A new method is presented for estimating urban hydroxyl radical (OH)
concentrations using the downwind decay of the ratio of nitrogen dioxide
over carbon monoxide column-mixing ratios (XNO2/XCO) retrieved from
the Tropospheric Monitoring Instrument (TROPOMI). The method makes use of
plumes simulated by the Weather Research and Forecast model (WRF-Chem) using
passive-tracer transport, instead of the encoded chemistry, in combination
with auxiliary input variables such as Copernicus Atmospheric Monitoring
Service (CAMS) OH, Emission Database for Global Atmospheric Research v4.3.2
(EDGAR) NOx and CO emissions, and National Center for Environmental
Protection (NCEP)-based meteorological data. NO2 and CO mixing ratios
from the CAMS reanalysis are used as initial and lateral boundary
conditions. WRF overestimates NO2 plumes close to the center of the
city by 15 % to 30 % in summer and 40 % to 50 % in winter
compared to TROPOMI observations over Riyadh. WRF-simulated CO plumes differ
by 10 % with TROPOMI in both seasons. The differences between WRF and
TROPOMI are used to optimize the OH concentration, NOx, CO emissions and
their backgrounds using an iterative least-squares method. To estimate OH, WRF
is optimized using (a) TROPOMI XNO2/XCO and (b) TROPOMI-derived XNO2 only.
For summer, both the NO2/CO ratio optimization and the
XNO2 optimization increase the prior OH from CAMS by 32 ± 5.3 % and 28.3 ± 3.9 %, respectively. EDGAR NOx and CO emissions
over Riyadh are increased by 42.1 ± 8.4 % and 101 ± 21 %,
respectively, in summer. In winter, the optimization method doubles the CO
emissions while increasing OH by ∼ 52 ± 14 % and
reducing NOx emissions by 15.5 ± 4.1 %. TROPOMI-derived OH
concentrations and the pre-existing exponentially modified Gaussian function fit (EMG) method differ by 10 % in summer and
winter, confirming that urban OH concentrations can be reliably estimated
using the TROPOMI-observed NO2/CO ratio. Additionally, our method can
be applied to a single TROPOMI overpass, allowing one to analyze day-to-day
variability in OH, NOx and CO emission.
Introduction
The rapidly growing urbanization has led to an increase in the number of big
cities globally. More than 55 % of the global population resides in
cities, and this fraction is projected to increase to 68 % in 2050
(United Nations, 2019). The associated rise in consumption of
energy and materials leads to severe air pollution that is estimated to have
caused premature deaths of 4 to 9 million people globally in 2015
(Sicard
et al., 2021; Pascal et al., 2013; Burnett et al., 2018). Air pollution
control measures and the application of cleaner technology reduced the
NO2 concentrations in developed cities such as Los Angeles and Paris by
1.5 % to 3.0 % yr-1 between 1996 and 2017
(Georgoulias et al., 2019). The CO
emission was reduced by 28.8 % to 60.7 % in these cities in the period
2000 to 2008 (Dekker et al., 2017). In
developing cities such as Tehran and Baghdad, however, NO2
concentrations increased by 8.6 % yr-1 and 16.9 % yr-1
between 1996 and 2017 (Georgoulias
et al., 2019). The CO emission increased by 15 % in New Delhi in the
period 2000 to 2008 (Dekker et al.,
2017). As a consequence, air pollution monitoring and mitigation in
developing cities are becoming increasingly important priorities.
Nowadays, urban air pollution can be studied using a combination of
ground-based measurement networks and satellite observations
(Sannigrahi
et al., 2021; Ialongo et al., 2020). Satellite observations have helped to
investigate urban air pollution, particularly in cities without a
ground-based monitoring network
(Beirle et al., 2019;
Borsdorff et al., 2019). In past decades, improvements in the quality and
spatial resolution of satellite measurements have allowed the detection of
trends in air pollutants and the quantification of urban emissions
(Lorente
et al., 2019; Verstraeten et al., 2018; Borssdorff et al., 2019). Several
studies have focused on NOx, using NO2 observations from the SCanning
Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY),
the Ozone Monitoring Instrument (OMI) and TROPOMI
(Ding
et al., 2017; Lorente et al., 2019). At the resolution and sensitivity of
TROPOMI, urban NO2 enhancements can be detected readily, even in a single
satellite overpass. OMI-derived NO2 data have been used to quantify
NOx emissions, as well as the urban lifetime of NO2, as
demonstrated by Beirle et al. (2011) using the
exponentially modified Gaussian function fit (EMG) method.
In the EMG method, the satellite-observed exponential decay of NO2
downwind of the city center is used to quantify the first-order loss of
NO2, which is used to quantify the hydroxyl radical (OH), neglecting
other NOx removal pathways. Liu et al. (2016) modified the EMG method for application to
complex emission patterns. The quantification of CO emissions from cities is
more complicated compared with NO2 because of its longer lifetime and
the related importance of CO sources from the surroundings of cities.
Nevertheless, a few studies have demonstrated the feasibility of quantifying
relative changes in urban CO emission using Measurement of Pollution in the
Troposphere (MOPPIT), Infrared Atmospheric Sounding Interferometer (IASI),
Atmospheric Infrared Sounder (AIRS) and TROPOMI observations
(Borsdorff
et al., 2019; Dekker et al., 2017; Pommier et al., 2013).
In recent years, methods have been developed that combine satellite
measurements of different trace gases (for example, the combined use of
NO2 and CO) to obtain specific information about pollutant sources
(Lama et
al., 2020; Hakkarainen et al., 2016; Miyazaki et al., 2017; Reuter et al.,
2019; Silva and Arellano, 2017). The emission factors of CO and
NOx from fuel combustion are uncertain and vary strongly with the
combustion efficiency (Flagan and Seinfeld, 1988).
The satellite-observed ΔNO2/ΔCO ratio
is particularly sensitive to this fuel-burning efficiency, as demonstrated
by Lama et al. (2020), and can be
used to evaluate emission inventories. However, another important
uncertainty arises from the removal of NO2 by OH. OH is an important
oxidant in the atmosphere, which determines the lifetime of trace gases such
as CO, NOx, sulfur dioxide (SO2) and volatile organic compounds (VOCs)
(Monks
et al., 2009). OH plays an important role in atmospheric chemistry on
scales ranging from urban air pollution to the global residence times of
greenhouse gases. The direct measurement of OH is possible using
spectroscopic methods, but the spatial representativeness of the data is
limited due to its short lifetime
(de Gouw et al., 2019). OH
estimates from global chemical transport models (CTMs) have an uncertainty
of > 50 %
(Huijnen et al., 2019). Urban
measurement campaigns point to large discrepancies between modeled and
observed OH abundances – for example, in Lu et al. (2013), who found a factor 2.6 difference in a campaign in the suburbs
of Beijing.
The aim of this study is therefore to estimate the average OH concentration
in the urban plume of large cities (hereafter referred to as urban OH) from
the downwind decay of the TROPOMI-observed NO2/CO ratio. The proposed
method makes use of the Weather Research and Forecast (WRF) model
(Grell et al., 2005) to
simulate the meteorological fields and atmospheric transport. The TROPOMI
instrument
(Veefkind
et al., 2012), launched on 13 October 2017 on board the Sentinel-5 Precursor
satellite, is particularly well suited for this task, as it measures both
compounds with high sensitivity and spatial resolution. Our method uses CO
because it has a longer lifetime than NO2 (weeks to months compared to a
few hours). Therefore, CO can be considered as an inert tracer at the
timescale of urban plumes. The difference in the rate of decay between
NO2 and CO therefore provides information about the photochemical
oxidation of NO2 because atmospheric dispersion is expected to have a
very similar impact on both tracers and therefore cancels out in their
ratio. The use of the NO2/CO ratio for estimating urban-scale OH is
further compared to the EMG
method using only satellite-retrieved NO2 (Beirle et al.,
2011).
The city of Riyadh (24.63∘ N, 46.71∘ E) is chosen as a
test case. Riyadh is an isolated city and a strong source of CO and NO2
pollution
(Beirle
et al., 2019; Lama et al., 2020). The frequent clear-sky conditions over
Riyadh yield a large number of valid TROPOMI CO and NO2 data. The
signal to noise in TROPOMI is high enough to detect the enhancement of CO
and NO2 over Riyadh in a single overpass
(Lama et al.,
2020). Model results from the Copernicus Atmospheric Monitoring Service
(CAMS) for Riyadh show a distinct seasonality in OH (see Fig. S1 in the Supplement), which we
attempt to evaluate using TROPOMI data for summer and winter.
This paper is organized as follows: Sect. 2 describes the TROPOMI NO2
and CO data, the WRF model setup that was used and the optimization method
that is used for estimating OH. Optimization results and comparisons between
TROPOMI and WRF are presented in Sect. 3, followed by a summary and
conclusion of the main findings in Sect. 4. Additional figures and
information about the optimization method are provided in the Supplement.
Data and methodTROPOMI NO<inline-formula><mml:math id="M61" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> tropospheric column
We used the offline TROPOMI level-2 tropospheric column NO2 [mol m-2] data from retrieval versions 1.2.x for 2018 and 1.3.x for 2019,
available at https://s5phub.copernicus.eu; http://www.tropomi.eu (last access: 21 September, 2020). NO2 data of
versions 1.2.x and 1.3.x have minor processing differences such as removal
of negative cloud fraction, better flagging and uncertainty estimation.
However, they use the same retrieval algorithm applied to level-1b version
1.0.0 spectra (Babic et
al., 2019) recorded by the TROPOMI UV-Vis module in the 405–465 nm spectral
range. The TROPOMI NO2 differential optical absorption spectroscopy (DOAS) software, developed at KNMI, is used for
the processing of NO2 slant column densities
(van Geffen et al., 2019). The improved
NO2 DOMINO algorithm of
Boersma et al. (2018)
has been used to translate slant columns into tropospheric column densities.
In this algorithm, stratospheric contributions are subtracted from the slant
column densities, and the residual tropospheric slant column density is
converted to tropospheric vertical column density using the air mass factor
(AMF). The AMF depends on the surface albedo, terrain height, cloud height,
cloud fraction and a priori NO2 profiles from the TM5-MP model at 1∘×1∘
(Eskes et
al., 2018; Lorente et al., 2017). The comparison of multi-axis differential optical absorption spectroscopy (MAX-DOAS) ground-based
measurements in European cities shows that TROPOMI underestimates NO2 columns by 7 % to 29.7 %
(Lambert
et al., 2019). To reduce the differences between satellite and model, we
re-calculated the AMF by replacing the tropospheric AMF based on TM5-simulated vertical NO2 columns with the WRF-Chem equivalent
(Lamsal
et al., 2010; Boersma et al., 2016; Visser et al., 2019; Huijnen et al.,
2010) using the equation provided in Appendix A. After the AMF
recalculation, the NO2 vertical profiles are consistent between
satellite and model. Furthermore, the use of WRF-Chem has the advantage that
it resolves NO2 gradients between urban and downwind regions better
than the coarser-resolution TM5-MP model
(Russell
et al., 2011; McLinden et al., 2014; Kuhlmann et al., 2015). During summer, the AMF recalculation increases TROPOMI NO2 by 5 %
to 10 % and by 25 % to 30 % in winter in the urban plume over
Riyadh, whereas background areas are less affected (see Fig. S2). The
Sentinel-5P Product Algorithm Laboratory (S5P-PAL) reprocessed NO2 data available at https://data-portal.s5p-pal.com/products/no2.html (last access: 1 September 2022) differ by 7.5 % to
10 % in summer (June to October 2018) and 13.5 % to 16 % in
winter (November 2018 to March 2019) compared to the AMF-recalculated
TROPOMI NO2 data used in this study. These differences have been used
to quantify the systematic uncertainty of the NO2 data and its
contribution to the uncertainty in the NOx emission and lifetime derived
using our method (see Tables S1, S2 and S3 in the Supplement).
TROPOMI CO
For CO, the offline level-2 CO data product version 1.2.2 has been used,
available at https://s5phub.copernicus.eu (last access: 20
September 2020). The Shortwave Infrared Carbon Monoxide Retrieval (SICOR) algorithm is applied to TROPOMI 2.3 µm
spectra to retrieve CO total column density [molec. cm-2]
(Landgraf et al., 2016). The
retrieval method is based on a profile-scaling approach, in which
TROPOMI-observed spectra are fitted by scaling a reference vertical profile
of CO using the Tikhonov regularization technique
(Borsdorff et al., 2014). The
reference CO profile is obtained from the TM5 transport model
(Krol et al., 2005). The averaging kernel (A) quantifies the
sensitivity of the retrieved total CO column to variations in the true
vertical profile (ρtrue) as follows
Borsdorff et al., 2018a):
Cretrieval=A⋅ρtrue+∈CO,
where Cretrieval is the retrieved column-average CO mixing ratio, and
∈CO is the retrieval error, statistically
represented by the retrieval uncertainty that is provided for each CO
retrieval.
The comparison of TROPOMI-derived XCO to the 28 different Total Carbon Column Observing Network (TCCON) ground-based
stations suggests that the difference between TCCON and TROPOMI is in the range of
9.1 ± 3.3 % (Shah et al.,
2020). Such difference is used to estimate the uncertainty in the emission
and lifetime (see Tables S1, S2, S3 and Text S6).
Satellite data selection and filtering criteria
As NO2 and CO are retrieved from different channels of TROPOMI using
different retrieval algorithms, the filtering criteria and spatial
resolutions of CO and NO2 are different. The data filtering makes use
of the quality assurance value (qa) and is provided with the CO and NO2
retrievals, ranging from 0 (no data) to 1 (high-quality data). We selected
NO2 retrievals with qa ≥ 0.75 (clear-sky condition) and CO
retrievals with qa ≥ 0.7 (clear sky or low-level cloud), as in Lama et
al. (2020). The
SICOR algorithm was originally developed for SCIAMACHY to account for the
presence of low-elevation clouds, increasing the number of valid
measurements (Borsdorff et al., 2018a). In
addition, the CO stripe-filtering technique is applied as described by
Borsdorff et al. (2018a). Using
dry-air column density derived from the surface pressure data in CO and
NO2 TROPOMI files, the total CO column and tropospheric NO2 column
densities are converted to dry column-mixing ratios XCO (ppb) and XNO2
(ppb). The spatial resolution of the NO2 data is finer compared to the
CO data (3.5×7 km2 versus 5.5×7 km2). After the CO and NO2
retrievals pass the filtering criteria, their co-location is approximated by
assigning the center coordinates of an NO2 retrieval to the CO
footprint in which it is located
(Lama et al.,
2020).
Weather Research Forecast model (WRF)
We have used WRF chemistry model (http://www.wrf-model.org/, last access: 22 August 2019),
version 3.9.1.1 to simulate NO2 and CO mixing ratios over Riyadh. WRF
is a non-hydrostatic model designed by the National Center for Environmental
Protection (NCEP) for both atmospheric research and operational forecasting
applications. For this study, we have set up three nested domains in the
model at resolutions of 27, 9 and 3 km, centered at 24.63∘ N,
46.71∘ E. The first and second domain cover Saudi Arabia and
provide the boundary conditions for the nested third domain (see Fig. S3).
The analysis in this paper uses the 500×500 km2 sub region around
Riyadh in the third domain, containing 161 by 161 grid cells. All domains
are extended vertically from the Earth's surface to 50 hPa, using 31 vertical layers, with 17 layers in the lowermost 1500 m. WRF simulations are
performed using a time step of 90 s for the period June 2018 to March 2019, using a spin-up time of 10 d.
We have used the Unified Noah land surface model for surface physics
(Ek
et al., 2003; Tewari et al., 2004), an updated version of the Yonsei
University (YSU) boundary layer scheme
(Hu et al., 2013) for the
boundary layer processes, and the rapid radiative transfer method (RRTM) for
shortwave and longwave radiation
(Mlawer et al., 1997). Cloud
physics is solved with the new Tiedtke cumulus parameterization
scheme (Zhang and Wang, 2017). The WRF
Single-Moment 6-class scheme is used for microphysics (Hong et al., 2006). The WRF coupling with chemistry (WRF-Chem) allows the simulation
of tracer transport and the chemical transformation of trace gases and
aerosols. Here, we used the passive tracer transport function instead of the
encoded chemistry in WRF to speed up the model simulation and to reduce the
computational cost. In addition, the passive-tracer option helps in
separating the influences of wind, OH and the rate constant of the
NO2+OH reaction (KNO2⋅OH) on the NO2/CO ratio in the
downwind city plume. Compared to previously used methods
(Beirle et al., 2011;
Valin et al., 2013) which did not use a transport model at all, we consider
this an important improvement. The function of different tracers, their
acronyms and an explanation of different WRF simulations are provided in Table 1.
The meteorological initial and boundary conditions are based on NCEP data at
1∘×1∘ spatial and 6 h temporal resolutions, available at
https://rda.ucar.edu/datasets/ds083.2/ (last access: 10 September 2019). Nitrogen oxides (NOx= NO2+NO) and CO anthropogenic emissions have been taken from the
Emission Database for Global Atmospheric Research v4.3.2 (EDGAR) 2012 at
0.1∘×0.1∘ spatial resolution
(Crippa et al., 2016). The EDGAR
2012 data have been re-gridded to the resolution of the WRF domains, and
hourly, weekly and monthly emission variations are taken into account using
the temporal emission factors provided by van der Gon et al. (2011). The chemical boundary conditions for CO and NOx are based
on the CAMS chemical reanalysis product at 0.75∘×0.75∘ spatial and 3 h temporal resolutions
(Inness et al., 2019), retrieved from
https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=_form, last access: 1 November 2020). XCO and XNO2 boundary
conditions based on CAMS are assumed to be representative as background values
within the domain. Since we do not explicitly compute the sources and sinks
of background NO2 inside the domain, we decide to transport the
boundary conditions as background passive tracers.
Summary of WRF simulations and the definition of tracers and
acronyms used.
WRF simulation or tracerWRF input and tracer definitionPriorWRF run using NCEP meteorological data, EDGAR CO and NOx emissions, CAMS OH, and CAMS CO and NOx as initial and lateral boundary conditions.WRFOH×1.1Prior run with CAMS OH increased by 10 %Optimized run1st iterOptimized state (emission, OH, background) after iteration 1Optimized run2nd iterOptimized state (emission, OH, background) after iteration 2CO XCOemisThe contribution of urban CO emissions to XCOXCOBgThe contribution of the background to XCOXCOWRFXCO from the prior runXCOWRF, 1st iterXCO from optimized run1st iterXCO WRF, optXCO from optimized run2nd iterNO2XNO2 emisThe contribution of urban NOx emissions to XNO2, ignoring the OH sinkXNO2 (emis,OH)As XNO2 (emis) accounting for the OH sinkXNO2(emis,OH×1.1)As XNO2(emis,OH) with CAMS OH increased by 10 %XNO2 BgThe contribution of the background to XNO2XNO2 WRFXNO2 from the prior run.XNO2(WRF,OH×1.1)XNO2 from WRFOH×1.1XNO2 WRF 1st iterXNO2 from optimized run1st iterXNO2 WRF optXNO2 from optimized run2nd iterRatio (NO2/CO) Ratiowithout OHRatio of XNO2 emis and XCOemisRatiowith OHRatio of XNO2 emis,OH and XCOemisRatioBgRatio of XNO2 Bg and XCOBgWRF ratioRatio of XNO2 WRF and XCOWRFWRF ratioOH×1.1Ratio of XNO2(WRF,OH×1.1) and XCOWRFWRF ratio1st iterRatio of XNO2 WRF, 1st iter and XCOWRF, 1st iterWRF ratiooptRatio of XNO2 WRF, opt and XCOWRF, opt
The atmospheric transport in WRF causes the influence of
NOx and CO emissions from
Riyadh on their column-average mixing ratios to be linear. Instead of a
simplified photochemistry solver, we make use of a WRF-Chem module for
passive tracer transport for transporting NOx. This WRF module has been
modified to account for the first order loss of NOx in the reaction of NO2
with OH, using NOx/NO2 ratios from CAMS to translate NOx into NO2
and CAMS OH fields to compute the chemical transformation of NO2 to
HNO3 (see Text S1 for detail).
This is a simplified
treatment of the lifetime of NOx as other photochemical pathways play a
role, such as
the oxidation of NO2 in reaction with organic radicals (RO2) to
form the alkyl and multifunctional nitrates (RONO2)
(Romer Present et al., 2019);
NOx loss due to the formation of dinitrogen pentoxide (N2O5) followed
by heterogeneous transformation to HNO3
(Shah et al., 2020);
peroxyacetyl nitrate (PAN) formation in equilibrium between NO2 and the
peroxyacetyl radical (Moxim, 1996);
the dry deposition of NO2 on the surface and plant stomata
(Delaria et al., 2020).
The loss of NO2 by OH to HNO3 accounts for 60 % of the global
NOx emission (Stavrakou
et al., 2013). Macintyre and
Evans (2010) showed that
the N2O5 pathway reduces NOx concentrations by 10 % in the
tropics (30∘ N to 30∘ S) and 40 % at northern latitudes. The
NOx loss through N2O5 hydrolysis is largest at northern latitudes
during winter (50 % to 150 %), unlike in the tropics where, its
seasonality is small. Moreover, the removal of N2O5 is primarily
important during nighttime because of its photolysis during daytime,
whereas our analysis focuses on the midday overpass time (13:30 LT) of TROPOMI
when OH abundances are highest. For these reasons, we consider it safe to
neglect the loss of NOx through N2O5 in our analysis for
Riyadh. The dry deposition flux is also expected to be low, as it is
controlled largely by stomatal uptake, which is assumed to be insignificant
for the low vegetation cover of Riyadh. The same is expected to be true for
PAN formation because of its thermal decomposition at increasing
temperatures. We acknowledge that our OH estimates should be regarded as
upper limits due to the neglect of other NOx transformation pathways. A
quantification of the combined effect would require full chemistry
simulations, which we consider to be outside of the scope of this paper.
Note that, in this study, OH is only applied to the urban NOx emission tracer
(XNOxemis). The CAMS NOx background tracer
(XNOxBg) is transported in WRF without OH
decay, since it already represents the balance between regional sources and
sinks. CAMS hydroxyl radical (OH) data at 0.75∘×0.75∘ spatial and 3 h temporal resolutions
(Inness et al., 2019), retrieved at
https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=_form, last access: 1 July 2020), are spatially, temporally and
vertically interpolated to the WRF grid. The NOx lifetime is derived as
follows:
2dNO2dt=KNO2OH⋅[OH]⋅[NO2],3fact=NOxNO2,4τNOx=1KNO2OHfact⋅[OH],
where, KNO2OH is the International Union of Pure and Applied
Chemistry's (IUPAC's) second order rate constant for the reaction of
NO2 with OH; “fact” represents the fractional
contribution of NO2 to NOx (NOx/NO2). This NOx-to-NO2 conversion factor is derived from the CAMS reanalysis and
is re-gridded to WRF to account for its spatial and temporal variation.
τNOx is the lifetime of NOx.
TROPOMI-derived XCO (a) and average wind speed and wind
direction from the surface to the top of the boundary layer (b) derived from
the CAMS global reanalysis eac4 data at the TROPOMI overpass time over
Riyadh for 4 August 2018. The white star represents the
center of Riyadh. The black box (B1), with a dimension of 300×100 km2,
is rotated in the average wind direction at a 50 km radius from the center of
Riyadh at the TROPOMI overpass time, resulting in the red box. For the
calculation of cross-directional averaged NO2 and CO, the red box is
divided into 29 smaller cells with the width (Δx) ∼ 11 km. For this, TROPOMI-derived XCO is gridded at 0.1∘×0.1∘.
The components of NOx (NO and NO2) have short lifetimes during daytime
because of the photo-stationary equilibrium exchanging NO and NO2 into
each other. For this reason, we estimate the lifetime of their sum
(NOx), which is determined largely by the reaction with OH. In earlier
work with satellite NO2 data, the Jet Propulsion Laboratory (JPL) high-pressure limit was used as the rate constant to represent the first order loss
of NO2
(Beirle
et al., 2011; Lama et al., 2020; Lorente et al., 2019). However, we found
this approximation to be too crude and therefore apply the full IUPAC-recommended pressure-dependent formula for the second order rate constant.
Supplement Fig. S4 shows the difference between the three rate constants,
i.e., JPL high-pressure limit, JPL second order and IUPAC second order,
confirming the importance of accounting for the pressure dependence.
WRF output for the third domain is interpolated spatially and temporally to
the footprints of TROPOMI. The interpolated WRF NOx tracers are converted
to NO2 using the conversion factor derived from the CAMS reanalysis,
accounting for its spatial and temporal variation (for the names and
functions of tracers, see Table 1). The averaging kernel available for each
TROPOMI CO and NO2 observation is applied to the WRF output after
interpolation to the vertical layers of the TROPOMI retrieval. To compare
WRF output to TROPOMI, WRF-derived XNO2(XNO2WRF) is calculated by combining the NO2 tracer that accounts for
the OH effect (XNO2 (emis,OH)) and the CAMS
NO2 background (XNO2 Bg) (see Figs. S5 and
S6). Similarly, the CO emission tracer (XCOemis)
is added to the CAMS CO background (XCOBg) to
calculate WRF-simulated XCO (XCOWRF) (see
Figs. S7 and S8).
<inline-formula><mml:math id="M250" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mo>/</mml:mo><mml:mi mathvariant="normal">CO</mml:mi></mml:mrow></mml:math></inline-formula> ratio calculation using box rotation
The variation of the NO2/CO ratio in the downwind city plume is
calculated as a function of distance x from the city center in a downwind
direction. We select days with an average wind speed (U) in the range of 3.0 m s-1 (Beirle et al., 2011) <U<8.5 m s-1 (Valin et al.,
2013) within a 50 km radius from the center of Riyadh (24.63∘ N,
46.71∘ E). The horizontal distribution of EDGAR emissions over
Riyadh is used within this 50 km radius (Fig. S9). A total of 95 d in summer
and 70 d in winter meet the wind speed criteria over Riyadh for the ratio
calculation. The boundary layer average wind speed and direction are
calculated using the CAMS global reanalysis eac4 (retrieved at https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=_form, last access: 1 August 2020) at a 0.75∘×0.75∘ spatial and 3 h temporal resolution.
For this, the CAMS wind vector is spatially and temporally interpolated to
the central coordinate of TROPOMI pixels.
To compute the NO2/CO ratio as function of the downwind distance x,
TROPOMI and WRF data have been re-gridded at 0.1∘×0.1∘. A box (B1) is selected with a width of 100 km, from 100 km in upwind to
200 km in downwind direction of the city center (see Fig. 1a). The dimension
of the box is motivated by multiple TROPOMI overpasses over Riyadh, showing
NO2 and CO enhancements advected downwind over a ∼ 200 km
distance without other large sources of NO2 and CO within a 100 km
radius of the city center (see Fig. 1a). Figure 1b shows the boundary
layer averaged wind speed and wind direction over Riyadh, indicating flow
towards the northeast on 4 August 2018. The box is rotated for
every TROPOMI overpass, depending upon the daily average wind direction
within a 50 km radius from the center of Riyadh, as shown in Figs. 1a and
S10. The rotated box B1 is divided into N rectangular boxes,
orthogonal to the wind direction with length (Δx) ∼ 11 km (see Figs. 1 and S10). The XNO2 and XCO grid cells that fall
within the N rectangular boxes are selected to derive zonally averaged
XNO2 and XCO for summer and winter.
Unlike the enhancements over the city, ΔXNO2 and ΔXCO
become smaller than retrieval uncertainties at large distance from the city,
where the ratio ΔXNO2/ΔXCO becomes ill defined.
Therefore, we decided to use the ratio of mean XNO2 and XCO instead of
enhancements over the background. To analyze the influence of atmospheric
transport and the OH sink on the WRF-derived XNO2/XCO ratio, two
different ratios are derived: (1) XNO2 emisXCOemis,
named “ratiowithout OH”, and (2) XNO2 (emis,OH)XCOemis, named
“ratiowith OH” (see Table 1). The CAMS
background accounts for the balance between regional source and sink in CTMs,
so it is excluded to analyze the influence of atmospheric transport on the
ratio. For the comparison between TROPOMI and WRF, the CAMS backgrounds are
included in “WRF ratio” (XNO2 WRFXCOWRF) (see Table 1). The
comparison of WRF ratio to TROPOMI ratio and the contribution of its
components are presented in Sect. 3.2.
OH estimation: satellite data only
In the EMG method, following Beirle et al. (2011), 2D NO2 column
density maps are assigned to eight equal wind sectors, spanning 360∘ for summer and winter; 1D column densities per wind sector are
computed by averaging in a cross-wind direction. This way, average NO2
column density functions of the downwind distance to the city center have
been constructed for summer and winter (see Fig. S11). Using the EMG method,
as in Beirle et al. (2011), the e-folding distance
(x0) and NO2 emissions have been estimated. The NO2 lifetime is
derived by dividing x0 by the average wind speed (5.46 and 5.24 m s-1 for winter and summer, respectively) and is provided in Table 2.
The OH concentration is derived from the inferred NO2 lifetime using
the IUPAC second order rate constant (for details, see Text S2 and
S3 in the Supplement). Rate constants at the time of TROPOMI overpasses are obtained from WRF
by averaging the IUPAC second order rate constant from the surface to the top of
the planetary boundary layer (PBL). The PBL height at the time TROPOMI overpass
has been taken from WRF. EMG-derived NO2 emissions are also converted
to NOx emissions using the CAMS-derived conversion factor. Summer- and winter-averaged CAMS-derived conversion factors for the box of 300 km × 100 km are
1.28 and 1.31, respectively.
OH estimation: WRF optimization
To jointly estimate the NOx and CO emissions as well as the OH
concentration from the TROPOMI data, a least-squares optimization method is
used. This method fits the model to the data by minimizing a cost function
(J) (see Text S4 for details). The reaction of NO2 with OH introduces a
non-linearity in the OH optimization. To account for this non-linearity, we
linearize the problem around the a priori starting point using the small
perturbations (10 %) Δbackground, Δemission and ΔOH.
The non-linear model is fitted to the observations by optimizing scaling
factors fBg, femis, fOH to the perturbation functions
Δbackground, Δemission and ΔOH, respectively. This
process is repeated iteratively, updating the linearization point and
re-computing the perturbation functions. The scaling factors femis,
foh and fbg represent the modification of the prior in percentage
change.
We estimate OH by optimizing WRF with TROPOMI in two ways: (1) optimizing the
simulated NO2/CO ratio using TROPOMI-derived ratios, named as “ratio
optimization”, and (2) optimizing NO2 and CO separately using TROPOMI-derived XCO and XNO2, named as “component-wise optimization”. First,
the ratio optimization is described, followed by the component-wise
optimization. Optimized ratios are derived as follows:
5FTROPOMI=F+ΔFΔemis⋅femis10+ΔFΔOH⋅fOH10+ΔFΔBg⋅fBg10,6F=XNO2 WRFXCOWRF,7XNO2 WRF=XNO2 (emis, OH)+XNO2 Bg,8XCOWRF=XCOemis+XCOBg,9ΔFΔemis=XNO2 (emis, OH)⋅1.05+XNO2 BgXCOemis⋅0.95+XCOBg-F,10ΔFΔOH=XNO2(emis,OH×1.1)+XNO2 BgXCOemis+XCOBg-F,11ΔFΔBg=XNO2 (emis, OH)+XNO2Bg⋅1.05XCOemis+XCOBg⋅0.95-F,
Here, FTROPOMI is the TROPOMI-derived NO2/CO
ratio, F is the WRF ratio, ΔFΔemis is the change in F due to an increase
in the NO2 emission by 5 % and a decrease in the CO emission by 5 % (1.05/0.95=∼ 10 %), ΔFΔOH is the change in F due to an
increase in OH by 10 %, and ΔFΔBg
is the change in F due to an increase in the XNO2 background
by 5 % and a decrease in the CO background by 5 %.
XNO2 (emis, OH) is the contribution of city NOx
emissions to XNO2, accounting for the OH sink.
XNO2 Bg is the NO2 background.
XCOemis is the contribution of the EDGAR city CO
emissions to XCO, and XCOBg is the CO background
derived from CAMS. XNO2 WRF and
XCOWRF are the WRF-derived XNO2 and XCO,
respectively. XNO2(emis,OH×1.1) is the contribution of city NOx emissions to XNO2 after
increasing CAMS OH by 10 %. The scaling factors femis, fOH and fBg obtained from the ratio optimization have been divided by 10
because ΔFΔemis,ΔFΔOH and ΔFΔBg are defined as the change in F due to
modification of emission, OH and background by 10 %.
Although the ratio optimization is sensitive to the emission ratio and the
OH sink of NO2, it is not sensitive to the absolute emissions of CO and
NO2. Therefore, we performed component-wise optimizations for XCO and
XNO2 to optimize absolute emissions. We also compare the OH factor
obtained from the ratio optimization and component-wise optimization to test
the robustness of the method. The optimized XNO2 is derived using Eq. (12). XCO is optimized using the same equation but without considering the
OH sink (see Appendix B).
12XNO2 TROPOMI=XNO2 WRF+ΔXNO2 emis⋅femis10+ΔXNO2 OH⋅fOH10+ΔXNO2 Bg⋅fBg1013ΔXNO2 emis=XNO2emis, OH⋅1.10-XNO2emis, OH14ΔXNO2 OH=XNO2emis, OH×1.1-XNO2emis, OH15ΔXNO2 Bg=XNO2 Bg⋅1.10-XNO2 Bg
Here, XNO2 TROPOMI is the TROPOMI-derived XNO2, XNO2 WRF is the WRF
XNO2. ΔXNO2 emis is the
change in XNO2 due to an increase in emissions by 10 %,
ΔXNO2 OH is the change in XNO2 due to an
increase in CAMS OH by 10 %, and ΔXNO2 Bg is a change in the background XNO2 by 10 %. The scaling
factors femis, fOH and fBg are divided by a factor 10
because ΔXNO2 emis,
ΔXNO2 OH and ΔXNO2 Bg are defined as 10 % changes in NOx emission, OH
and background level.
Comparison between XNO2(a, c) and XCO (b, d) from TROPOMI and
WRF over Riyadh, averaged over June to October 2018. Panels (a) and (b) show TROPOMI
data, and panels (c) and (d) show the corresponding co-located WRF results.
XNO2 WRF is derived by adding
XNO2emis, OH and
XNO2 Bg.
XCOWRF is derived by adding
XCOemis
and XCOBg. The white star
represents the center of the city. TROPOMI and WRF results are gridded at
0.1∘×0.1∘.
Results and discussionXNO<inline-formula><mml:math id="M377" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> and XCO over Riyadh
In this subsection, we compare WRF-derived XCOWRF and XNO2 WRF with TROPOMI for
summer (see Fig. 2) and winter (see Fig. S12) over Riyadh. TROPOMI and WRF-derived XCO and XNO2 are averaged from June to October 2018 for summer
and November 2018 to March 2019 for winter in a domain of 500×500 km2 centered around Riyadh. The comparison for summer in Fig. 2 shows
TROPOMI NO2 after replacing the TM5-based tropospheric AMF with WRF
profiles, as described in
Visser
et al. (2019). The enhancement of XNO2 and XCO over Riyadh due to urban
emissions is clearly separated from the background for TROPOMI and WRF,
showing that the city of Riyadh is well suited to investigating the use of the
NO2/CO ratio to quantify OH in urban plumes. Due to the longer
lifetime of CO, the TROPOMI-observed XCO plume extends further in the
southeast direction compared to XNO2. Figure 2 shows that our WRF
simulations are able to reproduce the TROPOMI-retrieved XNO2 (r2=0.96) and XCO (r2=0.78) plumes, confirming that WRF-derived
XNO2 WRFXCOWRF is suitable for the optimization of
CTM-derived OH concentrations using TROPOMI data.
XNO2 WRF is higher by 25 % compared to TROPOMI
in the city center. In the background, XCOWRF shows
a similar spatial distribution as TROPOMI XCO, but the values are higher by
5 % to 10 % (see Fig. 2). Close to the city center,
XCOWRF is ∼ 5.7 % higher than
TROPOMI XCO. In EDGAR 2011, emission sources are located in the center of
Riyadh (see Fig. S9). However, as noted by Beirle et al. (2019), they extend
to a larger part of the city in reality. This difference in spatial
distribution leads to higher XNO2WRF and
XCOWRF close to the center of Riyadh compared to
TROPOMI.
In winter, the wind direction is predominantly from the southeasterly
sector in WRF and TROPOMI (see Fig. S12). The spatial distribution of
XCOWRF (r2=0.73) and
XNO2 WRF (r2=0.88) matches
quite well with TROPOMI. Therefore, the difference between summer and winter
should offer the opportunity to quantify the seasonality in emissions and OH
concentrations over Riyadh. In winter, XCOWRF is
∼ 5 % to 10 % higher than TROPOMI, while
XNO2WRF is higher by 40 % to 50 %. The difference could either point to uncertainties in the NO2/CO
emission ratio, uncertainties in the NO2 lifetime or inaccuracies in
the background. By quantifying OH, we can evaluate these explanations (see
Sect. 3.3). XNO2 WRF is higher by 20 % in winter than in summer. Contrarily, TROPOMI NO2 is lower by
∼ 30 % in winter (Fig. S12) compared to summer (Fig. 2).
Again, to disentangle the role of changing sources and sinks, we need an
independent estimate of OH.
The <inline-formula><mml:math id="M409" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">XNO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mo>/</mml:mo><mml:mi mathvariant="normal">XCO</mml:mi></mml:mrow></mml:math></inline-formula> ratio and OH
Before comparing TROPOMI- and WRF-derived XNO2/XCO ratios, we first
analyze the influence of atmospheric transport and the OH sink on the WRF-derived XNO2/XCO ratio. To do this, three ratios are used: (1) ratiowithout OH, (2) ratiowith OH, and (3) WRF ratio (see Table 1). As
seen in Figs. 3, S13 and S14, WRF is able to reproduce the TROPOMI-observed
downwind evolution of XNO2 and XCO in summer and winter. The peak of
the XNO2 and XCO plumes is shifted away from the city center due to the
balance between the accumulation of urban emissions in the atmospheric
column and atmospheric transport
(Lorente et al., 2019).
As expected, ratiowithout OH shows an
approximately straight line when the background is removed because
transport influences NO2 and CO in the same way and therefore cancels
out in the ratio (see Fig. 3b). The ratiowith OH
however, shows an approximately Gaussian relation with distance due to the
influence of the sink on NO2. This comparison demonstrates the
sensitivity of the relation between the XNO2/XCO ratio and downwind
distance to the NO2 lifetime, which we want to exploit to quantify OH.
When including the background, the shapes of the functions in Fig. 3c
change (not shown) because the relative weights of the background and city
contributions to the ratio vary with distance from the city center. In summer,
the WRF ratio is higher by ∼ 15 % close to the center-of-city
TROPOMI due to the overestimation of XNO2 WRF in WRF (see Fig. 3d). However, in the downwind plume, at a distance
of 100 km, WRF ratio is higher by 20 % to 50 % compared to TROPOMI.
Comparison of WRF and TROPOMI averaged across the wind for each
small box (a) XNO2, (b) XCO, (c) WRF ratio (XNO2/XCO) without
CAMS background and (d) WRF ratio (XNO2/XCO), with background and TROPOMI as
a function of distance to the center of Riyadh for summer (June to
October 2018).
In winter, ratiowithout OH and
ratiowith OH show relations with downwind
distance that are similar to summer, confirming that an OH sink leads to a
Gaussian structure of the ratio (see Fig. S14). The winter WRF ratio is 40 % to 60 % higher than TROPOMI due to the overestimation of XNO2
by 40 % to 50 %. The WRF ratio close to the center of the city is also 20 % higher in winter than in summer due to higher winter
XNO2 WRF (see Figs. S12
and S15). In contrast, TROPOMI shows a higher ratio in summer compared to
winter (see Fig. S15). These differences between TROPOMI- and WRF-derived
ratios offer an opportunity to address uncertainties in CTM-computed urban
OH and emission inventories, which will be explored next.
WRF optimization using synthetic data
To translate the discrepancies between TROPOMI- and WRF-derived ratios of
Sect. 3.2 into implied differences in emissions, OH and background, the
least-squares optimization method has been used as described in Sect. 2.7.
Before optimizing WRF using TROPOMI, pseudo-data experiments in WRF have
been carried out to test if the optimization method is capable of recovering
true emissions and OH levels. To this end, changes in OH concentrations,
emissions and background by known scaling factors have been applied to the
WRF prior simulation to create a synthetic dataset. This process is repeated
multiple times to create thousands of synthetic datasets. Subsequently, the
scaling factors are obtained in the inversion procedure. These tests reveal
that the estimation errors for femis, fOH and fBg are less
than 2.5 % (see Fig. S16). This confirms that the least-squares
optimization method works, with two iterations leading to a sufficient
accuracy, and can be used to estimate emissions and OH from TROPOMI data.
Using TROPOMI data, estimation errors for femis, fOH and
fBg are expected to be higher due to atmospheric transport errors,
simplified chemistry, and XCO and XNO2 retrieval uncertainties. These
errors did not play a role in the pseudo-data experiments, in which perfect
transport and sampling were assumed.
To obtain a more realistic estimate of the uncertainty in least-squares-optimized OH, TROPOMI data have been replaced by NO2, CO and the
NO2/CO ratio derived from WRF-Chem using the Carbon Bond Mechanism Z
(CBM-Z) gas-phase chemical mechanism (Zaveri and Peters, 1999). EDGAR-based
VOCs, NOx and CO emissions have been used in combination with boundary
conditions for NO, NO2, CO and ozone (O3) from CAMS to run WRF-Chem
for 17 August and 18 November 2018, representing a
summer and winter day, respectively. For 17 August 2018, the ratio and XNO2 optimization increase the CAMS-based prior OH
of 1.19×107 molec. cm-3 by 15.7 % and 13.4 %, respectively
(see Fig. S17). In the fully coupled online chemistry with WRF simulation,
the boundary layer averaged OH for the box of 300 km × 100 km amounts to
1.33×107 molec. cm-3, which is < 5 % lower than the
optimized OH value that is derived using our method. The optimized NOx and
CO emissions differ by < 11 % from the emission input used in the
full-chemistry-version WRF. In winter, the optimization
increases CAMS-based OH of 1.03×107 molec. cm-3 by 19.4 %.
The OH derived from WRF with full online chemistry is 1.07×107 molec. cm-3 and lower by 15.2 % than the optimized OH value. The
component-wise optimization increases the EDGAR NOx and CO emissions by 23.1 % and 10.5 %, respectively (see Fig. S18). Overall, the uncertainty in
optimized NOx, CO emission and OH derived from this test is < 11 % in summer and 10 % to 23 % in winter. Since the lifetime of NOx
is determined by other reactions in addition to the oxidation to HNO3
considered in our method, it is expected to overestimate the real OH value.
The test using WRF full chemistry confirms that this is indeed the case. The
uncertainties for OH, NOx emission and CO emission are in good agreement with
the CLASS computations explained in detail in Text S6.
WRF optimization using seasonally averaged TROPOMI data
The results for summer are summarized in Fig. 4, showing the optimized fit
to the TROPOMI data as well as the corresponding scaling factors femis, fOH and fBg that are estimated. The optimized emission, OH and
Bg obtained from the second iteration are divided by the prior to derive the
femis, fOH and fBg (see Text S5 for details). The
convergence of the iterative procedure is shown in Figs. S19 and S20. The
estimated uncertainties for the scaling factors femis, fOH and
fBg are derived by summing the contribution of wind speed, length and
width of the box, NO2 bias, CO bias, and the different pathways of NOx
loss in quadrature (see Text S6, Tables S1 and S2). For summer and winter,
the uncertainties of the optimized OH concentrations are < 17 %
and < 29 %, respectively. For NOx and CO emissions, the
uncertainty is < 29 % in summer and winter. Figure 4a shows WRF
ratios for summer in comparison to TROPOMI, before and after optimizing the
OH concentration. The optimized WRF ratios fit the TROPOMI ratios well, with
X2=0.1 (for the derivation of X2, see
Text S7). The prior and optimized emission ratios, OH concentrations and
background ratios obtained from component and ratio optimizations for summer
and winter are provided in Table S4. According to the ratio optimization, the
emission ratio and CAMS OH are underestimated by 155 ± 26 % and 32 ± 5.3 %, respectively (see Table S4). The optimized CAMS
background ratio is lower by 70 ± 6.5 % compared to prior. It
should be realized here that the ratio optimization does not estimate the
absolute emission of NO2 and CO, only their ratio.
Comparison between TROPOMI and WRF, before and after optimization
for summer (averaged over June to October 2018) (a)XNO2/XCO ratio, (b) XNO2 and (c) XCO in comparison to TROPOMI; fOH, femis and
fBg are optimized scaling factors obtained iteratively for OH,
emissions and background by the least-squares optimization method; femis,
fOH and fBg are derived by accounting the total change in
emission, OH and background using the corresponding scaling factors obtained
from the first and second iterative steps. The unit of the scaling factor is in
percent (%).
To derive the absolute emission, we performed component-wise optimizations
of WRF-derived XCOWRF and
XNO2 WRF. Optimized
XCOWRF and
XNO2 WRF fit well to the TROPOMI data (see Fig. 4b and c). In the XNO2 optimization, the EDGAR NOx emission is
increased by 42.1 ± 8.4 %, and the CAMs background is reduced by
75.9 ± 10.0 %. CAMS OH is increased by 28.3 ± 3.9 %, which
is close to the results obtained from the ratio optimization (see Table S4).
In the XCO optimization, EDGAR CO emissions are roughly doubled, and the
background is reduced by 4.5 ± 0.7 % compared to CAMS (see Table S4).
As Fig. 4, for winter (averaged over November 2018 to March 2019).
The summer optimized NOx/CO emission ratio derived from the component-wise
optimization is 0.55 ± 0.09. The optimized emission ratio from ratio
optimization is larger by factor 3.6 compared to component-wise optimization
(see Table S4). The difference between two estimates can be explained by
different constraints on the solution in the two methods. In particular, the
ratio inversion allows emission adjustment in a fixed relation between
NO2 and CO emissions, whereas the component-wise has the full
flexibility to adjust CO and NO2 emissions. The NO2/CO ratio over a
city is the sum of the contributions of the background and the city
emissions. The relative weight of the two is determined by the absolute
background levels and absolute emissions of CO and NO2. Therefore, the
emission ratio estimated by ratio optimization is sensitive to the
XNO2 Bg. However, the difference between the two estimates is larger
than expected but does not affect the OH estimation. Lama et al. (2020) inferred an
NO2/CO emission ratio over Riyadh of 0.47 ± 0.1 for 2018 from
TROPOMI, favoring the Monitoring Atmospheric Chemistry and Climate and
CityZen (MACCity) emission ratio over that of EDGAR. The optimized emission
ratio obtained from component-wise optimization is consistent with Lama et
al. (2020) and with
MACCity summer emissions. This shows that, for the accurate estimation of the
emission and emission ratio, the component-wise optimization method is
preferable.
Figure 5 presents optimization results for winter, where optimized WRF is in
similar good agreement with TROPOMI as for summer, with X2=0.11. For winter, the ratio optimization increases the emission ratio by 58.8±33 % and OH by 52.0 ± 14 %. The ratio and component-wise
optimizations again show similar OH adjustments, demonstrating the
robustness of our method. The background ratio is reduced by 66.8 ± 11 %. The XNO2 optimization reduces the EDGAR NOx emission by 15.45 ± 4.1 % and the CAMS background by 70.2 ± 6.1 %. For XCO,
the WRF XCOBg is reduced by 1.74 ± 0.1 % in combination with a doubling of the EDGAR CO emission.
The optimized emission ratio (NOx/CO) derived from component-wise
optimization is 0.36, which is 4 times lower than the optimized emission
ratio obtained from ratio optimization (see Table S4).
Overview of WRF-optimized OH and NOx emissions for Riyadh and
comparison to the EMG method. The estimated uncertainty for EMG- and WRF-derived NOx emissions and OH concentrations is the sum of the contribution of
wind speed, length and width of the box, NO2 bias correction, CO bias, and
the different pathways of NOx loss provided in Tables S1, S2 and S3.
To demonstrate the application of our WRF optimization method to a single
TROPOMI overpass, results are presented in this subsection for 18 August 2018. This date was selected for clear-sky conditions, with most
of the TROPOMI NO2 and CO pixels passing the data quality filter.
During this day, the urban plume was transported in a southwestern direction
over Riyadh. The spatial distribution of
XNO2 WRF (r2=0.76) and
XCOWRF (r2=0.65) matches quite well with
TROPOMI (see Fig. S21). The optimized ratio, XNO2 and XCO for a single
day fit well with TROPOMI (X2=0.1, 0.3 and 0.7), comparable
to the summer-averaged plumes, indicating that the optimization method can be
applied to a single TROPOMI overpass. The ratio optimization increases the
emission ratio and CAMS OH by 111 ± 18.4 % and 37.9 ± 6.2 %, respectively, whereas the background is reduced by 51.5 ± 5.2 % (see Fig. S22a). The XNO2 optimization increases the
EDGAR NOx emission by 25.5 ± 5.1 % and CAMS OH by 32.3 ± 4.4 %, whereas the NOx background is reduced by 54.4 ± 7.0 % (see
Fig. S22b). The CO optimization doubles the EDGAR CO emission and reduces
the background by 6.1 ± 0.97 % (see Fig. S20c). The optimized NOx
and CO emissions for 18 August are 8.9 ± 1.7 and 18.9 ± 4.0 kg s-1, respectively, and differ by < 25 % from the
summer optimized emission (see Tables 2 and S5). The optimized OH derived
from a single TROPOMI overpass is 1.73×107± 0.3 molec. cm-3 and differs by < 5 % from the summer-averaged OH, i.e., 1.7×107± 0.3 molec. cm-3, confirming that the method yields
realistic results for a single overpass.
WRF optimization vs. the EMG method
To investigate the consistency between our method and the EMG method, the
derived NOx lifetimes, emissions and OH concentrations using both
methods are listed in Table 2 for winter and summer. Our optimization and
the EMG method agree well on the seasonal change in NOx emission and OH
concentration. Both methods result in higher NOx emissions and shorter
lifetimes in summer and lower NOx emissions and longer lifetimes in winter.
Riyadh has dry and warm summer days, and the increase in power consumption
due to the use of air conditioning contributes to the higher emissions in
summer than in winter (Lange et al., 2022).
During the summer, EMG and the WRF optimization method both increase the NOx
emission and OH concentration compared with the prior. The sizes of the
NOx emission and OH concentration increase, obtained using the WRF
optimization method, which is higher than the EMG method by 10 % to 29 %.
However, the differences between the EMG method and the component optimization method are smaller compared to the uncertainties of the emission
and OH concentration derived for the optimization method. For winter, the
difference between the EMG and WRF-optimized results are smaller than the
difference between the EMG results and the prior. The NOx emission after
optimization differs from the EMG method by 33 %. Optimized OH
concentration and NOx lifetime differ by < 10 % compared to the EMG
method. In general, the difference between the EMG and optimization results
is within the uncertainty range of 20 % to 30 %, confirming their
consistency and strengthening the confidence in the estimates that are
obtained from TROPOMI data. In contrast to the EMG method, the optimization
method can be used for a single TROPOMI overpass (see Sect. 3.5) and does
not require yearly averaged NO2 data, allowing analysis of day-by-day
OH, NOx and CO emissions (see Sect. 3.3). Segregation and averaging of
NO2 urban plumes by wind sector is not required in the optimization
method. The effect of transport cancels out in taking the NO2/CO ratio,
and loss of NO2 is mostly governed by OH during the midday. In this
study, NOx emission and OH concentration are estimated iteratively, whereas
the EMG method arrives at the solution in a single step. However, since our
optimization method requires a WRF model simulation, it is computationally
more expensive. Uncertainties in transport may create mismatches with the
satellite observations, leading to errors in the optimized fit. This
influences the quality of derived emission estimates
(Dekker et al., 2017). Therefore,
finding a simplified approach using satellite data to derive the emission
ratio and to estimate OH concentration in urban plumes will be our focus in
the future. In the future, the accuracy of our method can be further
improved by accounting for other NOx removal pathways.
WRF optimized emissions and emission trends
It should be realized that the a priori EDGAR emissions and TROPOMI optimized estimates represent different years (2012 and 2018, respectively).
To check whether the emission differences that are found may be explained by
trends in emissions, we compare EDGARv5.0 2012 NOx and CO emissions with
2018, accounting for seasonal and diurnal emission variations using temporal
emission factors from van der Gon et al. (2011). EDGAR 2018 NOx and
CO emissions are derived by linear extrapolation using emissions from 2000 to
2015 (see Fig. S23). For summer, midday NOx emissions, the EDGAR emissions
increased by 16.7 % from 2012 to 2018, which is lower than our
optimization results. For winter, midday NOx emissions increased in EDGAR by
15.2 % from 2012 to 2018, whereas the WRF optimization yielded reductions
of 15.6 %. In EDGAR, summer and winter CO emissions increased from 2012 to
2018 by 38.5 %. However, the WRF optimization suggests that the EDGAR CO
emissions for summer and winter need to be doubled (see Table S5). Borsdorff
et al. (2018b) mentioned that EDGAR
CO emissions have to be increased significantly to match with TROPOMI CO
observations over middle eastern cities such as Tehran, Yerevan, Tabriz and
Urmia. Overall, this points to a significant uncertainty in the EDGAR
emission inventory at the city scale.
To test the accuracy of the linear extrapolation of EDGAR data, we compare
the relative change in NOx and CO emissions in 2012 to 2018 using CAMS Global
(CAMS-GLOB) anthropogenic v4.2 emission datasets (https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-emission-inventories?tab=overview, last access: 15 June 2021). CAMS-GLOB shows that, for summer and winter, NOx emissions
increased by 26 % from 2012 to 2018, which is higher than
EDGAR by a factor of 1.7. CAMS-GLOB-based summer and winter CO emissions increase by 20 %
from 2012 to 2018, which differs by ∼ 40 % compared to
EDGAR. In general, the relative increase in CO and NOx emissions from EDGAR
and CAMS-GLOB is much smaller compared to the difference with our
optimization method.
Discussion
The TROPOMI-retrieved XNO2/XCO ratio is useful for estimating midday
OH over isolated localized sources, such as the city of Riyadh, showing a
clear contrast between the urban plume and the background. Such TROPOMI-derived OH estimates offer a new opportunity to evaluate urban
photochemistry in chemistry transport models. OH depends non-linearly on NOx
and VOC emissions, meteorological conditions, etc. (Sillman et al.,
1990), which vary substantially between cities that are monitored by
TROPOMI. Therefore, the application of our method to the global and
multi-year dataset that is available could contribute substantially to the
understanding of urban photochemistry and the development of effective
pollution mitigation strategies. In addition, the method requires local
sources with NO2 and CO emissions that are large enough to be detected
by TROPOMI. Especially in European cities with lower CO emission, where
TROPOMI cannot detect the CO enhancement along with NO2, this method
cannot be applied.
We realize that our method only considers the first order loss of NO2
by OH-forming HNO3. In reality, the NO2 lifetime is influenced by
more spatially and temporally varying factors such as temperature, ozone
and radiation (Lang
et al., 2015; Romer et al., 2018). In cities, the loss of NO2 via the
formation of alkyl and multifunctional nitrates (RONO2) is also an
important reaction influencing the lifetime of NO2 (Browne
et al., 2013; Sobanski et al., 2017). For CO, secondary production from
short-lived volatile organic compounds can also play an important role in
urban pollution plumes. The application of full chemistry that includes all
the sources and losses of NO2 and CO could therefore further improve
the accuracy of OH estimates.
For cities at higher latitudes, especially in winter, it becomes more
critical to account for the contribution of other pathways of NOx loss than
OH oxidation. Isolated tropical and subtropical cities are therefore best
suited for application of our current method.
A sensitivity test has been performed in which XNOx, Bg is lost by OH.
In this case, the optimized NOx emission and OH for summer and winter differ
by < 7 % from the default method, where the background is treated
as an inert tracer (see Table S6). Furthermore, a
sensitivity test has been performed in which the prior emission has been
changed. The optimized emission varied by < 5 %, demonstrating
robustness of the method to the choice of prior (see Fig. S24). This also
indicates that the optimization method can be used to study emission
changes. Figure S25 shows that road transport, power
plants and manufacturing industries are the largest pollutant emitter over
Riyadh (Beirle et al., 2019). In this study, NOx
and CO anthropogenic emissions are introduced at the surface, whereas the
emission height of different sources is expected to vary in reality. The
different emission heights for NOx and CO emission sources can also
influence the result. In the future, realistic emission heights should also
be incorporated in WRF for accurate estimation of OH. Moreover, the temporal
emission factors that have been used by van der Gon et al. (2011) are based on European countries. The comparison of van der Gon et
al. (2011) with the Copernicus Atmosphere Monitoring Service
TEMPOral profiles (CAMS-TEMPO)
(Guevara
et al., 2021) suggests that temporal emission factors for weekend road
transport and monthly residential combustion are different in Riyadh
compared to European countries. CAMS-TEMPO is expected to provide a more
accurate representation of emission variation due to the information on
temporal and spatial variations that is included. Road transport CO emissions
are the largest contributor by ∼ 75 % to the total emissions
over Riyadh, whereas NOx emissions from the road contribute by 24 % to the
total NOx emission. Residential combustion has the smallest contribution of
∼ 0.3 % to 0.4 % to total NOx and CO emissions (see Fig. S25). In the future, the application of accurate diurnal emission factors for
road transport (see Fig. S26) can further improve the accuracy of urban OH
concentrations estimated using TROPOMI-derived XNO2/CO ratios. In
addition, the seasonality for NOx and CO emissions is different in Riyadh
than in Europe, which should also be accounted for in future studies.
Conclusions
In this study, a new method is presented for estimating OH concentrations in
urban plumes using TROPOMI-observed XNO2/XCO ratios in combination with
WRF simulations of the downwind pollution plume of large cities. Our new
method has been tested for the city of Riyadh using synthetic as well as
real TROPOMI data. Seasonal emissions and OH concentrations have been
estimated for summer (June to October 2018) and winter (November 2018 to March 2019). WRF is well able to reproduce the spatial distribution of TROPOMI-retrieved XNO2 and XCO plumes over Riyadh during the summer and winter
seasons. However, before the optimization, WRF overestimates XNO2 by 15 % to 30 % in summer and 40 % to 50 % in winter compared to
TROPOMI. In both seasons, TROPOMI XCO agrees within 10 % with WRF. The
WRF-derived XNO2/XCO ratio is higher by 15 % to 30 % in summer
and 40 % to 60 % in winter compared to TROPOMI, explained mostly by
differences in XNO2.
The differences between WRF and TROPOMI observations have been used to
optimize emissions and the NO2 lifetime. To this end, scaling factors
for the city emissions, OH and the background level have been optimized
iteratively using a least-squares method. Ratio and component-wise
optimizations have been compared to test the overall consistency of the
method. In summer, the ratio and XNO2 optimization for XNO2
suggest that the OH prior from CAMS is underestimated by 32 ± 5.3 % and 28.3 ± 3.9 %, respectively . The OH estimates obtained from the ratio and NO2-only
optimization differ by < 10 %, demonstrating the robustness of
the method. Summertime emissions of NOx and CO from EDGAR are increased by
42.1 ± 8.4 % and 101 ± 21 %. For winter, the ratio and
component-wise optimizations increase OH by ∼ 52 ± 14
% to fit TROPOMI-inferred ratios. In the optimization of winter data, NOx
emissions are reduced by 15.5 ± 4.1 %, and CO emissions are doubled.
In the future, the remaining differences between TROPOMI observations and
WRF simulations could be reduced further by the use of precise temporal and
monthly emission factors, emission heights and full chemistry to account for
secondary sources and sinks of CO and NO2.
TROPOMI-inferred OH concentrations obtained from the least-squares
optimization method have been compared to the EMG method. For the summer and
winter, the optimized OH concentrations differ by 10 % between the two
methods. These results confirm that urban emissions and OH concentrations
can be estimated robustly from TROPOMI data. With our method, single TROPOMI
overpasses can be used to estimate OH, whereas the EMG method requires averaging
of urban NO2 plumes by wind sector. The iterative approach allows one to
test the factors, i.e., femis, foh and fbg, obtained from the
optimization method, whereas the EMG method does not allow such flexibility.
An important remaining uncertainty is the bias correction of the TROPOMI
XNO2 retrieval. Following the recommended procedure, the air mass
factor AMF is recalculated by replacing the tropospheric AMF based on TM5,
which is provided with the data, with WRF-Chem. The TROPOMI XNO2 bias
correction increases the mixing ratio in the urban plume of Riyadh by 5 %
to 10 % in summer and 25 % to 30 % in winter. The background is
less affected by the bias correction. Without TROPOMI XNO2 bias
correction, the uncertainty in the scaling factor for OH can vary up to 20 %
and up to 60 % for NOx emissions over Riyadh.
AMF recalculation
The air mass factor (AMF) used in the retrieval of TROPOMI XNO2 has
been re-calculated by replacing the tropospheric AMF, calculated from the
NO2 column simulated by TM5, with its WRF-Chem equivalent, as described
by Lamsal et al. (2010) and
Boersma et al. (2016),
using the following Eq. (A1):
Mtrop,WRF=Mtrop,TM5×∑l=1LAtrop,lxl,WRF∑l=1Lxl,WRF,
where Mtrop,WRF and
Mtrop,TM5 are the tropospheric air
mass factors derived from WRF and TM5, respectively.
Atrop,l is the tropospheric averaging kernel,
ranging from the surface to the uppermost layer of the troposphere in the
TM5 model (l). xl,WRF is the equivalent NO2
column density in model layer l, based on WRF. Atrop
in Eq. (A1) is derived using Atrop=A×MMtrop, where M and
Mtrop are the total and tropospheric AMFs,
respectively. Finally, the bias-corrected NO2 vertical column density
is computed using
NO2, bias corrected=Mtrop,TM5Mtrop,WRF×NO2,
where NO2 is the TROPOMI tropospheric NO2
vertical column density, and NO2, bias-corrected is the bias-corrected TROPOMI tropospheric NO2
vertical column density.
XCO component-wise optimization
The component-wise optimization of XCOWRF to
estimate the emission and background of CO uses the following equations:
B1XCOTROPOMI=XCOWRF+ΔXCOemis⋅femis10+ΔXCOBg⋅fBg10,B2XCOWRF=XCOemis+XCOBg,B3ΔXCOemis=0.10⋅XCOemis,B4ΔXCOBg=0.10⋅XCOBg.
Here, XCOTROPOMI is TROPOMI XCO,
XCOWRF is the WRF-simulated XCO accounting for
emissions and background CO, XCOemis is the XCO
contribution from the urban CO emission, and XCOBg
is the CAMS-derived XCO background. ΔXCOemis is the change in XCO due to emission, and
ΔXCOBg is the change in the XCO
background level.
Data availability
TROPOMI CO and NO2 data can be downloaded from https://cophub.copernicus.eu/s5pexp (ESA, 2018). EDGAR emission data are available at
https://edgar.jrc.ec.europa.eu/emissions_data_and_maps (last access: 23 June 2021, Crippa et al., 2016). CAMS data can be downloaded
from https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=_form (last access: 1 November 2020, Inness et al., 2019).
WRF simulations outputs are available at 10.5281/zenodo.5752219 (Lama et al., 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-16053-2022-supplement.
Author contributions
SL performed the data analysis and data
interpretation and wrote the paper. SH supervised the study. SH, FKB, IA,
MK and HACDG discussed the results. All co-authors commented on the paper
and improved it.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We are thankful to the team that designed the TROPOMI instrument, consisting
of the partnership between Airbus Defence and Space Netherlands, KNMI, SRON
and TNO, commissioned by NSO and ESA. We acknowledge the free availability of the
WRF-Chem model (http://www.wrf-model.org/, last access: 22 August 2019). Thanks to SURFSara
for making the Cartesius HPC platform available for computations via
computing grant no. 17235.
Financial support
This research has been supported by the Aard- en Levenswetenschappen, Nederlandse Organisatie voor Wetenschappelijk Onderzoek (grant no. 2017.036).
Review statement
This paper was edited by Bryan N. Duncan and reviewed by two anonymous referees.
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