Introduction
In response to changing air quality and climate, there is a growing interest
in quantifying emissions of pollutants and greenhouse gases from urban areas
(UNEP, 2013; EEA, 2014). Urban emissions are usually known through the
combination of direct and indirect geospatial energy use statistics with
emission factors for individual source sectors. The heterogeneity of the
input data in space, time and type makes it difficult to monitor the
uncertainties of these inventories. Such monitoring actually receives little
incentive at the international level (e.g., Bellassem and Stephan, 2015), but it
has been an active topic for scientific research. Some studies have been
based on measurement campaigns dedicated to specific sectors, such as
air-composition measurements in road tunnels for traffic emissions (e.g.,
Touaty and Bonsang, 2000; Ammoura et al., 2014), or in ambient air for
power plants (Zhang and Schreifels, 2011), waste water treatment plants
(Yoshida et al., 2014; Yver-Kwok et al., 2015) or for the overall city-scale
emissions (Lopez et al., 2013; Turnbull et al., 2011, 2015; Xueref-Remy et
al., 2016). Measurements made in the ambient air are affected by dilution in
the atmospheric boundary layer, but this effect is cancelled out for mole
fraction ratios between the considered species. The mole fraction ratios
estimated from ambient air can also be directly interpreted in terms of
emission ratios provided that the measured molecules share the same origin
(e.g., Turnbull et al., 2006). Ultimately emission ratios may be interpreted
in terms of sectoral emissions. In practice, the mixing of air parcels of
various origins and ages largely hampers the interpretation. To isolate the
local urban signal, measurements for species with a significant lifetime in
the atmosphere have to be corrected from background influence (Turnbull et
al., 2015), usually based on other measurements made in the free troposphere
or at a remote site (e.g., Lopez et al., 2013; Turnbull et al., 2015).
Isotopic measurements, like those of 14CO2, can also allow the
analysis to be more specifically focused on anthropogenic activities (e.g.,
Levin and Karstens, 2007; Turnbull et al., 2011). Finally, atmospheric
transport models are used in a few studies to quantify the contributions of
the different sources within an inverse modelling approach (e.g., Saide et al., 2011; Lauvaux et al., 2013; Bréon et al., 2015).
Here, we investigate the possibility of benefiting from an enhanced local
urban signal at low wind speed for estimating emission ratios from
atmospheric composition measurements. Indeed, when the atmosphere is not
well ventilated, emission plumes get trapped in the atmospheric boundary
layer close to their origin. The resulting large peaks in mole fractions
time series are easily visible compared to typical background variations. In
this paper, we make the first attempt to fully exploit this well-understood behaviour. We use several measurement campaigns of CO2, CO
and volatile organic compounds (VOCs) performed in Paris in 2010, 2013 and
2014 to validate the approach and to evaluate local emissions ratios. Paris
is the third largest megacity in Europe and the largest city in France. It
comprises around 12 million people when including its suburbs. The
population density is one of the highest in Europe, with 21 347 inhabitants
per km2 (INSEE, 2014). According to the latest Paris
inventory of Airparif (association in charge of monitoring the air quality
in the Paris region) provided for the year 2010, emissions of CO2 are
mainly from the traffic (29 %) and residential and service sectors
(43 %) (AIRPARIF, 2013). Airparif also estimated VOC emissions and their
main anthropogenic origins are the same as those of CO2 (such as
traffic or residential heating).
The paper is structured as follows. Section 2 presents the measurements and
the data. Section 3 starts with a presentation of typical measurements and a
discussion about the choice of the background level, presenting two
different options. The analysis method itself developed to estimate urban
emission ratios is described in Sect. 3.3, including sensitivity tests
(Sects. 3.3.2 and 3.3.3). Section 4 presents the results obtained for
different periods of the year and different years. Section 4.1 gives the
interpretation of the ratios determined with our method and discusses the
representativeness of these ratios. Section 4.2 presents the seasonal
variability of the ΔCO / ΔCO2 ratio in Paris and
Sect. 4.3 compares all ratios between co-emitted species obtained during two short
campaigns in Paris.
Methods
Site description
All atmospheric composition measurements presented in this study have been
made in the centre of Paris. The instruments were installed at two sites.
The first one is located on the Jussieu campus of University Pierre et Marie
Curie (UPMC) at the QualAir station (http://qualair.aero.jussieu.fr). This station stands on the roof of a
building, on the left bank of the river Seine (48∘50′ N,
2∘21′ E; 23 m above ground level). A botanical garden of 28 ha, the Jardin des Plantes, lies about 500 m from the measurement
site. The closest motorways are about 4 km to the south and south-east, but the university is surrounded by many streets which are
particularly congested during rush hours. The emission activities in the
centre of Paris essentially originate from road traffic activities and from
the residential and service sectors, since most industrial activities have
been removed in the 1960s (AIRPARIF, 2013).
The second measurement site is the roof of Laboratoire d'Hygiène de la
Ville de Paris (LHVP) located about 2 km south-east of the Jussieu campus (48∘49′ N, 2∘21′ E; 15 m a.g.l.). It dominates a public garden of 4.3 hectares, the Parc de Choisy.
Residential buildings and arterial roads also surround this site. The
closest expressway is a few hundred metres south of the site.
Instrumentation and air sampling
Joined MEGAPOLI/CO2-MegaParis winter campaign
Our first campaign was performed jointly within the MEGAPOLI European
project (Megacities: Emissions, urban, regional and Global Atmospheric
POLlution and climate effects, and Integrated tools for assessment and
mitigation project; http://megapoli.info/) and the CO2-MegaParis project
(https://co2-megaparis.lsce.ipsl.fr). This “winter campaign” took place in
Paris during January–February 2010 (Dolgorouky et al., 2012; Lopez et al.,
2013).
Two instruments were deployed at the LHVP. A gas chromatograph equipped with
a flame ionisation detector (GC-FID, Chromatotec) sampled non-methane
hydrocarbons (NMHCs). Mole fractions of acetylene, ethylene, propene,
i-pentane, n-pentane, ethane and propane were obtained with a time resolution
of 30 min (air is sampled during the first 10 min and analysed
during the next 20 min). More details can be found in Gros et al. (2011)
and Dolgorouky et al. (2012).
A cavity ring-down spectrometer (CRDS, model G1302, Picarro Inc.) was also deployed
to analyse CO2, CO and H2O mole fractions with a time resolution
of 1 s (see Lopez et al., 2013, for more details).
Long-term continuous CO2 and CO measurements
The CRDS performed continuous
CO2, CO and H2O measurements in Jussieu from 4 February 2013 to 11 June 2014 with a time resolution of 1 s. This instrument was calibrated
about every 2 months using three 40 L aluminium gas tanks. These cylinders
were previously calibrated for CO2 and CO dry air mole fractions
against the NOAA-X2007 scale for CO2 and the NOAA-X2004 for CO. A
fourth gas cylinder was used as a target to evaluate the repeatability of
the data and the drift of the instrument. This target was analysed for 20 min every 12 h between 4 February 2013 and 25 August 2013 and for 15 min
every 47 h since 26 August 2013. Using the target gas measurements,
we estimate the repeatability and the trueness (closeness of agreement
between the average of a huge number of replicated measured species
concentrations and a reference concentration; BIPM, 2012) of the 1 min
averaged data to be, respectively, 0.05 and 0.03 ppm for CO2 and
6.8 and 3.7 ppb for CO. The instrument was compared to that of the
MEGAPOLI/CO2-MegaParis used in 2010 and the repeatability and the
trueness of the 1 min average data were found to be almost the same.
“Multi-CO2” field campaign
Several instruments were installed next to the CRDS analyser in Jussieu from
11 October until 22 November 2013 within the Multi-CO2 project.
For the compounds of interest for this study (CO2, CO and light VOCs),
the same instruments that were used during the joined
MEGAPOLI/CO2-MegaParis campaign were deployed (see Sect. 2.2.1). VOC
mole fractions were measured using a gas chromatograph (Chromatotec)
calibrated against a reference standard (National Physics Laboratory,
Teddington, UK). Some VOCs were selected for this study because they share
the same origins (such as traffic or residential heating) than other VOCs,
CO and CO2: ethane, ethylene, acetylene, propane, propene, i-pentane
and n-pentane. The total uncertainty of the data was estimated to be better
than 15 %.
Meteorological parameters (wind speed and direction, temperature) were also
monitored (instrument WMR2000, OREGON Scientific).
Data processing
As the time resolution was different for both instruments (CRDS and GC-FID),
the data have been synchronised. 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 given time stamp corresponding to the
beginning of the measurement. Thus for each compound measured by the other
instruments (CRDS and meteorological instruments), data have been averaged
on the same 10 min interval. Finally, in this study, all the data have the
same time step of 30 min.
Results
Typical time series and identification of specific meteorological
events
(a–d) Temporal variation of the mole fraction of selected compounds
monitored during the Multi-CO2 campaign (30 min time step). The
black lines represent the background levels defined with the calculation of
the 5th percentile (black disks). (e) Wind speed during the campaign.
Time is given in UTC.
Figure 1 shows an example of atmospheric gas dry air mole fractions time
series collected during the Multi-CO2 campaign in 2013, with a time
step of 30 min. The wind speed during the same period is also represented in
the figure (Fig. 1e). Time series recorded during the joined
MEGAPOLI/CO2-MegaParis campaign in 2010, as well as the continuous
measurements of CO2 and CO in Jussieu, are shown in the supplementary
material.
Mole fractions of the different species appear to covary significantly despite the
different lifetime of the species: CO2 and CO have typical lifetimes in
the atmosphere (τ) that are much longer than the observation period, whereas
acetylene has a τ of a 13 days and ethylene has a τ of a few
hours. In comparison, the meteorological events in Paris during the campaign
lasted from a few hours to 1 day so that VOCs with a τ longer than
2 days, like acetylene, can be almost considered as non-reactive species.
For shorter-lived species, here only ethylene and propene (1 day > τ > 5 h), we computed the correlations between these species and acetylene. When considering all the data of the
Multi-CO2 campaign (without any selection), coefficients of
determination are high (r2>0.70). These tight
correlations between VOCs with different reactivity suggest a limited impact
of the chemistry.
In Fig. 1, we identify some events when the mole fractions of all species
were significantly higher than elsewhere over the campaign duration (1.25 to
6 times as high). These periods (30 and 31 October, 10 and 11 November)
appear to be systematically linked to specific meteorological conditions
when the wind speed was very low (less than 1 m s-1). The mole
fractions obviously increased as the result of the stagnation of local
emissions in the atmosphere. However, three periods with low wind speed do
not correspond to significant peaks in mole fractions (on 5, 6 and 7 November 2013). These three periods were too short (they last around 2 h) for the
accumulation of emissions in the atmosphere to have taken place and did not
result in high mole fractions. There is one more period that we can
highlight and for which the wind speed was less than 1 m s-1: from 17 November, 15:00 UTC, to 18 November, 07:00 UTC. The mole fractions were
higher than the common baseline due to changes in synoptic conditions.
However, no significant peaks are visible. We notice that during this
period, even though the wind speed was low, wind came from one sector only
(from 90 to 190∘), whereas there is no specific wind direction
associated to the large peaks of the other periods (turning wind, see Fig. 2a). In the case of a dominant wind direction, and despite low wind
speeds, emissions did not seem to have accumulated in the atmosphere (there
may have been slowly evacuated). The wind roses in the two different cases
are represented in Fig. 2. To summarise, periods with low wind speed and
non-directional winds are the focus of the present study because they show a
distinct local emission signal in the mole fractions.
Background levels
The previous data selection does not remove all influence of long-range
transport (advection) and dispersion in the measurements and there is still
a need to remove a background level, especially in the case of species with
significant lifetime in the atmosphere like CO2. Most of the previous
studies whose main interest was CO2 defined a continental clear-air
background to correct the CO2 data. For example, data from Mace Head in
Ireland (Lopez et al., 2013) or from Jungfraujoch in Switzerland (Vogel et
al., 2010) are often considered as background data for measurements in
Europe, but strictly speaking they are too far from Paris to isolate the
city signal. Measurements in the free troposphere have also been used as a
baseline (Miller et al., 2012; Turnbull et al., 2011), but they are particularly
expensive to make and are not available for our study period. Furthermore,
continental and free-tropospheric measurements may be misleading for the
interpretation of local emissions (Turnbull et al., 2015). For short-lived
species, the definition of the background is not as critical and the
smallest measured value is often used.
Here, we investigate two options to define the urban background levels. The
first option takes advantage of the fact that the urban emissions are
positive fluxes, i.e. they increase local atmospheric mole fractions. We
define background mole fractions as all measurements smaller than the 5th
percentile of the species over a moving window. The moving window allows
accounting for the dependence of the background on the synoptic situation or
on the time of year, as the background changes seasonally for many gases. As
the average characteristic time of synoptic changes is a few days, and in
order to gather a significant amount of data, we define overlapping windows
of 3 days that start every day at 00:00 UTC in increments of 1 day.
Figure 1 displays the selected lowest 5 % as black disks for some species
measured during the Multi-CO2 campaign. In order to avoid
discontinuities, we linearly interpolate the selected data to obtain a
background mole fraction time series with a time resolution of 30 min
(black curves in Fig. 1).
Wind roses for two low wind speed situations. (a) Wind rose for
10–11 November 2013 (significant peak in mole fractions). (b) Wind rose for
18 November 2013 (no significant peak in mole fractions). The percent scale
is not the same for the two wind plots.
This background definition is simple to implement because it does not
require additional measurements. It samples different wind sectors and not
just clean air ones. For instance, we noticed a difference of 8 ppm between
continental (0–180∘) and oceanic (180–360∘) sectors
for the averaged CO2 background derived from the 5th percentile
calculation. This background definition is expected to work well for all
species that do not have local sinks in the atmosphere or at the surface. We
saw in Sect. 3.1 that chemical sinks can be neglected for our
measurements, but, in the case of CO2 during the vegetation-uptake
season (summer in particular), vegetation within Paris also contributes to
populating the 5th percentile.
Our second option (for CO2 only) defines the background from a publicly
available analysis of the global atmospheric composition. We test it for
CO2, the species for which the first definition may be the least
appropriate. The definition of the background level of CO2 relies on
the global inversion product of the Monitoring Atmospheric Composition and
Climate project (MACC v13.1, http://www.copernicus-atmosphere.eu/; Chevallier et al., 2010). This
product has a resolution of 3.75∘ × 1.9∘
(longitude–latitude) in space and of 3 h in time. It combines the
information from 131 CO2 stations over the globe and a transport model
within a Bayesian framework and estimates the CO2 surface fluxes over
the globe together with the full 4-D CO2 field.
We extracted the 3-hourly time series of the CO2 concentrations from
the MACC database for the eight grid points that surround our two
measurement sites, Jussieu and the LHVP. The CO2 background mole
fraction is estimated as the linear interpolation in time of the analysed
CO2 concentrations averaged over the eight grid points. In the
following, we call Δspecies the mole fractions excess from the background
as defined by either method.
A comparison of the results obtained using the two background definitions
successively is presented in Sect. 3.3.3.
Determination of the ratios between co-emitted species
Description of the method
We present next the method to evaluate ratios of excess mole fractions
between two species (Δspecies1 and Δspecies2). We
consider a moving window of 4 h in increments of 30 min (each period
contains eight points). In each period, we compute the coefficient of
determination r2 between Δspecies1 and Δspecies2 and use a linear regression to evaluate the slope (type II
model regression in which errors on both axes are accounted for). This slope
defines a ratio between the two considered Δspecies over the 4 h
period. We also calculate the difference between maximum and minimum Δspecies1, which is plotted on the x axis, over this period (we name it
δΔspecies1). The motivation for this amplitude
computation will be developed in Sect. 4.1. These calculations are made when
more than five points exist during the time period and when species excesses are
linearly related (a p value test relative to linear relationship of species
excesses is conducted and p value < 0.001 are selected). As an
example, on a 4 h period, we compute (i) the coefficient of determination
r2 between ΔCO and ΔCO2, (ii) the slope,
which well fits the considered dataset (thus giving the ΔCO / ΔCO2 ratio over this period), and (iii) δΔCO2.
In Fig. 3, we show some examples of ratios determined on each 4 h period
against the local corresponding species offset δΔCO2.
They have a simple structure with a horizontal asymptote when δΔCO2 is high. The equation of the asymptote defines the
average ratio. Interpretation and representativeness of this ratio are
discussed in Sect. 4.1.
In order to unambiguously define the equation of this horizontal asymptote,
and the related value of the ratio, we apply a filter on r2
and on δΔspecies1 that isolates the asymptote. We apply
this criterion to measurements spread over a month. The sensitivity of the
ratios to all tested criteria is presented in Sect. 3.3.2. The final
choice of a criterion is a compromise between a cautious selection of points
(derived from the criterion on r2 and δΔspecies1) to clearly extract the local-signal asymptote and a
selection of enough points to get a robust ratio. Finally, the equation of
the horizontal asymptote is the ratio (we impose a slope of 0). The ratio
uncertainty is computed at a confidence level of 68 % (1σ).
Sensitivity to the criterion on r2 and δΔCO2
We present here a sensitivity test for the criterion on r2
and δΔCO2 in the case of the ΔCO / ΔCO2 ratio during the Multi-CO2 campaign. We evaluate this ratio
using the method described in Sect. 3.3.1 and vary the thresholds on
r2 (with values 0.6, 0.7, 0.8 and 0.9) and on δΔCO2 (with values 15, 20, 25, 30, 35 and 40 ppm).
Selected ratios to ΔCO2 plotted vs. the local
CO2 offset (δΔCO2) from the measurements acquired
during the Multi-CO2 campaign. Black data points were selected to
determine the equation of the horizontal asymptote using the criteria
described in Sect. 3.3.2 (the used criteria depend on the considered
species).
Considering a given r2 (δΔCO2 can vary
and be higher than 15, 20, 25, 30, 35 or 40 ppm), we find less than 10 %
difference between all the derived ratios. For the other case, considering a
fixed δΔCO2 offset and a varying r2,
differences between all ratios were found to be less than 6 %. However,
tighter restrictions on the criterion result in fewer available data points, which sample the emission conditions within the month less well. As an
example, for the couple (r2 > 0.6, δΔCO2 > 15 ppm), 211 points are selected in the
asymptote, whereas for the one (r2 > 0.9, δΔCO2 > 30 ppm), only 39 points remain. We choose the
criterion r2 > 0.8 and δΔCO2 > 20 ppm to determine the ΔCO / ΔCO2
ratio during the Multi-CO2 campaign: it keeps more than 100
points to define the asymptote. The same test was conducted on all studied
ratios and differences between derived ratios do not exceed 10 %, which is
lower than the 15 % error imposed by the uncertainty on VOC data. The data
selection for several ratios, including ΔCO / ΔCO2, is
presented in Fig. 3.
Sensitivity to the background choice
Days (weekdays in red crosses and weekends in blue crosses) and hour
sampled per month with our method.
In this section, we test the influence of the chosen background definition
on the obtained ΔCO / ΔCO2 ratio using the methods
described in Sect. 3.3.1. We compare ΔCO / ΔCO2 ratios
for 2013 using the 5th percentile or MACC simulations as background
levels (MACC simulations for 2014 were not available when this study was
conducted). The evolution of the ratios for both options is presented in
Fig. 5. We evaluate the relative difference between the ratios derived
from the two options (in percent of the ratio obtained with the 5th
percentile as background). Differences vary from -17 % in August 2013 to
+11 % in September 2013. The highest differences are found for the
summer months (11 % on average) and the lowest ones for the winter months
(3.2 % on average). These results show that the definition of the
background does not significantly affect the derived ratios, even during the
summer months when MACC and its 3-hourly resolution explicitly account for
the daily cycle of vegetation activity, while the 3-day moving window does
not. This comes from the fact that urban mole fractions during low wind
speed periods are usually larger enough than the background mole fractions
(from around 1.25 to 6 times more).
After these analyses, we finally choose to define background levels using
the 5th percentile on a running window of 3 days as described in Sect. 3.2.1. However, tests were conducted using the 10th percentile (and a
running window of 3 days) or changing the length of the running window
between 1 and 5 days (but still considering the 5th percentile). No
significant difference was found using the 10th percentile (less than 2 %
difference between the two derived ΔCO / ΔCO2 ratios).
Comparing ΔCO / ΔCO2 ratios obtained with different
lengths of the running window, ratios differ by less than 6 % from one
case to another, thus consolidating our choice for background levels.
Discussion
We apply the method presented in Sect. 3.3.1 to assess ratios between
co-emitted species in Paris. In this section, we first discuss the
interpretation and the representativeness of the ratios determined using the
method previously presented. Then, we divide the analysis in two parts.
First we focus on the seasonal variability of the ΔCO / ΔCO2 ratio using continuous measurements acquired from February 2013 to
June 2014. Then we compare the ratios between co-emitted species and
CO2 obtained for the two short campaigns (in Sect. 4.3).
Interpretation and representativeness of the ratios determined with the
asymptotic method
The x axis in Fig. 3 (δΔspecies1) represents the
variability of the species excess over a 4 h period. Large values correspond
to a strong increase or decrease in the species local emissions, and
highlight the concentration peaks that occur at low wind speed. The presence
of an asymptotic value in the monthly ratio plots like that of Fig. 3
suggests that the ratios do not vary much within the month. This stability
is also confirmed by the regular spread of the selected events throughout
the month and even throughout the day. For instance, applying our method to
the continuous CO and CO2 measurements acquired in 2013/2014 in Paris,
we notice that all days (weekdays and weekends) and all hours of the day
were sampled equally: no period type is systematically missing (see Fig. 4). This feature allows our method to yield a robust average ratio per month
in Paris despite, for example, boundary layer dynamics during the day.
Our study focuses on low wind speed periods (less than 1 m s-1, i.e.
less than 3.6 km h-1). Considering this speed and a typical event
length of about 3 h, the extension of the influence zone would be a circle
with a radius of 11 km if the wind direction was constant. With a
non-directional wind, as in our case, the influence area is much smaller,
likely spreading only a few hundred metres around the site. Urban model
simulations could confirm this point but this would involve different
resources and expertise than those of our study.
Seasonal variability of the ΔCO / ΔCO2 ratio in
Paris
The evolution of the ΔCO / ΔCO2 ratios in Jussieu
between March 2013 and May 2014 is presented in Fig. 5. It shows a large
seasonal variability with a maximum value in winter and a minimum value in
summer. There is a difference of around 60 % between these extreme values
(minimum value: 3.01 ppb ppm-1; maximum value: 6.80 ppb ppm-1). The impact of
the biosphere in this seasonality seems to be negligible because night-time
and daytime measurements yield the same ratios (i.e. the same asymptotes
with our method).
Monthly ΔCO to ΔCO2 ratios in Paris. Results
using background levels defined with the 5th percentile are given in
violet. The ones using the MACC simulations are in blue. Error bars on the
ratios correspond to 1σ. The ratio from the
MEGAPOLI/CO2-MegaParis campaign and the corresponding average
temperature are represented by a black disk. Temperature corresponding to
the selected data for the ratio calculation averaged by month is represented
in green as a proxy for season.
Given the large seasonal cycle observed, we hypothesise that temperature is
an important driver of the ΔCO / ΔCO2 ratio. The monthly
atmospheric temperature measured during the low wind speed periods is also
shown in Fig. 5. The two curves are much anti-correlated (r2=0.75):
when the temperature is high, the ratio is low – and vice versa. This is
likely the consequence of higher emissions when temperatures are low because
residential heating is important, whereas in summer, when temperatures are
high, emissions mainly come from traffic, residential cooking and service
sectors, which all together seem to correspond to a lower ΔCO / ΔCO2 ratio. The difference in emissions between the two extreme
seasons relies on the importance of residential heating use. The differences
in the ratios may indicate that higher ratios are observed for residential
heating than for other sources. This is not in agreement with data from the
Airparif inventory (2010): the annual CO / CO2 for residential heating
and for the other sectors is respectively 2.7 and 7.1 ppb ppm-1.
However, we cannot exclude the impact of other drivers such as traffic as
several studies previously showed that CO emissions are more important when
vehicles work at lower temperature than the optimal value (Ammoura et al.,
2014; SETRA, 2009). However, to our best knowledge, no study characterised
the link between vehicle emissions and ambient temperature so far. The
Airparif inventory does not show a seasonal variability as there is almost
no difference on CO / CO2 ratios between winter and summer: 3.1 ppb ppm-1
in January against 3.6 ppb ppm-1 in August. The comparison between these
estimates and our observations suggests the possible influence of another
source. Indeed, wood burning is a major part of CO emissions from the
residential sector (around 90 %) the Airparif inventory does not include
biogenic and/or natural sources of CO2 for two reasons (AIRPARIF,
2013): (1) Airparif respects the definitions given by the UNFCCC and (2) the
carbon cycle of the biomass lifetime is estimated too short to account for
this emission sector. However, our study shows that CO2 emissions from
biomass burning might represent a non-negligible part of the Paris CO2
budget, but we could not confirm it. The differences may be adjusted
to account for this source for CO2 emissions as well and may explain
why
there is no seasonal variability in the Airparif inventory. However, we were
not able to evaluate this point in our study.
Comparison between Multi-CO2 and MEGAPOLI/CO2-MegaParis
campaigns
CO to CO2 emission ratios in Paris
The ratios between the co-emitted species for the Multi-CO2 and
MEGAPOLI/CO2-MegaParis campaign, derived from our method, are presented
in Table 1.
Observed ratios between co-emitted species derived from our method
for the Multi-CO2 and MEGAPOLI/CO2-MegaParis (in red) campaigns.
Numbers in parentheses correspond to 1σ. The mole fraction ratio is
reported in ppb ppm-1 for ΔCO / ΔCO2; all other ratios
to ΔCO2 are reported in ppt ppm-1. Those that do not include ΔCO2 are reported in ppb ppb-1. Ratios in bold mean that there is a
satisfactory agreement between the two campaigns (less than 15 % of
difference).
ΔCO2
ΔCO
ΔAcetylene
ΔEthylene
ΔPropene
Δi-pentane
Δn-pentane
ΔEthane
ΔPropane
ΔCO2
–
5.55/6.33 (0.24)
24.82/25.21 (2.13)
52.55/33.51 (3.87)
11.18/6.26 (2.51)
13.57/ 11.47 (2.34)
9.27/3.41 (0.97)
49.81/31.70 (5.10)
32.07/20.38 (2.92)
ΔCO
–
3.48/2.78 (0.28)
5.47/5.13 (0.39)
1.32/0.88 (0.08)
2.18/2.04 (0.15)
1.15/0.73 (0.11)
6.56/3.09 (0.59)
3.19/2.27 (0.30)
ΔAcetylene
–
1.09/0.84 (0.06)
0.21/0.17 (0.01)
0.28/0.34 (0.02)
0.17/0.11 (0.01)
0.75/0.53 (0.10)
0.48/0.35 (0.04)
Generally, ratios are different between the two campaigns. We notice
differences from -120 to +63 %. A satisfactory agreement is found
between the two campaigns for the ratios that are reported in bold in Table 1 (less than 15 % of difference). Several explanations can be given for
these differences. First, measurements were not carried out in the same
year: 2010 for the joined MEGAPOLI/CO2-MegaParis campaign and 2013 for
the Multi-CO2 one. The differences in the ratios may illustrate some
evolution in the emission structure (as an example, some technological
improvements can occur for vehicles or heating systems). Secondly, these
differences may highlight the importance of the seasonal variability of the
ratios, which was shown in Sect. 4.2. Indeed, measurements were performed
in autumn (October–November) for the Multi-CO2 campaign and in winter
(January–February) for the MEGAPOLI/CO2-MegaParis one. The ΔCO / ΔCO2 ratio from the latter campaign is also reported in
Fig. 5 for the corresponding month of the year: it aligns well on the
seasonal variability observed in Jussieu, even though this campaign was made
4 years before. Furthermore, average temperatures during the low wind
speed periods were not the same: 10 ∘C during the Multi-CO2
campaign and 3 ∘C during the MEGAPOLI/CO2-MegaParis one. This is
in agreement with the argument developed in Sect. 4.2: residential heating
is more important in the heart of winter and its emissions make the ΔCO / ΔCO2 ratio higher. Finally the instruments were not
installed at the same location in the centre of Paris (there are 2 km
between the two locations). Thus the emission area of influence could be
different because the local activities are not exactly the same around the
two sites. As an example, expressways, where the vehicle speed is limited to
80 km h-1 and the vehicle flow is high, are closer to the LHVP
(MEGAPOLI/CO2-MegaParis measurements), leading this site to be more
influenced by large traffic emissions. This spatial variability of the
ratios in Paris is confirmed by the Paris emission inventory Airparif 2010.
Airparif provides annual CO and CO2 emissions by districts in Paris.
Jussieu is in the 5th district and the LHVP in the 13th. According
to the latest Airparif inventory, the annual CO / CO2 ratios are
respectively 2.43 and 3.74 ppb ppm-1 for the 5th and the
13th districts. However, the good agreement between the ratio from the
MEGAPOLI/CO2-MegaParis campaign (measurements in 2010) and the one
derived in Jussieu (measurements in 2014) indicates that the seasonal
variability is the main driver for the evolution of the ratios.
VOCs emission ratios in Paris: multi-CO2 vs.
MEGAPOLI/CO2-MegaParis
This section analyses the VOC emission ratios more specifically, as these
compounds (which share common sources with CO and CO2) were also
measured during the two campaigns (Multi-CO2 and
MEGAPOLI/CO2-MegaParis). In the presence of nitrogen oxides (NOx),
VOC oxidation leads to the formation of ozone and secondary organic aerosols,
which impacts air quality and climate. Therefore characterising VOC
emissions in urban areas (which are always associated to high NOx
conditions) is of importance. VOCs include a large variety of compounds and
information on their sources and sinks will be given here only for the
compounds selected in this study. As already mentioned, among the various
non-methane hydrocarbons measured during these campaigns, the selected
compounds were the ones which presented a strong correlation with CO2
and CO (r2>0.8), allowing the use of our approach for the
ratio determination. In urban areas, anthropogenic sources of VOCs are
dominated by traffic, residential heating (including wood burning), solvent
use and natural gas leakage, as was recently shown in Paris (Baudic et al.,
2016) as well as in other cities (Niedojadlo et al., 2007, in Wuppertal,
Germany; Lanz et al., 2008, in Zurich, Switzerland; Morino et al., 2011, in
Tokyo, Japan). VOC levels,
diurnal and seasonal variability and source contributions in Paris have been
thoroughly described by Baudic et al. (2016). Therefore only minimal
information is reported here. Ethane and propane are mainly associated with
natural gas leakage sources (and to wood burning to a lesser extent),
whereas acetylene, ethylene and propene predominantly come from combustion
sources (which include wood burning and vehicle exhausts). Finally pentanes
are associated with traffic emissions (vehicle exhaust and /or gasoline
evaporation). None of them are tracers of a specific source and therefore
characterisation of sources is usually made by using either a ratio
approach, often using CO or acetylene as tracer (see Borbon et al., 2013, and
references therein), or an approach based on the determination of sources
composition profiles (see Baudic et al., 2016, and references therein). The
studied compounds usually show a seasonal cycle with a minimum in
spring/summer and maximum in autumn/winter. This typical seasonal cycle is due
to the combination of several factors: emissions (the wood burning source
has a pronounced maximum in winter), photochemistry (OH, which presents
higher values in summer, is the main sink of all the studied compounds) and
finally dynamics (a shallower boundary layer in winter leads to more
accumulation of the pollutants). We note that all compounds selected here
have a lifetime (which ranges from a few hours for ethylene to almost 40 days for ethane) shorter than CO.
Ratios obtained during the Multi-CO2 campaign are reported along with
the results obtained for the MEGAPOLI/CO2-MegaParis campaign in Table 1. For consistency, we note that the comparison is restricted to the
MEGAPOLI/CO2-MegaParis campaign. Indeed ratios presented in this table
have been determined according to the method described previously in Sect. 3.3.1, which differs from the traditional ratio approach (where the ratio
directly represents the slope of the scatter plot between two compounds).
Ratios between the campaigns appear to agree within a 2-fold factor (except
for Δn-pentane / ΔCO2) but present quite heterogeneous
results. The previous section mentions the importance of the seasonal
variability for the ratio ΔCO / ΔCO2, as the
Multi-CO2 campaign occurred in autumn, whereas the
MEGAPOLI/CO2-MegaParis campaign occurred in winter, associated with a
higher residential heating contribution. If seasonality was the main driver
of the ratio ΔVOC / ΔCO2, we would observe higher ratios
in winter as well (for compounds largely emitted by residential heating like
acetylene and ethylene), which is not the case (ratio Δacetylene / ΔCO2 is not significantly different between both campaigns and
Δethylene / ΔCO2 is lower during
MEGAPOLI/CO2-MegaParis). Another possible driver of the ΔVOC/ΔCO2 variability between the two campaigns is the
interannual variation of VOCs (2010 for MEGAPOLI/CO2-MegaParis, 2013
for Multi-CO2). Indeed, a recent study has shown significant trends of
non-methane hydrocarbons in urban and background areas in France (Waked et
al., 2016). These trends (from -3.2 to -9.9 %) have been determined
for acetylene and ethylene in Paris and are likely explained by efficient
emission control regulation. Nevertheless, these trends would suggest lower
ratios in 2013 than in 2010, which was not the case. As the temporal
variability does not seem to be the main driver of the ΔVOC / ΔCO2 difference, and given the complexity of VOC emission profiles,
which differ within a same source (e.g., emissions from vehicle exhaust vary
as a function of motor temperature and engine type; see Salameh et al.,
2015, and references therein), we suggest that this difference arises from the
heterogeneity of the VOC sources in the vicinity of the two measurements
sites. For instance, remember that one, and only one, of the two sites is
located close to an expressway. This would imply a low spatial
representativeness of our VOC results obtained in very low wind conditions.
Conclusions
We have investigated the possibility to characterise local urban emissions
through atmospheric mole fraction measurements collected during low wind
speed periods. In the case of Paris, we have shown that this approach
significantly reduces the sensitivity of the results to the species
background level definition, even in the case of CO2. Thanks to
continuous long-term measurements, we have also shown that the low wind
speed conditions in the centre of Paris (especially in Jussieu) sample the
hours of the day and the days of the week rather evenly, so that the method
characterises an average urban atmosphere.
The comparison of ratios obtained for the two measurement campaigns,
Multi-CO2 and MEGAPOLI/CO2-MegaParis, shows differences from
-120 to +63 % for nine atmospheric species. Such differences may reveal
spatial and seasonal variability in the ratios because the two campaigns
took place at different sites, during different years and seasons. However,
the evolution of the ratios seems to be mainly influenced by the seasonal
changes. This seasonal variability was assessed for the CO to CO2
ratios for the period from February 2013 to June 2014, showing a strong
anti-correlation with monthly atmospheric temperature, likely linked to
seasonal changes in emissions sources (for example, domestic heating is
predominant in winter and non-existent in summer). We provide evidence on
the importance of residential heating in the total ΔCO / ΔCO2 ratio. This ratio is higher than the ones for other sectors,
which is in contradiction to current estimates from the Airparif inventory.
Due to the heterogeneity of VOC sources, ratios that include VOCs are more
difficult to interpret in terms of representativeness in low wind speed
conditions.
The determination of these average ratios may be useful to assess the
estimates provided by emission inventories. Indeed, city-scale emission
inventories mainly focus on air quality, and the link with greenhouse gases,
especially with CO2, is not well made. The combination of the
well-known total pollutant emissions with the ratios estimated by our
experimental approach should allow a better quantification of total CO2
emissions.