dataesr/scientific-paragraphs-categorization huggingface dataset:
, in preparation)  and in the near future (aeronautical certification of the instrument already approved) greenhouse gases (CO 2 , CH 4 )  (Filges et al. , 2015) , while developments for aerosol measurements are still on-going  (Bundke et al. , 2015). So far, many studies have used the IAGOS data to investigate air pollution throughout the troposphere, including the planetary boundary layer (PBL) (e. g. Ding et al. , 2008; Elguindi et al. , 2010; Kalabokas et al. , 2007 Kalabokas et al. , , 2013;; Petetin et al. , 2016a Petetin et al. , , 2016b;; Sahu et al. , 2014; Sauvage et al. , 2005; Sheel et al. , 2016; Tressol et al. , 2008; Yamasoe et al. , 2015). However, a part of the scientific community seems reluctant to use the IAGOS data for studying the lowermost PBL due to concerns about the representativeness of the IAGOS measurements close to the surface.
While ozonesondes are usually launched in remote, rural or low-density urban areas, the specificity of the IAGOS measurements relies on the fact that the ascent/descent profiles are (i) slanted rather than strictly vertical, (ii) performed close to international airports (with potentially high emissions on the tarmac) (iii) located in the vicinity of large cities (among the 290 airports visited since 1994, the average population of the nearby city is 1. 6 millions inhabitants), and (iv) along flight corridors frequented by other aircraft. For these reasons, questions arise on the representativeness of the IAGOS measurements in the lower troposphere (roughly, the 2 first kilometres). For instance, the Air Quality (AQ) community working on the validation of regional chemistry-transport models (thus focussing on the surface and the PBL) has sometimes argued that the IAGOS data are likely strongly influenced by very local pollution sources (e.
g. airport, other aircraft) that cannot be correctly represented in the models with the current spatial resolution (a few kilometres). This would prevent relevant model-observation comparisons, in the same way that urban traffic surface stations are rejected for the validation of models at the surface (contrary to rural and urban background stations). However, the situation changed over the last years with model inter-comparison programs like AQMEII (Air Quality Model Evaluation International Initiative) or CAMS (Copernicus Atmospheric Monitoring Service) that benefited from the IAGOS dataset to evaluate models in the lower troposphere (e. g. Elguindi et al. , 2010; Solazzo et al. , 2013). Another example is given by the community working on the validation of CO tropospheric columns observed by satellite.
This is required to better identify the type of applications allowed by the IAGOS dataset. Such analysis is of primary importance since, compared to the ~68,000 ozone soundings (since 1924) publicly available in the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) database, the IAGOS database now represents about 70,000 O 3 profiles since 1994 and 40,000 CO profiles since 2002 which, thus, contributes a large proportion of the whole dataset of in-situ profiles available in the troposphere. Over the last two decades, several studies have investigated the consistency of the IAGOS O 3 dataset with radiosondes and surface stations  (Thouret et al. , 1998; Chevalier et al. , 2007; Logan et al. , 2012; Tilmes et al. , 2012; Zbinden et al. , 2013; Staufer et al. , 2013 Staufer et al. , , 2014;; Tanimoto et al.
Although this study greatly improved our confidence in the representativeness of the IAGOS O 3 measurements, a similar assessment of the representativeness of the IAGOS CO measurements is still missing. In addition, for the comparison between IAGOS and other datasets, some airports like Frankfurt (not taken into account in  Tanimoto et al. (2015) ) offer a much larger number of profiles, which can allow more in-depth analysis. Although its secondary sources (oxidation of CH 4 and non-methanic volatile organic compounds (NMVOCs)) account for a large part of its global budget (around 57% of the total source  (Stein et al. , 2014) ), CO is characterized by strong emissions associated with all types of incomplete combustion and a long lifetime (from a few weeks to a few months), which traditionally makes it a very useful tracer of pollution  (Heald et al.
, 2003; Liu et al. , 2003). Conversely, O 3 is a secondary pollutant, formed by a complex chemical mechanism involving notably NO x , VOCs and CO, and likely to be quickly removed by surface dry deposition  (Monks et al. , 2015). The overall objective of this study is to investigate more deeply the representativeness of the IAGOS O 3 and CO measurements in the lower troposphere. More specifically, this study aims at analysing how such airborne measurements are comparable to the surface observations measured by the nearby AQ monitoring network (AQN). Despite (usually) larger distances from the IAGOS airports, the comparisons will be extended to the GAW stations in order to assess how much the IAGOS observations throughout the PBL are influenced by the regional background. The subsequent objective of this study is to give recommendations on the possible use of the IAGOS dataset in the lower troposphere.
IAGOS observations This study mostly relies on the O 3 and CO observations available in the framework of the MOZAIC-IAGOS program (http://www. iagos. org) (Marenco et al. , 1998; Petzold et al. , 2015). Observations have been performed by commercial aircraft from several airlines since 1994 for O 3 and 2002 for CO. In both the MOZAIC and IAGOS programs, the same instruments are used in all aircraft. During the 2011-2014 overlapping years, inter-comparisons were systematically performed between the MOZAIC equipped aircraft and the IAGOS fleet, demonstrating a good consistency in the dataset  (Nédélec et al. , 2015). In MOZAIC, O 3 was measured using a dual-beam UV-absorption monitor (time resolution of 4 seconds) with an accuracy/precision estimated at about ±2 ppbv/±2%  (Thouret et al. , 1998) , while CO was measured by an improved infrared filter correlation instrument (time resolution of 30 seconds) with an accuracy/precision estimated at ±5 ppbv/±5%  (Nédélec et al.
, 2003). In IAGOS, both compounds are measured with instruments based on the same technology used for MOZAIC, with the same estimated accuracy and the same data quality control. A more detailed description of the IAGOS system and its validation can be found in  Nédélec et al. (2015). We use the barometric altitude available in the IAGOS database. It is deduced from the pressure measured by the aircraft, assuming standard conditions at the surface (temperature of 288. 15 K, pressure of 1013. 25 hPa). It is worth noting that this leads to an uncertainty on the actual altitude of the aircraft above the surface. In particular, under specific atmospheric conditions (cyclone), this barometric altitude can thus sometimes be located below the actual airport altitude. To tackle this uncertainty, it would be useful in the future to take into account the real conditions of temperature and pressure observed at the surface based on meteorological stations available in or around the airport.
For now, such developments are yet not available and in this study, we will therefore consider all IAGOS measurements associated to their altitude above sea level (ASL), no matter if the altitude is below the actual elevation of the airport. Note also that the GPS altitude (measured by the aircraft system) is now available in the IAGOS dataset, but only since late 2014. It is also worth noting that IAGOS profiles are not exactly vertical. The ascent/descent rates of IAGOS aircraft are 7. 3 ± 2. 0 m s -1 on average, i. e. 9 min to reach an altitude of 4 km. In parallel, the aircraft's horizontal speed (available in the IAGOS database) increases with altitude from about 85 m s -1 at 0-1 km to 166 m s -1 at 3-4 km on average. The horizontal distance between Frankfurt airport and IAGOS aircraft depending on the aircraft altitude is plotted in Figure  1.
Surface observations Surface O 3 and CO data from the local AQN are taken from the AIRBASE database (https://www. eea. europa. eu/ data-and-maps/data/airbase-the-european-air-qualitydatabase-7). AIRBASE data are available until January 2012. For comparison, some results in two other European cities, Vienna and Paris, will also be discussed. These two cities are chosen because they offer the largest number of IAGOS profiles (after Frankfurt) and a dense network of surface stations. For Paris, instead of AIRBASE, data are taken directly from AIRPARIF, the local agency in charge of the air quality monitoring network (https://www. airparif. asso. fr, data downloaded the 20 th July 2017). In addition, we also use the CO and O 3 measurements available at GAW stations in West-Central Europe (see Table  1 ) in order to investigate the representativeness of IAGOS at a regional scale.
Some comparisons between the two datasets are also made at the daily and monthly scale. For these analysis, hourly data are aggregated and averaged daily and monthly, without any minimum data coverage required. These daily and monthly data are used only for comparison, but do not correspond to real daily and monthly averages as they only include data sampled simultaneously by both aircraft and surface stations. total), the comparison is thus performed on 9% of the hourly dataset (8,348 hours). At daily and monthly scales, the availability of IAGOS data increases to 75 and 88%, respectively. A roughly similar amount of IAGOS data is available during all four seasons (for O 3 : 24% in winter, 20% in spring and summer, 23% in autumn).
Diurnal variability As mentioned in Sect. 3. 1, the exact comparison between IAGOS and urban background stations gives small differences. Reasonable correlations are also found between the two datasets (r = 0. 58 and 0. 81 for CO and O 3 , respectively). In this section, we investigate the diurnal variability of these differences. The biases between the two datasets are calculated for each hour of the day, and are plotted in Figure  4. Here, the bias designates IAGOS minus the urban background stations. These biases show a substantial diurnal variability. Both the negative bias of CO and the positive bias of O 3 at urban background stations are greater (in absolute value) during the morning (06:00-09:00 local time (LT = UTC+1)) when they reach -40% for CO and +70% for O 3. After 10:00 LT, these biases get smaller until a minimum is reached during afternoon, when they range between ±20% for both CO and O 3.
g. presence of strong low-pressure systems). Both the mean (points) and the 5 th and 95 th percentiles (horizontal lines; only for IAGOS and GAW) are shown. A good agreement between IAGOS and all surface stations is found, except for traffic and some GAW stations (below 500 m ASL) for CO. DOI: https:// doi. org/10. 1525/elementa. 280. f5 For O 3 , a common seasonal pattern is observed at the surface stations and at altitude, with a broad springsummer maximum and a minimum in late autumn-early winter. This is in good agreement with other observations in the lower troposphere  (Parrish et al. , 2013). As for CO, the relative amplitude of the IAGOS dataset quickly decreases with altitude from about 100% close to the surface to 30-40% above 1 km ASL. The relative amplitudes observed at the GAW stations are in close agreement with IAGOS at the corresponding altitude levels (relative mean bias of amplitude of -1%).
However, the O 3 relative amplitude observed at the urban background stations range between 121-144% (134% on average) and is thus slightly stronger compared to what is observed both by aircraft and GAW stations at these altitudes. This is mainly due to a lower minimum of O 3 in winter at the urban background stations. The absolute amplitude (maximum minus minimum) is quite similar to the other datasets. As for CO, the bias on O 3 between IAGOS and urban background stations thus depicts some seasonal variations, from +28% in winter to -12% in summer (against +3% at the annual scale, see Sect. 3. 1). These higher differences for both CO and O 3 in winter could be due to less dispersive conditions during the cold season.
5 to -6% yr -1. Such differences of CO trends between traffic and urban background stations have already been highlighted in Europe by  Guerreiro et al. (2014). Among the GAW stations, 7 stations have a negative trend ranging between -4 and -1% yr -1 (JFJ, SNB, SSL, RIG, PAY, NGL, KMW), while the trends at the 3 other stations are not significant (KVV, HPB, KOS). In the lowest altitude levels, IAGOS data show a significant decrease of CO mixing ratios at the same rate as urban background stations (around -4% yr -1 ). Contrary to most traffic stations, the difference of trends between IAGOS and urban background stations are usually statistically insignificant. The IAGOS negative trends decrease with altitude, down to -2% yr -1 at 2 km. Such a decrease of CO in the IAGOS dataset of Frankfurt has already been highlighted by  Petetin et al.
(2016a). Thus, as for the distribution of CO mixing ratios, IAGOS observations progressively shift from a behaviour similar to urban background stations to a behaviour in reasonable agreement with the highest GAW stations, as one moves toward higher altitudes. Quite similar features are observed with seasonal trends (not shown). All O 3 annual trends from IAGOS and the AQN appear statistically not significant (see. No significant differences between these two datasets are highlighted. Among the GAW stations, 2 stations (KOS and NGL) out of 10 show a significant decrease of O 3 over the period 2002-2012, although these trends are not strongly significant (about -1. 5 ± 1. 2% yr -1 ). They are mostly driven by a significant decrease in summer and/or autumn. Slightly negative or insignificant trends of O 3 in Europe over the 2000s have already been highlighted in other studies (e.
Each grey circle represents the correlation between one given GAW station and the IAGOS data at one given altitude level, plotted against the difference of altitude between the two datasets (i. e. the difference between the elevation of the GAW station and the given IAGOS altitude level). For clarity, the correlations between GAW and IAGOS data are shown every 3 IAGOS altitude levels (i. e. at 25 m, 175 m, 325 m, etc. up to 4 km ASL). The correlations between IAGOS and GAW are found to be in the same order of magnitude of than the correlations among GAW stations themselves. Although variable, these correlations tend to be higher for lower differences of altitude. DOI: https://doi. org/10. 1525/elementa. 280. f8 To investigate this point, we compared the O 3 and CO IAGOS profiles during these two phases over the period 2002-2012.
Higher in altitude, these negative trends decrease to rates typical of GAW stations (-2% at 4 km ASL). DOI: https://doi. org/10. 1525/elementa. 280. f9 simultaneous landing and take-off data), stronger differences are found due to the fact that (i) both O 3 and CO depict a substantial diurnal variability in the lower troposphere, and (ii) landings and take-offs are not similarly distributed over the day in the IAGOS database (landings are more numerous during the morning while take-offs are more equally distributed along daytime).
However, some general comparisons can still be performed at few other airports in order to assess how far these previous results may change at other locations. In this section, we thus extend the previous analysis to two other airports of West-Central Europe: Vienna and Paris. The analysis is restricted to a comparison of the overall distribution of mixing ratios and a discussion on the correlation of hourly and daily datasets. For information purposes, the corresponding figures are given in the Supplement (Figures S-8 to S-10 for Vienna, Figures S-11 to S-13 for Paris). To facilitate comparisons among airports, the mean CO and O 3 mixing ratios at the different surface stations and in altitude are reported in Table  2  for the 3 cities.
For O 3 , the mean mixing ratios are 33 ± 19 and 35 ± 19 ppbv at urban and rural background stations, respectively. Compared to Frankfurt, IAGOS data at Vienna show lower O 3 mixing ratios in the first levels, but the agreement is good higher in altitude (mean difference below 3 ppbv above 400 m ASL), in agreement with the results of  Logan et al. (2012)  in the low/middle troposphere (2. 6-5 km). Considering exact comparisons between IAGOS and surface stations, the mean difference is only +2 and +10% for rural and urban background stations, respectively (Table  3 ). As for Frankfurt, these differences show diurnal variations with highest biases during the morning and lowest during the second half of the day (not shown). The correlation between the two datasets is strong (r = 0. 82). Therefore, for both CO and O 3 , IAGOS aircraft at Vienna measure mixing ratios in agreement with the urban background observed at the surface by the close-by AQ monitoring stations.
They show mean O 3 mixing ratios of 28 ± 19 and 22 ± 19 ppbv at the rural and urban background stations, respectively. The IAGOS data show slightly lower O 3 mixing ratios, with mean differences of -22 and -11% for rural and urban background stations (Table  3 ). Similarly to Frankfurt and Vienna, both are well correlated (r > 0. 7). The bias of O 3 between IAGOS and rural background stations can be greatly reduced by restricting the dataset to days with windy conditions. This is clearly illustrated in Figure  11  in which the relative bias is shown for different values of minimum wind speed from 0 to 10 m s -1. This bias progressively decreases when data associated with low wind speed (measured by IAGOS aircraft) are removed: applying a minimum wind speed of 2, 4, 6, 8 and 10 m s -1 leads to a bias of -21, -16, -14, -11 and -10%, respectively.
For wind criteria above 8 m s -1 , biases are not significant. However, such restrictions of the dataset slightly reduce the correlations. Such an influence of the wind was not observed at the other airports. A good agreement between IAGOS and rural background stations is not expected under very low wind conditions, as mixing in the vicinity of the surface station is reduced. However, the persistent decrease of the bias for higher wind speed was not expected. It may be due to the flat orography of the Paris region that favours homogenisation of the spatial O 3 distribution at a regional scale when the wind is sufficiently high (contrary to Frankfurt and Vienna where the surrounding orography is more complex). Note also that a similar reduction of the bias with the wind speed is highlighted between IAGOS and the EIFF3_U station located at the 3 rd floor of the Eiffel tower (315 m ASL).
Discussion and conclusion The CO and O 3 mixing ratios measured by IAGOS at different altitude levels in the lower troposphere around three European airports have been compared with surface measurements available from local air quality monitoring networks (within 50-80 km from the airport) and regional GAW stations (within 500 km from the airport). A focus was made on Frankfurt airport where the IAGOS data record is the densest and longest. At Frankfurt, the measurements at (urban) traffic stations show a very specific behaviour, with very high CO mixing ratios, and a strong negative trend over the period 2002-2012. Conversely, the CO and O 3 measured at urban background stations were found to share common characteristics with the CO and O 3 measured by IAGOS in the altitude levels closest to the surface, in terms of distribution, seasonal variations and trends (Figures  3, 6, 9 ).
IAGOS data showed slightly lower CO and slightly higher O 3 , which suggests a smaller influence of local emission sources than at urban background stations. These differences exhibit a diurnal variation with largest biases during the morning rush hours when the local traffic emissions increase the CO mixing ratios at both urban background and traffic stations (Figure  4 ). In addition, the CO and O 3 mixing ratios measured at the nearby surface stations appeared reasonably well correlated with the IAGOS airborne data at the lowest altitude levels (r ~ 0. 6-0. 7 at the daily scale, 0. 7-0. 8 at the monthly scale) (Figure  7 ). At higher altitudes, these characteristics of the IAGOS observations quickly deviate from the urban background and approach the regional background observed by the different GAW surface stations located in and around Germany.
This is demonstrated by the fact that the correlation between IAGOS and GAW datasets increases with altitude, with maximum correlations mostly obtained at an altitude close to the elevation of the surface station (Figure  6 ). The correlation between IAGOS and GAW stations is in the same range as the correlation among the GAW stations themselves (Figure  8 ). The vertical distribution of CO and O 3 derived from the IAGOS airborne measurements was found to be in close agreement with the vertical distribution deduced in the first 4 km from the GAW stations at different elevations (Figure  5 ). A comparison between concomitant ascent and descent profiles at Frankfurt have shown a good agreement (insignificant bias) on CO below 300 m ASL and above 1,000 m ASL but some small significant differences between 300 and 1,000 m ASL, with lower CO during landings (Figure  10 ).
The agreement on O 3 was very good whatever the altitude. Despite sparser IAGOS data, some comparisons were extended to Vienna and Paris airports. In both cities, the CO mixing ratios measured by IAGOS in the first altitude levels are always lower than the urban background given measured at the surface. At Vienna, the agreement with Frankfurt is good, as illustrated by a mean difference of CO between IAGOS and urban background stations of -17% on average (against -26% at Frankfurt). At Paris, much lower CO mixing ratios are measured by IAGOS (difference of -46%), at least partly due to a high proportion of data during the morning, when higher biases were also observed at Frankfurt and Vienna. With biases below ±12%, the agreement of O 3 between IAGOS and urban background stations appears very satisfactory at all 3 airports.
The boundary of transition between these two variability regimes likely depends on the local meteorological conditions, and in particular the PBL height and state. The transition may thus be more abrupt than our results suggest since in this study, numerous profiles of different PBL height are combined, which automatically smoothes the features that may exist at the transition between PBL and lower free troposphere. The good consistency between GAW and IAGOS observations at the different altitudes (more particularly above the first 500 m) gives confidence on the ability of IAGOS data to measure a reliable vertical distribution of the pollutants in the lower troposphere. Several reasons can explain the absence of strong influence of the local emissions from the airport activities and/or other aircraft.
For instance, the vertical separation minimum between two aircraft is fixed to 300 m (1,000 ft) below ~8. 8 km of altitude (and the double above) (ICAO, 2016). In addition, although the minimum duration between two consecutive takeoffs/landings can be as short as a few minutes (it varies depending on the aircraft and the airport), commercial aircraft are not allowed to follow each other too closely on the same track. Considering the fact that the dispersion of pollutants is more efficient at altitude (stronger wind, absence of re-circulation loops caused by buildings and/or canyon streets), these different separation standards appear sufficient to avoid measuring directly the exhausts from other aircraft. Detailed studies on airport air quality aspects with IAGOS data are subject of on-going work.