Quantifying burning efficiency in Megacities using NO 2 /CO ratio from the Tropospheric Monitoring Instrument (TROPOMI)

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Introduction
The rapid urbanization and economic growth in developing countries has led to a strong increase in urban air pollution (Pommier et al., 2013;United Nations, 2018).In the south Asian cities of Kabul and Dhaka, for instance, nitrogen dioxide (NO 2 ) increases have been reported in the order of 10 % yr -1 (Schneider et al., 2015).In New Delhi, emissions of carbon monoxide (CO) increased by 22.4 % in the period 2000-2008 (Jiang et al., 2017).In European countries, on the other hand, the use of modern technology and other air pollution abatement measures have decreased NO 2 concentrations by 10-50 % in the period of 2004 to 2010 (Castellanos & Boersma, 2012) and CO by 35 % between 2002to 2011(Guerreiro et al., 2014).
To develop effective air pollution control strategies, accurate information on local emission sources and combustion processes is important (Borsdorff et al, 2004).However, developing countries and remote areas lack the local infrastructure needed to obtain detailed information e.g. about energy consumption, fuel type and technology.Limited process information contributes largely to the uncertainty in emission inventories (Silva & Arellano, 2017).For example, the range of uncertainty in the Chinese NOx and CO emissions in the period of 2005to 2008, has been estimated at -20 to +45% due to inadequate information about the fuel consumption and rough estimates of emission factor (Zhao et al., 2011(Zhao et al., , 2012)).In the global emission inventory EDGAR v4.3.2, uncertainties in regional emissions have been estimated at 17 to 69% for NOx, and 25 to 64% for CO (Crippa et al., 2016).In this study, we investigate the use of satellite remote sensing to improve the emission quantification for these important air pollutants.
In global emission inventories, combustion related emissions are computed as the product of the amount of fuel burned (activity data), and the composition of the emissions as represented by the emission factor (EF) (Vallero, 2007).Emission factors depend strongly on the burning conditions (Sinha et al., 2003;Ward et al., 1996;Yokelson et al., 2003), in particular on the combustion efficiency (CE).CE is defined as the fraction of reduced carbon in the fuel that is directly converted into CO 2 (Yokelson et al., 1996).Usually, emission factors are measured in laboratories under controlled burning conditions.However, in ambient environment, combustion conditions are highly variable (Andreae & Merlet, 2001;Korontzi et al., 2003) introducing large uncertainties in global emission inventories through the impact of CE on EF.A case study (Frey & Zheng, 2002) for NOx emission estimates from the coal fired power plants with dry-bottom wall-fired boilers using low NOx burner showed that the EF for NOx can vary by factor of 4 or more within a same technology.The application of mean EF introduces uncertainties in the range of -29 % to +35 % with respect to mean emission estimates (Frey & Zheng, 2002;Tang et al., 2019).Fuel type, fuel composition, combustion practices and technology are the main factor influencing combustion efficiency in the ambient environment (Silva & Arellano, 2017).To improve the accuracy of global inventories, a better quantification of combustion efficiency and EFs is needed.
In recent years, the availability of atmospheric composition measurements from Earth orbiting satellites has strongly improved.Sensors such as Scanning Imaging Absorption spectroMeter for Atmospheric Chartography (SCIAMACHY) (Bovensmann et al., 1999) and Tropospheric Monitoring Instrument (TROPOMI) (Veefkind et al., 2012) deliver global datasets of multiple species.The satellite observations from SCIAMACHY have been used in combination with inverse modelling techniques to test and improve emission inventories (Konovalov et al., 2014;Mijling and van der A, 2012;Reuter et al., 2014;Silva et al., 2013).By combining observations of different species (e.g.CO, CO 2 , NO 2 ) information about common sources is obtained, and potentially also about emission ratios (Hakkarainen et al., 2015;Miyazaki et al., 2017;Reuter et al., 2019;S. Silva & Arellano, 2017).
In this study, measurements from the TROPOMI are used to investigate the combustion efficiency in megacities.TROPOMI is a push broom grating spectrometer on board of Sentinel 5 Precursor launched by ESA on 13 October, 2017 (Veefkind et al., 2012).We use the ratio of the TROPOMI retrieved tropospheric column of NO 2 and the total column of CO, which is formally not equivalent to combustion efficiency but can nevertheless serve as a useful proxy (Silva & Arellano, 2017;W. Tang & Arellano, 2017).The reason for this is that NO x emission increases with combustion temperature, which is high during efficient combustion.In contrast, CO is a product of incomplete combustion, and is produced when combustion efficiency is low (Flagan & Seinfeld, 1988).The combination of these effects makes the NO 2 /CO ratio highly sensitive to combustion efficiency.To correct for differences in the NO 2 and CO background concentrations, the enhancement ratio ΔNO 2 /ΔCO is used.Here ΔNO 2 and ΔCO represent concentration increases compared with their respective backgrounds.
The ΔNO 2 /ΔCO ratio is insensitive to atmospheric transport, as NO 2 and CO emissions are dispersed in a similar manner by the wind.Therefore, the impact of transport cancels out in the ratio.Because of this, TROPOMI observed ratios close to emissions sources can be directly related to emission ratios.The aim of this study is to investigate the local relation between TROPOMI retrieved ΔNO 2 /ΔCO ratios and emission ratios in a quantitative manner, focusing on megacities showing significant concentration enhancements in the TROPOMI data.In the past studies, NO 2 from the Ozone Monitoring Instrument (OMI) and CO from Measurement of Pollution in the Troposphere (MOPITT), have been used to derive CO/NO 2 ratios (Silva & Arellano, 2017;W. Tang & Arellano, 2017).MOPITT also has a SWIR channel (or near IR) and the multispectral (TIR/NIR) product, with near-surface sensitivity over some land regions, was used in both Silva & Arellano, 2017;Tang & Arellano, 2017.TROPOMI provides a unique opportunity to measure CO and NO 2 using the same instrument at unprecedented high spatial resolution (7x7 km 2 at nadir) and daily global coverage (Borsdorff et al., 2018b;van Geffen et al., 2019) making this instrument ideally suited for investigation of NO 2 /CO ratios from space.Additionally, TROPOMI CO retrievals make use of the short-wave infrared, improving the sensitivity to surface emissions of CO compared to the thermal infrared sounders MOPITT and Infrared Atmospheric Sounding Interferometer (IASI).However, TROPOMI NO 2 retrievals are less sensitive to the lower troposphere, causing ΔNO 2 /ΔCO to be influenced by vertical sensitivity (Eskes & Boersma, 2003).We derived a correction factor to take this influence into account, as will be explained in detail in Section 2.5.This paper is organized as follows: Section 2 provides detailed information about the TROPOMI CO and NO 2 retrieval, the approach used to quantify the ΔNO 2 /ΔCO column enhancement ratio over megacities, and how to relate it to the corresponding emission ratio.Results comparing satellite and emission inventories derived ratios are presented in section 3.
Finally, section 4 summarizes our findings and presents the main conclusions.

TROPOMI CO retrievals
For this study, we are using the TROPOMI CO scientific beta data product provided by SRON (ftp://ftp.sron.nl/open-accessdata-2/TROPOMI/tropomi/co/7_7/).The output is identical to the one of European Space Agency (ESA) 's operational data product but provides in addition the TM5 a priori profiles (http://tm5.sourceforge.net/)that are used in the retrieval.The SRON CO product also supplies more data for the early months of the mission which are not included in the operational product.Total column densities of CO [molecules/cm 2 ] are retrieved from spectral radiance measurements from the TROPOMI short wave infrared (SWIR) module at 2.3 µm using the SICOR algorithm (Landgraf, Brugh, et al., 2016).In this profile scaling algorithm, the TROPOMI observed spectra are fitted by scaling a reference vertical profile of CO using the Tikhonov regularization technique (Borsdorff et al., 2014).The reference a priori CO profile is derived from the TM5 transport model (Krol et al., 2005) as described in Landgraf (2016).The averaging kernel (A) is an essential component of the CO retrieval, which quantifies the sensitivity of the retrieved CO column to a change in the true vertical profile ( following Borsdorff et al., (2018c) Where, is the error in the retrieved CO columns.

TROPOMI NO 2 retrievals
The UV-Vis module of TROPOMI is used to retrieve NO 2 in the 405-465nm spectral range.NO 2 slant column densities are processed using the TROPOMI NO 2 DOAS software developed at KNMI (van Geffen et al., 2019).The retrieval algorithm is based on the NO 2 DOMINO algorithm ( Boersma et al., 2011) which has been improved further in the QA4ECV4 project (Boersma et al., 2018).The algorithm subtracts the stratospheric contribution to the slant column densities, and then converts the residual tropospheric slant column density into the tropospheric vertical density via the air mass factor (AMF).The AMF is computed using co-sampled, daily NO 2 a priori vertical profiles from output of the TM5-MP chemistry transport model at 1˚ x 1˚ resolution (Williams et al., 2017).AMF depends on the surface albedo, terrain height, cloud height and cloud fraction (Eskes et al., 2018;Lorente et al., 2017).We have used the offline level 2 NO 2 data [molem -2 ] available at (https://s5phub.copernicus.eu;http://www.tropomi.eu).The TROPOMI NO 2 product has been successfully used in various studies so far (Griffin et al., 2019;Reuter et al., 2019).There are indications that NO2 is biased low by approximately 30% in the tropospheric columns because of issues with the cloud pressure and a priori NO2 profile used in the AMF calculation (Lambert et al., 2019).

Data Selection
We used TROPOMI CO and NO 2 retrievals from June to August, 2018 because of the large number of clear sky days during this period over megacities of our interest.Megacities are strong sources of air pollution and can readily be observed in TROPOMI data (Borsdorff et al., 2018c).Since CO and NO 2 are retrieved from different instrument channels using different algorithms, the filtering criteria and spatial resolutions are also different.To facilitate data filtering, both algorithms provide a quality assurance value (qa value).The qa value for both products ranges from 0 (no data) to 1 (high quality data) For our data analysis, we selected NO 2 retrievals with qa values equal or larger than 0.75, indicating clear sky conditions (Eskes & Eichmann, 2019), and CO retrievals with qa values equal or larger than 0.7, representing measurements under clear sky conditions or the presence of low-level clouds (Apituley et al., 2018).The application of SICOR algorithm on SCIAMACHY CO retrievals with low-level clouds increases the number of measurement and hardly disturb on the detection of CO sources (Borsdorff et al., 2018a).CO retrievals are filtered for stripes as described in Borsdorff et al., (2018c).The CO retrieval has a factor 2 coarser spatial resolution than the NO 2 retrieval (7x7km2 versus 3.5x7km2).To collocate NO 2 and CO retrievals, we combine those NO 2 pixels which centres fall within a CO pixel, selecting only those pixels for which both the NO 2 and CO retrievals pass the filtering criteria.The total CO column and tropospheric NO 2 columns are converted into the dry column mixing ratio XCO (ppb) and XNO 2 (ppb) using the dry air column density calculated using the collocated surface pressure data included in the CO data files as described in Borsdorff et al., (2018c).

Calculation of NO 2 /CO
This study focuses on the following megacities (population > 5 million): Mexico City, Tehran, Riyadh, Cairo, Lahore and Los Angeles.These six megacities are well isolated from surrounding sources and frequently experience cloud -free conditions, allowing the retrieval of a large number of XCO and XNO 2 data from TROPOMI.Los Angeles and Mexico City have automated air quality monitoring networks, measuring CO and NO 2 at different locations in the city.These measurements are used in section 3.3 to validate the results obtained using TROPOMI.In addition, these megacities are expected to span a sizeable range in burning efficiency by including urban centres in developed (US/ Los Angeles) and developing countries( Mexico/ Mexico City, Egypt/ Cairo, Saudi Arabia/Riyadh, Pakistan/ Lahore).
The concentration gradient between the background and the city centre is used to determine the ΔXNO 2 /ΔXCO enhancement ratio.To determine this ratio, we divide each city into a core city area and a background area.Every city has a different size and different neighbouring CO and NO2 emission sources and therefore the appropriate choice of radii for the background and outskirt areas varies between cities (detail explanation in Supplements Section 1).This is important mostly to have a significant signal from city emissions in CO and NO2.However, since the same regional definition is used for NO2 and CO, the enhancement ratio is not so sensitive to the details of the region selection.To maximize the size of the city enhancement, we exclude the diffuse outskirt area in between the city centre and the background.For the location of the city centre we use the weighted average emission centre of NO 2 , derived from the EDGAR emission database (Dekker et al., 2017).The derived centre coordinates, and the radii of the city core and background area are listed in Table 1.We test the robustness of the satellite-derived emission ratio using two different methods, which are explained in detail below.

Upwind background
To determine the upwind background (UB) column mixing ratio, we select a section of the background region that is upwind from the city centre using the average wind direction over the core city area (Fig. 1and  (2) (3) (4) The background area might contain free tropospheric NO 2 from lightning and convectively lofted surface NO 2 from elsewhere.However, these contributions vary on scales that are usually large compared with the scale of a city.Therefore, the calculated ΔXNO 2 and ΔXCO enhancements are caused predominantly by emissions from the city.

Plume Rotation
The daily TROPOMI-observed city images are rotated in the direction of wind using the city centre as the rotation point to align each CO and NO 2 plume in upwind-downwind direction (Pommier et al., 2013).Rotated images for June to August 2018 are averaged together (see FigS8).∆XNO 2 and ∆XCO are determined by subtracting the average of the first quartile XNO 2 , XCO values in a 100 km x 20 km region upwind from the city centre from the average of the fourth quartile XNO 2 , XCO values in a 100 km x 20 km region downwind from the city centre.Finally, the enhancement of XNO 2 and XCO is calculated as described in Eq. ( 5) and the enhancement ratio is derived by using Eq. ( 4).

∑ ∑
where, n downwind = number of observation ≥ 75 th percentile, n upwind = number of observation ≤ 25 th percentile

NO 2 /CO emission ratio
Local TROPOMI derived ratios in column abundance are compared with emission ratios derived from the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) at 0.1˚ x 0.1˚ spatial resolution for the most recent year of 2012 and the database provided by Monitoring Atmospheric Chemistry and Climate and CityZen (MACCITY), for 2018 available at 0.5˚ x 0.5˚ resolution (Granier et al., 2011).MACCity has been re-gridded to a spatial resolution of 0.1˚ x 0.1˚ assuming a uniform distribution of the emissions within each 0.5˚ x 0.5˚ grid box.Both emission inventories contain total emissions of NOx and CO.NOx emissions are converted into NO 2 by dividing NOx by the conversion factor of 1.32.This conversion factor is based on Seinfeld and Pandis (2006) and represents urban plumes at 13.30 local time.The emission ratio of NO 2 and CO (E NO2 /E CO ) is calculated from total emissions (sum of all processes) within the core city area, for the EDGAR and MACCity emission inventories.
To compare TROPOMI to inventory derived ratios, the NO 2 tropospheric column has to be corrected for its limited atmospheric residence time.The CO lifetime is long enough compared with the transport time out of the city domain to be neglected.In addition, we need to account for differences in the vertical sensitivity of TROPOMI to NO 2 and CO, as quantified by their respective averaging kernels (A) shown in Fig. 2. To compare TROPOMI to EDGAR and MACCity, we formulate a relationship between the emission ratio (E NO2 /E CO ) and the column enhancement ratio (ΔXNO 2 /ΔXCO) taking into account the combined effect of atmospheric transport, chemical loss and the averaging kernel.This relationship is as follows (see Appendix A for its derivation).

( [ ])
Where, U is the is the 200m wind speed (ms -1 ), lx is diameter of the city centre (m), K is the rate constant of the reaction of NO 2 with OH of ( ) cm 3 molecule -1 s -1 (Burkholder et al., 2015).T (K) and OH (molecule cm -3 ) are respectively the boundary layer average temperature and OH concentration and A influence is the influence of the averaging kernel on ΔXNO 2 /ΔXCO (see section 3.For CO, the TROPOMI data provide column A's from the surface to the top of atmosphere.For NO 2 , tropospheric A is derived using the air mass factor for the troposphere as fraction of the total column (Boersma et al., 2016).For further details see Appendix B.

Uncertainty
To quantify the uncertainty in TROPOMI-derived ΔXNO 2 /ΔXCO ratios for the plume rotation method, we use the error propagation method of Pommier et al.,(2013) and boot strap for the upwind background, as explained further below.

Bootstrapping
The boot-strapping method is a statistical resampling method, used here to calculate the uncertainty in the daily enhancement ratio of .The first step is to generate a new set of samples by drawing a random subset with replacement from the full dataset of N daily ratios.The subset has the same number of samples as the full dataset, from which a mean ratio is calculated.This procedure is repeated a thousand times for each city.Finally, the standard deviation of the resulting ratios is taken and used to represent the uncertainty in daily .

Error propagation
To calculate the uncertainty in by error propagation, we first determine the uncertainty in the enhancements ΔXNO 2 and ΔXCO, which are derived from the uncertainty in the mixing ratios upwind and downwind of the source as follows where, X is XNO 2 or XCO.
Here, we assume that the upwind and downwind uncertainties are independent.The uncertainty for the column enhancement is:

Detection of NO 2 and CO pollution over megacities
The collocated TROPOMI XNO 2 and XCO data have been averaged for June to August 2018, for domains of 500 x 500 km 2 centred around the selected megacities as described in section 2. Results are shown in Fig. 3 for Mexico City and Cairo.The enhancements of XCO and XNO 2 over Mexico City and Cairo are clearly separated from the surrounding background areas and are prominent in several overpasses of TROPOMI (Fig. S9).This demonstrates that a relatively short data averaging

Comparison between TROPOMI and inventory derived ratios
In this subsection, we attempt to compare TROPOMI-derived NO 2 /CO column enhancement ratios to emission ratios from EDGAR and MACCity for the six selected megacities (see Fig. 4).As explained in section 2, column enhancement ratios 255 from TROPOMI are obtained using the upwind background (UB) and plume rotation (PR) methods.These estimates differ by 5 to 20 % across the six cities, providing an initial estimate of the accuracy at which the column enhancement ratio can be derived (see Table S1 for details).The EDGAR and MACCity inventories show a substantial variation in emission ratios between cities, with relatively high emission ratios for Riyadh and the lowest for Lahore.TROPOMI-derived ∆XNO 2 /∆XCO Figure 4. Comparison of TROPOMI-derived ΔNO 2 /ΔCO enhancement ratios, calculated using different methods shown in blue shades, to corresponding emission ratios from the EDGAR (red shades) and MACCity (yellow shades) emission inventories for six megacities.The dark solid shades for emission inventories represent the annual average inventory derived ratio whereas faded shades represents the June to August average inventory derived ratio.Error bars represent 1σ uncertainties calculated using boot strapping (upwind background) and error propagation (plume rotation method).The upwind background corrected emission ratio (UBCER) and Plume rotation corrected emission ratio (PRCER) account for the impact of photochemical NO 2 removal and the averaging kernel.column enhancement ratios for the UB and PR methods show similar patterns as EDGAR and MACCity with Pearson correlation coefficients of 0.85 and 0.7 respectively (Fig. S10).However, inventory-derived emission ratios are clearly larger than TROPOMI-derived column enhancements ratios by 60 to 85%, explained largely by the impact of the limited NO2 lifetime and the averaging kernel, as will be discussed further after explaining the differences between EDGAR and MACCity.
Emission ratios from MACCity are lower than from EDGAR by 10 to 75%, except for Los Angeles and Mexico City.To understand the differences in emission ratios between MACCity and EDGAR, we selected two cities, Cairo and Mexico City, which present the largest and smallest differences in emission ratio.The CO and NO 2 emissions are categorized into seven sectors: agriculture, residence, energy, industries, transportation, shipping and waste treatment.Sectors are compared that contribute most to the total emission.In the case of Cairo and Mexico City these are the transportation, industries, energy and resident sectors (Fig. S11 a and b).For Cairo, the total CO emission is lower in EDGAR than in MACCity by a factor 2, whereas the total NO 2 emission is 10% higher in EDGAR.This results in an emission ratio that is higher by a factor 3. The largest discrepancy between EDGAR and MACCity CO emission is due to the resident sector followed by energy.
For NO 2 , the energy, transportation and resident sectors explain most of the difference between EDGAR and MACCity.In Mexico City, EDGAR total CO and NO 2 emission are both higher by a factor 2 compared to MACCity, cancelling out in the ratio leading to the best agreement of all selected megacities.However, it is complicated to identify the main factors explaining the differences between EDGAR and MACCity at the sector level due to the combined influence of differences in activity data, emission factors and the methods used to disaggregate country totals.To understand the disaggregation of emission in EDGAR and MACCity, we compared the country total CO and NO 2 of Mexico/ Mexico City and Egypt/ Cairo.
The comparison shows that EDGAR and MACCity country CO total and NO 2 total of Mexico shows a small differences (~12%) whereas in Mexico city the difference is about factor of 2 (Fig. S11c).For Egypt, EDGAR and MACCity CO total shows the similar differences as Cairo whereas EDGAR NO 2 country total emission is lower by factor 2 (Fig. S11d).This shows that EDGAR attribute CO and NO2 emission to the city and MACCity smears them out over the country.
The difference between satellite-derived column enhancement ratios and inventory-based emission ratios can be explained in part by the relative short lifetime of NO 2 , reducing columnar NO 2 /CO ratios compared to the emissions.In addition, the sensitivity to the planetary boundary layer is smaller for NO 2 than for CO TROPOMI measurements, reducing the satellite observed column enhancement ratio further.Taking these influences into account using Eq. ( 6) leads to the Upwind Background Corrected emission ratio (UPCER) and Plume rotation Corrected Emission Ratio (PRCER) in Fig. 4, which have been calculated on a daily basis before averaging over the full period.Due to the short lifetime of OH, its concentration depends strongly on the local photochemical conditions (de Gouw et al., 2019).Therefore, to account for the local lifetime of NO 2 , we need an estimate of the OH that is representative for the photochemical conditions inside cities. Figure 5 shows the boundary layer OH concentration at the time TROPOMI overpasses from CAMS for Mexico City, averaged over June-August, 2018.The Fig. 5 shows a clear enhancement of OH in the city centre, confirming that the spatial resolution of CAMS is sufficient to resolve urban influences on OH in megacities.
UB and PR column enhancement ratios increase by 60 to 85 %, when accounting for the NO 2 lifetime (see Table S1).The boundary layer OH concentrations and mean wind speeds for the six cities are listed in Table 2.
The impact of differences between the XNO 2 and XCO averaging kernels is calculated using vertical profiles of NO 2 and CO taken from CAMS.These profiles were used to calculate XNO 2 and XCO using either the TROPOMI A's or A's replaced by identity matrices.The relative difference quantifies the impact of differences between the averaging kernels (see Appendix C for the derivation).The CAMS daily simulated city enhancements over June to August, 2018 did not compare well with TROPOMI for CO over Tehran, Cairo, Riyadh and Lahore, possibly due to the coarse resolution of CAMS (see Fig S14, S15, S16and S17).Therefore, for Mexico and Los Angeles is determined to calculate the averaging kernel impact (Fig S12 and S13).CAMS derived enhancement ratio for Mexico City differs by 5 % compared to UB and PR but for Los Angeles the ratio (0.094) is higher by 75% compared to UB and PR (0.034).To test the accuracy of , a few days for Tehran, Cairo, Riyadh and Lahore were selected when CAMs CO and NO 2 enhancements did compare relatively well with TROPOMI.For the six megacities, TROPOMI derived ΔNO 2 /ΔCO ratios are 10 to 15 % lower than the 'ideal' ΔNO 2 /ΔCO ratio that would be measured if both retrievals had uniform vertical sensitivities, i.e. every molecule in the column receives equal weight.Details about the selected days and calculated corrections for each city are listed in Table S2.
After correction, UBCER and PRCER for Tehran and Mexico City are close to EDGAR and MACCity (10 to 25%).This confirms that the emission factors for Mexico City is well represented in the EDGAR and MACCity emission inventories.
The difference between corrected and uncorrected ratios in Fig. 4 highlights the importance of the correction, in particular the influence of OH, for assessing emission ratios using TROPOMI.For Cairo the correction also reduces the difference between TROPOMI and the emission inventories, although the EDGAR ratios remain higher by about 65% for Cairo than UBCER and PRCER.For MACCity, the emission ratios are close to TROPOMI derived UBCER and PRCER for Cairo (within 20%), pointing to a more accurate representation of emission ratios in MACCity than in EDGAR.However, for Riyadh UBCER and PRCER are close to MACCity (~10 -20 %) whereas EDGAR is higher by 35%.Similarly, for Lahore, PRCER is close to EDGAR ratio whereas, MACCity is lower by factor of 2.5.For Los Angeles, the ratios from MACCity and EDGAR are higher by 70 % and 50 % respectively than UBCER and PRCER after correction, suggesting poorer burning conditions than represented by the emission inventories.To further investigate this discrepancy for Los Angeles, we included the Hemispheric Transport of Air pollution version 2 (HTAP-v2) emission inventories for 2010 in the comparison.HTAP-v2 has a resolution of 0.1˚ x 0.1˚ and makes use of emission estimates from the Environmental Protection Agency (EPA) for the USA (Janssens-Maenhout et al., 2015).The HTAP-v2 derived emission ratio over Los Angeles is 0.074, which is close to UBCER and PRCER (within 20 %).This result provides further confidence in TROPOMI derived emission ratio.However, different sources of uncertainty play a role as discussed further below.
To account the effect of seasonality on the annual average EDGAR ratio, we quantify the seasonal correction factor using EDGAR v4.3.2 2010 since monthly data for EDGAR 2012 is not available.June to August (JJA) EDGAR ratio reduces by 12.5 % for Tehran whereas <5% for Cairo, Riyadh, Mexico City and Los Angeles.For Lahore, ratio increased by 24.3 % compared to annual average EDGAR ratio (Figure 4 and Figure S18).The JJA MACCity 2018 ratio is higher by 27.0 % for The ozone concentration and the photolysis rate impact the partitioning of NO and NO 2 (Jacob, 1999) influencing the applied conversion factor of 1.32.To further investigate the uncertainty introduced by this factor, we analysed CAMS surface NO and NO 2 at the time of the TROPOMI overpasses (see Table 2).The CAMS-derived conversion factor varies <10 % compared with the standard value of 1.32, introducing a <10 % uncertainty in the inventory derived emission ratio.
However, given the uncertainty in the CAMS simulated urban NO, NO 2 and OH concentrations (Huijnen et al., 2019) the actual uncertainty is probably higher.Additionally, TROPOMI underestimates NO 2 column by 7 % to 29.7 % relative to MAX-DOAS ground based measurement in European cities (Lambert, et al., 2019).However, since we don't know yet how representative this estimate is for the cities that we study so, the impact of the bias is accounted as an additional the source of uncertainty of 25% of the TROPOMI inferred NO2/CO ratio (see Table S3).We calculated the wind direction and wind speed at different height i.e. 200m to 1000m and the ratio changes<10 % for all the cities (FigS19 and S20).The initial uncertainty for CAMS OH was ±50 % (Huijen et al., 2019).The bootstrapping method show that the concentration of OH varies from 8.0 -15 % for six different megacities resulting similar uncertainty to the TROPOMI derived emission ratio.If the CAMS overestimate OH concentration systematically, the TROPOMI derived emission ratio will decrease.To estimate the effect of predefined areas as background, we simultaneously increase the outskirt and background radius by 10 km for all the cities for four times.The effect is about 20 % for Riyadh whereas for other cities, the effect is < 12 % (Fig S21 using error propagation method for TROPOMI derived emission ratio ranges from 27 to 35 % and the detail is provided in Table S3 (see supplements).
We also acknowledge that our treatment of the photochemical removal of NO 2 is simplified.In reality, NO 2 is influenced by several other factors including meteorological parameters such as temperature, wind speed and radiation (Lang et al., 2015;Romer et al., 2018), causing the formation and loss of NO 2 to vary spatially and temporally.In the corrected ratio, we only consider the first order loss of NO 2 by OH forming HNO 3 .Several studies show that in cities surrounded by forested areas, loss of NO 2 through the formation of alkyl and multifunctional nitrates (RONO 2 ) can play a more important role than nitric acid production (Browne et al., 2013;Farmer et al., 2011;Romer Present et al., 2019;Sobanski et al., 2017).In addition, secondary production of CO from VOC oxidation may play a role.However, this only affects our ratios if it changes the CO gradient between the city and the background.Hence, to further improve the accuracy of TROPOMI supported evaluation of emission ratios a more sophisticated treatment of urban photochemistry is required.

Validation using ground based measurements
To further evaluate TROPOMI's ability to quantify burning efficiencies, TROPOMI derived ∆XNO 2 /∆XCO ratios have been To compare with TROPOMI, all measurement sites are spatially averaged.
Ground based CO and NO 2 at Mexico City and Los Angeles are strongly correlated with a Pearson correlation coefficient of r = 0.95 and 0.80 respectively, confirming that the observed signals reflect NO 2 and CO emissions from common sources.
The slope of the regression line for Mexico City amounts to 0.048, which is 45 % higher than the TROPOMI derived column enhancement ratio using the UB and PR method.The ∆NO 2 /∆CO ratio that is observed at ground level is likely influenced less by photochemical removal of NO 2 than the TROPOMI retrieved columns, and therefore closer to the inventory derived ratio, consistent with our results.This comparison suggests that removal of NO 2 reduces the ratio in ground-based measurements by 35 % compared to EDGAR and MACCity.Overall, the emission ratios in EDGAR and MACCity for Mexico City are consistent with both the ground-based measurements and TROPOMI, i.e. within the uncertainty of introduced by the chemical removal of NO 2 .
For Los Angeles, the regression slope is 0.042, which is 20% larger than the TROPOMI derived column enhancement ratios using the UB and PR method.However, the EDGAR and MACCity ratios are higher by a factor 5 compared to the ∆NO 2 /∆CO ratio observed at ground level.The ground-based measurements point to similar ratios for Mexico City and Los Angeles, confirming the HTAP-v2 supported TROPOMI finding that the emission ratio in EDGAR and MACCity is too high for Los Angeles.Therefore, the ground-based measurements for Los Angeles provide independent support for the TROPOMI derived ratios pointing to poorer burning conditions in Los Angeles than indicated by the emission inventories, and confirm the value of TROPOMI for monitoring the burning efficiency of megacities.

Conclusion
In this study, we investigate the use of TROPOMI XCO and XNO 2 retrievals for monitoring the burning efficiency of fossil fuel use in megacities.To improve the accuracy of the global emission inventories, the burning efficiency and emission factor is quantified using collocated XCO and XNO 2 enhancements over the megacities Tehran, Mexico City, Cairo, Riyadh, Lahore, and Los Angeles.TROPOMI is well capable of detecting XCO and XNO 2 enhancements over these megacities with relatively short averaging time and shows the expected spatial correlation.
TROPOMI derived column enhancement ratios have been compared with emission ratios from EDGAR and MACCity.The TROPOMI derived column enhancement ratios are strongly correlated with EDGAR and MACCity inventory derived emission ratios (r = 0.85 and 0.7) showing the highest emission ratio for Riyadh and the lowest for Lahore.This shows that Lahore has the poorest burning efficiency whereas over Riyadh, fossil fuel burning is the most efficient of all megacities that were analysed.
The impact of the short NO 2 lifetime and differences in the vertical sensitivity of the TROPOMI XCO and XNO 2 retrieval on the ΔNO 2 /ΔCO enhancement ratio has been quantified.Correcting for these factors significantly improves the agreement between ratios derived from TROPOMI and emission inventories.The comparison indicates that emission ratio in MACCity and EDGAR is well represented for Mexico City and Tehran.EDGAR emission ratios for Lahore are better quantified whereas MACCity emission ratio over Cairo and Riyadh are close to TROPOMI derived emission ratio.However, emission ratios in MACCity and EDGAR remain higher by 50 to 70 % for Los Angeles.The total uncertainty on TROPOMI derived emission ratio ranges from 27 to 35 %.The bias in S5P TROPOMI NO2 retrievals accounts for the major contribution for the uncertainties in the TROPOMI derived emission ratio.
TROPOMI derived ΔXNO 2 /ΔXCO column enhancement ratios for Mexico City and Los Angeles have been validated using ground-based measurement from local air quality monitoring networks.For Mexico City, the enhancement ratio derived from ground-based measurements is consistent with EDGAR, MACCity and TROPOMI derived emission ratio.CAMS derived enhancement ratio over Mexico City differs by 5 % compared to UB and PR.For Los Angeles, TROPOMI derived enhancement ratios are consistent with the ground-based measurements as well as the HTAP-v2 inventory based on EPA statistics, whereas EDGAR and MACCity-derived emission ratios appear to be overestimated by a factor 3. Similarly, CAMS derived enhancement ratio for Los Angeles is higher by 75 % in contrast to UB and PR.This demonstrates the potential of TROPOMI data for monitoring burning efficiency and evaluating emission inventories.
for detail seeFig S7).Generally, more than 75% of all pollutants are emitted between the surface and 200m altitude(Bieser et al., 2011).Therefore, average wind speed and direction from surface to 200m altitude are derived from the ERA-interim reanalysis, provided at 0.75˚x0.75˚and 3 hourly resolutions.The wind vector components of ERA-interim are spatially and temporally interpolated to the central coordinate of TROPOMI pixels.Using this information, daily enhancement ratios are calculated as follows.

Figure 1 .
Figure 1.ERA interim average wind speed and direction from surface to 200m at the time TROPOMI overpasses (left ) and TROPOMI derived CO total column over Mexico City (right) for 5 th of June, 2018.The black star represents the centre of the city.In the right panel, the white circle is the background area for Mexico City and the blue section represents the upwind background area that we selected depending upon the wind direction in the core city area.P0,P1,P2 and P3 are the points where north, east, west and south wind directions intersects at the inner rim of the background area.

Figure 2 .
Figure 2. TROPOMI total CO and tropospheric NO 2 column averaging kernel (A) for June 1 st , 2018 over Mexico.The error bars represents the standard deviation of the mean A at each vertical level.
2).OH, CO and NO 2 fields from the Copernicus Atmospheric Monitoring Service (CAMS) real time are used to account for the impacts of chemical loss and the averaging kernel.The CAMS data, at 0.1˚ x 0.1˚ and 3 hourly resolutions are spatially and temporally interpolated to the TROPOMI footprints.CAMS CO and NO 2 vertical mixing ratio profiles are converted into vertical column densities using ERA Interim reanalysis surface pressure.

Figure 3 .
Figure 3. Collocated TROPOMI retrieved XNO 2 (left) and XCO (right) data over Mexico (top) and Cairo (bottom) averaged for June to August, 2018.De-striping is applied to CO total columns (Borsdorff et al., 2018c) and CO and NO 2 retrievals have been re-gridded to 0.1˚x0.1˚.The white stars represent the centres of Mexico City and Cairo, respectively.The red circle in panels c) and d) points to an industrial area eastward of Cairo.

Figure 5 .
Figure 5.The boundary layer average OH concentration at the time of TROPOMI overpasses during June -August, 2018 over Mexico City.The white star represents the centre of Mexico City.
compared with ground-based measurements from Mexico City and Los Angeles.For this purpose, twenty ground-based stations in Mexico City with hourly measurements of CO and NO 2 //www.aire.cdmx.gob.mx/).Similarly, for Los Angeles twelve ground based stations from South Coast Air Quality Management District (AQMD) monitoring network (www.aqmd.gov/)have been selected.For the details of the names and locations of these sites see Table S4.For Mexico City, data were only available for June 2018.For Los Angeles, data for the June to August 2018 period were used but the periods 25 July to 11 August and 17 to 26 August were excluded to avoid the influence of wild fires on the observed urban pollution level.The validation results are presented in Fig. 5 for spatially averaged, hourly CO and NO 2 measurements for Mexico City and Los Angeles collected during noon (12:00 to 14:00 local time).To determine the enhancement in CO and NO 2 due to local emissions for each groundbased station, the 5 th percentile of hourly CO and NO 2 measurements 16 is used as background.CO and NO 2 enhancements for individual monitoring stations are calculated as -.

Table 2 . Average wind speed and boundary layer CAMs OH concentration for June-August, 2018, used to correct for the limited lifetime of NO 2 .
Tehran, 10 % for Mexico City, 50 % Cairo and 71 % for Lahore.The JJA MACCity ratio is close to UBCER and PECER (within 10 %) for all the cities except Los Angeles.EDGAR and MACCity do not agree with the effect of seasonality on the emission and comparison of seasonal ratio might result uncertainty in inventory derived ratio.