The Tropospheric Monitoring Instrument (TROPOMI) on the ESA Copernicus Sentinel-5
satellite (S5-P) measures carbon monoxide (CO) total column concentrations as
one of its primary targets. In this study, we analyze TROPOMI observations
over Mexico City in the period 14 November 2017 to 25 August 2019 by means of
collocated CO simulations using the regional Weather Research and Forecasting coupled with Chemistry
(WRF-Chem) model. We draw conclusions on the emissions from different urban districts
in the region. Our WRF-Chem simulation distinguishes CO emissions from the
districts Tula, Pachuca, Tulancingo, Toluca, Cuernavaca, Cuautla, Tlaxcala,
Puebla, Mexico City, and Mexico City Arena by 10 separate tracers.
For the data interpretation, we apply a source inversion approach determining
per district the mean emissions and the temporal variability, the latter
regularized to reduce the propagation of the instrument noise and forward-model errors in the inversion. In this way, the TROPOMI observations are used
to evaluate the Inventario Nacional de Emisiones de Contaminantes Criterio
(INEM) inventory that was adapted to the period 2017–2019 using in situ
ground-based observations. For the Tula and Pachuca urban areas in the north
of Mexico City, we obtain 0.10±0.004 and 0.09±0.005Tgyr-1 CO
emissions, which exceeds significantly the INEM emissions of <0.008Tgyr-1 for
both areas. On the other hand for Mexico City, TROPOMI estimates
emissions of 0.14±0.006Tgyr-1 CO, which is about half of the INEM
emissions of 0.25 Tgyr-1, and for the adjacent district Mexico City Arena
the emissions are 0.28±0.01Tgyr-1 according to TROPOMI observations versus
0.14 Tgyr-1 as stated by the INEM inventory. Interestingly, the total emissions
of both districts are similar (0.42±0.016Tgyr-1 TROPOMI versus 0.39 Tgyr-1
adapted INEM emissions). Moreover, for both areas we found that the TROPOMI
emission estimates follow a clear weekly cycle with a minimum during the
weekend. This agrees well with ground-based in situ measurements from the
Secretaría del Medio Ambiente (SEDEMA) and Fourier transform spectrometer
column measurements in Mexico City that are operated by the Network for the
Detection of Atmospheric Composition Change Infrared Working Group
(NDACC-IRWG). Overall, our study demonstrates an approach to deploying the large
number of TROPOMI CO data to draw conclusions on urban emissions on sub-city scales
for metropolises like Mexico City. Moreover, for the exploitation of TROPOMI
CO observations our analysis indicates the clear need for further improvements
of regional models like WRF-Chem, in particular with respect to the prediction
of the local wind fields.
Introduction
Carbon monoxide (CO) is an atmospheric trace gas emitted by incomplete
combustion to the atmosphere (e.g., biomass burning, industrial activity, and
traffic). Its background concentration is relatively low with an atmospheric
residence time varying from days to month depending on the
atmospheric concentration of the hydroxyl radical .
These characteristics established CO as a tracer for air pollution and
transport processes in the atmosphere (e.g., ).
The Tropospheric Monitoring Instrument (TROPOMI) launched in 2017 as the single payload
of ESA's Copernicus Sentinel-5 Precursor mission aims to monitor CO as one of its
primary targets. The operational CO column product is inferred from TROPOMI's
shortwave infrared measurements with daily global coverage and a high spatial
resolution of 7×7km2. Early in the mission, the TROPOMI
CO dataset was validated with ground-based measurements of the Total Carbon
Column Observing Network (TCCON) and inter-compared
with simulated CO fields of the European Centre for Medium-Range Weather
Forecasts (ECMWF) Integrated Forecasting System . On 11 July 2018, it was concluded that the TROPOMI CO data quality is fully compliant
with the mission requirements of 15 % precision and 10 % accuracy and so it was
released for public usage (https://scihub.copernicus.eu, last access: 16 December 2020).
illustrated the capability of
TROPOMI to detect CO emissions from pollution hot spots of medium-sized
to large cites (e.g., Yerevan, Tabriz, Urmia, and Tehran), industrial
areas (e.g., Po Valley in Italy), and even from pollution along arterial
roads in Armenia. To monitor the emissions of metropolises, data
interpretation of multi-annual datasets is required. The different
inversion techniques discussed by for plume
inversions, i.e., the source pixel method, the mass balance method, and
the inversion of a Gaussian plume model, are appropriate for interpreting
emissions of point sources but are less suitable for flux inversion of
spatially extended sources. Therefore, in this study we estimate CO
emissions by inverting simulations of the regional atmospheric modeling
Weather Research and Forecasting coupled with Chemistry (WRF-Chem) as an atmospheric tracer
transport model, which allows us to simulate the CO column at the same spatial
resolution as TROPOMI. Possible error sources of this type of flux
inversion are the limited validity of the simulated wind fields, prior
assumptions on the spatial distribution of emissions, and the simulated
atmospheric dispersion .
Mexico City is a prime example of a CO pollution hot spot that is
clearly detectable by TROPOMI. It is a fast-growing megacity located
at an altitude of 2240 m on the Central Plateau which is surrounded by
mountains. The urban area is divided into 10 different urban districts
(Tula, Pachuca, Tulancingo, Toluca, Cuernavaca, Cuautla,
Tlaxcala, Puebla, Mexico City (CdMx), and Mexico City Arena
(ACdMx)), and the metropolis has a long history of atmospheric
pollution measurements. More than 29 in situ CO measurement stations
are distributed over the city, operated by the Secretaría del Medio
Ambiente (SEDEMA, Mexican Ministry of the Environment). About every
2 years, the ministry reports on the CO emissions of Mexico City.
Based on a bottom-up approach using the in situ measurements, it is
concluded that a major part of Mexico City's CO emissions is caused by
light-duty motor vehicles with a significant decline in recent
years. For the zona metropolitana del valle de México (ZMVM, Greater Mexico City), SEDEMA
reported a reduction in CO emissions from 2.04 to 0.7 and 0.28 Tgyr-1
in the years 2000, 2014, and 2016 .
These in situ measurements are complemented by ground-based Fourier transform infrared (FTIR)
observations of the NDACC (Network for the Detection of Atmospheric
Composition Change) IRWG (Infrared Working Group) network, which
among other products regularly provides CO total column concentrations.
Using NDACC and IASI satellite observations of CO,
estimated the overall annual CO emissions of
Mexico City to be about 2.15 Tgyr-1 for the year 2008. Building on
this, TROPOMI CO observations add new possibilities for air quality
monitoring due to its regional coverage and daily overpass combined
with the high precision of its data.
In this study, we analyze about 2 years of TROPOMI CO
measurements using collocated WRF-Chem CO simulations for Mexico to
get more insight into the emissions of Mexico City. To this end, in Sect. 1 we
introduce the TROPOMI CO dataset and the simulation of
the WRF-Chem model, and Sect. 2 describes our methodology to fit the
WRF-Chem model to the TROPOMI data for emission estimates. Section 3
discusses our findings, and Sect. 4 gives the summary and conclusion.
TROPOMI CO dataset
To investigate CO emissions of the Mexico City metropolis, we select
the TROPOMI dataset of CO total column observations
between 14 November 2017 and 25 August 2019 over Mexico.
The data are processed with the shortwave infrared CO retrieval
(SICOR) algorithm that was developed for Copernicus operational
data processing . Algorithm settings like the
spectral windows, a priori profiles, and other auxiliary data are reported by
. The SICOR algorithm accounts for atmospheric scattering by retrieving effective cloud parameters
(altitude, optical thickness) together with the total column
concentrations of CO and of the interfering gases H2O, HDO,
and CH4.
The radiative transfer simulation uses the HITRAN2016 database for
all species as described by , and the inversion
deploys the profile-scaling approach that scales a reference profile to
fit the spectral measurement . Here, the a priori
profile is taken from spatio-temporally resolved atmospheric
transport simulations of the TM5 model . The TROPOMI
CO data product includes the total column averaging kernel acol
that relates the true vertical CO profile ρtrue to the
retrieved total column concentration cret following the equation
cret=acolρtrue+ϵ,
with the noise contribution ϵ. This study limits the analysis
to scenes under clear-sky and low-cloud atmospheric conditions. This
corresponds to a quality assurance value q>0.5 which is also
provided by the S5P data product. Finally, the individual TROPOMI CO
orbits show an artificial striping in flight direction, probably due
to a deficient instrument calibration. To reduce this feature, we apply an a
posteriori data correction as discussed by
based on frequency filtering in the Fourier space. Finally, on 5 August 2019, the spatial sampling of the data product with
satellite nadir geometry was improved from 7×7 to 7×5.6 km2
due to a shorter readout time of the detectors. This event is covered
by our dataset.
We simulate the TROPOMI CO column concentrations by
deploying the WRF-Chem model version 3.9.1.1. The simulation covers the time
period of TROPOMI measurements on the regional domain shown in Fig. .
We ignore photo-chemical oxidation and secondary production of CO in
the atmosphere (chem_opt option 106 (RADM2-KPP), as a tracer with
gaschem off), which is justified by the long lifetime of CO compared
with the size of the model domain as discussed by
. Especially for the region of Mexico City, the
contribution of atmospheric chemistry to the total CO concentration is
less than 3 % as presented by . Hence, WRF-Chem
simulates the transport of CO surface emissions as traces as done by
e.g., and . The spatial
resolution of the simulation is chosen to be comparable with the
TROPOMI CO product sampling. Each grid cell of the considered
simulation domain (270×270 km2) is 3×3 km2. The WRF-Chem
simulation employs the emission inventory Inventario Nacional de
Emisiones de Contaminantes Criterio (INEM) for the year 2013 but
scaled by a factor of 0.48 to make it applicable to the years 2017 to
2019. Here the scaling factor is based on recent SEDEMA surface
measurements . The inventory includes diurnal,
week-to-week, and monthly variations in the CO emissions, where weekly
and daily temporal profiles are derived from traffic counts in Mexico.
The inventory is described in more detail by .
Finally, the model run is constrained by National Centers for Environmental Prediction (NCEP) North American Mesoscale
(NAM) 12 km analysis wind fields and yields vertical
CO concentration profiles for every latitude and longitude grid cell and
every model time step and tracer run.
The WRF-Chem simulation uses 10 independent tracers to estimate the CO
emissions of the areas Tula, Pachuca, Tulancingo, Toluca, Cuernavaca,
Cuautla, Tlaxcala, Puebla, the metropolitan area of Mexico City CdMx,
and the adjoint urban area ACdMx. The total simulated CO field is
given by the sum of the simulated CO fields of the tracer. Since no
atmospheric chemistry is accounted for, each CO tracer field is linear in terms of
the corresponding emissions per district:
FWRF(α1,⋯,α10)=∑i=110kiαi,
where αi is the corresponding scaling factor and
ki represents the CO tracer field for the reference
emissions (adapted INEM data) for αi=1.
Before using our model to simulate TROPOMI data, we interpolate the
model fields to the geolocation and time of the TROPOMI observations.
Subsequently, we integrate the model CO profiles to total column densities by
applying the total column averaging kernel of the TROPOMI CO retrieval
following Eq. (). We summarize this numerical step
in the observation operator O, which transforms the forward
model into
Fsat(α1,⋯,α10)=∑i=110O(ki)αi.
Hence, the operator O accounts for the TROPOMI-specific vertical
sensitivity, which can change from measurement to measurement and so ensures
that the comparison between TROPOMI and WRF-Chem is free of the null space or
smoothing error .
Here, the scaling factors αi are not affected by the operation.
In a next step, we transform Eq. () to
Fsat(E1,⋯,E10)=∑i=110O(k̃i)Ei,
where k̃i=kiEi,INEM and
Ei=αiEi,INEM with the corresponding
emissions Ei,INEM of the INEM inventory interpolated to the TROPOMI overpass time.
To improve the capability of our forward model to fit TROPOMI
observations, we introduce a spatially constant CO background field
kbg and an altitude dependence term
kelv=z-zref with corresponding
scaling factors αbg and αelv.
Here, z is the respective elevation of the TROPOMI CO ground pixels
and zref=2240 m is an arbitrary reference altitude set to
the elevation of Mexico City:
Fsat(E1,⋯,E10,αbg,αelv)=∑i=110O(k̃i)Ei+kbgαbg+kelvαelv.
These two effective model components account for the CO contribution over the
Mexico City area originating from outside the model domain such as from fires, power
plants, biogenic production, and other cities as well as long-range transport
and an altitude-dependent linear vertical gradient of
the CO columns. Both do not interfere with any localized emission sources. They
mitigate shortcomings of the WRF-Chem simulations ignoring CO boundary
conditions in the model domain.
Finally, for the interpretation of our CO forward simulations, we make
an important assumption. Although the WRF-Chem simulations account for the
temporal accumulation of the localized CO emissions over days and
weeks, we allocate an emission estimate of the corresponding overpass
time to each TROPOMI overpass. Here, we assume that a TROPOMI CO image
is dominated by the emissions of the urban districts for the
particular observation day, where the temporal accumulation of CO from
previous days is partly described by the WRF-Chem simulation due to the
corresponding scaling of the inventory and partly mitigated by fitting
the nuisance parameters αbg and
αelv.
Inversion methodology
Interpreting a series of n TROPOMI CO images,
y=(y1,⋯,yn),
at overpass times t0,⋯,tn means
estimating the corresponding emissions given by the state vector,
x=(x1,⋯,xn),
where each element comprises
xi=(E1,i,⋯,E10,i,αbg,i,αelv,i)
at the corresponding time ti. Our linear forward model
in Eq. () describes the measurement vector y
by
y1y2⋮yn=K10⋯00K2⋯0⋮⋮⋱⋮00⋯Knx1x2⋮xn,
with the forward-model Jacobian matrix
Ki=(O(k̃1,i),⋯O(k̃10,i),kbg,i,kelv,i),
in short y=Kx.
Equation () can be inverted by
xest=minx||y-Kx||Se2,
which is equivalent to the solution
(xest,1,⋯,xest,n)
of the individual problems
yi=Kixi due to the block
diagonal form of Eq. (). Here, the norm of an arbitrary
vector p is defined by
||p||Se2=pTSe-1p and Se is the measurement error covariance matrix
with the variance of the TROPOMI retrieval error on the diagonal.
Due to measurement noise and forward-model errors, the least-squares
inversion of Eq. () results in unfavorable error propagation and so requires
regularization. Because our problem is linear in the state vector
x, regularization can be performed as part of the fitting approach or
a posteriori to the least-squares solution, without loss of
generality. To regularize the noise propagation, we first derive the
temporal mean,
x¯est=1n∑i=1nxest,i,
from the unregularized solution in Eq. (). This
modifies our cost function to
xest=minx||y-Kx||Se2withx¯=1n∑i=1nxi.
In this way, we divided the solution of the original inversion problem () into two steps: first we determine the
mean emissions from the individual least-squares solutions
xest,i, which yields the constrained least-squares problem
in Eq. () to describe the temporal
variability. The side constraint
guarantees that measurement information is not used twice. Finally, we
add an additional Tikhonov side constraint to Eq. () to regularize
the error propagation,
xest=minx||y-Kx||Se2+||x-x¯||Γ2,
with
x¯=1n∑i=1nxi.
Here, Γ is an appropriate regularization matrix. For
a block diagonal form of Γ analogous to the Jacobian matrix in Eq. (), namely
Γ=Γ10⋯00Γ2⋯0⋮⋮⋱⋮00⋯Γn,
the minimization problem () decomposes into
n problems,
xest,i=minxi||yi-Kixi||Se2+||xi-x¯||Γ2,
which are only coupled by the external side constraint (). A closer look at our inversion problem
shows that the two constraints have similar effects. The Tikhonov
constraint ||x-x¯||Se minimizes the variation in the state vector around its
mean depending on the regularization parameter
λ, whereas the external constraint requires strict
conservation of the mean.
Therefore, in practice, we solve the inversion () and evaluate the external constraint on
the mean afterwards to confirm proper use of the measurement
information. Its solution is given by
xest,i=Gi(yi-Kix¯i)+x¯i
with the gain matrix
Gi=(KiTSe,i-1Ki+Γi)-1KiTSe,i-1.
The inversion's averaging kernel relates the “true” state vector
xtrue,i to xest,i; namely
xest,i=Aixtrue,i-xi+x¯
with
Ai=GiKi.Ai represents the derivative Ai,kl=∂xest,j∂xtrue,l,
where its diagonal elements describe the retrieval sensitivity of a
state vector element to its true value.
The degree of freedom for signal
DFSi=trace(Ai),
indicates the total number of independent pieces of
information.
To evaluate the fit quality for each overpass, we consider the fit
residuals δi=yi-Kixest,i.
Additionally, we evaluate the goodness of the fit described by the reduced chi-squared
value,
χi2=1νi∑l=1L(δi,l/yerr,ik)2.
Here L is the number of observations of a single overpass, yerr,i is the
retrieval error, and νi=L-DFSi.
Estimate of the mean emissions
The first step of the inversion described in the previous section
means determining the a priori emissions from a set of TROPOMI data
with highest information content using a unregularized least-squares
fit, Γ=0. Here, the individual emission estimates
may be noisy due to enhanced error propagation in the inversion; however,
averaging all inversions reduces noise contribution and so gives a reliable
estimate of mean emissions for the different districts. The validity of this
approach highly depends on the selected dataset of TROPOMI overpasses.
On the one hand, the ensemble should be large enough to estimate mean emissions for
the considered time period, but on the other hand it should be strictly filtered
for cases where the forward model is in good agreement with the measurement such
that a stable inversion of all the emissions is possible.
The information content of a single overpass varies and depends on several
aspects: (1) the number of useful measurements and their cloud coverage changes
between different TROPOMI overpasses. Here, clouds shield the lower troposphere,
where atmospheric measurements are particularly sensitive to the surface emissions
Ei. (2) The pixel size at the swath edge is about 32 km and so about 5 times
larger than at the sub-satellite point. This reduces not only the number of
pixels covering a certain area but also the sensitivity of the individual
TROPOMI observations. (3) The quality of the forward model depends on the
meteorological situation, where we consider model simulations for low wind
speeds more reliable.
These considerations lead to the criteria of the data
filtering to determine the mean emissions for each district. We only select overpasses which meet both filter criteria:
TROPOMI observations cover 70 % of the data domain.
For all observations the across-track pixel size is <15 km.
The filter criteria reduce the original set of 551 overpasses to 199,
which we consider to be sufficient to estimate the overall average emission
rate per district, yielding x¯.
For this we use the median instead of the mean because of its
robustness against outliers. With the same reasoning we define the
percentile difference,
δPj=|Pj(84.1)-Pj(15.9)2|,
to describe scattering in the data, which corresponds to the standard deviation
of normally distributed parameters. Finally, we calculate the error in the mean
using the percentile difference.
Final data product
Subsequently, the final data reduction step is
performed solving the inversion problem ().
For all overpasses, we choose Γi to be a diagonal matrix with
diagΓi=[γ1,γ2,⋯,γ10,0,0],
where the zeros ensure that the elements of the state vector αbg and
αelv are not regularized. Obviously, the
regularization parameter γk must be well-chosen to optimize
the balance between minimum error propagation on the fit parameter and
maximum information content inferred from the measurement. If
γk is chosen to be too small, the propagation of the TROPOMI
measurement noise as well as retrieval biases and forward-model errors
dominates the inversion. If γk is chosen to be too large, the
estimated state vector reproduces the a priori estimate without
appropriate use of the information content of the measurement. For our
application, we fix the regularization parameter γk for
k=1,⋯,10 to constant values such that the scatter of the
retrieved emissions stays within predefined boundaries.
Considering the temporal variation in the INEM emissions to be about
40 %, we adjusted the regularization parameter
γ1,⋯,γ10 such that the retrieved emissions vary
within 60 % of their average. The value 60 % is empirically chosen
to balance information content against noise propagation. It puts a
moderate constraint on the inversion, ensuring on the one hand a
stable inversion and on the other hand a realistic variation in the
retrieved emissions around the a priori.
One great advantage of the final retrieval product is that it includes the
averaging kernel Ai. This can be used to
filter the data with respect to the information provided by the TROPOMI
measurements. For the emissions of each tracer, we filter on the individual
emissions El, considering averaging-kernel values Al,l>0.3.
This form of data mining optimizes the data use, keeping in mind that
TROPOMI overpasses may be appropriate for determining one specific source
but not all sources simultaneously. The concept of information-content-based filtering turned out to be very useful and enhances the
data exploitation compared to the unregularized least-squares
fitting used to determine the mean emission values.
Background CO concentration for the domain shown
in Fig. estimated by fitting the WRF-Chem simulation
to the TROPOMI data. (a) Background CO for individual collocations
from 9 November 2017 to 25 August 2019. (b) Monthly mean
background CO based on the individual collocations. The error
bars are the standard error in the mean, and the light-blue line is the time-invariant a priori used in the fit.
Results
Figure shows the CO background that was fitted as
an auxiliary parameter during the inversion described in
Sect. . The concentration and its annual cycle are
shown. Here, the biomass burning season between February and June
causes the corresponding CO enhancement, whereas lower CO
concentrations are observed during the rainy season between June and
November. The extremely high CO column values on 15 May 2019 are
due to the transport of CO-enriched air from wildfires in the
southwest of Mexico into the model domain. Figure
shows the CO concentration in the state of Mexico under normal
conditions and after the fires, which caused a serious health hazard
in Mexico City. These types of fires outside the model domain create an
inhomogeneous background CO field over Mexico City, which cannot be
described by our forward model. Only fitting a constant
background is not sufficient in these extreme cases, and so during
the fire season many data cannot be used (we excluded the months
May and June 2019).
Example cases for fitting the WRF-Chem simulation to the TROPOMI data
deploying the “final-fit” approach for
(a) 20 September, (b) 7 November, (c) 19 November 2018,
and (d) 17 August 2019. TROPOMI CO retrievals (left column), WRF-Chem
simulation fitted to the TROPOMI data (middle column), and the residual
(right column, TROPOMI – WRF-Chem).
Figure shows three examples of TROPOMI overpasses,
which include a pixel resolution of 7×7 km2 (panels a, b, and
c) and the enhanced spatial resolution of 7×5.6 km2 (panel d),
where the latter represents the TROPOMI instrument baseline since 6 August 2019. Focusing on the dry season, the TROPOMI instrument can
detect distinct CO enhancements over the different emission areas in
central Mexico with the retrievals from single-orbit overpasses (see
left column of Fig. ). After fitting our forward model
to the TROPOMI measurements, simulated data and observations are brought
into good agreement as illustrated in Fig. .
Particularly for low-wind-speed conditions in Fig. a,
TROPOMI and WRF-Chem show distinct CO enhancements over the different
emission areas of Mexico. Furthermore, the transport of CO-enhanced
air from Mexico City towards the south following the mountain
orography and the accumulation of CO in the south is seen by TROPOMI
in agreement with the WRF-Chem simulation (Fig. c). This
clearly shows that regional models like WRF-Chem have a great
potential to reproduce the large-scale patterns seen by the TROPOMI
instrument. However, we also found clear localized residuals in the
difference δj between observations and the forward model (right
column of Fig. ). For atmospheric conditions under
high wind speeds the WRF-Chem simulations can deviate more from the
TROPOMI measurements as shown in Fig. c. Here, the
plume of CO-enriched air extending from Mexico City towards the north
is simulated as very narrow compared to the more dispersed plume seen by
TROPOMI. This points to a possible underestimation of the
atmospheric dispersion in the WRF-Chem simulation. A very prominent
residual between TROPOMI and WRF-Chem not only is shown in Fig. d
but also is present in Fig. a and b. Here TROPOMI measures
a strong CO enhancement in the north of Mexico City that is not
reproduced by the WRF-Chem model. This points at a deficient spatial
distribution of INEM emissions.
Statistics of CO emissions averaged from 9 November 2017
to 25 August 2019 for the tracer domains shown in Fig. . (a) Median of the a priori emissions (adapted INEM
inventory) used
for the WRF-Chem simulation (grey) and retrieved from the TROPOMI data
(unregularized fit in blue, regularized fit in green). The error bars indicate the
standard error in the mean calculated from the delta percentiles (b)
used as a robust estimation of the standard deviation; (c) the median of
the goodness of the fit (χ2), and (d) the number of
collocations. The number of collocations and the χ2 values
of the a priori simulation and unregularized fit are the same
for all tracer domains (blue and grey line), but in the final regularized fit they change
due to the information content filtering. Here, a collocation
corresponds to a specific day because TROPOMI overpasses the region only
once.
For each tracer domain Fig. a shows the mean
emissions of the adapted INEM inventory, the unregularized least-squares fit, and the final data product. The mean emissions agree very
well between the last two approaches, indicating that the final
inversion in Eq. () satisfies the constraint of the predefined
mean value. This supports our assumption that the external
constraint does not have to be accounted for in the chosen Tikhonov
constraint of the inversion. The scatter of the least-squares product
is high and in most cases exceeds 100 % (see Fig. b), which is expected for the non-constrained inversion.
Moreover, we find significant
differences between the emissions of the a priori and the final data
product. The retrieved emissions for the urban districts Tula (0.10±0.004Tgyr-1) and Pachuca (0.09±0.005Tgyr-1) in the north
of Mexico City seem to be underestimated by the emission inventory
(both were less then 0.008 Tgyr-1). It is not yet clear what sources
are missing in the inventory; this needs to be addressed in future
studies. However, we identified an oil refinery and a power plant near
to Tula and cement and lime kilns near to Pachuca that could have
contributed to the CO emissions. Furthermore, we found that the
emissions of the central part of Mexico City (CdMx) are assumed to be too high
in the adapted INEM inventory (0.25 Tgyr-1), where TROPOMI measurements
indicate lower values for CdMx (0.14±0.006Tgyr-1). This is accompanied by higher values for the adjacent district ACdMx (0.28±0.01Tgyr-1). The sum of both emissions (0.42±0.016Tgyr-1) is
similar to the a priori emissions (0.39 Tgyr-1). This may mean that the
total emissions of the domain including CdMx and ACdMx is well
represented in the emission inventory but only the spatial
distribution of the source intensity needs refinement.
(a) The goodness of the fit (χ2) of the TROPOMI CO
measurements and the simulation of the WRF-CHEM model for single-orbit
overpasses. We distinguish the cases, only fitting background parameters
(a priori) and additionally fitting the 10 tracer fields (unregularized
and regularized). (b) Improvement of the goodness of the fit (χ2)
when fitting the 10 tracer fields (unregularized and regularized)
relative to the (a priori) case. For the regularized fit we
only show the χ2 of the urban district ACdMx because it provides
good data coverage when filtering for the degree of freedom.
The χ2 values in Fig. clearly show that the
agreement between TROPOMI and WRF-Chem can be improved by fitting the
emissions of the different city districts (blue line) instead of using
the INEM inventory (grey line). The regularization approach increases
the χ2 values (green bars) because the inversion cannot
compensate so well for differences between TROPOMI and WRF-Chem by choosing unrealistic
emissions. However, the χ2 values of the final fit are still
lower than the ones for the a priori INEM emissions (grey line). Overall,
the χ2 values exceeds 1 which indicates that the difference
between TROPOMI and WRF-Chem is dominated by systematic errors in the WRF-Chem
simulation. Figure shows the χ2
values for the difference between the TROPOMI CO observations of single overpasses and the WRF-Chem simulations
over the considered time range of the
study. The χ2 values follow a seasonal pattern with enhanced values
during the biomass burning season between February and June and low values
during the rainy season between June and November. As mentioned before, in the
vicinity of other pollution sources (e.g., wildfires) the background
variability in CO becomes more complex and can interfere with the retrieved local emissions of Mexico City. Hence, a better model of the CO background concentration
and its variability is needed to cope with this effect. However,
Fig. also shows that
fitting the emissions of the different city districts significantly
improves the χ2 between TROPOMI and WRF-Chem over the whole
time range and the improvement can be even more than 50 % for single overpasses
compared to cases of simulations only using the emission values provided by the INEM inventory.
Averaging-kernel matrices showing the sensitivity and
cross-sensitivities for the scaling of the different tracer fields. The
same cases as in Fig. are shown for the dates (a) 20 September, (b) 7 November, (c) 19 November 2018, and (d) 17 August 2019 but deploying the regularized retrieval.
For a correct interpretation of the retrieved emissions the averaging
kernel, as shown in Fig. for four example cases, offers
several advantages. The figure shows that generally the averaging
kernels have high values on the diagonal, indicating high sensitivity
to the quantity to be retrieved. It indicates that TROPOMI
measurements can be used to distinguish emissions of the different
urban districts of Mexico, with the exception of the emissions of the
Tulancingo district. Due to the small mean emissions, the averaging
kernel indicates a low sensitivity of the data product. Furthermore, the
averaging kernel shows cross-correlations between the different
elements of the state vector due to the regularization. Although these
interdependencies exist, e.g., between the emissions of CdMx and ACdMx
as shown in panel (d) of Fig. , these are still small
compared the diagonal. The averaging-kernel information is very
useful to filter the emission product with respect to the information
provided by the TROPOMI measurements. Using the sensitivity of
individual sources, this results in a different number of coincidences
for the different districts (panel c of Fig. ).
This form of data mining optimizes the data use, keeping in mind that
TROPOMI overpasses may be appropriate for determining one specific source
but not all sources simultaneously. In this manner, error propagation
in the inversion can be minimized.
Retrieved CO emissions from the TROPOMI data for the tracers CdMx
(left column) and ACdMx (right column). (a) Time series of individually
retrieved CO emissions. The error bars indicate the error in the fit, and
the black line is the time-invariant a priori used in the fit. (b) Degree of
freedom of the scaling factor for the tracer field. Only data with degrees of freedom (dofs) >0.3 are
accounted for. (c) Weekly cycle of the CO emissions. Median values are shown, and
the error bars are the standard error in the mean deploying the delta percentile
as a robust estimation of the standard deviation.
Due to the little scatter and the higher number of data of the final data
product for the suburbs CdMx and ACdMx, we can draw conclusions about the time-dependent variability in emissions. Figure a
shows the time series of the emissions for CdMx and ACdMx, which vary
around the a priori value. This temporal variation is determined from
the measurements as all a priori information is assumed to be time
invariant. The scatter of the data is still high and even includes
negative values. Even though negative emissions are not physical, we
need to keep them in our analyses because filtering negative noise
can induce a positive bias in the mean. Panel (b) of
the figure shows relatively high values of the diagonal elements of
the averaging kernel for the emissions of the two urban districts.
Finally, panel (c) of the figure indicates a clear weekly CO cycle in
the data with low values during weekends. During the week, the CO
emissions of the two districts do not differ significantly due to the
error estimates, and more TROPOMI data are required to further constrain
the weekly cycle. We found that the
CO values on Saturday and Sunday are equally low. An explanation for this
could be that the main source of CO in Mexico City during the week is
traffic which is responsible for the weekly cycle and
the remaining sources of cooking, water heating, etc. should not
change much during the weekend.
Weekly cycle of the CO concentration. (a) Based on 29 in situ
measurement station operated by SEDEMA. (b) Ground-based FTIR
vertical column measurements of an instrument located in Mexico. Median values are
shown, and the error bars are the standard error in the mean deploying the delta
percentile as a robust estimation of the standard deviation.
A similar weekly cycle is observed by Mexico City in situ measurements
provided by 29 SEDEMA ground stations. For each of the sites, we use
data from 2017 to 2018 for the overpass time of TROPOMI (12:00–15:00 local
time), calculate a weekly cycle, and group the data into the stations
located in the CdMx urban area and those located in the wider area of
the metropolis. Figure a depicts the median of all
weekly cycles and the standard error in the mean
with a clear minimum during weekends. The error
bars indicate that the overall shape of the weekly cycles for the
remaining days varies a lot from station to station.
The lower CO concentrations during the weekend are also detectable
with column retrievals from ground-based FTIR measurements in Mexico
City, 2280 m a.s.l.; 19.32∘ N, -99.18∘ E, at the campus
of the National Autonomous University of Mexico by the Center of Atmospheric Sciences (CCA).
The spectra used are recorded in the mid-infrared with a resolution of
0.075 cm-1, and the CO
column and profile is retrieved using the standard NDACC retrieval
strategy . Figure b
shows the averaged weekly cycle with the standard error derived from the
column measurements. Due to the low data density at weekends we used
the full time range from 5 December 2010 to 10 September 2019 without filtering for the overpass time of TROPOMI. These
independent ground-based measurements confirm the weekly CO cycle
found in the TROPOMI data. In general, the TROPOMI CO data product agrees
very well with retrievals from ground-based FTIR measurements performed by the TCCON
network worldwide with an averaged bias of less than 6±3.8 ppb, and the bias with
retrievals from NDACC measurements is even lower .
Conclusions
In this study, we analyzed TROPOMI CO retrieval from 551 overpasses of
the instrument over central Mexico, which corresponds to about 2 years
of measurements starting from the 14 November 2017 until the 25 August 2019. We found that urban pollution can be monitored by the TROPOMI CO
data. The high signal-to-noise ratio of the measurements allowed us to
distinguish distinct CO enhancements over the various urban districts
of central Mexico using single-orbit overpasses of TROPOMI with a high
spatial resolution of 7×7 km2 that is enhanced to 7×5.6 km2 from
6 August 2019 onwards.
With a dedicated WRF-Chem tracer simulation for the full-time range of
the current TROPOMI data record, we could distinguish the contribution
of 10 urban districts: Tula, Pachuca, Tulancingo, Toluca, Cuernavaca,
Cuautla, Tlaxcala, Puebla, CdMx, and ACdMx. The model data were
collocated with the TROPOMI measurements and convolved with the total
column averaging kernel to account for the vertical sensitivity of the
instrument. Here, the WRF-Chem tracer simulation does not account for
atmospheric chemistry, and so the simulated CO tracer fields are linear
in the emission rates of the different districts. The model is
extended by two effective parameters describing a spatially constant
CO background and a dependency of the simulated column on terrain
height.
The CO emissions are determined in two steps. First, we apply an
unregularized least-squares fit of the model to the TROPOMI
observations to determine the averaged emissions per district. A
strict data screening based on the measurements and WRF-Chem model
simulation reduced the TROPOMI dataset from 551 to 199 overpasses.
Second, we solve a regularized least-squares problem, which minimizes
the variation in the emissions around their mean to reduce the noise
propagation in the inversion. By means of appropriate regularization
parameters, we reduce the scatter of the retrieved emissions to about
60 % of the median for all urban districts. For data interpretation
and screening, the use of the averaging kernel is of great
advantage. The final retrieval product includes an averaging kernel as
a retrieval diagnostic, which allows us to analyze retrieval
sensitivities and cross-correlations between the inferred emission
rates.
The derived averaged emissions for the various urban districts of
Mexico deviate significantly from emission estimates of the
Inventario Nacional de Emissions de Contaminantes Criterio (INEM)
inventory adapted to the period 2017–2019. The TROPOMI emissions from
the urban districts Tula (0.10±0.004Tgyr-1) and Pachuca (0.09±0.005Tgyr-1) in the north of Mexico City deviate significantly
from the INEM inventory with 0.008 Tgyr-1 for both areas. For the
emissions of the central part of Mexico City (CdMx), TROPOMI indicates
0.14±0.006Tgyr-1 versus 0.25 Tgyr-1 INEM emissions, and for the ACdMx district, TROPOMI indicates 0.28±0.01Tgyr-1 versus 0.14 Tgyr-1 INEM emissions.
Together, both districts have similar emissions with 0.42 Tgyr-1 seen
by TROPOMI versus 0.39 Tgyr-1 from the inventory, pointing to a
different relative distribution of the CO emissions seen by TROPOMI.
Moreover, using a posteriori data screening to optimize data selection
per emission source allows us to distill a weekly cycle of CO emissions
at the districts CdMx and ACdMx from the dataset with a clear minimum
during weekends. This finding is in agreement with in situ
observations and ground-based FTIR measurement in the metropolis.
Our study shows the need for and the potential of regional atmospheric
transport modeling for the interpretation of TROPOMI CO data over
metropolitan areas like Mexico City. Here, the CO pollution is a
composite of emissions from different districts, and its transport
leads to complex CO enhancement patterns in the atmosphere. The
WRF-Chem tracer model could simulate the TROPOMI measurements to a
great extent; however model errors are still significant and further
improvement is required to fully explore the TROPOMI CO observations
of urban sources. Another potential error source of our method is
the accuracy of the week-to-week and monthly variations in the
emissions in the INEM inventory considering the fixed overpass time of
TROPOMI. Furthermore, basin cities can be problematic with low wind
speed for days, which could lead to the accumulation of signals from more than
1 d in the basins, and this is not yet covered by our approach. To
account for this effect in our inversion needs major adjustments,
which will be investigated in follow-up studies.
Data availability
The TROPOMI CO dataset of this study is available for
download at ftp://ftp.sron.nl/open-access-data-2/TROPOMI/tropomi/co/ (last access: 16 December 2020, ).
The in situ measurements in Mexico City were downloaded from http://www.aire.cdmx.gob.mx (last access: 16 December 2020, ).
The ground-based FTIR measurements in Mexico can be downloaded from http://www.epr.atmosfera.unam.mx/ftir_data/UNAM/CO/VERTEX/v1/ (last access: 16 December 2020, ).
Author contributions
TB and JL performed the TROPOMI CO retrieval and data
analysis. AGR, GM, and BMM performed the WRF-Chem simulation. WS and MG provided the ground-based FTIR measurements.
All authors discussed the results and commented on the
manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
The presented work has been performed in the frame of the
Sentinel-5 Precursor Validation Team (S5PVT) or Level-1 and Level-2
Product Working Group activities. Results are based on preliminary
(not fully calibrated or validated) Sentinel-5 Precursor data that will
still change. The results are based on S5P L1B version 1 data.
Special issue statement
This article is part of the special issue “TROPOMI on Sentinel-5 Precursor: first year in operation (AMT/ACP inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The presented material contains modified Copernicus data (2017, 2018).
The TROPOMI data processing was carried out on the Dutch National
e-Infrastructure with the support of the SURF cooperative.
This project was partially supported by the LANCAD-UNAM-DGTIC-179 supercomputer system.
Review statement
This paper was edited by Hartmut Boesch and reviewed by two anonymous referees.
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