Introduction
In 2011 more than half of the world population was living in urban areas,
and this proportion is expected to reach 67 % in 2050 (United Nations,
2012). Cities are therefore strategic places to address the impact and
mitigation of climate change and, in particular, the reduction of greenhouse
gas emissions (Duren and Miller, 2012). Here we focus on the Paris metropolitan area. Paris is part
of the Île-de-France region which is inhabited by about 12 million
people. Out of these, Paris and its suburbs concentrate 11 million
inhabitants and constitute the third largest megacity in Europe. The most
detailed description of its emissions is provided by the regional inventory
developed by Airparif, the association in charge of monitoring the air
quality in Île-de-France. According to the Airparif 2010 emission
inventory, CO2 emissions of Île-de-France represent about 13 %
of the total French anthropogenic CO2 emissions – a surface that
extends over only 2 % of the French territory (AIRPARIF, 2013). Most of
the regional CO2 emissions are concentrated in the Paris metropolitan
area. They are attributed essentially to the residential and service sectors
(43 %) and to traffic (29 %). As is quite common for emission
inventories, there is no independent quantitative assessment of the Airparif
database, and its uncertainties are poorly constrained. Moreover, Airparif
emission estimates are based on activity proxies calibrated from benchmark
situations that may significantly differ from real ones. For instance,
chassis dynamometer tests (Bosteels et al., 2006) characterise the vehicle
emissions under controlled conditions and fuel composition but cannot
represent well the diversity of real driving conditions and fleet
composition. Therefore independent evaluations of the inventory are needed; atmospheric measurement programs around the Paris megacity such as the
CO2-MEGAPARIS project, which sample the actual emission plume
(Xueref-Remy et al., 2013), may provide new reference information to anchor
the inventory. In the framework of the MEGAPOLI and CO2-MEGAPARIS
research projects, Lopez et al. (2013) measured the mole fractions of
CO2 and its carbon isotopes in winter 2010 in the centre of Paris and
in the southwest peri-urban area. Using the 13CO2 and radiocarbon
(14CO2) signatures, 77 % of the total CO2 was attributed to
anthropogenic sources and 23 % to biospheric sources. The anthropogenic
emissions were identified to originate 30 % from traffic and 70 % from
gas heating. Measured emission ratios were compared to the Airparif
emissions inventory and showed good consistency with it. First encouraging
estimates of the total CO2 anthropogenic emissions of the Paris
megacity by atmospheric inverse modelling have been obtained by Bréon et al. (2014), who compared their results to the Airparif inventory. In urban
areas, Volatile Organic Compounds (VOCs) are also controlled by
anthropogenic sources and thus represent potential tracers for inferring
CO2 urban emission sources. Gaimoz et al. (2011) set up such
measurements in the centre of Paris in spring 2007 and identified major VOC
sources. Traffic activities (exhaust and fuel evaporation) were found to be
responsible for 65 % of the total VOC emissions, industrial sources for
14 %, natural gas and background for 8 %, local sources for 4 %,
biogenic evaporation for 8 % and wood burning for 1 %. The study of
VOCs and of tracers of anthropogenic CO2 like CO or NOx is motivated
by their impact on human health and by their production of photo-oxidants
(such as ozone) in ambient air. As they are major pollutants emitted by
traffic activities, they are regulated by European emission standards. As an
example, the Euro 3 norm set strong limits in emissions for gasoline
vehicles (2.2 g km-1 for CO, 0.15 g km-1 for NOx). Euro 4 and 5
accentuated these limits (1.0 g km-1 for CO and 0.08 g km-1 for
NOx). Euro 5 is the first norm in the series that also controlled NMHC
emissions (limited to 0.068 g km-1).
In this paper we use new atmospheric mole fraction data acquired in real
conditions in Paris to evaluate the emission ratios of CO, NOx and VOCs
relative to CO2 for the traffic sector in the Airparif inventory.
These ratios carry the signature of the traffic emission plume because,
during the combustion processes of fossil fuels, CO2 is co-emitted with
other species in ratios that are characteristic of each emission sector and
fuel type. In order to focus on the traffic sector and be representative of
the vehicle fleet, we have performed our atmospheric measurements in a road
tunnel. Such an approach has been previously used in several tunnels around the
world to study emission factors of VOCs (Ho et al., 2009) and trace gases
(Chirico et al., 2011). In western Europe, Popa et al. (2014) and Vollmer et
al. (2007) provided CO / CO2, N2O/CO2 and CH4/CO2
ratios for vehicular emissions. In the Paris area, one study was conducted
in a road tunnel in August 1996 (Touaty and Bonsang, 2000) to evaluate
hydrocarbon vehicle emissions and to determine emission factors for
non-methane hydrocarbons (NMHC) and CO.
Like the study of Touaty and Bonsang (2000), our experiment was carried out
in the Guy Môquet tunnel in Thiais, located about 15 km south of Paris
centre. The campaign took place over 10 days from 28 September to 8
October 2012. CO2, CO, VOCs and NOx mole fractions were measured inside
the tunnel in order to determine their ratios to atmospheric CO2 for
traffic in the Paris megacity. Our measurements enable us to update the
results from the study of Touaty and Bonsang (2000). To our best knowledge they
also constitute the first study in a French tunnel that involves CO2, VOCs
and NOx all together and quantifies the ratios of these co-emitted
species to CO2 in Paris for the traffic sector.
This paper is structured as follows. The instrumental methods are described together with the Airparif inventory
in Sect. 2. Section 3 starts with a
general description of the data (Sect. 3.1) and a discussion about the
definition of background level mole fractions (Sect. 3.2). In Sect. 3.3
we identify the co-emitted species from road traffic by evaluating the
correlations between these species and CO2. Then, in Sect. 3.4, we
quantify the emission ratios between these species and CO2 for the
present vehicle fleet. Finally (Sect. 4.1), we compare these measured
ratios with the ones provided by previous experiments and by the most recent
regional emission inventory of Airparif (2010) (Sect. 4.2). Section 4.3
refines the comparison with the latest European tunnel study.
Methods
Site description
The Guy Môquet tunnel (48∘77′ N, 02∘39′ E) is
located in Thiais, about 15 km south of the centre of Paris. This tunnel
was built on a highway and has been used since 1990. It is 600 m long with
a rectangle cross-sectional area of 64 m2. It contains two
separate tubes, one for each traffic direction. Each bore contains three
lanes of traffic. The two tubes are not connected. The average traffic in
each bore of the tunnel is about 60 000 vehicles per day. The speed limit is
90 km h-1.
The tunnel is equipped with a longitudinal ventilation mode: a system of jet
fans at two places on the tunnel ceiling. The aim of this ventilation system
is to speed up the airflow towards the tunnel exit in case of fire
emergency, pushing smoke outside (O'Gorman, 2012). Under normal traffic conditions the
tunnel is self-ventilated as traffic through the tunnel induces the airflow
direction. We cannot be sure that the ventilation system was never activated during the whole measurement campaign. However, we will mainly
focus on traffic peaks during which the traffic signal on the mole fraction
ratios between species (which is the heart of this study) is strong enough
not to undergo significant ventilation/dilution.
(a-c) and (e-g) Temporal variation of the mole fraction of
the selected compound during the whole tunnel campaign. (d) Average speed.
(h) Vehicle density. Time is given in local (UTC + 2 h). Minor ticks on the
horizontal axis are distributed every four hours.
Vehicle speed and traffic counts were available every 6 min. All these
data were provided by the Direction Régionale et Interdépartementale
de l'Équipement et de l'Aménagement d'Île-de-France (DRIEA-IF).
Vehicle speed and density are shown in Fig. 1d and h. Around 61 000 vehicles crossed the
tunnel daily on workdays (from 1 to 5 October 2012), 58 000 on Saturday (6 October 2012) and 55 000 on Sunday
(7 October 2012). Traffic density during the night (between 23:00 and
04:00 LT) was low with around 500 vehicles per hour, unlike traffic
density during rush hours which was around 3100 vehicles per hour.
Air sampling and instruments
Air measurements were made at a single location within the tunnel, in the
bore that leads to the city of Créteil, 550 m from the tunnel entrance
and 50 m from its exit, from 28 September to 8 October 2012. Time is
given as local (Central European Summer) time (UTC + 2 h).
Several instruments were operating during this study and those relevant
to our study are presented here. A Cavity Ring-Down analyser (Picarro,
model G2401) performed continuous CO2, CO and H2O measurements
with a time resolution of 1 s. This instrument was calibrated at the
beginning of the campaign using three 40 L gas tanks. These cylinders
were calibrated for CO2 and CO dry air mole fraction using a gas
chromatograph, against the NOAA-X2007 scale for CO2 and the NOAA-X2004
for CO with a precision better than 0.1 ppm. During the campaign, a fourth
gas cylinder was analysed for 30 min every 8 h. It was used as a
target to evaluate the repeatability of the data and the drift of the
instrument. During the campaign no significant drift was detected for
CO2 and CO measurements, and the precision of the data (1σ) was
estimated to be 0.04 ppm for CO2 data and 16 ppb for CO data on 1 min averages. Thanks to the use of a sequencer, CO2 and CO mole
fractions in the ambient air (outside the tunnel) were also measured with
this analyser for 30 min every 4 h. The sequence of CO and CO2
measurements was: tunnel air for 4 h, ambient air for 30 min, tunnel air
for 3 h 30, target gas cylinder for 30 min, ambient air for 30 min.
Two gas chromatographs equipped with a flame ionisation detector (GC-FID)
were installed to measure non-methane hydrocarbons (NMHCs). Both instruments
are described in detail in Gros et al. (2011). Measurements of
C2-C6 and C6-C10 hydrocarbons were provided with a time
resolution of 30 min. Air was sampled during the first 10 min of
each 30 min segment and analysed during the next 20 min. Previous
measurements and tests have shown a good stability of the detector over
several weeks (Gros et al., 2011). Therefore only one calibration was
performed during the campaign (1 October) and consisted of the direct
injection (repeated 3 times) of a 4 ppb calibration gas mixture (National
Physics Laboratory, Teddington, UK). Mean response factors of these three
injections were used to calibrate NMHCs during the campaign. NMHC mole
fractions in ambient air were estimated on 2 October 2012 between 13:50 and
16:30 LT. The total uncertainty of the data was better than
15 %.
A chemiluminescent analyser (API TELEDYNE, model T200UP) continuously
measured nitrogen oxides (NO and NO2) mole fractions with a time resolution
of 1 min. Calibration of the instrument is regularly checked at the
laboratory by injecting 30 ppb from a 10 ppm NO calibration gas mixture (Air
Liquide, France). In order to check the calibration parameters within the
range of values expected in the tunnel, 500 ppb of NO from the Air Liquide
standard were injected into the instrument prior to the campaign. The response
of the instrument was found very good (506.5 ± 4.5 ppb, variability
coefficient < 1 %, n=35) and therefore the instrument was
operated with the same parameters during the campaign. NOx mole fractions in
ambient air were also measured on 2 October 2012 between 13:51 and 16:39 LT. For NO mole fractions over 2300 ppb the instrument showed
saturation and was no more quantitative.
Data processing
As the temporal sampling was different for each instrument, a common
average time was defined a posteriori to get all data sets on a similar
temporal resolution. The chosen time interval was the one imposed by GC-FID
measurements. Data from GC-FID were acquired for 10 min every 30 min, the reported time corresponding to the beginning of the
measurement. Thus for each compound measured by the other instruments, data
were averaged over the same 10 min interval. Doing so, all the final data
have a time step of 30 min with a resolution of 10 min.
NO and NO2 data were screened because of the characteristics of the
analyser. Since the instrument saturated when NO mole fractions reached 2300 ppb, a filter was applied to remove the NO and NO2 data when the NO
mole fraction exceeded 2200 ppb.
Airparif inventory
Airparif (http://www.airparif.asso.fr/en/index/index) has been
developing an inventory of emissions for greenhouse gases and air pollutants
with a spatial resolution of 1 km × 1 km and a temporal resolution of
1
h for Île-de-France. The emissions are quantified by sectors: energy,
industry, road transport, agriculture, solvent uses, waste treatment, etc.
Emissions (in tons) are assessed for five typical months (January, April,
July, August and October) and three typical days (weekday, Saturday and
Sunday) to account for seasonal and weekly cycles. A speciation matrix is
used to extract emissions for each specific VOC from the total VOCs
emissions in the inventory. This speciation matrix is provided by the
Institute for Energy Economics and the Rational Use of Energy (IER). The
extraction is possible for each specific VOC and by SNAP (activity).
Thanks to in situ vehicle counters, Airparif also provides emission
estimates specific to some roads. Such information was available for this
study in the Thiais tunnel.
The latest version of the inventory, the results of which are used in this study,
was made for the year 2010, but the speciation matrix for VOCs was
established in 1998 and has not been updated yet.
Results
Data overview
The temporal evolution of the mole fractions for the whole campaign with
a time step of 30 min is shown in Fig. 1. The average speed and density
of vehicles in our tunnel section are also represented in this figure.
During workdays, rush hours are easily identifiable for all studied
species by one peak in the morning, between 06:00 and 09:00 LT,
and another one in the afternoon, between 16:00 and 19:00 LT.
Almost 4050 vehicles cross the tunnel per hour at the beginning of the rush
periods, but the vehicle density and speed then decrease along with the
congestion in the tunnel. The average speed of vehicles during rush hours is
lower than 20 km h-1, whereas otherwise it is faster than
60 km h-1. These peaks are linked to the commutation of Paris active
inhabitants going to and from their workplace. For comparison
purposes, the average vehicle speed in Paris has been determined to be
15.9 km h-1 from a recent study performed by the Paris city local
administration.
Mole fractions vary significantly over the course of the day. The mean
diurnal cycle (±1σ) amplitudes are
summarised in the supplementary material. Mole fractions were significantly
higher during traffic peaks than at night or other times of the
day. Compared to traffic peaks, we notice a decrease in mole fractions
at night by 40 % for CO2 and propane and by 80 to 94 % for the other compounds. For periods during daytime out of traffic
peaks, the decrease, compared to traffic peaks periods, was about 15 % for
propane, 30 % for CO2 and between 65 % and 90 % for the others.
Since the traffic signal in terms of gas mole fractions is so much stronger
during rush hours, we will focus on these periods in the following. Indeed,
in order to evaluate mole fraction ratios, enough mole fraction variability
is required (differences to the background level can thus be robustly
calculated) and these strong signals were encountered only during traffic
peaks periods.
Background levels
The long lifetime of some of the studied species, like CO2, induces a
large variety of emission origins and potentially elevated background levels
in the measured mole fractions. Since we aim at extracting the traffic
signal as accurately as possible and characterising the ratios of the
studied species relative to CO2 for tunnel traffic activity only, the
mole fractions in principle have to be corrected from other influences, such
as the nearby biogenic contribution or the baseline level.
In previous tunnel studies (Popa et al., 2014; Touaty and Bonsang 2000,
Vollmer et al., 2007; Ho et al., 2009; Araizaga et al., 2013; Na, 2006), two sampling
sites were installed: one near the entrance of the tunnel, representing the
background mole fractions, and another one near the exit. The difference in
mole fractions between these two samples represented vehicle emissions in
the tunnel. The current configuration of the Thiais tunnel did not enable us
to install two sampling sites, and background levels had to be defined
differently. Apart from CO2 and CO, it was not possible to use the few
measurements made outside the tunnel (Sect. 2.2) because they do not
include all species and are not performed on a regular basis, while, according
to previous measurements, ambient VOCs mole fractions vary significantly
during the day and from one day to another (Gros et al., 2011).
Given the available information, background mole fractions can be
approximated (i) by nighttime mole fractions (as performed by Chirico et
al., 2001) or (ii) by daily mole fractions out of the traffic peaks. In our
case, nighttime mole fractions were the lowest measured mole fractions of
the whole campaign. Vehicle density was quite low at around 500 vehicles h-1,
and average vehicle speed was relatively high at more than 70 km h-1.
For (ii), the daytime mole fractions outside rush hours were higher than
nighttime ones by 10 % (CO2) to 60 % (propene). Vehicle density
during these periods was high as well, around 3500 vehicles h-1. For our
study, we choose option (i) because it corresponds to the lowest density of
vehicles. We focused on four nights during weekdays and evaluated the averaged
mole fractions between 23:00 and 04:00 LT. We define the background as the
average measurement values per species in the tunnel between 23:00 and 04:00 LT
on Monday–Thursday nights (i.e. four nights per week) and we characterise
its uncertainty by the corresponding measurement standard deviation. For
instance, for CO2 our background is 495.92 ± 23.46 ppm. Tests
with option (ii) or using the sparse measurements made outside the tunnel
are presented in the supplementary material: they show that the definition
of the background does not affect the estimated ratios to CO2 showed in
the following. This comes from the fact that the traffic signal during rush
hours inside the tunnel is much larger than the mole fractions measured
during all other periods of time, inside or outside of the tunnel (from
2 to around 10 times more).
Correlations between co-emitted species and CO2
Gros et al. (2011) and Gaimoz et al. (2011) characterised the VOC sources in
Paris and identified the main traffic-related VOCs. Based on their results,
we select benzene, toluene, xylenes, ethylbenzene, n-propylbenzene,
m&p-ethyltoluene, propene, acetylene, ethylene, i-pentane, n-pentane,
i-butane, n-butane and propane for the correlation study to CO2. We
also consider CO, NO, NO2 and NOx, as done by Chirico et al. (2011).
Correlations between ΔCO2 and selected co-emitted
species. The red line represents the linear regression fit between ΔCO2 and the considered species. The linear regression does not
intercept the (0, 0) point because of the uncertainty on the background
level.
For background mole fractions we use the average values during the night
(cf. Sect. 3.2). We focus on workdays (5 days between Monday, 1
October and Friday, 5 October 2012) only. For each species, we calculate
Δspecies as the differences between each mole fraction point
measured in the tunnel during traffic peaks and the average mole fraction
calculated for the nights of workdays only. We compute the coefficient
of determination r2 for all corrected mole fractions Δspecies and ΔCO2 using the scatter plot between the two
(Fig. 2). Generally, tight correlations are found between the selected compounds
and CO2 (r2=0.58–0.89). In all cases a p-value
test was performed, resulting in each p-value lower than 0.001. However,
correlations were poor for propane, i-butane and n-butane with respective coefficients of determination r2 = 0.15, r2=0.22 and r2=0.031. All coefficients of determination are listed in Table 2.
Inside the Thiais tunnel, CO is exclusively emitted by traffic activities.
The strong correlation between ΔCO and ΔCO2,
r2=0.89, supports that the emitted CO2 in the tunnel
has the same origin as CO, i.e. traffic. Strong correlations are also found
between CO2 and benzene, toluene, xylenes, ethylene, acetylene and
propene (r2 = 0.60–0.81) because these compounds dominate in
vehicle exhaust (e.g. Gaimoz et al., 2011 and Chirico et al., 2011). This is
also consistent with the high coefficient of determination
(r2=0.85) seen between CO2 and NOx, which are also
traffic tracers.
Propane is one of the main compounds emitted by fuel evaporation. Fuel
evaporation does not emit CO2 and this can explain the poor correlation
between Δpropane and ΔCO2 (r2=0.15).
Coefficients of determination for i-butane and n-butane, which also come
from fuel evaporation, were also low (respectively r2=0.22 and r2=0.031). Therefore, these compounds (propane, i-butane and n-butane) will not be further considered in this study.
Observed emission ratios to ΔCO2 and coefficient of
determination (r2). Numbers after ± signs correspond to 1σ. Mole fraction ratios for ΔCO, ΔNO and ΔNO2
are reported in ppbppm-1, all others are reported in
pptppm-1.
Species
Observed ratios
Coefficient of
to ΔCO2
determination (r2)
ΔCO
8.44± 0.45
0.89
ΔNO
3.32± 0.23
0.85
ΔNO2
1.10± 0.09
0.82
Δi-pentane
35.22± 4.43
0.60
ΔToluene
24.26± 2.91
0.63
ΔAcetylene
20.14± 1.67
0.79
ΔEthylene
14.01± 1.91
0.60
ΔPropene
13.17± 1.37
0.69
Δn-pentane
12.93± 1.45
0.66
ΔBenzene
8.84± 0.67
0.81
Δm&p-xylenes
6.06± 0.63
0.70
Δo-xylene
4.38± 0.43
0.72
ΔEthylbenzene
3.32± 0.36
0.67
Δn-propylbenzene
3.12± 0.41
0.58
Δm&p-ethyltoluene
1.75± 0.18
0.69
Ratios of co-emitted species to CO2 in traffic peaks
In the following, we assess the ratios between co-emitted compounds and
CO2 in the traffic peaks. We define the ratio as the slope of the
scatter plot between Δspecies and ΔCO2 using a linear
regression fit (Bradley et al., 2000; Turnbull et al., 2011; Borbon et al., 2013; Popa et al., 2014). For each co-emitted species, the error on the ratio was computed using a confidence interval at
68 % (1σ). Note that our use of a constant background value per
species in our main results implies that our calculated ratios do not depend
on the actual value of these constants; however, the uncertainty of the constants
is accounted for in the confidence intervals of the ratios given in the
tables (we evaluate the extreme linear regression fits for the data weighted
with their uncertainties; the difference between the two extreme ratios is
defined as the uncertainty on the ratio). Our method seems more robust than
the calculation of instantaneous ratios. Indeed, it constrains the ratio to
be unique. The uncertainty is thus lower (instantaneous ratios show a larger
variability, which leads to large uncertainty). The ratios of the selected
co-emitted species to CO2 are presented in Table 1. We notice that the
outliers do not influence the linear regression within a 1σ
uncertainty.
Comparison between observed ratios to CO2 and emission
ratios provided by the 2010 Airparif inventory for only the traffic source.
In blue are the ratios from the Airparif inventory for the whole Île-de-France
region; in green are the ratios from the Airparif inventory using emissions only in
the tunnel area; in red are the ratios from our study. Ratios for CO and NOx are
reported in tkt-1; all others are reported in kgkt-1.
ΔVOCs to ΔCO2 ratios are shown in decreasing order of
magnitude. The higher the ratio is, the more the corresponding species is
emitted. In the tunnel, i-pentane and toluene were the most emitted VOCs.
This result combined with the VOCs profile determined for the traffic sector
from this tunnel campaign (Gros et al., 2014) is in good agreement with the
vehicle exhaust source profile published in Gaimoz et al. (2011).
Discussion
Comparison with previous campaigns
Of the studies that focused on traffic emissions, few have evaluated mole
fraction ratios to CO2. To our best knowledge, none of previous tunnel
studies reported ΔVOC to ΔCO2 ratios. Table 2 lists
ΔCO to ΔCO2 ratios for vehicle emissions from previous
studies. Generally their ratios are higher than ours, except for the latest
Swiss study (Popa et al., 2014). The comparison with the oldest studies shows
indeed a significant difference in ΔCO to ΔCO2 ratios:
the ratio from the study of Bradley et al. (2000) is almost 500 % higher
than ours. For more recent studies, the ratios reported by Bishop and
Stedman (2008) and Vollmer et al. (2007) were respectively 11 to 120 and
9 % higher than ours. There are fifteen years between the campaign of
Bradley et al. (2000) and ours, during which vehicles benefited from
significant technological improvement, especially catalytic converters that
reduce vehicle CO emissions. Furthermore, fuel use is not the same in
France, in the USA and in Switzerland. American vehicles have been mostly
using gasoline for decades (diesel vehicles only reached 3 % in 2012),
whereas in France and particularly in the Île-de-France region, diesel
is the most used fuel (according to Airparif, 78 % of vehicles use
diesel). Switzerland is in between with 22 % of the fleet using diesel
(2010). Furthermore, gasoline vehicles are known to emit much more CO than
diesel vehicles. Thus, European emission policies set higher thresholds for
CO emissions from gasoline consumption (about a factor 3 in 2000 and a
factor 2 since 2005 compared to diesel) while their CO2 emissions are
only of a few percents higher (ADEME, 2013). This results in a much higher
CO / CO2 emission ratio for gasoline vehicles than for diesel ones. The
large differences in the fuel partition of each national fleet is thus
likely one main reason why the ΔCO / ΔCO2 ratios measured
in the United States are effectively higher than the ones observed in
Switzerland – and higher still in France. However, this point cannot be more
detailed because we did not have further information on the fleet
composition evolutions between 1997 and 2012.
Finally, the ratio from the latest study (Popa et al., 2014 in Switzerland)
is half the value of the one measured during our campaign. Measurement years
were almost the same (2011 for Popa et al. (2014) and 2012 for our study), and no
significant evolution occurred in the fleet composition during this year.
Furthermore, the mean age of the Swiss fleet and the French one is also
nearly the same, around 8 years. The comparison with this study will be
further analysed in Sect. 4.3.
ΔCO to ΔCO2 ratios for traffic emissions,
comparison with previous studies (continued from Popa et al., 2014). Results
of this study are shown in bold.
Reference
ΔCO/ΔCO2
Location
Measurement
(ppbppm-1)
year
Bradley et al. (2000)
50± 4
Denver, CO, USA
1997
Vollmer et al. (2007)
9.19± 3.74
Gubrist tunnel, Switzerland
2004
Bishop and Stedman (2008)
9.3 …18.4
Chicago (IL), Denver (CO),
2005–2007
Los Angeles (CA), Phoenix (AZ), USA
Popa et al. (2014)
4.15± 0.34
Islisberg tunnel, Switzerland
2011
This study (congested traffic)
8.44±0.45
Paris, France
2012
This study (fluent traffic)
5.68±2.43
Paris, France
2012
Comparison of the measured ratios with the
Airparif inventory
In this section we compare the emission ratios derived from our
observations in the tunnel during our campaign with those given by the
Airparif 2010 inventory.
Comparison between mass observed ratios to CO2 and mass
emission ratios provided by the 2010 Airparif inventory, only for the
traffic source. The first column shows ratios from the Airparif
inventory for the whole Île-de-France region, the second one shows
the specific Airparif ratios for the tunnel road. Observed ratios are
in bold. The last column reports the relative differences between the
specific Airparif ratios for the tunnel road and observed mass
ratios. Emission ratios for CO and NOx are reported in tkt-1, all others are reported in kgkt-1.
Airparif (2010)
Airparif (2010)
Observed mass
Relative difference between
(mean in
in the tunnel road
ratios 2012
inventory ratios in the tunnel
Île-de-France region)
area and observed mass ratios
(in % of the observed mass ratio)
Compound i
i/CO2
i/CO2
Δ i/ΔCO2
CO
9.7
5.3
5.4
-2
NOx
4.4
4.6
6.5
-30
i-pentane
64.3
21.6
57.7
-63
Toluene
176.9
68.3
50.8
+34
Acetylene
44.6
16.5
11.9
+39
Ethylene
94.2
37.2
8.9
+318
Propene
52.5
20.6
12.6
+63
n-pentane
34.9
18.0
21.2
-15
Benzene
74.1
33.7
15.7
+115
m&p-xylenes
67.6
24.0
14.6
+64
o-xylene
2.6
2.1
10.2
-79
Ethylbenzene
32.8
12.4
8.0
+55
n-propylbenzene
22.8
7.7
8.5
-9
As Airparif provides emission estimates in tons for each compound, we
convert our measured mole fraction ratios (in ppbppm-1 or pptppm-1) into mass
ratios (tkt-1 or kgkt-1). Our measurements were made in September and
October,
and we notice that these months were typical months in regards to annual
average traffic emissions. According to the Airparif inventory, there is a
small seasonal variation in the traffic emissions. Nevertheless, September
and October contributions to the whole year are close to the yearly average
and therefore can be considered as representative. Thus, we use annual
emissions from the Airparif inventory to evaluate the ratios. The comparison
is summarised in Table 3 and Fig. 3.
The Airparif inventory is sufficiently detailed (Sect. 2.4) to provide
emissions estimates related to the specific area of this tunnel road, where
our experiment was conducted. These ratios are shown in the second column in
Table 3 and in green bars in Fig. 3. We notice a good agreement for the
ΔCO to ΔCO2 ratio: the difference between the ratio
inferred from our observations and the one from the Airparif inventory is
less than 2 %. The agreement is also satisfactory for the n-pentane to
CO2 and n-propylbenzene to CO2 ratios for which we notice a difference
with the observed ratios lower than 15 %. Airparif overestimates most of
the other ratios by 34 % or more (up to about 318 % for ethylene). NOx
to CO2, o-xylene to CO2 and i-pentane to CO2 ratios are
underestimated by 30 to 79 %. The case of xylenes can be
distinguished. Indeed, if we consider the separation between m&p-xylenes
on the one side and o-xylene on the other side, we note significant
differences between the specific Airparif ratios and the observed ones.
However, if we evaluate the ratio considering total xylenes we obtain a
better agreement with only 5 % difference between the two (observed
Δxylenes to ΔCO2: 24.4 kgkt-1; Airparif xylenes to
CO2: 26.1 kgkt-1). A problem in the speciation of xylenes may be
responsible for this change.
Airparif accounts for the specific fleet composition in the tunnel, which is
different on this highway than in the centre of Paris, for instance. Heavy
goods vehicles do not drive through the centre of Paris, whereas two-wheelers
represent 16 % of the total of vehicles. In the tunnel, heavy vehicles are
allowed (5 % of the fleet composition) whereas motorised scooters
constitute less than 2 % of the total vehicles. To assess the impact of
this specificity on our study we also compare our results to the traffic
ratios obtained from the whole regional emission inventory. The Airparif
regional ratios are given in the first column in Table 3 and in blue bars in
Fig. 3. These results indicate a significant spatial variability in the
whole Airparif inventory, which makes it important to select inventory data
from the specific tunnel road for proper comparison. Doing otherwise
systematically increases the misfits (except for NOx, i-pentane and
o-xylene) up to about 960 %. The Thiais tunnel is a highway
tunnel where motorised scooters are not allowed, whereas they constitute an
important source of traffic emissions around Paris, particularly of CO
emissions. Almost half of traffic-emitted CO is due to scooters and
motorbikes: 57 210 tyear-1 of a total of 117 170 tyear-1 for the whole traffic sector (Airparif, 2013). We notice the same trend in regards to total VOC emissions: 6990 tyear-1 are emitted by two-wheeled vehicles from a total of 14 850 tyear-1 for traffic.
Even if we use the inventory data from the relevant geographical area, our
calculated ratios mostly do not agree well with the ones from the inventory,
especially for VOCs to CO2 ratios. This may be caused by some out-dated
features of the speciation matrix that was made in 1998 (see Sect. 2.4).
For instance, the regulation of benzene in fuel became stricter in
2000: instead of the prior 5 %, benzene has since been limited to 1 % in the fuel composition. The fuel composition was also regulated in aromatic
compounds content, becoming limited to 35 % since January 2005 instead of
42 % prior. The impact of these changes on the benzene and aromatics
emissions is not yet taken into account in the speciation matrix of the
inventory and may explain why the related ratios to CO2 are
overestimated for the emission inventory.
CO to CO2 ratios (ppbppm-1) for gasoline and
diesel contributions in Switzerland and in Île-de-France,
using annual emission inventories.
COCO2gasoline
COCO2diesel
COCO2total
Switzerland (2010)
13.52
1.32
10.84
Île-de-France (2010)
37.44
1.41
9.34
Additional investigation in the comparison with
the latest Swiss study
The comparison with the Airparif inventory in Sect. 4.2 suggests some
refinement to our comparison in Sect. 4.1 to the recent tunnel
measurements made in Switzerland by Popa et al. (2014). The Swiss fleet
composition and the French one are very different, in particular for diesel
use (Sect. 4.1). In order to assess the impact of this difference on the
emission ratios we separately compute CO to CO2 ratios for gasoline
and diesel fuel in Île-de-France and in Switzerland, based respectively
on the emission inventories delivered by Airparif and by the Swiss
Department of Environment, Transport and Energy (OFEV, 2010). Using the
distribution diesel vehicles/gasoline vehicles in each region we can then calculate the total CO to CO2 ratio. Results are compiled in Table 4.
COCO2gasoline emission ratio is almost 3 times
higher in France than in Switzerland and reflects the impact of two-wheeler
emissions. Indeed, motorcycles in Île-de-France, around 8 % of the
total fleet, only use gasoline fuel and, as stated previously, they emit
almost half of the CO emissions. In Switzerland, less than 4 % of vehicles
are motorcycles and they emit around 20 % of the total traffic-emitted CO.
COCO2diesel ratios are lower than
COCO2gasoline ratios in both cases. The total ratios,
which are the product of COCO2 and of the relative
percentage of diesel and gasoline vehicles in each case, are almost the same
in Switzerland and in the Paris region even if Swiss and French fleet
compositions are different. Therefore the difference in diesel and gasoline
vehicles in the two fleet compositions does not seem to explain the
difference between the ΔCO to ΔCO2 ratio from Popa et al. (2014) and ours.
Then we note that the two campaigns have been made in different traffic
conditions. On the one hand, the ratio of Popa et al. (2014) is representative of fluent
highway traffic: driving conditions stayed constant while vehicles crossed
the tunnel and the average vehicle speed was higher than 80 km h-1.
On the other hand, in our study we have focused on traffic jam periods with frequent stops and low speed (less than 20 km h-1), during which
the combustion and the catalytic converter are less efficient (Weilenmann et al., 2010). According to
SETRA (SETRA, 2009), a branch of the French Department of Energy and
Environment, vehicles emit twice as much CO when they work at a temperature 40 % of the optimal value, whereas CO2 emissions remain
almost the same (CO emissions are multiplied by 3 if vehicles are completely
cold). Based on these results, ΔCO to ΔCO2 ratios are
therefore expected to be 2 or 3 times higher in the case of less effective
combustion. Looking back at the analysis from Sect. 4.1, the quality of
the combustion could therefore not explain the differences of the previous
studies (except Popa et al., 2014).
ΔCO to ΔCO2 ratios in the Thiais tunnel
depending on average vehicle speed.
Low speed period
High speed period
(<20 kmh-1)
(>50 kmh-1)
ΔCO/ΔCO2
Coefficient of
ΔCO/ΔCO2
Coefficient of
(ppbppm-1)
determination r2
(ppbppm-1)
determination r2
8.44± 0.45
0.89
5.68± 2.43
0.45
To further assess the influence of the vehicle speed on ΔCO to
ΔCO2 ratio we evaluate this ratio in the tunnel when the speed
was higher. We use daily data between 12:00 and 16:00 LT on
workdays only, when the speed was higher than 50 km h-1 and vehicle
density was still important (around 3800 vehicles h-1). We use the method
presented in Sect. 3.3 and 3.4 to evaluate the ratio. The comparison
between the two periods is shown in Table 5.
Vehicle speed appears to affect the ΔCO to ΔCO2 ratio:
it decreases when the average speed is increasing but the standard
deviation shows a larger variability. However, we cannot rule out the
possibility of a dilution effect in the tunnel with ambient air outside.
Indeed, in the Swiss study, air flow in the tunnel is well known and the two
sampling sites allow isolating vehicle emissions from the tunnel. In our
study it may be possible that, when average speed was high and the tunnel was
not congested, some ambient air was brought into the tunnel and mixed with the
tunnel air thanks to a piston effect, thus changing the ratios compared to rush
hours. This dilution effect, combined with a random use of the ventilation,
may explain the weak correlations between co-emitted species and CO2
found out of traffic peaks and justifies the focus on rush periods.
Summary and conclusion
This study pioneered the measurement of CO to CO2 and VOCs to CO2
ratios for traffic emissions in the Paris area. 15 co-emitted species
characteristic of traffic emissions were found to strongly correlate with
CO2. VOCs to CO2 ratios enabled an identification of the most emitted
species: here i-pentane and toluene were the most emitted VOCs. We compared
our results with other studies made in the United States and in Switzerland. Differences from 9 % to more than 500 % were found between previous tunnel studies, only reporting CO to CO2 ratios, and the ratio inferred from our
observations. Such differences may be explained by the significant
technological improvements of vehicles (such as the development of catalytic
converters) as well as by the large differences in fleet composition
(diesel/gasoline use) and driving conditions (traffic jams/fluent traffic and
high-/low-speed regimes). A comparison with the latest Paris regional
inventory was done focusing on the specific road of the Thiais tunnel. In
most cases it indicated that the inventory overestimates the ratios to
CO2, even though a satisfactory agreement is found for the CO to
CO2, n-pentane to CO2, n-propylbenzene to CO2 and xylenes
to CO2 ratios. VOC emissions for the traffic sector are the most
overestimated, suggesting that the VOCs speciation matrix should be updated
in the inventory in order to account for the latest regulations for fuel
composition. The evaluation of the mean ratios for the whole regional
inventory indicated significant spatial variability in the inventory data.
The fact that the best fit to our measurements is seen when the inventory
data for the tunnel road are isolated suggests some skill in this inventory
variability. The satisfactory agreement found for several ratios to CO2
suggests that data from the inventory are representative of low-speed
regimes. Our data suggests a ΔCO / ΔCO2 ratio smaller by
about one third in high-speed regimes but with much higher uncertainty. This
point also confirms the limited representativeness of specific campaigns
like the previous ones or ours. In our case more measurements are needed
within the Paris megacity to draw a general picture of the emission ratios of the traffic sector around Paris, which is characterised by a large
spatial (highways vs. small streets) and temporal (weekday vs. weekend)
variability. The varying ratios of co-emitted species to CO2 also imply
that traffic does not have a unique imprint on the urban plume but rather
leaves various signatures. Depending on whether these signatures overlap
with those of the other emission sectors, such as domestic heating, the ratios
may or not allow the identification of the emission composition of the urban plume.
Finally, this variability of the ratios bears important consequences for
atmospheric inverse modelling. Indeed it has been suggested that
measurements of CO and possibly of other co-emitted species could help to
constrain the estimation of fossil-fuel CO2 emissions (Levin and
Karstens, 2007; Kort et al., 2013; Lopez et al., 2013; Rayner et al., 2014).
Our study shows that this is possible only through a good quantitative
knowledge of the large spatio-temporal variations in emission ratios,
which somehow shifts the difficulty without necessarily reducing it. In this
respect, isotopic measurements of CO2 are still currently the most
well-suited data for bringing information about fossil fuel vs. natural CO2 emissions (e.g. Levin et al, 2003; Lopez et al., 2013), even though such measurements are expensive and much more difficult to make.