PollyXT Raman polarization lidar observations were
performed at the Remote Sensing Laboratory (RS-Lab) in Warsaw
(52.2109∘ N, 20.9826∘ E), Poland, in the framework of
the European Aerosol Research Lidar Network (EARLINET) and the Aerosol,
Clouds, and Trace gases Research Infrastructure (ACTRIS) projects. Data collected in
July, August, and September of 2013, 2015, and 2016 were analysed using the
classical Raman approach. In total, 246 sets of intact profiles, each set
comprising particle extinction (α) and backscatter coefficients
(β) as well as linear particle depolarization ratios (δ) at 355 nm
and 532 nm, were derived for statistical investigations and stored in the
EARLINET/ACTRIS database. The main analysis was focused on intensive
optical properties obtained within the atmospheric boundary layer (ABL). Their
interrelations were discussed for different periods: the entire day; nighttime, with respect to the nocturnal boundary layer (NL) and the residual boundary layer (RL); at sunrise, with respect to the morning transition boundary layer (MTL); and from late afternoon until sunset, with respect to the well-mixed boundary layer (WML). Within the boundary layer, the lidar-derived optical
properties (entire day, 246 sets) revealed a mean aerosol optical depth
(AODABL) of 0.20±0.10 at 355 nm and 0.11±0.06 at 532 nm;
a mean Ångström exponent (ÅEABL) of 1.54±0.37; a
mean lidar ratio (LRABL) of 48±17 sr at 355 nm and 41±15 sr at
532 nm; a mean linear particle depolarization ratio (δABL) of 0.02±0.01 at 355 nm and 0.05±0.01 at 532 nm; and
a mean water vapour mixing ratio (WVABL) of 8.28±2.46 g kg-1. In
addition, the lidar-derived daytime boundary layer optical properties (for the
MTL and WML) were compared with the corresponding daytime columnar aerosol
properties derived from the multi-filter rotating shadowband radiometer (MFR-7) measuring within the
National Aerosol Research Network (PolandAOD-NET) and the CE318 sun photometer of the Aerosol Robotic NETwork (AERONET). A high linear correlation of the
columnar aerosol optical depth values from the two latter instruments was obtained in
Warsaw (a correlation coefficient of 0.98 with a standard deviation
of 0.02). The contribution of the aerosol load in the summer and early-autumn
free troposphere can result in an AODCL value that is twice as high as the AODABL
over Warsaw. The occurrence of a turbulence-driven aerosol burst from the
boundary layer into the free troposphere can further increase this
difference. Aerosol within the ABL and in the free troposphere was interpreted
based on comparisons of the properties derived at different altitudes with
values reported in the literature, which were characteristic for different
aerosol types, in combination with backward trajectory calculations,
satellite data, and model outputs. Within the boundary layer, the aerosol consisted of
either urban anthropogenic pollution (∼ 61 %) or mixtures of anthropogenic aerosol with biomass-burning aerosol (< 14 %), local pollen
(< 7 %), or Arctic marine particles (< 5 %). No
significant contribution of mineral dust was found in the boundary layer.
The lidar-derived atmospheric boundary layer height (ABLH) and the AODABL
exhibited a positive correlation (R of 0.76), associated with the local
anthropogenic pollution (most pronounced for the RL and WML). A
positive correlation of the AODABL and LRABL and a negative
correlation of the ÅEABL and LRABL, as well as the expected
negative trends for the WVABL (and surface relative humidity, RH) and
δABL, were observed. Relations of the lidar-derived aerosol
properties within the ABL and the surface in situ measurements of particulate
matter with an aerodynamic diameter less than 10 µm (PM10) and less than 2.5 µm (PM2.5) measured by the Warsaw Regional Inspectorate
for Environmental Protection (WIOS) network, and the fine-to-coarse mass
ratio (FCMR) were investigated. The FCMR and surface RH showed a positive
correlation even at nighttime (R of 0.71 for the MTL, 0.63 for the WML, and 0.6 for the NL), which generally lacked statistically
significant relations. A weak negative correlation of the FCMR and δABL
(more pronounced at 532 nm at nighttime) and no casual relation
between the FCMR and ÅEABL were found. Most interestingly, distinct
differences were observed for the morning transition layer (MTL) and the
well-mixed layer (WML). The MTL ranged up to 0.6–1 km, and was
characterized by a lower AODABL(<0.12), wetter conditions
(RH 50–80 %), smaller particles (ÅEABL of 1–2.2; FCMR from 0.5 to
3), and a low LRABL of between 20 and 40 sr. The WML ranged up to 1–2.5 km
and exhibited a higher AODABL (reaching up to 0.45), drier conditions
(RH 25–60 %), larger particles (ÅEABL of 0.8–1.7; FCMR of
0.2–1.5), and a higher LRABL of up to 90 sr.
Introduction
Atmospheric aerosol can impact climate (e.g. Feingold et al., 2016; Seinfeld
et al., 2016), weather (e.g. Fan et al., 2016; Gayatri et al., 2017), air
quality (e.g. Fuzzi et al., 2015), and human health (e.g. Zheng et al., 2015;
Trippetta et al., 2016). Aerosol particles affect the Earth's radiative
budget, as they interact with the incoming solar short-wave radiation and
the outgoing terrestrial long-wave radiation. Depending on the aerosol type,
they can scatter and/or absorb the radiation, causing local warming or
cooling of the atmosphere, at the surface and at the top of
atmosphere (Kaufman et al., 2002). The variety of aerosol sources, those of
natural and anthropogenic origin, as well as the influence of diverse
meteorological conditions on aerosol characteristics and transport, lead to
strongly variable aerosol contents within the troposphere. Aerosol optical
properties, size, and composition are important for aerosol–cloud–radiation
interaction studies (Seinfeld et al., 2016) and for radiative transfer
modelling (Lolli et al., 2018).
The Intergovernmental Panel on Climate Change (IPCC) reported that the
sparse and/or poorly-known information on the aerosol temporal and spatial
variability causes high uncertainty in the assessment of their influence on
the global radiation budget (Stocker et al., 2013). The latest IPCC report
indicates that the reduction of uncertainties is still necessary, as it can
improve the ability to accurately forecast global climate change
(Masson-Delmotte et al., 2018). Several past studies have been dedicated to
investigating and improving the above-mentioned uncertainties. Pan et al. (2015)
identified the major discrepancies between the state-of-the-art global
aerosol models and observations with respect to simulating aerosol loading over South
Asia, thereby providing directions for future model improvements in this
important region. Ghan et al. (2016) found that uncertainty regarding anthropogenic
aerosol effects on cloud radiative forcing arises from uncertainty in
several relationships, including the choice of parameter values and numerical
integration methods. Koffi et al. (2016) provided further spatial and
temporal details on the state-of-the-art AeroCom II global aerosol models
and investigated the reasons for the model discrepancies and diversity,
which provided a good foundation for further evaluation of the models' performance
at a global scale. Kipling et al. (2016) investigated the impact of a wide
range of processes (emission, transport, deposition, and microphysical and
chemical processes) on aerosol vertical distribution in the
aerosol–climate model via a series of limiting case process-based
sensitivity tests; they showed that the processes that have the
greatest impact on the vertical distribution vary both between different
aerosol components and over the particle size spectrum. Seinfeld et al. (2016)
provided strategies for improving estimates of aerosol–cloud
relationships in climate models, for new remote sensing and in situ
measurements, and for quantifying and reducing the model uncertainties.
Air pollution is one of the major environmental issues in metropolitan areas
due to its adverse effects on human health (e.g. Chen et al., 2008;
Lelieveld et al., 2015). Juda-Rezler et al. (2012) reported that air quality
is related to anthropogenic emissions and natural emissions and that a
change in temperature, water vapour, precipitation, wind speed, and wind
direction can affect atmospheric chemical reactions, atmospheric transport,
and deposition processes, as well as the rate of pollutant transport from
urban environments to global-scale environments. The emission of pollutants
changes the chemical composition of the atmosphere, which in turn has a
feedback effect on the regional and global climate (Juda-Rezler et al.,
2012). Strong emissions, e.g. from traffic, industry, or heating, can
drastically decrease air quality, particularly when meteorological
conditions prevent an exchange of polluted and clean air, (Juda-Rezler et
al., 2011). In Europe, surface particulate matter (with an aerodynamic
diameter below 10 µm, PM10, and 2.5 µm, PM2.5) is one of
the most serious air quality problems (e.g. De Leeuw et al., 2001; Wolff and
Perry, 2010). As atmospheric aerosol can affect air quality, health, and the
environment, joint studies of the aerosol optical property measurements in
combination with surface observations of PM10 (e.g. Guo et al.,
2009, 2017; Stachlewska et al., 2017b; Popovici et al., 2018), PM2.5
(e.g. Schaap et al., 2009; Stachlewska et al., 2017b, 2018), and PM1
(e.g. Qin et al., 2018) concentrations can improve our knowledge of the
atmospheric environment.
The atmospheric boundary layer (ABL) can affect the dispersion of pollutants
within the mixing layer (Wałaszek et al., 2018). The knowledge of
ABL characteristics and ABL dynamics, both related to
ambient meteorological conditions, is helpful to model and predict
mechanisms of importance in weather forecasting, air pollution, and climate
change studies (Barlage et al., 2016). Therefore, it is meaningful to
acquire knowledge of the ABL top height distribution along with the
aerosol optical properties within the ABL.
Lidar techniques seem to be an optimal tool to provide height-resolved
aerosol data products. Several lidar techniques suitable for aerosol studies have recently matured. In the last 10 years, rapid progress in laser
technology, measurement techniques, and data acquisition systems has
contributed to a much wider use of these techniques for aerosol
monitoring, ranging from simple elastic backscatter lidar/ceilometer
networks (Flentje et al., 2010; Lolli and Di Girolamo, 2015) to
advanced multi-wavelength Raman lidar system networks (Baars et al., 2016).
The European Aerosol Research Lidar Network (EARLINET;
https://www.earlinet.orglast access: 15 October 2019) conducts lidar observations and provides relevant
sets of lidar data products on the aerosol distribution over Europe, which are stored in a comprehensive, quantitative, and
statistically significant database
(Pappalardo et al., 2014). A quality assurance programme (Freudenthaler et al., 2018) and lidar data evaluation algorithms (Böckmann et al., 2004) have
been developed and assessed at each lidar station, as well as during
lidar intercomparison campaigns (e.g. Wandinger et al., 2016), to meet
the accuracy standards required for calculations of aerosol radiative forcing. The unique
dataset of lidar observations conducted over Europe allows for the
classification of the aerosol type (e.g. Nicolae et al., 2018;
Papagiannopoulos et al., 2018). The EARLINET network is an integral part of
the Aerosol, Clouds, and Trace gases Research Infrastructure (ACTRIS;
https://www.actris.eu, last access: 29 August 2019) – a pan-European initiative consolidating actions from European partners producing high quality observations of aerosol,
clouds, and trace gases. As different atmospheric processes are increasingly
becoming the focus of many societal and environmental challenges, such as air
quality, health, sustainability, and climate change, ACTRIS initiatives aim
at contributing to resolving these challenges by providing a platform for
researchers to more effectively combine their efforts, and by making
observational data of aerosol, clouds, and trace gases openly available to other external users.
The aerosol optical properties derived for the boundary layer from lidar have
been studied using a statistical approach at several EARLINET sites in Europe
(e.g. Matthias et al., 2004; Mattis et al., 2004; Amiridis et al., 2005;
Sicard et al., 2011; Siomos et al., 2018). However, most of these studies
have been restricted to the analysis of either AOD and/or LR values in the ABL (e.g.
Matthias et al. 2004; Amiridis et al. 2005, 2009; Sicard et al., 2011), or
relations between LRABL and AEABL (Giannakaki et al., 2010). Comprehensive investigations of interrelations of various aerosol optical
properties have not been reported thus far. Therefore, the current study is the first to report on these interrelations (or lack of) for different times of
day (nocturnal, sunrise and sunset, daytime) in summer (July and
August) and early autumn (September) over Warsaw. The relations of surface
PM2.5, PM10, and AODCL have been reported by e.g. Guo et al. (2009),
Filip and Stefan (2011), Zang et al. (2017), and Szczepanik and Markowicz (2018),
and the relations of AODCL and ambient RH were reported by e.g. Bergin
et al. (2000) and Altaratz et al. (2013). Stachlewska et al. (2017b, 2018),
investigated the relations of PM2.5, PM10, RH, LRABL,
AODABL, AEABL, and δABL, based on case studies. In
this paper, we analyse and report on those sets of properties based on long-term
observations. The combination of such study with hygroscopic growth monitoring
is the next step in the future (Navas Guzmán et al., 2019). The results
reported in the current paper will enrich the state of knowledge on
boundary layer aerosol optical properties by building a seasonal climatology
over Warsaw (even if it is limited to July–September of the 3-year period – 2013, 2015, and 2016) and, thus, providing a reference for comparative studies with the other EARLINET (e.g. Papayannins et al., 2008) and AERONET sites (e.g. Siomos et al.,
2018). The dataset obtained has excellent potential for aerosol
microphysical parameter inversion (e.g. Veselovskii et al., 2002;
Böckmann et al., 2005) and can also be used to test aerosol
typing algorithms (e.g. Nicolae et al., 2018; Papagiannopoulos et al.,
2018). This research provides parameters for studies of boundary layer
meteorology, and the derived aerosol optical properties can serve to
verify aerosol transport models (e.g. Binietoglou et al., 2015); furthermore, these properties can be used in the estimation of the aerosol radiative forcing effect in radiative
transfer modelling (e.g. Lisok et al., 2018; Lolli et al., 2018) as well as
for the validation of space-borne lidar (e.g. Illingworth et al., 2015;
Papagiannopoulos et al., 2016; Proestakis et al., 2019).
In the framework of EARLINET, extensive observations at a continental,
urban site in Warsaw at the Remote Sensing Laboratory (RS-Lab) of the
Institute of Geophysics at the Faculty of Physics of the University of
Warsaw have been performed since July 2013. Within the current paper the
data products from the Warsaw site published in the EARLINET/ACTRIS database were
utilized (The EARLINET Publishing Group, 2018). The study was dedicated to the
calculation and analysis of the aerosol optical properties derived within
the atmospheric boundary layer, at various times of the day, during summer (July
and August) and early autumn (September) of 2013, 2015, and
2016, over a continental urban site in Warsaw. The following three specific goals were
addressed: (i) investigation of interrelations between the different
aerosol optical properties; (ii) investigation of relations between the
aerosol optical properties, particulate matter (PM2.5 and PM10),
and the relative humidity; and (iii) assessment of the quantitative
contribution of the boundary aerosol optical depth to the columnar aerosol
optical depth. In Sect. 2, the instrumentation and datasets are described.
Section 3 presents the methodology regarding the boundary layer height derivation and the
aerosol optical property retrieval approaches. Section 4 focuses on
comparisons of different types of mean optical properties as derived within
the boundary layer and in the atmospheric column: PMs, ABLHs, and near-surface
relative humidity. Conclusions are given in Sect. 5.
Instrumentation and dataset
The Raman polarization and water vapour lidar (52.2109∘ N,
20.9826∘ E, 112 m a.s.l.) is located at the Remote Sensing
Laboratory (RS-Lab, https://www.igf.fuw.edu.pl/en/instruments, last access: 1 May 2019) of the
Institute of Geophysics at the Faculty of Physics of the University of
Warsaw, Poland. The location of the RS-Lab is shown in Fig. 1. The RS-Lab
conducts observations as a part of the European Aerosol Research Lidar
Network (EARLINET, https://www.earlinet.org, last access: 15 October 2019, Pappalardo et al., 2014) and provides
regular measurements within the worldwide Polly.NET lidar network
(http://polly.tropos.de/, last access: 23 September 2019, Baars et al., 2016) and within the National
Aerosol Research Network PolandAOD-NET (http://www.polandaod.pl, last access: 15 October 2019, Supplement in
Markowicz et al., 2016). The so-called “next generation PollyXT”
lidar deployed in Warsaw was described in a great detail in Engelmann et al. (2016).
The location of the Remote Sensing Laboratory (RS-Lab) at UW Ochota
Campus in Warsaw, Poland (in red), the WIOS monitoring stations in Ursynow
and Bielany (in green), and the Institute of Geophysics at the Polish Academy of Sciences (IGF-PAS) Observatory in Belsk (arrow).
Since July 2013, the PollyXT lidar has performed quasi-continuous 24/7 observations.
Powerful laser pulses (180, 110, and 60 mJ) at 1064, 532, and 355 nm are
emitted co-axially and vertically, with a 20 Hz repetition frequency, into
the atmosphere. Detection is performed with a Newtonian telescope at
eight channels (so-called 2α+3β+2δ+ WV), which
enables the determination of the particle extinction coefficient profiles
(α) at 532 and 355 nm, the particle backscatter coefficient
profiles (β) at 1064, 532, and 355 nm, the linear particle
depolarization ratio profiles (δ) at 532 and 355 nm, and the water
vapour mixing ratio (WV). The signals at all channels are recorded up to
48 km with a standard 7.5 m vertical and 30 s temporal resolution. Measured
signals are affected by an incomplete geometrical overlap between the
emitted laser beam and the receiving telescope; therefore, the signals in
the range below an altitude of 400 m are rejected from further evaluation. The
overlap-range issue posed a first constraint on the selected dataset, i.e.
constraining analyses to the summer and early-autumn (July–September) data.
In winter, the atmospheric boundary layer height derived at noon and
midnight from the radiosounding profiles was found often enough below the
complete lidar's overlap range. Hence, in winter, within the range in which
the divergent lidar laser beam is not fully received by the full field of
view of the lidar telescope, the detection of the boundary layer height by
lidar is limited. In contrast, in summer and early autumn, the boundary
layer height is always above the complete lidar's overlap range; thus, it
does not affect the derived profiles. Therefore, we restricted the analyses
of the optical properties within the boundary layer to the latter period.
Moreover, the analysis was restricted to the complete sets of profiles of
aerosol properties (i.e. 2α+3β+2δ+ WV) derived
in 2013, 2015, and 2016, as for the above-mentioned period, for this site, it was
feasible to obtain the highest number of quality controlled lidar profiles.
They were then calculated and stored in the EARLINET/ACTRIS database.
Although the PollyXT lidar in Warsaw performs quasi-continuous 24/7
observations, the data cannot be obtained every single day. Unfavourable
weather conditions (e.g. precipitation or thick low-level clouds)
significantly limit the number of observations in spring and late autumn,
and practically prevent observations in winter. Instrumental issues occur (e.g.
technical failures), which are season independent but in some periods lasted
several weeks or months. Finally, even if observations were performed, it
was not always feasible to derive the aforementioned full sets of aerosol
properties, e.g. due to overly strong background light at daytime or an overly low
(high) aerosol load. Note that, for the July–September period
of 2013, 2015, and 2016, no other site provided such a high
number of complete sets of measurements within the EARLINET/ACTRIS database.
Quality-controlled profiles of optical properties are stored in the
EARLINET/ACTRIS database (http://www.earlinet.org, last access: 15 October 2019). The statistical analysis
covers profiles derived for EARLINET regular measurements (Mondays and
Thursdays, ±2 h from zenith and sunset) and for dedicated
measurements (e.g. diurnal cycles, special alert events). From the Warsaw site, only cloud-screened profiles evaluated using the classical Raman approach
are feed into the database. The profiles obtained for lidar observations in
July, August, and September of 2013, 2015, and 2016 were analysed
(2014 was excluded from analyses due to that fact that the data availability was too sparse). In
total, 246 lidar profiles were collected for this study (denoted as
contributing to “entire time”), 113 profiles were obtained for the “nocturnal time” (21:00–02:00 UTC),
and 37 profiles were derived at sunrise (03:00–08:00 UTC) and 63 profiles at
sunset (16:00–20:00 UTC), here defined as the “sunrise/sunset or transition time”. The precise sunrise and
sunset times are available at http://www.timeanddate.com/sun/poland/warsaw (last access: 5 May 2019). The
analysis period is separated into three periods, because the change in
atmospheric conditions is driven by different processes during the designated times; this allows for change in the aerosol optical properties to be investigated. Note, that only 29 profiles were available under
daytime conditions (08:00–16:00 UTC), which was considered to be too low
to consider the daytime category separately – thus, these profiles joined the entire time category. However, comparisons of the optical properties derived
from the lidar and photometer measurements were carried out for a subset of daytime profiles
(03:00–19:00 UTC).
A multi-filter rotating shadowband radiometer (MFR-7; Yankee Environmental
Systems) was used for continuous passive measurements at the RS-Lab in the
frame of the PolandAOD-NET network activities. The instrument operates at
six narrow-band channels (415, 500, 615, 673, 870, and 940 nm) and one
broadband channel. It measures direct, diffuse, and total solar radiation,
from which the spectrally resolved aerosol optical depth is obtained. In situ calibration using the classic Langley approach is applied on a regular basis. Details on the instrument design and uncertainty analyses are
reported in Harrison et al. (1994). Cloud-screened products used in this
study are the AODCL(415) and AODCL(500), with uncertainty at the
±0.025 level, and ÅECL(415/500), with uncertainty at
the ±0.04 level. AODCL values less than 0.03 were excluded from the analyses.
A sun photometer (CE318; CIMEL Electronique) is operated at the Institute of
Geophysics at the Polish Academy of Sciences (IGF-PAS) in the Observatory in
Belsk (51.8366∘ N, 20.7916∘ E, 190 m a.s.l.), which is located
43.7 km south-west of the RS-Lab in Warsaw and provides the longest record of
passive measurements in Poland. The same instrument was recently installed
in the RS-Lab in Warsaw, and since January 2018 it has provided data to the AERONET database. Passive measurements of direct and diffuse solar irradiance and sky
radiance at the Earth's surface at nine wavelengths in a spectral range from
340 to 1640 nm are used for retrieval of the AOD and ÅE. The data are
calibrated once a year at the PHOTONS/AERONET-EUROPE calibration centre
(http://loaphotons.univ-lille1.fr, last access: 3 September 2019) and processed by the Aerosol Robotic
NETwork (AERONET, http://aeronet.gsfc.nasa.gov, last access: 15 October 2019, Holben et al., 1998). The
Version 3 products (Giles et al., 2019) of AERONET Level 2.0 cloud-screened
AODCL(380) and AODCL(500), with uncertainty at the
±0.01 level, and ÅECL(380/500), with uncertainty at the
±0.03 level, were used. AODCL values less than 0.03 were excluded from the analyses. Note that, the AODCL data
from MFR-7 and CE318 are scaled using the Ångström power law
(Ångström, 1929; Iqbal, 1983) to match either one another or the
lidar wavelength.
The sun photometer is located about 2 km from the
village of Belsk, in a typical agricultural region with fertile soil and
trees. Note that the AERONET data in Belsk were only used in the current study
to check data consistency and only as an indicator that the free-tropospheric aerosol load existed above Warsaw and in its vicinity; therefore, sun-photometer data from Belsk do not contribute to the core
results. Note that, the AERONET CE318 sun photometer is also
operated (since January 2018) at the RS-Lab of the University of Warsaw. The
data from the latter instrument, specifically the data for the intercomparison of AODs obtained
with the co-located CE318 and MFR-7 instruments at the RS-Lab, constitute a
vital part of the results.
Particulate matter concentrations for particles with an aerodynamic diameter
of less than 2.5 and 10 µm (referred to as PM2.5 and PM10
respectively) were measured at the air quality monitoring site of the Warsaw
Regional Inspectorate for Environmental Protection (WIOS) in Ursynow, Warsaw,
located 6.5 km from the RS-Lab. The daily and hourly averaged PM2.5 and PM10 data are available at
http://sojp.wios.warszawa.pl/raport-dobowy-i-roczny (last access: 15 October 2019). The data measurements
conducted at the State Environmental Monitoring stations are gathered in
the JPOAT 2.0 air quality database of the National Chief Inspectorate for
Environmental Protection (GIOS). These official, calibrated datasets of
PM2.5 and PM10 are accessible at
http://powietrze.gios.gov.pl/pjp/archive (last access: 15 October 2019). The measurement uncertainty is
below 30 % for the hourly concentrations. Products used in this study are as follows:
surface daily and hourly mean particulate matter concentrations for
PM2.5 and PM10, and the fine-to-coarse mass ratio (FCMR) defined
as PM2.5/ (PM10- PM2.5) (Zawadzka et al., 2013). A FCMR greater than 1.5 denotes “fine particle” domination (diameter < 2.5 µm); a FCMR less than 0.5 means “coarse particle” domination (diameter between 2.5 to 10 µm).
FCMR values between 0.5 and 1.5 indicate
that fine and coarse particles are distributed approximately equally.
The temperature, pressure, relative humidity, wind speed, and wind direction at
the surface (p, T, RH, V, and Vdir), were measured by the WXT510 (Vaisala) weather transmitter
mounted on the roof platform of the RS-Lab at 21 m above
the ground's surface. The atmospheric pressure, temperature, and relative
humidity profiles were obtained from the RS92 (Vaisala) radiosonde launched
at two World Meteorological Organization sites located in Poland: WMO 12374
station in Legionowo (52.40∘ N, 20.96∘ E, 96 m a.s.l.,
25 km north of Warsaw) and WMO 12425 station in Wroclaw (51.78∘ N,
16.88∘ E, 122 m a.s.l., 300 km south-west of Warsaw). The noon and
midnight radiosounding profiles (launch at 11:15 and 23:15 UTC respectively, with a duration of
circa 1.5 h) were visualized and downloaded from the University of Wyoming
Upperair Air Data website (http://weather.uwyo.edu/upperair/sounding.html, last access: 27 August 2019).
Note that the shadowband radiometer-derived and the sun-photometer-derived AOD and
the in situ measured PM concentration values are averaged so as to correspond to the time of the lidar-derived optical profiles available for given period for
the Warsaw site in the EARLINET/ACTRIS database. Moreover, the MFR-7 and CE318
instruments only collected data at daytime. The measurement sites are shown in Fig. 1. The PM site is located in the residential suburb of Ursynow. The
RS-Lab is located at the university campus in the suburb of Ochota, which is shrouded by
green parks. Between the three sites Ochota and Ursynow (in Warsaw) and
Belsk, there are no industrial pollution sources and the anthropogenic pollution
in summertime is related to traffic (Zawadzka et al., 2013).
Methodology of lidar product retrieval
The atmospheric boundary layer is regarded as the lowest layer of the
troposphere; it is directly influenced by the Earth's surface and reacts
quickly to the surface forcing (Stull, 1988). The atmospheric boundary layer
height (ABLH) can be derived from the lidar elastic-scattering aerosol
backscatter signal, which relies on a higher aerosol load within the boundary
layer than in the free troposphere. Dang et al. (2019) recently summarized the available methods for ABLH retrievals. The limitations and capabilities
of mixing height retrieval algorithms for different lidar and ceilometer
systems were investigated by Haeffelin et al. (2012). Comerón et al.
(2013) discussed the wavelet correlation transform method and the gradient
method for the determination of the ABLH by lidar. Stachlewska et al. (2012)
used the latter method for the first retrievals of the ABLH in Warsaw. Wang et
al. (2019) reported that the wavelet covariance transform method (WCT) is the most optimal technique for ABLH retrieval for the PollyXT lidar data in Warsaw.
As for the WCT method applied in our study, the covariance transform Wf
of the Haar wavelet function h, is defined as in Eq. (1):
Wfa,b=1a∫z1z2P(z)⋅hz-badz,hz-ba=+1b-a2≤z≤b-1b≤z≤b+a20elsewhere,
where P(z) is the lidar elastic signal, z1 and z2 are the respective lower
and upper limits of the signal, z is the altitude, and a and
b are the dilation and the centre of the Haar wavelet function respectively. The WCT calculations were carried out
with the dilation of 30 range bins applied on signals averaged over 7.5 m
and 30 min, and the ABLH was derived at all three elastic wavelength signals
(355, 532, and 1064 nm) and then averaged for a final result.
Aerosol optical properties can be derived in different ways. The
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) can provide the
range-resolved profiles of aerosol and clouds (Winker et al., 2007). The
combined ground-based lidar and cloud radar is capable of obtaining the vertical
structure of cloud and aerosol properties (e.g. Delanoë and Hogan,
2008). The high spectral resolution lidar technique allows for the
separation of molecular and aerosol signals (e.g. Grund and Eloranta, 1991;
Burton et al., 2012). The aerosol and cloud information can be retrieved by
multi-wavelength Raman lidars (e.g. Müller et al., 2007; Giannakaki et
al., 2010; Alados-Arboledas et al., 2011; Baars et al., 2016; Lolli et al.,
2018; Ansmann et al., 2018). The latter lidar technique was used for this
study.
Lidar signals stored in the EARLINET/ACTRIS database were evaluated using
the classical Raman retrieval approach. The particle extinction coefficient
profiles at 355 and 532 nm were calculated from the so-called Raman lidar
equation using the Rayleigh law for molecules and the Ångström power law
(usually with ÅE = 1) for aerosol particles. The particle extinction
coefficient (αp) was derived as in Eq. (2):
αp=ddzlnNPNz2-αλ0m-αλNm1+λ0λNk,
where N denotes the molecular number density of the nitrogen, λ0 is the emitted wavelength (355 or 532 nm), λN is the
wavelength of the nitrogen channel (387 or 607 nm), PN is the Raman signal
at the nitrogen channel, z is the distance from the lidar, αλim (i=0, N) is the molecular extinction coefficient at the emitted or
the nitrogen wavelength, and k is the wavelength dependence of particle.
The particle backscatter coefficient profiles (βp) at 355, 532,
and 1064 nm are derived with the use of the obtained particle extinction
coefficient profiles at 355 and 532 nm (utilized for both larger wavelengths)
and calibrated at the height range free of aerosol. More details on the
exact procedure are given in Baars et al. (2016).
The linear particle depolarization ratio (δp) was derived as
the ratio of the cross channel and the corresponding total channel at the
same wavelength, using Eq. (3):
δp=1+δmδv1+βpβm-1+δvδm1+δm1+βpβm-1+δv,
where δv is the linear volume depolarization ratio, δm is the linear depolarization ratio of air molecules, and βm is the molecule backscatter coefficient. The procedure for the
±45 depolarization calibration is used as in Engelmann et al. (2016).
The water vapour mixing ratio (WV) was obtained as the ratio of the Raman
water vapour channel and the Raman nitrogen channel, as described in
Stachlewska et al. (2017a), using Eq. (4):
WV=CPλH2OPλNexp-αλH2O-αλN,
where PλN is the Raman backscatter signal of the nitrogen
channel wavelength λN=387 nm, PλH2O is the
Raman backscatter signal of water vapour channel wavelength λN=407 nm, and C is the calibration constant.
For all analysed data products, low- and mid-altitude clouds are screened
prior to the retrieval. The sets of profiles (αp, βp, and
σp) were averaged over the same time interval, which was either 60 min (60 %), 45 min (23 %), or 30 min (17 % of profiles used), depending on the variability of the atmospheric conditions and the signal-to-noise ratio. Iarlori et al. (2015) reported
that smoothing procedures can affect the features of the lidar signal profile.
For the EARLINET/ACTRIS database, we store the files with profiles smoothed
with the running mean over 49 (b-file) and 101 (e-file) range bins (length
of a single range bin is 7.5 m), which were found to be optimal for the PollyXT lidar
data products derived at the Warsaw site. Typically, the αp
profiles need higher smoothing than βp and δp
profiles. However, we keep two versions of the smoothed profiles in the database. In this paper, the profiles smoothed over 49 range bins were used.
Keeping the smoothing at the same level is not only advantageous for the
error estimations and the profile comparisons but also for the
microphysical parameter inversion (Janicka et al., 2017). The profiles of
the WV were averaged over 30–60 min and 60 m with no smoothing applied. With respect to the
lidar retrieval, the atmospheric profiles obtained by radiosounding at
Legionowo or Wroclaw were used depending on the approaching direction of the
air mass transport.
After the ABLH was determined, the mean values of different optical
properties within the boundary layer were derived. For the incomplete overlap
region, data in the lowermost altitude range required special treatment. The lowest value of the available particle extinction coefficient was
assumed to be representative down to the ground surface, which is the commonly accepted approach in lidar studies, e.g. Matthias et al. (2004). Therefore,
the mean extinction coefficient of the entire ABL was obtained by
extrapolating the extinction profile using this value down to the ground.
Similarly, the mean backscattering coefficient and the particle
depolarization ratio of the ABL were extrapolated. Although the δ
profiles can be derived almost to the ground, for the EARLINET/ACTRIS database profiles are only stored down to 400 m, so that extrapolation was also required here. The water vapour mixing ratio profiles were also extrapolated,
from 100 m down to the ground. The WV profiles were calculated using the
ratio of two signals at the 407 and 387 nm Raman channels. The overlap term of
those two channels (which is close in spectral range – only 20 nm) practically cancels
when calculating their ratio. Similarly, for the particle depolarization
ratio (the cross and total ratio at the same wavelength), the overlap term also
cancels. Therefore, the water vapour (and the depolarization ratio profile)
can be obtained almost down to the ground. The water vapour nighttime
detection in July–September is typically performed from 20:00 to 04:00 UTC;
thus, only the data corresponding to the nocturnal time (21:00 to 02:00 UTC)
were analysed.
Within the ABL, the vertical distribution of the lidar ratio (LRABL) was
derived as a ratio of the aerosol extinction to backscatter coefficient
profiles at 355 and 532 nm (LR =αp/βp), and then
the mean LRABL was calculated.
The vertical distribution of the Ångström exponent ÅEABL(355/532)
was computed by using the profiles of the aerosol
extinction coefficient (not AOD) at 355 and 532 nm; the mean ÅEABL was then
calculated using Eq. (5):
ÅE=-lnαpλ1αpλ2lnλ1λ2.
The aerosol optical depth within the boundary layer (AODABL) was calculated
by integrating the extrapolated (to the ground) aerosol extinction profile
at 355 and 532 nm from the height of z1=0 to the height of the
top of the boundary layer z2= ABLH, as in Eq. (6):
AOD=∫z1z2αpdz.
There is a threshold set on the mean values of an AODABL(355) of less than 0.05 and an AODABL(532) of less than 0.03 within the lowermost 1 km, i.e.
the aerosol extinction coefficient profiles are extrapolated according to
the AOD threshold for each wavelength. If the AOD within 1 km is below the
threshold, re-extrapolation is applied from a range bin just above the
initially chosen one down to the ground in an iterative manner until the
AOD values within 1 km meet the above-mentioned thresholds.
Stachlewska et al. (2018) reported the uncertainty of the AODABL at 355 and
532 nm derived from the Raman extinction coefficient profiles as being less than 20 %, the uncertainty of LRABL derived by
extinction-to-backscattering coefficient ratios at 355 and 532 nm as less than 35 %, and the uncertainty of δABL at 355 and 532 nm
as less than 20 % of the derived value. The uncertainty of extinction-derived
ÅEABL(355/532) is less than 30 %. The uncertainty of the ABLH
retrieval from PollyXT lidar is ±70 m (Wang et al., 2019).
Literature review based on the values of the lidar-derived
particle optical properties used for the interpretation of aerosol
measured over the RS-Lab in Warsaw. The listed properties are assessed in
each aerosol layer based on a manual approach in combination with
case-to-case air mass transport analyses.
LR (sr) δ (%) ÅERHAir massNo. of cases355532355532(355/532)(%)transport*(in the ABL) (%)Anthropogenic pollution50–6533–723–63–110.7–1.850–90Local (Warsaw), advective (western Europe)151 (61 %)Biomass burning50–9560–902–64–120.8–2.060–80Advective (eastern Europe, North America)34 (14 %)Pollen50–7546–695–176–20–< 50Local16 (7 %)Arctic marine16–3018–261–71–11-0.6–0.7–Advective (Arctic, subarctic)14 (5 %)Dust50–7045–6524–2925–430.1–1.520–40Advective (Africa)0Undefined Mixtures31 (13 %)
* Calculations of up to 10-day backward trajectories for aerosol layers in the
free troposphere and in the boundary layer, and the assessment of possible sources of
aerosol by interpreting them against satellite data (MODIS, MSG, and CALIPSO) and
model outputs (NAAPS).
Finally, we attempt to interpret the aerosol measured within the boundary
layer during the specified observational period over Warsaw. A number of
publications have highlighted aerosol studies that use backward
trajectories to interpret the possible aerosol source/type based on optical
properties of observed aerosol and on the origin of air masses containing
aerosol (e.g. Dörnbrack et al., 2010; Mona et al., 2012; Nemuc et al.,
2014; Burton et al., 2015; Granados-Muñoz et al., 2015; Jung et al.,
2016; Papagiannopoulos et al., 2016; Schmeisser et al., 2017; Szkop and
Pietruczuk 2017; Ansmann et al., 2018; Di Biagio et al., 2018; Horvath et
al., 2018; Foth et al., 2019). In this paper, the analysis was undertaken in a standard manner,
case by case, based on the comparison of aerosol optical properties (LR, δ,
ÅE, and RH) with the values reported in the literature (an excellent
review of these values is provided by e.g. Nicolae et al., 2018), as well as by
using the HYbrid Single-Particle Lagrangian Integrated Trajectory
(HYSPLIT) model to calculate trajectories (Stein et al., 2015). The 246 profiles analysed were checked against the 3–10 day backward trajectories obtained
in the boundary layer and the free troposphere, starting at altitudes of
0.5, 1.2, and 3 km, applied on the Global Data Assimilation System (GDAS)
and CDC1 meteorological data (trajectories are available via the
PolandAOD-NET website). We assessed possible aerosol sources by inspecting
the trajectories and the aerosol optical properties against the satellite
data, i.e. from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), and the Cloud-Aerosol Lidar and
Infrared Pathfinder Satellite Observations (CALIPSO), and model outputs, i.e. the Navy Aerosol Analysis and Prediction System (NAAPS),
http://www.nrlmry.navy.mil/aerosol, last access: 4 May 2019), as described by Stachlewska et al. (2018).
Roughly speaking, a LR greater than 75 sr can indicate the existence of aerosol
particles related to biomass burning, values between 40 and 50 sr can indicate mineral dust, values between 50 and 60 sr can indicate
anthropogenic pollution, and values between 20 and 30 sr can indicate Arctic marine particles, although
these values depict an ideal case (a pure aerosol type rarely exists)
and the values overlap for the different aerosol species, as
indicated in Table 1.
The particle linear depolarization ratio (δp) can be used as a
tracer of spherical and non-spherical particles: the low values
(δ<0.01) can be regarded as being due to very small spherical
particles (e.g. pollution); δ values between 0.2 and
0.35 can be regarded as beng due to dust (polluted dust values are lower – even as low as 0.1); δ values between 0.04 and 0.08 can be regarded as
characteristic of biomass burning aerosol, values of approximately 0.1 indicate pollen, and a value less than 0.2 indicates urban pollution. Also here, values can
overlap for the different aerosol species, as indicated in Table 1.
Mean values of the aerosol optical properties with standard
deviations derived within the atmospheric boundary layer (ABL) from the PollyXT
lidar at the EARLINET site, Warsaw, for measurements at 355 and 532 nm
conducted for the July–September period of 2013, 2015, and 2016. Symbols
denote the particle extinction coefficient (α), the particle
backscatter coefficient (β), the aerosol optical depth (AOD),
the lidar ratio (LR), the linear particle depolarization ratio (δ), the Ångström exponent (ÅE), and the atmospheric boundary layer height
(ABLH). Mean values are obtained for different times of the day with respect to
the boundary layer type.
λαABLβABLAODABLLRABLδABLÅEABLABLH(nm)(Mm-1)(Mm-1sr-1)(sr)(km)Entire time (ET) WML, RL, MTL355142±683.1±1.20.20±0.1048±170.02±0.011.54±0.371.33±0.36(246 cases)53283±432.2±0.70.11±0.0641±150.05±0.01Nocturnal time (NT) (21:00–02:00 UTC) NL355129±563.0±1.10.11±0.0444±120.02±0.011.58±0.360.76±0.12(113 cases)53269±391.9±0.70.07±0.0238±120.05±0.01RL355137±533.1±1.00.18±0.0847±150.02±0.011.61±0.381.34±0.17(105 cases)53275±372.3±1.00.11±0.0540±130.04±0.01Transition time (TT) during sunrise (03:00–08:00 UTC) and sunset (16:00–20:00 UTC) MTL355128±543.8±1.10.11±0.0337±140.02±0.011.53±0.300.70±0.10(37 cases)53273±352.3±0.60.06±0.0232±130.04±0.01WML355163±632.8±1.10.24±0.0155±180.02±0.011.37±0.341.60±0.38(63 cases)53296±491.9±0.80.14±0.0549±160.05±0.02
The Ångström exponent (ÅE) can be used as an indicator of the
size of atmospheric aerosol particles. ÅE values of less than or equal to 1 indicate
particle size distribution dominated by the coarse-mode aerosol (radii ≥ 0.5 µm,
here referred to as “large particles”) that are typically associated with dust and
sea salt particles (Perrone et al., 2014). ÅE values greater than or equal to 1.5
indicate size distributions dominated by the fine-mode aerosol (radii < 0.5 µm,
here referred to as “small particles”) that are associated with urban
pollution (Perrone et al., 2014). ÅE values between 1 and 1.5
belong to the accumulation mode (here referred to as “medium-size particles”) and are
associated with biomass-burning aerosol (Janicka et al., 2017). The use of the
ÅE nomenclature with respect to small, medium, and large particles is for clarity, as not to confuse them
with the fine-to-coarse mass ratio (FCMR).
For brevity, hereafter the αp, βp, and δp are denoted as α, β, and δ.
Results and discussion
Table 2 shows the mean particle extinction coefficient (α), the particle
backscatter coefficient (β), the aerosol optical depth (AOD), the lidar ratio
(LR), the linear particle depolarization ratio (δ), and the Ångström
exponent (ÅE) derived at the 355 and 532 nm channels within the atmospheric
boundary layer (ABL) for the entire (ET), nocturnal (NT), and transition (TT)
times obtained for the July–September measurement period of 2013, 2015,
and 2016. Different mechanisms govern the sunrise and sunset conditions; the
first is driven by the development of the convective boundary layer; the
latter lessens the convection which causes more stratification within the residual
layer (RL). Thus, the ABLH algorithm developed determines the atmospheric
boundary layer top as a morning transition layer (MTL), a well-mixed layer
(WML), a nocturnal layer (NL), and/or a residual layer (RL).
The mean entire time (24 h) ABLH was 1.33±0.36 km for
July–September of 2013, 2015, and 2016. Wang et al. (2019) reported that the decadal mean ABLH over Warsaw in summer was 1.34±0.15 km based on
the 10-year dataset. This indicates that, in terms of the ABLH, the
dataset analysed can be regarded as representative of the long-term
analysis. The mean values of the αABL, AODABL, and LRABL
calculated at the two wavelengths in the transition time are higher during the
sunset period (WML) and lower during the sunrise period (MTL), the latter being similar to the
NL. The mean ÅEABL in the given periods was high (1.37–1.61, in
Table 2), indicating small particles. (Later on, we will show that the ÅEABL values ranged between 0.7 and 2.9 for
the nocturnal period,
whereas the range narrowed to 0.8–2.2 in the sunrise/sunset period.)
Frequency distribution of the aerosol optical depth (AOD),
the lidar ratio (LR), the linear particle depolarization ratio (δ),
and the Ångström exponent (ÅE) at 355 and 532 nm derived within
the atmospheric boundary layer at the EARLINET/ACTRIS lidar site, Warsaw, for
the July–September period of 2013, 2015, and 2016. The corresponding fine-to-coarse mass
ratio (FCMR) derived from measurements of surface particulate matter with an
aerodynamic diameter below 10 µm (PM10)
and below 2.5 µm (PM2.5) at the WIOS
site in Ursynow, Warsaw. The period of measurement is divided into the following time spans: the entire
time (ET; 24 h); the nocturnal time (NT), between 22:00 and 02:00 UTC – including the
residual boundary layer (RL) and the nocturnal boundary layer (NL); and the
transition time (TT), after sunrise between 03:00 and 08:00 for the morning transition
layer (MTL) and before sunset between 16:00 and 20:00 UTC for the well-mixed boundary
layer (WML).
The frequency distribution plots for the AODABL, LRABL, and
ÅEABL derived at 355 and 532 nm, and the FCMR derived for
PM2.5 and PM10 for the entire, nocturnal, and sunrise/sunset periods
are shown in Fig. 2. The mean AODABL mainly ranges from 0.1 to 0.3:
80 % of the occurrences are attributed to AODABL(355) in
the 0.1–0.3 range and AODABL(532) of below 0.2. The
mean LRABL(355) and LRABL(532) values mainly range from 30 to 70 sr, which accounts for more than 75 % of total data. The majority of
the ÅEABL(355/532) values are between 1.0 and 2.0 (more than
90 % of the total data), which indicates mid-sized and small particles (≤ 500 nm) within the boundary layer. The FCMR values
between 0.5 and 1.5 constitute around 70 % of the total data, indicating a
more or less equal distribution of fine and coarse particles with a size
between 2.5 and 10 µm at the surface. Most of the AODABL in the
MTL is below 0.2, and the LRABL in the MTL ranges from 25 to 50 sr at
both wavelengths. The δABL in the MTL between 0.04 and 0.06 accounts
for around 50 % of occurrences. The fine particles are expected in the MTL, as the majority of the FCMR values are above 1.5.
Amiridis et al. (2005) reported a 4-year mean AODABL of 0.44±0.16 at 355 nm and a mean LRABL of 49±25 at 355 nm in summer
at Thessaloniki, Greece. According to Papayannis et al. (2008), this much
higher value of AODABL(355) can be attributed to a significantly
stronger impact from the summertime Saharan dust events on Thessaloniki than
on Warsaw. Sicard et al. (2011) reported a low AODABL of 0.07±0.02 at 532 nm in north-eastern Spain, and attributed it to the influence of the
sea breeze on the Barcelona area. Mattis et al. (2004) reported a
3-year mean AODABL(355) of 0.38 and a AODABL(532) of 0.18 for Leipzig, with a mean LRABL(355) of 58 sr and a LRABL(532) of 53 sr, and a mean ÅEABL(355/532) of 1.4. However, the ÅEABL
was between 1.8 and 2.2 in the upper boundary layer during summer in Leipzig
(Matthias et al., 2004); thus, only slightly larger particles
are observed in the ABL in Warsaw during summer compared with Leipzig. Using Raman lidar observations at 10 EARLINET stations, Matthias et al. (2004) derived the lowest AODABL values in northwestern Europe (Aberystwyth) and the highest values in southeastern Europe (Athens), which was again attributed to the impact of Saharan dust events on the aerosol distribution in southern
Europe.
The lidar ratio was used for the aerosol type characterization in various
aerosol typing algorithms (e.g. Papagiannopoulos et al., 2018; Nicolae et
al., 2018). Alados-Arboledas et al. (2011) reported lidar ratios for a fresh
biomass-burning pollution plume of 60–65 sr at 355 and 532 nm at
Granada. Müller et al. (2007) reported lidar ratios of 45–60 sr
with a mean value of 53 sr at 532 nm, and a particle depolarization ratio
at 532 nm of below 0.05 for Leipzig, under local and regional urban and
anthropogenic haze conditions. Amiridis et al. (2005) reported a continental aerosol 4-year mean lidar ratio of 40–47 sr at 355 nm for
Thessaloniki, and Giannakaki et al. (2010) reported a biomass-burning aerosol 7-year mean lidar ratio of 70 sr at 355 nm. Optical properties of eight aerosol types were derived by Burton et al. (2012) over North America for urban
aerosol (lidar ratio of 53–70 sr at 532 nm with a particle depolarization
ratio of 0.03–0.07) and for smoke particles (lidar ratio of 33–46 sr
with a particle depolarization ratio of 0.04–0.09). A lidar ratio of marine
particles of 20–26 sr at 532 nm was found in North Atlantic and the tropical
Indian Ocean by Müller et al. (2007), and Masonis et al. (2003) reported a value of 25±4 sr at 532 nm in
Hawaii. Dawson et al. (2015) presented a global
mean lidar ratio for marine aerosol of 26 sr, with a range from 22±7 to 32±17 sr, depending on variation of the mean ocean surface wind
speed. Haarig et al. (2017) reported lidar
ratios varying from 19 to 27 sr at 355 nm and 23 to 25 sr at 532 nm for marine particles, and a particle
depolarization ratio of between 0.05 and 0.12 sr at 355 nm and 0.07 and 0.15 sr at 532 nm. A review
of aerosol types reported by Groß et al. (2013, 2015) includes a
classification scheme based on the following values: the lidar ratio for the marine
particles varies from 16 to 30 sr at 355 nm and 18 to 26 sr at 532 nm; biomass burning aerosol ranges from 50 to 95 sr at 355 nm and 60 to 90 sr at 532 nm; mineral
dust ranges from 50 to 70 sr at 355 nm and 45 to 65 sr at 532 nm; and anthropogenic pollution ranges from 50 to 65 sr
at 355 nm and 50 to 60 sr at 532 nm. In the current study,
LRABL values in the range of 25–30 sr at both wavelengths were obtained in several cases
(Fig. 2), and were interpreted as likely being due to the transport of a clean
air mass of Arctic marine particles injected into the boundary layer in
Warsaw during the analysis period. Such cold air masses can be transported
from the Arctic to eastern Europe (Costa-Surós et al., 2015).
Intercomparison of hourly averaged
clear-sky daytime aerosol optical depth measured within the atmospheric
column (AODCL) with the CE318 instrument at the AERONET site in
Warsaw (Version 3 Level 2.0 data) and the MFR-7 shadowband radiometer at the
PolandAOD-NET site in Warsaw (Level 1.5 data). Note the high agreement
obtained for both wavelengths for a month (July 2018) of co-located
measurements (instruments at a distance of 3 m and at the same altitude) at the
RS-Lab in Warsaw. The AODCL at 415 nm from the MFR-7 was
scaled to 380 nm using the Ångström power law.
The linear particle depolarization ratio is an indicator of non-spherical
particles (Ansmann et al., 2009; Sakai et al., 2010; Gasteiger and
Freudenthaler, 2014). The total depolarization ratio in dust episodes have been
reported to be above 0.2, whereas anthropogenic pollution aerosol have a total
depolarization ratio below 0.1 (Xie et al., 2008; Nemuc et al., 2013).
Heese and Wiegner (2008) reported a particle depolarization ratio for dust
(∼ 0.25) and biomass-burning aerosol (< 0.1) over
Sahel (West Africa). Particle depolarization ratios of dust particles in
the range of 0.1–0.25 were reported in Leipzig (Matthias et al., 2004), whereas values of
0.3–0.35 were found at Ouarzazate, Morocco (Freudenthaler et al., 2009). The particle
depolarization ratios of urban haze and fire smoke have been reported to be less than
0.05 at different sites (Müller et al., 2007). A linear particle
depolarization ratio for marine aerosol, in the range from 0.01 to 0.03, was
reported by Groß et al. (2011). In the current study, the
δABL values obtained (shown in Fig. 2) are within the range of
the values listed above, characterizing different aerosol types. As δ
is sensitive to the size of the sensed non-spherical particles, small particles (< 300 nm) sensed with a
wavelength that is twice as large (532 nm) can be under the limit of detection (as seen in Fig. 2). The
dust cases detected in the free troposphere during the given measurement
period in Warsaw (e.g. Janicka et al., 2017) were excluded from the current
analyses (four cases); however, the derived δ values of the entire observation
time are less than 0.1, which means that there were no cases of dust
particles being deposited or advected into the ABL, although the existence of polluted dust cannot be entirely excluded.
Overall, during the July to September period of 2013, 2015, and 2016 in
Warsaw, the aerosol within the ABL consisted mainly of the following: (i) urban
anthropogenic pollution of local origin or pollution transported from areas below or
above of the Czech Republic via Silesia and/or Germany (61 %), and its
mixtures with (ii) grass and peatland biomass-burning aerosol transported
from Russia, the Ukraine, and Belarus (14 %), (iii) pollen emissions of strictly
local origin from Warsaw's semi-natural green parks (7 %), and (iv) Arctic
marine particles transported mainly from the Arctic over the Baltic Sea (5 %). For the
remaining cases, the aerosol composition was regarded as being due
to a mixture of more than two subcomponents (13 %), and was consequently
not interpreted. The percentages given were derived by relating the number
of profiles with an estimated origin to the total number of profiles, and are
therefore given without uncertainties. No significant contribution of
mineral dust into the boundary layer was found for the 246
profiles analysed, although transport pathways from the Sahara over the Iberian Peninsula or
via Italy were identified for the upper troposphere.
Hourly averages of the aerosol optical depth (AOD) and the
Ångström exponent (ÅE) derived within the atmospheric boundary
layer at 355 and 532 nm from the PollyXT lidar at the EARLINET/ACTRIS site,
Warsaw, plotted along with the corresponding MFR-7 shadowband radiometer values from
the PolandAOD-NET site, Warsaw, for July–September 2013, 2015, and 2016 (leap
year). These were co-located measurements at the RS-Lab in Warsaw.
The AODCL from the MFR-7 measurements at 415 and 500 nm were
scaled to 355 and 532 nm using the Ångström power law.
For the co-located, simultaneous Raman lidar measurements and the
shadowband radiometer measurements, we assessed the AODABL contribution within the boundary layer to the AODCL within the column of air, keeping in mind that such co-located measurements never sample an identical section of
air (lidar sampling in the zenith position vs. radiometer sampling at
angles related to sun's elevation over the horizon). To make sure that
the columnar measurements with the less commonly used (in the lidar community) shadowband
radiometer do provide high quality data products, an intercomparison of the
MFR-7 shadowband radiometer (PolandAOD-NET, Level 1.5 data, 415 nm and 500
nm) and the CE318 sun photometer (AERONET, Version 3, Level 2.0 data, 380 and
500 nm) was performed. The use of the above-mentioned level of data for both
networks refers to clear-sky, manually cloud screened data products that have been calibrated within the past 12 months. One month of co-located daytime (03:00–19:00 UTC)
measurements in July 2018 at the RS-Lab in Warsaw was chosen for the
intercomparison and confirmed the high quality of the measurements performed
using both instruments. Figure 3 shows high correlation coefficients
(R=0.98) and low standard deviations (STD = 0.02) for 144 data points
obtained at 380 and 500 nm for the intercomparison of the CE318 and
the MFR-7. Note that the AODCL value at 415 nm from the MFR-7 was
scaled to the value at 380 nm (corresponding to CE318 wavelength) using
the Ångström power law (Fig. 3).
Figure 4 shows the daytime mean 30–60 min average of the aerosol optical
depth within the atmospheric boundary layer, AODABL, at 355 and 532 nm,
calculated from the mean extinction coefficient profiles of the EARLINET
PollyXT lidar in Warsaw and the columnar daytime mean 1 h average (with a
threshold of at least five data points) of the AODCL at 415 and 500 nm
(scaled to lidar wavelengths) derived from the PolandAOD-NET MFR-7
shadowband radiometer measurements in Warsaw. Note that the AODCL values of
the CE318 in Belsk were in good agreement with Warsaw results, with Belsk values
being slightly lower than those in Warsaw (compare values in Table 3).
Figure 4 also depicts the extinction-derived ÅEABL(355/532) of the
lidar and the ÅECL(355/532) computed from the (scaled)
shadowband radiometer AODCL. Products presented in Fig. 4 were
derived for the July–September period of 2013, 2015, and 2016, for a subsample
of cases when all three instruments were conducting observations at the same
time (i.e. 41 cases).
The mean daytime (03:00–19:00 UTC) aerosol optical depth (AOD)
and Ångström exponent (ÅE) with standard deviations derived
within the atmospheric boundary layer at 355 and 532 nm from the PollyXT lidar at
the EARLINET site, Warsaw, and in atmospheric column measured by the MFR-7
shadowband radiometer (415 and 500 nm) at the PolandAOD-NET site, Warsaw.
For reference, the mean values derived from Version 3 Level 2.0 CE318 CIMEL
(380 and 500 nm) at the AERONET site in Belsk are given. The
AODCL of the CE318 and the MFR-7 were scaled to the lidar
wavelength (355 and 532 nm respectively) using the Ångström power law. The
mean values were obtained for July–September of 2013, 2015, and 2016, when
instruments operated simultaneously (41 cases). The mean values
derived for cases with no aerosol in the free troposphere (12 cases), as
given in EARLINET/ACTRIS database, are shown in parentheses and emphasized using italic font.
AODAODÅE (355/532)ÅE (380/500)ÅE (415/500)AERONET Belsk (columnar) reference site CE318 photometer 355 nm532 nm0.41±0.170.23±0.091.49±0.23All cases(0.28±0.12)(0.16±0.06)(1.57±0.10)(no FT aerosol)380 nm500 nm0.36±0.160.24±0.121.51±0.23(0.26±0.10)(0.19±0.08)(1.60±0.13)PolandAOD Warsaw (columnar) MFR-7 shadowband radiometer 355 nm532 nm0.45±0.170.25±0.111.56±0.21All cases(0.30±0.09)(0.15±0.06)(1.71±0.17)(no FT aerosol)415 nm500 nm0.36±0.150.27±0.121.50±0.31(0.26±0.08)(0.17±0.07)(1.66±0.35)EARLINET Warsaw (within the aerosol boundary) PollyXT-UW Raman lidar 355 nm532 nmAll cases0.20±0.060.13±0.031.53±0.23(no FT aerosol)(0.16±0.08)(0.10±0.03)(1.60±0.15)
In Table 3, the CE318-derived mean AOD is 0.41±0.17 at 355 nm and
0.23±0.09 at 532 nm, the MFR-7-derived mean AOD is 0.45±0.17 at 355 and 0.25±0.11 at 532 nm, and the PollyXT-derived mean
AODABL is 0.20±0.06 at 355 and 0.13±0.03 at 532 nm,
for the 41 points. The columnar AODCL values of the two instruments are the same within the given uncertainty range, despite the 43.7 km distance between the two sites (Warsaw and Belsk). It can be expected that the
AODABL is less than the AODCL (e.g. Sicard et al., 2011; Szczepanik and Markowicz, 2018). The results obtained indicate that the mean values of the
AODABL are twice as large as the mean values of the AODCL.
The high contribution of the aerosol load in the free troposphere can be related
to the summertime long-range transport of the biomass-burning aerosol to
Warsaw (e.g. Markowicz et al. 2016; Szkop and Pietruczuk, 2017; Janicka et
al., 2017; Stachlewska et al., 2017a, 2018). This is why the mean values derived for the cases with no long-range
transport in the free troposphere are listed in parentheses in Table 3 (i.e. no allocation to categories such as forest-fire or dust in the EARLINET/ACTRIS database).
Excluding those cases from the mean results in an AODCL that is
roughly 30 to 40 % lower in Warsaw and ∼ 30 % lower in Belsk. At the
same time, the mean values of the AODABL do not decrease as much, i.e.
the AODABL is less than 20 % lower. For conditions with no aerosol layers in the free troposphere, Szczepanik and Markowicz (2018) proposed the following
approximation of the daytime boundary layer aerosol load for a rural
mountainous site (Strzyzow, Poland, elevation 899 m a.s.l.):
the AODABL(500) equals approximately 80 % of the AODCL(500). Based on the values listed in the Table 3, such approximation for the urban continental site in Warsaw (elevation 112 m a.s.l.) would indicate AODABL(532) ≈ 53 % AODCL(532) and AODABL(355) ≈
67 % AODCL(355).
A closer look at Fig. 4 shows that the lidar-derived AODABL values are less than 0.2 on a few days (e.g. case numbers 9–13), although corresponding values of the radiometer-derived AODCL are more than 0.7. This is not an error. On 10 July 2013, biomass-burning aerosol from Canadian wildfires was detected by lidar in Warsaw, with an apparent optically thick aerosol layer suspended in the lower troposphere just above the top of the boundary layer, as reported by Janicka et al. (2017) and Ortiz-Amezcua et al. (2017). Due to the low ABLH (< 1000 m) on this day, not unusual under high-pressure system conditions over Poland e.g. Stachlewska et al. (2018), the optical
depth contribution of the aerosol smoke layer in the free troposphere dominated
over the optical depth contribution of the aerosol within the boundary
layer, which explains the much higher columnar values compared with the boundary layer AOD.
Markowicz et al. (2016) reported aerosol layers in the free troposphere
having a significant (up to 55 %) contribution to the total optical depth,
which is consistent with the results obtained in the current paper.
The results in Fig. 4, obtained for the ÅE with relation to the
particle size, show that retrievals from different instruments have a similar
trend of variation with time. The mean ÅECL values given in Table
3 are the same with respect to the given variability range for all 41 cases, despite
the differences in the measurement wavelengths. The values in parentheses (no
long-range transport of aerosol in free troposphere) show consistent
results, i.e. higher ÅEABL values than ÅECL values were
obtained, which indicates pollution dominating within the boundary layer.
Relation of the ABLH with optical properties and surface PM in summer and early autumn over Warsaw
The comparison of the AODCL and AODABL with the ABLH for 41 cases of temporally co-located measurements conducted with the MFR-7 and the
PollyXT shows a positive correlation (0.74 at both wavelengths) of
AODABL and ABLH, which is even higher than the correlation coefficient of
0.55 between AODABL and ABLH reported for Leipzig by Mattis et al. (2004). Similar results were reported for Warsaw by Stachlewska et al. (2017b, 2018),
i.e. when there is no aerosol in the free troposphere above Warsaw; however,
Stachlewska et al. (2017b, 2018) found that both the AODCL and AODABL increased with increasing ABLH. In the current study, no significant correlation was
observed for the AODCL and ABLH, which is related to differences in the
load and type of aerosol in the free troposphere and within the boundary
layer. When an aerosol layer containing particles of dust, smoke, pollution, or
a mixture of the above is suspended in the free troposphere, an increase in the columnar
AODCL values can be observed. Marinou et al. (2017) reported that dust particles can be transported far from their source of origin and are
frequently observed over central and northern Europe, with higher
occurrences during summer. Dust particles can occur over Warsaw, mainly in
the free troposphere (at a height above 2–3 km) during the spring and
summer period (Chilinski et al., 2016; Janicka et al., 2017; Szczepanik et
al., 2019). Biomass-burning particles and smoke layers were detected over
central Europe in the summers of 2013 (Janicka et al., 2017; Ortiz-Amezcua et
al., 2017; Trickl et al., 2015), 2015 (Stachlewska et al., 2017b; Szkop and
Pietruczuk, 2017), and 2016 (Stachlewska et al., 2018). Biomass burning
and dust were detected and analysed in southern Europe using a 10 year lidar
dataset (Siomos et al., 2017, 2018). Canadian wildfire smoke detected in
the troposphere and the stratosphere in summer 2017 over central Europe was also reported by Haarig et al. (2018), Ansmann et al. (2018), Hu et al. (2019),
and Baars et al. (2019).
In cases where there is a lack of clouds (as in this study), the less sufficient growth
of the ABLH could be explained as being partly due to the aerosol suspended in
the free troposphere; this aerosol reduces the solar radiation reaching the surface and, thus, suppresses the thermal turbulence, leading to a lower boundary layer
height. Hence, for certain conditions, the relation of AODCL and the ABLH
can be expected to exhibit a negative correlation.
The majority of the 41 cases of daytime, clear-sky, summertime
measurements analysed were related to observations of biomass-burning aerosol in
the free troposphere. Over an urban site, the AODABL can increase due
to pollution in the boundary layer, which adds to the growth of the ABLH due to the
sun-driven turbulence. At the same time, the AODCL can decrease
when free-tropospheric aerosol (e.g. absorbing particles) causes
negative radiative effects at the surface. Another aspect that has to be
accounted for is the presence of aerosol particles in the
troposphere directly above the top of the boundary layer, which may follow from the
dynamics of turbulent eddy structures in the layer. Even in the absence of
convection, a typical feature of turbulent boundary layer flows is the
presence of abrupt bursting and sweeping events (Pope, 2000). Bursts can
eject aerosol particles to the upper regions of the boundary layer. The
interactions between vortical structures are responsible for the balances of
the particle concentration in the boundary layer flows (Béghein et al.,
2014).
The AODABL is calculated as an integral of the extinction coefficient within the ABLH, and the ABLH is a variable of AODABL; therefore, the ABLH is not expected to be strongly related to the aerosol conditions above in the free troposphere. The aerosol optical depth is a unique parameter to determine the
atmospheric aerosol load, and the ABLH derived by lidar relies on a
higher aerosol concentration within the boundary layer than in the free
troposphere. Therefore, a positive correlation between the AODABL and the ABLH can
be expected. Note that although an intrusion of biomass-burning smoke into
the boundary layer can contribute strongly to the suppression of the growth of
the ABLH, as reported by Stachlewska et al. (2018), it still did not result in a
negative correlation.
Hourly averaged aerosol optical depth (AOD) and
Ångström exponent (ÅE) derived within the boundary layer at 355 and
532 nm vs. the atmospheric boundary layer height (ABLH), all derived from the
PollyXT lidar at the EARLINET/ACTRIS site of the RS-Lab,Warsaw, for
July–September of 2013, 2015, and 2016. A linear fit to data points is shown for
correlation coefficients (R) greater than 0.6.
Hourly averages of the surface fine-to-coarse mass ratio
(FCMR) and particulate matter (PM2.5 and
PM10) measured at the WIOS site in Ursynow, Warsaw,
vs. the atmospheric boundary layer height (ABLH) derived from the PollyXT lidar
at the EARLINET/ACTRIS site at the RS-Lab, Warsaw, for the July–September period
of 2013, 2015, and 2016. Thresholds of the FCMR are marked using horizontal lines.
The relations of lidar-derived AODABL, ÅEABL, and the ABLH at
different times of the day for the July–September period of 2013, 2015, and
2016 (246 cases) are depicted in Fig. 5. As the AODABL is related
to the ABLH, there is a higher aerosol load in the ABL than in
the free troposphere – thus, a positive correlation can be observed. A
relatively high correlation coefficient (0.76 at 355 nm and 0.75 at 532 nm)
between the ABLH and the AODABL occurred in the MTL, whereas their correlation
coefficients were slightly weaker (0.66 and 0.61 respectively) during the
nocturnal time (residual layer effect), when aerosol load within the ABL
basically remained stable due to much weaker vertical mixing at night.
The mean ÅEABL is 1.54±0.37, indicating the dominance of
relatively small particles during the observation period. No obvious relation
between ÅEABL and the ABLH was obtained, but the highest values
(ÅEABL> 2) were only observed at night, i.e. there was either
more pollution or less humidity. Indeed, a higher concentration of
PM2.5 was measured during the nocturnal period (16.75±6.86µg m-3) compared with the other time spans (15.74±7.24µg m-3 during sunrise and 10.94±4.13µg m-3 during
sunset). Note that the standard deviations given indicate high variability
in the values obtained.
Figure 6 illustrates the relations between the ABLH, PM2.5, PM10,
and the FCMR. A slightly negative trend between the FCMR and the
ABLH can be observed for the well-mixed layer, which may suggest an increase in coarse particles
(number or/and size) at the surface with an ascending ABLH. The fact that adiabatic effects have partly influenced the growth of
particle size cannot be excluded. Schäfer et al. (2006) found a high negative correlation
between PM10 and the ABLH in Hanover and Munich in winter. Rost et al. (2009) reported a strong negative relation between PM10 and the ABLH in
Stuttgart. Similarly, Du et al. (2013) found that PM2.5 and the ABLH
exhibited a negative correlation in Delhi and Xi'an. Geiß et al. (2017)
reported that the link between PM and the ABLH can be attributed to several
different reasons, such as meteorological conditions, terrain, local
particle sources, and even the ABLH retrieval method itself. This
was also the case for Warsaw during aerosol
injections into the boundary layer that had undergone long-range transport (Stachlewska et al., 2017b, 2018).
However, in general, no pronounced relationship between PM10 and
the ABLH are expected for Warsaw, as also shown in Zawadzka et al. (2013). Furthermore, in the
current study, no significant link between particulate matter (PM10 and
PM2.5) and the ABLH was found for Warsaw during summer and early autumn
(Fig. 6), which can be partly attributed to relatively low records
of PM10 emissions (hourly values < 60 µg m-3) and
relatively high summer ABLH values (up to 1.6 km, Table 2). The highest PM10
and PM2.5 values are observed at night (NL and RL), lower values are found for the MTL, and the
lowest values are noted for the WML. Reizer and Juda-Rezler (2015) reported that either regional
background pollution or local emission sources are mainly responsible for
the high PM10 and PM2.5 concentrations in Polish urban areas.
Clearly, the ABLH is not the main factor controlling the surface pollution
in summer in Warsaw, which is consistent with reports by Bonn et al. (2016), Stachlewska et al. (2017b, 2018), and Geiß et al. (2017).
Hourly averaged aerosol optical depth (AOD), lidar ratio
(LR), linear particle depolarization ratio (δ), and
Ångström exponent (ÅE) derived within the atmospheric boundary layer
at 355 and 532 nm by the PollyXT lidar at the RS-Lab, EARLINET/ACTRIS site in
Warsaw, for the July–September period of 2013, 2015, and 2016 vs. the surface fine-to-coarse
mass ratio (FCMR) derived from particulate matter (PM2.5 and PM10) measured at the WIOS site in
Ursynow, Warsaw. Thresholds of the ÅE and FCMR are marked using horizontal and
vertical lines respectively.
Interrelations between different optical properties and with surface PM in summer and early autumn over Warsaw
Figure 7 presents the relationship of AODABL, ÅEABL,
LRABL, δABL, and the surface FCMR for the nocturnal and
transition times during July, August, and September of 2013, 2015, and 2016.
The separation thresholds for the FCMR are defined as follows: an FCMR greater than 1.5 (vertical line) means that fine particles (< 2.5 µm)
dominated and an FCMR less than 0.5 (vertical dashed line) means that
coarse particles dominated (2.5–10 µm). The given thresholds for the ÅE roughly
indicate the dominant particle size distribution mode, with separation
thresholds of for small particles ÅE > 1.5 (horizontal line)
and large particles ÅE < 1 (horizontal dashed line). However,
the relation between the ÅE and the aerosol size distribution is complicated, as is the relationship with the FCMR.
For all time periods, FCMR values between 0.5 and 1.5 constitute the
largest proportion in total. During the nocturnal period there are more fine
particles than during sunrise (MTL) or sunset (WML). There is clear
separation mark at a FCMR of 1.5 regardless the time period, and no clear
correlation of ÅEABL with FCMR; however, for an ÅEABL between 1 and 2 during the nocturnal period and at sunrise, there is a higher PM2.5 contribution at the surface.
Because of weak vertical air motion (no convective mixing) and a lower ABLH
during nighttime, relatively large aerosol particles are deposited in the
ABL and most of small aerosol particles stack below the inversion of the top of the
boundary layer (or residual layer). This can lead to an increase in the
number of small particles accumulated within the nocturnal ABL, which manifests
as a fine particles increase at the surface. In general, urban pollution, regarded
as road traffic, industrial emissions, and the chemical reaction of gases
(SO2, NO2, NOx), causes an increase in both PM10 and
PM2.5 (He et al., 2008). The sunrise period in July–September
(05:00–09:00 local time) corresponds to urban traffic emission, which can
cause the lifting of particles from the ground (Zawadzka et al., 2013). The
relationship of LRABL and the FCMR in Fig. 7 shows a clear separation of
data, which is mainly the result of a higher abundance of fine particles in
the MTL. On the contrary, more coarse particles (PM10 with a higher
LRABL of 40–80 sr) occur in the WML.
The relationship of δABL and the FCMR in Fig. 7 indicates
possible negative trends (stronger at 532 nm) for all time periods. The
abundance of the fine (coarse) particles at the surface is related to the
abundance of the spherical particles within the ABL.
Hourly averaged aerosol optical depth (AOD),
Ångström exponent (ÅE), and lidar ratio (LR) derived within
the atmospheric boundary layer at 355 and 532 nm from the PollyXT lidar at the RS-Lab, at the
EARLINET/ACTRIS site in Warsaw, for the July–September period of
2013, 2015, and 2016. The lack of a correlation between the linear particle depolarization
ratio (δ) and the lidar ratio (LR) is not shown for brevity.
The linear fit to data points is shown for correlation coefficients (R)
greater than 0.6.
Bennouna et al. (2016) reported that a significant positive correlation of
PM10 and AODCL, and an increasing correlation coefficient for daily, monthly, and yearly averages, relays on the aerosol characteristics of the site. Zawadzka et al. (2013) reported a negative correlation between
PM10 and AODCL for long-term monthly mean values in winter in
Belsk and Warsaw, and a positive relation for unstable (meaning strong
turbulent vertical mixing in summer) atmospheric conditions in Warsaw.
The relation between optical properties and the surface aerosol mass concentration
depends on boundary layer processes, chemical composition, source regions,
weather conditions, and aerosol type, which are challenging to
characterize well with the columnar AODABL and the surface PM concentrations
alone. As depicted in Fig. 7, the AODABL and FCMR indicate a higher
AODABL for coarse particles (PM10) in the WML and a lower AODABL for fine
particles (PM2.5) in the MTL; however, no significant correlations of
AODABL with PM10 or PM2.5 are reported in the current
study.
Significant correlations (R>0.5) of AODCL and
PM2.5 were reported mainly for the eastern cities of China (Guo et al.,
2009; Zheng et al., 2015; Zang et al., 2017) and the United States (Liu et al.,
2007; Hutchison et al., 2008; Wang and Christopher, 2003), where the main
industrial regions with extreme pollution are located. In these cases, the
majority of aerosol was expected to be present within the boundary layer.
The anthropogenic pollution in Warsaw is much lower compared with the above-mentioned regions: Zawadzka et al. (2013) reported an R value of 0.42 between
AODCL and PM2.5 in Warsaw, which was attributed to a
significant load in the free troposphere affecting the relationship. The
current study shows that there is also no
significant correlation for AODABL and PM2.5, which can be explained by the low values of
AODABL and PM2.5 measured during the investigated period; these low values of AODABL and PM2.5 are, in turn, attributed to the lack of high pollution events in summer and early autumn in
Warsaw. Stachlewska et al. (2017b, 2018) showed that AODABL and
PM2.5 in Warsaw can be correlated, under high aerosol load
conditions, during events involving an injection of
pollution or biomass burning that has undergone long-range transport into the boundary layers (but with no aerosol
in the free troposphere).
As for the relation between AODABL and PM10, in the current study no significant linear correlation was found for the nocturnal time
(similar to Filip and Stefan, 2011), and a weak positive relation was
observed for the sunrise/sunset period (similar to Zawadzka et al. 2013). The
ABL in summer is primarily driven by intensive convective mixing, resulting
in a significantly higher ABLH than in other seasons (Wang et al., 2019). In
summer, the ABL aerosol can be elevated by effective convection into the free
troposphere, and this can lead to a decrease in the aerosol loading within the ABL, as
reported by e.g. He et al. (2008) and Tian et al. (2017). The emission of
PM10 in summer is lower than in other seasons in Warsaw (Zawadzka
et al., 2013). Even lower urban emissions at night reduce the mass
concentrations of surface PM10, and the aerosol
properties within ABL are concurrently relatively stable due to the stable boundary layer
at nighttime. Therefore, no apparent relationship can be observed during the
nocturnal period.
Interrelations of optical properties within the ABL are given in Fig. 8. A
positive correlation between the AODABL and LRABL is observed for all
times, which is higher for sunrise and sunset (0.64–0.72) and lower for the
nocturnal time (∼ 0.56). The AODABL and LRABL depend
on the extinction coefficient derived within the ABL; thus, both values will
increase when the fine particle contribution increases and/or when there is
an increase in the absorption capability of the particles within the ABL,
and vice versa. This may be partly due to the presence of biomass-burning
particles inside the ABL, as reported e.g. in Stachlewska et al. (2018).
Hourly averaged near-surface relative humidity (RH)
measured by the WXT510 (Vaisala) weather transmitter at the RS-Lab in Warsaw for the July–September period of 2013, 2015, and 2016 vs. surface particulate
matter (PM2.5 and PM10) measured
at the WIOS site in Ursynow, Warsaw. The linear fit to data points is shown for
correlation coefficients (R) greater than 0.6.
The relation between the ÅEABL and LRABL shows a weak negative trend
during the analysed period, which may be due to larger size particles
being injected into the ABL, particles growing in the ABL, or the smoke
contribution in the composition of the ABL aerosol. As for the latter, the
presence of smoke particles results in high negative correlations,
e.g. correlations between LRABL and ÅEABL of -0.79 (Giannakaki
et al., 2010) and -0.84 (Amiridis et al., 2009) were found for smoke
particles. Moreover, Stachlewska et al. (2018) showed negative correlation of
ÅEABL and LRABL for smoke particles. An alternative explanation could be the condensation of large organic molecules and particle
coagulation from the upper atmosphere into the ABL, as reported by e.g.
Pósfai et al. (2004) and Fiebig et al (2003). Giannakaki et al. (2010),
showed no significant correlation between ÅEABL and LRABL for continental and urban aerosol related to anthropogenic pollution. Moreover,
Mattis et al. (2004) reported no relationship between ÅEABL and
LRABL when anthropogenic particles dominated in Leipzig. The results obtained in the current paper suggest that a mixture of local urban anthropogenic aerosol with a natural
source aerosol (local or long-range transported into ABL) during summer and early autumn in Warsaw, might be a
reasonable explanation for the weak negative tendency of
ÅEABL and LRABL observed.
Relations of optical properties, surface PM, and relative humidity in summer and early autumn over Warsaw
Relations between the near-surface relative humidity (RH) and surface
PM10 and PM2.5 and FCMR, and relations between the near-surface RH and the lidar-derived aerosol
properties (AODABL ÅEABL, LRABL and δABL)
were investigated for the entire, nocturnal, and sunrise/sunset times. Additionally, the nighttime relations between the lidar-derived
water vapour mixing ratios (WVABL) and the aforementioned quantities were investigated.
Nighttime hourly averaged water vapour mixing ratio (WV)
vs. aerosol optical depth (AOD), lidar ratio (LR), Ångström
exponent (ÅE), and linear particle depolarization ratio (δ), all derived within the atmospheric boundary layer from the PollyXT lidar
at the RS-Lab, at the EARLINET/ACTRIS site in Warsaw, and vs. the fine-to-coarse
mass ratio (FCMR) and surface particulate matter
(PM2.5 and PM10) measured at the
WIOS site in Ursynow, Warsaw, during the July–September period of 2013, 2015, and 2016. Note that the lidar-derived WV is only available at nighttime.
Generally, as seen in Fig. 9, a weak positive trend of RH and PM2.5 is in
agreement with the results of Sharma et al. (2017). The RH and FCMR exhibit a
positive correlation, with correlation coefficients of 0.6 for the NT, 0.63 for
the WML, and 0.71 for the MTL. Zhang et al. (2015) reported that high RH values can led to high PM2.5 in Beijing. In the summer
urban environment, Li et al. (2017) showed that an increase of
the RH can lead to growth of fine particles (PM2.5),
but not to a growth of PM10 due to the hygroscopic effect on aerosol, and mainly attributed this to the effects of the
wet scavenging under the high summer rainfall. The mean RH
obtained in the current study was highest for the MTL at 63±10 %,
was 57±12 % for the NL, and was lowest for the WML at 43±10 %.
Small particles have a greater possibility to aggregate into relatively
large particles at nighttime; thus, the lower correlation coefficients
for RH and PM2.5 (and FCMR) are found for the nocturnal time.
Figure 10 presents no clear relation of WVABL with surface PM2.5,
PM10, and FCMR at nighttime in Warsaw. The correlation coefficient of
WVABL and PM2.5 is a little higher than WVABL and PM10,
indicating that the water vapour in the ABL can affect the surface fine
particles more than the surface coarse particles at night. This could be due to the
presence of anthropogenic particles in the ABL, as these hygroscopic
particles can absorb water vapour and gradually increase in size, although
the growth of particles due to particle coagulation within the ABL cannot be
excluded (Fiebig et al., 2003).
Figure 10 also does not depict a clear relation between WVABL and
AODABL or ÅEABL and LRABL – only δABL and
WVABL show a negative trend. The hygroscopicity of particles increases
with decreasing particle size (Petters et al., 2009). At the same time, the
more fine particles the lower the depolarization (see Fig. 7). Hence, an
increase in water vapour and the presence of hygroscopic particles leads to a
decrease in depolarization. When biomass-burning aerosol that has undergone long-range transport occurs over Warsaw, hygroscopic effects can be well
captured with quasi-continuous profiling of the water vapour (Stachlewska et
al., 2017a). Note that the relation of the AODABL, ÅEABL, and
LRABL with the surface RH also does not show much of a trend for
the NL, although for the WML the negative trends and property groupings becomes more
visible (Fig. 11).
Hourly averaged near-surface relative humidity (RH)
measured by the WXT510 (Vaisala) weather transmitter at the RS-Lab, Warsaw,
vs. the aerosol optical depth (AOD), the lidar ratio (LR), the linear particle
depolarization ratio (δ), and the Ångström exponent
(ÅE) derived within the boundary layer at 355 and 532 nm from the PollyXT lidar
at the RS-Lab, at the EARLINET/ACTRIS site in Warsaw, for the July–September
period of 2013, 2015, and 2016.
The AODCL was reported to increase with an increase in the ambient
relative humidity for the hygroscopic particles due to hygroscopic
growth (Bergin et al., 2000; Altaratz et al., 2013). In Fig. 11, a
negative relation between AODABL and surface RH is found (with a higher surface RH, a lower AODABL is observed), except for during the nocturnal period (no trend in Fig. 11, similar to Fig. 10 for AODABL and WVABL).
The size and optical properties can change as aerosol particles take up water
and a higher RH is more favourable for hygroscopic growth of pollution
particles (Tang, 1996). During the observational period, urban anthropogenic aerosol was observed most frequently within the boundary
layer. An
increase in RH led to an increase in the pollution particle size, which was visible
at nighttime (ÅE and RH show slight negative trend). A slight positive
trend between ÅEABL and RH is seen at transition times (different
grouping in the moister MTL and drier WML), which is in accordance with
the FCMR and ÅE scatterplots (more coarse particles in the MTL and more
fine particles in the WML). Pollution particles within the boundary layer, due to a
weaker convective mixing at nighttime, are prone to water uptake, which
contributes to an increase in the aerosol particle size Cheng et al. (2008). The
relation between LRABL and RH shows practically no correlation during the nocturnal time, but it is well separated in the transition time (a lower LR for the MTL). At nighttime, an increasing LR due to the accumulation of hygroscopic smoke
particles was reported by Giannkaki et al. (2010). The convection and the
energy exchange is stronger in the transition time (Stull, 1988), leading to
anthropogenic aerosol being dominant in the atmospheric boundary layer.
Increasing the surface RH results in a rise in the aerosol particles' size,
and thus contributes to a decrease in the LRABL. The negative trend in the
relation between δABL and the surface RH (clearly visible during the
nocturnal time) is in agreement with the trend obtained for WVABL and δABL.
Conclusions
This study focuses on the optical properties derived within the atmospheric
boundary layer, based on a dataset comprising measurements from July to September in 2013, 2015, and
2016 from the EARLINET/ACTRIS site in Warsaw. Interrelations between the different
optical properties within ABL as well as relations between the optical properties and the
surface PM concentrations and RH measurements were studied by conducting
comparisons of various parameters during different periods: the entire time
(24 h, ABL), nighttime (21:00–02:00 UTC; NL and RL), sunrise (03:00–08:00 UTC; MTL), and sunset (16:00–20:00 UTC; WML).
At both wavelengths, AODABL and LRABL at sunset were found to be higher (0.14–0.24 for AODABL and 49–55 sr for LRABL) than for the
other two periods (0.06–0.18 for AODABL and 32–47 sr for LRABL).
During the nocturnal period, ÅEABL values were higher (1.58±0.36 for NL, 1.61±0.37 for RL) than for the two remaining periods (1.53±0.30 for MTL, 1.37±0.34 for WML). For the entire dataset, the
AEABL was distributed between 1 and 2 (> 90 % of data
points) and the surface FCMR ranged between 0.5 and 1.5 (∼ 70 % of
data points). The aerosol composition within the ABL in summer and early autumn in
Warsaw consisted of urban anthropogenic pollution and its mixtures with
local pollen and long-range transported biomass-burning aerosol and Arctic
marine particles.
The boundary layer AODABL contribution to the columnar AODCL was assessed. The latter was more than twice as high when optically thick aerosol
layers were observed above the ABL, and less than twice as high for cases
with no aerosol layers in the free troposphere but with the presence of
pollution aloft due to convection.
The AODABL and ABLH exhibit a positive correlation (∼ 0.75)
during the observation period, which is highest for the WML and MTL. When the ABLH
increases, a declining trend of the FCMR is observed for the WML, indicating an increase
in the coarse particle fraction. However, there is no clearly apparent link
between PM10 or PM2.5 and the ABLH. For the MTL, the ranges of ÅEABL and FCMR values obtained are higher than for the WML. A negative
correlation of δABL and the FCMR was found for all time periods,
which indicates the higher sphericity of fine particles. As reported in literature,
different correlations obtained for AODABL and PM10 are attributed to complicated atmospheric and weather conditions. In summer and
early autumn in Warsaw, high-pressure systems generally govern the dynamics
of the atmosphere, there is significantly less traffic pollution (people on
holidays, on bicycles), and the pollination of plants also plays a role.
The relation between AODABL and FCMR reported here displays a
negative correlation at sunrise and sunset, which can be related to the
traffic peaks. Due to lower urban emissions (no traffic nor domestic
heating in summer) and a stable boundary layer, no apparent relationship between
AODABL and the FCMR was observed at night. The AODABL and
LRABL depend on the extinction coefficient within the ABL – thus, a
positive correlation is observed. The relation of ÅEABL and LRABL reveals a weak negative trend. The positive trends of the RH and FCMR were found at night and for sunrise and sunset, although the sunrise trend was most pronounced.
The weak positive relation at night for WVABL and PM2.5 is higher than for PM10, indicating that the water vapour in the NL and RL affects
surface fine particles more than surface coarse particles. The increasing
WVABL (and RH at the surface) can lead to a decrease in depolarization
(both relations are very similar at night) in the presence of
hygroscopic particles. For high near-surface RH, lower AODABL values were
derived in the MTL and WML, which was not seen in the NL and RL. A negative trend of
δABL and WVABL and of δABL and RH is due to
the hygroscopicity of particles. A negative relation between AODABL and surface RH is found for the transition time (in both the MTL and WML), which is
followed by a weak negative correlation of LRABL and RH observed only during sunset (in the WML).
The co-located measurements conducted in Warsaw with the MFR-7 shadowband
radiometer (PolandAOD-NET) and the CE318 sun photometer (AERONET) showed
strong agreement with a correlation coefficient (R) of 0.98 and a standard
deviation of 0.02 at both 380 and 500 nm.
The results obtained increase the current knowledge on the variability of
optical properties within the summer and early-autumn atmospheric boundary layer
at a continental urban site in central Europe. The relations found in previously
published research, obtained using a case-study approach, do not
necessarily apply and are not necessarily seen in the long-term study. Therefore, special
care should be taken when interpreting and comparing the different results.
The bottom line is that regular, automated observations of the next generation
PollyXT lidar conducted at the EARLINET site in the framework of the ACTRIS
infrastructure activities allow for such studies. The excellent capabilities
of this lidar enabled the combination of the results derived within this study with other data sources (e.g. AERONET, WIOS, and PolandAOD networks). Hypotheses regarding
boundary layer aerosol properties interrelations were proposed and will be
further verified using additional lidar data from regular observations in Warsaw.
The expansion of the existing high quality lidar data
sample would allow for an improvement in the investigation of the subgrouping of aerosol properties, which could
provide statistically significant correlations. Moreover, more observations
would allow for an extension of the investigation of differences to other seasons, would enable daytime
analyses, and would aid with better distinguishing between sunset and sunrise aerosol
property relationships; furthermore, a separation of aerosol properties
accordingly to aerosol content, i.e. urban aerosol vs. urban aerosol mixtures with
other aerosol types, would be possible (currently too few data exist in the mixed
categories), followed by an estimation of their radiative effect.
Data availability
The lidar data used in this study are available upon registration from http://data.earlinet.org (last access: 15 October 2019). The PM2.5 and
PM10 data are publicly accessible via the data archive of the National
Chief Inspectorate for Environmental Protection (GIOS) at
http://powietrze.gios.gov.pl/pjp/archive (last access: 15 October 2019).
The sun photometer AOD data are publicly available via the AERONET data
website at https://AERONETl.gsfc.nasa.gov (last access: 15 October
2019). The shadowband radiometer AOD data and the surface RH data are
available via the Polish aerosol Research Network PolandAOD-NET website
at https://polandaod.pl (last access: 15 October 2019). The
radiosounding profiles are available via the University of Wyoming
Upperair Air Data website at
http://weather.uwyo.edu/upperair/sounding.htm (last access: 15 October
2019).
Lists of symbols and physical quantities
Entire timeETNocturnal timeNTTransition rimeTTAtmospheric boundary layer (derived by lidar)ABLAtmospheric boundary layer height (derived by lidar)ABLHResidual layer (derived by lidar)RLNocturnal layer (derived by lidar)NLMorning transition layer (derived by lidar)MTLWell-mixed layer (derived by lidar)WMLParticle extinction coefficient (within the atmospheric boundary layer)αABLParticle backscatter coefficient (within the atmospheric boundary layer)βABLAerosol optical depth (within the atmospheric boundary layer, derived by lidar)AODABL(λ)Aerosol optical depth (columnar, derived by sun photometer or radiometer)AODCL(λ)Lidar ratio (within the atmospheric boundary layer)LRABL(λ)Linear particle depolarization ratio (within the atmospheric boundary layer)δABL(λ)Ångström exponent (within the atmospheric boundary layer, derived by lidar)ÅEABL(λ1/λ2)Ångström exponent (columnar, derived by sun photometer or radiometer)ÅECL(λ1/λ2)Water vapour mixing ratio (within the atmospheric boundary layer)WVABLRelative humidity (at the near-surface)RHParticulate matter with diameter < 10 µm; < 2.5 µmPM10; PM2.5Fine to coarse mass ratioFCMRWavelengthλ
Author contributions
ISS wrote the paper, obtained funding for the research, came up
with the study approach, designed the methodology, performed the experiment,
contributed to the development of the PollyXT lidar (Warsaw) and the lidar evaluation
algorithms, took care of the quality assurance of lidar measurements and data
products, and holistically interpreted the lidar results with respect to other data
sources' results (AERONET, PolandAOD-NET, and WIOS Monitoring Network).
DS was responsible for the calculation of the aerosol optical property
profiles (α, β, and δ) and their categorization for
the EARLINET/ACTRIS database. DW was responsible for an extensive literature
review, wrote the codes for the ABLH and the WV retrieval algorithms, and
performed statistical analysis of the optical properties (ÅE, LR, δ, and WV). All authors contributed to revisions of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “EARLINET aerosol profiling: contributions to atmospheric and climate research”. It is not associated with a conference.
Acknowledgements
This research was mainly carried out in the frame of the Technical
assistance for Polish Radar and Lidar Mobile Observation System
(POLIMOS) project funded by ESA-ESTEC.
The PollyXT lidar (Warsaw) was developed via a scientific collaboration
between the Faculty of Physics at the University of Warsaw (FUW) and the
Institute of Tropospheric Research (TROPOS). The development was
financed by the Polish Foundation of Science and Technology (FNTP). We
especially acknowledge members of the PollyXT lidar group lead by
Dietrich Althausen.
The authors acknowledge the Warsaw Regional Inspectorate for
Environmental Protection (WIOS) of the National Chief Inspectorate for
Environmental Protection (GIOS) for the provision of the processed and
quality assured PM2.5 and PM10 data.
We acknowledge Brent Holben for processing the AERONET data; Aleksander
Pietruczuk and Piotr Sobolewski of the Institute of Geophysics at the
Polish Academy of Sciences for performing the AERONET measurements in
Belsk; Wojciech Kumala of the Faculty of Physics at the University of Warsaw
(FUW) for performing the AERONET measurements in Warsaw; Phillippe Golub
for performing instrument calibrations at the PHOTONS/AERONET-EUROPE
calibration centre.
The AERONET sun photometer at the RS-Lab site in Warsaw is the property
of the National Institute of Research and Development for
Optoelectronics (INOE 2000) in Romania, and was kindly lent by the
research group led by Doina Nicolae, within the framework of the
SAtellite based Monitoring Initiative for Regional Air quality (SAMIRA)
funded by ESA-ESRIN.
We acknowledge Krzysztof Markowicz for processing the MFR-7 data within
the Polish aerosol Research Network PolandAOD-NET. The purchase of the
MFR-7 instrument was funded by the Polish Ministry of Science and High
Education.
The EARLINET/AERONET site in Warsaw established at RS-Lab at Faculty of
Physics, University of Warsaw (FUW) acknowledges the vital support of
the ACTRIS Research Infrastructure in developing the site.
Financial support
This research has been supported by the European Space Agency ESA-ESTEC
(grant no. 4000119961/16/NL/FF/mg), the Polish Foundation of Science and
Technology (grant no. 519/FNITP/115/2010), the Polish Ministry of
Science and High Education (grant no. 1283/B/P01/2010/38), and the European
Space Agency ESA-ESRIN (grant no. 4000117393/16/INB).
The EARLINET activities have been supported by the ACTRIS project,
funded within the European Commission Seventh Framework Programme under
the research infrastructures for atmospheric research (grant agreement
no. 262254); the ACTRIS-2 project, funded by the European Union Research
Infrastructures action under the H2020 specific programme for integrating
and opening existing national and regional research infrastructures of
European interest (grant agreement no. 654109); and the ACTRIS PPP 65
project supported by the European Commission under the Horizon 2020 –
Research and Innovation Framework Programme,
H2020INFRADEV-2016-2017 (grant agreement no. 739530).
Review statement
This paper was edited by Lucia Mona and reviewed by three anonymous referees.
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