The regional concentrations of airborne ultrafine particulate matter mass
(Dp<0.1µm; PM0.1) were predicted in 39 cities across
the United States (US) during summertime air pollution episodes.
Calculations were performed using a regional source-oriented chemical
transport model with 4 km spatial resolution operating on the
National Emissions Inventory created by the U.S. Environmental Protection Agency (EPA). Measured source
profiles for particle size and composition between 0.01 and 10 µm were
used to translate PM total mass to PM0.1. Predicted PM0.1
concentrations exceeded 2 µgm-3 during summer pollution episodes
in major urban regions across the US including Los Angeles, the San
Francisco Bay Area, Houston, Miami, and New York. PM0.1 spatial
gradients were sharper than PM2.5 spatial gradients due to the
dominance of primary aerosol in PM0.1. Artificial source tags were used
to track contributions to primary PM0.1 and PM2.5 from 15
source categories. On-road gasoline and diesel vehicles made significant
contributions to regional PM0.1 in all 39 cities even though peak
contributions within 0.3 km of the roadway were not resolved by the 4 km
grid cells. Cooking also made significant contributions to PM0.1
in all cities but biomass combustion was only important in locations
impacted by summer wildfires. Aviation was a significant source of
PM0.1 in cities that had airports within their urban footprints.
Industrial sources, including cement manufacturing, process heating, steel
foundries, and paper and pulp processing, impacted their immediate vicinity
but did not significantly contribute to PM0.1 concentrations in any of
the target 39 cities. Natural gas combustion made significant contributions
to PM0.1 concentrations due to the widespread use of this fuel for
electricity generation, industrial applications, residential use, and commercial
use. The major sources of primary PM0.1 and PM2.5 were notably
different in many cities. Future epidemiological studies may be able to
differentiate PM0.1 and PM2.5 health effects by contrasting
cities with different ratios of PM0.1/PM2.5. In the current
study, cities with higher PM0.1/PM2.5 ratios (ratio greater
than 0.10) include Houston, TX, Los Angeles, CA, Bakersfield, CA, Salt Lake
City, UT, and Cleveland, OH. Cities with lower PM0.1 to PM2.5 ratios (ratio lower than 0.05) include Lake Charles, LA, Baton Rouge, LA,
St. Louis, MO, Baltimore, MD, and Washington, D.C.
Introduction
Airborne particulate matter (PM) has been linked with premature mortality
and numerous other health risks in cities across the world (see, for example,
references Dominici et al., 2006; Franklin et al., 2007; Pope et al.,
2002, 2009; Ostro et al., 2006; Laden et al., 2000; Kheirbek et
al., 2013; Aneja et al., 2017). Despite years of progress (EPA, 2017a),
PM concentrations in many urban regions in the US still exceed
health-based standards, resulting in an increase in non-accidental mortality
(Franklin et al., 2007; Baxter et al., 2013). Toxicology
testing suggests that ultrafine particles with a diameter <0.1µm may be the most harmful size fraction within PM2.5 (Li
et al., 2003; Oberdorster, 2000; Ostro et al., 2015; Oberdorseter et al.,
1995; Pekkanen et al., 1997). Initial attempts to analyze ultrafine particles
in epidemiology studies have used particle number concentration as a
surrogate for ultrafine particle exposure, but this approach has not found
consistent relationships with health effects (HEI, 2013). In contrast, a
recent epidemiology study based on ultrafine particle mass (PM0.1)
found significant associations with premature mortality (Ostro
et al., 2015). In addition, ultrafine (UF) mass concentrations are highly
correlated with particle surface area and can be a good metric for
potential exposure to UF particles (Kuwayama et al.,
2013; Ostro et al., 2015). Follow-up studies have also found significant
associations between PM0.1 and reproductive outcomes, including low
birth weight and preterm birth (Laurent et al., 2016; Bergin et al.,
1996). These findings have biological plausibility, since ultrafine
particles may cross cell membranes and interfere with internal cell function
(Sioutas et al., 2005). Ultrafine particles have greater surface
area per volume due to the small particle diameter, making them more available
for chemical reaction. Ultrafine particles can therefore have a larger
impact when deposited deep into the lung cavity, from which they are not easily
removed (Nel et al., 2006; Li et al., 2003).
A monitoring network for PM10 and PM2.5 has been operating
throughout the continental US for almost 20 years. Multiple studies have
performed source apportionment calculations for coarse and fine PM using
these measurements (see, for example, Reff et al., 2009; Zhang et al., 2014; Zheng et al.,
2002; Ham and Kleeman, 2011). In contrast, measurements of PM0.1 are
limited to focused field campaigns lasting for short time periods with even
fewer studies attempting source apportionment calculations
(Kleeman et al., 2009). Multiple barriers have
prevented the widespread deployment of PM0.1 monitoring networks
including (i) the low concentration of PM0.1 mass, which challenges
the detection limits of analytical methods, (ii) the artifacts associated
with collecting PM0.1 samples, (iii) the additional workload involved
in operating the collection devices, and (iv) the sharp spatial gradients of
PM0.1 concentrations. Expensive investments in PM0.1 monitoring
are unlikely to be purchased without compelling evidence linking PM0.1 to
public health. Early epidemiological studies for PM0.1 must therefore
use techniques other than direct measurements to calculate population
exposure.
Various methods, such as the source-resolved PMCAMx chemical transport model,
the chemical mass balance (CMB) model, photochemical box models, and land use
regression (LUR) models, have been used to track source contributions to
primary organic matter, elemental carbon, and in some cases particle number
concentration (Nx) over areas in the Eastern US and parts of Europe
and Asia (Lane et al., 2007; Posner and Pandis, 2015; Wang et al.,
2011; Cattani et al., 2017; Wolf et al., 2017; Simon et al., 2018; Gaydos et
al., 2005; Zhong et al., 2018). However, these methods are limited in one or
more aspects of their ability to predict population exposure to ultrafine
particles over large analysis domains. Source-resolved models, such as
PMCAMx, have been used to resolve composition for Nx in the Eastern
US but not for PM0.1 (Posner and Pandis, 2015). CMB models
need measurements of specific molecular markers at numerous sites to resolve
the sharp spatial gradients of ultrafine particle source contributions. LUR
models need comprehensive measurements that act as training data sets in
order to extend throughout a modeling domain (Lane et al.,
2007).
Hu et al. (2014) calculated population exposure to
PM0.1 in California using a regional source-oriented chemical transport
model supported by measured profiles for the size and composition of
particles emitted by dominant sources. Predictions were compared to all
available fine and ultrafine particle measurements over the period 2000–2010
with good agreement observed for the dominant chemical components of
PM0.1 mass including organic aerosol, elemental carbon, and numerous
trace metals (Hu et al., 2014). The 4 km spatial resolution
used in these calculations supported multiple epidemiological studies based
on spatial gradients of exposure (Ostro et al., 2015; Laurent et al.,
2016). These encouraging results motivate the expansion of the PM0.1
exposure technique to other locations.
Here we use the Eulerian source-oriented UCD–CIT chemical transport model to
predict the concentration of PM0.1 in 39 urban regions
throughout the US during summer pollution events in 2010. The calculation
tracks contributions from 15 primary particle sources through a
simulation of all major atmospheric processes while retaining information
about particle size, composition, and source origin (Hu et al.,
2014). The results of this calculation reveal US national trends in
PM0.1 concentrations for the first time and suggest locations where the
differential health effects of PM0.1 and PM2.5 can best be
studied.
MethodsSimulation dates
A total of 39 of the largest cities in the continental US were selected as
the primary target locations in the current study (Fig. 1). These cities have
been used to characterize atmospheric reactivity across the US in previous
air pollution studies (Carter, 1994, 2010; Venecek et al.,
2018a, b). Simulations within each target city were
carried out during peak summer air pollution events in 2010. Dates were
selected based on an initial investigation of measured 1 h ozone (O3)
across all monitors in a core-based statistical area (CBSA). A CBSA is
defined as a US geographical area that consists of one or more counties
anchored by an urban center of at least 10 000 people plus adjacent counties
that have a high degree of social and economic integration with the core as
measured by commuting (United States Census Bureau, 2018).
Map of 39 cities used for the prediction of PM2.5 and PM0.1 source contributions across the continental United States during summertime air pollution events.
The selected air pollution events within each CBSA typically had measured
1 h maximum O3 concentrations greater than 70 ppb. Regional pollution
events caused by atmospheric stagnation were selected whenever possible as
opposed to special events caused by unusual occurrences such as wildfires
that affected only one city at a time. The simulation dates in each city are
listed in Table 1. Figure 2 illustrates the average 1 h maximum O3
concentration across all monitors within each CBSA during the selected
regional events. Simulation periods are organized in chronological order for
the year 2010, and cities within the same geographical region are grouped
together. Measured 24 h PM2.5 concentrations during peak summer
pollution events ranged between 3.2 and 30 µgm-3 depending on the
location. The aggregation of these events across the US enables a
comparison of typical summertime air pollution episodes within different
cities.
Average 1 h maximum O3 across all monitors in each domain.
Cities are grouped by corresponding extreme O3 dates (that averaged
>70ppb) and US geographical region.
City, city code, simulation date, 2010 population, and geographical
region.
The UCD–CIT model predicts the evolution of gas- and particle-phase
pollutants in the atmosphere in the presence of emissions, transport,
deposition, chemical reaction, and phase change (Held et al., 2005)
as represented by Eq. (1):
∂Ci∂t+∇⋅uCi=∇K∇Ci+Ei-Si+RigasC+RipartC+Riphase(C),
where Ci is the concentration of gas- or particle-phase species i at a
particular location as a function of time t, u is the wind vector, K is the
turbulent eddy diffusivity, Ei is the emissions rate, Si is the loss
rate, Rigas is the change in concentration due to gas-phase
reactions, Ripart is the change in concentration due to
particle-phase reactions, and Riphase is the change in
concentration due to phase change (Held et al., 2005). Loss rates
include both dry and wet deposition. Phase change for inorganic species
occurs using a kinetic treatment for gas–particle conversion
(Hu et al., 2008) driven towards the point of
thermodynamic equilibrium (Nenes et al., 1998). Phase change for
organic species is also treated as a kinetic process with vapor pressures of
semi-volatile organics calculated using the two-product model
(Carlton et al., 2010). More sophisticated approaches for
secondary organic aerosol (SOA) formation (Cappa et
al., 2016) were also tested in the current study but these required a larger
number of assumptions and they did not produce higher SOA concentrations in
the PM0.1 size fraction.
Nucleation was included in the model using the ternary nucleation (TN)
mechanism involving H2SO4–H2O–NH3
(Napari et al., 2002). A tunable nucleation parameter equal
to 10-5 was used based on results from previous studies across
California for the year 2012 (Yu et al., 2019). Yu et al. (2019) found
good agreement between predicted and measured concentrations of
daily-averaged PM0.1 and N7 source contributions in California. The current study expands these nucleation
calculations to investigate new particle formation across all major US
cities, but the data needed to evaluate the accuracy of these calculations
are generally not available outside California, and particle number
concentrations will not be a focal point of this work. The model spatial
resolution was 4 km over the 4.2 million km2 of simulated urban areas,
so near-roadway concentrations of ultrafine particles on spatial scales
of ∼0.1km will not be presented.
A total of 50 particle-phase chemical species are included in each of 15 discrete particle size bins that range from 0.01 and 10 µm in particle
diameter (Held et al., 2005). Artificial source tags are used to
quantify source contributions to the primary particle mass and the secondary
organic aerosol (SOA) mass for a specific bin size, thereby allowing
the direct contribution of each source of PM2.5 and PM0.1 mass
to be determined. Gas-phase concentrations of oxides of nitrogen (NOx),
volatile organic compounds (VOCs), oxidants, O3, and semi-volatile
reaction products were predicted using the SAPRC-11 chemical mechanism
(Carter and Heo, 2013).
Model inputs
Anthropogenic emissions were generated using the Sparse Matrix Operator
Kernel Emissions (SMOKEv3.7) modeling system applied to the 2011 National
Emissions Inventory. The NEI reports county-wide emission totals from all 50
states that are then mapped using spatial surrogates. Temporal profiles are
also used to account for variation by time of month, week, and day; however,
the NEI does not account for “no-burn” days that would impact residential
wood combustion or precipitation events that would impact paved–unpaved road
dust. These default profiles may result in larger model performance bias
when comparing predictions to measured values. Emissions from each of the
four major source sectors (area, mobile, non-road, and point) were tagged to
create 15 different emissions groups: on-road diesel, on-road
gasoline, off-road diesel, off-road gasoline, biomass, cooking, natural
gas, process heaters, distillate (oil), aviation, cement, coal, steel
foundries, paper products, and all other emissions. Size- and
composition-resolved source profiles were then assigned to the PM emissions
within each of these groups using the UCD–CIT emissions processor based on
the most recent measurements available in the literature (Robert et al.,
2007a, b; Kleeman et al., 2008). Some of the 15
source categories were represented using weighted-average source profiles
from multiple sources as described in Table S1 in the Supplement.
Daily values for 2010 wildfire emissions were generated using the Global
Fire Emissions Database (GFED) (Giglio et al., 2013). Biogenic
emission rates were generated using the Model of Emissions of Gases and
Aerosols from Nature (MEGANv2.1). The gridded geo-referenced emission
factors and land cover variables required for MEGAN calculations were
created using the MEGANv2.1 preprocessor tool and the ESRI_GRID leaf area index and plant functional type files available at the
Community Data Portal (Guenther et al., 2012).
Meteorology parameters used to drive the UCD–CIT chemical transport model (CTM) and the MEGANv2.1
biogenic emissions were generated using the Weather Research and Forecasting
model (WRFv3.6) and WRF preprocessing system (WPSv3.6). Meteorological
fields were created within three nested domains with horizontal resolutions of
36, 12, and 4 km. Each domain had 31 telescoping vertical
levels up to a top height of 12 km. Four-dimensional data assimilation (FDDA)
or “FDDA nudging” was used to anchor meteorological predictions to
measured values (Hu et al., 2010). Meteorological data and gridded map
projections needed for 2010 emissions modeling were taken from the
corresponding WRF simulations using the meteorology–chemistry interface
processor (MCIP).
Supporting measurements
Ambient hourly O3 measurements and daily PM2.5 measurements were
obtained from the Environmental Protection Agency (EPA) AQS API/Query AirData (EPA, 2017b). Model predictions
are compared to these measurements to build confidence in the accuracy of
the overall modeling system since PM0.1 measurements are not available
during any of the peak summer pollution events studied here.
Results
Predicted maximum 1 hO3, NO2, SO2, and CO concentrations
were compared to measurements at all available monitors within each study
CBSA to indirectly evaluate the accuracy of the emissions inventories and
meteorology fields. Many of the sources that emit O3 precursors also
emit ultrafine particles. Likewise, meteorological parameters like wind
speed and mixing depth influence the concentrations of all pollutants,
including ultrafine particles. Successful prediction of gas-phase species is
therefore a necessary step in the accurate prediction of ultrafine particle
concentrations during summer photochemical smog episodes. Predicted 24 h
PM2.5 concentrations were also compared to measurements at all
available monitors within each study CBSA. Many of the combustion sources
that emit primary particles within the PM2.5 size fraction also emit
primary PM0.1 and/or precursor gases that can condense into the
PM0.1 size range. The Chemical Speciation Monitoring Network (CSN)
operated by the U.S. Environmental Protection Agency (EPA) measures
PM2.5 mass and chemical composition at more than 260 sites throughout
the US, including many of the 39 cities studied in the current analysis
(Solomon et al., 2014). Full monitor information
including latitude, longitude, and total number of available measurements for
comparison within the simulation period are show in Tables S2–S6.
Model performance statistics for predicted maximum 1 hO3
against measured values. The red line represents performance criteria of 0.15
for NMB and 0.25 for NME. NMB and NME were calculated for available
measurements against predictions at every monitor in the CBSA based
on the U.S. EPA AQ Data Mart. Monitor number (horizontal axis), latitude and
longitude, name, MO, MP, NMB, NME, FB, and FE values are available for all
monitors in the Supplement.
Figure 3 illustrates the normalized mean bias (NMB) and normalized mean
error (NME) for predicted 1 h maximum O3 against measured 1 h maximum
values for each monitor within a specific modeling domain. Figure S1 in the
Supplement illustrates the fractional bias (FB) and fractional
error (FE) for predicted 1 h maximum CO, NO2, and SO2 against
measured 1 h maximum values. Figure 4 illustrates the NMB and NME for 24 h
average predicted PM2.5 concentrations against measured 24 h average
PM2.5 concentrations at each available monitor over the specific
simulation period. A time series of predicted vs. measured O3 concentrations is displayed in Fig. S2.
Model performance statistics for predicted 24 h average PM2.5 against measured values. The red line represents performance criteria of ±0.30 for NMB and 0.50 for NME. Normalized mean bias and normalized mean
error were calculated for available measurements against predictions at
every monitor in the CBSA based on the U.S. EPA AQ Data Mart. Monitor
number (horizontal axis), latitude and longitude, name, MO, MP, NMB, NME, FB,
and FE values are available for all species in the Supplement.
Percent of monitors throughout the entire US domain that met
performance criteria for normalized mean error (NME).
Table 2 summarizes the total number of available monitors for a comparison of
measured values vs. predicted values for O3 and PM2.5. Emery et
al. (2017) recommend model performance criteria for 1 hO3 NMB less
than or equal to ±0.15 and NME less than or equal to 0.30. The 24 h
PM2.5 model performance recommendations, also based on Emery et al. (2017), are NMB less than or equal to ±0.30 and NME less than or equal
to 0.50 (Emery et al., 2017). Table 2 displays the percentage of
measured vs. predicted comparisons that met the performance criteria for NME
over the entire US modeling domain. In summary, 95 % of all locations
met NME performance criteria for O3 predictions, and 85 % of all
locations met NME performance criteria for PM2.5 predictions.
Elemental carbon (EC) and organic carbon (OC) are the chemical components
most relevant for both the PM2.5 and the PM0.1 size fractions.
Figure 5 illustrates predicted vs. measured 24 h PM2.5 EC and OC
concentrations for all 39 cities. Primary organic matter tracked by model
calculations is converted to OC by dividing by a factor of 1.2
(Russell, 2003). Secondary organic aerosol tracked by model
calculations is converted to OC by dividing by a factor of 1.5. In general,
the model slightly underpredicts PM2.5 EC, OC, and mass with
regression slopes ranging from 0.62 for EC to 0.71 for OC. The
negative bias in model predictions may stem from the 4 km spatial averaging
inherent in the calculations vs. the influence of sources closer than 4 km
to the measurement site in urban environments, such as highways and
restaurants. Model performance statistics for PM2.5 predictions
are summarized in Table S6.
Predicted average 24 h vs. measured 24 h average (a) organic
carbon and (b) elemental carbon (µgm3). Predicted OC was
converted from predicted organic matter (OM) and secondary organic aerosol
components using a ratio of 1.2 and 1.5, respectively (Russell, 2003).
PM0.1 measurements are not available for model evaluation in the 39
cities across the US in 2010 at the core of the current study, but
measurements are available in California in the years 2015 and 2016 that can
be used to evaluate similar modeling procedures. Yu et al. (2019)
compared PM0.1 concentrations in Los Angeles, Fresno, East Oakland, and
San Pablo, California, predicted using the UCD–CIT air quality model to
receptor-based source apportionment calculations based on measured
concentrations of molecular markers in the ultrafine particle size fraction
(Xue et al., 2018). Good agreement was found between predictions
for PM0.1 concentrations associated with gasoline engines, diesel
engines, cooking, wood burning, and “other sources” from these two
independent techniques. Further details on this comparison are provided by
Yu et al. (2019). This evaluation of the modeling procedures
builds confidence in the PM0.1 source predictions across the US in
the current study, but new measurements would be helpful to fully evaluate
model predictions in the future.
(a) PM2.5 and (b) PM0.1 24 h average mass (µgm-3) during summer air pollution event. Scale drawn to highlight all
areas of the US. Actual maxima: (a)94.25µgm-3; (b)9.43µgm-3.
Figure 6 illustrates a composite representation of PM2.5 and
PM0.1 mass across the US during the summer pollution episodes listed
in Table 1. The spatial plot in Fig. 6 is
constructed using the intermediate 12 km simulation results from multiple
simulations stitched together to cover a broader geographical area. Regional
PM0.1 concentrations reach a maximum value of 5 µgm-3 in a
few isolated grid cells with wildfires, but concentrations generally exceed 2 µgm-3 in major urban regions across the US, including Los
Angeles, the San Francisco Bay Area, Houston, Miami, and New York. The
comparison between PM2.5 mass (Fig. 6a)
and PM0.1 mass (Fig. 6b) shows that
predicted PM0.1 spatial gradients are sharper, with fewer regional
contributions between “hot spots”. Locations in the Midwestern and Eastern
US outside cities with high PM2.5 concentrations due to
secondary formation (sulfate and secondary organic aerosol) did not have
corresponding high concentrations of PM0.1. Most major urban centers
had noticeable peaks of both PM2.5 and PM0.1. This pattern
presents a challenge for epidemiological studies seeking to differentiate
the effects of PM2.5 and PM0.1 because the locations with
differential exposure (high PM2.5 but low PM0.1) have a low
population density, which will reduce the power of the analysis.
PM0.1 source contribution for 39 cities across the
continental US.
The UCD–CIT model explicitly tracks source contributions to particle mass in
each size bin using artificial source tags. Pie charts of PM2.5 and
PM0.1 source contributions are illustrated in
Fig. 6 for selected major cities. Pie charts for
PM0.1 source contributions in all 39 US cities are shown in
Fig. 7. The detailed source profiles within each
city are based on the nested 4 km simulation results during the pollution
events listed in
Table 1. Source contribution spatial plots for the entire US are shown in
Figs. S3 through S5, and pie charts for
PM2.5 source contributions in all 39 US cities are shown in Fig. S6. On-road gasoline and diesel vehicles
made significant contributions to regional PM0.1 in all 39 cities even
though peak contributions within 0.3 km of the roadway were not resolved by
the 4 km grid cells. Cooking also made significant contributions to
PM0.1 in all cities, but biomass combustion was only important in
locations impacted by summer wildfires. Residential wood combustion is not
typically a strong source in the summer due to warmer temperatures;
however, in the wintertime biomass would most likely be a dominant source.
Aviation was a significant source of PM0.1 in cities that have airports
within their urban footprints. Industrial sources including cement
manufacturing, process heating, steel foundries, and paper and pulp
processing impacted their immediate vicinity but did not significantly
contribute to PM0.1 concentrations in any of the target 39 cities.
Natural gas combustion made significant contributions to PM0.1
concentrations due to the widespread use of this fuel for residential,
commercial, and industrial applications. Natural gas contributions were
especially significant in locations with high levels of industrial use, such
as chemical refineries, and in locations with significant levels of natural-gas-fired power plants.
The major sources of primary PM0.1 and PM2.5 were notably
different in many cities (compare Fig. 6a and
b). The sources that contribute most
strongly to PM2.5 are on-road diesel, gasoline, cooking, coal,
and “other”, which includes brake and tire wear from mobile sources and
dust. Natural gas combustion makes minor contributions to primary PM2.5 mass since particles from this source have a mass distribution peaking at
∼0.05µm in particle diameter (Chang
et al., 2004), with all of the emitted mass in the PM0.1 size fraction.
In contrast, other combustion sources using more complex fuels, such as
on-road vehicles, have a mass distribution peaking at ∼0.1µm, with at least half the emitted mass outside the PM0.1 size
fraction (Robert et al., 2007a, b).
Likewise, cooking contributes strongly to PM2.5 concentrations,
but the emitted particle mass distribution peaks at 0.2 µm, with the
majority of the mass outside the PM0.1 size fraction.
The fraction of PM that is primary within each CBSA is listed in Tables S7–S16. Averaged across the US, PM2.5 was found to
be approximately 62 % primary material, while PM0.1 was found to be
approximately 87 % primary material.
Population-weighted average source contribution across the 39 major
cities in the continental US for (a) PM2.5 and (b) PM0.1.
Discussion
Figure 8 illustrates the population-weighted
average PM0.1 source contributions across all 39 study cities shown in
Table 1. These predictions are based on source profile measurements for
wood burning, cooking, mobile sources, and nonresidential natural gas
combustion reported in multiple peer-reviewed studies (Taback et al.,
1979; Cooper, 1989; Houck et al., 1989; Hildemann et al., 1991a, b; Harley et al., 1992; Schauer et al., 1999a, b, 2001, 2002a, b; Kleeman et al., 2008, 2000; Robert et al., 2007a, b). In addition, new measurements made by Xue
et al. (2019) were conducted to confirm previous measurements of the
particle size distribution associated with natural gas and biomethane
combustion particles.
(a) Natural gas compressor stations and pipelines across the US
and (b) PM0.1 natural gas combustion concentrations (µgm-3).
Scatter plot showing correlation between 24 h average PM2.5 and PM0.1 for the 39 cities.
PM0.1/PM2.5 ratio for each city.
PM0.1/PM2.5 ratio across the US.
The results summarized in Fig. 8 highlight the importance of natural gas
combustion particles in the PM0.1 size fraction and the minor role that
these natural gas combustion particles play in the PM2.5 size
fraction. Natural gas typically consists of +93 % methane, with the
balance of the fuel made up by higher-molecular-weight alkanes and trace
impurities. In addition to background sulfur compounds in the natural gas,
sulfur-containing odorants such as mercaptans are commonly added to aid in
leak detection. Natural gas combustion does not emit high amounts of
particulate matter per joule of energy in the fuel, but the widespread use of
natural gas suggests that it could still contribute significantly to ambient
PM0.1 concentrations. Natural gas combustion accounted for 29 % of
total US energy consumption in 2016 (U.S. Department of
Energy, 2017). In contrast, gasoline combustion accounted for 17 % of US
energy consumption, and diesel fuel combustion accounted for approximately
6 % of US energy consumption in 2016. Less than half of the PM emitted
by gasoline and diesel fuel combustion is in the PM0.1 size
fraction (Robert et al., 2007a, b), whereas all of the PM emitted by natural gas combustion is in the
PM0.1 size fraction (Chang et al., 2004). Taken
together, these facts support the potential importance of natural gas
combustion for ambient PM0.1 concentrations.
The five states with the highest consumption of natural gas in 2016 were
Texas (14.7 %), California (7.9 %), Louisiana (5.7 %), New York
(5 %), and Florida (4.8 %). These consumption patterns are reflected in
the natural gas distribution system (Fig. 9a) and
the predicted PM0.1 concentration field associated with natural gas
combustion (Fig. 9b). Natural gas end use
included electric power generation (36 %), industrial applications
(34 %), residential use (16 %), commercial use (11 %), and
transportation (3 %).
Lane et al. (2007) used a source-resolved version of PMCAMx and individual
emission inventories to determine source contributions of primary organic
material (POM2.5) (Lane et al., 2007). Lane et al. (2007) note
that POM2.5 associated with natural gas sources ranged from 0.1 to 0.8 µgm-3. Chang et al. (2004) measured emitted particle size
distributions for gas-fired stationary combustion that fell between 10 and 100 nm. The combination of these two
results indicates that the natural gas mass component of POM2.5 predicted by Lane et al. (2007) is consistent with the magnitude of the
PM0.1 mass associated with natural gas combustion found in the
current study. Lane et al. (2007) were not studying PM0.1, so the major
role of natural gas in this size fraction was not identified.
Posner and Pandis (2015) utilized PMCAMx with the LADCO 2001 BaseE
source-resolved mass emissions inventory for a July 2001 prediction of
Nx over the Eastern US with 36 km resolution (Posner and
Pandis, 2015). Posner and Pandis used a “zero-out” method in combination
with source-specific size distribution to study the percent contribution of
six major sources (on-road gasoline, industrial, non-road diesel, on-road
diesel, biomass, and dust) of Nx. They found that Nx was made up of
36 % on-road gasoline, 31 % industrial, 18 % non-road diesel, 10 %
on-road diesel, 1 % biomass burning, and 4 % long-range transport
(Posner and Pandis, 2015). The emissions particle number inventory
was normalized based on PM10 mass from each source and particle
emissions from natural gas sources were assumed negligible, which
effectively removed natural gas sources from the simulation. This has minor
effects on PM2.5 and PM10 predictions, but the results of the
current study suggest that natural gas combustion significantly contributes
to ultrafine particle concentrations.
Future epidemiological studies may be able to differentiate PM0.1 and
PM2.5 health effects by contrasting cities with different predicted
ratios of PM0.1/PM2.5. Although the current study does not
calculate the annual average concentrations that would be needed for such an
analysis, the results for the peak photochemical episodes may provide some
useful insights to guide future studies. Figure 10
illustrates the correlation between predicted PM2.5 and PM0.1
concentrations in the 39 cities considered in the current analysis,
Fig. 11 illustrates the ratio of
PM0.1/PM2.5 for each city, and Fig. 12 illustrates a field
plot showing the ratio of PM0.1/PM2.5 across the continental US.
Cities with higher PM0.1/PM2.5 ratios include Houston, TX, Los
Angeles, CA, Salt Lake City, UT, Cleveland, OH, and Bakersfield, CA. Cities
with lower PM0.1 to PM2.5 ratios include Lake Charles, LA, Baton
Rouge, LA, St. Louis, MO, Baltimore, MD, and Washington, D.C. Measurements should
be conducted in these locations to verify the contrast in PM0.1/PM2.5 concentrations in preparation for future exposure analysis.
Conclusion
The UCD–CIT regional chemical transport model was used to predict source
contributions to PM0.1 across the continental US during peak
photochemical smog periods in the year 2010. Performance for PM2.5 and O3 predictions met or exceeded the criteria typically used for
regional air quality model applications, building confidence in the emissions
inputs and meteorological fields used to drive the calculations. Similar
model exercises carried out for episodes in California in 2015 and 2016 find
good agreement between predicted PM0.1 source contributions and
receptor-based PM0.1 source contributions calculated using measured
concentrations of molecular markers (Yu et al., 2019). In the
current study, predicted regional PM0.1 concentrations exceeded 2 µgm-3 during summer pollution episodes in major urban regions
across the US including Los Angeles, the San Francisco Bay Area, Houston,
Miami, and New York. Predicted PM0.1 spatial gradients were sharper
than predicted PM2.5 spatial gradients due to the dominance of
primary aerosol in PM0.1. This finding suggests that the PM0.1
measurement networks needed to support epidemiology must be denser than
comparable PM2.5 measurement networks. Nonresidential natural gas
combustion was identified as a major source of PM0.1 across all major
cities in the US. On-road gasoline and diesel vehicles contributed on
average 14 % to regional PM0.1 even though peak contributions within
0.3 km of the roadway were not resolved by the 4 km grid cells. This is
consistent with other studies that have found an exponential decrease in
ultrafine particle concentrations downwind of major roadways
(Wang et al., 2011) due to the sharp gradient of PM0.1. Cooking also made significant contributions to PM0.1 in all cities, but
biomass combustion was only important in locations impacted by summer
wildfires. Aviation was a significant source of PM0.1 in cities that
have airports within their urban footprints. The major sources of primary
PM0.1 and PM2.5 were notably different in many cities. Future
epidemiological studies may be able to differentiate PM0.1 and
PM2.5 health effects by contrasting cities with different ratios of
PM0.1/PM2.5 sources.
Data availability
All of the PM0.1 and Nx outdoor exposure
fields produced in the current study are available free of charge at
http://faculty.engineering.ucdavis.edu/kleeman/ (last access: July 2019). The model
source code and input data are available to collaborators through direct
email request to the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-9399-2019-supplement.
Author contributions
MAV prepared model input data, performed model simulations,
postprocessed model output, and prepared the initial draft of the
paper. XY created the nucleation module used in model calculations.
MJK designed the study, created the models used for the calculations,
assisted in model simulations, assisted in postprocessing model output, and
revised the final paper.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Neither CARB nor any person acting on their behalf (1) makes any warranty, express or implied, with respect to the use of any
information, apparatus, method, or process disclosed in this report or (2) assumes any liabilities with respect to the use and/or damages resulting from the
use or inability to use any information, apparatus, method, or process
disclosed in this report.
Acknowledgements
This research was supported by the California Air Resources Board under
project no. 14-314.
Financial support
This research has been supported by the California Air Resources Board (grant no. 14-314).
Review statement
This paper was edited by Veli-Matti Kerminen and reviewed by three anonymous referees.
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