Introduction
Urbanized and industrialized areas are estimated to produce more than 70 % of the global CO emissions based on the consumption of fossil fuels (IEA, 2008; Seto et al., 2014). Furthermore, due to increased urbanization especially in developing countries, urban CO emissions are projected to grow rapidly in the next decades (e.g., Wolf et al., 2011). Understanding the contribution of cities to climate change will help stakeholders to become active at the city level in making proper decisions regarding CO emission reduction (United Nations, 2011a). Megacities especially are places where human activities release large quantities of CO into the atmosphere and they require scientific and political interest (Rosenzweig et al., 2010; Duren and Miller, 2012).
Based on the 2010 population census, the Paris metropolitan area has
10.5 million inhabitants and is ranked as the 21st largest megacity in the world and second in
Europe after Moscow (United Nations, 2011b). Paris is centered in the region
Île-de-France (IdF) that contains 18 % of the French population
(INSEE, 2012) while covering only 2 % of the territory. The emission
inventory reported by AIRPARIF (Association de surveillance de la qualité
de l'air en IDF;
In recent years, there has been a growing international interest in
quantifying urban CO fluxes from atmospheric top-down approaches (e.g.,
Duren and Miller, 2012; McKain et al., 2012). Large projects developed in
Indianapolis (INFLUX;
Several studies highlighted the fact that the CO concentrations measured in and around cities are directly sensitive to factors that control the CO fluxes: proximity to urban centers and industrial sources, ground and air traffic, vegetation distribution and rates of primary productivity (e.g., Wentz et al., 2002; Grimmond et al., 2002; Apadula et al., 2003; Nasrallah et al., 2003; Gratani and Varone, 2005; Strong et al., 2011). Furthermore, advection and vertical mixing strongly influence the urban CO signal (e.g., Idso et al., 2002; Moriwaki et al., 2006). At low wind speeds, urban CO emissions that accumulate over the city were observed to generate a CO urban dome of several tens of ppm at night and several ppm in the afternoon compared to surrounding rural areas, reaching, for example, 100 ppm in Phoenix, Arizona, just before pre-dawn (Idso et al., 1998, 2001). At higher wind speeds, the strength of the CO urban dome decreases through ventilation processes to take the shape of a plume and is considered in some former studies for other cities to reach an asymptotic value (e.g., Rice and Bostrom, 2011) which was sometimes considered representative of the regional background CO concentration (Garcia et al., 2010, 2012; Massen and Beck, 2011).
In the Paris region, no continuous atmospheric CO observation network existed before the present study, apart a couple of intensive campaigns: (1) Widory and Javoy (2003) performed CO measurements very close to the ground level (mostly under the influence of car exhausts) that we think is not representative of the urban scale; and (2) in winter 2010, Lopez et al. (2013) showed an increase of several ppm in the atmospheric CO concentration in Paris (30 m above ground level, a.g.l.) in comparison with the CO levels measured in the Gif-sur-Yvette station (GIF, 12 m a.g.l.), located in a remote peri-urban area 20 km SW of Paris. Furthermore, the Mace Head station (MHD – west coast of Ireland) is generally used as the reference site for European CO background measurement (Bousquet et al., 1996), as it has been the case in the Heidelberg (Germany) study of Vogel et al. (2010) or in the Paris study of Lopez et al. (2013). The relevance of this remote coastal site as a regional background site, especially for studying the regional impact of the Paris megacity on atmospheric CO remains to be assessed at the diurnal to the seasonal scales, as no regional in situ network measurements were available to tackle this question.
In the framework of the CO-MegaParis project, we deployed a network of
in situ CO stations along the path of the dominant winds and developed
high-resolution top-down modeling frameworks dedicated to study the Paris
CO emissions (Lac et al., 2013; Bréon et al., 2015). Our
observation network consisted of three new continuous sites installed in and
around the Paris megacity, among which was one on top of the Eiffel Tower
(317 m a.g.l.). These three stations (named Montgé-en-Goële – MON; Gonesse – GON; the Eiffel Tower – EIF) were deployed
in summer 2010 within the AIRPARIF infrastructure. They ran for several
months of the CO-MegaParis project lifetime and delivered almost 1 year
of CO concentration datasets for the Paris megacity area.
Additional datasets were provided by two long-term stations operated by LSCE
named Traînou (TRN) (Schmidt et al., 2014) and GIF (Lopez et al., 2012) that are part
of the national monitoring network SO-RAMCES (now called ICOS-France;
This work aims to understand the diurnal, synoptic and seasonal variability of the atmospheric CO concentration observed at each of the five stations of the Paris megacity network from the analysis of the first 1-year time series (8 August 2010–13 July 2011). We also compare the regional CO concentration datasets to those at MHD in order to assess how relevant this remote site is in defining the CO background level in the Paris region. Section 2 introduces the observation network and reports the data treatment and the quality of the CO time series. We also present the meteorological fields used over the period of study as well as observations of the atmospheric boundary layer (ABL) height, collected at the QUALAIR site (QUA) in the center of Paris, that cover a large part of the period of study (8 August 2010–31 March 2011). In Sect. 3, we present air mass back trajectories and the different wind sectors covered to assess the variability of the time series over the year of study (Sect. 3.1). We then analyze the diurnal variations of the CO concentration at the five sites that we compare to the MHD record (Sect. 3.2). A specific focused analysis is carried out on the case of the Eiffel Tower station. We also estimate the weekday versus weekend variability (Sect. 3.3) and analyze the seasonal variations of the CO concentration at each site (Sect. 3.4). Finally, we study the role of wind speed and direction on the CO signal collected at the five regional network stations (Sect. 3.5) and we assess the impact of local (< 10 km), regional (10–100 km) and remote (> 100 km) fluxes on the observed CO concentrations. We come to conclusions on the representativeness of each site for assessing how the Paris CO emissions impact the atmospheric CO concentration at the regional scale and on the lessons learned for regional urban network design.
Experiments
The measurement network
Geography of IdF and CO emissions from the Paris region and western Europe
Paris is located in the region of IdF in a relatively flat area and benefits
from a temperate climate, with frequent rain events in all seasons and
changing weather conditions. IdF covers 12 011 km, i.e., only 2.2 % of
the national territory. In 2010, land usage was 47 % by agriculture,
31 % by forests and natural areas and 22 % by urbanized areas
(
Coordinates of the stations used in this study (a.s.l. stands for above sea level; a.g.l. for above ground level).
| Station | Code | Latitude ( ) | Longitude ( ) | Site ground | Sampling height |
|---|---|---|---|---|---|
| elevation a.s.l. | a.g.l. | ||||
| Montgé-en-Goële | MON | 490141.79 N | 24455.54 E | 160 m | 9 m |
| Gonesse | GON | 485924.56 N | 22721.90 E | 68 m | 4 m |
| Eiffel Tower | EIF | 485129.71 N | 21739.92 E | 33 m | 317 m |
| Gif-sur-Yvette | GIF | 484235.82 N | 20851.55 E | 163 m | 7 m |
| Traînou | TRN | 475753.08 N | 20645.42 E | 133 m | 50 m, 180 m |
| Mace Head | MHD | 531933.00 N | 95412.00 W | 25 m | 15 m |
| QUALAIR | QUA | 485047.26 N | 22121.40 E | 35 m | 25 m |
Figure 1 shows the total annual CO emissions emitted from IdF at the
resolution of 1 1 km (AIRPARIF, 2010). As shown in Fig. 1, there is a
large spatial variability of CO emissions in IdF which is mainly
driven by the population density and the location of highways. Each year,
average emissions in the center of Paris are estimated to be 70 000 t CO km compared to 5000 t CO km
at the suburban borders. Emissions have a temporal variability on
diurnal, synoptic and seasonal scales, mainly because CO emitted by
heating varies with temperature and season, and CO emitted by traffic
changes with the time of the day, day of the week and vacation periods (see
Fig. 3, Bréon et al., 2015). Figure 1 also shows emissions from
industry and energy production that come from point sources here
distributed on 1 1 km grid cells. According to AIRPARIF, these sources are
located mostly in the north and northeastern areas of Paris compared with
the southern part of Paris (Lopez et al., 2013). Detailed and public
information on a total of 123 point sources of
CO in IdF can be found online for the year 2010 at the following
address:
Annual emissions of CO from Île-de-France at a spatial resolution of 1 1 km (AIRPARIF, 2010) and our Paris megacity CO in situ network: the red points indicate the CO-MegaParis stations (MON is a NE rural site at 9 m a.g.l.; GON is a NE peri-urban site at 4 m a.g.l.; EIF is an urban site at 317 m a.g.l.); the dark blue points are stations from the ICOS-France network (GIF is a SW peri-urban site at 7 m a.g.l.; TRN is a SW rural site at 50 and 180 m a.g.l.). The QUALAIR station for monitoring the atmospheric boundary layer height in the city of Paris is also shown (green point).
[Figure omitted. See PDF]
Sampling sites
The locations of the observation sites are represented in Figs. 1 and 2. Table 1 gives their exact geographical coordinates. The sites were carefully chosen so that they would not contaminate the CO measurements by their own emissions.
The EIF station was installed on the highest floor accessible to tourists, in a closed room of 1.5 m under the stairs providing access to the tower communication antennas. To prevent contamination by the visitors' respiration, the air inlet was elevated to about 15 m above the last floor accessible to tourists, at the antenna level (317 m a.g.l.), where it was protected from uplifted air by several intermediate metallic floors. The instrument was set up in a Faraday cage to avoid interferences from strong electromagnetic radiations from the antennae. The location of the Eiffel Tower is not exactly central within Paris. The 0–180 (N, E and S) wind sector of the station is exposed to a larger urbanized and industrialized area than the 180–270 sector (S to W). In the 0–180 wind sector, the urbanized area covers a radius of about 20 km and includes two large point sources that are the waste burning facility of Ivry-sur-Seine (in the SE direction of the Eiffel Tower) and the heating facility of Saint-Ouen (in the north). In the 180–270 wind sector, the urbanized area extends barely within a 10 km radius before entering into broadleaved forests covering 2300 ha. The 270–360 wind sector is also mostly urbanized over a radius of about 15 km, although it comprises the woods of Boulogne (about 840 ha) which are located only 2 km NW of the Eiffel Tower.
Location of the Paris megacity on a map of CO anthropogenic
emissions from western Europe, adapted from the EDGAR 2009 inventory
(
[Figure omitted. See PDF]
The GON station was set up about 20 km northeast of the Eiffel
Tower at the local fire station in a residential area comprising a
combination of streets and lawn gardens with a few trees around. The analyzer
was hosted in a shelter equipped with a mast of 4 m standing below
the canopy level ( 15 m a.g.l.). However, the distance from the mast
to the closest trees was at least 20 m and the station was well exposed to
wind from all directions. GON is located on a small hill relative to the
center of Paris, and in the southerly direction, the station benefits from an
open view of the city. About 3 to 4 km to the southeast and east of the
station is a highway which carries high traffic during rush hours, as early
as 5:00 UTC. The highway connects the center of Paris and CDG
Airport, which is located about 7 km northeast of GON. The station is also
close to the Bourget Airport located about 2.5 km to the south. Finally, in
the W–NW sector, two noticeable industrial sources located at about 5 km
from Gonesse (Fig. 1) should be mentioned as they might have an influence on
the CO measurements (Sect. 3.5.2): a thermal plant in Sarcelles that
emitted 44 kt CO yr in 2010 and an energy production plant in Le
Plessis-Gassot that emitted 128 kt CO yr in 2010 (source:
The MON station was set up in the small village of
the same name with approximately 700 inhabitants located on the middle of
the slope of a small hill ( 20 m high). The analyzer was
installed on the top of the three-floor city hall building ( 9 m a.g.l.).
The air inlet was set up on an arm pointing about 1.5 m outside of the
window towards the south (200) opening onto fields. The north
sector was covered by a few houses situated at the edge of a forest of
broadleaved trees. The city hall is located on the southern side of the
main road of the village which approximatively follows a
northwest–southeast axis. Most of its close surroundings are agricultural fields and
small villages connected by secondary roads. Montgé-en-Goële is
located approximately 10 km east of CDG Airport. Two noticeable point
sources are relatively close to the station (Fig. 1) and could influence the
measurements (Sect. 3.5.2): a cement plant 3 km east in Saint-Soupplets
(43 kt CO yr in 2010; source:
The GIF station, previously described in Lopez et al. (2012,
2013), has been running continuously since 2001 at LSCE (Laboratoire des
Sciences du Climat et de l'Environnement). The air inlet is set up on the
roof of a building at 7 m a.g.l.. The site is located 20 km
southwest of the center of Paris on the Plateau de Saclay and surrounded
mainly in the 0–90 sector by agricultural fields and by a few
villages. A few hundred meters further in this direction, a national road
passes on a north–south axis with high traffic levels during the morning and
in the evening during rush hours. About 1 km further in the
270–360 sector, the atomic and environmental research agency (CEA
of Saclay) holds approximately 7000 employees and is equipped with a thermal
plant (17 kt CO in 2010; source:
The TRN station, previously described by Schmidt et al. (2014),
has been running continuously since 2007. It is located about 120 km south
of the center of Paris in the region “Centre”, within the Orléans forest
(50 000 ha). A 200 m transmitter mast was equipped with four sampling levels:
5, 50, 100 and 180 m a.g.l.. TRN is located 13 km
northeast of the city of Orléans which has about 120 000 inhabitants.
There are a few villages around the station, including Traînou village
with 3195 inhabitants in 2012
(
The MHD station has already been described by Biraud et al. (2000) and Messager et al. (2008). Atmospheric CO has been continuously measured there since 1992. This station, located on the west coast of Ireland, is an important site for atmospheric research in the Northern Hemisphere, as its remote location facilitates the investigation of trace constituent changes in marine and continental air masses. Most often, the station receives maritime air masses, although sometimes it is in the footprint of continental air masses coming from Europe, or more locally from Ireland and the UK (see Messager et al., 2008 for further details). In this study, MHD was evaluated as a potential background site for urban regional studies in the European continent.
The QUA station is located in the Paris city center on the campus of Université Pierre et Marie Curie in Jussieu on the top floor of a building (25 m a.g.l.), about 4 km east of the Eiffel Tower along the Seine river. It is briefly described in Dieudonné (2012). This station allows monitoring the height of the urban atmospheric boundary layer (ABL) above the Paris megacity.
CO measurements
Measurement system and calibration procedure
The CO datasets of the CO-MegaParis stations (MON, GON and EIF) were collected from 8 August 2010 to 13 July 2011 using cavity ring-down spectroscopy (CRDS) analyzers (Picarro, model G1302) at 0.5 Hz. These three stations were identically set up: atmospheric air was pumped through short inlet lines made of Synflex® (4.3 mm inner diameter) with a flow rate of 0.15 L min. The cell temperature of the analyzers was controlled at 45 C and the cell pressure at 140 Torr. At EIF, the analyzer was specifically designed to undergo higher temperatures inherent to the metallic structure of the tower and the cell temperature set point was set higher (60 C). No specific impact of this set point was observed on the measurements. Air was not dried before analysis at the 3 stations and we applied the automatic CO water correction implemented on the CRDS instruments (Rella, 2010) to our datasets.
The GON and MON stations were equipped with four high-pressure aluminum cylinders containing gas mixtures of CO in synthetic air (matrix of N, O and Ar) for instrument calibration. Before on-site deployment, the CO concentration of the cylinders was assigned at LSCE on the WMO-X2007 scale by a gas chromatograph (GC) described in Lopez et al. (2012). It spanned a range from 370 to 500 ppm. At each site, three of the tanks were used for instrument calibration and measured every 2 weeks. The calibration sequence consisted of four cycles (6 h total). Each cycle measured the tanks one after the other for 30 min each. The fourth tank called “target” was run for 30 min every 12 h. The target was used to monitor the instrumental drift and to assess the dataset accuracy and repeatability. At EIF, for safety reasons, it was not possible to leave any gas tanks on the site so the target tank was measured every 2 weeks and the calibration gases every 3 months only (two calibration cycles of 20 min for each gas, for a total sequence of 2 h). The instrumentation and the calibration procedure of the two SO-RAMCES stations (GIF and TRN) have already been described in Lopez et al. (2012, 2013) and Schmidt et al. (2014).
Calibration and target frequencies, accuracy and repeatability of the CO-MegaParis stations. The accuracy is given as the difference of the target CO concentrations measured by the CRDS analyzer and the GC.
| EIF | MON | GON | |
|---|---|---|---|
| Calibration sequence | 2 h every 3 months | 6 h every 2 weeks | 6 h every 2 weeks |
| Target sequence | 30 min every 2 weeks | 30 min every 12 h | 30 min every 12 h |
| Accuracy (ppm) | 0.13 | 0.04 | 0.07 |
| Repeatability (ppm) | 0.38 | 0.10 | 0.07 |
Data processing and quality control
The CRDS CO data were calibrated by applying a linear fit to the CO concentration of the calibration tanks as measured by the CRDS analyzer versus the CO concentration as measured by the GC. Gas equilibrium issues implied retaining only the last calibration cycle of the four cycles at MON and GON (and of the two cycles at EIF) to compute the calibration equation. For all of the calibration and target gas cylinders, the CRDS CO concentration was calculated as the average of the last 5 min of each gas. The accuracy of the datasets was calculated as the mean difference between the CO concentration reported by the CRDS analyzer and by the GC for the target gas. The long-term repeatability of each dataset was calculated as the standard deviation of the mean concentration of the target gas reported by the CRDS analyzer over the year of observations.
Table 2 summarizes the accuracy ( 0.13 ppm) and repeatability ( 0.38 ppm) calculated from the 5 min averaged data for MON, GON and EIF. As expected, the dataset of EIF shows larger deviations compared to GON and MON due to less frequent calibration and target gas measurements and a shorter calibration procedure.
The data of GON, EIF and MON were automatically filtered against cavity pressure () and cavity temperature () departure to the set points ( and according to the ICOS procedure (Hazan et al., 2016), keeping only points for which < 0.1 Torr and < 0.004 C for MON and EIF (0.006 C for EIF). Furthermore, dead volumes in the setup led to instability in the response of the analyzer for 2 min after switching from one gas line to another. These 2 min periods were automatically removed from the datasets.
The data were also manually inspected to remove CO spikes due to very local influences (fire training at the GON station, breathing of a maintenance operator on the sampling inlet, etc.). Very local influences were identified from the short duration of the events (a few seconds to some minutes) and from the large standard deviation of the CO averages associated with these events. This amounted to less than 1 % of the total datasets, resulting in 91 % of the data validated after the (, ) filtering and the manual quality control.
The GIF, TRN and MHD data processing and quality check were assessed in previous studies by Schmidt et al. (2014) and Messager et al. (2008): the repeatability of the 1 h average CO concentration of the target gas is 0.05 ppm at GIF, 0.06 ppm at TRN and 0.05 ppm at MHD. The instrumentation at these three sites is directly linked to the WMO-X2007 scale (Zhao and Tans, 2006).
Monthly means and standard deviation (1 of the CO concentration (in ppm) measured at each site and data coverage of each month (in percent).
| MON | GON | EIF | GIF | TRN50 | TRN180 | MHD | |
|---|---|---|---|---|---|---|---|
| Spring | |||||||
| March | 410.4 9.4 | 420.3 19.1 | 411.8 16.7 | 414.4 13.7 | 408.9 9.3 | 405.5 7.9 | 398.6 4.4 |
| Coverage | 99.9 | 97.3 | 95.6 | 93.0 | 57.7 | 66.8 | 87.6 |
| April | 402.1 11.0 | 421.2 32.6 | 403.0 13.2 | 408.7 15.3 | 401.3 11.2 | 396.8 7.1 | 398.6 4.9 |
| Coverage | 100.0 | 95.3 | 94.6 | 94.2 | 69.0 | 79.6 | 77.6 |
| May | 394.7 8.9 | 405.5 20.0 | 398.0 10.6 | 398.7 11.2 | 395.0 9.9 | 391.2 5.9 | 396.3 2.4 |
| Coverage | 99.9 | 97.3 | 98.8 | 98.3 | 81.2 | 82.8 | 95.6 |
| Summer | |||||||
| June | 400.1 11.9 | 406.2 27.3 | 396.9 8.2 | 400.9 12.8 | 398.4 10.7 | 394.5 4.7 | 394.5 3.5 |
| Coverage | 98.1 | 0.65 | 95.3 | 84.9 | 88.2 | 69.3 | 92.9 |
| July | 393.1 6.9 | 398.6 17.3 | 393.4 6.6 | 397.2 8.3 | 392.4 6.2 | 389.8 3.2 | 392.1 5.0 |
| Coverage | 96.8 | 96.8 | 78.1 | 62.4 | 51.4 | 78.1 | 97.1 |
| August | 390.8 10.2 | 401.9 29.6 | 387.1 7.9 | 392.2 11.8 | 389.8 10.8 | 384.9 5.6 | 381.4 2.5 |
| Coverage | 99.6 | 94.6 | 90.5 | 78.6 | 95.8 | 96.1 | 99.9 |
| Autumn | |||||||
| September | 395.3 12.7 | 410.9 34.0 | 391.0 11.1 | 395.3 11.1 | 392.5 11.8 | 385.7 5.7 | 384.0 3.3 |
| Coverage | 72.9 | 96.0 | 97.8 | 83.1 | 91.1 | 90.4 | 96.8 |
| October | 402.8 9.8 | 413.9 24.7 | 400.8 12.0 | 403.0 11.3 | 400.3 10.6 | 395.0 7.2 | 390.9 6.2 |
| Coverage | 100.0 | 96.0 | 98.9 | 82.7 | 92.5 | 90.5 | 98.7 |
| November | 408.3 10.4 | 414.9 15.9 | 407.7 15.1 | 411.2 12.9 | 401.8 9.4 | 399.3 8.6 | 393.6 3.8 |
| Coverage | 100.0 | 97.2 | 99.6 | 67.4 | 34.3 | 31.5 | 97.1 |
| Winter | |||||||
| December | 417.0 13.9 | 424.5 17.9 | 414.2 16.9 | 415.4 13.9 | 408.3 9.5 | 406.0 10.4 | 396.8 3.8 |
| Coverage | 100.0 | 73.9 | 71.9 | 77.4 | 82.4 | 87.5 | 97.2 |
| January | 408.9 9.4 | 415.8 16.7 | 408.4 13.2 | 410.1 13.0 | 405.7 10.1 | 403.1 9.3 | 396.1 2.3 |
| Coverage | 100.0 | 96.2 | 78.9 | 78.5 | 95.6 | 94.5 | 98.7 |
| February | 411.9 12.2 | 423.1 20.7 | 410.5 14.7 | 409.8 10.5 | 405.4 7.8 | 402.8 7.3 | 396.3 2.0 |
| Coverage | 100.0 | 97.0 | 93.2 | 97.0 | 84.8 | 88.5 | 98.4 |
At each station, some instrument failures occurred during the period of the CO-MegaParis study. The amount of available data points in the final datasets which are all provided as hourly averages is reported in Table 3 for each month and for each site, and is in most cases above 80 %.
In the following study, we will use CO hourly means for all of the stations. Apart from a few exceptions that will be identified, time is always given in UTC. Local time in Paris is UTC 2 from April to October and UTC 1 from November to March.
Atmospheric boundary layer height measurements
ABL heights over Paris were determined using the 532 nm elastic lidar of the
QUALAIR station (
The ABL height database was constructed by applying this detection method to hourly average lidar data, leading to hourly average ABL depth values. The data were acquired during daytime and weekdays, since an operator had to be on site to shut down the system in the case of rain. The dataset covers 70 % of the year of study.
Meteorological fields
Urbanized areas are characterized by specific meteorological patterns (e.g., Masson, 2000). For example, the urban heat island effect was observed to generate a gradient of temperature of a few degrees and a gradient in the ABLH of several percent between Paris city center and its rural surroundings (Pal et al., 2012; Lac et al., 2013). As far as possible, it is thus appropriate to use local meteorological fields for each of the regional atmospheric CO stations. Since our sites were not equipped with their own meteorological sensors, the Meso-NH model was run over the full period of study at a time step of 60 s and a spatial resolution of 2 km to generate wind speed and direction over a domain including Île-de-France (Lac et al., 2013). This modeling framework includes the land– and surface–atmosphere interaction model SURFEX with an urban scheme (town energy balance, TEB; Masson, 2000) and a vegetation scheme (Interactions between Soil, Biosphere, and Atmosphere, ISBA-A-gs; Calvet et al., 1998; Noilhan and Planton, 1989). It was already validated against observations for 1 week in March 2011 in Lac et al. (2013), where it is described in detail. The meteorological fields were extracted for the present study from the model with an output frequency of 1 h at the sampling height of each station. About 1.5 km north of GIF at the CEA of Saclay (SAC), a mast equipped with meteorological sensors provided wind field data at 10 m a.g.l. from August 2010 to April 2011. In that period, the observed SAC and the modeled GIF meteorological datasets match each other on average within 0.8 m s for wind speed and 3.7 for wind direction, giving additional confidence in the average behavior of the model, at least in such peri-urban areas.
For wind fields at MHD, we use a local meteorological hourly observation
dataset provided by Met Éireann (
Figure 3 shows the wind roses at GIF for each season (using Meso-NH modeled data), given that the synoptic features are broadly similar to all of the regional stations. Two dominant wind regimes were observed according to the general meteorological features of the region: the southwest regime dominates mostly in summer, autumn and winter, and a northern regime (northeast and northwest sectors) mostly in spring and winter. Wind speed varied from 0 m s on 18 September 2010 to a maximum of 11.1 m s on 13 November 2010, the mean wind speed being 3.4 m s. The first (25 %) and third (75 %) quartiles were 2.2 and 4.4 m s, respectively. The main variations of wind speed occurred during changes of synoptic conditions. In MHD, winds blew mostly from the Atlantic Ocean in all seasons, including both the southwest and the southeast sectors. MHD also sometimes received continental air masses mostly in winter, spring and autumn. At this station, wind speeds ranged from 0.1 to 25.3 m s with a mean at 7 m s and the first and third quartiles at 4.1 and 9.5 m s, respectively.
Regarding temperature, field observations were available over the full period of study at 100 m a.g.l. at SAC (but not closer to the surface). Since we are mostly interested in relative variations of the temperature at the seasonal scale, we use this dataset as a proxy of the air temperature for all stations located in IdF (although we know that the urban heat island can generate differences of a few degrees between the city and its surroundings, as shown in Pal et al., 2012). The hourly temperature dataset collected at SAC 100 m a.g.l. over the whole period of study is shown in Fig. S2 in the Supplement. Temperature ranges from a minimum monthly mean of 0 C in December to a maximum monthly mean of 18.8 C in August.
Wind rose at GIF (7 m a.g.l., SW peri-urban site) given by season over the period of study (8 August 2010–13 July 2011) from the Meso-NH modeled wind fields. Colors indicate the wind speed according to the given scale (in m s.
[Figure omitted. See PDF]
Results and discussion
Air mass back trajectories and wind classification of the CO concentration time series
In order to get information about the origin of the air masses that reached
our stations, back trajectories from the Hybrid Single-Particle Lagrangian
Integrated Trajectory model (HYSPLIT;
(a) Time series of CO concentration (1 h averages) recorded during the CO-MegaParis period and colored by wind classed for sites MON (NE rural site, 9 m a.g.l.), GON (peri-urban site, 4 m a.g.l.), EIF (urban site, 317 m a.g.l.) and GIF (SW peri-urban site, 7 m a.g.l.). (b) Time series of CO concentration (1 h averages) recorded during the CO-MegaParis period and colored by wind classed for sites TRN50 (rural SW, 50 m a.g.l.) and MHD (coastal remote site, 15 m a.g.l.).
[Figure omitted. See PDF]
In Fig. 4a and b, as expected, wind direction and wind speed appear to be
part of the main controlling factors of the CO mixing ratio values
recorded in the different stations. The urban and peri-urban stations are
characterized by higher mixing ratios and a much larger variability than the
rural and background sites. The highest variability is observed on the GON
time series, followed by EIF and GIF. We note as well that the highest mixing
ratios recorded at the southern rural sites (TRN50 and TRN180) and remote
station of MHD occur usually during local events, likely from the influence
of local emissions or remote events with northeast winds that passed over the
Benelux and Ruhr areas (see back trajectories in Fig. S1 in the Supplement)
and were loaded with anthropogenic emissions (Xueref-Remy et al., 2011)
before reaching IdF. We also observe simultaneous variations between the
sites for the local wind class: for example, peaks of the CO mixing
ratio are observed in all the stations of IdF in mid-February and the end of
March 2011, which correspond to two pollution events reported by AIRPARIF
(
CO diurnal cycles
Mean CO diurnal cycles
Diurnal cycles of atmospheric CO are affected by local sources and sinks, regional transport and ABL dynamics (Fang et al., 2014; Garcia et al., 2010, 2012; Rice and Bostrom, 2011; Artuso et al., 2009; George et al., 2007; Gerbig et al., 2006). The mean CO diurnal cycles and associated 1 standard deviation are shown in Fig. S3 for the different stations.
Noticeable differences are observed between the sites. The diurnal amplitude
of the CO concentration from the lowest to the highest point is 2.6 ppm
(MHD), 6.5 ppm (TRN180), 11.2 ppm (EIF), 14.9 ppm (MON), 15.5 ppm (TRN50),
18.2 ppm (GIF) and 30.6 ppm (GON). While the CO diurnal pattern at
TRN can mostly be explained by biospheric activity and vertical dilution in
the ABL (Schmidt et al., 2014), the peri-urban and urban stations are also
expected to be strongly influenced by the diurnal cycle of Parisian
anthropogenic sources. For all sites except EIF, the maximum concentration
occurs in the late night/early morning (04:00–05:00 UTC for TRN50, MON, GIF and GON;
07:00–08:00 UTC for TRN180) when the ABL is the most shallow, vegetation respires and
rush hours traffic occurs (05:00–09:00 UTC; source:
Comparing the 50 and 180 m levels at TRN, we observe a vertical gradient of CO concentration, along with a phase shift of the diurnal cycle: the maximum concentration is observed at 05:00 UTC at TRN50 versus 07:00 UTC at TRN180 due to the coupling of the CO fluxes with the ABL cycle. CO emitted during the night and early morning by anthropogenic sources and by the biosphere's respiration accumulates near the ground into the shallow nocturnal boundary layer (Schmidt et al., 2014) until the ABL develops in the morning, uplifting CO (from 05:00 to 07:00 UTC) to the 180 m level. In the afternoon, when the ABL is well mixed and deeper than 180 m, the mean difference between the concentration at the 50 and 180 m levels is very low (0.3 ppm). Furthermore, as noticed in Schmidt et al. (2014), the amplitude of the diurnal cycle decreases with increased sampling height as elevated sampling levels are decoupled from the CO sources during the night. As reported in Fang et al. (2014), this covariance between biospheric CO activities and the ABL dynamics can make it difficult for inversion models to properly reproduce the CO vertical gradient and thus use nighttime data for inversions. During mid-afternoon, the ABL is well mixed and the vertical bias would be very tiny.
There is a significant enhancement in the CO concentration observed at the regional stations compared to MHD, that increases the closer a station is to the city of Paris (apart from EIF). The difference of concentration observed between two sites depends on the time of the day, and its variation is mainly driven by the CO diurnal cycle at the continental sites. Apart from EIF, the more the station is surrounded by urbanization, the higher is the concentration enhancement compared to MHD, as the average levels of the CO concentration recorded at a station increases with a higher proximity to anthropogenic emissions from Paris. Figure 5a–g show that the hourly 1 variability of the mean diurnal cycle remains quite constant over the day at TRN50, TRN180 and MHD. It is a bit more variable for the rural and remote peri-urban stations that are located within IdF (MON and GIF). The variability changes significantly with the time of the day at EIF and even more at GON. We can conclude that (1) the more the station is within the urbanized part of the city, the more variable is the measured CO signal, which reflects the spatial and temporal variability of anthropogenic emissions coupled to atmospheric transport fluctuations; and (2) the MHD signal is several ppm below the continental signals, even at the rural site of TRN that has already been shown not to be significantly influenced by the Paris megacity fluxes (Schmidt et al., 2014). Thus, MHD does not reproduce the background diurnal variability observed in the rural stations of IdF and is clearly not a relevant background site for continental European urban studies at the diurnal scale and at the regional scale of 100 km.
(a–d) Diurnal cycles of CO from 1 h averages at (a) MON (NE rural site, 9 m a.g.l.), (b) GON (NE peri-urban site, 4 m a.g.l.), (c) EIF (urban site, 317 m a.g.l.) and (d) GIF (SW peri-urban site, 7 m a.g.l.). (a'–d') Diurnal cycles of CO by season at (a') MON, (b') GON, (c') EIF and (d') GIF. Note that the left and right plot scales are not the same. (e–g) Diurnal cycles of CO from 1 h averages at (e) TRN50 (SW rural site, 50 m a.g.l.), (f) TRN180 (SW rural site, 180 m a.g.l.) and (g) MHD (remote site, 15 m a.g.l.). (e'–g') Diurnal cycles of CO by season at (e') TRN50, (f') TRN180 and (g') MHD. Note that the left and right plot scales are not the same.
[Figure omitted. See PDF]
Figure 5a–g show the mean diurnal cycle at each site by season. The influence of anthropogenic activities on the observed CO concentration is expected to be the highest in wintertime when emissions from heating are superimposed on traffic and other sources, photosynthesis is minimal and the diurnal ABL is thinner. Although they vary with the time of the day, on average CO emissions from traffic are quite constant throughout the year but they vary at the hourly and daily scales (according to the AIRPARIF 2010 inventory: on average, 1.5 kt yr during weekends and 2.5 kt h during weekdays, and up to 4 kt h during traffic peaks; see Fig. 4 in Bréon et al., 2015). On the contrary, emissions from gas combustion (from the residential, public and commercial infrastructure that include mostly heating, production of hot water, air conditioning and cooking) show a seasonal cycle (mainly from heating), releasing about 2.5 kt h of CO in the atmosphere in winter versus approximately 1.5 kt h in summer (AIRPARIF, 2010; Bréon et al., 2015). The biospheric fluxes show large diurnal and seasonal cycles, as mentioned in Bréon et al. (2015) who reported net ecosystem exchange (NEE) outputs from the C-TESSEL model for the Paris region: NEE values are the highest in spring (10 to 25 kt h during daytime and 5 kt h during nighttime, and a daily mean of 5/10 kt yr which is the same order of magnitude as fossil fuel emissions, i.e., 7 to 9 kt h in spring), a bit lower in summer and autumn and much smaller in winter (3 kt h during daytime and 2 kt h during nighttime, and a daily mean of 1 kt h, which is much smaller than fossil fuel emissions that reach 10 kt h in winter). In Table S4 in the Supplement, we give for each site the annual and seasonal averages of the daily minimum and daily maximum of the hourly concentration, along with the annual and seasonal averages of the diurnal cycle amplitude (maximum–minimum concentration difference). The lines entitled “variation” give the mean of the hourly 1 standard deviation of the minimum and maximum of each diurnal cycle.
It is noticeable that the mean winter concentration is about 6 ppm higher at MON than in TRN50. Both stations are in a rural environment, but MON is closer to Paris than TRN. As the signals are quite similar in summer, this difference can not likely be explained by the biospheric activity and is more probably partly due to a higher anthropogenic influence in MON. However, here, we need to take into account the difference of the stations inlet height (9 m a.g.l. at MON, 50 m a.g.l. at TRN50): as shown in Schmidt et al. (2014) for the 2010 winter season at Traînou, during daytime, CO concentrations measured at 10 and 50 m a.g.l. are similar, but this is not the case during nighttime when the CO concentration is about 3 ppm higher at 10 m a.g.l. than at 50 m a.g.l. because atmospheric mixing is not existent at night and CO sources accumulate near the surface (Denning et al., 1995). This means that the difference between MON and TRN at the inlet height of MON is of the order of 6 ppm during daytime and twice as low during nighttime. This is consistent with the hypothesis of a higher impact of anthropogenic emissions in MON than in TRN, that according to AIRPARIF are lower during nighttime than during daytime, although we do not observe the same order of magnitude (AIRPARIF gives a ratio of daytime to nighttime emissions equal to 3–4 in wintertime, while we observe a ratio of 2; see Fig. 3 in Bréon et al., 2015). Remember though that the diurnal cycle of the emission inventory is an average for the whole IdF region and not only for the MON area. The impact from local sources and/or the CO emission plume of the Paris megacity on MON will be further inferred from the wind analysis in Sect. 3.5.
The influence of urban emissions in GIF, MON and GON results in a higher mean diurnal concentration of atmospheric CO at these sites compared to the others for all seasons (and mainly in winter) and of its variability. The impact of traffic emissions is well visible in GIF, MON and GON in the winter season only with two CO maxima during rush hours (morning and evening). Although traffic occurs throughout the year, these peaks are likely more or less masked by the biospheric activity and the ABLH dynamics during the other seasons (see above). In addition, the ABL is shallower during winter leading to higher CO concentrations. The amplitude of the morning and evening peaks is higher in GON than in GIF and MON, and denotes a stronger impact of traffic emissions in GON than in the two other stations. GON also shows the maximum interseasonal difference between summer and winter (31.3 ppm in the afternoon) which is higher than the mean annual afternoon dispersion, meaning, in other terms, that the seasonal variability is higher than the mean annual dispersion of the fluxes in the afternoon. Actually, the whole diurnal cycle is shifted towards higher concentrations at GON, the mean concentration being higher in GON than in GIF, TRN50, TRN180 and MHD for all seasons, with the largest differences in winter. The full variability observed at GON over the year can thus be explained partly by the seasonal variation of biospheric activity and ABL dynamics but also by a strong impact of anthropogenic CO emission variability. The impact of the Paris emissions versus more local sources around the station (highways, airports, heating, industrial facilities, etc.) will be further assessed in Sect. 3.5.
Mean altitude of the lowest estimate of the boundary layer height (LBLH) by season in the morning and early afternoon (hours are given in UTC; altitude in m a.g.l.). The number of points used to calculate the means are also given (). NaN indicates no available data.
| Time (UTC) | 05:00 | 06:00 | 07:00 | 08:00 | 09:00 | 10:00 | 11:00 | 12:00 | 13:00 |
|---|---|---|---|---|---|---|---|---|---|
| Spring | |||||||||
| LBLH | NaN | 410 | 442 | 520 | 593 | 697 | 833 | 899 | 935 |
| 0 | 9 | 11 | 11 | 12 | 12 | 12 | 13 | 13 | |
| Summer | |||||||||
| LBLH | 513 | 583 | 728 | 992 | 1178 | 1324 | 1400 | 1405 | 1531 |
| 7 | 13 | 13 | 13 | 13 | 13 | 11 | 11 | 7 | |
| Autumn | |||||||||
| LBLH | 351 | 394 | 451 | 615 | 751 | 837 | 896 | 947 | 940 |
| 16 | 25 | 31 | 34 | 33 | 33 | 33 | 31 | 30 | |
| Winter | |||||||||
| LBLH | NaN | 301 | 349 | 384 | 419 | 440 | 470 | 516 | 550 |
| 0 | 3 | 15 | 24 | 23 | 25 | 26 | 27 | 29 | |
The specific case of the top of the Eiffel Tower
In all seasons, the CO diurnal cycle at EIF is out of phase with the other stations, with a maximum occurring later, in the mid-morning instead of the late night/early morning (Figs. 5 and S3). EIF is significantly higher (317 m a.g.l.) than TRN180 (180 m a.g.l.) so when comparing these elevated sites to ground stations, the effect of the CO coupling with the ABL dynamics can be expected to appear stronger at EIF than at TRN180. Such coupling was already mentioned in the framework of a direct CO transport modeling study in March 2011 (Lac et al., 2013). Furthermore, Dieudonné et al. (2013) demonstrated the existence of a vertical concentration gradient between the bottom and the top of the Eiffel Tower for NO, a species co-emitted with CO during combustion processes especially by the traffic sector, and this vertical gradient was shown to be correlated with the ABL dynamics.
Diurnal cycles of the hourly LBLH estimate means (in black) 1 standard deviation (in grey) and of the CO hourly means (in red) observed by season at QUALAIR (urban site, 25 m a.g.l.) and EIF (urban site, 317 m a.g.l.), respectively. Time is in UTC. The blue horizontal line is the elevation of EIF. The violet circles give the CO concentration (according to the red scale) at the same moments when the LBLH (in black) was measured.
[Figure omitted. See PDF]
We show in Sect. S5 in the Supplement the hourly means of the LBLH observed at the QUALAIR station during daytime, colored by hour and compared with the level of the EIF station. These data are summarized in Table 4. We recall that the LBLH dataset does not cover the whole period of study but the most interesting parts of it, as it includes the cold months during which the LBLH and dynamics are at their lowest. The period of August to March allows us to observe a large portion of the seasonal cycle of the LBLH which is characterized by a change in its maximum value (on average, 1200 m in summer, 400 m in winter) and in the phase of its development, which starts earlier in summer. We do not have the proper data to quantify precisely this starting time; however, we note that the LBLH is always above the level of EIF in summer, while it is below it (at 301 m on average) before 06:00 UTC in winter (see Table 4). We can thus infer that the EIF station could be often above the nocturnal layer at night, inside the residual layer (but not in the free troposphere).
In Fig. 6, we show the CO diurnal cycle for each season computed using
only the data that were collected at the EIF station at the same time as the
LBLH data. The CO signal increases in the morning when the growing ABL
brings to EIF the nighttime and early morning CO emissions that got
trapped into the nocturnal and/or nascent boundary layer. However, compared
to TRN180, the effect at EIF is much stronger due to larger emissions in the
city, especially from the morning traffic peak (from 06:00 to 10:00 UTC,
i.e., 04:00–08:00 UTC in summer and 05:00–09:00 UTC in winter)
(
Autumn. The LBLH is close to the EIF altitude. The moderate development of the ABL during the morning does not compensate for the accumulation of the peak traffic emission in the ABL, so that the CO concentration increases from 05:00 to 10:00 UTC, leading to a CO increase of 17.1 ppm for an LBLH increase of 470 m. At the end of the afternoon, the LBLH decreases and it gets close to the level of EIF, decoupling the station from the surface. This could explain why the late night/early morning concentrations are relatively low and the morning bump of CO quite large. However, this remains a hypothesis as we do not have enough points for a robust demonstration.
Winter. As expected, the process of vertical mixing is quite slow in wintertime. The CO concentration increases in the morning ( 6 ppm) with the maximum concentration encountered at 13:00 UTC for a development of the LBLH of only 157 m within a 7 h time frame. After the morning flush of the surface emissions due to the growth of the ABL, the concentration decreases quite rapidly to reach its daily minimum at 16:00 UTC. At the end of the day, the LBLH falls and gets quite rapidly below the EIF station level, decoupling the EIF station from the surface. Although we do not have lidar data after 18:00 UTC to confirm it, this likely explains the relatively low level of CO concentrations observed late at night.
Spring. In spring, the CO signal increases until 10:00 UTC to a maximum of 420 ppm while the ABL height increases by 287 m. The shape of the CO mean concentration and LBLH diurnal cycles suggests that the relatively high CO concentrations encountered in the late night/early morning result from the evening high CO emissions trapped in the previous day's ABL that became the residual layer at night.
Summer. The CO concentration is on average lower than in the other seasons due to local and regional photosynthesis activity, lower anthropogenic emission levels and higher LBLH. In particular, the observed LBLH during daytime is always above the EIF station level (Fig. S5) so that one would expect CO concentrations to peak in phase with the traffic counter records, between 06:00 and 07:00 UTC. However, the CO diurnal cycle at EIF remains out of phase with those recorded at ground-level stations, though the delay with the morning peak is reduced compared to other seasons. The CO concentration remains quite stable between 07:00 and 09:00 UTC, despite the increasing LBLH (460 m) and the decreasing traffic counts. However, one must keep in mind that until late morning, the air dragged into the ABL by entrainment does not come from the clean free troposphere but from the polluted residual layer, explaining why high CO concentration can maintain. After 09:00 UTC, the CO concentration steadily decreases, though the average LBLH still increases. This drop in concentration can be explained both by an increase in the photosynthetic activity with increasing solar flux and by vertical dilution. Indeed, though the LBLH still rises after 10:00 UTC, the entrainment zone continues to grow until the mid-afternoon (Dieudonné, 2012), blending in clean air from the free troposphere. During the late afternoon, the CO concentration increases again as vertical mixing decays and as the evening traffic peak starts (around 15:00 UTC).
(a) CO diurnal cycle by day of the week at the different stations, calculated from CO hourly concentrations over the whole period of study. (b) Standard variation (1 of the hourly CO mean concentration.
[Figure omitted. See PDF]
This analysis confirms that the coupling of the urban CO emissions together with the dynamics of the ABL height is very likely a major controlling factor of the specific CO diurnal pattern observed at EIF. We lack data at night and in the early morning to make a deeper analysis of the ABL dynamics and especially of the role of turbulence on CO variability. We conclude that a vertical and fluctuating gradient of CO likely exists above the Paris megacity, between the ground level and 317 m a.g.l. (and likely higher). Quantifying such vertical gradients is of interest since they have to be correctly reproduced in urban mesoscale modeling frameworks for accurate atmospheric CO inversion purposes. This vertical gradient can be roughly estimated by subtracting the EIF signal from the GON or the GIF signal. In the early morning (04:00–05:00 UTC), the GON-EIF (respectively, GIF-EIF) gradient is 35 ppm (18 ppm) in spring, 31 ppm (17 ppm) in summer, 30 ppm (10 ppm) in autumn and 14 ppm (4 ppm) in winter. In the afternoon (14:00–16:00 UTC), the GON-EIF (respectively, GIF-EIF) gradient is lower in absolute values and changes of sign: 7 ppm (8 ppm) in spring, 4 ppm (3 ppm) in summer, 4 ppm (7 ppm) in autumn and 2 ppm (5 ppm) in winter. The gradient is thus at its maximum at night and in the warm seasons, which may also reflect the influence of the biospheric respiration at the stations close to the ground level, compared to EIF. In the future, we plan to equip the Eiffel Tower with two supplementary levels of sampling to collect observations that will allow us to well characterize the CO vertical profile over the city of Paris and its temporal variability, and its relation with ground emission variations and their coupling with atmospheric dynamics.
Weekday versus weekend
According to the AIRPARIF inventory, the total CO emissions of IdF are lower during weekends than during weekdays, with mean differences of the order of 30–40 % during daytime and 50–60 % during nighttime. We infer here the impact of such variations on the atmospheric concentrations. In Fig. 7, we show the mean diurnal cycles of the CO concentrations at each site for each day of the week, as well as the associated standard deviation (1.
In GON, the CO concentrations are systematically lower over the weekend, especially on Sundays (5–10 % decrease during daytime, 25–35 % decrease during nighttime). A similar pattern is observed for MON. The weekdays-to-weekend ratios observed for the CO concentrations are lower than those computed from the emissions given by the inventories. This could be due to an overestimation of the difference from the inventory; however, biospheric fluxes (e.g., Schmidt et al., 2014), wind speed and direction (see Sect. 3.5), and CO background signals (see Sect. 3.1 and Turnbull et al., 2015) are also factors that modulate the observed CO concentration at each site. Disentangling the role of each of these factors on the differences between the observed weekdays-to-weekend CO concentration ratios and the ones calculated from the inventory would require a dedicated analysis that is outside the scope of this paper. Note that while the variability of the CO means is very large in GON, it is lower during weekends than during weekdays. The CO diurnal cycle does not change much in GIF between a working weekday and a weekend (except for a small decrease during nighttime over the weekend), nor at EIF and TRN, possibly because of a larger influence of the biospheric fluxes (that do not depend on weekdays or weekends) at these stations compared to the contribution of anthropogenic emissions (that are different on weekdays and weekends according to AIRPARIF; see Fig. 4 in Bréon et al., 2015) and that are the strongest observed at GON (Sect. 3.2.1 and 3.5.2). During nighttime, at GIF, we observed the highest concentrations from Sundays to Wednesdays, with concentrations lower by 3–5 ppm (a 20–25 % decrease) from Thursdays to Saturdays. This could be due to a specific traffic pattern within the footprint of the station, but we currently do not have access to local traffic data for each day of the week to verify this hypothesis.
CO seasonal cycle
We computed the seasonal cycle of CO at each site, based on the monthly means of our 1-year datasets and including all hours of the day (Fig. 8a). The seasonal cycles of the air temperature and available LBLH data (at QUA) are also shown on the same figure.
(a) Seasonal cycles of CO concentration at the six sites based on monthly means. Monthly averages of air temperature at 100 m (Saclay tower near GIF) and of the LBLH (QUALAIR urban site, 25 m a.g.l.) are also shown. Note (in m a.g.l.): MON is at 9 m (NE rural site), GON is at 4 m (NE peri-urban site), EIF is at 317 m (urban site), GIF is at 7 m (SW peri-urban site), TRN50 is at 50 m (SW rural site), TRN180 is at 180 m (SW rural site), MHD is at 15 m (remote site). (b) Seasonal cycle (August 2010–July 2011) of CO at each of the Paris regional sites and at MHD, calculated from CO monthly means of hourly averages, with error bars showing 1 standard deviation ( 1 of the CO means. Note (in m a.g.l.): MON is at 9 m (NE rural site), GON is at 4 m (NE peri-urban site), EIF is at 317 m (urban site), GIF is at 7 m (SW peri-urban site), TRN50 is at 50 m (SW rural site), TRN180 is at 180 m (SW rural site), MHD is at 15 m (remote site).
[Figure omitted. See PDF]
Ignoring the specific case of EIF (Sect. 3.2.3), throughout the year we observe that the monthly mean CO concentration increases with the vicinity of the station to larger CO emission sources. The maximum CO enhancement compared to MHD is observed at GON which is our most anthropogenically influenced station (from 6.8 ppm in July to 27.5 ppm in December). Similarly to what is observed at the diurnal scale (Sect. 3.2), differences of several ppm are also observed between our rural sites and MHD, while the differences between the rural/peri-urban/urban stations in IdF are of the same order of magnitude. These differences of concentration between the stations located in IdF and MHD vary with the season, the seasonal cycle being much more well defined in the Paris rural stations than in MHD due to a higher biospheric activity in the IdF region than on the western coast of Ireland. This implies that background values of CO in IdF (i.e., without the impact of Paris emissions) should be defined at the regional scale near Paris ( 100 km) and not at the continental scale in MHD. Furthermore, in Sect. 3.1, we explained that the CO concentration fluctuates with the origin of the air masses that can be very variable, and therefore specific regional background should be selected in function of the wind direction, as also mentioned for the case of Indianapolis (Turnbull et al., 2015). In conclusion, MHD appears not to be relevant as a background site for defining the atmospheric plume of CO in the Paris region at the seasonal scale as well. Regional background stations ( 100 km) seem to be much better suited for urban regional studies in Paris and elsewhere in the European continent. Several methods are available to extract a background signal from a time series (e.g., Ruckstuhl et al., 2012; Ammoura et al., 2016). Quantifying precisely the Paris background signals values as well as the Paris plume and its variability requires a dedicated analysis that is outside the scope of the present paper: it will be specifically addressed within another dedicated study. At each station, the monthly mean CO concentration follows a seasonal cycle that reaches its maximum in winter and its minimum in summer. This is expected due to (1) the seasonal cycle of the biosphere; (2) the variability of anthropogenic emissions, mainly from the heating sector, which are directly linked to ambient temperature (see Sect. 3.2.2); and (3) the seasonal cycle of the ABL height (Sect. 3.2.3), which is the lowest in wintertime (e.g., Denning et al., 1995; Turnbull et al., 2009). It is difficult to estimate the biases due to missing data points in the time series (Sect. 2.2.2); however, as an indicator of robustness, the data coverage for each month and each station (given in Table 3) is very good overall.
To assess the variability of the seasonal cycle, Fig. 8b shows the CO monthly means at each station with error bars representing the associated 1 standard deviation. Note that the 1 dispersion is the highest at GON and the lowest at MHD. More generally, the variability increases with the level of urbanization around the station and the distance to anthropogenic CO emission sources. Therefore, increases in the variability from one month to the next can be used to track down the influence of more local and thus fresh sources, as a complement to the “local” wind sector (wind speed < 3 m s. Some specific seasonal patterns can be observed.
Winter. In winter, the lower biospheric activity makes the CO concentration relatively more sensitive to anthropogenic emissions (see Bréon et al., 2015). In Paris, January is usually the coldest month (meaning the month with the highest heating emissions). However, the months of December 2010 and February 2011 were characterized by cold episodes, while January 2011 was rather mild. This resulted in higher CO concentrations in December and February than in January for MON and GON. In GIF, EIF and TRN, the secondary maximum (February) is shifted to March. Indeed, in February, southerly winds prevailed (see Fig. S1 in the Supplement and also Fig. 4a and b), bringing Parisian anthropogenic CO emissions in the direction of GON and MON, and depleting the southern stations, while in March, winds blew mostly from the NE/SE sectors, bringing higher CO levels to GIF, TRN, EIF and also MHD. The higher CO concentration encountered in December compared to February or March can be explained by the ABL height being minimal in December (Fig. 8a). However, in February, the GON signal remains the highest of all stations, and the concentrations observed at MON are higher than those recorded at TRN. Here, we may see the impact of air masses advected from the NE with higher CO background levels and especially a sensitivity to upwind emissions at GON. Such influence of meteorological conditions on the seasonal cycle of continental stations was also reported in the literature (e.g., Fang et al., 2014; Zhang et al., 2008) and will be further assessed in Sect. 3.5.2.
Spring. Starting in April, we observe a decrease of CO at all stations except GON, as regional photosynthesis activity develops (Bréon et al., 2015). In April, the high variability of the GON signal and the prevailing local, SW and NW wind sectors show that the station experiences strong influence from anthropogenic emissions, local or advected, and explains why the CO concentration remains higher than at the other stations. From April to July, we observe that the CO concentration at TRN180 is always equal to or below MHD, showing the strong influence of regional biospheric activity on concentrations measured at continental stations. Indeed, this effect is also observed in TRN50 and MON in May when the biosphere was very active and winds blew mostly from the SE and SW, bringing air masses from the forests of the Centre region to IdF. During other spring and summer months, concentrations at TRN50 and MON remain higher than at MHD as the dominant winds were from the NE sector, likely bringing emissions from the Ruhr/Benelux areas to MON and TRN and/or from Paris to TRN.
Summer. For all stations except GON, the annual minimum of concentration is observed in August when the following occurs: (1) the minimum of anthropogenic emissions as given by the AIRPARIF inventory (see Fig. 3 in Bréon et al., 2015); (2) the maximum of photosynthetic activity (see Fig. 4 in Bréon et al); and (3) the maximum development of the ABLH (Fig. 8a). In GON, the contribution of the local wind sector is strong in August, as confirmed by the large 1 deviation, explaining why the minimum of concentration is shifted to July, another month with reduced economic activity and emissions (on top of a high level of photosynthesis and a relatively high ABLH). The higher concentrations in August at GON are also associated with slow winds blowing from the northwest direction, indicating an impact of relatively local emissions, possibly of the two point sources mentioned in this wind sector in Sect. 2.1.2.
Autumn. September is characterized by an increase of the monthly mean CO concentrations at all stations, although the increase is higher in GON (9 ppm) than elsewhere (3 to 5 ppm). As there were several local and NW events during that month, we infer that this larger increase is due to urban emissions in the vicinity of GON (e.g., from CDG Airport) or a bit further to the NW side of GON (among which the two industrial sites mentioned in Sect. 2.1.2).
The sensitivity of the stations to wind speed and direction, and especially the question of higher background CO levels advected from the NE sector, will be analyzed in more detail in the next section.
Wind study: from local to regional signals
Wind speed effect
Wind speed is a key factor in modulating the dispersion of CO emissions (e.g., Idso et al., 2002; Moriwaki et al., 2006, Rice and Bostrom, 2011; Garcia et al., 2010, 2012; Lac et al., 2013; Turnbull et al., 2015). Figure 10 shows the mean hourly CO concentrations and the associated standard deviations recorded at GON over the year of study for local afternoon hours only (11:00–15:00 UTC) as a function of the wind speed and colored by wind direction. The CO concentrations have been seasonally adjusted to avoid biases due to seasonal variability (Sect. 3.4), by applying the following treatment to the CO hourly dataset of each station: (1) computing the annual mean of the dataset; (2) computing the monthly seasonal index for each month by calculating the ratio between the monthly mean and the annual mean of the dataset; (3) interpolating the monthly seasonal indexes at an hourly scale over the full period of study; and (4) dividing the CO hourly dataset by the hourly seasonal index. Figure 9a shows that the amplitude of the CO concentration range and especially the maximum values decrease exponentially with wind speed because of the ventilation and dilution effects. Such behavior is observed at all the regional stations, although the wind speed maximum is higher at TRN ( 11 m s and even higher at EIF ( 20 m s due to the elevation of these stations. The 1 dispersion from the hourly means (called variability in Fig. 9b) shows a similar dependency on wind speed. At low wind speed, the relatively high level of variability can be associated with the impact of fresh and regional anthropogenic CO emissions. For high wind speeds, the hourly averaged CO concentration converges towards a mean value and the 1 variability drops below 1 ppm. Such behavior was previously reported at former CO urban stations for other cities (e.g., Garcia et al., 2010, 2012; Rice and Bostrom, 2011; Massen and Beck, 2011). However, and contrary to those studies, we do not think that this mean value can be considered as an asymptote, as it originates only from a few sparse events (spread over 7 days of the period of study), nor that it can be considered as a background CO concentration for the stations.
(a) Hourly means of the CO concentration recorded at GON (NE peri-urban site, 4 m a.g.l.) as a function of wind speed and colored by wind direction (the color scale is in degrees). (b) Same for the CO standard deviation (1 of the hourly CO concentration means).
[Figure omitted. See PDF]
Mean CO concentration (in ppm) observed at the different stations of the Paris regional network (TRN represents the measurements at 50 m a.g.l.) and at MHD for wind speed higher than 9 m s over the period of study (8 August 2010–13 July 2011). During such events, the synoptic conditions were mostly oceanic (wind blowing from the SW sector). Note (in m a.g.l.): MON is at 9 m (NE rural site), GON is at 4 m (NE peri-urban site), EIF is at 317 m (urban site), GIF is at 7 m (SW peri-urban site), TRN50 is at 50 m (SW rural site), TRN180 is at 180 m (SW rural site) and MHD is at 15 m (remote site).
[Figure omitted. See PDF]
Figure 10 shows this CO mean value at the different stations: a CO horizontal enhancement appears as stations get closer to the city of Paris (apart from EIF), with the maximum difference (6.6 ppm) observed between GON and MHD. The high wind speed events that occurred during the period of study correspond only to winds blowing from the southwest, mostly from the 200–220 sector. GON was thus immediately downwind of Paris emissions, most likely the reason why it exhibits the highest mean constant value. An enhancement is also observed at TRN and at GIF compared to MHD. As both TRN and GIF are located upwind of Paris, we see once again here that MHD does not provide an adequate CO concentration background level for Paris and other continental western European cities. The peri-urban upwind station of GIF has quite a similar mean constant value to the rural downwind station of MON. Indeed, MON station was not in the path of Paris CO urban plume in this 20 wind sector. The EIF value is also lower than at GIF and GON, supporting the fact that for such high winds, the top of the Eiffel Tower was not very sensitive to surface emissions, most likely because between 0 and 300 m a.g.l. ventilation of emissions was stronger than their vertical mixing.
Fine wind sector analysis
In order to distinguish the relative contributions of the local, remote and Paris megacity regional CO fluxes to the CO concentration observed at the five stations of the Paris network, we analyzed the dependence of the observed CO concentration and its variability on the horizontal wind speed and direction. Considering the diurnal variability of vertical transport dynamics (Sect. 3.2), we separately analyzed afternoon (11:00 to 15:00 UTC) and nighttime (22:00 to 02:00 UTC) data. For the TRN station, we consider that the TRN50 level is sufficient for this analysis.
Inner Paris extends to a diameter of 10 km, while the Paris metropolitan area extends to a diameter of 30 to 50 km. The distance of the peri-urban stations GON and GIF to the Paris inner city is about 10 and 15 km, respectively. The distance of the rural stations MON and TRN to inner Paris is about 30 and 100 km, respectively. Taking into account these distances, we set the hypothesis that we can assess the influence of local emissions using hourly means observed in low wind speed conditions (less than 3 m s while the influence of remote emissions can be analyzed using data recorded in relatively high wind speed conditions (more than 8 m s. This relies on considering the time given for atmospheric mixing of local and regional emissions (dominant at low to moderate wind speeds) versus their ventilation (dominant at high wind speeds): the integration of local and regional emissions into an air mass, which carries the signature of remote emissions when it is upwind of Paris, gets higher with decreasing wind speeds. For example, for wind speeds lower than 3 m s (11 km h, it takes 1 h or more for any air mass to flow over the center of Paris ( 10 km of diameter), allowing some time for local emissions to get mixed into the air mass, while at 8 m s or more ( 29 km h it takes about 20 min or less, allowing less time for the atmospheric integration of local to regional emissions. In the middle range of wind speed (3–8 m s, we expect most of the CO variability to be driven by the influence of the regional emissions coming from Paris.
(a) CO mean concentration as a function of wind speed (circles in m s and wind direction at MON (NE rural), GON (NE peri-urban), EIF (urban), GIF (SW peri-urban) and TRN50 (rural) stations using daytime data (11:00–15:00 UTC) for the period of study (4 August 2010–11 July 2011). (b) Mean 1 CO variability of each concentration (ws, wd) point. (c) Occurrence as the frequency of the (ws, wd) bin weighted by the square root of the CO concentration mean.
[Figure omitted. See PDF]
For all of the regional stations, Fig. 11 shows the pollution roses of the
mean afternoon CO concentration binned by wind speed (ws) and wind
direction (wd) with a resolution of 1 m s for ws and 10
for wd. We use here the CO hourly concentration dataset that has been
seasonally adjusted (Sect. 3.5.1). In order to assess the
representativeness of each (ws, wd) bin, the contribution of each
concentration mean for a given (ws, wd) bin on the total concentration is
also calculated, after applying a square root transformation on the CO
concentration to reduce any bias from the highest CO values (we used
the polarFreq function from the OpenAir work package for R with the option
“weighted mean”; more information can be found online here:
Influence of remote emissions (> 100 km)
The back trajectories (Fig. S1 in the Supplement) show that Paris was exposed to a range of synoptic air masses over the period of study, including clean oceanic ones and others with CO enriched by remote anthropogenic emissions especially from Benelux, the Ruhr area and the London megacity. Relatively high CO concentrations (> 410 ppm) were observed for high wind speeds (> 8 m s in the 0–45 NNE sector at the three stations located relatively close to the ground level (MON, GON and GIF). For the elevated stations (EIF and TRN), such concentration values also occur, but as expected at higher wind speeds (> 12–14 m s, reaching at least the 410 to 420 ppm range at all of the stations. The fraction of data falling in these (ws, wd) bins is large enough to consider these high concentration values to be statistically representative. Furthermore, the standard deviation of the signal at the upwind stations is quite low (less than 0.6 ppm), which indicates that the high concentration values observed upwind of Paris (GON and MON) are not associated with fresh emissions but with imported pollution that was already well mixed in the atmosphere. It is likely that we see here the signature of remote anthropogenic CO emissions from hot spots such as the Benelux and the Ruhr areas that bring higher CO background levels to all the stations. The high CO concentrations observed in the 0–35 NE sector at the downwind stations (EIF, GIF and TRN50) for moderate to high wind conditions ( 3 m s appear thus to be due not only to the Paris CO emission plume but also to enriched background CO levels advected from the NE. By comparison, the background levels that are observed in the 200 (SE) to 280 (NW) sector of GIF and TRN50 are lower than 400 ppm, while the 0–35 NE background levels at GON and MON are often above 400 ppm, reaching concentrations in the 410–430 ppm range. This shows that the Paris megacity background values can vary by several ppm depending on the wind direction, with the highest CO concentrations advected in the 0–45 wind cone. We note also that EIF shows higher concentrations in the 295–360 NW sector at high wind speeds that could be associated with long-range transport of anthropogenic plumes from the northern hot spot emissions mentioned and better seen at this elevated station. Also, TRN shows higher CO concentrations in the 345–360 NW sector for high wind speeds, which could be attributed to these hot spots – but also to Paris.
During nighttime, for wind speeds higher than 8 m s, all stations show higher CO levels in the 0–45 NE sector than in the other wind directions (see Fig. S6a).
Influence of local emissions (< 10 km)
In Sect. 3.2.1, we questioned whether MON was under the strong influence of
local signals. The MON CO wind rose shows that for wind speeds in the
0–2 m s range, higher CO concentrations (400 ppm to more
than 430 ppm) are observed in different wind sectors. Note that the
230–240 SW sector is where the bin contribution is the highest
( 0.8–1 %). These higher CO concentrations can most likely
be attributed to the influence of the point sources relatively close to MON
mentioned in Sect. 2.1.2 but also to relatively close diffuse emissions
(traffic, heating, etc.) from ground activity under the path of the air mass
and also possibly to aircraft emissions. Montgé-en-Goële is located
in the path of aircraft departing from CDG for easterly winds and aircraft
arriving to that airport for westerly winds
(
In Sect. 3.2.1 and 3.4, we questioned the influence of local sources on GON
(such as CDG and Le Bourget airports) but also of point sources mentioned in
Sect. 2.1.2 and diffuse sources around the station. As for MON, all these
types of sources in the vicinity of GON will likely influence it at low wind
speed. GON is also exposed to aircraft emissions as it lies close to the
lowest flight paths (0–1000 m a.g.l.) from the CDG and Le Bourget airports
(
At EIF, the influence of local emissions is expected mostly between the late morning and the late afternoon since, as we have seen in Sect. 3.2.2, the top of the Eiffel Tower receives surface emissions in this time period during all seasons. The CO pollution rose of Fig. 11 indicates high concentrations (400 to more than 430 ppm) in all directions around the stations for wind speeds comprised between 0 and 2 m s. The variability is quite large (1.5 to 5 ppm), indicating fresh emissions and reflecting the spatial and temporal variability of the emissions coupled to atmospheric transport variations. Carbon isotopes and CO co-emitted species measurements would be useful here to estimate the role of the different emission sectors (e.g., Lopez et al., 2013; Pataki et al., 2009).
In GIF, a few high CO spikes are observed for low wind conditions in diverse wind directions. These spikes are likely due to emissions from traffic and heating from the surrounding infrastructure, as observed from the corresponding relatively high standard deviation (> 5 ppm). Flight paths to and from Orly Airport for westerly winds pass several kilometers south of the station and likely have a weak local impact.
Similarly to what is observed at GIF, higher CO concentrations are observed at TRN50 in the wind sector of the city of Orléans, located 13 km SW of the station.
At night, MON and GIF show a higher local influence that still remains moderate. At EIF, no specific local influence is observed apart from a couple of (ws, wd) bins, confirming that the station is quite disconnected from the surface where urban emissions are diluted into the nocturnal layer. At GON, the influence of local emissions is strongly evident, with CO concentrations reaching greater than 460 ppm and standard deviation greater than 5 ppm. In the 2–3 m s range, the station shows the highest CO concentration in the direction of the CDG Airport, a source that seems to have an impact on GON even at night. CDG is one of the only airports in Europe to have nocturnal activity. TRN seems to be less influenced by local emissions than during daytime. With TRN not being impacted by the Paris urban heat island, the nocturnal boundary layer is very shallow there, so the 50 m level is probably often decoupled from fresh emissions during the night (Pal et al., 2012).
At all stations, except for a few points in the SW sector at MON and GIF, the bin contribution of the data recorded for wind speeds in the 0–3 m s range is quite low, which indicates that generally the low wind conditions do not bias the data very much. However, since local sources can be relatively strong, for regional studies, these local influences should be removed by filtering out the CO concentrations collected at wind speeds lower than 3 m s.
Influence of regional emissions (10–100 km)
Most of the data correspond to wind speeds between 3 and 8 m s, values for which we expect the regional influence of the Paris megacity on the downwind observed CO concentrations to be the highest.
In the 0–45 (NNE) sector, we observe relatively high CO signals (> 400 ppm) and low standard deviation values, even in stations upwind of Paris (GON and MON). In MON, the CO concentrations in this wind sector are even higher than the ones in the SW sector which is expected to be exposed to the Paris emission plume. This large NE signal can be attributed to the impact of remote emissions advected from that wind sector, as observed for higher wind speeds. In EIF and GIF (over and downwind of Paris in that wind sector), the CO concentration reaches even higher values (> 430 ppm, especially in EIF), which indicates the additional impact of the urban regional emissions. The contribution of each (ws, wd) bin is in the 0.4–1 % range and is thus significant. These high concentrations are associated with high standard deviations (> 1 ppm and even > 5 ppm at EIF), which results both from the high spatial and temporal variability of fresh emissions at the surface and from small-scale dynamic effects in the ABL such as turbulence (succession of updrafts bringing polluted air to the station and downdrafts bringing cleaner air). In TRN50, there are some bins where the signal is higher than in MON and GON, but overall the CO concentration is lower, indicating that the Paris plume does not pass the TRN tower (50 m level) very often.
In the 45–90 (ENE) sector, all stations but EIF show CO concentrations mostly in the 390–400 ppm range with some bins in the 400–410 ppm range. EIF shows more bins in the 400–410 ppm range, showing a higher exposure to urban emissions. However, while the standard deviation is relatively low in MON and TRN50, this is not the case at the GON, EIF and GIF stations, likely due to a higher proximity to sources of emissions, that, for GON include the CDG Airport.
In the SW wind direction, stations upwind of the Parisian emissions (TRN50 and GIF) mostly show CO concentrations in the 380–400 ppm range. In EIF, and even more in GON, we observe higher CO values reaching the 400–410 ppm range. Due to its geographical position, EIF is less exposed to Parisian emissions in this wind sector, while GON is directly downwind of Paris for the 175–235 wind sector, where the largest point contribution reaches 1.6 %. The standard deviation in EIF is above 1 ppm although lower than in the NE sector, while it is less than 1 ppm in GON, indicating that the emissions were mixed before arriving at the station. The MON station does not show specifically higher CO concentrations compared to the upwind GIF station, except in the direction of the CDG Airport. This latter source together with industrial emissions as well as other sources (highways, domestic and commercial heating, etc.) located in this direction (Fig. 1) seems to have more impact on the station than the Paris emission plume, which does not appear to often advect to the station.
In the NW wind sector, all stations except EIF are mostly in the 390–400 ppm range, with some values in the 400–410 ppm range (like in the 45–90 sector or NNE sector). EIF exhibits higher concentrations in the 325–360 sector, with values often in the 410–430 ppm range and even reaching more than 430 ppm. The associated standard deviation is also very high at EIF, in the 2–5 ppm range and even higher, indicating that emissions from the NW of Paris strongly impact this station. On the contrary, the variability is mostly below 1 ppm in the other stations. The highest values are observed at GIF in the 305–325 direction, which could be explained by the station receiving emissions from the Saint-Quentin-en-Yvelines metropolitan area located 10–15 km upwind of GIF in those wind directions.
In the SE wind sector, for moderate wind speeds, the MON, GIF and TRN50 stations show CO concentrations mostly below 400 ppm and a few (ws, wd) bins in the 400–410 ppm range, especially in GIF for the 3–4 m s range and in the 90–135 sector. This sector comprises the southern branch of the extension of the Paris megacity which likely impacts the station. It is surprising, though, that the 70–85 (ENE) sector does not show similar concentration ranges as it is urbanized at a similar level. At GON, the station is mostly sensitive to emissions in the 135–180 (SSE) sector although the standard deviation is quite low, indicating these emissions are not from nearby sources as they are already mixed into the atmosphere. The EIF signal is as high as in the NW sector, very variable from one wind direction to the next and shows a high standard deviation, again reflecting the large variability of surface emissions and possibly the impact of atmospheric turbulence on the observations.
At night, MON exhibits the highest CO concentrations in the 0–45 (NNE) sector with values reaching the 410–420 ppm range. Those higher concentrations probably correspond to the continental background signals of polluted air masses advected from the Benelux and Ruhr areas. At GON, the CO concentration reaches similar values but in all directions, showing on top of higher NE background values an impact of the regional urban emissions. As during daytime, EIF shows higher concentrations in the urbanized sectors upwind of the station (NE, SE and NW mainly), although the concentrations stay mostly below 410 ppm – as a result of the decoupling from surface emissions during nighttime. At GIF, the highest concentrations are encountered, like during daytime, mostly in the NE sector, which is the most exposed to Paris emissions. At TRN some (ws, wd) bins show higher CO concentration in the NE sector, although this remains at a moderate level. The levels of the standard deviation confirm these observations, and the data distribution plots show that generally most of the regional signal is contained in the 3–6 m s range.
Conclusions
This work forms the first study of 1 year of measurements of atmospheric CO in the region of the Paris megacity. We analyzed the CO diurnal, synoptic and seasonal variability at five stations in that region and carried out a comparison with the CO dataset recorded at the MHD remote site.
In all stations of the Paris network, the influences of anthropogenic emissions, biospheric fluxes, atmospheric dynamics and synoptic wind patterns were shown to be key factors of the diurnal, weekday/weekend and seasonal variability of the atmospheric CO concentrations.
At low wind speed, the stations receive local emissions from sources that could extend to a few kilometers, leading to a build-up of the CO concentration, especially over Paris at the top of the Eiffel Tower during daytime and at the GON peri-urban station, where the concentration increase can reach up to 60 ppm. For wind speed values comprised between 3 and 9 m s, advection leads to a decrease of the CO concentration at all stations by ventilation of the emissions. For wind speeds higher than 9 m s, as it was mentioned in previous urban studies, the CO concentration tends toward a mean constant value. However, contrary to previous studies, we showed that this value is different at each site and increases with the level of urbanization surrounding the station, leading to an enhancement of a few ppm at downwind stations compared to upwind ones. We argued that this value is based only on sparse meteorological events so it cannot be defined as an asymptotic value, nor should it be used as a regional background.
Our work shows large diurnal and seasonal differences in the CO concentration between the MHD site and the Paris upwind sites, as advected air masses undergo the influence of sources and sinks of CO encountered on their footprint before reaching the megacity. We demonstrated that such a remote coastal site should not be used as a background site to infer atmospheric regional CO signals ( 100 km) coming from emissions of urbanized regions located several hundreds of kilometers away from this remote site on the continent, as it was done in some previous studies. A similar conclusion was also highlighted by Turnbull et al. (2015) when analyzing atmospheric CO variability in the Indianapolis region. Furthermore, even at high wind speeds, higher CO concentrations (up to several ppm) are observed for air masses advected from the 0–45 NNE sector at all of the regional stations, compared to those advected from the SW sector, highlighting the impact of anthropogenic emissions from remote hot spots like Benelux and the Ruhr Valley on the Paris region CO background in the NNE sector. Indeed, the average CO concentrations measured at a given station when it is located downwind of the Paris megacity are not always higher than the concentrations measured at that same station when it is located upwind, and this concerns the hourly, diurnal and seasonal averages. This shows that the CO concentration advected from the polluted 0–45 NNE sector can overtake the sum of the CO plume coming out from Paris for SW winds and of the relatively low SW oceanic CO background signals. This leads to the conclusion that when further developing the Paris CO network, efforts must be made to carefully set up several regional background sites on the path of the different wind directions and ideally at the peri-urban/rural border of the city to constrain its signal as much as possible. Ideally, the network will also be designed to position the urban and peri-urban downwind sites on these same wind direction axes. The CO datasets presented here provide the basis for a study conducted on atmospheric inversion modeling of the Paris CO emissions (Staufer et al., 2016), where we quantified the need for eight more sites in the suburban/urban border of Paris to improve our top-down approach.
Furthermore, our analysis shows the strong coupling that exists between the CO concentration diurnal cycle and the boundary layer height cycle at the elevated stations and especially at EIF. We also highlighted how the high variability observed at EIF in the afternoon reflects the coupling of the highly variable urban emissions in the vicinity of the station with fluctuations of the wind speed and direction but also possibly with atmospheric fine-scale dynamic processes. These results have consequences for the assimilation of the EIF data for inverse modeling purposes. Tall towers have been for several years the first choice in the matter of sites selection for studying atmospheric CO at the regional to the continental scales (e.g., Andrews et al., 2014; Haszpra et al., 2015; Gloor et al., 2001; Vermeulen et al., 2011), but their use for understanding CO in urban environment seems to be more complicated, as this requires the proper representation of the underlying dynamic processes (including turbulence) that occur inside the boundary layer and their coupling with the highly variable ground anthropogenic CO emissions. For these reasons, we are for now not able to use data from EIF in our inverse modeling framework (Bréon et al., 2015). We plan to improve our instrumental setup on the Eiffel Tower with two additional sampling heights to gather vertical CO profiles and associated meteorological data: this will be of great help to understand the coupling between CO sources and atmospheric dynamics over the Paris megacity in the future. This recalls as well that the altitude relative to ground level and the distance to the emissions of a station are very important factors to take into account in the network capacity to properly detect a CO urban plume (see also the discussion about this topic in Boon et al., 2016).
In regard to gaining lessons on urban CO network design, with 13 observation towers located in and just around the city, the Indianapolis network is a good example to follow (see Turnbull et al., 2015) – as long as the budget allows it – that fulfills the urban network constraints we inferred from our analysis in Paris. Longer prospects on the Paris network design with cheaper sensors are discussed in the study of Wu et al. (2016). Note that these lessons are appropriate to cities having a flat continental topography. The situation would be different for coastal or mountain/valley cities, where complex meteorological features occur (breezes, katabatic winds, thermal inversion, etc.).
The fine classification of the CO concentrations collected at each site following wind directions and wind speeds allowed us to better define the footprint of each station and the impact of local, regional and remote CO fluxes on each station. In each of the regional sites, the high CO concentrations observed at low wind speeds (< 3 m s revealed the impact of local sources including likely emissions from aircraft and airports, cement plants and thermal plants. For moderate wind speeds (3 to 9 m s, the impact of the CO emissions of Paris is clearly seen at urban and peri-urban stations (GON, EIF and GIF) in the afternoon and much less at night. This impact, however, is barely seen in the two rural stations (MON and TRN) which ultimately do not seem to be relevant sites to study the CO emission plume from the Paris megacity.
At each station, the minimum of the seasonal cycle amplitude was found in summer due to high photosynthesis, lower anthropogenic emissions and higher ABL height. The maximum of the CO seasonal cycle was found in winter when the biospheric activity reaches its minimum, the Paris anthropogenic emissions get to their maximum and the ABL height is at its lowest. However, we could not separate the anthropogenic and biospheric CO signals, nor the role of the different emission sectors. This highlights the need for regular carbon isotopic measurements of CO at the regional network stations, together with measurements of anthropogenic co-emitted species such as CO, NO, black carbon and volatile organic compounds (e.g., Lopez et al., 2013; Ammoura et al., 2014, 2016). Finally, we show that ancillary data such as local meteorological data and parameters defining the structure of the atmosphere such as the ABL height are very important to understand the observed CO variability. Ideally, such measurements should also be included in the development of future urban CO monitoring networks.
The CO-MegaParis datasets are available from the AERIS/ESPRI data
center via the following secure FTP link (
The supplement related to this article is available online at:
The authors declare that they have no conflict of interest.
Acknowledgements
This work was mostly funded by the Agence Nationale de la Recherche (ANR) in the framework of the CO-MegaParis project and partly by the Ville de Paris through the “Le CO parisien” (Paris 2030) project. We deeply acknowledge the AIRPARIF technical team for the maintenance of the CO-MegaParis stations. The authors are very grateful to the RAMCES-ICOS team and they also thank Sandip Pal for technical help. The GIF and TRN stations are funded by INSU and CEA (SNO RAMCES/ICOS). Most of the figures shown in this work were produced with the OpenAir package for R (Carslaw and Ropkins, 2012; Carslaw, 2015). We especially acknowledge David Carslaw (OpenAir package) for helpful advice. We thank SPR at CEA Saclay very much for providing us with the meteorological measurements. The first author sends warm acknowledgments to Peter Rayner and Thomas Lauvaux for their scientific advice in building up the CO-MegaParis project, and to Cecilia Garrec and Peter Rayner (once again) for their help in coordinating it. The authors give special thanks to Steve Wofsy for his support and to Chris Rella from the Picarro company for his help with the CRDS analyzers. Edited by: Eliza Harris Reviewed by: Jocelyn Turnbull and one anonymous referee
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Abstract
Most of the global fossil fuel CO
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; Ravetta, François 8 ; Perrussel, Olivier 9 ; Ciais, Philippe 7 ; François-Marie Bréon 7
; Broquet, Grégoire 7 ; Ramonet, Michel 7 ; Spain, T Gerard 10 ; Ampe, Christophe 9 1 Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette, France; now at: Aix Marseille Univ, Avignon Université, CNRS, IRD, Institut Méditerranéen de Biodiversité et d'Ecologie marine et continentale (IMBE), Marseille, France
2 Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette, France; now at: Laboratoire de Physico-Chimie de l'Atmosphère (LPCA), Dunkirk, France
3 Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette, France; now at: European Organization for Nuclear Research (CERN), Meyrin, Switzerland
4 Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette, France; now at: Environment Canada, Climate Research Division, Toronto, Ontario, Canada
5 Centre National de la Recherche Météorologique (CNRM-GAME), Toulouse, France
6 Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette, France; now at: Institute of Environmental Physics (IEP), Heidelberg, Germany
7 Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette, France
8 Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Guyancourt, France
9 Association de Surveillance de la Qualité de l'Air en Île-de-France (AIRPARIF), Paris, France
10 National University of Ireland (NUI), Galway, Ireland