Introduction
Cyclic volatile methyl siloxanes (cVMSs) are present in a wide
range of personal care and cosmetic products (e.g., hair products, lotions,
antiperspirants, makeup, and sunscreens) as well as in sealers, cleaning
products, and silicone products (Wang et al., 2009; Horii and Kannan, 2008;
Dudzina et al., 2014; Lu et al., 2011; Capela et al., 2016). As high
production volume chemicals (> 1000 tyr-1 produced) (OECD
Environment Directorate, 2004), their environmental fate is an important
topic. The most prevalent cVMS species in personal care products is
decamethylcyclopentasiloxane (D5), although octamethylcyclotetrasiloxane
(D4) and dodecamethylcyclohexasiloxane (D6) are also emitted (Horii
and Kannan, 2008; Dudzina et al., 2014; Wang et al., 2009; Lu et al., 2011).
Atmospheric lifetimes (Atkinson, 1991) are approximately 5–10 days at
typical hydroxyl radical (OH) concentrations;
accordingly, long-range transport (Xu and Wania, 2013; Krogseth et
al., 2013a; McLachlan et al., 2010; Genualdi et al., 2011; MacLeod et
al., 2011) of cVMS occurs. The environmental fate and transport of cVMS has
been widely studied due to concerns of potential persistent, bioaccumulative,
and toxic (PBT) behavior in the environment; however, assessing the
environmental risk has been a subject of debate due to unique cVMS
properties, evolving scientific information on properties and presence in the
environment, and different interpretations of risk assessment information.
The parent cyclic siloxanes have been the subject of a number of regulatory
screenings including those by Canada (Environment Canada and Health Canada,
2008a, b, c), the UK (Brooke et al., 2009a, b, c), and the EU (ECHA, 2015);
comprehensive review articles (Rucker and Kummerer, 2015; Wang et al., 2013)
and recent environmental fate studies (Mackay et al., 2015; Gobas et
al., 2015a, b; Fairbrother et al., 2015) are also relevant. The conceptual
model of cVMS fate and transport is summarized as emission (mainly to the
atmosphere) in population centers as a result of personal care product use
(Mackay et al., 2015; Montemayor et al., 2013; Gouin et al., 2013), followed
by atmospheric transport and reaction by OH (Xu and Wania, 2013).
Emissions and concentrations are highly dependent on population, with urban
locations (Yucuis et al., 2013; Genualdi et al., 2011; Krogseth et
al., 2013b; Buser et al., 2013a; Companioni-Damas et al., 2014; Ahrens et
al., 2014) and indoor environments (Tang et al., 2015; Yucuis et al., 2013;
Companioni-Damas et al., 2014; Pieri et al., 2013; Tri Manh and Kannan, 2015)
having much higher concentrations than remote locations. As this work shows,
the population-dependent personal care product emissions are best validated
for D5, and the importance of other emission types, as well as the
variation in this by cVMS compound, is uncertain.
Substantial insights regarding cVMS fate, transport, and expected
concentrations have come from atmospheric modeling studies. McLachlan et
al. (2010) simulated atmospheric D5 concentrations using the Danish
Eulerian Hemispheric Model (DEHM), a hemispheric-scale 3-D atmospheric
chemistry and transport model. MacLeod et al. (2011) simulated D5
globally using the BErkeley-TRent Global Model (BETR Global), a multimedia
mass balance model at 15∘ horizontal resolution. Global zonally
averaged modeling using the multimedia GloboPop model has also been performed
(Xu and Wania, 2013; Wania, 2003). Emission estimates have been
back-calculated from measured atmospheric concentrations using a multimedia
model (Buser et al., 2013a, 2014), and compartmental model studies focusing
on specific partitioning or loss processes have also been conducted (Navea et
al., 2011; Whelan et al., 2004). These modeling studies have permitted
extension, both in time and space, beyond the sparse measurement dataset and
testing of key model processes (emissions, fate, and transport) versus
modeled concentrations. Latitudinal gradients, urban–rural–remote gradients,
seasonal patterns, sensitivity to processes and parameterizations, and diel
cycles have been explored using these models. Modeling studies have shown the
large-scale concentration patterns with OH as a dominant loss process, and
quantified the importance of the atmosphere (relative to sediment and surface
waters) for fate and transport. Seasonal and latitudinal trends can be
explained in part by availability of OH. Models estimate D5
concentrations of 50 ngm-3 and higher in well-mixed urban air
(Navea et al., 2011), while 0.04–9 ngm-3 is reported from
models for remote locations (Krogseth et al., 2013a).
Atmospheric measurements of cyclic siloxanes have been performed in ambient
air (McLachlan et al., 2010; Genualdi et al., 2011; Yucuis et al., 2013;
Ahrens et al., 2014; Kierkegaard and McLachlan, 2013; Krogseth et
al., 2013b, a; Buser et al., 2013a; Companioni-Damas et al., 2014). Higher
concentration microenvironments have also been surveyed through measurement
(wastewater treatment plants,
landfills, and indoor air) (Krogseth et al., 2013b; Cheng et al., 2011; Wang
et al., 2001; Pieri et al., 2013; Yucuis et al., 2013; Tri Manh and Kannan,
2015; Companioni-Damas et al., 2014; Tang et al., 2015). In several
instances, model–measurement comparison has been conducted and, to a large
extent, confirmed our understanding of emissions, fate and transport.
Generally good agreement for rural and remote locations have been observed
(McLachlan et al., 2010; Krogseth et al., 2013a; MacLeod et al., 2011; Navea
et al., 2011; Xu and Wania, 2013; Genualdi et al., 2011), while urban areas
tend to be underpredicted (Genualdi et al., 2011; Yucuis et al., 2013; Navea
et al., 2011). Measured seasonal concentration variations have been
replicated for sites in rural Sweden and the remote Arctic. However, it was
noted that the DEHM tended to have better agreement during late spring
(McLachlan et al., 2010) and late summer (Krogseth et al., 2013a) compared to
winter. The BETR model conversely had better agreement during winter compared
to late spring for the same rural Sweden site (MacLeod et al., 2011).
The majority of modeling and chamber study investigations, and all of the
ambient measurements for cVMS, have focused on the emitted or “parent” cVMS
compounds (i.e., D4, D5, and D6). The identity and fate of the
cVMS oxidation products has received less scrutiny until recently, compared
to the parent compounds. Sommerlade et al. (1993) reacted D4 with OH in
an environmental chamber and identified multiple reaction products by GC-MS,
with the single OH substituted silanol (D3TOH) as the most prevalent
resolved species, with species identification confirmed by matching retention
time and mass spectra compared to synthesized D3TOH. Because of the
method of collection (the product was collected from rinsing the
environmental chamber walls with solvent) confirmation of secondary
aerosol production from D4 oxidation was not possible from
Sommerlade et al. (1993). Chandramouli and Kamens (2001) reacted D5 in a
smog chamber, with separate analysis of gas and aerosol products, confirming
the presence of D4TOH in the GS/MS analysis of the condensed aerosol
phase.
Wu and Johnston (2016) conducted more exhaustive characterization of aerosols
from photooxidation of D5, using high-performance mass spectrometry,
revealing both monomeric and dimeric oxidation products, with molar masses up
to 870. Oxidation progressed not only by substitution of a methyl group with
OH (e.g., leading to D4TOH) but also by substitution with CH2OH;
linkages between Si-O rings to form dimers were through O, CH2, and
CH2CH2 linkage groups.
Aerosols containing Si and likely from photooxidation of gaseous precursors
have been recently identified in multiple locations in the US using laser
ablation particle mass spectrometry of ultrafine particles (Bzdek et
al., 2014). Bzdek et al. (2014) contend that a photooxidation source is most
consistent with observations because of the times of day of occurrence, short
atmospheric lifetime of the particle size in question (10–30 nm),
lack of wind direction dependence that would be expected from primary
sources, ubiquity across disparate measurement sites, and similarity in
temporal evolution of nanoaerosol Si to other species with known
photochemical sources. Except for the reports of the concentrations of
ambient oxidized cVMS in Bzdek et al. (2014), there are no ambient
measurements or model-based estimates of the potential aerosol concentrations
from cVMS oxidation. This work begins to address that gap by simulating the
gas-phase oxidation product concentrations using the atmospheric chemistry
and transport model Community Multiscale Air Quality (CMAQ). As experimental
determinations of aerosol yield become available, the simulations can be
updated to include secondary organosilicon aerosol concentrations.
This work builds on the limited information available on the oxidation
products. Properties relevant to fate and transport (e.g., Henry's law
coefficient) have been predicted in this work and in others based on
structure activity relationships (Buser et al., 2013b; Whelan et al., 2004).
Latimer et al. (1998) measured equilibrium gas–particle partitioning of
D5 and D4TOH on diesel, wood, coal soot, and Arizona fine dust
aerosols. Whelan et al. (2004) performed equilibrium air–particle and
air–cloud droplet partitioning modeling of multiple substituted OH silanols.
More extensive information is available about the gas–particle partitioning
(Latimer et al., 1998; Tri Manh and Kannan, 2015; Tri Manh et al., 2015; Kim
and Xu, 2016) and aerosol-phase reactions (Navea et al., 2011, 2009a, b) of
the precursor compounds, but these confirm that the gas-phase oxidation and
transport of the parent compounds are substantially more important than the
heterogeneous oxidation pathways and thermodynamic partitioning of the parent
compounds onto ambient aerosols.
In this work, atmospheric gas-phase concentrations of D4, D5,
D6, and its oxidization products are modeled comprehensively using the
CMAQ chemical transport model. The purpose of the model-based investigation
is twofold. First, it enables the highest resolution (36 km)
simulation to date of the parent compound over the US; the model simulates
vertical profiles, urban-to-rural transitions, and the dependence of these on
factors such as season and mixed layer height. Second, this paper reports,
for the first time in detail, concentrations of the cVMS oxidation products.
Some fraction of products is likely distributed into the aerosol phase, thus
contributing to aerosol Si concentrations on regional and global scales. We
expand upon the modeling first presented in Bzdek et al. (2014), but with
improved emission estimates, inclusion of wet and dry deposition, and
incorporation of season-dependent boundary conditions.
Methods
Cyclic siloxanes and oxidized cyclic siloxanes were modeled with the 3-D
atmospheric chemical transport model CMAQ (Byun and Schere, 2006), modified
to include cyclic siloxane species. CMAQ version 4.7.1 was used and the
modeling domain covered the contiguous US, northern Mexico, and southern
Canada. The domain had 14 vertical layers and a horizontal resolution of
36 km. Four 1-month simulations were performed for January, April,
July, and October to characterize seasonal variability in cyclic siloxane
atmospheric concentrations. A spin-up period of 7 days was used to minimize
the influence of zero initial conditions for the cyclic siloxanes species.
Meteorology was from the Weather Research and Forecasting (WRF) model
version 3.1.1 for the meteorological year of 2004. WRF was run with time
steps of 120 s, 30 vertical layers, the Morrison double-moment
microphysics scheme, the RRTMG longwave and shortwave physics scheme, the
Pleim–Xiu surface layer, the Pleim–Xiu land surface model with two soil
layers, and the ACM2 planetary boundary layer (PBL) scheme. Reanalysis
nudging using North American Regional Reanalysis (NARR) data was performed
every 3 h.
The cyclic siloxanes were added to the CMAQ model by adding D4, D5,
D6, and the oxidized species, o-D4, o-D5, and o-D6 to the
cb05cl_ae5_aq mechanism. Rate constants for the parent cyclic siloxanes
reacting with OH were used from Atkinson (1991), where D4 and D5 were
determined experimentally and D6 estimated from the reported D5 per
methyl rate. The rate constants used were 1.01×10-12, 1.55×10-12, and 1.92×10-12 cm3molecule-1s-1 for
D4, D5, and D6, respectively. Reactions of the oxidation
products are not included in the model. In part, this is because information
is limited on the kinetics of further oxidation and on the changes that this
would cause for fate, transport, and properties. Whelan et al. (2004) modeled
subsequent oxidation reactions, and chamber-based oxidation studies observe
multiple substitution products likely due to multiple substitution reactions
or auto-oxidation by internal rearrangement (Wu and Johnston, 2016). In the
model, only the first oxidation is computed. The oxidation products are
denoted o-D4, o-D5, and o-D6, and for calculation of physical
properties relevant to deposition, the single OH substitution is assumed.
Wet and dry deposition of the primary species (e.g., D4, D5) were
added to the model using Henry's law coefficients (Xu and Kropscott, 2012).
For the oxidized cyclic siloxanes, physicochemical parameters were estimated
using EPI Suite HENRYWIN v3.20 (EPA, 2012) for the single OH substitution of
one methyl group of the parent cyclic siloxane (e.g., D3TOH,
D4TOH). Deposition-related inputs necessary for the CMAQ deposition
routine included Henry's law coefficients, mass diffusivities, reactivity,
and mesophyll resistance. CMAQ calculates dry deposition as a deposition
velocity (dependent on mixing/turbulence, molecular properties, and land
type) multiplied by the lowest model layer concentration (Byun et al., 1999),
and wet deposition using Henry's law coefficients and precipitation rates
(Roselle and Binkowski, 1999). Dry deposition therefore treats the surface as
an infinite sink, which is consistent with other species in the model. The
mass diffusivity values were calculated by the Fuller, Schettler, and
Giddings (FSG) method (Lyman et al., 1982), where molar volume was estimated
based on element contributions. Sulfur molar volume contribution values were
substituted for silicon atoms since silicon values were not available.
Calculated mass diffusivity values, as estimated by the FSG method were
0.0512 (D4), 0.0454 (D5), 0.0411 (D6), 0.0527 (o-D4),
0.0464 (o-D5), and 0.0419 cm2s-1 (o-D6). The
reactivity parameter was set at 2.0 in common with methanol and other species
of limited reactivity. The mesophyll resistance, which is used to account for
uptake by plants, was set to zero (only a few species had mesophyll
resistances specified in CMAQ, such as NO2, NO, CO, and Hg gas).
Molecular weight for the oxidized cyclic siloxanes assumed the single
substituted OH species. The molecular weight of D6 and o-D6
exceeded the limit of the CMAQ dry deposition routine m3dry
(390 gmol-1) and values in excess of the limit were
set to the limit. The impact of this substitution is expected to be
minimal, since it is a minor adjustment to a minor pathway; dry deposition of
cVMS is relatively small (McLachlan et al., 2010; Xu and Wania, 2013; Whelan
et al., 2004).
Emissions of cyclic siloxanes were distributed according to gridded
population for the US, Canada, and Mexico, while Caribbean countries were
neglected. The US, Canadian, and Mexican per capita emission rates of D5
provided by personal communication (R. van Egmond, personal communication,
2013) and previously used and reported in McLachlan et al. (2010) were
adopted for this study. Briefly, as reported in McLachlan et al. (2010),
D5 emission rates were derived from country-specific market share based
on antiperspirant sales data combined with D5 consumption data from
antiperspirant plus 10 % to account for other sources. A table of many
available cVMS emissions rates from multiple methods are represented in
Table S2, and a wide variation exists. To calculate D4 and D6
emission rates, ambient measurements from Chicago (Yucuis et al., 2013) were
used to estimate emission ratios relative to D5. Chicago was chosen
since it is a major urban area and atmospheric measurements should be most
fresh and therefore the best representation of emission rates. However, since
OH reactivity (and other fate and transport properties) vary from compound to
compound, ambient measurements of compound ratios will not match emission
ratios, except in air parcels that are so fresh as to have seen no oxidation.
To check for the influence of air mass aging in the measurements of Yucuis et
al. (2013), the ratio NOx / NOy was used as a marker
of air mass age (Slowik et al., 2011). This ratio is high in fresh emissions,
and decreases as the air mass is oxidized. Hourly measurements of
NOx and NOy from Northbrook, Illinois (EPA), were
inspected during the time period of the Chicago sampling in Yucuis et
al. (2013). Using the NOx / NOy photochemical age
estimate, we calculated that emitted ratios vs. ambient ratios likely
differed by less than 1 % (see Supplement). The Chicago cyclic siloxane
measurements were therefore used as emission ratios without photochemical age
correction. The resulting emission ratios, 0.243 and 0.0451 for
D4 / D5 and D6 / D5, respectively, were
multiplied by the D5 emission rate to estimate the D4 and D6
emission rates. The resulting D4, D5, and D6 country emission
rates, which were constant for all simulations, were multiplied by gridded
population and merged with year 2004 emissions generated by the Sparse Matrix
Operator Kernel Emissions (SMOKE) model version 2.5. Population data were
from census-derived population surrogates from EPA 2011 v6.0 Air Emissions
Modeling Platform and are based on
permanent residency and does not include seasonal tourism. This may cause
inaccuracies in emissions near parks and other tourist destinations. SMOKE
emissions were calculated from NEI 2002, version 3, with on-road and point
sources projected to 2004 using EGAS, the EPA's point source and economic
growth analysis system. Biogenic emissions were from BEIS 3.13.
Boundary conditions were from previous DEHM modeling that modeled D5
concentrations using 2009 emission rates as described above (Hansen et
al., 2008; McLachlan et al., 2010). The DEHM was run for the Northern
Hemisphere at 150 km resolution. We extracted the D5 concentrations
from the DEHM for year 2011 meteorology along our model boundary. Boundary
concentrations were horizontally and vertically resolved, varied by month,
but were time invariant within each month. Since the DEHM only included
D5, D4 and D6 concentrations were estimated using measurement
ratios taken from a background site at Point Reyes, California (Genualdi et
al., 2011). Point Reyes samples had ratios of 0.646 and 0.0877 for
D4 / D5 and D6 / D5, respectively. The background
ratios combined with the “fresh” emission ratios (described previously)
were used to calculate a photochemical age. The calculation of a
photochemical age was necessary since the siloxanes have different OH
reaction rates and therefore the siloxane ratios change with season due to
varying OH concentrations. Using this method, we calculated an age of
17.6 days using the D4 / D5 ratios, and this is the age used
for further calculations. The calculated photochemical age was combined with
season-specific OH concentrations (Spivakovsky et al., 2000) to calculate
monthly resolved D4 / D5 and D6 / D5
“background” ratios. These monthly resolved D4 / D5 and
D6 / D5 ratios were then used for the entire model boundary.
Additional details are available in the Supplement.
Monthly minimum, maximum, and average D5 and o-D5
concentrations in the lowest modeled layer for the domain.
Domain
D5 concentrations (ngm-3)
o-D5 concentrations (ngm-3)
January
April
July
October
January
April
July
October
Minimum
0.14
0.27
0.024
0.27
0.0031
0.037
0.0021
0.0033
Maximum
432
379
265
301
3.19
4.86
9.04
5.21
Average
6.82
5.09
4.04
6.43
0.37
0.72
0.81
0.63
Monthly averaged surface layer D5 concentrations. The domain
average concentration is shown in the lower left for each month.
Results and discussion
Spatial variation in concentrations
Figures 1 and 2 show the 30-day averaged D5 and oxidized D5
(o-D5) modeled concentrations for January, April, July, and October. The
spatial distribution of cVMS and oxidized cVMS compounds show a strong
population dependence with major urban areas having elevated D5
concentrations and peak o-D5 concentrations occurring hundreds of km
downwind of source regions due to the time it takes for the parent compounds
to react with OH. Table 1 displays the monthly minimum, maximum, and average
concentrations for the entire modeled domain. The 36 km grid cell with the
highest 30-day average surface concentration of D5 was 432, 379, 301,
and 265 ngm-3 for January (Los Angeles – Long Beach), April
(Los Angeles – Long Beach), October (New York City), and July (New York
City), respectively. The domain-averaged surface concentrations of D5
were 6.82, 6.43, 5.09, and 4.04 ngm-3 for January, October,
April, and July. Simulated o-D5 was much lower than simulated D5
concentrations. For example, the 36 km grid cell with the highest 30-day
average surface concentration of o-D5 was 9.04, 5.21, 4.86, and
3.19 ngm-3 for July (NE of Los Angeles – Victorville), October
(E of Los Angeles – San Bernardino), April (SE of Los Angeles – Mission
Viejo), and January (Los Angeles – Long Beach), respectively. The domain
average surface concentration for o-D5 was 0.81, 0.72, 0.63, and
0.37 ngm-3 for July, April, October, and January, respectively.
The peak domain-averaged concentrations occurred during January for D5
and July for o-D5, which is expected based on seasonal trends of OH in
North America (Spivakovsky et al., 2000).
Monthly average surface layer oxidized D5 (o-D5)
concentrations. The domain average concentration is shown in the lower left
for each month.
Average monthly CMAQ modeled surface (a) cVMS and
(b) oxidized cVMS concentrations are plotted versus 36 km grid cell
population for 26 US and Canadian sites. These sites include the 10 most
populous US metropolitan areas, previous siloxane measurement sites, and
NOAA Climate Monitoring and Diagnostics Laboratory (CMDL) sites. See Table S3
for the listing of these sites.
Figure 3 shows the monthly averaged cVMS and oxidized cVMS concentrations
versus the model grid cell population for 26 US and Canadian sites. These
sites include the most populous 10 US metropolitan areas, siloxane
measurement sites, and NOAA Climate Monitoring and Diagnostics Laboratory
(CMDL) sites; see Table S3 for the full list. Modeled concentrations are
strongly dependent on population, with New York City and Los Angeles having
the highest concentrations (Table S4). In addition to the population
dependence, concentrations were greatest for D5 followed by D4 and
D6. This follows from our assumed emission ratios and agrees with North
American measurement data (Yucuis et al., 2013; Genualdi et al., 2011; Ahrens
et al., 2014; Krogseth et al., 2013b). The prevalence of D4 relative to
D6 is of interest because analysis of cVMS composition in consumer
products (Horii and Kannan, 2008; Wang et al., 2009; Dudzina et al., 2014; Lu
et al., 2011; Capela et al., 2016) suggests that D6 is more abundant
than D4 – while in our modeling (and atmospheric measurements) D4
concentrations are higher than D6 concentrations. Four explanations bear
further investigation: (1) non-personal-care emissions (e.g., cVMS residuals
from polymer production) may play a more important role for D4 than
other species based on UK emission estimates (Brooke et al., 2009a, b, c),
(2) possible siloxane conversion during sample collection (Kierkegaard and
McLachlan, 2013; Krogseth et al., 2013a), (3) higher D4 volatility (Lei
et al., 2010) could cause both more difficult detection in personal care
products and a larger fraction volatilization from products, and (4)
uncertainty and/or spatiotemporal variability in the D4 / D5
and D6 / D5 ratios from ambient measurements in Chicago used to
extend the D5 emissions estimates to D4 and D6.
Seasonal variation in concentrations
Since OH concentrations vary seasonally we expect higher cVMS in the winter
(low OH) and lower in the summer (high OH). This has been supported by
previous measurement studies. For example, McLachlan et al. (2010) measured
D5 at a rural site in Sweden (59∘ N) and observed reduced
D5 concentrations for the period of May–June compared to
January–April. Measurements in a remote Arctic location (79∘ N)
observed higher concentrations in the winter compared to late summer
(Krogseth et al., 2013a). For OH concentrations to influence cVMS
concentrations, time for oxidation is required – so the relationship between
seasonal OH and cVMS is expected at receptor sites where most cVMS is
transported from upwind locations. At source-dominated locations, the
influence of OH should be limited. For example, studies from Toronto
highlight local meteorological influences as important in determining
variation in siloxane (D3–D6) concentrations (Ahrens et al., 2014;
Krogseth et al., 2013b).
Figure 1 shows similar D5 spatial distribution between the 4 months,
especially for urban areas. Domain peak and average concentrations (Table 1)
have highest concentrations in January and lowest in July which agree with
seasonal OH concentrations, but specific grid cells (particularly urban
locations) often deviate from this. Rural and remote locations are more
likely to follow the OH-induced seasonal pattern. Seasonal variation for the 26 sites in Table S3 was examined using patterns in the month of highest concentration. Sites were classified as
either urban or rural based on summer D5 concentrations. For urban
sites, the most prevalent month with highest average D5 concentration
was October (59 %), followed by July (23 %) and January (18 %).
Restricting the analysis to the rural sites (summer D5 concentration
below 17 ngm-3), peak D5 concentrations occurred in January
(56 %), followed by October (33 %) and April (11 %). The month of
lowest average D5 concentrations occurred in July for 100 % of the
rural sites and 24 % of the urban sites. Similarly, looking at the
breakdown for the monthly averaged oxidized D5 concentrations, highest
concentrations generally occurred in July, which was true for 73 % of the
26 sites. Figure 2 shows differences in the spatial distribution of o-D5
between months. The analyzed sites therefore suggest less of a seasonal trend
for the parent compounds as compared to the oxidized products, and there are
differences in seasonal trends between source and non-source locations.
Remote and rural sites are more dependent on lifetime with respect to
reaction with OH, while source locations are less sensitive. This agrees with
previous modeling which showed reduced seasonal variability in D5
concentrations for urban areas compared to remote locations (McLachlan et
al., 2010; MacLeod et al., 2011; Xu and Wania, 2013).
Statistical relationships between D5, OH, PBL
height, and wind speed (WS) were explored using least squares multiple linear
regression. For the 26 analyzed sites, OH, PBL, and WS values were normalized
to their summer values and then used as predictive variables of the ratio of
D5 in each season to its summer value at the same site. In other words,
the regression analysis is testing the local season-to-season variability
across seasons and sites (e.g., whether winter : summer D5 concentration
is correlated with winter : summer OH-1). Sites were split between urban
and rural as described previously. For urban sites, D5 concentration was
only correlated to OH-1 when WS-1 was also included, with WS being
the dominant variable. The strongest predictive variables were PBL-1 and
WS-1 with an adjusted R2 fit of 0.50 and a p value of < 0.001.
The regression analysis supports the previous conclusion: ventilation of
local emissions through PBL height and local winds is the strongest influence
on urban siloxane concentrations.
For the rural sites, WS-1 was the only variable of significance but had
a low adjusted R2 of 0.10, p value of 0.056, and a negative coefficient
meaning lower wind speed results in lower D5 concentrations. Repeating
the linear regression, excluding Canadian sites and Point Reyes (California), led to
similar results. Canadian sites were excluded since non-siloxane Canadian
emissions were allocated by population and may cause errors in OH due to
misallocation of nitrogen oxides and reactive organic gases from some source
sectors (Spak et al., 2012). Point Reyes was excluded due to high grid cell
population despite low D5 concentrations. See Supplement for additional
regression results. From this analysis, we conclude that factors other than
local OH and local meteorology control rural/remote siloxane concentrations.
These factors likely include regional OH and regional transport patterns.
Model–measurement comparison
The model results were compared to measurement values in the Midwest (Yucuis
et al., 2013), North American measurements from the Global Atmospheric
Passive Sampling (GAPS) network (Genualdi et al., 2011), and several Toronto
measurements (Genualdi et al., 2011; Ahrens et al., 2014; Krogseth et
al., 2013b).
Midwest model comparison
In Yucuis et al. (2013) measurements were taken at three Midwest locations
during the summer (June–August) of 2011. The measurements were collected, in
duplicate, at sites with varying population density. Measurements from
Chicago, Illinois, were collected consecutively as sixteen 12 h samples from 13 to
21 August; from Cedar Rapids, Iowa, as four 24 h samples non-consecutively from
29 June to 26 July; and from West Branch, Iowa, as five samples that ranged from 30
to 47 h on 6 July and consecutively from 15 to 22 July. The measurements
were compared to the 1–30 July modeled hourly concentrations averaged as 12,
24, and 36 h intervals for the Chicago, Cedar Rapids, and West Branch sites,
respectively. These sampling periods and sample counts are insufficient to
establish representativeness of the values as monthly or seasonal averages.
The model results were averaged using time of day and duration matching the
measurements but do not correspond to the exact measurement days or
meteorology. Measurements are from 2011 and the model's meteorological fields
are from 2004; however, average wind speeds, wind directions, and boundary
layer heights are typically similar from year to year.
Model comparison to Yucuis et al. (2013). Model results from CMAQ
(1–30 July simulation); measurements were conducted in 2011 from 13 to
21 August (Chicago), 29 June to 26 July (Cedar Rapids), and 6 to 22 July (West
Branch), respectively. Hourly model data were averaged to 12, 24, and 36 h
periods, starting at typical measurement start times. Median concentrations
and number of observations are tabulated under the box plots.
Figure 4 displays the box plot comparison of the three Midwest sites of Yucuis
et al. (2013) and the modeled concentrations. The model does capture the
population dependence that the measurements show, with Chicago observing
highest concentrations followed by Cedar Rapids and West Branch. Modeled
concentrations, however, are lower for all three locations compared to the
measurements with fractional bias (Table S10) at Chicago of -0.31, -0.31,
and -0.28 (for D4, D5, and D6, respectively); Cedar Rapids of -1.25,
-0.93, and -1.51; and West Branch of -1.25, -0.78, and -1.23. Comparing the
relative percent error of the mean modeled concentrations to the measured
values, we found that Chicago sites had relative percent errors of around 25 %, while
the other sites had values ranging from 56 to 86 %. For Chicago, error
between the species was similar and this is most likely the result that
D4 and D6 emission rates were calculated based on the Chicago
measurements. For Cedar Rapids and West Branch, D5 had the lowest error,
while D4 and D6 were larger. This may indicate that the siloxane
emission ratios vary based on location.
One possible explanation for low model concentrations could be low emission
estimates. Current emission estimates (Table S2) vary considerably and the
estimates used in this work were 32.8, 135, and
6.10 mgperson-1day-1 for D4, D5, and D6,
respectively, for the US and Canada, while the Mexico emissions were 5.92,
24.4, and 1.10 mgperson-1day-1 for D4, D5, and
D6. Previous emission estimates have ranged 0.001–100, 0.002–1200, and
0.0009–80 mgperson-1day-1 for D4, D5, and
D6, respectively (Tang et al., 2015; Buser et al., 2013a, 2014; Navea et
al., 2011; Yucuis et al., 2013; Horii and Kannan, 2008; Dudzina et al., 2014;
Wang et al., 2009; Capela et al., 2016). Additionally, non-personal-care
product emissions could be important, as could potential geographical,
demographical, or temporal influences on siloxane emissions. As datasets of
cVMS concentrations, particularly those with simultaneous values for D4,
D5 and D6, become available
in more source-oriented locations and seasons, the emissions estimates,
particularly for D4 and D6, should be refined.
The treatment of deposition as an infinite sink could also cause low
gas-phase concentrations (deposition overpredicted) if surface concentration
are not degraded quickly. Experimental studies show the parent cVMS
degradation is slow in soil (Wang et al., 2013); however, this is likely
minimized due to low deposition potential as predicted by high air–water
(KAW) and low octanol–air (KOA) partitioning
coefficients (Xu and Wania, 2013). Octanol–air (logKOA)
partitioning values, which is an indication of the ability to partition to
soil and plants (Shoeib and Harner, 2002), are 4.29–5.86 for
D4–D6 (Xu and Kropscott, 2012), which is similar to or higher than
other organic species with modeled deposition such as methanol, aldehydes,
and carboxylic acids. The oxidized species are likely more sensitive due to
greater deposition potential as EPI Suite predicts lower logKAW
and higher logKOA values, however the surface degradation
kinetics of the oxidation products are not known.
GAPS model comparison
The model was also compared to measurements of Genualdi et al. (2011). These
measurements were collected from passive samplers as part of the GAPS network
over 3 months in 2009, generally from late March to early July. Figure 5
shows the CMAQ-modeled April versus measurements for eight locations
within our domain. Again, as with the Yucuis et al. (2013) comparison, the
modeled results do not explicitly represent meteorological conditions of the
measurement period. Fractional error (Table S11) for D4 varied from 0.02
to 1.93, with Point Reyes having the lowest and Ucluelet the highest. For
D5, fractional error values ranged from 0.02 to 1.24 with Fraserdale the
lowest and Bratt's Lake the highest. Similarly, for D6, the fractional
error varied from 0.11 to 1.71 with Bratt's Lake the lowest and Ucluelet the
highest. Averaged over the eight sites, the overall fractional biases were
-0.41, -0.03, and -0.90 for D4, D5, and D6,
respectively. The mean fractional error was 0.95, 0.66, and 0.98 for D4,
D5, and D6 species. Therefore, based on the fractional error
values, D5 had the best agreement followed by D4 and D6. This
is not surprising that D5 had the best agreement since D4 and
D6 emission rates are estimated based on Chicago measurements and would
have additional uncertainty compared to the D5 emission uncertainty.
Plot (a) shows CMAQ D4, (b) CMAQ D5,
(c) CMAQ D6, (d) BETR D5, and (e) DEHM
D5 modeled concentrations compared to Genualdi et al. (2011)
measurements. Plot (f) compares modeled CMAQ D5 versus DEHM
D5 concentrations. CMAQ model results are the April averaged
concentrations while BETR and DEHM model results are from Genualdi et
al. (2011) and represent the same period as the measurements.
Model resolution was 36 km for CMAQ, 150 km for DEHM, and 15∘ for BETR.
On average, fractional bias for D5 was close to zero while D4 and
D6 had greater negative bias due to significant deviations for
Fraserdale, Ucluelet, and Whistler. Aside from these three sites, the D4
predictions generally agreed well with the measurements. These same three
sites and Groton were also significantly underpredicted for D6, but
other sites were within a factor of 2 of the measurements. Possible
explanations for model deviation could be population errors (Ucluelet and
Whistler are tourist destinations and the population dataset used did not
include visitors), non-personal-care product emissions, or product
transformation of higher-molecular-weight siloxanes to D4 on sampling
media (Kierkegaard and McLachlan, 2013; Krogseth et al., 2013a), or that our
boundary conditions could be underestimating Asian cVMS transport. Genualdi
et al. (2011) hypothesized the high D4 concentrations measured at
Whistler and Ucluelet could be due to transport from Asia since D4
concentrations were greatest at west coast locations and especially at high-altitude sites.
Model overprediction for D5 occurred for the Point Reyes and Bratt's
Lake sites. Representation error is a likely cause of this, since the actual
sampling sites were upwind of large population centers (San Francisco and
Regina, Saskatchewan) in these grid cells; at 36 km resolution, the upwind sampling
sites and the downwind emission centers are not resolved. However, Point
Reyes and Bratt's Lake D4 and D6 concentrations were close to the
modeled values.
We also compare the 36 km CMAQ D5 concentration results to values from
the DEHM and BETR models. The BETR model did not report values for Ucluelet
or Groton so those sites are not included. The D5 modeling attempts were
ordered from most skilled to least skilled by using the mean of the
fractional bias and fractional error (in parentheses) scores: CMAQ -0.03
(0.66), DEHM -0.53 (0.73), and BETR -0.81 (1.08). The CMAQ and DEHM
models had similar performance for Fraserdale, Whistler, Ucluelet, and Point
Reyes, while the urban areas (Downsview; Sydney, Florida; and Groton) were better
predicted in the CMAQ model. Bratt's Lake was overestimated compared to the
DEHM and may have to do with the greater influence of Regina, Saskatchewan,
emissions due to improved model resolution. The differences in modeled
concentrations are most likely due to higher spatial resolution for CMAQ
(36 km) versus 150 km (DEHM), and 15∘ (BETR) resolutions.
Toronto cyclic siloxane comparison between the CMAQ model and
previous studies. Reported are the mean concentrations with ranges in
parentheses.
Period
Method
Averaging
Atmospheric concentration,
Reference
period
mean (range)
D4
D5
D6
(ngm-3)
January
CMAQ model
24 h
21.7 (5.4–45.1)
88.1 (21.5–184.8)
3.94 (0.95–8.31)
This study
April
CMAQ model
24 h
20.4 (4.6–43.7)
82.1 (17.1–178.2)
3.67 (0.74–8.01)
This study
July
CMAQ model
24 h
28.3 (7.5–57.0)
115.9 (30.5–233.8)
5.22 (1.37–10.54)
This study
October
CMAQ model
24 h
31.0 (5.4–60.6)
126.3 (20.8–247.7)
5.67 (0.90–11.13)
This study
March 2010–April 2011
Active sampling
24 h (not continuous)
16 (2.8–77)
91 (15–247)
7.3 (1.9–22)
Ahrens et al. (2014)
March 2010–April 2011
Passive sampling
∼ 28 days
21 (9.3–35)
140 (89–168)
11 (8.0–20)
Ahrens et al. (2014)
March 2012–June 2012
Active sampling
2–3 days
24.2 (4.7–90.9)
93.5 (22.4–355)
5.5 (1.6–17.4)
Krogseth et al. (2013b)
July 2012–October 2012
Passive sampling
80–92 days
41
122
–
Krogseth et al. (2013b)
April 2009–June 2009
Passive sampling
89 days
11
55
6.2
Genualdi et al. (2011)
April 2009–June 2009
BETR model
89 days
–
6.5
–
Genualdi et al. (2011)
April 2009–June 2009
DEHM
89 days
–
28
–
Genualdi et al. (2011)
Modeled monthly averaged D5 / D4 mole ratios by
season. Larger cVMS species react faster with OH. More reactive species are
in the numerator; therefore, ratios decrease with air mass age.
Toronto model comparison
Multiple measurement and modeling studies have investigated cVMS
concentrations in Toronto, Canada. Table 2 shows the mean and range of cVMS concentrations
in Toronto for each of the 4 months as simulated by the CMAQ model. Table 2
further includes the March 2010–April 2011 measured concentrations as
collected by both passive and active sampling (Ahrens et al., 2014), active
sampling from March to June 2012 and passive sampling from July to October
2012 (Krogseth et al., 2013b), and passive sampling (April–June 2009) from
the GAPS network (Genualdi et al., 2011). Finally, the BETR and DEHM modeled
D5 concentrations (Apri–June 2009) are also tabulated (Genualdi et
al., 2011). The CMAQ results compared favorably to the Ahrens et al. (2014)
measurements, with CMAQ monthly averages that generally fell within the
reported measurement concentration ranges. D4 monthly averages were
within a factor of 0.97–1.94, D5 within a factor of 0.59–1.39, and
D6 within a factor of 0.33–0.78 of the yearly averaged active and
passive sampling measurements. Comparison of the range of concentrations
showed that CMAQ 24 h averaged ranges were 4.6–60.6 (D4), 17.1–247.7
(D5), and 0.74–11.13 (D6) ngm-3 compared to Ahrens et
al. (2014) 24 h active sampling range of 2.8–77 (D4), 15–247
(D5), and 1.9–22 (D6) ngm-3. Similarly, good agreement
was observed for the active and passive sampling measurements from Krogseth
et al. (2013b), average April CMAQ D4, D5, and D6
concentrations were a factor of 0.84, 0.88, and 0.67, respectively, of the
measured average, the concentration ranges were similar, with higher peak
concentrations occurring for the measurements despite sampling for 2–3 days.
For the passive samples of Krogseth et al. (2013b), July and October average
CMAQ concentrations were 0.69–0.76 for D4 and 0.95–1.04 for D5
compared to the measurements. CMAQ April averages were 1.85, 1.49, and 0.59
times the Genualdi et al. (2011) measurements. Previous Toronto modeling
predicted 6.5 ngm-3 (BETR) and 28 ngm-3 (DEHM),
which were significantly lower than the spring CMAQ D5 concentration of
81.6 ngm-3. Overall, the CMAQ model was able to better predict
the higher observed concentrations of Toronto, which again can most likely be
attributed to increased model resolution.
Modeled monthly averaged D6 / D5 mole ratios by
season. Larger cVMS species react faster with OH. More reactive species are
in the numerator; therefore, ratios decrease with air mass age.
Compound ratios
Cyclic siloxane product ratios can be used to gain insight into emission
sources and OH photochemical aging (Ahrens et al., 2014; Kierkegaard and
McLachlan, 2013; Krogseth et al., 2013b, a; Yucuis et
al., 2013; Navea et al., 2011). Figures 6 and 7 show the model-predicted
seasonal plots of monthly averaged D5 / D4 and
D6 / D5 product ratios. It is important to note that the
modeling assumes D4 and D6 are emitted according to population
density, at constant ratios relative to D5 at all locations and times.
Thus, these figures emphasize the influence of differences in chemical aging.
Due to differences in OH reactivity rates, cyclic siloxane reactivity
increases with Si–O chain length (more methyl groups), so that D6 is the
most reactive and D4 the least (Atkinson, 1991). Therefore, siloxane
ratios depend on emissions, exposure to OH, and relative reactivity rates.
Mole ratios are plotted with the more reactive species as the numerator; as
air masses move away from emission sources and are exposed to OH, the ratio
decreases due to more rapid depletion of the more reactive species. This is
evident in the D5 / D4 and D6 / D5 maps, which
show urban areas have the highest ratios.
Seasonal differences of the product ratios are similar for both
D5 / D4 and D6 / D5 mole ratios. Urban areas
exhibit almost no season-to-season difference (Table S7), as they reflect the
local emission ratios. Seasonal differences are most apparent for rural and
remote locations. Domain average ratios are highest in January and lowest in
July which is consistent with seasonal OH fluctuations.
Since both SO2 and cVMS are precursors to secondary aerosol
formation, and both compounds have approximately the same OH rate constant,
the ratio of gas-phase SO2 to cVMS should predict aerosol-phase
ratios of S to Si in photochemically generated particles (Bzdek et
al., 2014). Figure 8 shows the seasonally modeled, monthly averaged gas-phase
SO2 / (D4 + D5 + D6) mole ratios. Urban
ratios exhibit lowest values which suggest photochemically generated aerosols
would have increased Si composition derived from siloxane oxidation.
Conversely, rural locations have high SO2 / cVMS ratios and
expected low Si aerosol composition. This is consistent with the high
nanoparticle Si measured in Pasadena, California, and Lewes, Delaware, by Bzdek et
al. (2014). Seasonal variation in the SO2 / cVMS ratio is minor.
Modeled monthly averaged
SO2 / (D4+ D5+ D6) mole ratio by season.
Monthly averaged vertical profiles for grid cells near Los Angeles.
Plot (a) shows D5 and (b) o-D5 model
concentrations. Grid cells refer to the location of maximum July D5,
maximum July o-D5, and a grid cell over the Pacific Ocean.
Vertical profile analysis
Modeled monthly averaged D5 and o-D5 vertical profiles are shown in
Fig. 9 for three grid cells near Los Angeles. The locations of the analyzed
sites include the highest monthly averaged surface July D5
concentration, the highest averaged surface o-D5 concentration, and a
grid cell over the Pacific Ocean. The grid cell with greatest D5
concentration (termed “Peak D5”) included cities such as Long Beach
and Anaheim while the grid cell with highest o-D5 (“Peak o-D5”)
was approximately 80 km northeast of the peak D5 grid cell and included
Victorville and Hesperia, California. The third location was over the Pacific Ocean
(“Pacific”), approximately 195 km southwest of Los Angeles (Fig. S9).
The CMAQ model was run with 14 vertical layers; plotted is the layer top
height versus the monthly averaged July D5 and o-D5 concentration.
For D5 concentrations, both the Peak D5 and Peak o-D5
sites had highest concentrations at the surface. Over the Pacific,
concentrations peaked above the surface at approximately 700–1700 m. Surface
D5 concentrations were 251, 103, and 0.3 ngm-3 for the
Peak D5, Peak o-D5, and Pacific locations, respectively.
From heights 475–3000 m, the Peak o-D5 site had higher D5
concentrations than the Peak D5 site and this is most likely due to
the plume dilution from the upwind LA source. For o-D5 concentrations,
surface concentrations were highest for the Peak o-D5 site
(9 ngm-3), followed by the Peak D5 site
(2 ngm-3), and the Pacific site (0.2 ngm-3).
From the surface to 3000 m the Peak o-D5 grid cell had
highest o-D5 concentrations as a result of being downwind of a major
emission source and the oxidation reaction takes times to occur. Both the
Peak D5 and Pacific sites have peak o-D5 concentrations not
at the surface (475 and 2300 m, respectively), while the “o-D5”
site is at the surface. The low surface o-D5 at the peak D5 site
could be due to low OH concentrations caused by urban OH sinks and is
consistent with low modeled surface OH (Fig. S10). Vertical concentrations
appear to be dependent on transport, reaction time, and OH concentrations.