Diesel-powered passenger cars currently outnumber gasoline-powered cars in many countries, particularly in Europe. In France, diesel cars represented 61 % of light duty vehicles in 2011 and this percentage is still increasing (French Environment and Energy Management Agency, ADEME).
As part of the September 2011 joint PM-DRIVE (Particulate Matter – DiRect and
Indirect on-road Vehicular Emissions) and MOCOPO (Measuring and mOdeling
traffic COngestion and POllution) field campaign, the concentration and
high-resolution chemical composition of aerosols and volatile organic carbon
species were measured adjacent to a major urban highway south of
Grenoble, France. Alongside these atmospheric measurements, detailed traffic
data were collected from nearby traffic cameras and loop detectors, which
allowed the vehicle type, traffic concentration, and traffic speed to be
quantified. Six aerosol age and source profiles were resolved using the
positive matrix factorization model on real-time high-resolution
aerosol mass spectra. These six aerosol source/age categories included a
hydrocarbon-like organic aerosol (HOA) commonly associated with primary
vehicular emissions, a nitrogen-containing aerosol with a diurnal
pattern similar to that of HOA, oxidized organic aerosol (OOA), and biomass
burning aerosol. While quantitatively separating the influence of diesel
from that of gasoline proved impossible, a low HOA : black carbon ratio, similar to
that measured in other high-diesel environments, and high levels of NO
Aerosols are known to have adverse effects on human health and on the global
climate. The World Health Organization (WHO) recently added anthropogenic
aerosol and air pollution to their list of known carcinogens (WHO, 2013), and
high mass concentrations of particles less than 2.5 micrometers in diameter
(PM
In France, the lower cost of diesel fuel (due to a lower taxation rate of diesel fuel versus gasoline fuel) and the generally higher fuel efficiency of diesel engines have increased the popularity of diesel passenger cars. In 2011, 82 % of the fuel consumed in France was diesel (World Bank, 2011). For comparison, this percentage in 2011 was 28 % in the US, 57 % in China, 70 % in the European Union, 49 % in Latin America and the Middle East, and 83 % in low-income countries.
The emission characteristics and emission limits of these two types of
engines (diesel and gasoline) are quite different: diesel vehicles have
higher emission factors for primary organic aerosol (POA) and BC, while
gasoline-powered vehicles have higher emission factors for carbon monoxide
(CO), carbon dioxide (CO
Aerosol and VOC emissions from both vehicle types, as well as biogenic emissions, industrial emissions, and emissions from other sources, will react together in the atmosphere and potentially form secondary organic aerosol (SOA). Thus, primary aerosol emissions may not be the most important emission factor to take into account for global reduction in anthropogenic aerosol. After emission, VOCs can react in the atmosphere and form SOA. From these reactions, gasoline VOC emissions could ultimately lead to the formation of higher concentrations of organic aerosol than organic aerosol released directly from diesel vehicles, as reported in a recent study comparing the SOA formation from a Euro 3 diesel light-duty vehicle (LDV) and a Euro 5 gasoline LDV (Platt et al., 2013).
A recent study by Bahreini et al. (2012) measured similar levels of SOA in the heavily traffic-influenced Los Angeles Basin during both weekend and weekday afternoons. While diesel-powered vehicle numbers on the road decrease significantly on the weekends in the LA area, the measured SOA does not, which leads to the conclusion that gasoline emissions are more responsible for SOA than diesel emissions (Bahreini et al., 2012). Nordin et al. (2013) performed smog-chamber studies on SOA formation from gasoline-vehicle VOC emissions during simulated cold start and idling driving conditions and confirmed the high potential of SOA formation from gasoline car exhaust. Another recent paper calculates the reactivity potential of diesel and gasoline fuel and comes to the opposite conclusion: due to the reactivity potential of diesel fuel, diesel-powered vehicles should contribute greater amounts of SOA than gasoline-powered vehicles to the atmosphere (Gentner et al., 2012). Thus, controversy still exists regarding the eventual aerosol emission factors of diesel and gasoline engines when considering both primary emissions and potential SOA formation.
Finally, gas-phase NO and NO
European vehicular emissions, near-highway pollution levels, and the chemical composition of highway pollution may be quite different than those measured in North America due to many factors, including (1) different emission standards and fuel regulations in the two regions, (2) different after-treatment devices to reduce the emission of certain pollutants, and (3) a much larger percentage of diesel-powered passenger cars on the road. A comparison between European and North American near-highway measurements could lead to further understanding of the effects of diesel versus gasoline on near-highway atmospheric chemistry.
To fully categorize the aerosol, VOC, and NO
The sampling site was located at 45.150641
The measurement site location is marked by a red square on the map, and the adjacent highway has been colored in red. A detailed view of the measurement site and the two measurement stations is shown in the lower right-hand corner; in the upper right-hand corner is the wind rose and polar plots for black carbon and NO, with the red lines denoting the direction of the highway. Grenoble is to the north.
Traffic cameras mounted to a roadway sign were used to capture the license plate numbers of vehicles driven on the highway close to the field measurement site. These numbers were later used to classify vehicular traffic into different categories: vehicle type (LDV, heavy-duty vehicles, buses) and age, vehicle size and engine capacity, fuel type (diesel or gasoline), and Euro number (i.e., the pollutant emission regulation that the vehicle complies with). The speed of the passing vehicles was also monitored with the classical traffic detector (double electromagnetic loops able to identify the passing of all vehicles and their speeds), which allowed the identification of periods of stop-and-go, dense, or free-flow traffic.
The MASSALYA platform is a mobile laboratory equipped for air quality
measurements with a hub located at the Aix-Marseille Université. For the
field campaign, PM
A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS,
Aerodyne) was used to analyze the chemical composition, size, and
concentration of non-refractory submicrometer particles in the ambient
atmosphere (DeCarlo et al., 2006). Instrument specifications have been
discussed in detail elsewhere (DeCarlo et al., 2006). Briefly, both
high-resolution and size-speciated chemical information for ambient aerosol
were obtained from this instrument. Aerosols were vaporized at
600
In addition to the HR-ToF-AMS, a size-mobility particle scanner (SMPS, TSI) was used to measure the size distribution and concentration of ambient aerosol and a multiangle absorption photometer (MAAP 5012, ThermoFischer) was used to measure the concentration of black carbon.
High-resolution mass spectra of VOCs were obtained using an Ionicon
proton-transfer-reaction time-of-flight mass spectrometer 8000 (PTR-ToF-MS,
hereafter referred to as PTR-MS) (Graus et al., 2010). The PTR-MS analyzes trace (parts per trillion by
volume) VOCs with high mass resolution, which allows the separation of
different species with the same nominal mass and the identification of each
peak's elemental formula. The PTR-MS was run with a 25 s time resolution and
a flow of 100 cm
The SMPS, PTR-MS, and HR-ToF-AMS were connected to the same sample inlet
with a PM
A Young meteorological station was also installed to capture wind speed, wind direction, relative humidity, and temperature data at the measurement location.
The Air Rhône-Alpes station collected PM
Organic compounds in these PM samples were also quantified by gas chromatography coupled with mass spectrometry, following the method detailed in El Haddad et al. (2009) and Favez et al. (2010). EC and OC measurements were performed using the thermal–optical transmittance (TOT) method on a Sunset Lab analyzer (Birch and Cary, 1996; Jaffrezo et al., 2005) following the EUSAAR2 temperature program (Cavalli et al., 2010). Ionic species were analyzed with ionic chromatography following the method described in Jaffrezo et al. (1998).
All filters used in this study were preheated at 500
In addition, NO
A detailed view of the measured traffic is presented in the Supplement
(Fig. S1). Briefly, the overall makeup of the traffic remained fairly steady
throughout the campaign. The bulk of the vehicles directly affecting the
measurement site were Euro 4 (released in 2005) or older; thus, the most
recent emission regulations had only a small effect on the air quality around
the field site. The ratio of diesel to gasoline cars was found to be
2.6, or 72 % diesel, with a high correlation (
The non-refractory submicrometer aerosol concentration in
Wind speeds were generally low throughout the campaign
(
The campaign time series concentration of submicrometer non-refractory
aerosol sulfate (SO
PM
These findings are similar to those presented recently by Sun et al. (2012), who measured aerosol size and chemical composition adjacent to the Long Island Expressway in New York and observed that traffic-influenced aerosol emissions were primarily small particles which varied in concentration with changes in traffic throughout the day. During periods with less traffic influence, more-oxygenated organic aerosol (OOA) and inorganic ions with larger mode diameters and lower temporal variations were observed (Sun et al., 2012).
The time series concentrations of selected VOC peaks are shown in Fig. 3.
Primary traffic-related VOC species, such as aromatics (benzene and
trimethylbenzene), were found to have high temporal variations similar to
those of traffic-related aerosol species and NO
The concentration in ppbv of PTR-MS VOC species-identified
isoprene and methyl vinyl ketone/methacrolein (left axis,
In addition to traffic-related VOC emissions, mass peaks corresponding in exact mass to biogenic emissions, such as isoprene, were measured in ppbv levels. These peaks were found to rise in concentration with the ambient temperature (Fig. 3a), typical of isoprene peaks. The presence of isoprene and its oxidation product, methyl vinyl ketone, or its isomer methacrolein in similar concentrations as that of the major traffic-related VOC peaks (ppbv levels) suggested that biogenic emissions also significantly influenced the local atmosphere despite close proximity to anthropogenic emission sources (i.e., road traffic).
The high morning concentrations of traffic-related pollutants, compared to evening concentrations, were caused in part by a low early morning boundary layer that rose during the day and fell during the night. Boundary layer heights (BLH) were estimated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) back-trajectory model. The HYSPLIT model either extracts the BLH from meteorological file input into the model or, if no BLH exists in the meteorological file, BLH is estimated using the vertical temperature profile. A selection of the BLH-scaled diurnal profiles of traffic and biogenic emission-related VOC concentrations are shown in Fig. 4a along with traffic (speed, vehicular flux) diurnal profiles and the calculated boundary layer heights and measured temperatures (Fig. 4b and c). This calculation was performed to more directly compare vehicle concentration and speed to vehicular emissions and temperature with biogenic emissions (by removing the dilution of emissions by the changing boundary layer height). Biogenic species, such as isoprene, peaked in concentration during the afternoon, when temperatures were the warmest. Aromatic species peaked in concentration, even after the rough boundary layer scaling was applied, during periods of low speeds. This is consistent with other findings that show cold starts and idling speeds cause an increase in aromatic VOC emissions from gasoline-powered vehicles (e.g., Broderick and Marnane, 2002).
Diurnal profiles of boundary-layer scaled VOC peaks from PTR-MS
measurements and BC peaks from MAAP measurements
The PMF model was applied to the HR-ToF-AMS
aerosol data using the process described in detail by Ulbrich et al. (2009).
Six aerosol factors were resolved by their source and relative aging using
the PMF model: a hydrocarbon-like organic aerosol (HOA) factor, a regional
oxidized organic aerosol (OOA-Reg) factor associated with sulfate aerosol,
two oxidized organic aerosol factors with opposing diurnal patterns, one more
oxidized than the other (less-oxidized organic aerosol, or LO-OA, with peak
concentration during the mornings/nights, and more-oxidized organic aerosol,
or MO-OA, with peak concentrations during the afternoons), a biomass-burning
organic aerosol factor (BBOA), and a nitrogen-containing organic aerosol
factor (NOA). The mass spectra for the six resolved factors is shown in
Fig. 5, labeled with their identifications. Evaluation graphs for the
six-factor PMF solution are shown in the Supplement (Figs. S5–S8). Polar
plots of the factor concentrations and wind direction are shown in Fig. S9. A
six-factor solution was the lowest number of factors where a BBOA factor was
resolved; BBOA was suspected to be present in the air mass measured during
the campaign due to periods of increased levoglucosan measured on filter
samples. However, its concentrations were very low (15 ng m
The mass spectra of the six resolved factors, more-oxidized organic
aerosol (MO-OA), less-oxidized organic aerosol (LO-OA), regional oxidized
organic aerosol (OOA-Reg), biomass burning organic aerosol (BBOA),
hydrocarbon-like organic aerosol (HOA), and nitrogen-containing organic
aerosol (NOA). Fraction of total signal is plotted against
PMF factors and their calculated organic mass to organic carbon (OM : OC) ratio, their hydrogen to carbon (H : C) ratio, and their oxygen to carbon (O : C) ratio. PMF factors are hydrocarbon-like organic aerosol (HOA), nitrogen-containing organic aerosol (NOA), less-oxidized organic aerosol (LO-OA), more-oxidized organic aerosol (MO-OA), biomass burning organic aerosol (BBOA), and regional oxidized organic aerosol (OOA-Reg).
The diurnal pattern and the relative concentrations of each resolved factor,
averaged over the campaign period, are shown in Fig. 6, along with the
standard deviation of their concentrations. Morning and evening peaks,
correlating in time to rush hour traffic, were clearly observable for the HOA
factor. Also clearly visible in Fig. 6a are the opposing diurnal trends of
LO-OA (peaking at night and early morning) and MO-OA (peaking around
3 p.m. LT each afternoon). OOA-Reg had no
discernable diurnal trend. An interesting finding in these data is that the
HOA and NOA factor concentrations both peaked during morning and evening high-traffic periods (Fig. 6a). This is not the general behavior demonstrated in
most studies for the NOA factor, although a similar NOA factor has been
previously measured in the Po Valley, Italy (Saarikoski et al., 2012). This
behavior was confirmed by examining HT periods, with an increase of
1.3
The diurnal profiles
The time series of the six-factor PMF solution
In Fig. 7, the time series of each factor are shown with oxalate
(C
Correlation between AMS organic PMF factors and other tracer species, from filter samples (oxalate, levoglucosan), AMS data (sulfate), and MAAP data (BC). PMF factors are hydrocarbon-like organic aerosol (HOA), nitrogen-containing organic aerosol (NOA), less-oxidized organic aerosol (LO-OA), more-oxidized organic aerosol (MO-OA), biomass burning organic aerosol (BBOA), and regional oxidized organic aerosol (OOA-Reg).
The BBOA factor was found to correlate with levoglucosan (
Oxalate and OOA-Reg covaried with an
Of the factors resolved, the HOA factor had the lowest O : C ratio (0.07)
and a good (
A source of uncertainty in the global particulate emissions of vehicles is
the formation of SOA from gas-phase emissions and the aging of POA. To
discriminate between the relative concentration of modern and fossil carbon,
and thus potentially discriminate between OOA from vehicular sources and from
modern sources, daily filter samples were collected at the sampling site and
As radiocarbon measurements have been performed through a thermal approach
(combustion of the samples at 850
Assuming that the majority of EC was traffic-related and thus from fossil
origin, the concentration of modern organic carbon and fossil organic carbon
was then calculated. While evidence for the presence of biomass burning
aerosol was measured at the field site, the main source of EC was likely
diesel exhaust. Figure 8 shows the fraction of EC and OC, HOA, and a
partitioning between fossil and modern carbon. In Fig. 8a, a rough
calculation was performed to determine the concentration of non-primary
fossil organic carbon. For a first estimate, all EC was assumed to be fossil
in origin. Additionally, the HOA aerosol was also assumed to be vehicular,
and thus fossil, in origin. The HOA factor concentration has been divided by
its OM : OC ratio to remove any non-carbon mass (HOA C, calculated from the
elemental formulas of the PMF factor mass spectra; Aiken et al., 2008). Both
EC and HOA C had high (
Measured EC and OC, with calculated contribution of non-primary
fossil organic carbon (assuming 100 % fossil EC and HOA,
This calculation provided a lower estimate of the amount of fossil carbon contributing to SOA mass and involves several assumptions and potential sources of error. Sources of error in this calculation include error in the PMF resolution of primary (HOA) organic aerosol spectra and error in the calculated OM : OC ratio of this factor species, biodiesel vehicular emissions contributing modern carbon to measured HOA, and biomass burning aerosol contributing modern carbon to measured EC.
As the measured HOA : EC ratio was in line with previous measurements in high-diesel environments, HOA concentrations did not appear to be significantly
over- or underestimated. Up to 7 % of fuel use in France was biodiesel;
thus, part of the HOA concentration could be from modern sources. While
research has shown that the use of biodiesel fuels reduces the overall
primary particulate matter emissions (Cheung et al., 2010), biodiesel could
still be a modern carbon contributor to OC and EC mass. Additionally,
although the concentration of BBOA was generally low (a campaign average of
0.34
Total organic carbon concentration appeared to be more driven by
processed/aged OOA concentrations than by primary emissions. During the
period with the highest organic concentrations (15–17 September), most of
the non-HOA carbon measured was modern carbon. Also during this time period,
the winds were slightly more southerly and SO
The high levels of modern carbon OOA suggested that biogenic compounds had a
large effect on the overall aerosol population in this location, even
directly adjacent to a large anthropogenic emission source (i.e., traffic).
Interaction between anthropogenic oxidants and biogenic VOCs (or BVOCs) has
been found to increase the formation of SOA (Chameides et al., 1988;
Goldstein et al., 2009; Shilling et al., 2013), isoprene oxidation reactions
leading towards SOA have been shown to vary depending on the level of NO
Older diesel vehicles have been shown to emit both higher levels of PM,
particularly BC, and higher levels of NO
High levels of BC were also measured in this work. A comparison of the HOA : BC ratio from this study and from previously reported field studies is shown in Fig. 9a. As expected, since the French fleet includes a much higher percentage of diesel cars with increased BC emissions, this ratio was significantly lower than that reported for an urban downwind site in Pittsburgh (1.41, Zhang et al., 2005), a highway-adjacent site in New York (1.02, Sun et al., 2012), an urban/highway site in Ontario (0.7–1.1, Stroud et al., 2012), a rural site in northwest England (1.61–1.91, Liu et al., 2006), and an urban site in Zurich, Switzerland (1.1, Lanz et al., 2007). As for measurements in France, a study in an urban site in Paris observed a HOA : BC ratio of 0.61 (Crippa et al., 2013); this site was most probably influenced by a vehicle fleet similar to Grenoble's, but measurements were collected during winter (lower temperatures) and within Paris (increased urban emissions). Tailpipe measurements of Euro 4 diesel and gasoline-powered vehicles (a Renault Kangoo and a Ford Ka, respectively) at IFSTTAR (Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux) performed during this PM-DRIVE research program also show a much higher HOA : BC ratio for gasoline vehicles versus diesel vehicles (unpublished data). This was due to much higher BC emissions from the diesel vehicle, as opposed to higher HOA emissions from the gasoline vehicle.
Calculated HOA and measured BC concentrations from the campaign and
HOA : BC ratios from previous field campaigns. Grey area is shaded to
include a diesel-only environment and two French HOA : BC ratios: from
Crippa et al. (2013) and from this study
Similarly, the HOA factor measured near Grenoble resembled that measured by Sun et al. (2012) in a high-gasoline environment next to a highway in New York, both in absolute concentration and chemical composition; thus, an increase in BC emissions (from diesel) rather than a reduction in HOA : vehicle number was likely the cause of our low HOA : BC ratio.
The change in HOA : BC ratio as a function of the diesel : gasoline fuel
use (Road sector, World Bank, 2011) is shown in Fig. 9b. A decrease in
HOA : BC with an increase in percent diesel is clearly observable with a
strong correlation (
Additionally, an AMS factor with a diurnal pattern peaking during rush hour
and with N-containing peaks was observed (see Sect. 3.3). Saarikoski et
al. (2012) found a similar amine-containing NOA factor in measurements taken
in the Po Valley (Italy) that also had a strong diurnal pattern. However,
their NOA factor was attributed to marine influence due to a correlation with
methanesulfonic acid (MSA)
(Saarikoski et al., 2012), although it is possible that MSA was from the
local industrial use of dimethyl sulfoxide as a solvent and
had a higher H : C ratio (1.91) than the factor resolved from this data set
(1.38). Like France, Italy has a large percentage of diesel fuel consumption
(71 %; World Bank, 2011). Aiken et al. (2009) and Sun et al. (2011) also
resolved N-containing OA factors from data measured in Mexico City and New
York, respectively, but did not observe a similar diurnal pattern. In the
PTR-MS mass spectra results obtained from Euro 5 vehicle emission smog
chamber studies, Hellebust et al. (2013) found higher nitrogen-containing
emissions from fresh and aged diesel mass than from fresh and aged gasoline
mass spectra (e.g., peaks such as CH
Finally, only small amounts OOA measured at the field site were calculated to contain fossil OC. Work by Bahreini et al. (2010) found that much of the measured SOA in the Los Angeles Valley was from gasoline passenger cars, not from diesel trucks, and perhaps the relatively low concentration of gasoline vehicles on the road in France is related to the low concentration of fossil OOA.
During this campaign, highly time-resolved particle and gas-phase chemical
composition and concentration measurements were obtained alongside parallel
traffic data of the speed, fluxes, vehicle type, and fuel type of passing
cars on a highway in the Grenoble Valley. An analysis of the local primary
(traffic) aerosol and the more regional, aged secondary organic aerosol was
performed for the PM
The resolved mass spectrum for the HOA factor was chemically similar to mass
spectra from both gasoline and diesel-emitted organic carbon and previously
resolved HOA factors in high-gasoline environments; however, the HOA : BC
ratio measured was low (
While high levels of both black carbon (5
This work was supported by the French Environment and Energy Management
Agency (ADEME, grant number 1162C0002). The authors gratefully acknowledge
the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT
transport and dispersion model (