Introduction
Motor vehicles are a large source of ambient fine particulate matter
(PM; Dallmann and Harley, 2010; Fraser et al., 1999; Kumar et al.,
2011; Zhang et al., 2015). Particles emitted from vehicle exhaust are
dominated by ultrafine particles (diameters <100 nm; Kleeman et al., 2000; Robert et al., 2007; Zhu et al., 2002), which
are a concern due to their potential impacts on public health (Health
Effects Institute, 2010; Hoek et al., 2009; Pope and Dockery,
2006). Vehicle-emitted PM largely consists of primary organic aerosol
(POA) and black carbon (BC; Dallmann et al., 2014; Maricq,
2007). Upon emission, vehicle exhaust undergoes rapid cooling and
dilution with ambient air on the road. Emissions undergo further
evolution from road to background-like conditions within a few hundred
meters downwind from the roadway (Robinson et al., 2010; Zhang et al.,
2004), which involves complex physicochemical processes. Subsequently,
the size distribution and physiochemical characteristics, and thus
the exposure characteristics and impacts, of aerosols evolve with downwind
transport.
A large portion of POA emitted from motor vehicle is semi-volatile material
(Grieshop et al., 2009; May et al., 2013a; Presto et al., 2012) that can
dynamically partition into the gas or particle phases with changing ambient
conditions (e.g., temperature, atmospheric dilution) and atmospheric
aging (Robinson et al., 2007, 2010). At equilibrium, the volatility of
organic species (saturation vapor pressure or, equivalently, saturation
concentration, C∗) dictates gas–particle partitioning (Donahue
et al., 2006). Enthalpies of vaporization (ΔHvap) also
influence the change in partitioning with temperature (Epstein et al., 2010;
Ranjan et al., 2012). Depending on the volatility of POA and atmospheric
perturbations (dilution, changing temperature), semi-volatile species in POA
will dynamically partition into gas or particle phases as they move downwind.
Therefore, gas–particle partitioning of POA likely plays an important role
in determining human exposure to traffic-emitted particles under varied
ambient conditions.
Fresh BC particles emitted from vehicles are typically fractal in
morphology (Bond et al., 2013; China et al., 2014) and may have
varying size, shape, and mixing state. BC may exist in the same
particle as OA and others species (internally mixed) or in separate
particles (externally mixed). The mixing state and morphology of BC
particles can influence their radiative absorption properties (Cappa
et al., 2012) and deposition in the human respiratory tract (Broday
and Rosenzweig, 2011). The mixing state and physiochemical
characteristics of BC particles evolve as they undergo atmospheric
processing and aging (Adachi and Buseck, 2013; Subramanian et al.,
2010). For example, the photochemical oxidation of volatile organic
compounds (VOCs), intermediate VOCs (IVOCs), and semi-volatile organic
compounds (SVOCs) forms condensable vapors. These condensable vapors can
partition into the particle phase either by absorbing into the organic
condensed phase or adsorbing onto nonvolatile BC cores (Donahue
et al., 2006; Pankow, 1994; Roth et al., 2005).
Spatial measurements of the volatility and mixing state of near-road aerosols
are critically important to better understand the evolution of
vehicle-emitted POA and BC under diverse ambient conditions (e.g., seasons)
as they are transported from roadways to assess human exposure and health
risks and to improve their representation in air quality and exposure
assessment models. Although a number of laboratory and field studies have
investigated the volatility (Kuwayama et al., 2015; May et al., 2013a, b; Li
et al., 2016; Biswas et al., 2007; Grieshop et al., 2009) and mixing state
(Tiitta et al., 2010; Liu et al., 2014; China et al., 2014; Willis et al.,
2016) of traffic-emitted particles using various techniques, they have
largely focused on source characterization or measurements at a fixed ambient location. To the best of our
knowledge, no studies have been conducted to systematically explore the
evolution of volatility and mixing state of near-road aerosols at different
distances from the roadway under diverse environmental conditions.
We measured the evolution of a highway plume at different downwind
distances under diverse environmental conditions under favorable wind
directions during summer and winter field campaigns. Heating (using
a thermodenuder) experimental data coupled with a mass transfer
kinetics model were used to investigate particle volatility, and
heating (V-TDMA: volatility tandem differential mobility analyzer) and
single-particle data (SP2: single-particle soot photometer) were used
to explore the mixing state of particles. The objectives of the study
were to (i) determine the spatial distribution of aerosol volatility and
mixing state in a near-road microenvironment, (ii) explore the
influence of seasonality and ambient conditions on the
phase partitioning of near-road aerosols, and (iii) evaluate the
representativeness of laboratory-derived POA volatility distributions
from vehicle exhaust to explain real-world observations of aerosol
volatility in a complex near-road microenvironment.
Methods
Measurement sites
Two month-long measurement campaigns were conducted at a site near Interstate
40 (I-40) outside Durham, North Carolina (35.865∘ N,
78.820∘ W) in summer 2015 (1 June–2 July) and winter 2016
(18 January–20 February). A map of the measurement site is shown in Fig. S1
in the Supplement. Detailed descriptions of the measurement site and
campaigns are included in Saha et al. (2018); they are only described briefly
here. At the measurement location, I-40 has eight lanes and an annual average
daily traffic volume of 140 to 145 thousand vehicles per day (4–6 % of
which are heavy-duty diesel vehicles; HDDVs). At our site, I-40 adjoins
a low-traffic rural road running almost perpendicular to the highway, which
is in line with the dominant wind direction (southwest; 225∘). This
minor road allows us to study the evolution of the highway plume at different
downwind distances.
Measurements were collected at a fixed-site trailer located
10 m from the highway
(continuous) and during downwind transects on the minor roadway at different
downwind distances (10, 50, 100, 150, 220 m) from the highway
(intermittent). Downwind transect measurements were performed on weekdays
with the wind coming off the highway consistently in summer (4 days) and
winter (3 days). A mobile platform (van) was used for transect measurements.
For a particular transect run, a sampling period at a downwind point was
∼20 min; four to five downwind points were sampled consecutively.
During sampling, the van engine was off to avoid self-contamination. A “full
transect run” took approximately 2 h. Typically, three transect runs were
completed per day: morning rush hour (∼ 07:00–09:00), midday (∼12:30–14:30), and evening rush hour (∼ 16:30–18:30).
To explore the spatial distribution of the volatility and mixing
state of particles, measurements collected during transect runs are the major
focus of this paper.
Measurements
Thermodenuder (TD) experiments
Various configurations of heating (thermodenuder; TD) experimental approaches
were applied to explore the volatility and mixing state of near-road
aerosols. The methods applied here fall into two general categories:
(i) heating of the polydisperse particle distribution (Huffman et al., 2008;
Lee et al., 2010; Saha et al., 2017) and (ii) heating of monodisperse
particles selected by the differential mobility analyzer (DMA-selected;
volatility tandem DMA approach, V-TDMA; Biswas et al., 2007; Kuhn et al.,
2005; Tiitta et al., 2010). A custom-built, multi-tube thermodenuder (MT-TD)
system was used for high-time-resolution volatility measurements. The MT-TD
consists of four separate heated lines controlled by automated valves that
can be switched in approximately 1 s, enabling quick alternation among four
different set temperatures. While measuring the evaporation of a polydisperse
distribution, the MT-TD was coupled with a scanning mobility particle sizer
(SMPS; TSI Inc.; 3010 CPC, 3081 DMA; scan time 2.5 min) to measure
thermodenuded distributions (10–400 nm) after heating at 60, 90,
120, and 180 ∘C with a residence time (Rt) of 30 s. All Rt
values reported in this paper are volumetric residence time at room temperature unless
otherwise stated. Dump flows were used to maintain constant flow in all lines
during MT-TD operation. A full set of temperature scans required ∼10 min. Another SMPS (3010 CPC, 3081 DMA; scan time 2.5 min) continuously
measured particle size distributions (10–400 nm) at ambient
temperature. In a subset of transect runs,
the V-TDMA configuration was used. In this approach, DMA size-selected
monodisperse particles (25, 50, 100, 250 nm) were heated at different
TD temperatures (60–180 ∘C) with an Rt of 30 s and the
thermodenuded distributions were measured using an SMPS.
In the stationary roadside trailer, a temperature stepping TD (TS-TD; Huffman
et al., 2008; Saha et al., 2017) was continuously operated at
four temperature steps (60, 90, 120, 180 ∘C; Rt =30 s)
upstream of an aerosol chemical speciation monitor (ACSM; Aerodyne Inc.;
75–650 nm) and SMPS (10–400 nm). In this configuration,
instruments were alternated between the bypass (ambient) and TS-TD lines at
10 min intervals using an automated three-way valve. TD–ACSM provides
chemically resolved (organic, sulfate, nitrate, ammonium, and chloride)
volatility data. Because TD data at different residence times provide
additional constraints on the volatility parameter extraction process (Saha
et al., 2015, 2017), TD–SMPS data (10–400 nm) using the MT-TD setup
were collected over a wide range of temperature and Rt conditions (T=60,
90, 120 ∘C; Rt =9, 13, 19, 30 s) during some of the
summer campaign at the roadside trailer. An extra flow controller was used to
vary Rt (Saha et al., 2015). In all measurements, a silica gel diffusion
dryer was placed upstream of TD inlets and aerosol instruments to maintain
relative humidity (RH) <30–40 %.
Single-particle soot photometer (SP2)
An SP2 (Droplet Measurement Technologies) was deployed at the
roadside trailer during the winter campaign to measure the size
distribution and mixing state of BC. The SP2 uses a laser-induced
incandescence (Nd:YAG laser; 1064 nm) technique (Stephens
et al., 2003) to measure refractory BC mass (rBC) in individual
particles. The rBC-containing particles passing through the laser beam
scatter laser light and at the same time absorb energy and are heated
to their vaporization temperature and incandesce (McMeeking et al.,
2011a; Moteki and Kondo, 2007; Shiraiwa et al., 2007; Stephens et al.,
2003). The incandescent light is proportional to rBC core mass. The
SP2 incandescence response was calibrated with DMA-selected dried
fullerene soot particles. A calibration curve is derived from the SP2
incandescence response and mass of the calibration particles from the
mobility diameter and assuming an effective density of
1.8 gcm-3. The scattering detectors were calibrated using
dried PSL (polystyrene latex sphere) particles by relating the
detector response to the PSL sizes. Ambient particles were dried
before introduction into the SP2.
Other supporting measurements
Measurements of traffic (volume, composition, speed), meteorological data
(ambient T, RH, wind speed, and direction), and various gaseous and
particulate air pollutant concentrations were collected throughout the
campaigns and are discussed in detail in Saha et al. (2018). A 10 m
meteorological tower recorded meteorological data at the roadside trailer
location. An existing remote traffic microwave sensor (RTMS) maintained by
the North Carolina Department of Transportation (NC-DOT) provided traffic
data. Particle size distributions (SMPS; 10–400 nm), chemical
composition (ACSM; 75–650 nm), BC (photoacoustic extinctiometer;
PAX-870; Droplet Measurement Technologies), NO/NO2 (2B
Technology 401/410), and CO2 (Li-cor Li-820) were continuously
measured at the roadside trailer.
During transect runs, particle sizing (SMPS), NO, NO2, BC, and
CO2 instruments from the trailer were placed in the transect van to
collect these parameters at different distances from the highway. Particle
size distribution (SMPS; 10–400 nm), NO/NO2
(Ecotech 9841), and BC (microAeth AE51) were continuously monitored in an
upwind stationary background site located on the opposite side of I-40
approximately 400 m away from the highway.
Data reduction
Evaporation of particles at a particular TD operating condition (T, Rt) is
described in terms of volume fraction remaining (VFR; for SMPS data) or mass
fraction remaining (MFR; for ACSM data). VFR (MFR) is the ratio of volume
(mass) concentration measured in heated line (CTD) to that in
unheated bypass line (CBP). The size distribution for the heated,
size-selected monodisperse particles (V-TDMA approach) was typically bimodal;
one mode did not shrink upon heating (nonvolatile mode) and the other did
(volatile mode). VFR for the size-selected particles was estimated as
(Dp,heated)3/(Dp,ambient)3,
where Dp,ambient is the mode diameter of the selected
monodisperse particles at ambient temperature and Dp,heated is the mode diameter of the volatile mode after
heating at a particular temperature. Therefore, estimated VFR for the
size-selected particles excludes the nonvolatile
population. Empirical particle loss correction factors (Saha et al.,
2015), estimated as a function of TD operating conditions (T, Rt),
were applied to the VFR from integrated SMPS volume and MFR from ACSM
data. Because VFR for size-selected particles was calculated from the
change in mode diameter, particle loss correction factors are not
required in this calculation.
SP2 data were processed using the PSI (Paul Scherrer Institute) SP2 Toolkit.
The rBC-containing particles are treated as an rBC core coated by a shell of
other material. The size distribution of rBC cores was derived based on the
mass equivalent diameter (MED) of an rBC core assuming a density of
1.8 g cm-3. The delay time
between the peak of the incandescence and scattering signals is an indicator
of the coating thickness (mixing state; Moteki and Kondo, 2007) and was used
to determine the number fraction of “thinly coated” and “thickly coated”
rBC particles (McMeeking et al., 2011a; Shiraiwa et al., 2007; Subramanian
et al., 2010).
Parameterizing volatility
An evaporation mass transfer kinetics model (Lee et al., 2011) was applied to
infer particle volatility distributions by fitting TD data. The volatility
distribution extraction framework used here is similar to that described in
Saha et al. (2015). The resulting fit empirically describes the particle
volatility distribution using a volatility basis set (VBS) framework (Donahue
et al., 2006, 2012), in which the material is lumped over a logarithmically
spaced set of C∗ (effective saturation concentration) bins at
a reference temperature of 25 ∘C. A set of fi describes the
distribution of semi-volatile species (particle + gas phase) in selected
C∗ bins under a gas–particle equilibrium and is usually known as
a volatility distribution. A six-bin
log10VBS with a C∗ bin range of 10-4 to
101 µgm-3 at 25 ∘C was selected to describe
the particle volatility distribution empirically. Before the TD inlet, an
initial gas–particle equilibrium at ambient temperature (summer
30 ∘C, winter 5 ∘C) and campaign-average aerosol mass
loading (COA ∼5µgm-3) were assumed. The
Clausius–Clapeyron equation was applied to calculate temperature-dependent
C∗ (Saha et al., 2015).
The mass transfer kinetics model tracks particle- and gas-phase
concentrations of the surrogate species (represented by C∗ bins) as
they pass through the TD. The TD-derived volatility distributions from
kinetics model fits are sensitive to assumptions of the enthalpy of
vaporization (ΔHvap) and evaporation coefficient
(γe); these values are generally unknown a priori (Cappa and
Jimenez, 2010) and γe is often assumed to be in unity in
fitting TD data (Grieshop et al., 2009; Li et al., 2016). However, recent
studies reported γe values between 0.01 and 1 for different
aerosol systems (Cappa and Jimenez, 2010; Saha et al., 2017; Saha and
Grieshop, 2016; Saleh et al., 2013). Similarly, in the literature, different
ranges of ΔHvap values are reported for different aerosol
systems (Epstein et al., 2010; May et al., 2013c; Ranjan et al., 2012). TD
data collected at varying T and Rt provide additional constraints on
feasible γe and ΔHvap values (Saha et al.,
2015, 2017; Saha and Grieshop, 2016). TD data over a wide range in (T, Rt)
space were collected during the I-40 summer campaign at the near-road trailer
and are shown in Fig. S2a–d. Following Saha et al. (2015, 2017), we used
this data set to optimize a set of γe and ΔHvap values that best explain the evaporation observed in
near-highway aerosols (see Fig. S2e for details). A γe value
of 0.25 and ΔHvap of 100 kjmol-1 provided the
overall best fit for this data set. We adopted these estimated
γe and ΔHvap values for the near-highway
aerosol system for further fitting of TD data from different distances from
the highway across all seasons and sizes. Saha et al. (2017) reported similar
γe and ΔHvap values for ambient TD data
from two sites in the southeastern US under diverse conditions. Saleh
et al. (2012) derived a γe value of 0.28–0.46 for ambient
aerosols in Lebanon. Therefore, given the consistency in reported
γe and ΔHvap values across diverse
settings (Saha et al., 2017; Saleh et al., 2012), it is reasonable to use
constant values for further fitting of TD data from the same site under
different conditions. Other inputs to the mass transfer model include
diffusion coefficient (D), surface tension (σ), molecular weight
(MW), and density (ρ); the assumed values generally have a smaller
influence on modeled evaporation in TDs (Cappa and Jimenez, 2010; Saha
et al., 2015) and are approximated from the literature (Table S1 in the
Supplement).
Results and discussion
Observed evaporations in TD with downwind distance
Figure 1 shows the measured VFR at 60 ∘C as a function of distance
from the highway. The particle volume fraction that evaporates at low and
moderate TD temperature (e.g., VFR at 60 ∘C) consists of semi-volatile species, presumably OA. VFR
data are shown for different monodisperse particle sizes (e.g., 25, 50, 100,
250 nm) and for the integrated volume of polydisperse distributions.
Results shows that the evaporation observed in a TD at 60 ∘C
decreases with downwind distance during transects in both seasons, which
suggests a reduction in the relative abundance of the semi-volatile fraction
in particles with distance. This reduction is especially pronounced over the
ultrafine particle range (<100 nm). Two plausible reasons could
contribute to this observation. First, a fraction of semi-volatile species in
vehicle-emitted fresh particles may be evaporating during transport due to
dilution-driven processes (Choi and Paulson, 2016; Robinson et al., 2007;
Shrivastava et al., 2006). Second, since the concentration of vehicle-emitted
particles decreases rapidly with distance from the highway, the relative
proportion of background particles in the sampled aerosol
(vehicle-emitted + background) increases with distance. If one assumes
that background particles are less volatile than vehicle-emitted fresh
particles, the relative abundance of the less volatile material in the
sampled aerosols will increase with distance. The influence of each factor
cannot be isolated directly from TD measurements. However, the particle size
distributions measured at background (upwind) and downwind locations from the
highway (Fig. S3) indicate that vehicle-emitted fresh particles are dominated
by ultrafine particles (<100 nm), while background particles are
predominantly in a relatively larger mode. When polydisperse particles
(10–400 nm) were heated at a moderate TD temperature
(60 ∘C), the changes in the larger size range (>100 nm)
were observed to be minimal (Fig. S4). Larger particles also do not show
significant downwind gradients in evaporation upon heating at 60 ∘C
(Fig. S4). Therefore, the observed downwind decrease in the evaporation of
ultrafine particles at 60 ∘C is likely more influenced by the
dilution-driven losses of semi-volatile species during downwind transport. It
should be noted here that other processes, including coagulation, may
influence the evolution of near-road aerosol size distribution. However,
Zhang et al. (2004) reported that after the initial stage of dilution
(tailpipe to on-road), the second phase (road to ambient) of aerosol size
distribution evolution is dominated by condensation and dilution, while
coagulation and deposition play minor roles. Therefore, we do not expect that
coagulation is a dominant process in altering the physiochemical properties
of transported traffic particles within the near-road domain
(10–200 m from the highway edge) where we conducted our
measurements.
Campaign-average downwind evolution of volume fraction
remaining (VFR) of near-road particles after heating at
60 ∘C in a TD (Rt =30 s) during (a)
summer and (b) winter. VFR of size-selected particles
(e.g., 25, 50, 100, 250 nm) obtained from V-TDMA
measurements. VFR of PM0.1 and PM0.1-0.4
estimated from integrated SMPS volume between 10 and 100 nm and
between
100 and 400 nm, respectively. OA MFR was measured using
a TD–ACSM system (Rt =30 s) in the near-road trailer. The
shaded area represents the interquartile range of all measurements for
each season.
The general trends in evaporation at 60 ∘C observed as
a function of downwind distance were consistent between summer and
winter (Fig. 1). However, the evaporation observed in winter was
slightly greater than that in summer, specifically closer to the
highway and for smaller particles. This observation is consistent
with that of Kuhn et al. (2005), who reported greater evaporation of
near-road particles in winter at a particular TD temperature. Two
possible factors may contribute to this inter-seasonal
difference. First, the initial partitioning of SVOCs entering the TD
is different; at colder temperatures, a higher fraction of
semi-volatile materials is expected to partition into the
particle phase. An analysis of temperature effects on the partitioning
of semi-volatile materials from vehicular emissions (see Fig. S5)
indicates that while 40–70 % of semi-volatile emissions reside in
the particle phase under typical winter conditions
(0–10 ∘C), only 10–20 % do so under summer conditions
(20–30 ∘C). This analysis used the gasoline POA volatility
distribution from May et al. (2013a) and ΔHvap
from Ranjan et al. (2012) and considered a range of OA concentrations
for a typical roadside environment (e.g., 0.5 to
5 µgm-3). Second, the difference could be due
inter-seasonal differences in emission properties (the volatility of
emissions) and atmospheric dilution. The effect of these two effects
cannot be isolated directly from TD observations, but the application of
an evaporation kinetics model can disentangle them to some extent. For
example, during modeling, initial gas–particle equilibrium at ambient
temperatures (winter ∼5 ∘C, summer ∼30 ∘C) was assumed before the TD inlet, which will account
for the ambient temperature effect on initial SVOC partitioning.
Therefore, inter-seasonal differences in the volatility of emissions (if
any) should be reflected in the resulting fitted volatility
distributions; these modeling results are discussed in Sect. 3.3.
Figure 2 shows the evaporation observed at a higher TD temperature
(180 ∘C). VFR of PM0.4 (integrated volume between 10 and
400 nm) at 180 ∘C decreases with downwind distance. The
particle volume fraction that does not evaporate at 180 ∘C will
consist of BC, other refractory materials (e.g., metals, crustal materials),
and/or extremely low-volatility organics (ELVOCs; C∗<10-3 µgm-3; Donahue et al., 2012). ELVOCs in the
atmosphere are formed from multiple sources and chemical processes (Ehn
et al., 2014; Jokinen et al., 2015). Organic mass fraction remaining (OA MFR)
at 180 ∘C measured in the roadside trailer (∼10–20 %)
using the TD–ACSM likely provides an approximate estimate of ELVOCs
(shown with green circles in Fig. 2a and b). Similar values were measured
during ambient TD measurements in urban background and rural sites in the
southeastern US (Saha et al., 2017). Recent laboratory-derived POA volatility
distributions suggest that the presence of ELVOCs in fresh traffic-emitted
POA may not be significant (May et al., 2013a, b). Therefore, as
a first-order approximation, ELVOCs measured in the near-highway environment
are likely dominated by regional background aerosol, and thus a gradient
downwind of the roadway is not expected. On the other hand, traffic emissions
are a major contributor of BC in near-highway environments (Baldauf et al.,
2008; Bond et al., 2013; DeWitt et al., 2015) and a rapid downwind decay of
BC concentrations was observed in our site (Saha et al., 2018), which is
consistent with past studies (Karner et al., 2010).
Campaign-average downwind evolution of volume fraction
remaining (VFR) of PM0.4 (integrated SMPS volume over
10–400 nm) at 180 ∘C in a TD (Rt =30 s)
and black carbon (BC) fraction in PM0.4 during
(a) summer and (b) winter. Points are the mean and
the shaded area represents the interquartile range. OA MFR was measured
using a TD–ACSM system (Rt =30 s) only in the near-road
trailer. (c) Correlation between the BC fraction and VFR of
PM0.4 (at 180 ∘C) at various downwind distances
after subtracting OA MFR (at 180 ∘C) measured at
10 m. (d) Comparison of SMPS-measured thermodenuded
size distribution at 180 ∘C and SP2-measured BC size
distribution.
The downwind gradients of VFR of PM0.4 at 180 ∘C
correlate well with that of BC (Fig. 2a and b). A less sharp decay of
BC during winter was also consistent with the gradient of VFR of
PM0.4 at 180 ∘C in winter. Figure 2c shows
a scatter plot of the BC fraction in PM vs. VFR of PM0.4
(at 180 ∘C) after subtracting OA MFR (at 180 ∘C)
measured at 10 m for the winter data set; a similar plot for
the summer data set is shown in Fig. S6. These correlation analyses
indicate that the observed downwind evolution of VFR at
180 ∘C is likely dictated by the BC component of the
aerosol. The BC fraction in this analysis was estimated as the ratio
of the PAX-measured BC to PM mass concentration from integrated
volume-weighted SMPS size distributions with an estimated effective
density of 1.5 gcm-3. See Fig. S7 for details on the
estimation of effective density and a comparison of submicron mass
concentrations measured by SMPS and ACSM+PAX.
The diurnal profile of SP2-measured BC size distribution, shown in Fig. S8,
indicates that BC is strongly correlated with the diurnal profile of traffic
volume, indicating that vehicles are the major source of BC at this
near-highway site. Figure 2d explores the contribution of BC to the
thermodenuded SMPS size distribution at 180 ∘C. In Fig. 2d, to
directly compare with the volume-weighted SMPS distribution, the
mass-weighted BC size distribution was converted to a volume-weighted
distribution by assuming a BC density of 1.8 gcm-3. The BC
distribution accounts for approximately 35 % of the area under the
thermodenuded particle size distribution at 180 ∘C (Fig. 2d). The
remaining approximately 65 % of material should consist of different
low-volatility species (e.g., ELVOCs and others). This is broadly consistent
with the measured OA MFR at 180 ∘C at the roadside trailer, which
explained ∼50 % of measured VFR at 180 ∘C at that location
(Fig. 2a and b). Therefore, by combining measurements from different
instruments and approaches, the analysis summarized in Fig. 2 indicates that
in a near-road environment, denuded particle volume at very high temperature
(at 180 ∘C) can be approximated as ∼ ELVOCs + BC, with
a spatial distribution that is dictated by that of BC.
Mixing state of near-highway particles
Figure 3 examines the mixing state of near-highway particles using
V-TDMA data. The heated size distributions of a size-selected
monodisperse (at ambient temperature) aerosol at different TD
temperatures are referred to as volatility spectra.
Campaign-average V-TDMA volatility spectra of (a) 100 nm, (b) 50 nm, and (c) 25 nm
particles measured 10 m from the highway in summer. Figure S9 shows similar plots for the winter data set.
Figure 3 shows the measured average volatility spectra of 25, 50, and
100 nm particles collected 10 m from the highway
during summer; similar winter observations are shown in
Fig. S9. Heated monodisperse particles yield a bimodal size
distribution; one mode (less volatile; LV mode) shows almost no change
from its original diameter with heating, and the other mode (more
volatile; MV mode) shrinks substantially with heating. Similar bimodal
distributions have been observed in previous near-road studies (Biswas
et al., 2007; Kuhn et al., 2005; Tiitta et al., 2010). The general
trend was found to be consistent across seasons.
A large fraction of LV-mode particles is expected to be fresh soot
from traffic emissions. Figure 3 suggests that LV-mode particles are
externally mixed (e.g., soot and OA exist in different particles)
because if they were internally mixed with semi-volatile organics or
others compounds (i.e., were coated), the coating material would
evaporate with heating and a substantial diameter reduction would be
observed. Several studies have shown that these LV-mode particles are
less hygroscopic using a V-TDMA coupled with an H-TDMA system (Kuwata
et al., 2007; Tiitta et al., 2010). The presence of externally mixed
LV particles was observed for all sizes studied (25, 50, 100, and
250 nm). However, the LV mode was relatively less pronounced
for smaller sizes (e.g., 25 nm) compared to larger particles
(e.g., 100 nm). The SP2-measured BC number size distribution
peaked around 100–130 nm (see Figs. 2d and S8c),
which is consistent with this observation. Kuhn et al. (2005) and Biswas
et al. (2007) also reported that the less volatile and nonvolatile fraction of
near-road aerosols increased with size within the size range studied
(20–120 nm) in near-road V-TDMA measurements in California.
Figure 4 explores the mixing state of near-highway BC particles using
the SP2 lag-time approach (Moteki and Kondo, 2007; Schwarz et al.,
2006; Shiraiwa et al., 2007; Subramanian et al., 2010). The delay time
between the occurrence of scattering and incandescence peaks observed
in the SP2 can be used as an indicator of relative coating thickness
(Δτ=τincandescence-τscattering= time to “boil off” coating; McMeeking et al., 2011a; Moteki and
Kondo, 2007). Figure 4 shows the frequency distributions (histograms)
of delay time (Δτ). Following McMeeking et al. (2011a), the
entire ensemble of refractory-BC-containing particles with scattering
responses within the detection range was considered in this
analysis. Measurements are stratified by wind direction to separate
those measured during wind events coming off the highway
(southwesterly; 225±45∘) to the monitoring site and the
opposite wind direction (northeasterly; 45±45∘). Two
distinct peaks near Δτ∼0.5 and ∼3.5 µs appear in the Δτ frequency
distribution. We use this to classify BC particles into two types
using a threshold Δτ of 2 µs: thinly coated BC
(Δτ<2µs) and thickly coated BC (Δτ>2µs). The threshold criterion is based on the observed
minimum in the bimodal frequency distribution of Δτ
(McMeeking et al., 2011a; Moteki and Kondo, 2007).
Figure 4 shows that a large fraction (up to 80 %) of rBC-containing particles
at this near-highway site are thinly coated
(externally mixed) and are likely fresh soot particles from traffic
emissions. The observed relative proportion of thinly coated (fresh)
particles increases when the wind comes off the highway to the
monitoring station (southwesterly wind; see Fig. 4), suggesting that
the local source (I-40 traffic) was the main contributor to this
fraction. Using the data collected with the wind coming off the
highway, Fig. S10 shows the linkage among the diurnal variation of
Δτ frequency distributions, BC size distributions, and
thermodenuded SMPS size distribution at 180 ∘C. The
thinly coated fraction was found to be slightly higher in the midday
and morning compared to the evening. This trend correlates with the
diurnal variation of the heavy-duty vehicle (HDV) fraction (indicated in
inset of Fig. S10), suggesting that HDVs are the dominant contributor
to the observed fresh (thinly coated) BC fraction. The thickly coated
fraction is likely contributed by regional background aged BC
particles. However, approximately 10 % of fresh BC from vehicular
emissions could be thickly coated as reported by Willis
et al. (2016). With an opposite wind (northeasterly), a minimum direct
influence from I-40 traffic is expected at our monitoring
location. The observed thickly coated fraction at that wind condition
went up to 41 %. This range of values is consistent with past
studies. For example, a thickly coated rBC fraction of approximately
30–40 % is reported in previous measurements in diverse urban
environments (McMeeking et al., 2011a; Shiraiwa et al., 2007;
Subramanian et al., 2010).
Campaign-average frequency distributions (histograms) of SP2
lag time (Δτ) for refractory-BC-containing particles
measured during periods with winds from the highway (red) or from
the opposite direction (black). Measurements were collected at
a distance of 10 m from the highway in winter.
The substantial presence of thinly coated (fresh) rBC suggested by the
SP2 data (Fig. 4) is consistent with our independently measured V-TDMA
observations of externally mixed characteristics for LV-mode particles
(Figs. 3 and S9). These observations are also in agreement with
several recent studies that examined the mixing state of rBC from
traffic emissions using a range of techniques (China et al., 2014;
Kuwata et al., 2009; Liu et al., 2014; McMeeking et al., 2011b; Willis
et al., 2016). Willis et al. (2016) reported that approximately 90 % of
rBC mass resides in rBC-rich particles using soot photometer–aerosol mass spectrometer (SP-AMS) measurements of traffic emissions
in an urban setting, whereas the remaining 10 % were mixed with
hydrocarbon-like OA (HOA). China et al. (2014) reported that ∼72 % of soot particles from vehicle exhaust are barely or thinly
coated using a microscopic imaging technique. Traffic-dominated rBC
particles were reported to be uncoated or very thinly coated by
Laborde et al. (2013) and Liu et al. (2014) using SP2 measurements in
urban environments.
Figure 5 compares V-TDMA
measurements of 100 nm particles at different distances from the
highway to examine the evolution of the mixing state of particles with
downwind transport. The overall concentration of both LV- and MV-mode
particles rapidly decreases with distance due to dilution and mixing with
cleaner background air. However, LV-mode particles (e.g., BC) remain mostly
externally mixed at 220 m of downwind distance. This result indicates
that there is minimal change in the mixing state in traffic-emitted particles
between the near-road (∼10 m) and far-road (∼220 m) locations. Specifically, the proportion of internally vs.
externally mixed particles does not appear to change, nor is there evidence
of substantial coating on externally mixed “nonvolatile” particles. The
evolution of BC mixing state is typically observed in the atmosphere with
photochemical aging; externally mixed BC particles (thinly coated) become
progressively internally mixed (thickly coated) via the formation of
condensable vapors via photochemical processes followed by condensation on
BC. Timescales on the order of 1 h are typically required to observe
a significant change in BC coating (Adachi and Buseck, 2013; McMeeking
et al., 2011a; Shiraiwa et al., 2007; Subramanian et al., 2010). Since the
transport times of particles at 220 m downwind from the highway are
on the order of a few seconds to minutes, it is not surprising to observe no
significant change in the mixing state of traffic-emitted particles (e.g.,
BC) within this short distance.
Similar to Fig. 3 showing average volatility spectra of
100 nm particles at 10 and 220 m downwind of the
highway. Measurements were collected during transect runs in
summer.
Inferred volatility distributions from TD data
This section discusses TD-derived volatility distributions at different
distances from the highway to provide insight into the evolution of the
volatility of traffic-emitted particles and provide parameterizations to
explain the phase partitioning of near-road particles in similar
microenvironments and laboratory observations. For this, we focus on the
measured evaporation of ultrafine particles (25, 50, and 100 nm) at
10 and 220 m distances. Figure 6 shows measured and modeled
thermograms (plots of VFR vs. TD temperature) for TD measurements of varying
particle sizes collected at different distances and seasons. At a particular
TD temperature, smaller particles evaporate more than larger
particles (Fig. 6). Size or
composition (volatility distribution) may contribute to the differential
evaporation observed for different size particles (Saleh et al., 2011); both
factors were taken into account during evaporation kinetics modeling
following the framework described in Saha et al. (2015). The evaporation
kinetics model tracks changes in diameter as aerosols with prescribed
properties pass through the TD at a particular operating condition (T, Rt),
as described in Sect. 2.4; model VFR was estimated based on predicted change
in particle diameter. In our fitting, we solved for particle volatility
distributions (particle-phase distribution; {x}i) via least-squares
fitting of modeled and measured VFR. Fitted distributions are listed in
Table S2 and model lines in Fig. 6 are shown using these best-fit
distributions. Our fitting results (Fig. 6) show that at a particular
downwind point, a single volatility distribution can explain the observed
evaporations for different sized particles, suggesting that particles within
this size range have a consistent volatility distribution or chemical
signature. We also report fi distributions (Table 1) after converting
our TD-derived particle-phase distributions (xi) to total
(gas + particle) distributions (fi) under gas–particle equilibrium
conditions and assuming a typical near-road OA loading of ∼5 µgm-3 (see Sect. S3 in the Supplement for details).
Campaign-average measured (points) and modeled (line)
thermograms for different sized particles measured at 10 and
220 m downwind during summer (a–b) and winter
(c–d). Model lines are shown using the best-fitted
volatility distributions listed in Tables 1 and S2;
ΔHvap =100 KJmol-1 and
γe=0.25.
TD-derived particle volatility distributions (at 298 K)
at 10 and 220 m of downwind distance from the highway I-40 during summer and winter.
Laboratory-derived gasoline POA distribution by May et al. (2013a) is also
listed.
logC∗
TD-derived fi distributiona
Gasoline POAb
at 298 K
10 m (summer)
220 m (summer)
10 m (winter)
220 m (winter)
(May et al., 2013)
-4
0.07
0.10
0.18
0.28
-3
0.13
0.21
0.07
0.08
-2
0.16
0.20
0.14
0.20
0.14
-1
0.27
0.37
0.15
0.30
0.13
0
0.12
0.06
0.27
0.09
0.15
1
0.25
0.06
0.20
0.06
0.26
2
0.15
3
0.03
4
0.03
5
0.01
6
0.11
a TD-fitted particle-phase distributions (xi) with
γe=0.25 and ΔHvap = 100 KJmol-1 (reported in Table S2) are converted to total
(gas + particle) distribution (fi) under gas–particle equilibrium
conditions and assuming a total aerosol loading of ∼5 µgm-3 (conversion equations are given in S1,
Sect. S3).b Chromatographic analysis
Figure 7 shows simplified representations of particle volatility
distributions at roadside (10 m) and downwind (220 m)
locations across summer and winter seasons. In this figure,
distributions of particle-phase material are shown in two broad
volatility categories: extremely low + low volatility
(ELVOC+LVOC; C∗ bins ≤0.1 µgm-3) and semi-volatile (SVOC; C∗ bins ≥1 µgm-3; Donahue et al.,
2012). A laboratory-derived POA volatility distribution of gasoline
vehicle exhaust by May et al. (2013a; derived from chromatographic
analyses of filter samples) is also shown under a typical near-road
aerosol loading (COA=5 µgm-3). The
volatility distributions shown in Fig. 7 and also reported in Tables 1
and S2 are at a reference temperature of 25 ∘C, which allows
for a convenient side-by-side comparison across seasons and with previous
studies. The gasoline POA distribution by May et al. (2013a) places
∼45 % of OA in the SVOC bins under this condition. Our
TD-derived results show that the overall volatility of near-road particles
is lower than laboratory-derived POA distribution, varies across
seasons and decreases with distance. The extracted volatility
distributions of near-road particles are a mixture of traffic-emitted
POA and background particles. Therefore, it is not expected that the
overall volatility of near-road particles would be the same as that of
vehicle POA. In Sect. 3.4, we use our spatial measurements of
near-road aerosol (traffic + background) volatility to assess
how and whether this laboratory-derived POA distribution can be used to
represent the overall near-road volatility under real-world
conditions.
Comparison of volatility classification of near-road
particles measured at 10 and 220 m (this study) for
(a) summer and (b) winter at a reference
temperature of 25 ∘C. Distributions of particle-phase
material are shown using two broad volatility categories. Also
shown in both panels is the POA distribution from gasoline vehicle
exhaust by May et al. (2013a) under typical near-road aerosol
loading (COA∼5 µgm-3).
Figure 7 indicates that the overall volatility of near-road aerosols
decreases with distance from the highway in both seasons. For example, the
TD-derived distributions apportion approximately 20–30 and 10 % of
particle-phase mass as SVOC at 10 and 220 m, respectively, which is
consistent with the dilution-driven evaporation of SVOCs and/or mixing with
the background particles. When a volatility comparison is made at a common
temperature of 25 ∘C (Fig. 7), the particle volatility was found to
be slightly higher for the winter data set than summer, especially closer to
the highway. The extent of dilution and the temperature of dilution air
dictate the overall partitioning of semi-volatile emissions. Atmospheric
dilution was substantially lower in winter at our site (and generally) due to
more stable atmospheric conditions under colder weather (Saha et al., 2018).
Therefore, when comparing particle volatility at the same temperature, lower
dilution during winter likely explains the observed higher SVOC fraction.
The volatility comparison in Fig. 7 is shown at a reference temperature of
25 ∘C; actual partitioning will vary with ambient temperature. Under
a particular COA loading (atmospheric dilution), it is expected
that a higher fraction of semi-volatile material partitions into the particle
phase at colder temperatures. Figure S11 is an alternate display of the data in Fig. 7, showing the volatility comparison at
campaign-average ambient temperatures of ∼5 and ∼30 ∘C
in winter and summer, respectively. After accounting for seasonal temperature
difference, Fig. S11 indicates that the particle-phase SVOC fractions are
approximately 2.5 times higher in winter (SVOC: 45 %) than summer (SVOC: 18 %) at the roadside location (10 m). During winter,
a higher fraction of semi-volatile particles may form via homogeneous
nucleation during a rapid cooling of vehicle exhaust under a lower ambient
temperature (Kittelson et al., 2006). This fact was supported by our observed
threefold increase in ultrafine particle and HOA emission factors during
winter compared to summer, as discussed in Saha et al. (2018). This result
implies that human exposure to semi-volatile particles at a near-road
location could vary substantially across seasons and would be more extreme in
colder weather.
Evaluation of laboratory-derived POA distributions to
explain roadside partitioning
Particle mass concentrations measured next to a highway are a mixture of
a traffic contribution and background particles (roadside
PM = traffic + background). It can be reasonably hypothesized that
the volatility distribution of roadside particles at a particular downwind
location is a superposition of that from background particles and
traffic-contributed particles at that location (Eq. 1). One can test this
hypothesis if the volatility distributions of different populations of
particles (roadside, background, traffic) are known. Vehicle-emitted POA
volatility distributions have been derived in laboratory studies (May et al.,
2013a, b) of a relatively small number of vehicles in controlled
tests; they have also recently been
measured in a traffic tunnel study (Li et al., 2016). Here, we used our
spatial measurements of particle volatility distributions along with
a laboratory-derived POA distribution from May et al. (2013a) to assess our
ability to represent the volatility of POA from the overall traffic fleet in
a complex near-road microenvironment.
COA, roadside×xi,roadside=COA,
traffic×xi,traffic+COA, background×xi,background
COA (µgm-3) is the organic aerosol
(OA) mass concentration, and xi is the distribution of OA mass
concentrations at different volatility bins, following the volatility
basis set (VBS) approach (Donahue et al., 2006).
To test Eq. (1), we conducted an analysis using an example data set from
a morning transect on 12 June 2015 (summer), with the wind consistently
coming off the highway; the details of the analysis are given in the
Supplement, Sect. S4. Analysis results are shown in Fig. 8. The contribution
of traffic particles in roadside measurements was estimated as the difference
between concentrations measured at the roadside (downwind) and upwind
background location. Figure 8a shows the measured upwind (background) and
downwind OA mass
concentrations as a function of distances from the highway. It should be
noted here that approximate estimates of OA mass concentrations in Fig. 8a
are calculated from integrated volume from SMPS measurements and an estimated
effective density of 1.5 gcm-3 (Fig. S7) and subtracting the
contribution of BC (as a function of distance), nitrate, and sulfate aerosols
(measured by an ACSM at the near-road fixed-site trailer).
Comparison of measured roadside volatility distribution (at
10 m) with a reconstructed distribution using traffic and
background contributions. Analysis conducted using an example data
set from a transect measurement on 12 June 2015 (summer), with the
wind consistently coming off the highway. (a) Measured
upwind (background) and downwind concentrations of particle mass
loading as a function of distances from the highway. (b)
Comparison of measured roadside volatility distribution (at
10 m) with the reconstructed distribution using
laboratory-measured POA volatility distribution from May
et al. (2013a)
as representative of traffic particles and our measured volatility
distribution at our most downwind location (220 m) as
representative of background particles.
Figure 8b compares our measured roadside volatility distribution (at
10 m) with a distribution reconstructed from traffic and background
distributions. Distributions of particle-phase-only OA concentrations are
shown. In this analysis, the volatility distribution of POA emissions from
gasoline vehicle exhaust from May et al. (2013a; Table 1) is assumed to be
representative of the overall traffic-emitted OA. Since we did not measure
particle volatility at our upwind background site, we assume that the
volatility distribution measured at our farthest downwind location (220 m) is a representative
distribution for background particles. This is a reasonable approximation as
particle concentrations approach background levels within 200–300 m
from the highway (Saha et al., 2018). It should be noted that this
“background” OA contains a non-negligible (∼25 %) contribution
from traffic emissions (Fig. 8a), and so likely has a slightly greater
contribution from higher-volatility components than what would be measured in
an actual background location. Our measured distributions from the summer
campaign (Table 1) were used to be consistent with the OA concentration
measurements in this particular example. Overall, a good agreement was found
between the measured distribution at 10 m downwind and superimposed
distribution of background + traffic POA (Fig. 8b). In particular, the
contribution from more volatile materials (C∗=1 and
10 µgm-3) from traffic POA is required to explain the
greater contribution from these more volatile species at 10 m vs.
220 m from the roadway. Therefore, this analysis indicates that the
laboratory-derived volatility distribution from May et al. (2013a) can do
a reasonably good job in explaining the observed partitioning of vehicle
emissions in a complex near-road environment. In addition to the analysis in
Fig. 8b, a similar analysis is shown in Fig. S12 using the “coarse” tracer
m/z-based factor analysis approach to decompose OA mass spectra (Ng et al.,
2011) in which the hydrocarbon-like OA (HOA) factor is assumed to represent
traffic-sourced OA, and oxygenated OA (OOA) is assumed to represent background OA. The details of this analysis
are discussed in the Supplement, Sect. S5. While this simplified
“superposition” analysis suggests that our data are able to capture the
influences of near-road evolution on emissions, further measurements and
modeling work are needed to represent these dynamic processes.
Conclusions and implications
Field experiments were conducted across two seasons in an effort to
explore the evolution of the volatility and mixing state of near-road
particles within a few hundred meters downwind of a highway. The
spatial distributions of the volatility of near-road aerosols varied
with distance from the highway and season. The overall volatility of
near-road particles decreases with distance from the roadway. For
example, at a reference temperature of 25 ∘C, while
approximately 20–30 % of particle mass was classified as
semi-volatile (SVOCs; C∗≥1 µgm-3)
10 m from the roadway, only ∼10 % of particle mass
was attributed to semi-volatiles at 220 m. The decrease in the
semi-volatile fraction in the particle phase with downwind distance is
likely due to dilution-driven evaporation of SVOCs as fresh
vehicle-emitted particles are transported downwind and/or mix with
background particles. The relative abundance of semi-volatile material
in the particle phase increased during winter, especially closer to
the highway, reflecting the effect of temperature on semi-volatile
partitioning. The nonvolatile fraction in roadside aerosols appeared
to be mostly externally mixed, and their mixing state showed minimal
change within a few hundred meters from the highway.
This research has several important implications for the measurement and
modeling of emissions and exposure to ultrafine particles (UFPs) in
a near-road microenvironment and its regulation. First, the measured particle
number (PN) concentrations in near-road settings are dominated by UFPs. In
a companion paper (Saha et al., 2018), we showed that UFP number emission
factors are substantially higher and their dispersion is slower during
winter, indicating that human exposure to UFPs would be significantly higher
in colder conditions. This paper shows that a significant fraction of UFPs is
semi-volatile in nature, and hence a larger portion of semi-volatile
materials likely exists in the particle phase in colder conditions. Current
European vehicle particle number emission standards use measurements of
thermally treated exhaust that strips the semi-volatile particle components
to constrain variability among measurement approaches (Wang et al., 2017). As
a result, this regulatory measurement likely does not address the seasonally
and spatially varying real-world particle number concentrations and
compositions to which people are exposed. Several recent toxicology studies
(Biswas et al., 2009; Keebaugh et al., 2015) reported that the semi-volatile
species in traffic-sourced particles could be more toxic than less volatile
components. Therefore, our observed seasonal variation in UFP emission
factors and semi-volatile components in the particle phase suggest that human
exposure to UFPs and their toxicity in a near-road microenvironment could
vary with seasons and environmental conditions and would be more extreme in
colder weather. The elevated fraction of semi-volatile materials in roadside
particles and their potentially higher toxicity suggest that an equivalent
amount of exposure (concentration × duration) to roadside vs.
background particles could have significantly different health impacts.
However, the toxicity of different volatility and size classes of PM is not
well established in the current literature. For example, Cho et al. (2009)
reported no significant difference in the overall toxicity end points for PM
samples collected at 20 and 275 m from an interstate highway. Further
research is needed to better understand the toxicity and health impacts of
different volatility and size classes of PM from different sources and
environmental conditions. Second, our finding of externally mixed near-road
particles suggests that exposure to BC- and OA-containing particles could be
different across seasons. For example, OA-containing particles will be more
dynamic under changing ambient conditions. Environmental conditions
(temperature, atmospheric dilution) will influence the gas–particle
partitioning of SVOCs and thus exposure to condensed- vs. vapor-phase SVOC
under changing ambient conditions. On the other hand, exposure to BC would be
less influenced by changing ambient conditions. Finally, the volatility
distributions and mixing state characteristics of near-road particles derived
here can be used to examine the representativeness of laboratory-derived
results in a complex real-world scenario (as shown via an example in this
paper) and to improve the representation of traffic-sourced aerosols in air
quality, exposure assessment, and chemical transport models.