Introduction
Atmospheric aerosols, with variable size ranging from a few nanometers to
tens of micrometers in diameter, have an influence on the atmospheric
radiative budget, global climate change, local air quality and visibility,
as well as directly or indirectly on human health (Seinfeld and Pandis,
2006; Kim and Ramanathan, 2008; Levy et al., 2013; Viana et al., 2014;
Karanasiou et al., 2012). Moreover, aerosol properties and vertical
distribution vary greatly with geographical location. Therefore, it is
important to obtain a comprehensive knowledge on the spatiotemporal
distribution of aerosol characteristics in terms of chemical composition and
physical properties. Measurements of aerosol optical and physical
properties, including the aerosol extinction coefficient (AEC), aerosol
optical depth (AOD) and single scattering albedo (SSA), provide information
for a better understanding of the role of aerosols in atmospheric processes.
Additionally, more accurate vertically resolved measurements of aerosol
optical properties are still needed to further assess the aerosol
environmental and radiative effects (IPCC, 2013).
Based on molecular ultraviolet–visible (UV–VIS) light absorption, the differential optical
absorption spectroscopy (DOAS) remote sensing technique is an effective tool
for air pollution measurements (Platt and Stutz, 2008). By observing
scattered sunlight at several elevations close to horizon and at zenith,
multi-axis DOAS (MAX-DOAS) is capable of retrieving information of the
vertical distribution of numerous trace gases (Hönninger et al., 2004;
Wittrock et al., 2004). Since the oxygen collision complexes O4
vertical profile is well known and nearly constant, the observed O4
absorption can serve as an indicator for the atmospheric distribution of
photon paths (Wagner et al., 2004; Frieß et al., 2006). Therefore, the
retrieved O4 differential slant column densities (DSCDs) at different
elevations can provide information about the impact of aerosol scattering on
photon paths. By combining measurements of the O4 absorption with
radiative transfer model simulations, ground-based MAX-DOAS has been used in
previous studies to determine atmospheric aerosol vertical extinction
profiles and optical depths (e.g., Irie et al., 2008, 2009; Li et al., 2010;
Clémer et al., 2010; Hartl and Wenig, 2013; Hendrick et al., 2014;
Vlemmix et al., 2015; Frieß et al., 2016).
Madrid, the capital of Spain, located in the southwest of Europe, is the
third largest city in the European Union. The city of Madrid covers a total
area of 604.3 km2. The residential population is almost 3.2 million,
with a metropolitan area population of around 6.5 million. Due to the dense
population and traffic, the city of Madrid has, in recent years, suffered
severe levels of air pollution by nitrogen dioxide and occasionally by
suspended particulates and ozone (Ayuntamiento de Madrid (AM), Madrid's Air
Quality Plan 2011-2015, 2012). Air pollution episodes in the Madrid air basin
are generally caused by local traffic emissions and domestic heating in
winter (Plaza et al., 2011). The Madrid metropolitan area is bordered to the
north-northwest by a high mountain range about 40 km away from the city and
to the northeast and east by lower mountainous terrain. This specific
topography results in particular meteorological conditions and typical
transport patterns that significantly influence air pollution dynamics in
Madrid (Salvador et al., 2008). In addition, the city of Madrid also suffers
from significant aerosol contributions from natural sources, e.g., occasional
Saharan dust intrusions.
Location of the measurement sites in Madrid.
To meet the current legislation targets on air quality, a new monitoring
network of in situ instruments for NO2, SO2, CO, O3, PM10
and PM2.5 has been operational in Madrid since 2010. This monitoring network
comprises 24 automatic measuring stations and two additional sampling points
for PM2.5 suspended particulates. The in situ PM2.5 concentrations
are measured at six stations as shown in Fig. 1, i.e., Esc. Aguirre, Casa de
Campo, Quatro Caminos, Méndez Alvaro, Castellana and Pza. Castellana.
Only a few studies have reported measurements of vertical aerosol optical
properties in Madrid using lidar systems over short periods of time (Molero
et al., 2014; Fernández et al., 2014).
DOAS retrieval settings for O4.
Parameter
Specification
Fitting window
339–387 nm
Wavelength calibration
Calibration based on reference solar atlas (Chance and Kurucz, 2010)
Cross sections
O4
293 K, Thalman and Volkamer (2013)
HCHO
298 K, Meller and Moortgat (2000)
O3
223 and 243 K, I0-corrected, Serdyuchenko et al. (2014)
NO2
294 K, I0-corrected, Vandaele et al. (1998)
BrO
223 K, Fleischmann et al. (2004)
Ring
Calculated with QDOAS
Additional adjustment
All spectra shifted and stretched against the reference spectrum
Polynomial order
5
Intensity offset
Linear correction
Here we present aerosol optical properties in the urban center of Madrid,
Spain, retrieved from MAX-DOAS observations of O4 in the UV
spectral region. This is the first time that measurements of aerosol
extinction properties in Madrid have been reported for several months, allowing
investigation of the temporal variation of aerosol optical properties in the
urban area. We compare the AOD and surface aerosol extinction coefficient
retrieved from MAX-DOAS with correlative sun photometer data and in situ
PM2.5 concentrations. Finally, we explore a case study of the aerosol
optical properties during an intrusion of Sahara dust arriving in Madrid,
which demonstrates the MAX-DOAS measurements as a useful monitoring tool to
obtain vertically revolved aerosol optical properties during dust episodes.
Measurements and method
MAX-DOAS instrument and setup
Our MAX-DOAS instrument consists of three main parts, i.e., the scanning
system, the spectrometer–detector equipment and the computer as the control
unit (e.g., Mahajan et al., 2012; Prados-Roman et al., 2015). The scanning
telescope was driven by a stepper motor to collect scattered sunlight from
different elevation angles. The light is focused by a lens (F=200 mm)
with a diameter of 50.8 mm to a bundle of 15 individual quartz fibers. The
light is fed by the fiber bundle to a Princeton Instruments SP500i
spectrometer with a Princeton Instruments Pixis 400B charge-coupled device (CCD) camera. The light is
dispersed by a 600 grooves mm-1 grating, resulting in a spectral window
of 90 nm and spectral resolution of 0.5 nm full width at half maximum (FWHM). The computer is
responsible for running the overall system and storing the spectral data.
During the spectra recording process, the offset was removed automatically.
The signal of dark current was measured every night and subtracted from each
spectrum according to the corresponding average exposure time. Depending on
the intensity of the received scattered sunlight, the exposure time was
adapted automatically between 0.1 and 1 s in order to optimize the total
signal and avoid saturation.
Ground-based MAX-DOAS measurements were carried out from 15 March to
15 September 2015. The telescope scanning system was mounted on the top roof
of a 25 m tall building at the main campus of the Spanish National Research
Council (CSIC, 40.44∘ N, 3.69∘ W; 700 m a.s.l.) in Madrid,
Spain (see Fig. 1). The spectra were recorded with an elevation angle
sequence of -4, -2, 0, 1, 3, 5, 7, 10, 20, 30 and 90∘ for each
scanning cycle. After each completed cycle, the grating was shifted between
the UV and VIS spectral regions, centered at 350 and 440 nm,
respectively. The telescope was pointed approximately to the south-southeast
(around 206∘ azimuth angle, red arrow in Fig. 1), towards the city
center of Madrid. This measurement site is located in the downtown area of
Madrid and is classified as a typical urban site, where the air quality is
mainly impacted by nearby traffic emissions.
Spectral analysis
The O4 DSCDs were derived from UV spectra covering 305 to 395 nm for
the retrieval, and only few data from VIS spectra were available due to
the instrument problem. The QDOAS software developed by BIRA-IASB
(http://uv-vis.aeronomie.be/software/QDOAS/) was applied to analyze
O4 absorption in the interval between 339 and 387 nm, encompassing the
three absorption bands at 343, 360 and 380 nm. Trace gas absorption cross
sections for O4 (Thalman and Volkamer, 2013), NO2 (Vandaele et al.,
1998) and O3 (Serdyuchenko et al., 2014) at 223 and 243 K, BrO
(Fleischmann et al., 2004) and HCHO (Meller and Moortgat et al., 2000) and a
synthetic ring spectrum (calculated by QDOAS) were included in the spectral
fitting analysis. During each measurement cycle, the corresponding zenith
spectrum was taken as a Fraunhofer reference spectrum for the lower viewing
elevation angles. The relevant configuration for the spectral analysis is
listed in Table 1. Afterwards, the O4 DSCDs were introduced into the
aerosol retrieval algorithm if relative errors yielded from QDOAS are less
than 10 %.
Other datasets
Cloud-screened AOD level 1.5 products from a CIMEL sun photometer situated on
the roof of the Agencia Estatal de Meteorologia in Madrid, Spain
(40.45∘ N, 3.72∘ W; 680 m a.s.l.), with a distance of
3.8 km from our measurements site (see Fig. 1) are compared to the
column-integrated aerosol profile retrieved from MAX-DOAS measurements. The
sun photometer is part of AERONET (Aerosol Robotic Network,
http://aeronet.gsfc.nasa.gov/), which provides standards for
instruments, calibration techniques, processing and data distribution (Holben
et al., 1998). The AERONET data are automatically cloud-screened if the direct
view of the sun is blocked by clouds. Thus, the absence of AERONET data can
serve as a temporal index for the presence of clouds and further help to
filter the MAX-DOAS measurements (Smirnov et al., 2000). The AOD at
wavelengths of 340, 380, 440, 500, 675, 870 and 1020 nm are available from
the AERONET database at the Madrid site.
Additionally, time series of PM2.5 concentrations measured by in situ
instruments were acquired from the air quality network of the Integral System
of Air Quality Madrid City Council (http://www.mambiente.munimadrid.es)
and were used to compare surface PM2.5 concentrations with the surface aerosol extinction coefficient derived
by the MAX-DOAS instrument. The six automatic measuring stations are located
throughout the urban area of Madrid (see Fig. 1).
Meteorological data, including temperature, relative humidity, wind speed and
wind direction, with a temporal resolution of 30 min, were obtained from the
Adolfo Suárez Madrid–Barajas Airport meteorological site
(40.47∘ N, 3.56∘ W; see Fig. 1)
(http://www.wunderground.com). All data are normalized to 1 h averages.
The HEIPRO aerosol retrieval algorithm
Basic principle
Since the zenith spectrum of each elevation sequence was taken as the
reference, the DSCDs at lower elevations are sensitive to the trace gases in
the lower troposphere. For the aerosol retrieval, O4 DSCDs from
different elevations derived from the MAX-DOAS spectral analysis are fed into
the aerosol inversion algorithm along with local atmospheric vertical
pressure, temperature profiles and a suitable aerosol a priori profile. In
this study, the HEIdelberg PROfile (HEIPRO, developed by IUP Heidelberg,
Frieß et al., 2006, 2011) retrieval algorithm, with the SCIATRAN
radiative transfer scheme (Rozanov et al., 2005) as forward model, was used
for the retrieval of aerosol vertical profiles.
Based on the well-established optimal estimation method (OEM) (Rodgers,
2000), the HEIPRO algorithm determines the most probable atmospheric state
x (aerosol extinction coefficient at a series of discrete altitude
intervals) given a set of measurement ym and a priori state
vector xa. The so-called maximum a posteriori (MAP) solution
x^ is approached by minimizing the cost function (Frieß et
al., 2006, 2011, 2016):
χ2(x)=y-F(x,b)TSϵ-1y-F(x,b)+x-xaTSa-1x-xa,
where the m-dimensional vector x defines the state of interest
within the measured system. The radiative transfer model or forward function
F(x,b) simulates the corresponding modeled value for the atmospheric state
x, which is also dependent on the parameters b including
temperature, pressure and aerosol profiles etc. y denotes the
measurement vector, i.e., measured O4 DSCDs at different elevation angles
in the case of the aerosol profile retrieval. The a priori state vector
xa serves as an additional constraint.
Sϵ and Sa denote the diagonal
measurement covariance matrices, representing the uncertainties in the
measurement and the a priori state, respectively.
The optimal state x^, which minimizes χ2, can be found
using the Levenberg–Marquardt method. The vertical resolution of the
retrieval and the sensitivity of the retrieved state x^ to the
true atmospheric state x is quantified by the averaging kernel matrix
A=∂x^/∂x. The retrieved profile
x^ can be represented as the true profile x, smoothed by
the averaging kernel matrix A according to following
Eq. ():
x^=xa+Ax-xa
Apart from the retrieval of aerosol properties, the HEIPRO algorithm can
also yield the vertical profile of trace gases by combining the resulting
aerosol extinction profiles, serving as forward modeling parameters, with
measured DSCDs and a priori profiles of trace gases. However, this paper
only focuses on the aerosol retrieval.
Aerosol inversion
In the forward radiative transfer model, atmospheric pressure and temperature
profiles were adapted from the climatological database employed in SCIATRAN,
which contains monthly and latitudinal-dependent vertical distribution of
atmospheric trace gases, pressure and temperature. The surface albedo was set
to 0.1 for the Madrid urban area. Aerosol extinction profiles from the
surface up to 4 km height were retrieved on a vertical resolution of 100 m
with a fixed temporal interval of 15 min. As to aerosol optical properties,
the single scattering albedo and the asymmetry parameter for a
Henyey–Greenstein phase function parameterization around 360 nm were set to
0.95 and 0.72 in the retrieval scheme, respectively. According to recently
measured aerosol loadings in the Madrid downtown area, the annual averages of
surface PM2.5 and PM10 in 2014 were around 10.8 and
19.4 µg m-3, respectively (Calidad del Aire Madrid, 2014),
and monthly averaged AOD at 440 nm ranged from 0.07 to 0.17. Based on these
data, an a priori aerosol extinction profile, with a surface extinction
coefficient of 0.05 km-1 and exponentially decreasing with scaling
height of 1.5 km, was assumed with 100 % error in retrieval. More
details about the sensitivities of different a priori profiles will be
discussed in Sect. 4.1.
The consecutive measured O4 DSCDs around the 360 nm band at different
elevation angles serve as measurement vector for the HEIPRO algorithm. It is
important to mention that the absorption of O4 simulated with the
absorption cross section from Hermans et al. (2003) was previously reported
with an underestimation of 25 % and suggested to be scaled by multiplying
it by 1.25 in several different aerosol retrieval schemes (Clémer et al.,
2010; Großmann et al., 2013; Vlemmix et al., 2015). However, good
agreement between modeled and measured O4 DSCDs in the Arctic is also
achieved without any correction for the same O4 cross section (Frieß
et al., 2011). In fact, varied correction factors to the retrieved O4
were applied depending on the uncertainty introduced by the absolute value of
the O4 absorption cross section (Zieger et al., 2011). Here, the new
cross section from Thalman and Volkamer (2013) is adopted to retrieve
O4; therefore, we first need to include the appropriate scaling factor
in the HEIPRO algorithm.
Diurnal variation of the measured O4 DODs and simulated O4
DODs with different scaling factors at elevation angles of 10, 20 and
30∘ on 4 April 2015.
Consequently, a cloud-free day with low aerosol load (AOD < 0.15), i.e., 4
April 2015, was chosen to test different scaling factors for O4
absorption, under which the O4 DSCDs between 15 and 30∘
elevation are sensitive to variations of atmospheric temperature and
pressure, as well as the aerosol optical properties (Frieß et al., 2006;
Wagner et al., 2009; Clémer et al., 2010). In the HEIPRO scheme, the
scaling only takes effects on the modeled value by multiplying with the
O4 cross section. Since Hermans et al. (2003) and Thalman and
Volkamer (2013) present different O4 cross section values at 360 nm, we
test different scaling factors, 0.875, 1.0, 1.125 and 1.25, within HEIPRO in
order to find the most appropriate for the adopted Thalman and
Volkamer (2013) cross section.
In Fig. 2, the modeled O4 absorption with different scaling factors
was compared to the measured O4 differential optical depth (DOD,
Frieß et al., 2006) at elevation angles of 10, 20 and 30∘. It is
obvious that the modeled O4 absorptions without scaling were
systematically about 20 % lower than the measured O4 at these
elevations and even more during the morning periods. However, the modeled
O4 absorption was overestimated with a scaling factor of 1.25 around noon
and even at elevation angles lower than 10∘, which was also found by
Irie et al. (2015). In the following aerosol retrieval at 360 nm, we assume
a scaling factor of 1.2 as the optimal correction for the uncertainties from
the newly available O4 cross section, which is comparable to those
previously reported for scaling O4 from Hermans et al. (2003) (Wagner et
al., 2009; Clémer et al., 2010).
Different a priori aerosol extinction profiles as input (a)
and relevant O4 DODs simulation compared with measured O4 DODs on
4 April 2015 (b).
Results and discussion
Sensitivity to the a priori profile
Since the information content of the measurement is usually too low to
reconstruct a full state vector, additional information concerning the
atmospheric state is provided by an a priori state vector xa
with covariance matrix Sa. As an important input
parameter, the a priori profile poses an additional constraint on the
retrieved profile. Unfortunately, the impact of the a priori is substantial
and there is no additional external information available that justifies the
selection of one specific a priori profile (Vlemmix et al., 2015). To investigate the
impacts of a priori profile shape, aerosol retrievals were performed with
only four different a priori extinction profiles available in the HEIPRO
algorithm, i.e., linear, exponential, Boltzmann (smoothed box-shaped) and
Gaussian distribution (peaking shape), as plotted in Fig. 3a. The same
cloud-free and low aerosol loading day of 4 April 2015 was chosen for the
sensitivity study of different a priori profiles. Besides the input
parameters in the forward model as mentioned in Sect. 3.2, other required
observed geometry parameters were set according to the real measurements,
e.g., solar zenith angle and relative azimuth angle.
Figure 3b shows the comparison between measured and simulated O4
absorptions resulting from the different a priori extinction profiles. When a
Gaussian a priori profile was applied, the simulated O4 absorption close to
surface (elevation angle 1∘) was much higher than the measured O4
absorption, and also resulted in underestimations at higher elevation angles. Except for the
Gaussian a priori, there were only small differences in the modeled O4
absorptions among the other three a priori profiles. By estimating the degree
of freedom for signal (DFS) and solution of cost function in the retrievals,
the statistics indicates that better performance of larger DFS and smaller
cost function were approached by utilizing an exponential a priori.
Considering the fact that the mass concentration profiles of the different
aerosol types usually decrease with altitude in the lower troposphere,
whereas the background profile in the free troposphere remains constant with
altitude, we have adopted the exponentially decreasing shaped extinction
profile as the a priori profile. We then test the appropriate scale height of an
exponential a priori profile, which defines the decreases of aerosol
concentration in the vertical. The results show that the retrieved aerosol was
constrained within lower altitudes by the algorithm if the scale height was
set to be too small, whereas large-scale height results in artifacts at
higher altitudes. Therefore, a moderate scale height of 1.5 km, where the
aerosol extinction coefficient decreased to half of the surface value,
was finally determined for the exponential a priori profile used in the
aerosol retrieval.
Aerosol optical characteristics
To evaluate the performance of the MAX-DOAS retrieval, the AOD from the
AERONET instrument at multiple wavelengths was interpolated to 360 nm using
the Ångström coefficient (α) (see Eq. ):
AOD=β×λ-α,
and then further averaged for hourly data series to be compared with the
MAX-DOAS-retrieved AODs. Since AERONET data are automatically cloud-cleared, the
hourly MAX-DOAS-retrieved AODs, which were calculated from the retrieved
aerosol extinction profiles in the HEIPRO algorithm, are normalized to the
timetable of AERONET data. Figure 4 shows the time series of hourly AODs
inferred from the sun photometer and MAX-DOAS measurements for several
months. AODs retrieved from these two methods exhibit similar temporal
trends. The occasional occurrences of high AODs (> 0.2) were attributed to
the influence of long-range transport of windblown dust from the Saharan
area, marked as gray hatched areas in Fig. 4, which are further discussed as
a case study in Sect. 4.4.
Time series of the AODs at 360 nm retrieved from MAX-DOAS and
AERONET.
The monthly averaged time series of AODs retrieved from MAX-DOAS and AERONET,
shown in Fig. 5, exhibit higher aerosol loadings from June to September 2015.
These higher AODs in the summer season are mainly due to long-range transport
of Saharan dust, which occurs more frequently in summer than in winter
(Sicard et al., 2011). Moreover, good agreement exists in both hourly and
monthly retrieved AODs between MAX-DOAS and AERONET, as high correlation
coefficients of R=0.87 (number of datapoints = 618,
AODMAX-DOAS= 0.6673 × AODAERONET+0.0294) and
R=0.96 (number of datapoints = 7, AODMAX-DOAS=0.6253× AODAERONET+0.0362), respectively, were obtained by
linear regression. However, these two datasets usually do not compare so well
in the case of higher aerosol loading, under which the absolute differences
between the MAX-DOAS and AERONET are relatively larger (Fig. 4). The MAX-DOAS
underestimation of the retrieved AOD could probably be explained by the
constraint that only aerosol extinctions below 4 km were considered in the
algorithm, especially for conditions of dust events when the air mass can
transport dust at higher altitudes. Furthermore, the exponentially decreasing
a priori extinction profile poses strong constraints on aerosols at higher
altitudes. Note also that the Saharan dust particles usually show the
characteristic optical properties of the single scattering albedo, the asymmetry
parameter and the Ångström coefficient, which are different to those
introduced in the basic scenario of HEIPRO retrieval (Gkikas et al., 2013).
Generally, although a rather good correlation was obtained between MAX-DOAS
retrieval and CIMEL instrument of AERONET, the deviation of these two datasets is still around 20 %. This is possibly due to an inhomogeneous
horizontal distribution of aerosols. Note that the AERONET site was located
in the northwest away from the MAX-DOAS instrument, whereas the telescope of
MAX-DOAS points in a southern direction. As a consequence, the MAX-DOAS-retrieved aerosol extinction profiles are representative of an average over
the light paths in the lower troposphere over several kilometers up to 15 km
in the horizontal, which was roughly estimated based on the O4 SCD at
0∘ elevation (Sinreich et al., 2013; Wang et al., 2014).
Statistics for monthly AODs at 360 nm retrieved from
MAX-DOAS and AERONET. The upper and lower boundaries of box indicate the
1st and 3rd quartile respectively. The whiskers show the 5th and 95th
percentiles. Mean and median are represented with open square and centerline
in the box.
Surface aerosol extinction
Owing to the absence of extinction coefficient measurements at ground surface
and in the vertical, another semiquantitative way to validate the aerosol
extinction coefficient retrieved by MAX-DOAS is to compare it with particle mass
concentrations. As indicated in Fig. 1, surface PM2.5 concentrations
from six in situ stations were averaged to represent the aerosol loading
throughout the entire city, which are then compared to the MAX-DOAS retrieval
of surface aerosol extinction coefficient; i.e., the bottom layer below 100 m
of the aerosol extinction profile was considered as being representative of
the surface extinction. Figure 6 shows the comparison between surface aerosol
extinction coefficient retrieved from MAX-DOAS and PM2.5 concentration
measured with in situ instrumentation.
Time series of surface aerosol extinction coefficient retrieved from
MAX-DOAS and comparison with in situ PM2.5 concentration (a).
Linear regression plots of daily and hourly data are shown in (b)
and (c), respectively.
Wind roses of AOD (a), surface aerosol extinction
coefficient and PM2.5 as a function of wind direction.
(b) shows the frequency and the dependence of regression coefficient
between surface extinction coefficient and PM2.5 on wind direction.
As shown in the upper panel of Fig. 6, a good agreement exists between the
daily aerosol surface extinction coefficient from the MAX-DOAS retrievals and
the ground PM2.5 concentration. This implies that the surface extinction
coefficient obtained from MAX-DOAS retrieval truly reflects the number of
particles near the ground. The linear fitting for aerosol extinction
coefficient and particle mass concentration yielded a correlation coefficient
R of 0.89 in daily average and 0.64 in hourly average, respectively.
Different explanations for the discrepancy in the hourly data include,
firstly, that the regression analysis was based on the comparison between two
different types of aerosol parameters and that the aerosol extinction coefficient
ranged basically with particle mass concentration; however, it is also
influenced by multiple factors including particles constituent,
hygroscopicity and meteorological conditions (Zieger et al., 2011). Secondly,
the in situ PM2.5 data were measured directly at or close to
the surface, whereas the aerosol surface extinction coefficient was extracted
from the MAX-DOAS-retrieved profile from ground to 100 m, averaged over a
large horizontal distance. As a consequence, the inhomogeneous distribution of
particles, both in vertical and horizontal directions, decreases the consistency
of these two data series (Frieß et al., 2016). It also should be
mentioned that the whole retrieved dataset was included in Fig. 6c without
cloud filtering.
Diurnal variation of meteorological parameters (a)
and (b), in situ particle concentrations (c) and MAX-DOAS-retrieved AOD (d) and aerosol extinction profiles for a dust
intrusion case study on 12 May 2015. Aerosol extinction profiles with default
setting and corrected with AERONET products are shown in (e)
and (f), respectively. (g) shows the difference of aerosol
extinction between (e) and (f) (corrected – default).
We then evaluate the impacts of air mass transport on the surface extinction
coefficient and PM2.5. The wind roses in Fig. 7a indicate that the
AERONET AODs and retrieved surface extinction coefficient, as well as in situ
PM2.5, generally exhibit a similar dependence on wind
direction. As it can be seen in Fig. 7a, combined high PM2.5
concentrations but low surface extinction conditions are present when the
winds blow from a S–SW–W direction. However, there was an inverse situation
under northerly winds. In view of the specific geographic characteristics,
i.e., the north of Madrid metropolitan is surrounded by mountains, the
dispersion pathway of particles was significantly impacted by wind
direction. Winds from N and S to SW components are dominant from March to
September in Madrid; see Fig. 7b. Using regression analysis of hourly PM2.5
and the surface extinction coefficient for 16 different wind directions,
correlation coefficients are found to be lower for northeastern winds along
with calm conditions, suggesting that the particles transported from the
northeast show different extinction properties to those from other wind
directions.
Case study of Saharan dust intrusion
Previous studies showed that the transport of Sahara dust resulted in high
levels of aerosol pollution in the Iberian Peninsula including the city of
Madrid, while the Atlantic or polar air masses generally bring cleaner air,
significantly reducing particulate matter levels (Querol et al., 2009;
Díaz et al., 2012). During the period of MAX-DOAS measurements, several
Saharan dust intrusions affecting Madrid were reported by the
Directorate-General of Environmental Quality and Assessment (Dirección
General de Calidad y Evaluación Ambiental) at the Ministry of
Agriculture, Food and Environment (Ministerio de Agricultura,
Alimentación y Medio Ambiente),
as also shown in Fig. 4. The diagnosis of Saharan dust intrusions uses an
integrated approach based on data from back-trajectory analysis (Hysplit
model, http://www.ready.noaa.gov), simulated dust maps from the NRL
(http://www.nrlmry.navy.mil/aerosol/), SKIRON
(http://forecast.uoa.gr/forecastnewinfo.php) and BSCDREAM
(http://www.bsc.es/)
models and satellite images provided by the NASA SeaWiFS project, as
described in Díaz et al. (2012).
To investigate the capability of MAX-DOAS retrieval for dust intrusion, we
use a case study focusing on the most severe dust intrusion with extremely
high AOD up to 0.5, which occurred on 12 May 2015. Figure 8a and b displayed
the meteorological parameters during this episode, including temperature,
relative humidity and wind direction and speed. It can be found that the wind
direction changed from northern to southern directions around 09:00 UTC on
that day, while both PM2.5 and PM10 concentration, as well as AOD,
increased to high levels that lasted for the following few hours. Together
with high AOD, the arrival of the dry air mass is correlated with a relative
humidity decline from 30 to 15 %. Therefore, all these characteristics
identified this day as a typical Saharan dust intrusion event.
Air mass pathway analysis: (a) 48 h back-trajectories
arriving in Madrid at 15:00 UTC and NAAPS modeled dust optical depth at
(b) 06:00, (c) 12:00 and (d) 18:00 UTC on 12 May
2015 from NRL.
The Ångström coefficient, single scattering albedo and asymmetry
parameters, as extracted from the AERONET product, are used to represent the
characteristic optical properties of Saharan dust vs. those of local
aerosols. Figure 8e and f depict the initial retrieved aerosol profile and
the corrected profile after including the characteristic aerosol optical
properties (i.e., single scattering albedo and asymmetry parameters) of
Saharan dust, as registered by AERONET, in HEIPRO. The results show small
differences in both retrievals. Both of them capture the elevation of high
aerosol extinction between 13:00 and 16:00 UTC. The good performance of the
MAX-DOAS retrieval is validated by the air mass pathways shown in Fig. 9, which
provides information on possible aerosol sources and the advective
transport towards the Iberian Peninsula during the dust event. As
demonstrated in Fig. 9a, the 48 h backward trajectory that arrived in Madrid at
15:00 UTC on 12 May denotes that the air mass at 1500 m above ground level
(a.g.l.) is from the southwest, accompanied with an elevation of several
hundred meters in the vertical, which was also captured by the MAX-DOAS-retrieved
extinction profiles.
The initial and corrected AODs retrieved from MAX-DOAS, together with the
AERONET AOD, are shown in Fig. 8d. The agreement between MAX-DOAS and AERONET
datasets was only slightly improved with the correction for aerosol optical
parameters. However, the AOD was not improved as much as expected after
14:00 UTC. The remaining systematic underestimation of AOD is likely due to
the fact that the algorithm was constrained for aerosol extinction within the
first 4 km and has a lower sensitivity for high altitudes (Frieß et al.,
2016). The backward trajectory of the air mass at 3000 m a.g.l. reveals
that dust from the southwest at even higher altitudes passed over Madrid
(Fig. 9a). This may explain the underestimation of MAX-DOAS AODs, with
respect to AERONET (Fig. 5), during days of Saharan dust intrusions, since
during these events, the contribution of the partial optical depth above 4 km
to the total AOD is much higher than during typical non-intrusion days.
Further details of the dust intrusion are provided by the dust optical depth
at 550 nm at 06:00, 12:00 and 18:00 UTC on 12 May (Fig. 9b, c and d), which
was modeled by the Navy Aerosol Analysis and Prediction System (NAAPS)
provided by Naval Research Laboratory (NRL). This confirms that the Saharan
dust arrived in Madrid around 12:00 UTC and lasted the whole afternoon.
Considering the ability to capture the elevated layer, the Gaussian a
priori profile was attempted in order to retrieve the aerosol extinction profile of this dusty day.
As it can be seen in Fig. 3a, the Gaussian profile was distributed as a
peaking shape, which was defined and parameterized in the algorithm with two
variables, i.e., the aerosol extinction coefficient of the peak and the
altitude of the peak. However, the bottom of the Gaussian profile close to
the ground surface has an extremely low aerosol extinction coefficient, which
obviously deviates from the realistic condition that considerable aerosol
loaded within low altitude during dust days. Therefore, the modeled O4
optical depths deviate from the measurements. It implies that this simplified
Gaussian a priori is not suitable for the dust layer retrieval. More suitable
a priori profile for the elevated plumes retrieval deserves further investigation
along with other satellite and ground-based measurement in the future.
Summary and conclusions
In this paper, we present the retrieval of aerosol extinction coefficient and
resulting AODs from MAX-DOAS measurements carried out from March to
September 2015 in the urban area of Madrid. The O4 absorption in the
UV bands at multiple elevations angles served as input for the HEIPRO
algorithm to retrieve aerosol extinction profiles in the lower troposphere.
Based on tests for O4 absorption correction, a cross section scaling
factor of 1.2 was determined as the optimal correction for the uncertainties
arising from the inclusion of the newly available O4 cross section
measured by Thalman and Volkamer (2013) in the aerosol retrieval at 360 nm.
The influence of different types of a priori profiles on the retrieval, i.e., linear, exponential, Boltzmann (smoothed box-shaped) and Gaussian
distribution (peaking shape), was investigated, which suggests that an
exponential a priori profile with a moderate scale height of 1.5 km is
suitable for use in this typical urban site.
Retrieved hourly AODs at 360 nm from MAX-DOAS were compared to the
correlative AERONET sun photometer product. Both of them displayed a similar
temporal behavior with significant correlation coefficients, with an R of 0.87.
Due to the more frequent Saharan dust intrusions in summer, monthly mean AODs
showed highest values from June to September. Nevertheless, a deviation of
20 % exists between these two datasets, especially during high aerosol
loading conditions (AOD > 0.2), e.g., dust intrusions. The MAX-DOAS-retrieved surface aerosol extinction coefficient was also compared to the
averaged PM2.5 concentrations from six urban in situ stations. The good
agreement between both datasets indicates that the MAX-DOAS retrieval is an
effective tool to characterize the number of particles close to the surface.
In addition, the retrieved AOD and surface extinction coefficient, as well as
in situ PM2.5, exhibit a generally similar dependence on wind
direction.
A severe dust intrusion on 12 May 2015 was chosen to assess the capability of
the MAX-DOAS retrieval during dust events. The high performance of MAX-DOAS
retrieval to recognize the elevated layer of particles was aided by air-mass
backward trajectory analysis. After removing the uncertainties from the
Ångström coefficient, single scattering albedo and asymmetry
parameters, the absolute difference in AODs between the MAX-DOAS and AERONET
methods was mainly attributed to the low sensitivity of the MAX-DOAS
inversion algorithm to the partial optical depth above 3–4 km. Finally, we
suggest that the ability of the MAX-DOAS technique to retrieve information on
the vertical distribution of the aerosol extinction coefficient over long
periods of time provides an additional tool for the study of the air quality
in Madrid. More comprehensive and direct validation of the MAX-DOAS-retrieved
aerosol extinction profiles needs simultaneous combined ancillary
measurements, e.g., by lidars and ceilometers.