Introduction
Vehicular engine emissions are known to degrade air quality in urban areas.
Besides gaseous compounds (e.g. CO, NOx, hydrocarbons and volatile organic
compounds), vehicle exhaust contains significant amounts of primary
particulate matter (PM) (e.g. Maricq, 2007; Keuken et al., 2013; Gordon et
al., 2014a). Primary particulate matter refers to particles directly emitted
from the engine, the fuel combustion process or the brakes that have not yet experienced
any significant chemical transformation in the atmosphere. Depending on the
engine and fuel type, primary exhaust PM emissions from vehicles consist
mainly of soot and different fuel and lubricating oil components (Maricq,
2007; Canagaratna et al., 2010; Karjalainen et al., 2014). In addition to
primary PM, burning processes in engine cylinders produce so-called delayed
primary aerosol. Delayed primary aerosol includes species like sulfuric acid which
occur in tailpipe conditions in a gaseous phase but will condense or nucleate
immediately when the exhaust is cooled and diluted without any significant
chemical transformation in the atmosphere (Arnold et al., 2012;
Rönkkö et al., 2013; Pirjola et al., 2015). In particle number size
distribution, the exhaust PM formed by these different processes is
frequently seen in separate modes with different concentrations and particle
size ranges (Kittelson, 1998; Rönkkö et al., 2013). In addition to
primary emissions, large amounts of secondary particulate matter form after
the exhaust gases are released into the atmosphere (Chirico et al., 2010).
Secondary particulate matter forms in the atmosphere via gas-to-particle
conversion as oxidation processes typically lower the volatility (vapour
pressure) of gaseous compounds. The difference between delayed primary and
secondary emissions is that secondary emissions form through different
transformation processes in the atmosphere, whereas delayed primary
emissions form in the cooling process without any significant chemical
transformation due to external conditions such as ultraviolet light (UV) or
atmospheric oxidants. While a large number of studies have focused on
vehicular primary particulate emissions (Giechaskiel et al., 2005; Maricq,
2007; Lähde et al., 2010; Karjalainen et al., 2014), a relatively limited
number of studies have focused on secondary emissions.
Both batch chambers (such as smog chambers) and flow-through chambers
combined with modern online composition analysis (e.g. AMS) have been used to
study vehicular secondary aerosol emissions in both laboratory and ambient
conditions. Smog chambers have been used to study the composition of the
primary and secondary PM in the exhaust emissions of gasoline and diesel
vehicles, the influence of after-treatment on secondary aerosol formation for
diesel vehicles, the fraction of the emissions that forms secondary organic
aerosol (SOA) and the relative importance of primary PM emissions versus SOA
formation (e.g. Nordin et al., 2013; Platt et al., 2013; Chirico et al.,
2014; Gordon et al., 2014a; Presto et al., 2014). A batch chamber is good for
detailed oxidation process studies (e.g. Chirico et al., 2014; Suarez-Bertoa
et al., 2015), but cannot be used to differentiate the rapidly changing
driving conditions during the test driving cycle. Flow-through chambers, such
as the potential aerosol mass (PAM) chamber, are designed to simulate
secondary aerosol mass formation potential on a nearly real-time basis (Kang
et al., 2011; Lambe et al., 2011). Several studies have been recently
published for which the PAM chamber was used to study the vehicular emissions
from gasoline, diesel and flex-fuel vehicles (e.g. Kroll et al., 2012;
Suarez-Bertoa et al., 2015; Karjalainen et al., 2016; Jathar et al., 2017).
These studies have shown that secondary particulate emissions from combustion
engines mainly consist of organic compounds and ammonium nitrate (Karjalainen
et al., 2016; Suarez-Bertoa et al., 2015) and that the secondary PM emissions
can be significantly larger than the primary emissions if the conditions
favour secondary aerosol formation (Giechaskiel et al., 2005; Chirico et al.,
2010; Karjalainen et al., 2016). In gasoline vehicles, the exhaust
emissions of secondary aerosol precursors have been shown to depend on
driving conditions, fuels and the operation of catalytic converters (Durbin
et al., 2007; Maricq et al., 2012; Gordon et al., 2014b; Karjalainen et al.,
2016). Also, previous studies indicate that gasoline vehicles have an impact
on secondary aerosol concentrations in urban areas (Nordin et al., 2013;
Tkacik et al., 2014; Karjalainen et al., 2016; Suarez-Bertoa et al., 2015).
However, the secondary aerosol formation potential and the composition as a
function of the driving situation for fuels with different ethanol contents
(E10–E100) remain poorly characterized in the literature.
The hydrocarbons of gasoline typically include 4–12 carbon atoms with a
boiling range between 30 and 210 ∘C (Owen and Coley, 1995). These
may be present in the exhaust gases as unburned hydrocarbons. In addition,
exhaust gases contain compounds formed in combustion and originating
from engine oil. Lipari (1990) analyzed 103 individual hydrocarbons up to
C12 in a study with flex-fuel
vehicles (FFVs) using the gasoline- and methanol-containing fuels M85 and M100
(85 and
100 % methanol). Toluene, ethylene, propylene, isobutylene,
isopentane, pentane, benzene and iso-octane represented 55 % of the total
hydrocarbons for gasoline. These gaseous compounds are emitted into the atmosphere
directly, or they are evaporated from primary exhaust particles when the
exhaust is diluted (Robinson et al., 2007). The oxidation products of organic
compounds may contain one or more functional groups, such as alcohol (-OH),
aldehyde (-CHO), carboxylic acid (-COOH), nitro (-NO2) and nitrate
(-NO3) or organic sulfate (-OSO3). Ambient photochemical
reactions take place in the presence of NO2, volatile organic compounds
(VOCs), heat and sunlight. Hundreds of different VOC species can participate
in thousands of photochemical reactions (Drechsler et al., 2004). The different
possible photo-oxidation pathways are also dependent on the conditions. Aromatic BTEX (BTEX; benzene, toluene, ethylbenzene and xylenes; the VOCs
typically found in petroleum derivates) compounds have been suggested to
depend on, for example, the prevailing NOx concentrations during the aging
process (Andino et al., 1996; Hurley et al., 2001; Sato et al., 2007, 2012).
The European Union has set an obligation that the share of renewable energy
should be at least 10 % in the transportation sector by 2020 (Directive
2009/28/EC). Ethanol is the dominant bio-component in transport fuels
worldwide. However, in Europe its share in gasoline is limited to
10 % vol, which is equivalent to
approximately 6 % energy content (Directive 2009/30/EC). Higher ethanol
concentrations up to 85 % vol (E85) can be used in special flex-fuel
vehicles. Previous studies have shown that primary PM, CO, HC, NOx and
aromatic hydrocarbon emissions are typically lower for the E85 fuel than for
gasoline, whereas ethanol, acetaldehyde, formaldehyde and methane emissions
increase with the increasing ethanol content of gasoline (Yanowitz et al., 2013;
Aakko and Nylund, 2003; Karlsson et al., 2008; Westerholm et al., 2008;
Clairotte et al., 2013). In order to reduce the detrimental effects of
pollution caused by vehicles, the emission standards for vehicle PM emissions
are tightening globally. However, it must be noted that in the
emission standard laboratory tests, the PM mass is measured directly after the
tailpipe from a filter sample at an elevated temperature and thus represents
mainly primary non-volatile PM emissions. As previous studies have
demonstrated (e.g. Chirico et al., 2010; Nordin et al., 2013; Platt et al.,
2013; Suarez-Bertoa et al., 2015), the secondary PM emissions formed from
gaseous precursors can be significantly larger than primary PM emissions,
meaning that the emission limits do not necessarily regulate secondary PM
emissions.
In order to properly quantify vehicular engine emissions, the whole
transformation chain, from freshly emitted primary PM and gaseous compounds
to aged secondary PM measured in urban air quality stations, has to be better
understood. The main objective of this study was to investigate primary
particulate emissions and simulate the secondary aerosol formation potential
of vehicular emissions with an oxidation flow chamber when the ethanol
content in the fuel increases. The measurements were carried out with a modern FFV
using fuels with three different ethanol contents (10, 85 and
100 %). A comprehensive set of instruments was used for measuring gaseous
emissions together with the chemical composition and size distributions of
the
primary and secondary particles. All measurements were done with high time-resolution instruments and with the PAM flow-through oxidation chamber. The
measurement set-up enabled the characterization of the concentration and
composition changes during different parts of the driving cycle.
Experimental design
Measurement set-up and sampling
The measurement set-up of this study is described in detail by Karjalainen et
al. (2016). The article by Karjalainen et al. (2016) is focused on the
primary and secondary emissions of a flex-fuel vehicle using E10 fuel,
whereas this article is focused on the influences of the fuel alcohol content
on particulate and gaseous emissions and their composition. In this study,
the emissions from a flex-fuel passenger car (model year 2011; 1.4 L
turbocharged direct-injection spark-ignition (DISI) engine; Euro 5) were
measured on a chassis dynamometer at 23 ∘C using three different
fuels (E10, E85 and E100; gasoline with 10, 85 and 100 % alcohol). A
schematic figure of the measurement set-up is shown in Fig. S1 in the
Supplement. The FFV vehicle was conditioned according to the manufacturer's
instructions, and the adaptation of the car to the new fuel was monitored.
Preparation needs and stability issues related to the FFV cars were based on
an earlier project (Aakko-Saksa et al., 2014). The driving cycle was the New
European Driving Cycle (NEDC; cycle profile shown in Fig. S2). NEDC totals
11.0 km divided into three test phases to study emissions at a cold start
and with a warmed-up engine. The first part of the NEDC, the urban driving
cycle (UDC), is repeated twice. The first phase, CSUDC, represents urban
driving with a cold start (0–391 s; cold start UDC). The second phase,
HUDC, represents typical urban driving (392–787 s; hot start UDC). The last
phase, EUDC, represents highway driving (788–1180 s; extra-urban driving
cycle). NEDCs were run on separate days in order to enable cold start
conditions for each fuel. The test fuels were regular commercial E10 (max
10 % ethanol), E85 (85 % ethanol), and E100 (100 % ethanol). To
avoid engine problems related to lean ethanol, deionized water was added to
E100 to adjust the water content to 4.4 % (m/m). A more detailed
description of the car preparation and the driving cycle is given in the
Supplement. Particle sampling was conducted with a partial exhaust sampling
system (Ntziachristos et al., 2004) at the exhaust transfer line. The
sampling system consisted of a porous tube diluter (a PTD with a dilution
ratio, or DR, of 12), a residence time chamber (2.5 s) and a secondary
dilution conducted with a Dekati Diluter (DR = 8; Dekati Ltd., Kangasala,
Finland). Regarding particle formation by nucleation, the sampling system
mimics the exhaust dilution and nanoparticle formation processes in the
atmosphere (Rönkkö et al., 2006; Keskinen and Rönkkkö, 2010).
Two NEDC tests were conducted for each fuel. While some parameters were monitored similarly during both NEDC cycles (gaseous emissions, particle size distribution of primary exhaust particles; results shown in Table S1), the extensive study for the differences between primary and secondary particle emissions could only be conducted once per fuel.
The PAM chamber was used to evaluate the secondary aerosol formation potential
during the NEDC. The PAM chamber is a small flow-through
chamber that is irradiated with ultraviolet light (wavelengths of 185 and
254 nm) to form high concentrations of oxidants (O3, OH and HO2) that
can initiate the production of secondary aerosol particles (Kang et al.,
2007, 2011; Lambe et al., 2011). High oxidant concentrations (up to 1000-fold
to atmosphere, with the same oxidant ratios as in the atmosphere) and high UV
lights ensure the fast oxidation of compounds (Kang et al., 2007). The aging
as the sample flows through the chamber is shown to represent up to several
weeks of aging in the atmosphere (Kang et al., 2011; Ortega et al., 2013).
The PAM chamber has been thoroughly characterized in previous studies. These
studies include a loss characterization, a comparison to other chamber studies
and a comparison on how the SOA formed in a chamber compares to the SOA observed
in the ambient atmosphere and the SOA produced in large environmental chambers
(e.g. Kang et al., 2007, 2011; Lambe et al., 2011, 2015; Tkacik et al.,
2014; Peng et al., 2016). The PAM chamber used in this study is described in
detail by Karjalainen et al. (2016). The PAM chamber was installed
between the residence time chamber and the secondary dilution unit of the sampling
system (Fig. S1). The particle instrumentation was located downstream of the
secondary diluter. The sample flow through the PAM chamber was set to
9.75 L min-1, resulting in an average residence time of 84 s. The
voltage of the two UV lamps was at the maximum value of 190 V. The sample
conditions during the test were fairly stable; typically, the relative humidity
was 60 %, the temperature was 22 ∘C and the ozone concentration was 6 ppm. All
cycles were first run without the PAM chamber to measure the primary emissions and then with the PAM chamber in order to study the formation of secondary
particles. The secondary aerosol in the PAM chamber is formed when low volatility
vapours condense on aerosols or form new particles. In the PAM chamber, these
vapours may also condense onto walls, exit the chamber, or react with OH,
which leads to fragmentation and an increase in the saturation vapour
pressure. Thus, the potential aerosol mass is underestimated if these
chamber-related
losses of low volatile vapours are not taken into account. We used the
LVOC (low volatility organic compound) fate model presented by Palm et
al. (2016) to estimate the
losses of the condensing organic vapours in the PAM chamber (model available at
https://sites.google.com/site/pamwiki/hardware/estimation-equations).
PM losses in the chamber were studied in the laboratory using a similar PAM
chamber as in the measurements. The Supplement includes a detailed
description of the loss calculations and the measured PM losses as a function
of the
particle size. Losses of primary PM in a PAM chamber (Fig. S3) are
generally small, especially in the particle sizes that contain most of the
aerosol mass: 25 % at 50 nm, 15 % at 100 nm and below 10 %
above 150 nm. Also, because of the high condensational sink, over 95 %
of the LVOCs condensed on aerosol in all cases according to the LVOC fate model.
Thus, the chamber-related losses of the LVOCs and the PM are small.
The PAM chamber was calibrated following the procedure described by Lambe et
al. (2011). According to this offline calibration, the upper limit average of
OH exposure during the experiments was
1.0 × 1012 molec. cm-3 s, corresponding to an atmospheric
aging of 8 days (assuming an average OH concentration of
1.5 × 106 molec. cm-3 in the atmosphere; Mao et al.,
2009), but the real OH exposure is lower due to high concentrations of OH-reactive gases in the exhaust. This effect is significant, especially at the
beginning of the cycle (CSUDC) when the concentrations are high. The average
external OH reactivities (OHR) due to VOCs and CO in the CSUDC were 1246, 1141
and 3441 s-1 for E10, E85 and E100, respectively. The higher OHR of
E100 is due to the high ethanol and aldehyde concentrations. After the CSUDC, the
average OHR is below 90 s-1 for all fuels. More detailed OHR
calculations are shown in Tables S2–S4.
We estimate the OH exposure in the PAM by using a simple photochemical box
model made by William Brune, in which the differential equations describing
the chemical reactions are solved using Euler's method. More details and the
source code of the model are found in the PAM users manual
(https://sites.google.com/site/pamusersmanual/7-pam-photochemistry-model).
The free parameters in the model are photon fluxes at 254 and 185 nm of
wavelength. Based on the offline calibration, the best-fit values for the
photon fluxes are 7.3×1014 and 1.3×1013 photons cm-2 s-1 for the 254 nm wavelength and the 185 nm
wavelength, respectively. The inputs for the model are OHR due to VOCs, CO
concentration, NO concentration and NO2 concentration. In the model,
SO2 is used as a proxy for VOCs; i.e. in the model, the OHR of SO2
equals the input OHR due to VOCs. This method is reasoned to be a realistic
approximation by Peng et al. (2015) in terms of estimating the OH exposure.
The input values for the model are obtained from 1 s time resolution
measurements of CO, NOx and total hydrocarbons (THC) corrected with the
residence time distribution caused by the PAM chamber. The residence time
distribution is obtained from the CO2 pulse experiment presented by
Lambe et al. (2011). The concentrations of the individual VOCs are estimated
using the high time-resolution THC concentration and the distribution of VOCs
in different phases of the driving cycle (see Tables S2–S4). The OHR due
to VOCs is obtained from these concentrations and the respective reaction
constants. The OH exposure in the PAM was modelled at a 20 s time interval for
each driving cycle, and the average OH exposures for the cycles are presented
in Table 1.
Particle measurements
The Soot Particle Aerosol Mass Spectrometer (SP-AMS; Aerodyne Research Inc.,
Billerica, MA, USA) was used to measure the chemical composition of the emitted PM. The SP-AMS is
a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) with
an
added laser (intracavity Nd:YAG, 1064 nm) vaporizer. The dual vaporizer
system enables the real-time measurements of PM mass and the size-resolved
chemical composition of submicron non-refractory particulate matter,
refractory black carbon and some metals and elements (e.g. Na, Al, Ca, V, Cr,
Mn, Fe, Ni, Cu, Zn, Rb, Sr and Ba; Carbone et al., 2015). The HR-ToF-AMS is
described in detail by Jayne et al. (2000) and DeCarlo et al. (2006), and the
design of the SP-AMS is described by Onasch et al. (2012). In the SP-AMS, an
aerodynamic lens is used to form a narrow beam of particles that is
transmitted into the detection chamber. The particles are vaporized either by
a
tungsten vaporizer (600 ∘C) to analyze the non-refractory inorganic
species and organics and/or with the laser in order to analyze the refractory
black carbon (rBC) and the metals in addition to the inorganics and organics attached
to these particles. The vaporized compounds are ionized using an electron impact
ionization (70 eV), and the formed ions are guided to the time-of-flight chamber
and to the multi-channel plate (MCP) detector. A 5 s averaging time
and a dual vaporization system, with both laser and tungsten oven operating,
was used in the measurements. Only the V-mode data are used in this study. For
the SP-AMS, the 1 s and 1 min 3σ detection limits for submicrometer
aerosol are < 0.31 µg m-3 and
< 0.03 µg m-3, respectively, for all species in the V-mode
(DeCarlo et al., 2006; Onasch et al., 2012). The CO2 concentrations during
the measurements were significantly higher (up to 1450 ppm) than the atmospheric
values, and thus the CO2 time series was used to correct the artefact caused by
gaseous CO2.
The average OH exposures during the driving cycles.
Fuel
Average OH exposure (h)
CSUDC
HUDC
EUDC
E10
6.2
35.3
33.3
E85
5.0
13.0
14.1
E100
3.9
16.2
22.5
The collection efficiency (CE) value, representing the fraction of the sampled
particle mass that is detected by the MCP detector, is required for the
calculation of the aerosol mass concentration measured by the AMS. Previous
studies have shown that the collection efficiency of an aerosol mass
spectrometer is affected by particle losses (i) during transit through the
inlet and the lens, (ii) by particle beam divergence for both tungsten and laser
vaporizers and by (iii) the bounce effects from the tungsten vaporizer (Matthew et
al., 2008; Huffman et al., 2009; Onasch et al., 2012). Willis et al. (2014)
demonstrated that particle morphology also affects the SP-AMS particle beam
width, which in turn affects the collection efficiency through the overlap of
the particle beam and the laser beam. Similar to Karjalainen et al. (2015), a
CE of 1 was used in this study for all SP-AMS data. We acknowledge that it
is likely that the collection efficiency might be underestimated for thinly
coated primary emissions, whereas the used CE of 1 is likely closer to the
correct value for heavily coated spherical secondary aerosol. Also, we note
that gasoline soot, consisting of agglomerates with an average diameter below
90 nm, will likely have a low transmission efficiency in the aerodynamic lens
and thus might have a lower collection efficiency than regal black, which is
typically used for calibration.
The particle number size distributions were measured using a time resolution
of 1 Hz with a high-resolution low-pressure cascade impactor (HR-LPI,
Dekati Ltd., Finland;
Arffman et al., 2014) and an engine exhaust particle sizer (EEPS; TSI Inc.,
Shoreview, MN, USA;
Mirme, 1994; Johnson et al., 2004). The particle number concentration was
also measured with an ultrafine condensation particle counter (UCPC; TSI
Inc.;
model 3025). The UCPC was located downstream of an additional diluter
(the operation principle based on the partial filtration of the sample;
DR = 42) to ensure that the concentrations to be measured were within its
measurement range. All the data shown below have been corrected by a total
dilution ratio for each instrument; thus, the presented values represent the
tailpipe concentrations.
A summary of the measured gaseous compounds, the instruments used and their
detection limits.
Instrument
Sampling
Measured compound
Detection limit
Flame ionization detector (FID)
Online
Total hydrocarbon concentration (THC)
3 ppm
GC (HP 5890 Series II)
Offline collection with Tedlar bag
C1–C8 hydrocarbons including methane, ethane, ethene, propane, propene, acetylene, isobutene, 1,3-butadiene, benzene, toluene, ethyl benzene and m-, p- and o-xylenes
0.02 mol m-3, corresponding to approximately 0.1 mg km-1 for methane, 0.5 mg km-1 for 1,3-butadiene and 0.7 mg km-1 for benzene
HPLC (Agilent 1260 UV detector; Nova-Pak C18 column)
Offline collection with 2,4-dinitrophenylhydrazine (DNPH) cartridges
Aldehydes; formaldehyde, acetaldehyde, acrolein, propionaldehyde, crotonaldehyde, methacrolein, butyraldehyde, benzaldehyde, valeraldehyde, m-tolualdehyde and hexanal
0.01 mg km-1
Fourier transformation infrared (FTIR; Gasmet CR2000)
Online
CO, NO, NO2, N2O, ammonia, methanol, ethanol, isobutanol, n-butanol, ETBE, formaldehyde and acetaldehyde
2–13 ppm at 1 s measurement interval corresponding to mass concentration of 1–15 mg km-1 over the European test cycle (Table S6)
The particle number size distributions measured by the HR-LPI can be used to
estimate how the particle losses in the PAM affect the measured total
particle mass. If the measured HR-LPI number size distributions are corrected
with the particle loss curve (Fig. S3 in the Supplement), the total mass
calculated from the number size distribution increases by 9–16 %
depending on the phase of the cycle and the fuel (see Table S5 for details).
The masses measured by the SP-AMS cannot be corrected in a similar way, since
the SP-AMS did not measure the particle size distributions; different
chemical species might also be located in differently sized particles. Thus, the
SP-AMS results presented in the following sections are not corrected for the
particle losses in the PAM, but we expect that the loss of organic mass
due to PAM wall losses to be of a similar order as the loss of total HR-LPI mass.
Gaseous phase composition measurements
The total hydrocarbon (THC) concentrations were measured with a flame ionization
detector (FID) developed for the standardized exhaust emission test
procedures of cars. The FID detects all carbon-containing compounds, for
example carbonyl compounds, in addition to hydrocarbons (HC;
Sandström-Dahl et al., 2010; Aakko-Saksa et al., 2014). In addition,
samples were collected using Tedlar bags (Sigma-Aldrich, St. Louis, MO, USA) for subsequent analysis by a gas
chromatograph (HP 5890 Series II, Al2O3 KCl PLOT column; Agilent Technologies, Santa Clara, CA, USA; an external
standard method). The analyzed hydrocarbons (from C1 to C8)
included methane, ethane, ethene, propane, propene, acetylene, isobutene,
1,3-butadiene, benzene, toluene, ethyl benzene and m-, p- and o-xylenes.
Besides HCs, the selected aldehydes were analyzed by collecting diluted exhaust
gas samples from a constant volume sampler (CVS) using
2,4-dinitrophenylhydrazine (DNPH) cartridges. The DNPH derivatives were
extracted with an acetonitrile and water mixture and analyzed using HPLC (high
performance liquid chromatography) technology (Agilent Technologies, 1260 UV
detector;
Nova-Pak C18 column, Waters Corporation, Milford, MA, USA). The aldehydes analyzed include formaldehyde, acetaldehyde,
acrolein, propionaldehyde, crotonaldehyde, methacrolein, butyraldehyde,
benzaldehyde, valeraldehyde, m-tolualdehyde and hexanal. Ethanol and a number
of other compounds were measured online using a Fourier transformation
infrared (FTIR) analyzer (Gasmet Technologies, Helsinki, Finland; CR2000). A summary of the measured gaseous
compounds, the instruments used and their detection limits is shown in Table 2.
In these measurements, the sum of the hydrocarbons (HCs) analyzed by GC, FTIR and
HPLC (the sum of HC from the GC + HC portions of ethanol and acetaldehyde)
resulted in an HC sum of 15 mg km-1 for E10, 30 mg km-1 for E85
and 216 mg km-1 for E100. The respective THC (FID) results were 22, 30
and 193 mg km-1. This indicated that, on average, 73 % of THCs (FID)
were analyzed for E10, and 100 % were analyzed for E85 and E100 by the GC, FTIR and
HPLC.
Results
Gas phase emissions
The composition of the gas phase emissions was observed to change when the
ethanol content of the fuel changed (Fig. 1). As the ethanol content
increased, a clearly detectable decrease was observed in both the average NOx
and ammonia concentrations during the measurement cycle. A decrease in NOx
with increasing ethanol content is likely caused by decreased flame
temperature (Turner et al., 2011). For instance, Turner et al. (2011) reported
a a decrease of about 20 to 40 ∘C in the exhaust temperature of a DISI
engine when the ethanol content of the fuel changed from 0 to 100 %.
Simultaneously, they reported that the NOx emissions decreased from 8 to
0.5 g kWh-1, with both values also slightly depending on the ignition timing
and strategies. Decreasing trends in the NOx emissions have also been
observed by Maricq et al. (2012), who reported decreases of 20 % in the
NOx emissions when the ethanol content increased to values of more than
17 %.
The mean concentrations of the gaseous compounds for different fuels
measured during the NEDC.
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Ammonia is formed in the reactions of the three-way catalyst (TWC;
Mejia-Centeno et al., 2007). In theory, ammonia formation is enhanced in
slightly rich air-to-fuel ratios at high temperatures (aggressive
accelerations) when sufficient HC and NOx concentrations are present
(Heeb et al., 2006; Mejia-Centeno et al., 2007; Li et al., 2010). Engine-out
emissions were not measured here, but it is assumed that the HC and NOx
concentrations were not a limiting condition for ammonia formation. A decrease
in the ammonia emissions for the E85 fuel indicates an enleanment of the
conditions in the TWC catalyst when compared with those for the E10 fuel.
Clairotte et al. (2013) also reported lower ammonia emissions for the E85
fuel than for the E5 fuel. The decreases in both ammonia and NOx lead
to a decreased contribution to the secondary aerosol formation of ammonium
nitrate in the atmosphere when the ethanol content is increased.
The amount of hydrocarbon emissions typically depends on the combustion
conditions and exhaust after-treatment by catalytic devices. In this study,
the test vehicle was equipped with a three-way catalytic converter with an
effectivity which depends on the exhaust temperature and also on the hydrocarbon
properties. In this study, the composition of the hydrocarbon emissions was
observed to be strongly dependent on the ethanol content; as the ethanol
content increased in the fuel, short-chain non-aromatic hydrocarbons and
aldehydes increased in the exhaust, while a decreasing trend was observed for
all measured aromatic hydrocarbon compounds. Also, as the ethanol content of
the
fuel increased, the exhaust concentrations of formaldehyde, acetaldehyde,
ethanol, methanol, ethene and acetylene increased, whereas the exhaust
concentrations of NOx, ammonia, PM and BTEX decreased (Fig. 1).
Composition of primary particulate<?xmltex \hack{\break}?> matter emissions
The chemical composition of the primary particulate emissions was observed to
vary for different fuels (Fig. 2). The concentration for each chemical component
in units of mg km-1 for both the primary and secondary emissions is shown in
the Supplement (Fig. S4). For E10, approximately
half of the primary PM emissions was composed of rBC, and the other half was composed of organics. The
contribution of inorganic species (sulfate, nitrate, ammonium and chloride) to
PM mass was small (1.2 %). From inorganic ions, sulfate had the highest
contribution (47–67 % of the mass of the inorganic ions) for all fuels. A clear
decrease in the rBC concentration and its contribution to the total emitted
primary PM was observed as the ethanol content of the fuel increased (E10,
rBC 53 %; E85, rBC 31 %; E100, rBC 25 %). The contribution of
organic matter increased from 46 % (E10) to 65 % for E85 and 75 %
for E100. The organics-to-rBC ratios for the E10, E85 and E100 were 0.9, 2.1 and
3.1, respectively.
The chemical composition of the primary particulate emissions for E10, E85
and E100 (a). The concentrations of the inorganic ions (b).
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Figure 3a shows the elemental ratios (oxygen-to-carbon ratio, O : C;
hydrogen-to-carbon ratio, H : C) for the primary organic PM. All values are
average values over the NEDC. The average elemental composition and
elemental ratios (O : C and H : C) are calculated using a method developed
by Aiken et al. (2007) in which the elemental composition is calculated using all
measured fragment ions observed in high-resolution mass spectra and H : C
and
O : C calibration factors derived from laboratory measurements of standard
organic molecules. Canagaratna et al. (2015) further developed the elemental
analysis to contain a wider range of organic species that are more
representative of ambient organic aerosol (OA) species. The improved ambient
ratios (IA) calculated according to the method published by Canagaratna et
al. (2015) are also shown in Fig. 3. Rather similar O : C values
(0.35–0.4) were observed for the primary organic fraction for all fuels.
The observed H : C for the primary emissions of E10 and E85 (∼ 1.5) was
slightly lower than for E100 (1.7). The observed elemental ratios are comparable
to the elemental ratios typically observed for hydrocarbon-like organic aerosol
(HOA), representing traffic emissions in the ambient atmosphere and the elemental
ratios observed in chamber studies (Tkacik et al., 2012; Timonen et al.,
2013; Carbone et al., 2014; Canagaratna et al., 2015). We note that these
O : C ratios measured with an SP-AMS can be slightly higher than the ratios typically
measured with an AMS for the primary emissions from gasoline vehicles due to the
fact that the SP-AMS also detects the refractory organic species
(rCOx) incorporated on the structures
of black carbon particles (Corbin et al., 2014).
The average elemental composition (O : C and H : C ratios) of
the emitted primary (a) and secondary (b) PM during the NEDC
for E10, E85 and E100. The elemental composition is calculated using
the original method developed by Aiken et al. (2007) and using a revised method
(marked with IA to indicate improved ambient) containing the more atmospherically
relevant organic compounds developed by Canagaratna et al. (2015).
![]()
The composition of the particulate emissions after the PAM chamber for E10,
E85 and E100 (a; in the left figure, all E10 results are divided by
10 in order to show all results in the same y axis). The concentration of
the
inorganic ions and rBC after the PAM chamber for E10, E85 and E100 (b).
![]()
Composition of secondary aerosol simulated with a PAM chamber
The secondary aerosol formation in the PAM chamber increased the contribution of
both organic and inorganic compounds (Fig. 4). For all fuels, most of the
particulate matter observed after the PAM chamber (E10, 89 %; E85,
79 %; E100, 61 %) consisted of organic compounds. The organics-to-rBC ratios for the secondary emissions of E10, E85 and E100 were 12, 8.3 and
3.1, respectively. The main inorganic ions observed after the PAM chamber
were nitrate, sulfate and ammonium. When the ethanol content of the fuel
increased, the relative contribution of the inorganic ions (E10, 4 %;
E85,
12 %; E100, 19 %) in the exhaust PM increased after the PAM chamber.
Organic aerosol
Figure 3b shows the average elemental ratios over the NEDC calculated
for the organics from the SP-AMS data. For E10, an increase in O : C (from 0.4 to
0.6) and a decrease in H : C (from 1.55 to 1.45) was observed when the PAM
chamber was used. In contrast, no change in O : C or H : C was observed
for E85 when using the PAM chamber. For E100, a slight decrease in O : C
and H : C values was observed when using the PAM chamber. Similar elemental
ratios (O : C 0.4–0.7) (Nordin et al., 2013; Suarez-Bertoa et al., 2015)
have been observed for the secondary PM emissions in previous batch chamber
studies. As shown for the gaseous exhaust compounds (Sect. 3.1), the
composition and concentrations of the gaseous precursors change when the ethanol
content of the fuel increases, also causing a clear change in the observed secondary
aerosol composition and the oxidation state.
Figure 5 shows the contribution of the different organic fragments CxHy
(hydrocarbons with CxHy+), CxHyO (fragments with one
oxygen atom CxHyO+, e.g. CO+ and CHO+) and
CxHyOz (hydrocarbon compounds containing several oxygen atoms
CxHyOz,z>1+, e.g. CO2+) for all the fuels with and
without the PAM. The contribution of CxHyO+ to organics
increased after the oxidation of the exhaust sample in the PAM chamber for all
the fuels, whereas the contributions of CxHyOz,z>1+ and
CxHy+ decreased. For E10, the contribution of the sum of the oxidized
compounds (CxHyO+ and CxHyOz) on exhaust PM
increased from 35 to 62 %. For E85, the contribution of the oxidized organic
compounds increased from 42 to 57 %, whereas for E100 the contribution of
the
oxidized organic compounds (approximately 62 %) remained the same with
the PAM chamber when compared to the primary emissions. For all fuels, the total
contribution of the oxidized compounds (CxHyO,
CxHyOz) increased in the PAM chamber when compared to the contribution
of hydrocarbons. For E10 and E85, the absolute concentration organic fraction and
the total mass concentration of each organic hydrocarbon group increases in
the PAM chamber, although the contribution of CxHyO slightly decreases, as
shown in Fig. 5. Also, the mass spectra (Figs. S5–S16) show that in
the hydrocarbon composition, clear differences can be observed. For E100,
both the
contributions and concentrations of the different organic families are similar
with and without the PAM chamber; however, a change in the composition of these
hydrocarbon groups is observed once again.
Refractive black carbon
Refractive black carbon (rBC) is formed during incomplete combustion and is
always considered a primary emission; therefore, the rBC concentrations with and
without the PAM chamber should be the same. Also, the measurements of the regulated
emissions (Table S1) show that the cycles were repeatable and that the rBC
concentrations for both cycles should be on the same level. However, some
differences in the rBC concentrations were observed when the primary rBC
concentrations were compared to the rBC results measured after the PAM
chamber. For E85 and E100, a slight decrease in rBC after the PAM chamber was
observed (20–30 %). This decrease is likely explained by losses of the
primary PM in the chamber. Karjalainen et al. (2016) showed that the particle
losses in the PAM chamber were on a similar level as the rBC losses seen here
(approximately 8–30 % for particle sizes of 50–400 nm). However, in
contrast for E10, a clear increase in rBC (from 240 to
480 µg m-3) was observed when the emissions during the driving
cycle were measured with the PAM chamber. In the SP-AMS, rBC is calculated as
a sum of the carbon fragments Cx+. To explore the observed increase in
rBC for E10 after the PAM chamber, the Cx fragments before and after the
PAM chamber were studied. The increase after the PAM chamber for E10 was seen
in all Cx fragments (Fig. S17; the ratios w PAM and w/o PAM from 1.6 to 2.8
for the
C2–C5 fragments). The main fragments, C3+ and C2+,
of the rBC (contributions of 59 and 27 %, respectively) did not have any major
interference from isobaric ions (ions observed in the same nominal mass). Larger
fragments (e.g. C5+–C9+) had interfering isobaric organic
compounds, but their contribution to the total mass was less than 10 %, and
the influence of the interference was therefore considered to be insignificant.
The contribution of the different organic fragments with and without the PAM
chamber. CH refers to hydrocarbons (CxHy), CHO to fragments with
one oxygen atom (CxHyOz+) and CHOx to compounds
containing several oxygen atoms (CxHyOz, z>1).
![]()
The average concentrations of the main chemical compounds of PM (in
µg m-3 and µg km-1 for individual species or
mg km-1 for total mass) during the NEDC for the primary (w/o PAM) and
secondary (PAM) emissions.
Cycle
Org
NO3
SO4
NH4
CHL
RBC
Total mass
(µg m-3/
(µg m-3/
(µg m-3/
(µg m-3/
(µg m-3/
(µg m-3/
(µg m-3/
µg km-1)
µg km-1)
µg km-1)
µg km-1)
µg km-1)
µg km-1)
mg km-1)
E10 w/o PAM
207.0/158.5
0.8/0.6
3.1/2.4
0.4/0.3
1.3/1.0
239.9/183.7
452.6/0.346
E10 PAM
5928.3/4538.9
101.1/77.4
119.4/91.4
30.6/23.4
0.9/0.7
481.3/368.5
6661.7/5.10
E85 w/o PAM
157.3/122.1
0.9/0.7
7.4/5.8
1.3/1.0
1.8/1.4
76.7/59.5
245.4/0.190
E85 PAM
487.1/378.0
27.2/21.1
5.9/4.6
40.4/31.4
0.6/0.5
58.0/45.0
619.3/0.48
E100 w/o PAM
112.7/89.7
0.7/0.6
3.6/2.9
0.6/0.5
0.4/0.3
36.3/28.9
154.4/0.123
E100 w PAM
75.8/60.3
10.8/8.6
7.2/5.7
4.7/3.7
0.9/0.7
24.0/19.1
123.5/0.10
There can be several reasons why the SP-AMS detected more rBC in the measurements
conducted after the PAM chamber. Firstly, it must be noted that this increase
in rBC was only observed for E10, which had the highest secondary aerosol
formation potential and thus the largest increase in particle size in the PAM
chamber. Previous studies have shown that the soot particles emitted by DISI
vehicles are small, typically in the size range of 10–100 nm (Karjalainen
et al., 2014). Due to restrictions from the aerodynamic lens, particles
smaller than 50 nm are not effectively detected by the SP-AMS. The
difference in the rBC results for E10 between the primary emissions and the emissions
after the PAM is likely partly explained by the increased mean particle size due
to the SOA formation increasing the efficiency through which the particles are
detected by the SP-AMS (low volatility compounds formed in the PAM chamber
condense on the surfaces of soot particles, increasing their aerodynamic size
and thus the detection efficiency of soot and rBC). Also, Willis et al. (2014)
demonstrated that the thick coating increases the collection efficiency by
changing the particle morphology, thus decreasing the beam divergence and
increasing the particle and laser beam overlap. Based on the increased mean
particle size and the secondary-to-primary PM ratios, it can be assumed (see also
Sect. 3.4 and the size distributions with and without the PAM chamber in
Figs. S18 and
S19) that for E10 the soot particles are heavily coated with SOA after the
PAM chamber, and thus they are more effectively detected by the SP-AMS. In
contrast, it has been shown that the dispersion of small and nonspherical
particles in the aerodynamic lens inlet of the SP-AMS may cause particles to
miss the laser vaporizer (Onasch et al., 2012). Also, based on previous
studies, it is known that the black carbon particles emitted by engines are
typically agglomerates with irregular shapes and diameters of 10–90 nm (Happonen
et al., 2010; Lähde et al., 2010; Karjalainen et al., 2014; Liati et al.,
2016), which also might have decreased the detection efficiency of the primary
black carbon (soot) particles in this study. We also note that the losses for
PM in the PAM chamber are dependent on particle size. Karjalainen et
al. (2016) (Fig. S3 in the Supplement) found that the smallest particles
incurred the largest losses in PM. However, based on this study, it is not
possible to estimate which of the above-mentioned processes is the main
reason for the observed rBC increase for E10.
The primary submicron PM concentrations in the exhaust emissions measured
without the PAM chamber and the submicron PM concentrations measured with the PAM chamber
for E10 (a), E85 (b) and E100 (c). The
secondary-to-primary PM ratio for the submicron PM mass in the exhaust calculated from the
SP-AMS measurements (d). The concentration of the secondary particulate
emissions was calculated by subtracting the concentration of the primary PM from
the PM measured after the PAM chamber.
![]()
Inorganic ions
In comparison to organics, the observed inorganic ion concentrations after the
PAM chamber were moderate to low (ion contribution to PM mass for E10,
3.8 %; E85, 12 %; E100, 19 %). The main ions observed after
the PAM chamber were sulfate, nitrate and ammonium for all fuels. In this
study, the sulfur contents of the E10, E85 and E100 fuels were lower than 10 ppm
according to the specifications EN 228, EN 15293 and EN 15376, respectively.
These facts strongly suggest that most of the observed
sulfate emissions originate from lubricant oil, especially for E100.
Primary-to-secondary particulate matter ratios
Table 3 and Fig. 6 show the submicron PM concentrations for both the primary
emissions and for the potential secondary aerosol emissions measured after the
PAM chamber averaged over the driving cycle. PM was calculated as a sum of
all SP-AMS species in the size range of the SP-AMS (30–800 nm). The PM
concentration measured after the PAM chamber is a sum of both the primary
particulate emissions and the formed secondary aerosol. The secondary aerosol
concentrations were calculated by subtracting the concentrations of the primary
particles from the PM concentrations observed after the PAM chamber. It is
likely that the wall losses in the chamber will somewhat decrease the primary aerosol
concentrations and thus might increase the observed secondary-to-primary
PM ratio when the particles go through the PAM chamber. However, based on
laboratory PM loss measurements and modelled vapour losses, the influence of
these on the results is estimated to be small. Also, one has to note that it
is likely that in the PAM chamber new material originating from the gaseous
phase will condense on the existing particles, which can change the particle
morphology, increase the particle size and change their detection
efficiency in the SP-AMS (Onasch et al., 2012; Willis et al., 2014). The observed secondary-to-primary PM ratios are thus
also slightly affected.
Large differences in the concentrations between primary and secondary
emissions were observed for the different ethanol content fuels. The largest
primary and secondary PM concentrations were observed for E10
(0.45 mg m-3 for primary PM and 6.7 mg m-3 after the PAM chamber).
A clear decrease in the primary PM emissions (E85, 0.24 mg m-3; E100,
0.15 mg m-3) was seen as the ethanol content in the fuel increased.
Similar to our results, Maricq et al. (2012) observed a decrease in the primary
PM concentrations when the ethanol content of the fuel increased; however, they did
not measure the secondary aerosol formation potential. The ethanol content of
the
fuel also had a large influence on the secondary aerosol formation potential.
For E10, the PM measured after the PAM chamber was on average 14.7 times larger
than the primary PM. For E85, the secondary PM emissions after the PAM chamber
were on average approximately 2 times larger (0.62 mg m-3) than the
primary PM emissions. For E100, a slight decrease in PM mass (E100,
0.12 mg m-3) was seen after the PAM chamber, indicating that either the
secondary aerosol formation was insignificant (Fig. 2; Table 3) or extensive
fragmentation decreased the observed PM mass. Previous studies (e.g. Tkacik et
al., 2014) have shown that high OH exposures cause a reduction in the
observed mass because of fragmentation, which forms light organic compounds
that are more volatile and will evaporate from the particulate phase. In the
case of E100, the fragmentation probably did not occur at the beginning of
the cycle because of the low OH exposure. The corresponding secondary-to-primary
PM ratios were 13.4 and 1.5 for E10 and E85, respectively.
The influence of the fuel composition on secondary aerosol formation has been
studied in only a few articles. Suarez-Bertoa et al. (2015) studied
the secondary aerosol formation potential of exhaust for vehicles using high
ethanol content fuels (E75 and E85). They used a batch chamber in their study
and found that the secondary aerosol, mostly secondary organic aerosol (SOA),
was on average 3 times higher than the primary emissions for high ethanol
content fuels; however, they did not measure the secondary aerosol for a
standard low ethanol content fuel or for ethanol fuel (E100). This study
shows that the SOA formation from high ethanol content is moderate to low when
compared to low ethanol content fuel. Suarez-Bertoa et al. (2015) also
concluded that short-chain hydrocarbons could have a role in SOA formation,
but
not only the aromatic BTEX compounds. We observed an increase in ethanol and
total hydrocarbon emissions as the ethanol content of the fuel increased;
however, the secondary aerosol formation was observed to be lower for these
high ethanol content fuels when compared to low ethanol content fuel (E10).
In this study, the concentrations of BTEX and the secondary aerosol formation
potential both decreased as the ethanol content of the fuel increased, indicating
that the BTEX compounds had a large influence on secondary aerosol formation.
This conclusion is in line with the results of Nordin et al. (2013), who found
that light aromatic precursors (C6–C9) were responsible for 60 % of the
formed SOA in a batch-type smog chamber.
The SOA yields for the different VOCs. The vapour wall-loss correction
factors are obtained from Zhang et al. (2014).
Compound
Yield (low NOx)
Yield (high NOx)
Correction (low NOx)
Correction (high NOx)
Reference
Toluene
0.3
0.13
1.9
1.13
Ng et al. (2007)
Benzene
0.37
0.28
1.8
1.25
Ng et al. (2007)
m- and p-Xylene
0.38
0.08
1.8
1.2
Ng et al. (2007)
1,3-Butadiene
–
0.18
–
–
Sato et al. (2011)
o-Xylene
0.1
0.05
–
–
Song et al. (2007)
Acetylene
0.1
–
–
–
Volkamer et al. (2009)
Tkacik et al. (2014) studied the secondary aerosol formation from in-use vehicle
emissions using a PAM chamber in a highway tunnel in Pittsburgh. Similar to
our study, they observed secondary-to-primary PM ratios of up to 10 inside the
tunnel. They also found that the peak in the secondary aerosol production
occurred under conditions equivalent to 2–3 days of atmospheric oxidation. With
higher OH oxidation values, they saw a decrease in secondary aerosol formation
due to continued oxidation fragmenting the carbon compounds. In our experiments,
the equivalent atmospheric age was approximately 3.9–6.2 h during the
CSUDC, when the most SOA formation took place. Thus, our results are likely
on the lower end compared to the maximum secondary aerosol formation potential,
but similar OH exposures are reached when compared to SOA formation studies
conducted with batch chambers (e.g. Platt et al., 2013; Gordon et al., 2014a;
Nordin et al., 2013).
Predicted SOA formation
The SOA yield is defined as (Odum et al., 1996)
Y=ΔMOΔHC,
where ΔMO is the formed secondary organic mass and ΔHC is the reacted precursor mass. Using Eq. (1), the measured VOCs and
the previously measured yields for these VOCs, we can analyze why the SOA
formation potential decreases as the ethanol content in the fuel increases.
We assumed that ΔHC equals the measured VOC concentration before the
PAM.
Similarly to Platt et al. (2013), we use low NOx yields to get an
upper limit for SOA formation. The yields are listed in Table 4. For
ethylbenzene, the SOA yield of m-xylene (0.38) was used (Ng et al., 2007;
Platt et al., 2013). According to Volkamer et al., (2009), the acetylene
(C2H2) SOA yield strongly depends on the liquid water content of
the
aerosol. Here, a value of 0.1 was assumed. The yields are corrected with
corresponding wall-loss correction factors (Table 4) presented by Zhang et
al. (2014).
The time series of the organic compounds for the primary
emissions (a) and for the emissions measured after the PAM
chamber (b). The speed profile of the NEDC is also shown. The speed
profile and the mass concentration in (b) do not correspond to each
other directly due to the broad residence time distribution of the PAM
chamber.
![]()
The contribution of each measured VOC to the predicted SOA is shown in the
Supplement (Tables S2–S4; Figs. S20–S22). According to the predictions, the
decrease in the SOA formation is caused by the decrease in aromatic compounds
in the exhaust when the ethanol content in the fuel is increased. The
comparison between the predictions and the measurements is shown in Fig. S23.
The trends in the predictions generally agree with the measurements except for
E100, where the predicted SOA is higher than for E85. The predicted SOA for
E100 mostly comes from acetylene (Fig. S22). Thus, the measured SOA formation
potential seems to depend rather on the aromatic concentrations than on the
acetylene.
Temporal variation in chemical composition<?xmltex \hack{\break}?> during the driving cycle
for primary and<?xmltex \hack{\break}?> secondary emissions
Figure 7 and Figs. S5–S16 in the Supplement show the time series of the organic,
inorganic ion and refractory black carbon (rBC) compounds for the primary
emissions (panel a) and the emissions after the PAM chamber (panel b). The
measurement set-up used and the primary and secondary particulate emissions
for E10 have been published previously by Karjalainen et al. (2016). For E10, the largest PM, organic, rBC and nitrate emissions were observed at
the beginning of the cycle during the first acceleration (Karjalainen et al.,
2015). Occasional increases were also observed during deceleration and engine
braking conditions (Rönkkö et al., 2014; Karjalainen et al., 2014). At the end of the cycle, when
the
speed was above 70 km h-1, the largest inorganic ion (sulfate,
chloride and ammonium) emissions were observed. A moderate increase in
organics and rBC was also observed at the end of the cycle.
Compared to E10, for which the highest primary organic emissions (peak
concentration up to 25 mg m-3) were observed right after a cold start,
for E85 and E100 the largest primary organics (peak concentration up to E85,
1.5 mg m-3; E100, 0.8 mg m-3) were measured either in the middle of
the cycle or during the highway driving part at the end of the cycle. The time trend
of the
rBC emissions was similar for all fuels. Elevated rBC emissions were only
seen at the beginning of the cycle and during the highway driving part of the cycle
(Figs. S6, S10 and S16). The primary inorganic ion concentrations were the highest
during the highway driving part of the cycle for E10 and E85. For E100, the
primary inorganic ion levels were typically low, except for some elevated spikes
that were observed for sulfate.
The number size distributions for the primary particle emissions.
The measurements were made during the driving cycle for all tested fuels,
E10 (b), E85 (c) and E100 (d). The driving cycle
(NEDC) is shown in (a).
![]()
The time series observed for the secondary emissions was completely different when
compared to the primary emissions. For E10 and E85, the cold start had a
dominating role in secondary aerosol formation, with a clear increase after
a cold start in the first part of the cycle (0–390 s). A similar increase at the
beginning of the cycle was not observed for E100. During the second part of
the driving cycle (390–780 s), the secondary organic concentrations stayed at a
constant level until the end of the cycle for all fuels. In contrast for
E100, the organic PM concentrations measured after the PAM chamber were stable
through the cycle with no clear maxima. We note that the speed profile and
the mass concentration in Fig. 7b do not correspond to each other directly
due to the broad residence time distribution of the PAM chamber (Lambe et
al., 2011). Still, the figure shows that the most SOA formation is caused by
the cold engine and a cold after-treatment at the beginning of the cycle for
both E10 and E85.
The temporal behaviour of the ions during the driving cycle was very different
when compared to the organics. After the PAM chamber, elevated nitrate
concentrations were observed at the beginning of the cycle after the cold
start and at the end of the cycle during the highway driving part. For E85 and
E100, the nitrate concentrations measured after the PAM chamber were very low
(Figs. S5–S16). Elevated sulfate concentrations for all fuels were measured
after the PAM chamber in the middle part of the cycle and at the end during
the highway driving part of the cycle (Figs. S5–S16). Elevated ammonium
concentrations were observed at the end of the cycle during the highway driving
part for all fuels. The temporal behaviour of the ammonium concentration was
observed to be correlated with nitrate, suggesting ammonium nitrate
formation. Tkacik et al. (2014) measured high ammonium nitrate concentrations
(forming from NO oxidation to HNO3 with subsequent neutralization with
NH3) that exceeded the SOA concentrations by a factor of 2 in measurements
conducted in a highway tunnel. In this study, the average contribution of
inorganic ions to the submicron PM mass was always below 20 % and the
contribution of ammonium nitrate was always significantly lower when compared
to SOA.
The number size distributions for the secondary particle emissions
measured after the exhaust treatment by the PAM. The measurements were made
during the driving cycle for all tested fuels, E10 (b),
E85 (c) and E100 (d). The driving cycle (NEDC) is shown
in (a).
![]()
Temporal variation in size distributions of primary and secondary
PM
The number size distributions of the emitted particles were measured in order to
understand the changes in the particulate phase when the driving conditions, such
as speed and engine load, rapidly change. Figure 8 shows the number size
distributions of the primary particles for each fuel as a function of time during
the driving cycle. It can be seen that for the E10 fuel, the emissions of
particles in the size range of 25–100 nm (Dp) were far higher than for E85 and E100. The emissions of the
particles in the size range of 25–100 nm depended on the driving condition,
so they existed mostly during the acceleration parts of the NEDC.
These particles were most likely soot-mode particles consisting of black
carbon. This is in line with the chemical composition results, which show that
as the ethanol content of the fuel increased, the rBC emissions decreased.
Figure 8 also shows that from the viewpoint of particle number, the role of a
cold start remained important with the fuels of high ethanol content. In
fact, most of the particulate emissions for E100 are related to the cold
start situation. For E10, 37 % of the particle number was emitted during
the first part of the cycle (CSUDC; 0–391 s; see Karjalainen et al., 2016).
For E85 and E100, 43 and 77 % of the particle number was emitted
during the CSUDC. Although it seems that the mean particle diameter
slightly decreased when the ethanol content of the fuel increased, the larger
soot-mode particles existed in the exhaust with all fuels. However,
the concentration of the soot-mode particles over the NEDC
decreased significantly when the amount of ethanol in the fuel was increased.
Fuel changes also clearly affected the nanoparticle emissions; the emissions of
nanoparticles decreased as the ethanol content of the fuel increased. Still, there
were systematic and identifiable sub-10 nm particle emission bursts with all the
fuels tested, possibly linking the emissions of the smallest particles to
lubricant oil consumption. Overall, we note that the effect of the fuel was
larger for soot-mode particles than for nanoparticles. At the end of the cycle
(800–1000 s), two distinct peaks were seen for E100. The same peaks were
identified in the rBC time series (see Fig. S16 in the Supplement).
The aerosol formation after the engine cold start was also clearly seen in
the secondary aerosol concentrations (Fig. 9). The largest particles
downstream of
the PAM chamber were measured about 100 s after the cycle start when enough
diluted exhaust gas was accumulated in the PAM chamber. Under high pollutant
concentrations, practically no sub-20 nm particles were measured downstream
of the PAM chamber. After around 200 s of the cycle, the vehicle engine and the
exhaust system had seemingly warmed up, and the particle size
distributions for the rest of the cycle had similar patterns. As the fuel
ethanol content increases, the size of the particles during the cold start as well as
during the whole cycle decreases. As the ethanol content of the fuel increased, a
clear increase in the smallest nanoparticles after the PAM chamber was
observed, indicating smaller amounts of condensable vapours to grow particles
inside the PAM chamber. Because the nanoparticle emissions were observed to
decrease as the ethanol content of the fuel in primary emissions increased, this
observation indicates that small particles can also form in the PAM chamber
via condensation on particles smaller than the lower size limit of the
instruments used or via nucleation. Figure 9 indicates that the
average particle size in the exhaust emissions decreased as the ethanol content
increased (also shown for the average values in Fig. S18). This will likely
affect the efficiency of how these particles are detected with the SP-AMS
since the collection efficiency of the aerodynamic lens used in the SP-AMS
sharply decreases in particle sizes below 30 nm. It should be taken into
account that the size distributions shown here are number size distributions, not
mass size distributions.
Conclusions
Ethanol is used in fuels to decrease the CO2 emissions of
transportation and thus to reduce the adverse climate effects of traffic. This
study shows that the use of these fuels produces benefits by decreasing
exhaust PM concentrations, thus having a positive influence on air
quality. A decrease in PM was seen in both primary emissions and the secondary
aerosol formation potential of the exhaust emitted by a modern flex-fuel DISI
vehicle.
The composition of the primary emissions was observed to change as the ethanol
content of the fuel increased. The relative contribution of rBC to the particulate
matter decreased, whereas the contribution of organic particulate matter and
inorganic ions increased. The organics-to-rBC ratios for the primary
emissions of E10, E85 and E100 were 0.9, 2.1 and 3.1, respectively. For all
fuels, most of the particulate matter observed after the PAM chamber consisted
of organic compounds (E10, 89 %; E85, 79 %; E100, 61 %). The
organics-to-rBC ratios measured after the PAM chamber for E10, E85 and E100
were 12, 8.3 and 3.1, respectively. The role of the cold start was observed to
dominate in the secondary aerosol formation for E10 and E85. For E100, no
significant increase in the secondary aerosol concentrations due to the cold
start was observed. As the ethanol content of the fuel increased, secondary aerosol formation
was observed to decrease significantly. For E10, the secondary aerosol
formation was significantly larger than the primary PM emissions, with a
secondary-to-primary PM ratio of 13.4, whereas for E100 a similar increase
in the PM mass after the PAM chamber was not observed.
The large difference in the exhaust secondary aerosol formation between E10
and fuels with a higher ethanol content can be explained by considering the
emissions of potential aerosol precursors. The exhaust emissions for
low-ethanol fuels contained fewer short-chained organic species (ethanol,
formaldehyde, acetaldehyde, methane and ethene) than the exhaust for E85 and
E100, but significantly more aromatic compounds (benzene, toluene, ethyl
benzene and xylenes). The compounds with a low number of carbon atoms are unlikely
to form secondary aerosol due to their high vapour pressure; conversely,
aromatic compounds are considered the most important SOA precursors among
the anthropogenic hydrocarbons. Their major atmospheric sink is the reaction with
the hydroxyl radical (Andino et al., 1996). It is also known that the SOA
yields tend to decrease at high NOx concentrations (Henze et al., 2008).
In our case, both the NOx concentration and the aromatics concentration
decreased when the fuel was changed from E10 to high-ethanol fuels, but the
NOx decrease was comparatively minor. At the same time, the
concentration of OH-reactive, short-chained organic species increased. These
factors together cause a strong decrease in the production of aromatic
hydrocarbon oxidation products, which in turn decreases the production of
secondary organic aerosol. The decrease in aromatic emissions may by itself
be enough to explain the SOA reduction, but one should not omit the effect of
the added reactivity presented by increased ethanol emissions, for example, which may
have an inhibiting effect by taking up a larger fraction of OH (similar to
the inhibition caused by isoprene in the case of biogenic SOA formation; see
Kiendler-Scharr et al., 2009). The reduction in NOx should in principle
increase the SOA formation, but the effect is minor compared to the inhibiting
causes.
This study shows that the SP-AMS combined with the PAM chamber is an
efficient tool to investigate the differences in the secondary aerosol
formation potential between vehicle technologies (fuels) with a high time
resolution taking the driving conditions into account. However, the study
strongly recommends including a high time-resolution particle size
distribution measurement parallel with the SP-AMS. In general, the
information gathered in this study is important for legislative purposes as
well as for modellers and city authorities establishing emission estimates.