3−7 restaurants). These results are presented as “fall-off curves,” similar to those shown in Karner et al. for pollutant concentrations near roadways.17 In each of these four cases, the restaurant cluster was isolated from other potentially confounding OA sources (e.g., away from busy roads, bus routes, etc.). Downwind distance was calculated using the centroid of the restaurant cluster. We parked our mobile laboratory for 1−5 min periods, or drove at very slow speeds (0.82) and low 55:57 values ( 1.6 and similarity values between 0.6−0.9 were apportioned to cooking sources. Plumes that did not fit either of the above criteria are labeled “other.” AMS cooking spectra across a wide range of cooking methods and ingredients appear similar when grouped according to the above criteria. This idea is supported by recent work from Reyes-Villegas et al.37 who compared AMS spectra from a variety of cooked foods and cooking methods. They found similar mass spectra for fresh emissions from a variety of cooking sources. While AMS spectra may not be useful for distinguishing different cooking types of foods, the spectral differences between the broad POA categories (cooking, traffic, biomass-burning) are significant enough to accurately differentiate plumes into these classes. We tested our algorithm using reference spectra from the major sources of primary OA emissions from the AMS Spectral Database (http://cires1.colorado.edu/jimenez-group/AMSsd/ ): traffic, cooking, and biomass-burning. The reference spectra are from source-categorization experiments, smog chamber experiments, and ambient measurements. Ambient measurements included source-specific factors determined via positive matrix factorization (PMF; e.g. HOA, cooking OA, biomassburning OA). Near-source spectra from our mobile data set e.g., vehicle chase plumes or measurements collected directly outside of a restaurant exhaust ventwere used as well. Further details of all reference spectra (Table S2), and a visual presentation of the categorization method (Figure S1) are presented in the SI. As shown in Figure S1, all reference spectra (39 total), both from the AMS Spectral Database and our own source-profile sampling, were sorted correctly according to this empirical classification scheme, with the possible exception of one spectrum. The one exception was sampled downwind of a restaurant serving pizza and hamburgers from a wood-fired grill and oven. This plume was not sorted into the “cooking” category, but rather is located near biomass-burning plumes in this similarity vs 55:57 space. Two other data points from other wood-fired restaurants fall within the cooking category but are closer to

tions. All AMS data were corrected for residence time in the sampling inlet. AMS data points were then matched to the GPS location (sampled at 1 Hz) closest to the midpoint of the AMS collection cycle. We used the Squirrel 1.57I toolkit for analyzing AMS data at unit mass resolution (written by Donna Sueper at University of Colorado for Igor PRO software, v6.27, Wavemetrics, Inc.). We applied a collection efficiency (CE) of 0.5 to the measured organic mass, which is standard practice with ambient AMS data sets for OA quantification.32 CE is an estimate of particle losses within the AMS, which are largely driven by the mechanism of particle bounce upon impact with the vaporizer prior to electron impact ionization.32 Different OA types can have substantially different CE values,33,34 and the results of Ye et al.35 show clear external mixtures of OA in near-source environments in Pittsburgh. However, we were unable to measure the CE for individual components during this study and so apply a uniform CE of 0.5 in the absence of better information. On the basis of the results of Docherty et al.,36 who show CE as a function of oxidation state for lab-generated OA, CE = 0.5 seems like an appropriate choice for cookingrelated OA, though likely is too low for fresh vehicle emissions, possibly resulting in overestimating some OA mass attributable to traffic sources. Similarly, we use the standard AMS relative ionization efficiency (RIE) of 1.4 for all organic mass. Recent work from Reyes-Villegas et al.37 has suggested that this RIE is appropriate for fresh cooking-related aerosol emissions, but may overestimate OA mass for aged cooking-related aerosols. Given the focus on fresh cooking-related plumes in this work, we deemed RIE = 1.4 to be appropriate for this analysis. To quantify the contribution of local sources, we corrected the data to account for temporal changes in background concentrations within and between drives. First, we identified areas with no known sources within 200 m; these were either on our driving route (e.g., large city parks, residential streets with low vehicular activity, etc.) or at the CMU campus urban background site. OA data collected from these locations were averaged together, defining the background concentration for that time. For all drives, data collected at the CMU campus site before and after the drive were used to define the background. For most drives, we also collected background data during the middle of the drives at the aforementioned areas (parks, quiet streets). A linear interpolation in time was then applied between background averaging periods for each drive. This was subtracted from the total OA concentration to provide the above-background OA concentration. This above-background OA concentration should be indicative of local sources and processes affecting OA. We refer to this above-background OA concentration as “ΔOA” hereafter. We refer to “plumes” throughout the rest of the paper as those data where OA concentrations were greater than 2 μg m−3 above background (ΔOA > 2 μg m−3). The mass spectral information provided by the AMS allows us to attribute plumes to vehicle sources, cooking sources, and other sources. OA from vehicle sources, often referred to as HOA (hydrocarbon-like OA), is relatively unoxidized, with an oxygen-to-carbon (O:C) of ∼0.03.38 HOA characteristically has high signal at m/z 57 (largely C4H9+) and other unoxidized alkyl (CxHy+) fragments. OA from cooking activities is typically more oxidized (4−7×) than HOA38,39 (O:C ≈ 0.11−0.2), and has a high relative signal at m/z 55 (significant contributions from both C4H7+ and C3H3O+). We use the ratio of ions at m/ z 55 and 57 (denoted “55:57” hereafter) as one metric to D


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Figure 2. (a) Time series from a typical drive, where we sampled in three commercial districts containing restaurants and in afternoon rush hour on a highway. The plot shows OA mass concentrations (green line) in the three different districts (left axis) and similarity to HOA (circle markers, right axis) colored by the ratio of 55:57. Upper panels show two mass spectra of different plume event types: (b) mass spectra collected in the highway tunnel and (c) near the kitchen exhaust of a burger restaurant (c). Panel d shows a difference spectra illustrating the mass spectral differences between (c) and (b).

urban park (∼2 km2) provides a sampling area devoid of local OA sources, and a location where we collected AMS mass spectra for background averaging. The outbound highway (AADT of 20 685 vehicles/day) is one of the major commuter highways in the Pittsburgh area and includes a two-bore 1.3 km-long traffic tunnel within the stretch that we sampled. Each of the three neighborhoods contains one or multiple clusters of restaurants (ranging from 7 to 30 restaurants), which are located on streets with AADT between 4300 and 10 000 vehicles per day. The commercial districts of Neighborhoods A and B are situated within residential areas containing both apartment buildings and single-family houses, while the landuse of Neighborhood C is a mix of neighborhood commercial and neighborhood industrial. Our driving within these neighborhoods encircled all sides of their respective commercial district(s), in an effort to minimize any biases from dispersion conditions during a given drive (e.g., wind direction). In some cases during this drive, which are labeled in Figure 2a, we were able to pinpoint plumes to individual restaurants. The time series in Figure 2a illustrates multiple important findings from this study. First, concentrations of OA are highly variable in space and this variability is driven significantly by restaurants. Second, OA concentrations in commercial districts with restaurants are frequently much higher than on highways. For example, during the drive shown in Figure 2a, the highest OA concentration we measured during the 20 min of rushhour driving on the highway, on-ramps, and through a tunnel was 6 μg m−3, while there were many instances of OA concentrations exceeding 10 μg m−3 in the commercial business districts. Third, as discussed below, the AMS mass spectra had chemical signatures of cooking emissions. For

the biomass-burning plumes than the other cooking plumes in our data set. It seems reasonable that these plumes contain a mix of organic molecules from the incomplete combustion of wood as well as aerosolized constituents from meats or other oils. Thus, there is some potential for classifying restaurant plumes in the “other” category, if wood is used as a cooking fuel for some restaurants in this sampling domain. Similarly, there appears to be a continuous spectrum of plumes between our cooking and vehicle categories, and we determined the empirical boundary based on the reference spectra. Thus, there is some potential for misclassifying vehicle plumes as cooking and vice versa, but this was not an issue for the 39 reference spectra with which we tested our classification scheme. Our classification separates HOA from cooking plumes at 55:57 = 1.6, where there is a clear boundary between ambient HOA spectra and laboratory cooking spectra from the reference data in Table S2. Below, we present the sensitivity of our plume classification to the specific choice of 55:57 as the boundary between HOA and cooking plumes.

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RESULTS Figure 2a presents measurements collected on a typical drive, both in terms of sampling design and the important data types collected. In this time series, we show OA concentration and similarity to HOA for plumes (ΔOA > 2 μg m−3). Points are colored by 55:57. For this sample drive, our route passed through the following areas after departing CMU’s campus: a large urban park, the congested outbound lanes of an interstate highway during rush-hour, and three neighborhoods with restaurantcontaining commercial districts and roads spanning a wide range of traffic volumes, before returning to campus. The large E


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Figure 3. Composition analysis of all OA plumes greater than 2 μg m−3 above the background (ΔOA > 2 μg m−3). (a) Similarity to HOA values for all plume mass spectra as a function of ΔOA mass concentration. Each point is colored by the ratio of 55:57, which serves as an indicator of whether a plume is likely from traffic or cooking sources. Letters correspond to plumes identified with certainty as being emitted from vehicles or restaurant sources. Red and purple colored bands indicate the range of Similarity values used to classify spectra. (b) Histogram showing the frequency of plumes as a function of similarity, which shares a left-axis with panel (a). (c) Histogram summarizing the frequency of measured plumes as a function of ΔOA for each plume category.

between 0.7 and 0.9 illustrated by the histogram in panel b of the figure. The summary of the plume categorization is shown in Figure 3c as a function of ΔOA concentration. The majority of OA plumes in Pittsburgh neighborhoods during the sampling time frame have cooking-like mass spectra (71%). We do see a significant fraction of plumes with HOA-like mass spectra (20%), but this is more than a factor of 3 less than the frequency of cooking-like spectra. Nine percent of plumes were attributed to other, unidentified sources. The fraction of plumes we attribute to cooking increases with the measured OA concentration, as shown in Figure 3c. Therefore, plumes with the highest concentrations were most frequently due to cooking. During evening mealtime (5 pm to 9 pm local time), cooking appears to be the dominant source of high concentration OA plumes in the Pittsburgh area. As described in the Methods section and Figure S1, trafficand cooking-related plumes seem to form a continuum, with apportionment between these sources relying in large part on the choice of 55:57 to use as the boundary between plume classes. We chose 55:57 = 1.6 to define the boundary between traffic- and cooking-related mass spectra based on laboratory and ambient spectra from the literature. To assess how sensitive our results were to this cutoff, we adjusted the boundary to 55:57 = 2.05, which corresponds to the lowest 55:57 value of direct source-sampling from this data set for cooking plumes. Using 55:57 = 2.05 results in 53% of plumes attributed to cooking, 26% to vehicles/traffic, and 21% to “other.” Figure S1 shows this sensitivity test in similarity-55:57 space. Even this boundary (55:57 = 2.05), which does not seem reasonable based on the available reference spectra, does not change our conclusion that we see many more cooking-like OA plumes compared to HOA-like plumes.

example, almost all of the plumes in Figure 2a have similarity values less than 0.8 and high 55:57 values, which are characteristic of cooking OA. Almost 90% of the plumes in Figure 2 are attributed to cooking emissions versus 5% to traffic. Examples of plume mass spectra (MS) collected within the highway tunnel (Figure 2b) and next to the kitchen exhaust of a hamburger restaurant (Figure 2c) are shown in the upper panels of Figure 2. Both MS are background-subtracted, as discussed in the Methods section. Figure 2d shows a difference MS between background-subtracted tunnel and restaurant spectra to further illustrate differences between these two plumes. There are distinct mass spectral signatures for trafficand cooking-related OA. While both MS have aliphatic fragments, the series of fragments enhanced in the traffic tunnel spectra (m/z 57, 69, 71, 83, 85) is distinct from the series of fragments that are more abundant in the restaurant emissions (m/z 41, 55, 67), as shown in this difference plot (Figure 2d). These spectral differences form the basis of our plume categorization, and are also visualized from the colored similarity points in Figure 2a. Plume Categorization. The patterns observed in Figure 2a, that cooking plumes are more numerous and larger in magnitude than traffic-related plumes, apply to the rest of our mobile sampling data set as well. The composition of all OA plumes is shown in Figure 3. Figure 3a shows similarity to HOA as a function of ΔOA concentration, colored by 55:57 values. Letters in Figure 3a are from unambiguous (known a priori) restaurant and traffic sources sampled in this data set and are labeled above the plot. We sort all plume data by their background-subtracted mass spectra into three categories, as described in the Methods section: HOA-like, cooking-like, and other. The majority (93%) of the plumes have similarity values F


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Figure 4. ΔOA concentration as a function of distance from centroid of restaurant cluster. The clusters are named and displayed as follows: (a) Restaurant cluster 1, (b) Restaurant cluster 2, (c and d) Restaurant cluster 3 on two different dates. Each example shows the raw data (gray markers), binned-averages of the raw data (colored circles), and an exponential decay fitted to the binned averages. The right axis shows the enhancement above the background (all data are normalized by the upwind average concentration). The distance at which the fit fell to half of the binned-average max (D50) is displayed for each example. Vertical bars indicate the minimum and maximum concentration measured within each bin. Horizontal bars indicate the width of the bin. Two visits were made to Restaurant cluster 3 (panel (c) and (d)), which had the highest and lowest D50 values. This likely reflects variability in source strength and dispersion conditions.

Spatial Scale of Elevated Concentrations around Restaurants. Figure 4 shows OA concentrations as a function of upwind and downwind distance from three restaurant clusters. In each example, OA is elevated downwind of restaurants, but not upwind, and we measure OA concentrations significantly above the background level hundreds of meters downwind of the restaurant clusters. The mass spectra of each downwind plume was categorized as cooking-like. Measured ΔOA concentrations from these plumes are highly variable across these three restaurant clusters, but still greatly elevated above upwind concentrations. For example, ΔOA concentrations in the first 50 m downwind of Restaurant Cluster 3 were as high as 200 μg m−3 (mean 54 μg m−3) on 2016/12/14, but only as high as 40 μg m−3 (mean 7 μg m−3) on 2016/11/16. Restaurant Clusters 1 and 2 had maximum concentrations just above 30 μg m−3. The differences in plume magnitude and spatial extent could be caused by the volume or style of food cooking and/or dispersion conditions for each measurement set. Despite the variability in the plume concentrations, the binned-averages are significantly enhanced above the background, by tens of μg m−3 in the first 50 m downwind of the sources. The right axis of Figure 4 shows the downwind enhancement, defined by normalizing all downwind data to the upwind average, which is between 3−10× greater than the upwind concentrations for these four cases. This enhancement is much higher than that shown for pollutants downwind of roadways in Karner et al.17 The falloff curves for near-road environments vary by pollutant but are usually less than 3× the background value for particulate pollutants immediately downwind of road sources.

To quantify the spatial extent of the elevated OA concentrations downwind of the restaurant cluster, we fit an exponential decay to spatially binned average concentrations in Figure 4. Measurements were grouped into fifty-meter bins closest to the restaurant cluster, but larger bin widths were used when data was sparse such that there were always three data points per bin at minimum. The distance where the fit fell to half of the max value (D50) varied from 52−368 m for these three examples. There was both day-to-day and locationspecific variations in D50. The lowest and highest values of the range of D50 were from the same restaurant cluster (#3), but on different days. The falloff curves in Figure 4 demonstrate that restaurants substantially impact neighborhood-scale OA concentrations. The length scale of restaurant plume impacts in Figure 4 (52− 368m) is similar to the fall off for traffic-related pollutants downwind of roadways. Concentrations of CO, ultrafine particles, and particulate black carbon all have near-roadway D50 between 50 and 150 m.17 Massoli et al. observed a similar D50, between 100 and 200 m, for total PM emissions from traffic (sum of HOA and BC) downwind of a highway.42 Figure 4 shows that there is variability in the OA distribution downwind of restaurant clusters. This scatter is likely due to multiple factors. Changing wind direction and turbulence caused by complex urban topography affect pollutant dispersion and prevents a precise definition of the plume centerline. The distance from the restaurant cluster is calculated from the center of the cluster, which may not be the location of the exhaust for those restaurant(s) emitting the OA. Lastly, restaurant cooking is done in batches on the order of minutes to match customer demand. Thus, some scatter in G


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and restaurants having the highest ΔOA concentrations. These results, combined with the source attribution using chemical composition, present a strong case that OA concentrations in areas with both roads and busy restaurants are likely to be meaningfully higher than concentrations in urban background locations, and that restaurant emissions are a major contributor to elevated OA concentrations in these areas. The results also show that restaurants can be important contributors to local OA hotspots during mealtimes.

the data is inherent to restaurants as sources of OA. The source strength can be highly dynamic and collecting the data to define up- and down-wind concentrations took 30 min to an hour for each case. However, even with this variability, it is clear restaurants contribute significantly to outdoor OA concentrations at neighborhood scales. Distribution of ΔOA by Land-Use Category. Figures 2 and 4 show the presence and spatial extent of plumes associated with individual restaurants or clusters of restaurants. Here, we compare OA concentrations across the four land-use classes defined above: Background, Roads-only, Restaurantsonly, and Both restaurants and busy roads. These concentration differences are summarized in the normalized cumulative distributions of grid-cell average ΔOA concentrations for each land-use type shown in Figure 5. There are clear variations in ΔOA across the land use classes, with the lowest median concentrations in Background grid cells and highest in the Both category.

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IMPLICATIONS Restaurant emissions can create elevated neighborhood scale submicron organic PM concentrations. In Allegheny County, 260 000 people (21% of total population) live within 200 m of a restaurant (63 mi.2 total buffer area). This is comparable to the 432 000 people (35% of population) living within 200 m of a busy (>7058 AADT) road (164 mi.2 total buffer area). However, organic aerosol concentrations downwind of restaurant clusters often greatly exceed those near roadways. Busy roads are more spatially distributed than restaurants in Pittsburgh, but restaurants are often located in more-densely populated areas relative to busy roads. Despite busy roads being the larger areal source in Pittsburgh, we measured a much larger number of OA plumes from food cooking (71%) compared to vehicle sources (20%). A significant portion of people living near restaurants also live near busy roads (14.8% of people in Allegheny County live within 200 m of both). This represents the potential for compound exposure to primary pollutants from these two source classes. Restaurants may be important for driving acute ambient exposures to OA. Acute exposure to high PM2.5 concentrations can have strong associations with negative health outcomes43 as well as total mortality.44 Peak exposures to PM2.5 may have greater associations with some negative health outcomes than average PM2.5 exposures.45 The numerous, but localized, hotspots of elevated OA concentrations around many restaurants create acute exposures to high OA concentrations, up to ∼100 μg m−3, during times of heavy cooking. During evening meal-times, the largest plumes of OA (and thus PM) are attributable to cooking activities. The data presented here may not be representative of longterm, spatially resolved OA concentrations as the sampling was conducted only during winter and with mostly evening driving. However, we consistently observed OA plumes in the vicinity of many restaurants and in areas with high restaurant density. We confirmed that the origin of many of these plumes to be cooking through the chemical information provided by the AMS, which can identify the broad source categories of primary OA. Assuming that other urban areas have a similar source mix of vehicles and restaurants, our data suggest that restaurant emissions are likely a major contributor to large spatial gradients and high exposures to PM in many cities.

Figure 5. Normalized cumulative distributions of average ΔOA values for grid cells of each land-use category: Background, Roads-only, Restaurants-only, and Both.

For each of the four land-use types, the majority of grid cells have average ΔOA concentrations less than 2 μg m−3. Almost all (96%) of the grid-cells for the Background land-use type have ΔOA less than 2 μg m−3. Busy-roads only, Restaurantsonly, and Both land-use types have 86%, 79%, and 68% of their grid cells with ΔOA less than 2 μg m−3, respectively. While there are differences between the land-use types, the majority of cells within each land-use type do not meet the “plume” criteria (ΔOA > 2 μg m−3). Nonetheless, any grid-cell average ΔOA concentrations that are above zero still represent local variations in OA concentrations. For example, the median ΔOA grid cell concentration for the Both land-use type is 1.2 μg m−3, 39% higher than the mean OA concentration for all background-averaging periods in the data set. There were grid cells with significantly elevated average ΔOA concentrations, and the distribution of these highconcentration grid cells also appears to be correlated with differences in land use. For the top decile of ΔOA, for example, the difference between the Both land-use type and the other land-use types becomes more substantial, though the ordering remains the same. Both is the highest (ΔOA = 5.1 μg m−3), followed by the Restaurants-only (3.2 μg m−3), Busy roads only (2.5 μg m−3), and Background (1.3 μg m−3) land-use types. Land-use clearly has an effect on OA concentration patterns in Pittsburgh, with areas containing both busy roads

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b02654. Table with details of individual drives, table of known sources shown in Figure 3, and plot further detailing the similarity/55:57 space and plume categorization method (PDF) H


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T.; Marcon, A.; Eriksen, K. T.; Raaschou-Nielsen, O.; Stephanou, E.; Patelarou, E.; Lanki, T.; Yli-Tuomi, T.; Declercq, C.; Falq, G.; Stempfelet, M.; Birk, M.; Cyrys, J.; von Klot, S.; Nádor, G.; Varró, M. J.; Dėdelė, A.; Gražulevičienė, R.; Mölter, A.; Lindley, S.; Madsen, C.; Cesaroni, G.; Ranzi, A.; Badaloni, C.; Hoffmann, B.; Nonnemacher, M.; Krämer, U.; Kuhlbusch, T.; Cirach, M.; de Nazelle, A.; Nieuwenhuijsen, M.; Bellander, T.; Korek, M.; Olsson, D.; Strömgren, M.; Dons, E.; Jerrett, M.; Fischer, P.; Wang, M.; Brunekreef, B.; de Hoogh, K. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe − The ESCAPE project. Atmos. Environ. 2013, 72, 10−23. (13) Cyrys, J.; Eeftens, M.; Heinrich, J.; Ampe, C.; Armengaud, A.; Beelen, R.; Bellander, T.; Beregszaszi, T.; Birk, M.; Cesaroni, G.; Cirach, M.; de Hoogh, K.; de Nazelle, A.; de Vocht, F.; Declercq, C.; Dėdelė, A.; Dimakopoulou, K.; Eriksen, K.; Galassi, C.; Gra̧ulevičiené, R.; Grivas, G.; Gruzieva, O.; Gustafsson, A. H.; Hoffmann, B.; Iakovides, M.; Ineichen, A.; Krämer, U.; Lanki, T.; Lozano, P.; Madsen, C.; Meliefste, K.; Modig, L.; Mölter, A.; Mosler, G.; Nieuwenhuijsen, M.; Nonnemacher, M.; Oldenwening, M.; Peters, A.; Pontet, S.; Probst-Hensch, N.; Quass, U.; Raaschou-Nielsen, O.; Ranzi, A.; Sugiri, D.; Stephanou, E. G.; Taimisto, P.; Tsai, M.-Y.; Vaskövi, É .; Villani, S.; Wang, M.; Brunekreef, B.; Hoek, G. Variation of NO2 and NOx concentrations between and within 36 European study areas: Results from the ESCAPE study. Atmos. Environ. 2012, 62, 374−390. (14) Eeftens, M.; Tsai, M.-Y.; Ampe, C.; Anwander, B.; Beelen, R.; Bellander, T.; Cesaroni, G.; Cirach, M.; Cyrys, J.; de Hoogh, K.; de Nazelle, A.; de Vocht, F.; Declercq, C.; Dėdelė, A.; Eriksen, K.; Galassi, C.; Gražulevičienė, R.; Grivas, G.; Heinrich, J.; Hoffmann, B.; Iakovides, M.; Ineichen, A.; Katsouyanni, K.; Korek, M.; Krämer, U.; Kuhlbusch, T.; Lanki, T.; Madsen, C.; Meliefste, K.; Mölter, A.; Mosler, G.; Nieuwenhuijsen, M.; Oldenwening, M.; Pennanen, A.; Probst-Hensch, N.; Quass, U.; Raaschou-Nielsen, O.; Ranzi, A.; Stephanou, E.; Sugiri, D.; Udvardy, O.; Vaskövi, É .; Weinmayr, G.; Brunekreef, B.; Hoek, G. Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2 - Results of the ESCAPE project. Atmos. Environ. 2012, 62, 303−317. (15) Zimmerman, N.; Presto, A. A.; Kumar, S. P. N.; Gu, J.; Hauryliuk, A.; Robinson, E. S.; Robinson, A. L.; Subramanian, R. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmos. Meas. Tech. 2018, 11, 291−313. (16) Mohr, C.; DeCarlo, P. F.; Heringa, M. F.; Chirico, R.; Richter, R.; Crippa, M.; Querol, X.; Baltensperger, U.; Prévôt, A. S. H. Spatial Variation of Aerosol Chemical Composition and Organic Components Identified by Positive Matrix Factorization in the Barcelona Region. Environ. Sci. Technol. 2015, 49, 10421−10430. (17) Karner, A. A.; Eisinger, D. S.; Niemeier, D. A. Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data. Environ. Sci. Technol. 2010, 44, 5334−5344. (18) Hayes, P. L.; Ortega, A. M.; Cubison, M. J.; Froyd, K. D.; Zhao, Y.; Cliff, S. S.; Hu, W. W.; Toohey, D. W.; Flynn, J. H.; Lefer, B. L.; Grossberg, N.; Alvarez, S.; Rappenglück, B.; Taylor, J. W.; Allan, J. D.; Holloway, J. S.; Gilman, J. B.; Kuster, W. C.; de Gouw, J. A.; Massoli, P.; Zhang, X.; Liu, J.; Weber, R. J.; Corrigan, A. L.; Russell, L. M.; Isaacman, G.; Worton, D. R.; Kreisberg, N. M.; Goldstein, A. H.; Thalman, R.; Waxman, E. M.; Volkamer, R.; Lin, Y. H.; Surratt, J. D.; Kleindienst, T. E.; Offenberg, J. H.; Dusanter, S.; Griffith, S.; Stevens, P. S.; Brioude, J.; Angevine, W. M.; Jimenez, J. L. Organic aerosol composition and sources in Pasadena, California, during the 2010 CalNex campaign. Journal of Geophysical Research: Atmospheres 2013, 118, 9233−9257. (19) Sun, Y. L.; Zhang, Q.; Schwab, J. J.; Demerjian, K. L.; Chen, W. N.; Bae, M. S.; Hung, H. M.; Hogrefe, O.; Frank, B.; Rattigan, O. V.; Lin, Y. C. Characterization of the sources and processes of organic and inorganic aerosols in New York city with a high-resolution time-

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] (A.A.P.). ORCID

Ellis Shipley Robinson: 0000-0003-1695-6392 Rishabh Urvesh Shah: 0000-0002-4608-1972 Joshua Schulz Apte: 0000-0002-2796-3478 Allen L. Robinson: 0000-0002-1819-083X Albert A. Presto: 0000-0002-9156-1094 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This article was developed under Assistance Agreement No. RD83587301 awarded by the U.S. Environmental Protection Agency. It has not been formally reviewed by EPA. The views expressed in this document are solely those of authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. Additional funding comes from NSF grant number AGS1543786.

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