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Characterization of Natural and Affected Environments

Spatial Variability of Sources and Mixing State of Atmospheric Particles in a Metropolitan Area Qing Ye, Peishi Gu, Hugh Z. Li, Ellis S. Robinson, Eric M. Lipsky, Christos Kaltsonoudis, Alex K. Y. Lee, Joshua Schulz Apte, Allen L. Robinson, Ryan Christopher Sullivan, Albert A Presto, and Neil M. Donahue Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01011 • Publication Date (Web): 18 May 2018 Downloaded from http://pubs.acs.org on May 18, 2018

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Spatial Variability of Sources and Mixing State of Atmospheric Particles in a Metropolitan Area Qing Ye,† Peishi Gu,†,‡ Hugh Z. Li,†,‡ Ellis S. Robinson,†,‡ Eric Lipsky,¶ Christos Kaltsonoudis,† Alex K.Y. Lee,§ Joshua S. Apte,k Allen L. Robinson,†,‡ Ryan C. Sullivan,†,‡ Albert A. Presto,†,‡ and Neil M. Donahue∗,† †Carnegie Mellon University, Center for Atmospheric Particle Studies, Pittsburgh, PA 15213, USA ‡Carnegie Mellon University, Department of Mechanical Engineering, Pittsburgh, PA 15213, USA ¶Penn State Greater Allegheny, 4000 University Drive, McKeesport, Pennsylvania 15132, USA §National University of Singapore, Department of Civil and Environmental Engineering, Singapore 117576 kUniversity of Texas, Department of Civil, Architectural and Environmental Engineering, Austin, Texas 78712, USA E-mail: [email protected]

1

Abstract

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Characterizing intra-city variations of atmospheric particulate matter has mostly

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relied on fixed-site monitoring and quantifying variability in terms of different bulk

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aerosol species. In this study, we performed ground-based mobile measurements using

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a single-particle mass spectrometer to study spatial patterns of source-specific particles

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ACS Paragon Plus Environment

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and the evolution of particle mixing state in 21 areas in the metropolitan area of Pitts-

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burgh, PA. We selected sampling areas based on traffic density and restaurant density

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with each area ranging from 0.2 to 2 km2 . Organics dominate particle composition in

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all of the areas we sampled while the sources of organics differ. The contribution of par-

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ticles from traffic and restaurant cooking varies greatly on the neighborhood scale. We

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also investigate how primary and aged components in particles mix across the urban

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scale. Lastly we quantify and map the particle mixing state for all areas we sampled

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and discuss the overall pattern of mixing state evolution and its implications. We find

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that in the upwind and downwind of the urban areas, particles are more internally

15

mixed while in the city center, particle mixing state shows large spatial heterogeneity

16

that is mostly driven by emissions. This study is to our knowledge, the first study to

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perform fine spatial scale mapping of particle mixing state using ground-based mobile

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measurement and single-particle mass spectrometry.

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Introduction

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Atmospheric particulate matter is one of the least understood aspects of human-caused cli-

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mate change. 1 In addition, exposure to atmospheric particles is strongly associated with

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respiratory and cardiovascular diseases. 2–4 The physiochemical properties of particles, de-

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termined by their sources and atmospheric processing, governs their climate and health

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effects. 5,6 The mixing state of particles, defined as the distribution of chemical species in

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a particle population, influences particles’ optical properties, 7,8 their ability to nucleate

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clouds, 9–11 their tendency to participate in chemical reactions 12 and potentially their health

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effects. 13 Atmospheric processing such as coagulation but also evaporation and condensation

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of low- and semi-volatile compounds will mix various constituents and homogenize com-

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position among particles from different sources. 14–16 Therefore, mixing state measurement

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can constrain the influence of local and regional sources and identify important atmospheric

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processing. 17



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Real-time single-particle mass spectrometers measure the size, chemical composition and

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mixing state of individual particles. 18–21 They have been widely used to study particle

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sources, chemical evolution and mixing state at fixed sites. 22–26 Single-particle measurements

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have also been conducted on mobile platforms, mostly on aircraft or ships, which can cover

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large areas over a relatively short time period and thus are able to characterize the spatial

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variability of particle composition. 27–30 However, very few studies have performed on-road

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mobile single-particle measurements in urban areas.

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In 2010, more than 80% of the U.S. population lived in urban areas. 31 Influenced by

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intensive, complex human activities and the large variation in land use types and micro-

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environments, air pollution in urban areas is highly complicated, dynamic and has sharp spa-

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tial gradients. 32,33 Studies have used ground-based mobile measurements to investigate the

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intra-city gradients of ambient atmospheric pollutants on spatial scales down to neighborhood-

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and street-level. 32–36 These studies mainly used standard instrumentation and did not mea-

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sure particle mixing state.

46

Particle mixing state is expected to be spatially heterogeneous across an urban area.

47

Based on the proximity to emission sources in the city, pollution levels and particle composi-

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tion vary among emission plumes (e.g. traffic areas), urban background areas and suburban

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areas, as shown in Fuzzi et al. 37 However, the relative strength of these different categories

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is not well constrained, nor is the variation from one metropolitan area to another. Espe-

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cially uncertain is the variation among these categories in particle number and mixing state.

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Particles in places close to traffic emissions are found to be dominated by hydrocarbon like

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organic aerosols (HOA) 35,38 while in urban background locations and urban downwind loca-

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tions, particles are found to be complex internal mixtures of carbonaceous aerosols, either

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primary or secondary, and secondary inorganic species such as sulfate, ammonium and ni-

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trate. 25,39–41 These studies are all fixed-site measurements that are not sufficient to resolve

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the spatial pattern and evolution of single-particle sources and address the question of how

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quickly do particles from various sources mix in the cities.


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Mixing state may affect toxicity of particles as different compounds can work synergis-

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tically to enhance the adverse health effects. 13,42 Ristovski and coauthors found that the

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adsorbed organic compounds on the surface of diesel particles will trigger the cellular pro-

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cesses that may lead to inflammation and oxidative stress. 43 Overall, studies on effects of

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mixing state on human health are limited but extensive measurements of particle mixing

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state in populous urban areas can provide meaningful guidance on studying the health ef-

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fects of particles.

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Here we perform particle mixing-state measurements as part of the Center for Air, Cli-

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mate and Energy Solution (CACES). We performed ground-based mobile measurements of

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the composition of ambient single particles to investigate mixing state at the neighborhood

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level in Pittsburgh, PA and surrounding areas. We developed a sampling strategy to span a

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range of conditions based on the anticipated intensity of traffic and restaurant cooking emis-

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sions, coupled with variation in land use types. Our sampling domains cover upwind subur-

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ban areas, the city center near emission sources and downwind urban background/suburban

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areas. This constitutes a transect to systematically study two key questions: 1) how do local

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and background sources affect particle composition and 2) how does particle mixing state

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vary and evolve across the urban area.

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Materials and Methods

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Overview of Stratified Mobile Sampling

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We conducted measurements with a mobile platform of atmospheric chemistry measurement

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housed in the Carnegie Mellon Center of Atmospheric Particle Studies (CAPS) mobile lab-

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oratory (a Nissan van); a detailed description can be found in Tan et al. 44 and Li et al. 45 A

81

list of instruments on the van can be found in the Supplementary Information. This work

82

focuses on the data from the soot-particle aerosol mass spectrometer (SP-AMS, Aerodyne

83

Inc.). We see negligible self-sampling of the van’s exhaust. When the van is moving, we do



Table 1: Mobile sampling areas classified by traffic and restaurant density. Numbers in parentheses are number of particles analyzed of each area. Numbers in circles correspond to the numbers in Figure 5 which shows the location for each area.

High traffic

Low traffic

High restaurant Strip district (9.1K) , 1 Lawrenceville (3.1K) , 2 Downtown (11K) , Squirrel Hill 3 (4.3K) , 4 Southside (12.5K) 5

Highland Park neighborhood (2.8K) , Dormont (2.4K) , 12 13 Restaurant plumes (4.9K)

Low restaurant Chartiers high school (1.7K) , 6 Children’s Museum (4.6K) , 7 Aspinwall (1K) , Uptown (3K) , 8 9 Millvale (1.7K) , Ivory Ave 10 (1K) , 11 Tunnels (2.7K), Highways (2.8K) South Fayette (1.1K) , 14 Carnegie neighborhood (5.8K) , Schen15 ley Park (2.8K) , Beechview 16 (2.8K) , 17 Hill district (3.9K) , 18 Mt. Washington (3.7K) , 19 Fox chapel (1.4K) , Highland park 20 (1K) 21

84

not expect sampling from the exhaust because the aerosol inlet is upstream of the exhaust

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above the roof of the van. When the van is not moving with the engine on, we do not see

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any increase of particle mass measured by the AMS.

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We conducted the sampling in August, September (late summer season), December, Jan-

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uary and February (winter season) in the City of Pittsburgh, PA and surrounding suburban

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and rural areas in Allegheny County. With a population of 300 thousand, Pittsburgh locates

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at the confluence of the Allegheny, Ohio and Monongahela rivers. It has a long history of

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combined air pollution from various industrial and commercial sources. In our study, we em-

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ployed two principal sampling modes: mobile sampling in which we drove the van through

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different neighborhoods, and stationary sampling in which we parked the van at the Carnegie

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Mellon University (CMU) campus. Measurements at the CMU campus constitute an urban

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background with no large local sources. For mobile measurements around Pittsburgh, we

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selected 21 neighborhoods based on traffic and restaurant density, two major local sources

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in Pittsburgh. Neighborhoods were selected to span a wide range of these two land-use

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covariates. Each neighborhood is an area of 0.2-2 km2 and is relatively homogeneous with

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respect to land use covariates.


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Table 1 classifies all 21 areas based on traffic and restaurant density. High traffic density

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areas are those having an average of more than 50 vehicle/day/m (annual average, data

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from Pennsylvania Department of Transportation 46 ). Low traffic density areas are those

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having an average of fewer than 50 vehicle/day/m. Calculation of traffic density can be

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found in the Supplementary Information and is the same as in Li et al. 36 High restaurant

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density areas are those with 30 or more restaurants per km2 (data from Allegheny County

106

Information Portal 47 ) and low restaurant areas are those with 30 or fewer restaurants per

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km2 . There are 21 areas in total augmented by three additional data ensembles representing

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extreme emissions encountered when we sampled inside restaurant plumes, in tunnels and

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on highways. The summary map at the end of the paper, Figure 5, also shows the area

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locations.

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The complete dataset comprises 53 total visits for the 21 areas; we did not visit any areas

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more than once within a day. During each visit of an area, we tried to cover all of the roads

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and streets within the area once (with some exceptions that when we encountered traffic

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detours, we might have covered some streets more than once). Sampling time in each visit

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of an area ranged between 30 minutes and 1 hour. We avoided following buses or trucks

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when driving. We visited each area 1 to 6 times. We show the number of visits to each

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area in Table S1 in the Supplementary Information. For the areas visited multiple times

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we selected different hours of the day (rush hour, non-rush hour; meal time and non-meal

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time) to try avoid bias towards time of the days with high or low emissions. Sampling

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time ranged from 6 AM to 11 PM. Immediately before or after each visit to collect single-

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particle data in an area, we conducted a similar visit in the same area to collect AMS

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bulk data (conventional AMS V-mode measurement). All of our sampling occurred during

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weekdays free of precipitation. For 13 of the 21 areas, we also have long-term stationary

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real-time measurement of PM2.5 throughout the entire campaign (details found in Li et al.,

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manuscript in prep). Using the data from those stationary sites, we confirmed that the

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days when we have single-particle measurements are typical without significantly high or



127

low pollution levels (daily concentration fell between 10% and 90% of all days during our

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sampling seasons).

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Event-trigger Single-Particle Mass Spectrometry

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The mission-critical instrument in this study is a real-time soot-particle aerosol mass spec-

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trometer (SP-AMS) with an event-trigger single-particle mode (Aerodyne, Inc.). Details

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of the SP-AMS have been published in Onasch et al. 48 A more detailed description of the

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instrument can be found in the Supplementary Information. The SP-AMS measures non-

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refractory aerosol components as well as refractory black carbon (rBC). It does not measure

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road dust, which is an important particle type in urban environments. However, road dust

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exists primarily in the super-micron size range, so we do not expect it to be a major source

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for particles transmitted through the aerodynamic lens in the AMS. Air flow entering the

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SP-AMS is dried to less than 7% relative humidity by a Nafion dryer (Perma Pure LLC).

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During event-trigger single-particle measurements, the 1064 nm Nd:YAG laser was on con-

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tinuously to measure soot particles. During bulk measurements, the laser was off in order

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to minimize any uncertainty in the relative ionization efficiency induced by the IR laser 48,49

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and consequently to obtain accurate bulk particle mass quantification. Those bulk data are

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the subject of a complementary manuscript. Due to the low transmission efficiency of very

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small and supermicron particles in the inlet of the AMS, here we only study particles ranging

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from 50 – 1000 nm. In this paper we mostly focus on the single-particle data. An extensive

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analysis of bulk data from this campaign can be found in another paper from our group

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(manuscript in prep).

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The event-trigger (ET) mode obtains mass spectra from individual particles if the signals

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they generate surpass user-defined signal thresholds in specified regions of interest (ROIs).

150

Each ROI is a mass-to-charge range and an associated ion signal threshold. When the AMS

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is operating in ET mode, the mass spectrometer runs continuously and firmware on a fast

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data acquisition card evaluates the ROIs on-line. If an event surpasses the ROIs threshold,


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its mass spectrum is recorded and downloaded to the computer and this event is treated as a

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single particle. The ROIs used in this study are m/z 41 to m/z 43 ≥ 4 ions; m/z 45 to m/z

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150 ≥ 5 ions and m/z 36 ≥ 3 ions (C3 + for black carbon detection) with logical “OR” filters

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to combine the ROIs into a single boolean trigger. These threshold values are determined

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before the campaign by sampling particle-free air and by laboratory generated particles

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to ensure that the thresholds are neither too low that they trigger too much background

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signals nor too high that we miss too many particles signals. Data are processed by Tofware

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developed by Tofwerk. Single-particle analysis is based on the cluster analysis panel (CAP)

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developed by Lee et al. 38 with customized modifications. We first perform k-means clustering

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with k=30 for all the particle spectra collected in each area. We then combine the clusters

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with similar spectra based on the knowledge of mass fragments in the AMS. 51 We ultimately

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categorize the particles into seven different groups as discussed below. In total, we analyzed

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470 thousand individual particles and 92 thousands of them were acquired during mobile

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sampling.

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Results and Discussion

168

Single-particle Characterization

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We first compare the overall bulk chemical composition of particles measured by the AMS

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when the van is parked at CMU campus, which represents the urban background, with

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corresponding ET ion signals. The upper panel of Figure 1 shows the composition of PM1

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derived from AMS bulk measurements without using the SP laser in Summer 2016 (August)

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and Winter 2017 (January and February). To this we add Black Carbon as measured by the

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Aethalometer. The average non-refractory PM1 mass from AMS was 11 and 6 µg m−3 for

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Summer and Winter, respectively, assuming a collection efficiency of 0.5. Organics dominate

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PM1 in both seasons (59% in summer and 36% in winter), followed by sulfate in the summer

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from strong photo-oxidation of SO2 and nitrate in the winter from elevated formation of



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ammonium nitrate due to high residual ammonia and low temperatures. Compared to the

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nearby urban background site for the Pittsburgh Air Quality Study conducted in 2001 –

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2002, there is a significant reduction in sulfate content. 52 During 2001 – 2002, sulfate mass

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concentration exceeded organics in PM2.5 . Fifteen years later the sulfate mass is less than

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half that of organics in PM1 .

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The lower panel in Figure 1 shows the aggregated ion signal for all detected particles from

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ET measurements at CMU campus during the same sampling periods. Similar to the bulk

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composition, the single-particle data are also dominated by organics (69% in summer and

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51% in winter) with a smaller contribution from sulfate and nitrate. In Figure S1 and Figure

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S2, we also compare the hourly composition observed in the single-particle mode and the bulk

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measurement mode for a 48-hour continuous measurement. The reduced fraction from black

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carbon measured by SP-AMS compared to the bulk measurements by Aethalometer may be

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due to the low transmission and detection efficiency of black carbon particles smaller than 50

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nm in the AMS inlet and incomplete overlapping between laser and the particle beam. 49,53

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The region-of-interest-based trigger of measurement in the ET mode may introduce bias by

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selectively acquiring particles producing specific ions. In addition, signals from the bulk

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measurement have been converted to mass assuming different relative ionization efficiency

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for different chemical species, which may also introduce some differences in single particle

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and bulk composition. Nonetheless, there is a high correspondence between the bulk and

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single-particle measurements.

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Next, we characterize the single-particle mass spectra we detected at the CMU campus.

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We assign each individual particle to one of seven groups and show the average cluster

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spectra in Figure 2. Broadly, the clusters appear to consist of four background types and

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three near-source primary types. We assign names to the clusters based on comparisons with

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reference mass spectra. The reference spectra for primary particles are collected when we

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sampled in the tunnels and inside restaurant plumes when the sources of fresh emissions are

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almost exclusively traffic and restaurant cookings, respectively.


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The background clusters include one inorganic rich and two organic rich types during

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each season. The inorganic-rich clusters favor sulfate in the late summer and nitrate in the

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winter. The organic-rich background clusters comprise one with relatively more oxygenated

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organic aerosols (OA) and one with relatively less oxygenated OA. The highest signal of the

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more oxygenated OA-rich mass spectrum is m/z 44 (CO+ 2 ) while the highest signal of the

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less oxygenated OA-rich mass spectrum is m/z 43 (mostly C2 H3 O+ ).

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The primary clusters are hydrocarbon OA-like (HOA-like), cooking OA-like (COA-like)

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and black carbon-like (BC-like). In Figure S3, we compare the mass spectra of HOA-

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like and COA-like clusters with source-specific particles from the AMS spectra database

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(http://cires1.colorado.edu/jimenez-group/AMSsd/) and show that they agree well, with

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r2 > 0.85. The spectrum of the BC-like cluster contains fragments of C+ , C2 + and C3 + .

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We also present the fraction of organic signals in each cluster (denoted as forg ). Even the

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inorganic-rich and BC-like clusters contain a significant fraction of organics. We do not

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include ammonium in the single-particle analysis due to the large interference from water.

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Spatial Variability of Source-specific Particles

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We designed the sampling protocol to assess the spatial variability of source-specific par-

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ticles, covering locations with significant differences in emissions of traffic and restaurant

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cooking. The major particle types associated with primary emissions are from traffic (HOA-

223

like particles) and restaurant cooking (COA-like particles). Figure 3A shows the number size

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distributions normalized by the highest bin concentration of particles measured by ET in the

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winter broken down into different cluster classes for areas with a range of traffic emissions,

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ranging from a tunnel to a park without local emissions. Due to the decreasing detection

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efficiency of particles with decreasing sizes, the signals drop at lower size range especially

228

under 100 nm. However, measuring single particles mass spectra down to 50 nm is com-

229

pelling. In Figure S4 in the Supplementary Information, we show the SMPS scaling curve

230

of number concentration for the ET-AMS. Figure 3 and Figure 4 are presented as detected



231

by the ET-AMS. We also show the absolute total particle number concentrations (10 – 400

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nm in mobility diameter) measured by the Nanoscan SMPS.

233

As Figure 3A show, particles collected in tunnels show a significant peak from HOA-like

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particles with a mode around 100 nm as measured by the AMS. This mode also appears on

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highways and in other high-traffic areas but as a lower fraction of the total particle count. In

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contrast, in places like parks with very low local traffic emissions, the traffic mode essentially

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disappears. In all areas, background particle types consistently dominate the accumulation

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mode.

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Figure 3B shows the breakdown by different ions rather than by cluster type. We de-

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convolve the organic signals into HOA and OOA using the methods described in Zhang et

241

al., 54 essentially the relative fraction of f57 and f44 . As with the clusters, HOA dominates

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the 100 nm mode in tunnels, on highways and in high traffic areas. Tunnels have a higher

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fraction of BC than other locations and a lower fraction of OOA, possibly due to less photo-

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chemistry. There is much more BC based on ions than just the “BC-like” cluster, as many

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HOA-like particles contain BC. Figure 3A and Figure 3B reveal a difference between the

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cluster classification and the overall ion composition. The “inorganic rich” cluster generally

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exceeds the “organic rich” clusters in terms of particle numbers, except on highways where

248

they are evenly split. However, the fraction of ions from OOA is comparable to the fraction

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of ions from sulfate and nitrate in the accumulation mode, and the highway samples are

250

unexceptional. This implies that the inorganic-rich cluster contains a considerable amount

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of oxygenated OA signal, and that inorganics and OOA are internally mixed in all of these

252

locations, though in varying proportions. This is confirmed by the fraction of organic signal

253

(forg ) in the inorganic-rich clusters shown in Figure 2. We shall explore this below.

254

In Figure 4 we plot the size distribution of particles organized by proximity to restaurants

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of selected areas. The left column is an extreme case where we parked our van inside a

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restaurant plume. The middle column is downtown area with high restaurant density, and

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the right column is a residential neighborhood with few restaurants. We again show the


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breakdown in terms of clusters in Figure 4A and ion composition in 4B. Because cooking

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OA contains more oxygenated carbon than HOA, we do not perform a simple deconvolution

260

of the OA signal but simply report all OA as a single composition group. The particle cluster

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types in these locations show a drastic difference. The restaurant plume is essentially pure

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COA-like, and pure OA. The PM1 inside the plume measured by the bulk mode of AMS is

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close to 100 µg/m3 assuming collection efficiency of 1. A significant portion of particles are

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COA-like in downtown, showing that restaurant cooking is an important source. This was

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a day when we sampled during dinner time. At other times, the contribution of COA-like

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particles in the downtown is less. The non-negligible fraction of COA-like particles in the

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suburban residential area could be transported restaurant COA or COA from household

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cooking. The ion distribution from the Figure 4B shows that in all of these areas, organics

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are the major contributor to particle composition; however particles collected downtown are

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dominated by fresh emissions with an average O:C of 0.19 and particles collected in the

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suburban residential area contain more aged organics with an average O:C of 0.59.

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Evolution of Particle Mixing State Across the Metropolitan Area

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We have shown the large spatial variability of particle composition and ion types in an urban

274

area by grouping particles using a relatively simple clustering analysis and looking at the

275

fractional contribution of different clusters to the entire particle population. We next will

276

investigate how particles with various origins evolve and mix across the city. To do this we

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segregate the sampling locations into three categories: upwind suburban areas, urban areas

278

near sources and urban background/downwind urban areas. We then compare the mixing

279

state of particles in these three areas.

280

To study the evolution of mixing state we focus on three representative ions (or ion

281

groups): f44 (the signal fraction of m/z 44, CO+ 2 , associated with oxygenated aged organics),

282

fSO4 (from sulfate) and f55 + f57 (C4 H7 + , C3 H3 O+ and C4 H9 + , associated with primary

283

organics from traffic and cooking emissions). We show the results for selected types of ion



284

pairs in Figure S5 in the Supplemental Information. Upwind particles largely contain a

285

mixture of sulfate and aged organics; however, in urban locations near emission sources,

286

most of the particles are on one axis or the other, indicating that sulfate and primary

287

organics are externally mixed. The internally mixed particles in urban near-source locations

288

are likely transported in from the upwind suburban locations. In the urban background,

289

more particles are internally mixed, containing sulfate and primary organics, but they are

290

less abundant than in upwind suburban areas. Also, in upwind suburban locations, a large

291

fraction of the particles contain oxygenated organics with few or no primary organic ions.

292

In urban near-source locations and the urban background, the particle population becomes

293

fresher and less oxygenated, with a reduced fraction of particles containing both fresh and

294

oxygenated signals.

295

Using the data we collected in all 21 areas described in Table 1, we create a map of particle

296

mixing state for Pittsburgh, shown in Figure 5. To quantify the mixing state we use the χ

297

metric developed in Riemer and West. 56 This parameter describes how chemical species (in

298

this work, organics, sulfate, nitrate, chloride and black carbon) distribute in single particles

299

with respect to the entire particle populations. More calculation details can be found in the

300

Supplementary Information and in Riemer and West. 56 The parameter χ can range from

301

0% to 100% with 0% meaning particles in a population are completely externally mixed and

302

100% meaning completely internally mixed. In an urban background site in Paris, χ has a

303

mean value of 59%, 41 about midway between a complete internal and a complete external

304

mixture.

305

In Pittsburgh, we calculate χ for each area we visited, averaging χ over all of the visits

306

in each area. In Figure 5 we color code each area according to χ. The number next to each

307

area corresponds to the number in Table 1. We also include an hourly averaged wind di-

308

rection frequency distribution recorded at Pittsburgh International Airport for the period of

309

our single-particle collection (www.wunderground.com). The prevailing winds in Pittsburgh

310

are southerly or westerly, and our sampling areas are designed to follow a path along the


Page 14 of 28

Page 15 of 28

311


prevailing wind direction.

312

The upwind areas (areas , which are rural or suburban locations, have high 14 15 and ), 6

313

mixing state metric χ of 60%– 70%. Closer to the city center (e.g. areas with 17 and ), 13

314

increased emissions from human activities, particles progressively become more externally

315

mixed. Areas in the city center have low χ values. The particle population in downtown

316

(area ) has χ = 43%. Area , 3 11 though it is approximately 5 km north of the city center,

317

is a small neighborhood sandwiched between an interstate highway and a busy road; with

318

χ = 36% it has the highest degree of external mixing of any area we sampled. In the

319

downwind areas the particles once again become progressively more internally mixed, with

320

an increased χ value, as air moves into suburban areas with fewer emission sources. However,

321

the downwind particle population is still more externally mixed than the upwind areas.

322

We also quantified the sub-grid variability of χ in an area with high emissions (downtown

323

) and in an area with low emissions (Schenley Park ) 3 16 by subdividing each area into

324

smaller boxes with side length of 300 m in longitude and latitude. The bar chart (Figure

325

S6) in Supplementary Information indicates the average χ of the sub areas and the standard

326

error of the mean of the χ of the sub areas. There is a slightly larger sub-area variability

327

of particle mixing state in downtown where sources are more spatially variable, while the in

328

Schenley Park which is a large urban park (∼300 acre) with few roads, particle populations

329

are more homogeneous. Although there may be sub-grid spatial variability of particle mixing

330

state inside each area, the overall trend across the entire sampling domain is clear.

331

Given the fact that we only visited some areas for a limited number of times, our data

332

are not designed to be representative of long-term particle mixing state and source appor-

333

tionment in any individual area, nor do they contains information on temporal variability of

334

mixing state in any areas. However, we perform stratified sampling by grouping the areas

335

based on emission levels and choose sampling areas located along the prevailing wind direc-

336

tion. We detected and analyzed a large number of particles (several thousands or more) in

337

each area, which should be representative to the entire particle population in those areas.



338

We believe our work provides an overview of the evolution of particle mixing state across an

339

urban area due to the influence of local sources and transport.

340

Implications

341

We observe a significant spatial variability of particle mixing state in Pittsburgh mostly

342

driven by variations in emissions. By conducting mobile measurements with a state-of-the-

343

art real-time single-particle mass spectrometer, we are able to quantify this variability by

344

measuring the fraction of different particle types and then mapping particle mixing state

345

on the neighborhood scale. Primary organic aerosols are reasonably volatile, 14 and thus the

346

semi-volatile constituents are expected to migrate among particle populations easily. We find

347

significant external mixing of particles persists in the urban center and urban background.

348

This could be due to inhomogeneous distributions of components other than organics in

349

particle populations. For example, sulfate is enriched in particles that have passed through

350

power-plant plumes but depleted in particles generated from cooking and low-sulfur diesel

351

fuels.

352

Our data imply that a large fraction of people in Pittsburgh and presumably many

353

other urban areas are exposed to highly externally mixed particles. While we find that in

354

Pittsburgh, traffic and restaurant cooking are the two major contributors to ambient PM1 ,

355

future studies should cover areas with other potential important PM sources such as the

356

coke plants in the southern Allegheny County.

357

Associated Content

358

Supporting Information

359

360

Instrumentation details, mobile laboratory details, mixing state metric quantification, supporting table and figures.


Page 16 of 28

Page 17 of 28


361

Acknowledgement

362

This publication was developed under Assistance Agreement No. RD83587301 awarded by

363

the U.S. Environmental Protection Agency. It has not been formally reviewed by EPA. The

364

views expressed in this document are solely those of the authors and do not necessarily reflect

365

those of the Agency. EPA does not endorse any products or commercial services mentioned

366

in this publication. This work is also supported by U.S. National Science Foundation under

367

grant ATM1543786, MRI−CBET0922643 and CHE-1412309. We also want to thank Faculty

368

for the Future Fellowship from the Schlumberger Foundation.

369

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BulkBulk mass composition mass Jan and winter Feb 2017 composition

Bulk mass composition Bulk mass August 2016 composition summer

8% (0.5) < 1% (< 0.1) 13% (0.9)

8% (0.9) 7% (0.8) 36% (2.3) 22% (2.6)

59% (7.0) 4% (0.5)

ET ionsET composition ions August composition2016 summer 2% 4%

15% (1.0) Org NO3 SO 4

NH 4 Chl BC

26% (1.7)

ET ions composition ET ions Jan and Feb 2017 composition winter 4%

12%

23% 13% 51% 2% 69% 21% Figure 1: Mass composition derived from AMS bulk measurements (upper panel) and ion composition derived from single-particle ET measurements (lower panel) during Summer 2016 (August) and Winter 2017 (January and February) from CMU campus. The blackcarbon (BC) values were derived from a co-located Aethalometer. Numbers in parentheses are the absolute mass concentrations (in µg/m3 ) measured by the AMS assuming a collection efficiency of 0.5. CMU campus represents an urban background environment for the City of Pittsburgh. The estimated error in OA measurement is ± 38%. 50


Page 25 of 28


0.15

Background particle clusters Freshly-emitted particle clusters Background particle clusters Background particle clusters Primary particle clusters Freshly-emitted particle clusters Background particle clusters Primary particle clusters 0.10 0.15 Inorganic-rich summer Inorganic-rich summer forg = 0.64 0.10 0.64 forg =0.05

0.10 0.05 0.00

0.00 40

20

0.1 20

0.3 0.2 0.1 20

0.2

0.2 0.1 0.0 40 0.3 0.2 0.1 0.0 40 0.2

0.1 0.0

Fractional ion signals

Fractional ion signals

0.3

0.2

0.0

f

0.05

HOA-like = 0.95 org

f

0.05

0.3

0.0

0.10

0.1 20

0.0 40

0.00

20 0.00 40 120 0.10 Inorganic-rich winter Inorganic-rich winter f = 0.32 org f =0.05 0.32 0.05 org

60 20

80 40

100 60

120 80

100 0.10

0.00 0.00 100 20120 40 0.6 0.6 More oxy OA-rich More oxy OA-rich = 0.80 f org 0.80 forg = 0.3 0.3

60 20

60 20

80 40

80 40

100 60

100 60

120 80

120 80

0.0 0.0 20 40 100 120

Less oxy OA-rich Less oxy OA-rich forg = 0.83 = 0.83 f org

60 80 20 40 m/z

100 60 m/z

120 80

60 20

80 40

60 20

80 40

100 60

Org NO3 SO 4

120

100

120

COA-like forg = 0.95 120 80

BC-like f = 0.39 org 60 80 20m/z 40

org

120 80

COA-like forg = 0.95

Chl BC

100

100 60

HOA-like = 0.95

100

120

BC-like f = 0.39 org

100 120 60 80 m/z

100

120

Org NO3 SO 4

Chl BC

Figure 2: Individual particles collected during mobile sampling are grouped into seven clusters based on their unit mass spectra. Shown here are the average mass spectra for each cluster. Each unit mass (m/z) is color-coded by chemical species classes that generate signal with that unit mass, as shown in the color legend. We broadly treat the first three clusters (inorganic rich, more oxygenated OA rich and less oxygenated OA rich) as background particles and treat the last three clusters (hydrocarbon OA (HOA)-like, cooking OA (COA)like and black carbon (BC)-like clusters) as primary particles. Fraction of organic signals (forg ) is also shown for each clusters.




(A)

Inorg-rich

More oxy OA-rich

1.0

1.0 On highways

In tunnels 38,000/cc

5,000/cc

0.5

0.0

50 100

300

0.0 1000

50 100

va

Normalized dion/dlogd

In an a site In areawith with high traffic traffic high density density

300

0.0 1000

va

NO 3

OOA

HOA

50 100

HOA-like

In a park 1,600/cc 0.5

300

0.0 1000

1.0

1.0

0.5

0.5

0.5

0.5

300

dva (nm)

0.0 1000

50 100

300

dva (nm)

300

1000

BC

1.0

50 100

50 100

dva (nm)

1.0

0.0

BC-like

1.0

d (nm) va

d (nm)

va

SO4

COA-like

0.5 6,500/cc

0.5

d (nm)

(B)

Less oxy OA-rich

1.0

Page 26 of 28

0.0 1000

50 100

300

dva (nm)

0.0 1000

50 100

300

1000

dva (nm)

Figure 3: Number size distributions normalized by the highest bin concentration for areas with various levels of traffic emissions. Panel A shows the distribution broken down into contributions from the six identified particle cluster types. Total particle number concentrations measured by the NanoScan SMPS are also shown in Panel A. Panel B shows the distribution broken down based on ions composition. Background particles have a similar ion composition across these areas with a dominant peak in the accumulation mode. In contrast, HOA particles and ions show a drastic difference depending on proximity to traffic emissions.


Page 27 of 28


Inorg-rich

Normalized d#/dlogdva

(A)

1.0

0.5

0.0

More oxy OA-rich

Less oxy OA-rich

1.0

Inside a restaurant plume

98μg/m3

0.5

50 100 300 d (nm)

0.0 1000

va

SO 4

Normalized dion/dlogd va

(B)

NO3

Org

Downtown: high restaurant density

8,600/cc

50 100 300 dva (nm)

COA-like

1.0

0.0 1000

A suburban residential area: site: low restaurant density

50 100 300 dva (nm)

1000

BC

1.0

1.0

0.5 O:C = 0.15

0.5 O:C = 0.19

0.5

50 100 300 dva (nm)

BC-like

0.5 2,700/cc

1.0

0.0

HOA-like

0.0 1000

50 100 300 d (nm)

0.0 1000

va

O:C = 0.59

50 100 300 d (nm)

1000

va

Figure 4: Size distributions normalized by the highest bin concentration for particular areas with various levels of restaurant-cooking emissions. PM1 in the restaurant plume measured by the AMS assuming collection efficiency of 1 is close to 100 µg/m3 . Total particle number concentrations measured by the NanoScan SMPS are also shown in Panel A for downtown and the residential area. Panel B shows the distribution broken down based on ions composition and O:C values for organic constituents. COA is a major contributor to particles in places like downtown with high restaurant density while in some residential areas with few restaurants, the major cluster types are background particles.



0

1

2

4

6

Page 28 of 28

8 Kilometers

⑳

Mixing state metric χ 2

70% more internally mixed

1.8 1.6

⑪

1.4

⑧

1.2 1 0.8

⑩

0.6 0.4

30% more externally mixed

0.2 0

0

0.5

1

1.5

2

0.3 NW 0.2 0.1 0 W

⑲ ③

NE

SE S

②

⑱ ⑨

④ ⑤ ⑯

E

SW

⑭

Downtown

N

⑦ ①

㉑ ⑫

⑮

⑰ ⑬

⑥ Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community

Figure 5: A map of the mixing-state parameter χ, which ranges from 0% to 100% with 0% being a fully externally mixed and 100% being fulling internally mixed population. The average χ for each area we visited ranged from 30% to 70%. The border of Pittsburgh city is outlined in black on the map, indicating areas with more intensive human activities. The hourly wind direction frequency distribution for the sampling period is shown as an inset. The overall pattern shows that in the upwind rural and suburban areas to the southwest, particles are more internally mixed. In the city center, where the emissions from traffic and restaurant cooking are high, particles are more externally mixed. In the downwind areas to the northeast, particles again become internally mixed but not as completely as in upwind areas. Base map sources: Light Gray Canvas Map, Esri, DeLorme, HERE, MapmyIndia. 55


(A) (B) - ACS Publications

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