The CO 2 integral emission by the megacity of St. Petersburg as quantified from ground-based FTIR measurements combined with dispersion modelling

. The anthropogenic impact is a major factor of the climate change which is highest in industrial regions and modern megacities. Megacities are a significant source of emissions of various substances into the atmosphere, including CO 2 which is the most important anthropogenic greenhouse gas. In 2019 and 2020, the mobile experiment EMME (Emission Monitoring Mobile Experiment) was carried out on the territory of St. Petersburg which is the second largest industrial city in Russia with a population of more than 5 million people. In 2020, several measurement data sets were obtained during the 15 lockdown period caused by the COVID-19 (COronaVIrus Disease of 2019) pandemic. One of the goals of EMME was to evaluate the CO 2 emission from the St. Petersburg agglomeration. Previously, the CO 2 area flux has been obtained from the data of the EMME-2019 experiment using the mass balance approach. The value of the CO 2 area flux for St. Petersburg has been estimated as 89±28 kt km -2 yr -1 which is three times higher than the corresponding value reported in the official municipal inventory. The present study is focused on the derivation of the integral CO 2 emission from St. Petersburg by 20 coupling the results of the EMME observational campaigns of 2019 and 2020 and the HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectories) model. The ODIAC (Open-source Data Inventory for Anthropogenic CO 2 ) database is used as the source of the a priori information on the CO 2 emissions for the territory of St. Petersburg. The most important finding of the present study based on the analysis of two observational campaigns is a significantly higher CO 2 emission from the megacity of St. Petersburg as compared to the data of municipal inventory: ~75800±5400 kt yr -1 for 2019, 25 ~68400±7100 kt yr -1 for 2020 versus ~30000 kt yr -1 reported by official inventory. The comparison of the CO 2 emissions obtained during the COVID-19 lockdown period in 2020 to the results obtained during the same period of 2019 demonstrated the decrease in emission of 10% or 7400 kt yr -1 . EM27/SUN which were used for ground-based remote sensing measurements of the total column amount of CO 2 , CH 4 and CO at upwind and downwind locations on opposite sides of the city. The applicability and efficiency of this measurement scenario and 115 EM27/SUN spectrometers have been shown by Hase et al. (2015), Chen et al. (2016), Dietrich et al., (2021). The description of the EMME experiment has been given in full detail in the paper by Makarova et al. (2021). This study has also reported the estimations of the area fluxes for the emissions of CO 2 , CH 4 , NO x and CO by St. Petersburg. In 2020, the EMME experiment was continued. It started in March before the COVID-19 pandemic lockdown and consisted of six days of field measurements (three days before the lockdown and three days during the lockdown). We use mobile FTIR measurements to obtain CO 2 column enhancements (ΔCO 2 ) related to urban anthropogenic 125 emissions. have shown that emissions from large CO 2 sources (cities, thermal power plants) can be characterized by the difference between the results of measurements of the carbon dioxide concentration in the dry atmospheric column inside and outside 425 of the pollution plume (ΔXCO 2 ). The results of measurement campaigns in 2019 and 2020 have shown that for St. Petersburg in a set of mobile experiments the values of ∆ XCO 2 averaged over the duration of FTIR observations were in the range of 0.05-4.46 ppmv. For comparison, similar studies revealed the following values of ∆ XCO 2 : 0.16-1.03 ppmv for Berlin, Germany (Kuhlmann et al., 2019), 0.80-1.35 ppmv for Paris, France (Pillai et al., 2016; Broquet et al. 2018), and 0-2 ppmv for Tokyo, Japan (Babenhauserheide et al., 2020). So, for St. Petersburg, the highest values of ∆ XCO 2 were detected 430 (4.46 ppmv), if compared to similar measurements in Berlin, Paris and Tokyo. It should be noted that the value of ∆ XCO 2 depends not only on the integral emission of the source, but also on its spatial allocation (compact or distributed), the geometry of the field experiment (location of observations relative to the pollution plume) and on the meteorological situation during the measurements. This is why dispersion modeling, taking into account inventories of emission sources, is the most appropriate tool for interpreting the results of such observations.

measurement campaigns organized in the framework of major scientific projects, such as InFLUX (sites.psu.edu/influx; Turnbull et al., 2014), Megacities Carbon Project (https://megacities.jpl.nasa.gov/portal/; Duren and Miller, 2012), MEGAPOLI (http://www.megapoli.info, Lopez et al., 2013), CO2-Megaparis project in Paris (https://co2-65 megaparis.lsce.ipsl.fr, Bréon et al., 2015), COCCON -Paris (http://www.chasing-greenhouse-gases.org/coccon-in-paris/), and VERIFY (https://verify.lsce.ipsl.fr/). The important goal is to improve existing techniques and to develop new algorithms for the space-borne detection of the CO 2 plumes originating from intensive compact sources such as large cities and big thermal power plants (TPP) (Kuhlmann et al., 2019;SMARTCARB project, https://www.empa.ch/web/s503/smartcarb). Bovensmann et al. (2010) and Pillai et al. (2016) proposed to create and launch 70 new specialised satellite instruments for studying natural and anthropogenic sources and sinks of carbon dioxide with high spatial resolution. At the same time, the variety of modelling tools used to simulate the atmospheric CO 2 fields and assimilate the results of observations is also quite large: ranging from simple mass balance models (Hiller et al., 2014;Zimnoch et al., 2010, Makarova et al., 2018 to modern transport and photochemical models (Ahmadov et al., 2009;Göckede et al., 2010, Pillai et al., 2011, Pillai et al., 2012. 75 The present study is focused on the CO 2 emission by St. Petersburg, Russian Federation. The area of St. Petersburg urban agglomeration is about 1440 km 2 , while the city centre, which is characterized by high construction density, occupies 650 km 2 . The city has a population of ~5.4 million people (the official data for 2019, St. Petersburg Center for Information and Analytics, 2020); according to unofficial data the population is now more than 7 million (Shevlyagina, 2020). The population density is ~3800 people/km 2 on average. It can reach ~7300 people/km 2 on the territories with high construction 80 density (Solodilov, 2005). The data on total emissions of anthropogenic air pollutants in St. Petersburg are provided in the annual reports of the municipal Environmental Committee Serebritsky, 2019). Published data are based on the emission sources inventory method ("bottom-up") where CO 2 fluxes for urban areas are calculated on the basis of information about the landscape and the type of anthropogenic activity (e.g., number and type of buildings, location of roads, traffic intensity, the presence and type of TPP, etc.) using appropriate emission factors (Gurney et al., 2002;Serebritsky, 85 2018). On average, the contribution of St. Petersburg to the total greenhouse gas emissions of the Russian Federation is about 1%. According to official inventory data for 2015, the integral CO 2 emission from the territory of St. Petersburg is about 30 Mt/year and the inter-annual variability of this estimate in the period 2011-2015 did not exceed 1 Mt/year . In the mentioned official inventory report, it is noted that most of St. Petersburg's emissions (more than 90%) are associated with power production. These estimates differ, for example, from the results obtained in the study of the 90 structure of anthropogenic CO 2 emissions by the city of Baltimore (Maryland, USA): Roest et al. (2020) have reported that electricity production in Baltimore emits only 9% of CO 2 and the main part of emissions is related to transport (automobile 34%, marine 4%, air and rail transport 2%), as well as to the commercial sector (20%), industry (19%) and private residential housing (12%). studies have demonstrated that for the territories with high population density carbon dioxide produced by human respiration process can make a significant contribution to total emissions (Bréon et al., 2015;Ciais et al., 2007;Widory and Javoy, 2003). According to some estimates, one person emits by breathing on average 1 kg of CO 2 per day (Prairie and Duarte, 2007), which would amount to about 3 Mt of CO 2 per year for St. Petersburg. Bréon et al. (2015) have shown that for Paris the CO 2 emission from human breathing constitutes 8% of the total inventory emissions of the metropolis due to the use of 100 fossil fuels.
Official inventory ("bottom-up") estimates of the CO 2 emissions for St. Petersburg  may have significant uncertainties both in the estimates of integral emissions and in the data on the spatial and temporal distribution of the CO 2 fluxes. This suggestion is confirmed, in particular, by the significantly different values of the CO-to-CO 2 emission ratio (ER) for St. Petersburg obtained by Makarova et al. (2021) from the field measurements (ER CO/CO2 ≈ 6 ppbv/ppmv) and 105 calculated using the official emission inventory data reported by Serebritsky (2018) (ER CO/CO2 ≈ 21 ppbv/ppmv). The ER CO/CO2 ratio is one of the most important characteristics of the source of air pollution, since its value can indicate the nature of the emission. For cities, ER CO/CO2 mostly reflects the structure of FF consumption.
In 2019, the mobile experiment EMME (Emission Monitoring Mobile Experiment) was carried out on the territory of the St. Petersburg agglomeration with the aim to estimate the emission intensity of greenhouse (CO 2 , CH 4 ) and reactive (CO,110 NO x ) gases for St. Petersburg (Makarova et al., 2021). St. Petersburg State University (Russia), Karlsruhe Institute of Technology (Germany) and the University of Bremen (Germany) jointly prepared and conducted this city campaign. The core instruments of the campaign were two portable FTIR (Fourier Transform InfraRed) spectrometers Bruker EM27/SUN which were used for ground-based remote sensing measurements of the total column amount of CO 2 , CH 4 and CO at upwind and downwind locations on opposite sides of the city. The applicability and efficiency of this measurement scenario and 115 EM27/SUN spectrometers have been shown by Hase et al. (2015), Chen et al. (2016), Dietrich et al., (2021). The description of the EMME experiment has been given in full detail in the paper by Makarova et al. (2021). This study has also reported the estimations of the area fluxes for the emissions of CO 2 , CH 4 , NO x and CO by St. Petersburg. In 2020, the EMME experiment was continued. It started in March before the COVID-19 pandemic lockdown and consisted of six days of field measurements (three days before the lockdown and three days during the lockdown). 120 The present study continues the analysis of the data of EMME-2019 and demonstrates the first results of the 2020 campaign. We concentrate our efforts on the CO 2 emissions leaving the results relevant to other gases beyond the scope of the research. As an extension to the work by Makarova et al. (2021) our goal in this paper is to estimate the integral CO 2 emission by St. Petersburg megacity rather than area fluxes. Completing this task consists of the following basic steps:

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We use mobile FTIR measurements to obtain CO 2 column enhancements (ΔCO 2 ) related to urban anthropogenic 125 emissions.

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We adapt the ODIAC database (Oda and Maksyutov, 2011) to construct a priori information on the spatio-temporal distribution of anthropogenic CO 2 emissions on the territory of St. Petersburg.
-We initialize the HYSPLIT dispersion model, HYbrid Single-Particle Lagrangian Integrated Trajectories (Draxler and Hess, 1998;Stein et al., 2015) with the ODIAC emissions to simulate CO 2 3D field over the city of We evaluate the performance of our HYSPLIT model setup by calculating the surface CO 2 concentrations and comparing them with the routine in-situ measurement results (Foka et al., 2019).
-We scale the emission input data for the HYSPLIT model simulations in order to reproduce the observed ΔCO 2 .
-Finally, from the scaled emission a priori data we get the estimate of integral CO 2 emission by St.Petersburg.
The paper is organized as follows. Section 2 describes the methods and instruments, including a description of the 135 EMME measurement campaign and the equipment used, methods for processing the measurement results, the configuration of the HYSPLIT model and its evaluation based on calculations of ground-level CO 2 concentrations. Section 3 presents main results of EMME-2019 and EMME-2020 including estimates of integrated CO 2 emissions derived from FTIR measurements of the 2019 and 2020 field campaigns, combined with HYSPLIT model simulations. Section 4 contains a summary of our findings. 140

Methods and instrumentation
The main goal of the EMME measurement campaigns in 2019 and 2020 organized jointly by SPbU (St. Petersburg State University, Russia), KIT (Karlsruhe Institute of Technology, Germany) and UoB (University of Bremen, Germany) was to evaluate emissions of CO 2 , CH 4 , CO and NO x from the territory of St. Petersburg. Similar to 2019, the EMME-2020 145 campaign was conducted in spring (March -early May). This time of the year is preferable for a successful study of urban emissions, especially CO 2 , due to the following reasons: (1) a daylight duration is sufficient for FTIR remote sensing measurements; (2) the influence of vegetation processes on the daily evolution of the CO 2 concentration in the atmosphere is negligible; (3) the winter heating of the city buildings is still active which is a significant source of the CO 2 emissions for northern cities such as St. Petersburg. In contrast to the 2019 campaign, when two mobile EM27/SUN FTIR spectrometers 150 were used in the field experiment for simultaneous measurements inside and outside of the air pollution plume, all measurements in 2020 were performed with one spectrometer which was moved between clean and polluted locations within one day. In 2019, the field measurements were carried out during 11 days in total, and on 6 days in 2020. The number of observations in 2020 was smaller than in 2019 due to the quarantine restrictions related to the COVID-19 pandemic. These restrictions were imposed in St. Petersburg on 28 March, 2020. During several days of the 2020 campaign, measurements 155 inside the city pollution plume were made at two locations, which allowed to increase the total number of observations. Details of both field campaigns are given in Tables 1 and 2 for 2019 and 2020, respectively. The tables contain the Fourier transform spectrometer (FTS) instrument IDs (#80 and #84 in 2019, #84 in 2020), the position on the upwind and downwind sides of the city (latitude and longitude), and the duration of observations. Note that each experiment presented in the tables Удалено: -In addition to the EMME-2019/2020 field campaign data we also use the results of routine in-situ measurements of local CO 2 concentrations (Foka et al., 2019). ¶ ¶ 2 The EMME measurement campaign (short summary) ¶

Удалено: only
Удалено: transported consists of a pair of series of measurements -from the upwind and downwind sides. In 2019, observations of two FTS 160 instruments (#80 and #84) simultaneously were used for this purpose (see Table 1). In 2020 the single FTS instrument (#84) was moved between the upwind and downwind positions (see Table 2). The average duration of measurements in 2019 was 3 hours within the period of ~12:00-15:00. In 2020, the duration of the measurements was limited to about 1 hour (sometimes less), and the observation time varied from 11:00 to 19:00. Since a single instrument was used in 2020, the time difference between upwind and downwind measurements in 2020 ranged from 3 to 5 hours. 165

Bruker EM27/SUN FTS and spectra processing
Bruker EM27/SUN (Gisi et al., 2012;Frey et al., 2015, Hase et al., 2016) is a portable robust FTS having low spectral resolution of 0.5 cm -1 . It was designed for accurate and precise ground-based observations of CO 2 , CH 4 and CO columnaveraged abundances (X CO2 , X CH4 and X CO ) in the atmosphere. These FTIR spectrometers were used to build the COCCON 170 network (COCCON, 2021;Frey et al., 2019). EM27/SUN is equipped with a Camtracker, a solar tracking system developed by KIT (Gisi et al., 2011). A Camtracker consists of an altazimuthal solar tracker, a USB digital camera and "CamTrack" software which processes an image acquired by a camera and controls the tracker's movement. EM27/SUN FTS is designed on the basis of a robust RockSolid™ interferometer having high thermal and vibrational stability; the detailed description of the instrument is given by Gisi et al. (2012). Therefore, this type of instruments is being successfully implemented for setting 175 up fully automated stationary city network MUCCnet (Munich Urban Carbon Column network, Dietrich et al., 2021) and for performing a number of mobile campaigns (Klappenbach et al., 2015;Luther et al., 2019;Makarova et al., 2021).
In our study, we used the dual-channel EM27/SUN with quartz beamsplitter. Additionally, two detectors allow observing X CO and future improvements of the X CO2 retrieval (Hase et al., 2016). FTS registers an interferogram which is the Fourier transform of the infrared spectrum of direct solar radiation. The processing of data acquired by EM27/SUN 180 spectrometer consists of the following stages: -deriving spectra from raw interferograms including a DC-correction and quality assurance procedures (Keppel-Aleks et al., 2007); -deriving O 2 , CO 2 , CO, H 2 O, and CH 4 total columns (TCs) from FTIR spectra by scaling a priori profiles of retrieved gases COCCON, 2021). 185 To process the spectral data we used the free software PROFFAST which had been specially developed for COCCON network (COCCON, 2021;Frey et al., 2019). PROFFAST has been developed by KIT in the framework of several ESA projects for processing the raw data delivered by the EM27/SUN FTS. For the retrievals of total columns (TCs) of target species the following spectral bands are used Hase et al., 2016;Frey et al., 2019): 4210-4320 cm -1 (target gas -CO, interfering gases -H 2 O, HDO, CH 4 ), 5897-6145 cm -1 (target gas -CH 4 , interfering gases -H 2 O, HDO, CO 2 ), 190 6173-6390 cm -1 (target gas -CO 2 , interfering gases -H 2 O, HDO, CH 4 ), 7765-8005 cm -1 (target gas -O 2 , interfering gases -

Отформатировано: надстрочные
Удалено: A number of studies (Pillai et al., 2016;Broquet et al. 2018;Kuhlmann Удалено: 2019;Babenhauserheide et al., 2020) have shown that emissions from large CO 2 sources (cities, thermal power plants) can be characterized by the difference between the results of measurements of the carbon dioxide concentration in the dry atmospheric column inside and outside of the pollution plume (ΔXCO 2  H 2 O, HF, CO 2 ), and 8353-8463 cm -1 (target gas -H 2 O). The retrieval algorithm requires the following input: temperature profile in the atmosphere, pressure at the ground level, and the a priori data on the mole fraction vertical distribution of the atmospheric trace gases. These data are generated by the TCCON network software which ensures their compatibility over the TCCON network (TCCON, 2021). The close agreement of EM27/SUN observations analyzed with PROFFAST with a 195 collocated TCCON spectrometer has been demonstrated in the framework of the ESA project FRM4GHG (Sha, 2020).
In order to obtain a reliable value of the CO 2 emission for St. Petersburg, it is necessary to eliminate possible systematic error caused by the instrument bias. This goal was reached by carrying out a cross-calibration of the instruments.
In April-May 2019 both instruments passed a four day cross-calibration. The comparison of side-by-side measurements of X CO2 by FTS#80 and FTS#84 allowed determining calibration factor which was used for converting X CO2 measured by 200 FTS#80 to the scale of FTS#84. Detailed information about side-by-side calibration of FTIR-spectrometers is given in the paper by Makarova et al. (2021).

A priori data on FF CO 2 emissions
The global emission inventory ODIAC (Oda and Maksyutov, 2011;Oda, Maksyutov and Andres, 2018) is used in the present study for characterisation of the area fluxes of the CO 2 emission from the territory of St. Petersburg and its suburbs. 205 ODIAC provides global information on monthly average CO 2 emissions due to consumption of fossil fuels. The high spatial resolution of ODIAC (1 km × 1 km) is achieved through a joint interpretation of the existing global inventory of anthropogenic CO 2 sources, data on FF consumption, and satellite observations of the night-time glow of densely populated areas of the Earth. We use the data for 2018 emissions given in the ODIAC2019 version .
The CO 2 emission data have been extracted from the ODIAC database for the domain that includes St. Petersburg and 210 its suburbs (59.60-60.29° N, 29.05-31.33° E, Fig. 1). The sources of anthropogenic CO 2 emissions are concentrated within the administrative borders of the city. Most of these sources have intensities of ~4000 tons/month/km 2 and higher and are located within the borders of the city ring road. Summing up the ODIAC data within the city borders gives an estimate of the average integrated CO 2 emission of ~2710 kt per month with variations from 2429 kt in July to 3119 kt in March (Fig. 2).
The emissions are maximal in late winter and early spring, and are minimal in summer. In general, the seasonal variability of 215 emissions is insignificant (~8%), therefore the data for 12 months of 2018 were averaged in order to obtain an estimate of the mean annual distribution of urban CO 2 emissions. The integrated annual emission of St. Petersburg equals to 32529 kt, which is in good agreement with published official estimates: about 30 million tons for the period from 2011 to 2015 .
The nominal latitude/longitude size of the ODIAC data pixel is 30 arcseconds (Oda and Maksyutov, 2011), which 220 defines a global spatial resolution of about 1 km × 1 km. Since the length of a degree of longitude changes with the latitude, Удалено: (ODIAC) the pixel size for St. Petersburg (~60° N) is smaller and equals to 0.93 km × 0.46 km (0.43 km 2 ). It should be noted that the average annual urban emission flux is ~26 kt km -2 while in the central part of the city it can reach up to 80 kt km -2 . There is one pixel in the ODIAC data located in the centre of St. Petersburg with an extremely high emission flux of 7000 kt km -2 .
Normally, power plants and industrial enterprises manifest themselves as point sources of strong emission. However, we 225 cannot confidently attribute this particular ODIAC pixel to any source of this type, since there is no such object near it.
There are about a dozen of large thermal power plants on the territory of St. Petersburg, but all of them appear to be rather far from this location. Despite the lack of published data on anthropogenic CO 2 emissions at the city scale, we were able to explore detailed reports from municipal inventories of stationary air pollution sources (unpublished, but available on request). According to the inventories of NO x , CO, SO 2 , NH 3 , VOC and PM10 pollutants, there are no stationary objects of 230 an extreme emission close to the point of our interest. Thus we feel confident to smooth out this outlier and replace it by the value averaged over the neighboring ODIAC pixels. As a result, it amounted to 42 kt km -2 .

The HYSPLIT model general setup
The spatial and temporal evolution of the urban pollution plume was simulated using the HYSPLIT model (Draxler and Hess, 1998;Stein et al., 2015). Calculations were performed for the territory of the St. Petersburg agglomeration using the 235 offline version of the HYSPLIT model with the setup similar to the one that was successfully used previously for the NO x plume modelling (Ionov and Poberovskii, 2019;Makarova et al., 2021). A 3-dimensional field of anthropogenic air pollution was calculated for a spatial domain with coordinates 54.8°-61.6° N, 23.7°-37.8° E; the domain grid size was 0.05°×0.05° latitude and longitude (see Fig. 3 Удалено: corresponds to an area of Удалено: Since such a high CO 2 emission at a particular location seems to be an Удалено: , this value was deleted and replaced Удалено: .2

Удалено: 2020
Удалено: is Spatial distribution of FF CO 2 emission sources and their intensities are taken from the ODIAC database. The original ODIAC data were converted into a set of larger pixels (~3.6 km 2 ). Pixels with the area fluxes lower than 8 kt km -2 have been filtered out in order to keep only the urban sources which could be attributed to the St. Petersburg agglomeration. The resulting array which was used as the input for HYSPLIT consisted of 376 pixels and is shown in Fig. 3 (bottom). The 255 integral CO 2 emission that corresponds to this array equals to 26316 kt year -1 ; this is the value being used as a HYSPLIT first guess hereafter.

Test simulations of ground-level CO 2 concentrations
Routine measurements of CO 2 surface concentrations have been carried out at the atmospheric monitoring station of It is important to emphasize that quantitative agreement is achieved by linear scaling of the a priori integral urban CO 2 emission. The scaling coefficient for emissions corresponds to the value of the integral urban CO 2 emission from the territory of St. Petersburg of 44800±1900 kt year -1 (the given uncertainty is due to the uncertainty of the fitted scaling factor). This value is noticeably higher than official estimates mentioned above and ODIAC data for 2018 (32529 kt). The average discrepancy between the measurement and simulation 275 data shown in Fig. 4 is 2±9 ppmv (model calculations are systematically lower).

The results of the EMME-2019 campaign
We simulated the CO 2 total column (TC) spatial distributions over the territory of the St.Petersburg agglomeration for the time periods of FTIR mobile measurements conducted in the framework of the EMME-2019 experiment in March-April 280 2019. Obviously, the anthropogenic contribution to the CO 2 TC is concentrated mostly in the lower boundary layer, with a

Удалено: and locations
Удалено: (Makarova et al., 2020) top height of ~200 to ~1600 m. Therefore, HYSPLIT model was configured to simulate CO 2 concentrations at 10 altitude levels (0-1500 m), which were then integrated to obtain the CO 2 column in the boundary layer. The maps of the CO 2 plume obtained in this way show that for all the analyzed experiments, the choice of the location of the upwind and downwind measurement points was correct (see Appendix A, Fig. A1). The differences between the results of FTIR measurements of 285 the CO 2 TC inside and outside the pollution plume (ΔCO 2 ) were compared with the differences in the CO 2 column in the boundary layer simulated by HYSPLIT at the corresponding locations. HYSPLIT calculations were performed with a temporal resolution of 15 minutes. The data series of measured and calculated CO 2 contents for the experiments involved in the analysis are shown in separate plots in the Appendix B, Fig. B1. It is clearly seen from the plots that the "downwindupwind" enhancements in CO 2 observed by the measurements are significantly higher than predicted by HYSPLIT, which 290 indicates an underestimation of inventory CO 2 emissions. An example of simulated CO 2 plume and a time series of CO 2 total column measurements and HYSPLIT calculations for a typical day of experiments in 2019 (April 4) is given in Fig. 5. For the sake of comparison, the simulation results and measurement data were averaged over time periods of field observations (the duration of each experiment is given in Table 1).
In order to obtain a quantitative agreement between simulated and observed ΔCO 2 , the inventory data (the ODIAC 295 emissions), which are used as input information for the HYSPLIT dispersion model, should be scaled (Flesch et al., 2004).
The scaling factor (SF) is derived as follows. The data from all days of measurements are compared to the corresponding model simulations, see Fig. 6 as an example of a scatter plot (left panel). The scaling factor is determined as a slope value of the following regression line (e.g. the slope is 2.88 ± 0.21 , as shown in Fig. 6): where ΔCO 2 [FTIR] i is is the difference between the downwind and upwind FTIR measurements averaged over the duration of experiment i (see Table 1 and Table 2, Appendix A and Appendix B for the details of every field experiment) and ΔCO 2 [HYSPLIT ODIAC ] i is the averaged difference between the downwind and upwind CO 2 column calculated using the HYSPLIT dispersion model for the location and time of FTIR observations, and initialized with ODIAC CO 2 emissions.
The error assessment for the scaling factor should be discussed in some detail. The 1σ precision for the XCO 2 305 individual measurement is of the order of 0.01 %-0.02 % (<0.08 ppm) (e.g. Gisi et al., 2012;Chen et al., 2016;Klappenbach et al., 2015;Vogel et al., 2019). The error of the scaling factor was estimated under the assumption that the measurement errors are the same for all days as well as the model simulation errors. The error bars indicated in  Fig. B1). Obviously, these quantities comprise both measurement errors and 310 simulation errors (including those associated with wind direction and speed uncertainty), and temporal variability of the CO 2 TC. One can see that these quantities differ from day to day. Удалено: 5a).

Удалено: 5a
The right panel of Fig. 6 demonstrates that the model reproduces well the evolution of ΔCO 2 recorded in field measurements; the correlation coefficient between the results of modelling and observations is 0.94. The derived scaling factor yields the integral anthropogenic CO 2 emission value of 75800±5400 kt year -1 ; i.g. the value of 75800 results from the 315 multiplication 26316×2.88 (the 2.88 here is the slope on the scatter plot, and 26316 is the model first guess, see Section 2.3).
Resulting CO 2 emission rate is almost twice as high as the above estimate, based on the analysis of ground-level CO 2 measurement data (Section 2.4, 44800±1900 kt year -1 ). This difference may indicate a significant contribution of elevated CO 2 sources (industrial chimneys) that could not be registered by the ground-level in situ measurements, as the elevated exhausts of pollution are more likely to further rise up, rather than descend to the ground. In contrast, FTIR measurements of 320 the total column keep being sensitive to this kind of emissions. In addition, while FTIR measurements implement a "cross section" of the urban pollution emission zone in a series of multidirectional trajectories (depending on the wind direction), local ground-level in situ measurements at a specific location (Peterhof) can not capture the contribution of the entire mass of urban emissions. Thus, estimates of integral CO 2 emissions based on the interpretation of ground-level measurements in Peterhof can be considered as a lower limit of an estimate. 325 An earlier analysis of the results of the EMME-2019 measurement campaign focused in particular on inferring the area fluxes of urban CO 2 emissions from St. Petersburg. In order to achieve this goal, the simplified mass balance approach was applied to the observed CO 2 enhancements (ΔCO 2 ) which were attributed to the accumulation of pollution during the air mass movement on its way from the upwind side to the downwind side of the megacity (see Makarova et al., 2021 for full details). Basically, the mass balance approach was adopted in the form of a one-box model, and the area flux F was 330 calculated using the following equation: where F is the CO 2 area flux, ΔCO 2 is the difference between the downwind and upwind FTIR measurements, V is the mean wind speed and L is the mean path length of an air parcel which goes through the urban area . The obtained mean value of the CO 2 area flux was equal to 89±28 kt yr -1 km -2 and was attributed to the emission from the city 335 centre. As shown above, in the current study, the application of the HYSPLIT model allowed us to estimate the integral anthropogenic CO 2 emission of the entire megacity. In order to check the consistency with previous results, in the present study we made calculations of area fluxes on the air trajectories of field measurements using the ODIAC emission database scaled to the integral CO 2 emission derived from the results of EMME-2019 combined with the HYSPLIT simulations (75800±5400 kt year -1 ). Schematically, the air trajectories corresponding to the 2019 FTIR measurement locations are shown 340 in Fig. 7. These trajectories were simulated as backward trajectories by the HYSPLIT model in the boundary layer of the atmosphere. The resulting values of anthropogenic CO 2 area fluxes calculated by integrating the ODIAC data along these  Fig. 8 in comparison with the experimental estimates by Makarova et al., 2021. As in the study by Makarova et al., 2021, the width of the air paths was assumed to be 10 km. On average, according to ODIAC data, the area flux for the 2019 measurement trajectories was 106±9 kt yr -1 km -2 , that is somewhat higher than the experimental estimates 345 (89±28 kt yr -1 km -2 ) but agree within the error limits. Significantly higher variability in the experimental data may be related to the variability of the wind field, which is not taken into account in the simplified mass balance approach.

The results of EMME-2020 and comparison with EMME-2019
The data of mobile FTIR measurements performed in March-early May 2020 were processed and analysed in the same way as it was done for data acquired during the measurement campaign in 2019. An example of simulated CO 2 plume and a time 350 series of CO 2 total column measurements and HYSPLIT calculations for a typical day of experiments in 2020 (April 8) is given in Fig. 9. The comparison of the observed and simulated mean values of ΔCO 2 is shown in Fig. 10. Similar to the results of 2019, the HYSPLIT simulations reproduce well the observed evolution of ΔCO 2 . The correlation coefficient between the simulations and observations is 0.78. The estimation of the CO 2 emission was done using the described above approach based on the pollution plume modelling by HYSPLIT and scaling the ODIAC data which were taken as an a priori 355 guess. For the EMME-2020 campaign, the derived integral anthropogenic CO 2 emission is 68400±7100 kt yr -1 , which is about 10% lower than the estimate obtained for 2019 (75800±5400 kt yr -1 ).
It should be noted that one can expect lower anthropogenic CO 2 emissions in the 2020 measurement data compared to the same period in 2019, since restrictive measures were imposed in St. Petersburg on March 28 due to the COVID-2019 pandemic. A number of studies have already reported significant reductions of air pollution that followed the lockdown 360 events in different regions of the world (see e.g. Petetin et al., 2020;Pathakoti et al., 2020;Koukouli et al., 2020). According to Yandex data (https://yandex.ru/covid19/stat) the traffic intensity in the city of St. Petersburg decreased to 12-26% of the usual value on weekdays in the first week of quarantine (from March 30 to April 3) and amounted to 28-33% in the following week (from April 6 to April 10). Since we have no official data on the CO 2 emissions by traffic at our disposal, we used the average estimate for European countries, according to which the contribution of traffic to total emission constitutes 365 30% (European Parliament News, 2020). Under this assumption, a reduction in traffic activity down to 30% of the normal level should result in a reduction in total anthropogenic CO 2 emissions by 21% ((1.0-(0.7+0.3×0.3))×100%).
The weak response of urban CO 2 emissions to restrictive quarantine measures may indicate a relatively small contribution of traffic to the total CO 2 emissions from the territory of St. Petersburg. This may be due to the higher contribution of emissions associated with residential heating (including consumption of natural gas in private residences, e.g. 370 stoves and water boilers), which is more important for such a northern city as St. Petersburg, unlike many European cities.
Normally, the heating is still working in St. Petersburg in March and April, and the corresponding CO 2 emissions cannot be reduced due to the quarantine. Our confident expectation to detect the transport contribution is based on the high sensitivity

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The estimated integrated CO 2 emission derived from the 2020 measurements is ~68400±7100 kt yr -1 . If we exclude from the scaling factor calculation the results of measurements performed before the start of the quarantine, than for the integrated emission we obtain ~70000±16000 kt yr -1 . The comparison with the same period of 2019 (~75800±5400 kt yr -1 ) gives the difference in emission of 8% or 5800 kt yr -1 . This difference is within the error limits of the estimates.

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The validity of our conclusion with regard to of FTIR measurements of X CO2 using EM27/SUN spectrometers and COCCON methodology. If the emission from traffic were higher it would have been definitely identified during the campaign. The high sensitivity of our measurements to the 375 CO 2 pollution from different sources is demonstrated by the following examples. The results of EMME-2019 revealed that the emission of a single TPP located on the north-eastern side of the city (see Fig. 11) can add ~5×10 19 molecules/cm 2 to the CO 2 TC (Makarova et al., 2021). During the 2020 measurement campaign, one of the series of FTIR measurements was performed near the Waste Processing Plant (WPP) on the eastern side of the city (see Fig. 11). The contribution of this local CO 2 source was ~1×10 19 molecules/cm 2 . We emphasise that these measurements, being significantly affected by local 380 sources, were excluded from statistical analysis. In general, for these reasons (including unfavorable weather conditions and wrong location of FTIR measurement points), data from only a few experiments were excluded: No.8 on April 18, 2019, No.10 on April 25, 2019, No.11 on April 30, 2019 (see Table 1) and No.4 on March 27, 2020 (see Table 2). However, the given examples indicate the crucial role of stationary, non-transport sources of emissions, which were not subject to restrictive quarantine measures. 385 A thorough analysis of all experiments performed during the 2019 and 2020 measurement campaigns has shown that there were days with similar air trajectories and similar downwind measurement locations. These situations occurred twice: on March 27, 2019 and April 5, 2020, and on April 1, 2019 and April 8, 2020 (see Fig. 11). Both series of 2020 measurements, on April 5 and April 8, were performed during the COVID-19 quarantine period. We calculated the CO 2 area fluxes for these days applying the mass balance approach which was used by Makarova et al., 2021. The results are 390 presented in Table 3. Unexpectedly, the estimates indicate an increase of area fluxes during the quarantine period in 2020, somewhat colder weather in 2020 may contribute to the increase of CO 2 emission due to the more intense residential heating. 395 However, the high uncertainty of the CO 2 area flux estimates due to the uncertainties of the wind field and of the effective path length (for details, see Makarova et al., 2021) does not allow us to gain sufficient confidence in the nature of the detected differences.
To our opinion, the most important finding of our study based on the analysis of two observational campaigns is a significantly higher CO 2 emission from the megacity of St. Petersburg as compared to the data of municipal inventory: 400 ~75800±5400 kt yr -1 for 2019, ~68400±7100 kt yr -1 for 2020 versus ~30000 kt yr -1 reported by official inventory. Besides, this finding is consistent with the estimate of the CO 2 emission area flux by Makarova et al., 2021 which was about double of the EDGAR inventory for St. Petersburg (EDGAR, 2019). The difference can be partly explained by the impact of diurnal and seasonal variations of anthropogenic activity, since our measurements were conducted during the period of maximum CO 2 emission (early spring and afternoon) and therefore represent the upper limit of the emission estimates. According to the 405 Удалено: 2020.

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Удалено: 2020 average. The global database of hourly scaling factors (Nassar et al. 2013) gives also a factor of ~1.07 for St. Petersburg to scale down the afternoon emission rates to the daily average. So, dividing our estimates twice by 1.07 gives ~59000÷66000 kt yr -1 , which is still higher than the official inventory value. Compared to other world cities, the integral CO 2 emission of St. Petersburg is not that high -e.g, the ODIAC inventory reports: ~18000 kt yr -1 for San Francisco, 410 ~37000 kt yr -1 for Paris, ~51000 kt yr -1 for Mexico, ~88000 kt yr -1 for Delhi, ~106000 kt yr -1 for Moscow, ~136000 kt yr -1 for Hong Kong, ~172000 kt yr -1 for Tokyo and ~227000 kt yr -1 for Shanghai (the data are taken from the paper by Umezawa et al., 2020, Fig. 3). Typically, these estimates of urban CO 2 emissions are strongly correlated with the city's populatione.g. ~1 million people at San Francisco and ~23 million people at Shanghai.

Summary and conclusions 415
In 2019 and 2020, in spring, the mobile experiment EMME (Emission Monitoring Mobile Experiment) was carried out on the territory of St. Petersburg, which is the second largest industrial city in Russia with a population of more than 5 million .
In 2020, several measurement series were obtained during the lockdown period caused by the COVID-19 pandemic.
Previously, the CO 2 area flux has been obtained from the data of the EMME-2019 experiment using the mass balance approach. The present study is focused on the derivation of the integral CO 2 emission from St. Petersburg by combining the 420 results of the EMME observational campaigns of 2019 and 2020 and the HYSPLIT model simulations. The ODIAC database is used as the source of the a priori information on the CO 2 emissions for the territory of St. Petersburg.
A number of studies (Pillai et al., 2016;Broquet et al. 2018;Kuhlmann et al., 2019;Babenhauserheide et al., 2020) have shown that emissions from large CO 2 sources (cities, thermal power plants) can be characterized by the difference  (Pillai et al., 2016;Broquet et al. 2018), and 0-2 ppmv for Tokyo, Japan (Babenhauserheide et al., 2020). So, for St. Petersburg, the highest values of ∆XCO 2 were detected 430 (4.46 ppmv), if compared to similar measurements in Berlin, Paris and Tokyo. It should be noted that the value of ∆XCO 2 depends not only on the integral emission of the source, but also on its spatial allocation (compact or distributed), the geometry of the field experiment (location of observations relative to the pollution plume) and on the meteorological situation during the measurements. This is why dispersion modeling, taking into account inventories of emission sources, is the most appropriate tool for interpreting the results of such observations. 435 The HYSPLIT model coupled with the scaled input from the ODIAC database reproduces well the results of FTIR observations of the CO 2 TC during both campaigns: the correlation coefficient between the results of modelling and observations is 0.94 for 2019 and 0.78 for 2020. Lower value of the correlation coefficient for 2020 can be partly explained by the change in the spatial distribution of the CO 2 emission sources during the COVID-19 pandemic lockdown which could differ from the ODIAC distribution of the FF CO 2 sources. However, the number of data is not sufficient to confirm this 440 suggestion. The most important finding of the study based on the analysis of two observational campaigns is a significantly higher CO 2 emission from the megacity of St. Petersburg as compared to the data of municipal inventory: ~75800±5400 kt yr -1 for 2019, ~68400±7100 kt yr -1 for 2020 versus ~30000 kt yr -1 reported by official inventory. The comparison of CO 2 emissions obtained during the COVID-19 lockdown period in 2020 to the results obtained during the same period of 2019 demonstrated a decrease in emission of 10% or 7400 kt yr -1 . 445 There was an attempt to simulate the in situ measurements of the CO 2 concentration performed at the observational site located in the suburb of the St. Petersburg megacity during the two-month period (March-April 2019). In this case the correlation coefficient between model simulations and observations was 0.72. In contrast to the estimates of the CO 2 emissions from FTIR measurements presented above, the simulation of in situ measurements gives a much lower value (by a factor of 1.5-1.7) of the CO 2 integrated emission: 44800±1900 kt year -1 . Similar differences were previously found between 450 estimates of the CO 2 area fluxes for the central part of St. Petersburg, obtained both from the analysis of FTIR measurements, and from in situ measurements of CO 2 concentration (Makarova et al., 2021). This fact may indicate a significant contribution of elevated CO 2 sources (industrial chimneys) that could not be registered by the ground-level in situ measurements (in contrast to FTIR measurements of the total column). The approach of monitoring the outflows of large cities using arrays of compact FTIR spectrometers seems a promising and cost-effective route for assessing and monitoring 455 the CO 2 emissions of these important sources. Recurring campaigns performed over extended periods or even the erection of permanent observatories as demonstrated by Chen et al. (Dietrichet et al., 2021) should be recognized as crucial components of strategies aiming at improved observational capacity for greenhouse gases on regional and urban domains.

Data availability
The datasets containing the EM27/SUN measurements during EMME-2019 and EMME-2020 can be provided upon request; 460 please contact Maria Makarova (m.makarova@spbu.ru)