NO_{2} vertical profiles and column densities from MAX-DOAS measurements in Mexico City

NO_{2} profiles and vertical column densities in Mexico City

NO_{2} vertical profiles and column densities from MAX-DOAS measurements in Mexico CityNO_{2} profiles and vertical column densities in Mexico CityMartina Michaela Friedrich et al.

Martina Michaela Friedrich^{1,2},Claudia Rivera^{1,3},Wolfgang Stremme^{1},Zuleica Ojeda^{1},Josué Arellano^{1},Alejandro Bezanilla^{1},José Agustín García-Reynoso^{1},and Michel Grutter^{1}Martina Michaela Friedrich et al. Martina Michaela Friedrich^{1,2},Claudia Rivera^{1,3},Wolfgang Stremme^{1},Zuleica Ojeda^{1},Josué Arellano^{1},Alejandro Bezanilla^{1},José Agustín García-Reynoso^{1},and Michel Grutter^{1}

^{1}Centro de Ciencias de la Atmósfera, Universidad Nacional
Autónoma de México, Mexico City, Mexico

^{2}Belgian Institute for
Space Aeronomie (BIRA-IASB), Brussels, Belgium

^{3}Facultad de Química, Universidad Nacional Autónoma de México,
Mexico City, Mexico

^{1}Centro de Ciencias de la Atmósfera, Universidad Nacional
Autónoma de México, Mexico City, Mexico

^{2}Belgian Institute for
Space Aeronomie (BIRA-IASB), Brussels, Belgium

^{3}Facultad de Química, Universidad Nacional Autónoma de México,
Mexico City, Mexico

We present a new numerical code, Mexican MAX-DOAS Fit (MMF),
developed to retrieve profiles of different trace gases from the network of
MAX-DOAS instruments operated in Mexico City. MMF uses differential slant
column densities (dSCDs) retrieved with the QDOAS (Danckaert et al., 2013) software. The
retrieval is comprised of two steps, an aerosol retrieval and a trace gas
retrieval that uses the retrieved aerosol profile in the forward model for
the trace gas. For forward model simulations, VLIDORT is used
(e.g., Spurr et al., 2001; Spurr, 2006, 2013). Both steps use
constrained least-square fitting, but the aerosol retrieval uses Tikhonov
regularization and the trace gas retrieval optimal estimation. Aerosol
optical depth and scattering properties from the AERONET database, averaged
ceilometer data, WRF-Chem model data, and temperature and pressure
sounding data are used for different steps in the retrieval chain.

The MMF code was applied to retrieve NO_{2} profiles with 2 degrees of
freedom (DOF = 2) from spectra of the MAX-DOAS instrument located at the Universidad Nacional Autónoma de
México (UNAM)
campus. We describe the full error analysis of the retrievals and include a
sensitivity exercise to quantify the contribution of the uncertainties in the
aerosol extinction profiles to the total error. A data set comprised of
measurements from January 2015 to July 2016 was processed and the results
compared to independent surface measurements. We concentrate on the analysis
of four single days and additionally present diurnal and annual variabilities
from averaging the 1.5 years of data. The total error, depending on the exact
counting, is 14 %–20 % and this work provides new and relevant information
about NO_{2} in the boundary layer of Mexico City.

Air pollution is a serious environmental problem due to its negative impacts
on human health and ecosystems. Fast-growing urban and industrial centers are
continuously affected by bad air quality, and in order to assess their current
efforts to mitigate emissions and plan for more efficient strategies to lower
the concentration levels of harmful contaminants, it is indispensable to have
the proper tools to measure them not only at ground level but also throughout
the boundary layer (García-Franco et al., 2018). The multi-axis differential
optical absorption spectroscopy (MAX-DOAS) technique
(e.g., Hönninger et al., 2004; Platt and Stutz, 2008) has rapidly developed in
recent years and has proven extremely valuable in tropospheric chemistry and
air pollution studies since it provides vertical distribution of trace gases
with high temporal resolution.

Figure 1Flowchart of the complete trace gas retrieval algorithm. Orange
boxes belong to the aerosol retrieval and green boxes to trace gas retrieval.
The light blue box encompasses forward model input calculation. The dark blue
boxes are in- and outputs of QDOAS. The yellow boxes represent the forward
modeling steps. The red boxes are the inversion steps, using Tikhonov
regularization for aerosol retrieval and optical estimation (OE) for trace
gas retrieval.

This remote-sensing technique is based on the spectroscopically resolved
measurement of scattered sunlight at different elevation angles, allowing for
the retrieval of total column amounts of aerosols and trace gases with
profiling capability. Powerful applications of this technique have been
demonstrated to provide useful information about the vertical distribution of
aerosols (Frieß et al., 2006; Wang et al., 2016) and photochemically relevant species
such as nitrogen dioxide (NO_{2}), formaldehyde (HCHO), glyoxal (CHOCHO) and
nitrous acid (HONO) among other gases (e.g., Wittrock et al., 2004; Wagner et al., 2011; Ortega et al., 2015; Hendrick et al., 2014).

Photochemical reactions involving NO_{2} play an important role in the
formation of O_{3}(Finlayson-Pitts and Pitts, 2000). The Mexico City
metropolitan area (MCMA) has been particularly affected since the 1990s by
high-O_{3} episodes threatening the population and forcing the
authorities to impose strict restrictions on the usage of motor vehicles
(Molina and Molina, 2002). Measurements have been performed in the region using
fixed and mobile DOAS zenith-scattered sunlight
(Melamed et al., 2009; Johansson et al., 2009; Rivera et al., 2013). In 2014, a MAX-DOAS network,
initially consisting of four instruments, was established in the Mexico City
metropolitan area (MCMA) and has been operating since (Arellano et al., 2016).

In this contribution, we describe the MMF (Mexican MAX-DOAS Fit) code that has
been implemented to retrieve vertical distribution of aerosols and trace
gases with emphasis on the errors and diagnostics of the results. An overview
of the MAX-DOAS instruments is provided in Sect. 2 and the
complete retrieval strategy from the measured spectra to vertical trace gas
profiles is summarized in Fig. 1.

Radiative transfer simulations (constituting the forward model in MMF, yellow
boxes in Fig. 1) are performed with VLIDORT
(Spurr, 2013) to derive simulated differential slant column densities
(dSCDs) at the middle of the corresponding wavelength interval used to derive
dSCDs from measured spectra. The dSCD retrieval (blue boxes in
Fig. 1) is performed with QDOAS (Danckaert et al., 2013) and is
described briefly in Sect. 3. The orange parts in the figure
refer to the aerosol retrieval while the green parts belong to the trace gas
retrieval. Details on the forward model choice, the forward model input
calculation (light blue box in Fig. 1) and processing of
output quantities in the inversion algorithm are described in
Sect. 4.

An error analysis has been included in Sect. 5,
in particular investigating the effect of the aerosol retrieval on the
NO_{2} results. Some examples of the NO_{2} variability are
provided from one of the network's stations and compared to surface
concentrations in Sect. 6, and a summary of the work and
an outlook on planned improvements to the retrieval code are presented in
Sect. 7.

An instrument based on the MAX-DOAS technique was designed and developed by
the Center for Atmospheric Sciences at the Universidad Nacional Autónoma de
México (UNAM). It consists of two main parts: the scanner unit, which
collects the scattered light, and the acquisition–control unit, containing a
spectrometer and computer that record and store the measurements. The two
components are connected by an optical fiber and a data connection cable.
Both are described briefly in the following sections. For a more complete
description and a map of the area with the station locations and their
measurement orientation, we refer the reader to Arellano et al. (2016).

2.1 The scanner

The scanner is composed of a plastic enclosure (NEMA-rated type 3) resistant
to sunlight and hermetically sealed to protect the inner parts from water and
bugs. This is important in order to assure long measurement periods under
harsh conditions (Hönninger et al., 2004; Galle et al., 2010; Arellano et al., 2016). The scattered
light is collected by a plano-convex quartz lens (Edmund Optics, ∅=25.4 mm, f=100 mm), focusing it into the entrance of an optical
fiber (Fibertech Optica, quartz, 6 m long, ∅=0.6 mm), which
transfers the light into the acquisition–control unit. These optical parts
are mounted inside a telescope housing constructed of Nylamid material. A
shutter system is also installed within the telescope. It consists of a
stepper motor (Mercury, 7.5^{∘} by step) that rotates a metal circular
plate to prevent the passage of light into the optical fiber and an optical
switch which is used to indicate the position of the plate. The shutter is
used to make measurements of dark spectra in between scans.

A stepper motor (Oriental Motors, PK266-02A) inside the enclosure allows the
movement of the scanner unit in a range of 180^{∘} with steps of
0.1^{∘}. A mechanical switch is used as a reference to indicate the
starting position of each scan. The motor is controlled by an electronic
board composed of an 8-bit microcontroller (AVR architecture), a RS-232 port
for communication with the acquisition–control unit, a temperature sensor
(Maxim 18B20, accuracy ±0.5 ^{∘}C) and a dual-axis accelerometer
(Analog Devices, accuracy ±0.1^{∘}) that provides an accurate
determination of the telescope's pointing elevation. The theoretical field of
view (FOV) of the optical system is 0.31^{∘}.

2.2 The acquisition–control unit

The second part of the instrument consists of a metallic housing receiving
the collected light through the optical fiber and sending it to the
spectrometer (Ocean Optics, USB2000+). This commercial device has a crossed
asymmetric Czerny–Turner configuration, diffraction grating
(1800 lines mm^{−1}) and a slit size of 50 µm
wide × 1 mm high, recording spectra in a wavelength range of
289–510 nm at a resolution of 0.69 nm (full width at half maximum). It
uses a charge-coupled device (CCD) detector array (Sony ILX511B) of
2048 pixels with an integration time adjustable between 1 ms and 65 s.

Because changes in temperature can affect the wavelength ∕ pixel ratio
and also modify the optical properties of the spectrometer like the alignment
and the line shape (e.g., Carlson et al., 2010; Coburn et al., 2011), a temperature control
system composed of a Peltier cell (Multicomp) and three temperature sensors
(Maxim 18B20, accuracy ±0.5^{∘}C) controlled by an electronic
board was implemented. The cooling side of the Peltier cell was placed on
top of the spectrometer and the heating side was attached to a heat sink. The
Peltier cell and the spectrometer were wrapped in a styrofoam box to keep the
temperature insulated from the outside. The three temperature sensors were
placed on the heat sink, the Peltier cell and the spectrometer to monitor
temperature changes. The temperature control system was wrapped in an
aluminum enclosure to prevent the heat spreading to other parts of the system
and a fan was installed to extract the heat from the enclosure.

The electronic board for the temperature control is composed of an 8-bit
microcontroller (AVR, architecture) and a RS-232 communication port. This
board is responsible for obtaining the data from the sensors and adjusting
the voltage in the Peltier cell to keep the temperature constant.

The acquisition–control unit has a laptop computer (Dell, Latitude 2021)
with a Linux operating system
contained within the enclosure. The program controlling the hardware is
written in C++ using Qt libraries. A script is used to carry out the
measurement sequence previously defined and to monitor the spectrometer
temperature.

2.3 Measurement strategy

A complete scan consists of a sequence that begins with a measurement towards
the zenith (90^{∘} elevation angle), followed by tree measurements
between elevation angles of 0 and 10^{∘} towards the west (for UNAM
station, this is an 85^{∘} azimuth angle); then 12 measurements are
taken with elevation angles between 10^{∘} towards the west and
10^{∘} towards the east (crossing the zenith, but without taking a
measurement), followed by three measurements between 10 and 0^{∘}
elevation angle towards the east. The same sequence is then repeated but in
reverse order. At the end of this cycle, a dark spectrum with the closed
shutter is taken. With this setup, a complete scan takes about 7 min. The
measurement sequence (90^{∘} zenith, 0, 2, 6, 13, 23, 36, 50, 65,
82^{∘} W, 82, 65, 50, 36, 23, 13, 6, 2, 0^{∘} E) is likely to
change in the future to use longer integration times but at the same time
reduce the number of viewing directions in the range between a 10^{∘}
elevation angle towards the west and a 10^{∘} elevation angle towards
the east in order to keep the total scan time roughly constant.

All data presented in this paper use this setup, meaning that we
include both westerly and easterly viewing directions in the same retrieval
and hence the result represents an average. This strategy leads to larger
fitting errors since this likely deviates further from the assumption of
horizontal homogeneity. With our retrieval chain, it is possible to consider
the easterly and westerly directions separately to investigate the
differences in viewing directions, which is the subject of current investigation.

The output from the MAX-DOAS instruments consists of five files per complete
scan: a file containing the metadata of each
spectrum (e.g., accelerometer data for each measurement, time of the
acquisition, temperatures within the acquisition unit), all the spectra in
non-zenith directions, the dark spectrum measured with the shutter closed, a
metafile for the dark spectrum and the first zenith reference measurement.
These files are stored for further processing.

3 Differential slant column density (dSCD) retrieval

The spectra were evaluated using the QDOAS (version 2.105) software
(Danckaert et al., 2013). As a preprocessing step before the QDOAS analysis, the dark
signal was subtracted from each of the measurement spectra. A wavelength
calibration was conducted in QDOAS by applying a nonlinear least-squares fit
to a solar atlas (Kurucz et al., 1984).

For NO_{2}, the retrieval was conducted in the 405 to 465 nm wavelength
range using the spectrum measured at the zenith position at the beginning of
each of the measurement sequences as reference. Using a single zenith
reference before the scan (both for NO_{2} and O_{4}) instead of a
linear interpolation of zenith measurements before and after the scan makes
correct measurements during twilight problematic: since upper atmospheric
contributions to the slant column density change rapidly during twilight,
stratospheric NO_{2} could falsely be attributed to the troposphere.
Hence, in the study presented here, no twilight measurements are included.
For the analysis, differential cross sections of NO_{2} at 298 K
(Vandaele et al., 1998), O_{3} at 221 and 241 K (Burrows et al., 1999), and
the oxygen dimer (Hermans et al., 1999) were convolved with the slit function
of the spectrometer and a wavelength calibration file (created using a
mercury lamp) and using the convolution tool of the QDOAS software. A Ring
spectrum, generated at 273 K from a
high-resolution Kurucz file using the Ring tool of the QDOAS software (Danckaert et al., 2013), was also included in the
analysis.

For O_{4}, the retrieval was conducted in the 336 to 390 nm wavelength
range. Differential cross sections of O_{4}(Hermans et al., 1999),
O_{3} at 221 and 241 K (Burrows et al., 1999), NO_{2} at 294 K
(Vandaele et al., 1998), BrO at 298 K (Wilmouth et al., 1999), HCHO at 298 K
(Meller and Moortgat, 2000), and a Ring spectrum
were included in the analysis. A third-degree polynomial was used for the
retrievals.

Figure 2 shows the dSCD retrieval error statistics for
NO_{2} as a function of elevation angle. The plot shows results of
31 448 scans from January to December 2016. Larger dSCD fitting errors are
found for viewing angles closer to the horizon (smaller elevation angles),
likely due to physical interferences during the measurements. As elevation
angles approach the zenith, retrieval dSCD errors decrease considerably. The
dSCD errors are the QDOAS calculated errors, a function of the fit residuals
and the degree of freedom; see Danckaert et al. (2013) for details.

Figure 2The dSCD measurement error statistics for NO_{2} at the UNAM
station as a function of elevation angle for data taken in the year 2016. The
box encloses the 25–75th percentiles, the whiskers are 5 %–95 %, and the
green bar is the mean and the red bar the median.

The method for the trace gas retrieval from slant column densities using the
MMF code is comprised of two parts, an aerosol retrieval using the known
O_{4} profile, and the trace gas retrieval
(e.g., Platt and Stutz, 2008). Both parts consist of the same steps:
a
forward model and an inversion algorithm.

In Sect. 4.1, details about the inversion strategy are
given. Our choice of forward model, VLIDORT
(e.g., Spurr et al., 2001; Spurr, 2006, 2013), and the input
parameter calculation are detailed in Sect. 4.2. How
forward model output quantities are processed for the inversion step is
detailed in Sect. 4.3.

The retrieval time per aerosol and trace gas retrieval with the Mexico City
setup is roughly half a minute for each scan, but highly dependent on the
conditions.

4.1 Inversion theory

The inversion strategy relies on the fact that the problem is sufficiently
linear so that in the iteration procedure, the new value for the quantity
vector in question x (either
the aerosol total extinction per layer or the trace gas optical depth per
layer) can be calculated using a Gauss–Newton (GN) scheme^{1} according to Eq. (1)
(Rodgers, 2000). This step corresponds to the red box and arrows
in Fig. 1.

Here, superscript “T” denotes transposed; superscript “−1” denotes the
inverse. The index i is the iteration index; the subscript “a” indicates
a priori values. S_{m} is the measurement error covariance
matrix, and y denotes the vector of measured differential slant column
densities. F(x_{i}) are the simulated differential slant column
densities, calculated using the forward model with input profile x_{i}.
Both y and F(x_{i}) are vectors of dimension, which is the
number of telescope viewing angles. ${\mathbf{K}}_{i}=\partial \mathit{F}(\mathit{x}{)}^{l}/\partial {\mathit{x}}_{n}$ is the Jacobian matrix at the ith
iteration describing the change in simulated dSCD for viewing angle l when
the profile x in layer n is varied.

In the case of optimal estimation (OE), the regularization matrix
R is equal to the inverse of the a priori covariance matrix,
$\mathbf{R}={\mathbf{S}}_{\text{a}}^{-\mathrm{1}}$. OE regularization is used for trace
gas retrieval. Other regularization matrices are possible; see, e.g.,
Steck (2002).

For the aerosol retrieval used in this study, we use the L1 operator
(R=L1^{T}αL1) where the scaling
parameter α is set to a constant value of 20 and is supplied via an
input script to limit the degrees of freedom (DOFs) to just slightly above 1.
Different scalings for the upper layers and lower layers could be supplied,
as well as a complete regularization matrix R.

4.2 Forward model

Several radiative transfer codes have been developed to serve as forward
models for this kind of retrieval algorithms. For example, there are Monte
Carlo (MC) codes, such as AMFTRAN (Marquard, 1998), TRACY
(von Friedeburg et al., 2002; von Friedeburg, 2003) or PROMSAR (Palazzi et al., 2005). All
these MC radiative transfer codes start with ejecting photons from the
instrument and following the photon path backwards (see also,
e.g., Perliski and Solomon, 1993; Marquard et al., 2000); hence they are sometimes
somewhat confusingly referred to as backward models. The advantage of MC
codes is their high accuracy; the disadvantage is the rather long calculation
time. SCIATRAN (e.g., Buchwitz et al., 1998; Rozanov et al., 2014, 2017) combines multiple-scattering radiative
transfer modeling using the Picard iterative approximation and a chemistry and
an inversion code. It has been heavily used in NO_{2}(e.g., Vidot et al., 2010) and ozone
(e.g., Rahpoe et al., 2013b, a) retrieval from
satellite observations.

Another class of radiative transfer codes, such as VLIDORT and DISORT
(Stamnes et al., 1988; Dahlback and Stamnes, 1991), uses the discrete ordinate
algorithm. This finite difference method is based on finding solutions for an
atmosphere consisting of a number of homogeneous layers using Gaussian
quadrature approximations to the integrodifferential radiative transfer
equation expressed using Legendre polynomials for the phase function and
Fourier series for the intensity.

The big advantage of the second class of codes is their high speed. Another
advantage of VLIDORT is that it calculates not only the intensity but also
analytic Jacobians. The number of calls to the program is not equal to twice
the number of layers (as it would need to be for finite difference
approximation), but a single call is sufficient. MMF uses the intensity part
of VLIDORT, version 2.7, released in August 2014. (V)LIDORT is configured to
return intensity Jacobians with respect to gas absorption layer properties or
aerosol total extinction layer properties.

Figure 3Calculating the gas and air (a) and aerosol (b)
contributions to the layer input parameters for VLIDORT. The green boxes are
primary input to MMF, blue boxes are intermediate quantities and red boxes
are final outputs that are combined for VLIDORT layer input. Here VMR denotes
the a priori volume mixing ratio. Instead of a VMR, an a priori trace gas
density can be supplied, too.

In MMF, the simulation grid is supplied via an input file to the code
together with the corresponding a priori values of the trace gas
concentration and the aerosol profile. The retrieval can be performed on the
whole simulation grid or only on the lower part of the simulation grid. In
this study, the simulation grid is identical to the retrieval grid and the
layer distribution of the retrieval grid is not equidistant. The grid
consists of 22 layers up to 25 km. The layer thickness increases from 100 m
at the lowest layer to 5 km in the uppermost layer. The exact height
distribution can be seen in Fig. 5. The surface
albedo used in this study was set to 0.07; this value can be passed to MMF in
the configuration file. However, in practice the effect for downwelling
intensity and hence for the dSCD calculation was found to be
negligible.

For each simulated atmospheric layer, the following input needs to be
supplied to VLIDORT^{2}:

total layer optical depth, τ;

single-scattering albedo, ω;

phase function expansion coefficients, β.

Since Jacobians with respect to changes of quantity χ in each layer are
required, the normalized derivatives of the total primary optical quantities,
τ, ω and β, with respect to this quantity need to be
supplied as well. In the case of trace gas retrieval, χ=a_{gas}Δh, and in the case of aerosol retrieval, $\mathit{\chi}=({\mathit{\sigma}}_{\mathrm{aer}}+{a}_{\mathrm{aer}})\mathrm{\Delta}h$. Here, a_{gas} and a_{aer} are
the gaseous and aerosol absorption coefficients, respectively, and
σ_{aer} is the aerosol scattering coefficient. Δh is
the layer thickness. Hence, for each layer the following input is
additionally required:

4.

normalized rate of change in τ with respect to χ,

5.

normalized rate of change in ω with respect to χ,

6.

normalized rate of change in β with respect to χ.

In order to calculate this input, we first need to calculate the separate
properties for the trace gases and the aerosols. Afterwards, these quantities
are combined to yield the total layer quantities.

The calculation of the contribution from the trace gas and the air density
(through Rayleigh scattering) for the layer inputs (one to six) is presented in
Sect. 4.2.1. The aerosol contribution calculation is outlined
in Sect. 4.2.2. These two sections also contain information on
the source of information for the respective a priori values, as well as, for
gas, the error covariance matrix.

The calculation of the contributions from gas and aerosol to yield the
VLIDORT inputs (one to six) is detailed in Sect. 4.2.3.

4.2.1 Input for gases

In Fig. 3a, the steps to calculate the contribution of
the trace gas and the contribution from Rayleigh scattering in each layer are
outlined. Red boxes represent the trace gas (and air) contribution to VLIDORT
input quantities, green boxes are primary inputs to the inversion code and light
blue boxes are intermediate quantities that are neither direct input to MMF
nor final input for the forward model.

The a priori volume mixing ratio (VMR) profile and the covariance matrix for
this study are calculated from simulations covering the year 2011 using
WRF-Chem v3.6. The model domain covers Mexico and surrounding seas; the
domain has 200-by-100 square grid cells with approximately 28 km width and
35 vertical layers. The following WRF parameterizations were used: WRF
single-moment three-class scheme (mp_physics = 3), planetary boundary
layer (PBL, bl_pbl_physics = 1), and cumulus option Grell 3-D ensemble
scheme (cu_physics = 5). As a land-surface option, the unified Noah land
use surface model (sf_surface_physics = 2) was used. The time-varying
sea surface temperature parameter was set to “on”, the grid analysis
nudging parameter was set to “on” and the urban canopy model was set to
“off”. The emissions used were for the RADM2 mechanism and are from the
National Emissions Inventory 2008.

MMF takes pressure and temperature profiles on a separate height grid;
internal interpolation to the provided retrieval grid is performed. In the
processing chain implemented in Mexico, the temperature and pressure profiles
from radiosonde data for the specific day are downloaded from the University
of Wyoming
(http://weather.uwyo.edu/upperair/sounding.html, last access: 9 April 2019) from the Mexico City International Airport, station number 76679.
These temperature and pressure profiles are linearly interpolated to the
simulation grid used to calculate the dry air number density ρ; see
Appendix B.

The absorption cross section ξ_{λ} is taken at a wavelength in the
middle of the wavelength interval used for the QDOAS retrieval (see
Sect. 3): for aerosol retrieval it is 361 nm and for
NO_{2} it is 414 nm. The same cross-section tables as for QDOAS dSCD
retrievals (see Sect. 3) are used for NO_{2} and
O_{4}. However, since the cross section cancels out at the end of the
calculation, its exact value is not too important.

There are analytical fits to the temperature dependence, using linear
(e.g. Vandaele et al., 2003, for NO_{2}) or more complicated
(e.g. Kirmse et al., 1997) temperature dependences, for example. For a comparison
study of different fitting functions, see Orphal (2002). These could be
implemented; however, the cross section is currently not temperature
dependent, in agreement with the assumption made for defining dSCDs.

The trace gas absorption coefficient is calculated according to
Eq. (2):

For the calculation of the depolarization ratio Δ, the main
contributions are from N_{2}, CO_{2}, O_{2} and Ar. Our
implementation follows Bates (1984). Details are given in
Appendix B. For the calculation of the Rayleigh
cross section Q_{Ray}, we follow the implementation of
Goody and Yung (1989) (their Eq. 7.37); see also
Platt et al. (2007) (their Eq. 19.5). The air scattering coefficient
can then be calculated according to Eq. (3):

The (a priori) aerosol data for total optical depth, average single-scattering albedo ω and asymmetry parameter g (used to calculate the
phase function moments) are time interpolated values from the co-located
AERONET (Aerosol Robotic Network) station in Mexico City (V2, level 1.5 at
http://aeronet.gsfc.nasa.gov, last access: 5 November 2018). They are also extra- and interpolated at the retrieval wavelength. The
a priori shape of the profile is taken from hourly averaged ceilometer data
(García-Franco et al., 2018), interpolated at the middle layer height h of
each layer.

The first part of MMF, the aerosol retrieval, is limited to the aerosol
density profile and the total aerosol optical depth. The average single-scattering albedo ω and asymmetry parameter g are not subject to
retrieval and are assumed to be constant in all layers.

Figure 3b outlines the strategy how the aerosol
contribution to the VLIDORT layer input parameters is calculated.

The hourly averages of (relative) aerosol density profiles in arbitrary units
from ceilometer measurements between November 2009 and February 2013 are
interpolated at measurement day time t and at retrieval grid middle heights
h to provide a relative aerosol profile. This profile, turned into a
partial optical depth per layer by multiplying with the layer thickness, is
scaled to match the total aerosol extinction from AERONET
τ_{aer}. The profile is then converted back into an
intensive^{3} quantity by division by layer thickness, usually known as the
aerosol extinction profile (extinction per unit length, AE). This is then
used to calculate the aerosol scattering coefficient σ_{aer} in
each layer by multiplying the layer aerosol extinction with the aerosol
single-scattering albedo ω_{aer}.

The aerosol absorption coefficient a_{aer} is the layer aerosol
extinction times (1−ω_{aer}), and the aerosol phase function
coefficients β_{aer,l} can be calculated via the Henyey–Greenstein phase function and the asymmetry parameter g (see
Eq. 5) e.g., Hess et al. (1998), where l
denotes the moment,

As mentioned in the introduction to this section, the layer input parameters
for VLIDORT are total optical depth τ and single-scattering albedo
ω in the layer, as well as the phase function coefficients
β_{l}. The quantities which have been calculated so far are the
aerosol and air contributions to β_{l} and the air and aerosol
scattering coefficients σ_{air} and σ_{aer}, as
well as the absorption coefficients a_{gas} from the trace gas in
question and a_{aer} from the aerosol in each VLIDORT input layer
(red boxes in Fig. 3). These quantities need to be
combined to total layer input parameters.

The total layer optical depth is simply the product of the layer thickness
and the sum of all extinction and scattering coefficients; see
Eq. (6):

Since Jacobians with respect to changes in quantity χ in each layer are
required, the normalized derivatives of the total primary optical quantities,
τ, ω and β, with respect to this quantity need to be
supplied. In the case of trace gas retrieval, χ=a_{gas}Δh,
and in the case of aerosol retrieval, $\mathit{\chi}={\mathit{\sigma}}_{\mathrm{aer}}\mathrm{\Delta}h+{a}_{\mathrm{aer}}\mathrm{\Delta}h$. Therefore, what needs to be done is to calculate
the normalized derivatives of
Eqs. (6)–(8) with respect to these
quantities. It should be remembered that β is a vector.

The three quantities for aerosol are calculated according to
Eqs. (9)–(11).

The forward model outputs, intensities and intensity Jacobians need to be
converted into differential slant column densities and corresponding
Jacobians. For each set of dSCDs, we have to run the forward model two times:
Once with the gas absorption included and once without the gas absorption.
Each of these sets consists of simulations in the desired telescope
directions and one simulation towards the zenith. Note that the output from
VLIDORT is Kx; hence the immediate output from VLIDORT has
to be divided by x in case of linear retrieval to give the Jacobian
alone.

In the following, the intensity and Jacobian of the simulation towards the
desired angles without gas absorption are denoted ${I}_{\mathrm{0}}^{\mathit{\alpha}}$ and
${\mathit{K}}_{\mathrm{0}}^{\mathit{\alpha}}$, the intensity (Jacobian) with gas absorption towards the
zenith ${I}_{\mathrm{g}}^{\text{z}}$ (${\mathit{K}}_{\mathrm{g}}^{\text{z}}$), the
intensity (Jacobian) with gas absorption towards the desired angle
${I}_{\mathrm{g}}^{\mathit{\alpha}}$ (${\mathit{K}}_{\mathrm{g}}^{\mathit{\alpha}}$) and the intensity
(Jacobian) without gas absorption towards the zenith ${I}_{\mathrm{0}}^{\text{z}}$
(${\mathit{K}}_{\mathrm{0}}^{\text{z}}$). The dSCD can be calculated as in
Eq. (15).

The errors of gas profile retrievals in remote-sensing applications can be
divided into the following four different types (Rodgers, 2000):

smoothing error, arising from the constraint of the NO_{2} and aerosol
profiles;

measurement noise error, arising from the noise in the spectra;

parameter error, including different parameters as aerosol profiles and cross sections; and the

forward model error, which originates from the simplification and uncertainties in the radiative transfer
algorithm.

The gain matrix, as defined by Eq. (17), describes the
change in retrieved x when measurement y changes. It can
hence be used to map the uncertainty in measurement space to a state-vector
uncertainty.

The error analysis in general produces an error pattern ϵ_{y} in the
measurement space of vectors of the dSCDs retrieved by QDOAS and simulated by
MMF. With help of the gain matrix, this error pattern is then mapped
as ϵ_{x} into the space of the solution state.

5.1 Smoothing error

The retrieved atmospheric state vector x only represents a smoothed
version of the true real state. The partial column averaging kernel matrix
AK_{pcol} describes how a change in the true atmospheric
state vector x_{true} is translated into changes in the
retrieved state vector x, according to Eq. (18):

The averaging kernel of the partial column and total column
(AK_{tot}) describe how the retrieved solution profile
depends on the real atmosphere x_{true}. They have to be taken
into account if the profile and the column are used. The part which is not
explained by AK_{pcol} is explained by the retrieval error
ϵ_{x}.

As MMF uses constrained least-square fitting, the AK_{pcol}
is calculated analytically by MMF itself for each scan according to
Eq. (19):

The averaging kernel matrix is in general asymmetrical. The columns
represent response functions related to a perturbation in a certain layer.
The rows of the matrix represent the sensitivity of the NO_{2} partial column
of a certain layer (or concentration in a certain layer) to all the different
true partial columns or concentrations.

If AK_{pcol} is expressed in units of partial columns (as
here indicated by the subscript “pcol”), the sum of the rows is the total
column averaging kernel. It represents the sensitivity of the vertical column
density (VCD) to the anomalies in different heights.

The change in averaging kernel under transformation of the units is expressed
by two matrix multiplications: one by multiplying the diagonal matrix
containing the partial air column in its diagonal U_{aircols}
from the right with the averaging kernel and the other by multiplying the
inverse of the diagonal matrix ${\mathbf{U}}_{\text{aircols}}^{-\mathrm{1}}$ from the left
side with the averaging kernel; see Eq. (20):

The trace of the AK_{tot} matrix represents the DOF, the
number of independent pieces of information in the profile retrieval, which
is around two for the NO_{2} retrieval.

Equation (18) separates the smoothing effect described
with AK matrices from the other errors. Before comparing the
retrieved profiles of MMF with profiles from models or other measurements,
such as satellite measurements, it is necessary to smooth the profile from
the other source with the AK from MMF (Rodgers and Connor, 2003).
Alternatively, a smoothing error S_{smooth} for the profile
can be calculated according to Rodgers (2000) from the a priori
covariance matrix S_{a} and the averaging kernel. The latter
is describing how the true atmospheric state varies, as a best estimate.
Equation (21) describes how sensitivity of the retrieved atmospheric
state vector depends on the true atmospheric state:

Both quantities can be given in different representations adjusted to the
atmospheric state vector as a fraction of the a priori, the VMR
(“VMR”), as number densities or as partial column profiles (“pcol”) as used
above. Using AK_{VMR}, S_{smooth} can be
calculated according to Eq. (22):

The smoothing error for the VCD is then calculated from the full covariance
matrix of the smoothing error S_{smooth} using a total column
operator; see Eq. (23):

where g is the total column operator for partial column profiles:
${\mathit{g}}^{\text{T}}=(\mathrm{1},\mathrm{1},\mathrm{1},\mathrm{1},\mathrm{1},\mathrm{1},\mathrm{1},\mathrm{1},\mathrm{1},\mathrm{\dots}\mathrm{1})$.

As described before, the NO_{2} retrieval code uses a single a priori
profile
and covariance matrix taken from the chemical transport model WRF-Chem. This
results in the sensitivities and smoothing errors represented in
Fig. 4a.

The aerosol retrieval, however, uses a Tikhonov constraint and no covariance
matrix is available. The a priori values for the aerosol extinction profile are
reconstructed by the actual total aerosol optical depth reported by the daily
AERONET measurements and the average vertical distribution, reconstructed
from ceilometer measurements for each hour of the day. The covariance matrix
for the aerosols S_{a, aerosol} is obtained by assuming a
100 % variability of the used a priori profile and an exponential
correlation length of η=500 m between the different layers, as in the
recent work by Wang et al. (2017); see Eq. (24):

Figure 4Averaging kernel columns: (a) in VMR representation for
NO_{2} profile retrieval and (b) in the corresponding intensive
quantity for the aerosol extinction (AE) profile. Each colored line shows
the expected response on perturbation in a certain layer and belongs to the
lower axis. The total column averaging kernel, (a) for the
NO_{2} VCD and (b) for the total optical depth, is given as a
black line and belongs to the upper axis. The vertical dashed line in each
panel indicates an ideal sensitivity of 1.0 at all altitudes.

In the cases where no vertical aerosol extinction profile (AE profile)
retrieval is available because the retrieval failed, or no O_{4} dSCDs
were available in the first place, a NO_{2} profile can still be
retrieved by using the a priori AE profile instead of the retrieved one.
Since in that case no information of the O_{4} absorption is used, the
estimated smoothing error S_{smooth} is then directly given
by the a priori covariance matrix S_{a,aerosol} for the
AE profile. The a priori information about the optical properties described
by the aerosol extinction profile is designed for cloud-free days and
therefore the error analysis is just valid for such cloud-free days.

We have assumed a constant sensitivity AK and a constant a priori
covariance matrix S_{a} for the calculation. The
AK of the trace gas profile indeed depends strongly on the aerosol
profile. It also depends slightly on the trace gas profile itself. The
S_{a} covariance matrix of the aerosol extinction profile
should be given for each hour as the a priori profile varies with time of
day. Therefore, the estimation of the smoothing error as it is calculated
here gives just a rough general idea about the size of the smoothing error.

5.2 Measurement noise

The measurement noise error can be calculated from the gain matrix
and the measurement noise matrix (S_{m})
(Rodgers, 2000). The diagonal matrix (S_{m}) is
already used in the optimal estimation of the profile retrieval to weight the
different dSCDs of a scan according to the square of the errors from the
QDOAS retrievals. The statistics of the measurement dSCD errors and its
dependence on the elevation angle have been presented in
Fig. 2.

The measurement noise error matrix for both (1) the NO_{2} profile and
(2) aerosol extinction profile are calculated directly during the retrieval
and are available from the MMF output of each measurement sequence as a full
covariance matrix S_{noise}, given in units of partial
columns and partial optical depths. The NO_{2} total column error is
then calculated from this and the total column operator, and amounts to
around 2.4 % of the VCD (see line 2 in Table 1). The
errors in the profile are shown in Fig. 6.

Table 1Errors in NO_{2} vertical column density as a fraction of a
retrieved VCD of 3.2×10^{16} molec. cm^{−2} measured at 13:58 on
20 May 2016 (UTC − 6). The total error is calculated as the square root of the
sum of squares of different independent errors. Different total errors are
calculated, by including or not including the algorithm error
(Wang et al., 2017), considering the two cases: that an aerosol extinction
profile is retrieved from O_{4} absorption or just an a priori guess is
used. For the assumed variability of the aerosol extinction, the error due to
the uncertainty remaining after an aerosol extinction profile retrieval is
5.1 % instead of 9.8 %. However, if the algorithm error according to
Wang et al. (2017) is included, the remaining error due to the uncertainty in
the aerosol profile is slightly better: 9.3 % instead of the 9.8 %
without O_{4} retrieval.

The parameter errors originate from all uncertainties in input parameters in
the forward model that are not properly fitted. These are, for example, the cross sections
and, for the NO_{2} profile retrieval, also the used aerosol extinction
profile.

5.3.1 Parameter error from spectroscopy

The error originating from an uncertainty in the cross section of 3 %
(Wang et al., 2017) is also around 3.0 % in the vertical column (see line 3
in Table 1) and similar in the vertical profile. This error
can be calculated using the generalized measurement error covariance matrix
${\mathbf{S}}_{\text{spectroscopic}}^{y}$ in the measurement space assuming that
all retrieved dSCDs are 3 % too high (or low) and using the outer product
of the measurement state vector containing the retrieved dSCDs; see
Eq. (25):

From the ${\mathbf{S}}_{\text{spectroscopic}}^{\mathit{x}}$ matrix, the error in the
total column and profile is calculated as shown earlier in
Sect. 5.1. As expected, the error for the total column is 3 %
and the errors in the NO_{2} VMR profile are shown in
Fig. 6. The spectroscopic error is a purely systematic
error and affects all retrievals in the same manner.

5.3.2 Parameter error from aerosol profile

The AE profile is crucial for the NO_{2} profile retrieval because of
its strong contribution to the air mass factor. The uncertainties in this
vector of input parameters arise from errors in the aerosol extinction
profile retrieved from the spectral signature of the O_{4} dimer. For
the propagation of the error of the O_{4} AE retrieval into the
NO_{2} retrieval, we assume here that the total error in the AE profile
depends on just the two contributions: (a) smoothing error and
(b) measurement noise errors.

The effect of the assumed aerosol content in each layer on the retrieved
NO_{2} profile is calculated by a sensitivity study. For this, 22
NO_{2} retrievals are performed for the same measurement sequence
(dSCDs) using slightly modified aerosol extinction profiles. First, a normal
retrieval with the best estimated aerosol extinction profile is retrieved.
Then, the aerosol extinction profile is modified so that the partial
extinction of the ith layer is increased by 1 % of the total optical
depth with respect to the original aerosol extinction profile.

Figure 5Plots showing the results of how sensitive the NO_{2} profile
is to changes in the aerosol extinction profile. (a) Columns of the
matrix D created to describe the response in the NO_{2} VMR
profile (the differences with respect to the original retrieval).
(b) Same as (a) but the response to the perturbation in the aerosol
profile is given as a fraction of the retrieved NO_{2} profile.

The difference between the perturbed and original NO_{2} VMR profiles
is combined into the matrix D, thus describing how the
NO_{2} VMR profile responds to changes in the aerosol at different
heights. The result is presented in Fig. 5.

With the help of this matrix, D_{VMR}, the different errors in
the aerosol extinction profile, as measurement noise (see
Sect. 5.2), smoothing error (see Sect. 5.1) or even
due to the algorithm error according to Wang et al. (2017), can be propagated
to the NO_{2} profile and its VCD. The uncertainty in aerosol,
represented by its S_{a, aerosol} matrix (see
Eq. 24), translates into an uncertainty in NO_{2} VMR
according to Eq. (27):

The propagation of the smoothing and measurement noise errors of the
O_{4} retrieval into the NO_{2} retrieval (separately resulting in
4.6 % and 2.2 %; see lines 6 and 5 in Table 1, respectively) results in a
5.1 % error in the NO_{2} VCD (see line 9 in Table 1). If the
algorithm error introduced by Wang et al. (2017) (7.8 %, line 8 in Table 1)
is included in the error propagated to NO_{2}, the error in NO_{2}
VCD is 9.3 % (line 10 in Table 1). However the algorithm error is
calculated from the resulting residual of the fit and is dependent on the
other error sources as mentioned earlier.

If no O_{4} retrieval is performed successfully, the aerosol a priori
profile is used for the NO_{2} retrieval. The error in this aerosol
profile is estimated by its aforementioned assumed variability and
S_{a} covariance matrix: a variability of 100 % of the a
priori profile is assumed for calculating the diagonal elements and an
exponential correlation length of 500 m is used for calculating the off-axis
elements of the S_{a} matrix. The error in the NO_{2}
VCD would in this case be 9.8 % (see line 7 in Table 1).

Figure 6Altitude-dependent errors in a NO_{2} VMR profile measured at
13:58 on 20 May 2016 (UTC − 6). (a) The square root of the diagonal in
the covariance matrix describing the individual error contributions in the
VMR profile and (b) the retrieved NO_{2} VMR profile with
corresponding error bars (total error and variability). Panel (a) also
includes the a priori profile in green.

The forward model error could be evaluated if an improved forward model was
available. However, this is not the case. Errors in the forward model would
result in systematic structures in the residual and a larger residual than
expected from the error calculated by QDOAS for the slant columns. The least
supported simplification in the forward model is the assumption of a
horizontal homogeneity of gas and aerosols. A horizontal inhomogeneity leads
to a set of slant columns in a scan which cannot be simulated by VLIDORT and
the residuals of measured minus calculated slant columns indicate this error.
Following Wang et al. (2017), we calculate the variance in the residuals as a
function of viewing angle and hence the algorithm error. But as all errors
increase the residual the so-called algorithm error is not identical
to the forward model error. However, it is maybe a good way to check
empirically if there are important error contributions missing in the
analytical analysis.

5.5 Total error

The results from the error estimations are summarized in
Table 1. The overall error in the NO_{2} VCD is estimated
to be around 14.1 % (20.3 % including the algorithm errors for
NO_{2} and O_{4} calculated from the residuals). The contributions
are 12.5 % from smoothing, 2.4 % from measurement noise, 3 % from
spectroscopy and 5.1 % from errors in the aerosol extinction profile. The
algorithm errors are 12.3 % from the NO_{2} retrieval itself and
7.8 % from the O_{4} AE retrieval. These errors are similar to those
reported by Wang et al. (2017).

The error in the vertical column is smaller than the errors in the VMR
profile for almost all layers (Fig. 6). This can be
explained by an anticorrelation in different partial column errors indicated
by the full error covariance matrix. While the measurement noise seems to
play a minor role, the smoothing and the aerosol profile are the main sources
of error. Even though the error might seem large, the retrievals still
provide new and relevant information of NO_{2} within the boundary layer
of Mexico City.

In this section, we present results of the NO_{2} variability measured
at one of the stations comprising the MAX-DOAS network operated in Mexico
City, and compare them with in situ measurements performed at the surface.
The location of the MAX-DOAS station (see Arellano et al. (2016) for a map)
coincides with the AERONET station and the location of the surface sensor
(19.32^{∘} N, 99.18^{∘} W). The launch location of the
radiosondes is close to the airport, around 15 km northeast from UNAM at
19.40^{∘} N, 99.20^{∘} W.

A data set from January 2015 to July 2016 (approx. 19 months) is considered
in this study, in which both MAX-DOAS and in situ surface measurements were
available. A total number of 2531 coincidences of hourly averages were found
in this period, considering that at least six measurement sequences of the
MAX-DOAS instrument in each reported hour needed to be available. The
complete time series is presented in Fig. 7, showing hourly
NO_{2} averages of the in situ surface concentration in red, the total
vertical column from the MAX-DOAS in blue and the average VMR of its first
six
layers closest to the surface in green. This plot includes all MAX-DOAS
results regardless of the sky conditions.

Figure 7Hourly NO_{2} averages of in situ surface concentrations (red)
in parts per billion, total VCDs from the MAX-DOAS instrument (blue) in
molecules per cubic centimeter and the average VMR of the first six layers (up to 550 m)
in the retrieved MAX-DOAS profiles (green), also in parts per billion.

Apart from the large data gaps in the beginning of 2016, the time series is
quite complete and the fitted annual periodic functions (a Fourier series
constrained to a fixed seasonal cycle shape) applied to the three data sets
show a similar pattern. VMR values from the surface measurements are clearly
higher than the VMRs detected in the lowest layers of the MAX-DOAS
retrievals.

The 2531 coincidences were correlated averaging different numbers of layers
starting from the ground and the resulting Pearson's coefficients (R) are
plotted in Fig. 8a. Currently, the integration times in
the spectra from which the O_{4} dSCDs are calculated are not long
enough to ensure an O_{4} dSCD error resulting in DOF larger than 1 for
the aerosol retrieval. Since we use a Tikhonov regularization for aerosol
retrieval, this means that we can basically retrieve the total aerosol
extinction. Due to the topographical circumstances in Mexico City, where the
boundary layer rises steadily during the day, using an a priori profile that is
calculated from hourly averages of ceilometer data will generally provide a
good a priori profile shape. However, for days that likely have a very
different aerosol profile from our a priori profile, the aerosol profile
included in the trace gas forward model accounts for far more than the
estimated error in Sect. 5.3.2 since this estimation
uses a fixed percentage of the a priori profile in the S_{a}
matrix. For cloudy days, for example, the form of the profile will look
considerably different than the ceilometer averages. We are currently not
able to retrieve such profiles. Hence, we limit our analysis to cloud-free
days. Currently, the retrieval chain has no cloud-screening algorithm
included. As an ad hoc solution, we use the presence of AERONET data on that
day as an estimator for cloud-free days. For our entire data set, for about
half of the coincident measurements (1270) there was at least one AERONET
measurement available on that particular day while for the rest of our data
(1261), no AERONET data were available on that day.

Figure 8Pearson's correlation coefficients (a) obtained from hourly
surface and MAX-DOAS coincident measurements as a function of the number of
layers considered in the VMR averages. The slopes obtained from the linear
regressions, performed forcing the intercept to be at zero to the same data
sets, are shown in (b). The layers below layer 6 are very likely
inside the mixed boundary layer, while layers above layer 6 might be on the
edge or above the boundary layer during some hours in the morning
(García-Franco et al., 2018). The blue dashed lines only show data when AERONET
data are available within 2 h of the scan and hence limit the measurements to
cloud-free conditions. This selection criterion improves the correlation
significantly.

Generally, a better correlation is found when averaged MAX-DOAS VMRs are
compared to in situ surface measurements. Average R values larger than 0.7
are obtained in all cases when the first six to eight layers are considered in the
correlation. These correlations decrease rapidly when fewer layers are
considered due to increased errors in the lowermost part of the profile.
Naturally, the R value also decreases towards a larger number of layers
averaged since different air masses are measured at higher altitudes. As
expected, slightly larger correlations are obtained for MAX-DOAS retrievals
using a measured aerosol input from AERONET (blue traces in
Fig. 8), which corresponds to clear-sky conditions.

Linear regressions such as the ones shown in Fig. 9 were generated
for each data set, in order to gain more information as to how the number of
layers averaged relates to the VMR comparison with the in situ surface
measurements. The slope of all coincident data using the first seven to eight layers is
0.45, and it can reach values above 0.6 considering the first six layers in
clear-sky conditions (AERONET data available within ±1 h of the
measurement time). We thus decide to use the six-layer VMR
averages of our MAX-DOAS retrievals in this study, reaching a height of 550 m above the
ground level; in the comparisons we present with surface concentrations.

Figure 9Correlation plot of the MAX-DOAS six-layer VMR averages vs. the
surface measurements shown separately for clear-sky measurements in which
aerosol data on that day were available (blue) and for MAX-DOAS retrievals
with no aerosol optical depth availability from AERONET, corresponding most likely to cloudy
days (red).

To investigate the difference in our data set between clear and cloudy days,
a correlation plot is presented in Fig. 9 showing the linear
regressions produced from the MAX-DOAS six-layer VMR averages for the
retrievals with AERONET availability on that day in blue and those retrievals
using a default aerosol a priori profile in red. A significant improvement in the
correlation coefficient going from 0.54 to 0.74 is evident in the plot. When
all the coincident data are considered, regardless of if the retrieval had
data available from the AERONET instrument on that day or not, the R and
slope values are 0.62 and 0.39, respectively.

Examples of how the averaged six-layer VMRs and VCD compare to the ground
level concentrations on individual days are presented in
Fig. 10. These examples were chosen to depict distinct diurnal
patterns that occur in different times of the year. Again, the MAX-DOAS
six-layer product is consistently lower than the NO_{2} concentrations
measured at the surface. These, together with the corresponding VCDs plotted
on a different y axis, follow the pattern of the surface measurements quite
well. The MAX-DOAS instrument captures the features observed at the surface
but not without some interesting differences.

Figure 10Example days showing the NO_{2} variability in both VMR from
the MAX-DOAS (green, <550 m) and surface measurements (red), as well as
the total vertical column densities (blue).

Figure 11Diurnal NO_{2} variability from hourly means of the entire data
set spanning from January 2015 to July 2016. Only the data with coincident
surface and MAX-DOAS measurements are included for consistency in the
comparison. Vertical bars correspond to the standard deviations.

Figure 12Annual NO_{2} variability resulting from the monthly mean
coincident data (surface and MAX-DOAS) between January 2015 and July 2016.
The standard deviations are given in vertical bars.

The data of 4 May 2016 was in the middle of a high-pollution episode in
Mexico City, in which a restriction on the use of private vehicles was
declared for 4 d. Ozone had surpassed, at least in one of the stations from the
monitoring network operated by the city government, the 165 ppb 1 h average
concentration. It is interesting to see in the 4 May plot that indeed the
surface concentrations of nitrogen dioxide were high in the morning, a
condition favoring ozone production, but there is a large NO_{2} peak
just after noon in the MAX-DOAS VCD and six-layer product data which is not
detected at the surface. This is more evident from the individual
measurements shown as dots than from the hour averages.

Other differences have to do with the evolution of the mixed layer height,
which has been shown to have a rapid growth in the late morning, strongly
influencing the surface concentrations (García-Franco et al., 2018). This is
for example evident in the 22 December plot, where a peak is observed at 11 h
and a second one at 14 h. This peak is strongest at 11 h in the case of the
surface measurement, but the total column shows the peak at 14 h to be
dominant. The mixed layer has grown in the mean time so that the registered
14 h surface concentration is lower despite there being more NO_{2}
molecules in the atmosphere.

In order to see the diurnal and seasonal patterns from both MAX-DOAS and
surface measurements, all coincident measurements between January 2015 and
July 2016 including days with clouds were averaged to a specific hour or
month. For consistency, only the coincident 1 h data were considered in
order to make the data sets comparable. The mean diurnal evolution with
standard deviations as vertical bars is shown in Fig. 11. The
maxima of both surface and MAX-DOAS six-layer products are at around 10 h,
while that of the vertical column is shifted towards noon. This is expected
from what is known from the emissions, growing mixed layer and ventilation
patterns in Mexico City (e.g., García-Franco et al., 2018). Despite the fact
that the offset in the curves for surface- and MAX-DOAS measurements appears
to be nearly constant throughout the day, it would be interesting to
investigate further how this offset varies in different seasons, particularly
when vertical mixing is not favored.

Figure 12, however, presents the compiled data
considered in this study as monthly means in order to observe the seasonal
variability and compare the data sets from these two measurement techniques.
The three data products coincide in having the highest monthly mean values
between April and June. A difference which is probably interesting to note is
during December and January, when the concentrations from the surface
measurements are relatively higher than those from the MAX-DOAS products.
This might have to do with the fact that during these winter months the mixed
layer is shallower than during the rest of the year, and the sensitivity of
the MAX-DOAS instrument is reduced for the lowest layers as described above.

In this contribution we describe the methodology used to analyze the data
produced by the network of MAX-DOAS instruments (Arellano et al., 2016)
operating in the Mexico City metropolitan area. In general terms, MMF is a
retrieval code developed to process the data acquired by the instruments
converting the measured spectra together with other input parameters to
vertical profiles of aerosols and certain trace gases. We have tested the
performance of the code for NO_{2} at one of the stations and present
some diagnostics and a full error analysis of the results. In the case of
NO_{2}, all error sources amount to about 14.1 %, the smoothing
error being the dominating one (12.5 %) followed by the error due to the
uncertainties in the aerosol extinction profile (5.1 %).

Both the resulting total vertical column densities and a product consisting
of the average VMR in the first six layers (<550 m above the ground level)
were compared to ground level NO_{2} concentrations. Good correlations
were obtained between the six-layer product and the values from the surface
measurements, with R values between 0.6 and 0.7. However, the MAX-DOAS
systematically underestimates the ground level concentrations by a factor of
about 0.6. This is consistent with the total column averaging kernels and
variability S_{a} reported in Sect. 5.1, meaning
that the MAX-DOAS instrument has a significantly lower sensitivity near the
surface and is most sensitive at a height of around 1 km. It is shown,
however, that this underestimation is less for clear-sky conditions as
suggested from comparing data when aerosol optical depths were available
from independent measurements.

Although results are shown only from one instrument located in the southern
part of the city (UNAM), several years of data are available from three other
stations at different locations within the metropolitan area (Acatlán,
Vallejo and Cuautitlán) that are being analyzed and used for studying the
spatial and temporal variability of NO_{2} in conjunction with several
satellite products.

In this work we present a new and competitive retrieval code which has been
developed for retrieving trace gas profiles from MAX-DOAS measurements. Since
this study, it has been further improved to perform retrievals in logarithmic
space to avoid unphysical negative partial columns and oscillations; it uses
the more stable Levenberg–Marquardt iteration scheme and decouples the
retrieval and simulation grids^{4}.

Further efforts include the improvement in the measurement noise by
increasing the integration times during spectral acquisition. This will allow
us to improve the retrieval of other gases such as formaldehyde (HCHO) and
other weak absorbers. It will also enable us to use OE as a retrieval strategy
for aerosol retrieval and possibly increase the DOF to comparable values as
for the NO_{2} retrieval. This, together with an inclusion of a
cloud-screening algorithm in the retrieval chain, will make the retrieval
less dependent on the availability of AERONET data.

The MAX-DOAS data from the UNAM station can be accessed via
http://www.epr.atmosfera.unam.mx/maxdoas_data/no2/nidforval/ (last
access: 9 April 2019, Rivera et al., 2019). Data from other stations, with
other versions or periods measured within Mexico City's MAX-DOAS network,
should be requested from Claudia Rivera (claudia.rivera@atmosfera.unam.mx).
Measurements from the surface monitor (CCA station) are performed by Mexico
City's Automatic Monitoring Network and can be accessed via
http://www.aire.cdmx.gob.mx/ (last access:
9 April 2019).

In a recent update of the code, implemented after the analysis presented here
(i.e., not used for obtaining the results here), the retrieval space was
changed from linear space to logarithmic retrieval space. This means that the
retrieval works in a linear (dSCD measurement) and logarithmic (profile
retrieval) space now. This enhances the
nonlinearity of the problem and requires a change of iteration scheme. The GN
iteration scheme (Eq. 1) is replaced by a slightly slower but
more stable Levenberg–Marquardt (LM) iteration scheme; i.e.,
Eq. (1) is replaced by Eq. (A1) for more
nonlinear inversion problems (Rodgers, 2000).

The symbols mean the same as in Eq. (1): superscript “T”
denotes transposed, superscript “−1” denotes the inverse and subscript “a”
indicates a priori values. S_{m} is the measurement error
covariance matrix, y denotes the vector of measured differential
slant column densities, F(x_{i}) denotes the simulated differential
slant column densities at iteration i, calculated using the forward model
with input profile x_{i}, and K_{i} is the Jacobian matrix at the
ith iteration.

The new x_{i+1} in Eq. (A1) is only accepted if the
cost function in Eq. (A2) decreases with respect to the cost function of the
previous iteration.

If this is the case, x_{i+1} is accepted and if (1+γ) is not
yet equal to 1, the factor (1+γ) is halved for the next iteration.
If, however, the cost function increases, the newly calculated
x_{i+1} is discarded and the ith calculation repeated with a factor
(1+γ) increased by a factor of 16.

In order to counteract the slowdown of the retrieval, more restrictions were
placed on the observation geometry for a single scan: a single relative
azimuth angle and a single solar zenith angle per scan. This means in
particular that two different viewing directions cannot be treated as a
single scan any longer. Also, it is no longer necessary to have the full
simulation grid as the retrieval grid. The retrieval can be limited to the
lower part of the simulation grid. Although the former change (with respect
to observation geometry) means a significant cut in flexibility, it results
in a retrieval time speed up of a factor of 4, and a more typical retrieval time per scan and per constituent
(i.e., for both aerosol and trace gas) is around 5 s.

Tests using the logarithm of the partial layer vertical column density (for
NO_{2} retrieval) or layer extinction profile (for aerosol retrieval)
motivated the change to the LM iteration scheme due to the increased
nonlinearity when working in a semi-log space as state-measurement space.
With this new configuration, MMF has been participating in the round robin
comparison of different retrieval codes for the FRM_{4}DOAS project
(Frieß et al., 2018). It has also participated in the profile retrieval from dSCD
from the CINDI-2 campaign, both for NO_{2} and HCHO (Tirpitz et al., 2019).

If the retrieval is to be performed in log space, i.e., to work with ln (x)
instead of with x as the retrieval parameter, K in
Eq. (16) is multiplied by x (since the raw output
from VLIDORT is Kx, no division by x is necessary
because the quantity needed in logarithmic space is Kx) and
the uncertainty covariance matrix S_{a} in
Eq. (A1) needs to be left and right multiplied by 1∕x.

The LM scheme of Eq. (A1) has currently only been tested with
OE and not with Tikhonov regularization; i.e., the aerosol retrieval was also
performed using OE.

In this appendix, we outline the calculation of Z, the molar mass of air
M_{a} and the depolarization ratio Δ. We follow
Ciddor (1996). If T is given in kelvin (temperature in degrees Celsius is denoted as
T_{C}), σ the wavenumber per micrometer, pressure P in
pascals and volume mixing ratio of CO_{2} in parts per million, the constants in
Eqs. (B1)–(B14) have the following values.

First, the saturation vapor pressure 𝒮 is calculated according to
Eq. (B1), then the enhancement factor for water vapor in air,
ℱ, is calculated according to Eq. (B2) and with this,
the molar fraction of water vapor in moist air x_{w} is
calculated according to Eq. (B3), where P_{w} is partial water
vapor pressure in pascals.

The air density in dry air ${n}_{\mathrm{air},\mathrm{dry},\mathrm{450}}$ (0 % humidity) at
standard conditions (15 ^{∘}C, 101.325 Pa, xCO_{2}=450 ppm) can be calculated as in Eq. (B4) and from this
the dry air density at xCO_{2} is calculated according to
Eq. (B5).

Using Eq. (B10) once with the values for Z, R and T for
standard water vapor and once with the values for standard air, one can
calculate ρ_{ws} and ρ_{axs}, respectively.

Here, R_{gas}=8.3144621 J mol^{−1} K^{−1} is the gas
constant and M_{mwv}=0.018015 kg mol^{−1} is the molar mass of
water vapor. Z, the compressibility of moist air at T,
x_{w} and P can be calculated as

Using again Eq. (B10), with the actual values for P,
x_{w} and Z, the density of the dry air component can be
calculated according to Eq. (B12) and the density of the water
vapor component can be calculated according to Eq. (B13).

Here, $\mathit{Av}=\mathrm{6.02214129}\times {\mathrm{10}}^{\mathrm{23}}$ is the Avogadro number.

We use the N_{2}, O_{2} and CO_{2} depolarization factors from
Bates (1984) and fixed VMR as summarized in Table B1 and
calculate the wavelength-dependent depolarization factor via
Eq. (B16), where F is the so-called King factor and is calculated for N_{2}
(λ in micrometers) and O_{2} according to Eqs. (B18)
and (B19), respectively. The complete depolarization factor is then
calculated according to Eq. (B19).

Table B1Depolarization ratios and VMR for main air constituents

MMF is responsible for the retrieval code development and testing,
the retrieval chain setup from spectra to profiles, and the retrieval
parameter choices for MMF and software support. In terms of text
contributions, MMF wrote Sect. 4, Appendix A and B and part of the abstract
and Sect. 1. MMF produced Figs. 1–3. CR is responsible for the QDOAS
retrieval setup and parameter choices. CR wrote Sect. 3 and parts of Sect. 2.
WS and ZO are responsible for the data analysis and interpretation, as well
as for the sensitivity tests and the error analysis. They wrote Sect. 5 and
parts of Sects. 6 and 7. They produced Figs. 4–12. JA constructed and
installed the instruments and was responsible for their maintenance. JA wrote
Sect. 2. AB provided technical support for instruments and data management.
JAGR provided the WRF-Chem simulations that serve as a priori data for
NO_{2} retrieval. JAGR wrote parts of Sect. 4.2.1. MG was involved in
the data analysis and interpretation and wrote Sect. 7 and parts of Sects. 1
and 6.

We would like to thank the financial support provided by CONACYT (grants
275239 and 290589) and DGAPA-UNAM (grants IN107417, IN111418, and IA100716
and Martina M. Friedrich's postdoctoral fellowship). SEDEMA is acknowledged
for providing the in situ surface concentrations from their monitoring
network (RAMA) as well for partially supporting the MAX-DOAS network, and
Arne Krueger, Alfredo Rodríguez, Hector Soto and Delibes Flores are
thanked for their technical assistance. We thank Caroline Fayt
(caroline.fayt@aeronomie.be), Michel Van Roozendael (michelv@aeronomie.be)
and Thomas Danckaert for the free use of the QDOAS software and we thank
Robert Spurr for free use of the VLIDORT radiative transfer code package. We
thank the Mexican Solarimetric Service for their effort in establishing and
maintaining the AERONET Mexico City site. We thank the University of Wyoming
Department of Atmospheric Science for providing the sounding data on
http://weather.uwyo.edu/upperair/sounding.html (last access: 9 April
2019).

This research has been supported by the CONACYT (grant no. 275239), the
CONACYT (grant no. 290589), the DGAPA-UNAM (grant no. IN107417), the
DGAPA-UNAM (grant no. IN111418) and the DGAPA-UNAM (grant no. IA100716).

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In a
recent update, the GN scheme was replaced by the more stable Levenberg–Marquardt (LM) iteration scheme; more details on recent changes can be found
in Appendix A.

We present a new numerical code to retrieve NO_{2} vertical column densities (VCDs) and concentrations from previously retrieved slanted columns from spectroscopic measurements in the visible and ultraviolet spectral ranges in different vertical viewing directions. We use this code on 1.5 years of data from a measurement station located in the south of Mexico City and compare daily and monthly averages to averages of in situ measurements. We show daily NO_{2} VCD time series for a few example days.

We present a new numerical code to retrieve NO_{2} vertical column densities (VCDs) and...