An increase in the sulfate aerosols observed in the period 1–6 April 2014
over Austria is analyzed using in situ measurements at an Austrian air quality
background station, lidar measurements at the closest EARLINET stations around
Austria, CAMS near-real-time data, and particle dispersion modeling using
FLEXPART, a Lagrangian transport model. In situ measurements of
Sulfate is one of the major aerosol components for particles with a diameter
smaller than 2.5 anthropogenic sources: a major contribution from combustion of fossil fuel
(about 72 %) and a small contribution from biomass burning (about
2 %); natural sources: from dimethyl sulfide (DMS) emissions by marine
phytoplankton (about 19 %) and from volcano eruptions (about 7 %).
A recent review of
In addition to chemical processes,
The purpose of this study is
to assess the relation between the excess with respect to monthly
averaged values observed in the in situ measurements of to estimate the potential sources of sulfate aerosols.
The study is based on the synergy of the remote-sensing instruments from the
European Aerosol Research Lidar Network (EARLINET)
The back-trajectory analysis relates the aerosol mass loading changes at a
receptor location to spatially fixed sources, identifying the sources by a
source–receptor matrix calculation
The synergy of the in situ remote-sensing data and models was used in more atmospheric studies related to
long-range-transported aerosols and estimation of their potential sources; see for example
The optical properties of the aerosol considered in this analysis are backscatter coefficients, extinction coefficients, volume depolarization ratio, particle depolarization ratio (PDepR), lidar ratio (LR), and Ångström exponent (AE).
In this paper, all times are given as UTC times, in the format HH:mm, HH being the hour and mm the minutes. The altitudes are given as ground-level altitudes (a.g.l.).
Whenever referring to measurements, the geographical name is used as an indicator for the station location (e.g., Pillersdorf means Pillersdorf site; Leipzig means Leipzig lidar station). In the plots, the stations are represented as Pillersdorf (red circle), Leipzig (green circle), Munich (magenta triangle), Garmisch (blue rhombus), and Bucharest (black square).
The in situ measurement of daily mean concentration and the maximum half-hour mean value per day for
daily mean concentration for PM maximum value per day of hourly mean concentrations and maximum value per
day of 8
The
The EARLINET lidar stations high-spectral-resolution lidar (HSRL) portable Raman multispectral lidar system
PollyXT Raman multispectral lidar system (RALI) ceilometers
The measurements were performed at the following wavelengths: 355, 532, and
1064
The lidar and the ceilometer measurements provide the vertical distributions of aerosols, retrieved from the range corrected signal (RCS, the preprocessed lidar/ceilometer signal corrected with squared range), and the vertical distributions of aerosol polarization, if the instrument is equipped with a polarization channel.
For the remote-sensing sites Leipzig, Munich, and Bucharest, the column-integrated AOD measurements for various wavelengths were taken from the AERONET sun–sky photometer measurements, the AERONET instruments being collocated with the lidar stations.
In this paper, products from CAMS, the Copernicus Atmosphere Monitoring
Service
For this analysis, the CAMS products for “model levels” and “surface
level”
from the NRT “Atmospheric Composition” dataset were selected for the times 00:00,
06:00, 12:00, and 18:00 for the analysis data and a step of 3
In this paper, the models FLEXPART and FLEXTRA were used for atmospheric transport modeling.
FLEXPART (FLEXible PARTicle dispersion model) is a Lagrangian particle
dispersion model designed for calculating the long-range and mesoscale
transport, diffusion, dry and wet deposition, and radioactive decay of air
pollutants from point, line area, and volume sources. FLEXPART can be run in
forward mode, simulating the transport and dispersion of emissions from given
sources towards receptor points or producing gridded output concentration and
deposition, or in backward mode from given receptors to produce source–receptor
relationships with respect to a point source or gridded sources
FLEXTRA is a kinematic trajectory model. It simulates only the transport of air parcels by mean winds, ignoring turbulence and convection, and does not provide concentrations, deposition, etc.
For both models the ECMWF (European Centre for Medium-Range Weather Forecasts)
ERA-Interim meteorological fields with a horizontal resolution of 0.5
For the determination of the aerosol optical properties for sites without lidar
measurements, where the aerosol composition is determined from CAMS products,
the aerosol model from
For the comparison with optical properties obtained from lidar measurements, the
optical properties computed in the model are rescaled to the lidar wavelengths
(355, 532, and 1064
The vertical profiles of the backscatter coefficients were determined using the
Fernald–Klett method
The LR was computed as the ratio of the extinction coefficient to backscatter
coefficient. For ceilometers, lidars with only elastic channels, and lidar
measurements during the day (when only backscatter coefficients can be
retrieved), the value of the LR was taken from the NATALI aerosol model, which
gives an estimate of the LR for 14 aerosol types. The values for 532
The aerosol layers are identified from the lidar measurements with the gradient method, applied to the RCS profiles
The aerosol type is determined from the lidar measurements using the NATALI
typing algorithm, described in
The values of the CAMS products (mixing ratios, temperature, specific humidity, etc.) for a given location were computed by interpolating the gridded CAMS values, using the inverse weighting distance interpolation.
The air density and the altitude specific to the model levels were computed
according to CY42R1 from IFS documentation
The concentrations of
The CAMS products are retrieved for the in situ site. The time series of mixing ratios of sulfate, dust, organic matter, and total aerosols are then analyzed for the same period as the in situ data. If one of the aerosol components has no significant contribution to the aerosol concentration, this component can be neglected in the subsequent analysis of the aerosol. The time series are also retrieved for the lidar stations around the in situ site.
To assess if the excess is caused by a local event or long- or medium-range-transported aerosol is involved, a qualitative analysis of the in situ concentration measurements and the time series of mixing ratios at the in situ station and at the lidar stations around the in situ station is performed. If the event is present only at the in situ station, we can assume that it is a local event. If the event is seen at some of the lidar stations around the in situ site, the event has contributions from an aerosol transport event.
The layers for the event at the in situ site are then determined by applying the same gradient method as for lidar data processing, but applied to the altitude profiles of aerosol concentrations. The concentrations are computed by multiplying the CAMS mixing ratios and the air density.
A statistical analysis of trajectories is then performed for each layer
identified at the in situ site. Three-dimensional kinematic hourly trajectories
are computed with the FLEXTRA model, run in backward mode for a transport time
of 10–20
A trajectory is associated with a lidar station if the projection of the
trajectory on the Earth's surface intersects a
0.5
If a trajectory overpasses a lidar station, the lidar measurements for the
overpass time are analyzed. The aerosol layers are identified with the same
method
The layers determined from lidar measurements are then compared with the
altitude of the trajectories overpassing the lidar station. If the altitude
matches a layer within a reasonable distance, the trajectory is associated with
the layer. The matching distance is defined as
The source–receptor sensitivity (SRS) is then computed for each layer identified
in the sulfate profile at the in situ station using FLEXPART with sulfate as
a
passive tracer. The receptor is set to the location of the in situ station, at
the altitude determined for that layer and the corresponding event time
interval. Sources are considered to be situated between 0 and 100
A cross-check of sulfate concentrations from lidar measurements, CAMS sulfate products, and FLEXPART is carried out for the layers at the lidar stations associated with the layers at the in situ station. One expects the values from the three methods to be in agreement.
The optical properties of the aerosol from each layer at the in situ station are
then computed according to
Sect.
The in situ measurements of
In situ
The time series of aerosol mixing ratios from CAMS near-real-time
data for Pillersdorf are shown for the same period in
Fig.
Time series of CAMS mixing ratios for total aerosol
Similar distributions, also retrieved from CAMS near-real-time data, are
observed for the lidar stations around Pillersdorf, as shown in
Fig.
Time series of CAMS mixing ratios for sulfate for Munich
The vertical profiles of sulfate, dust, and total aerosol concentrations are
shown in Fig.
CAMS total aerosol, sulfate, and dust profiles for 2 April 2014, Pillersdorf. The gray area represents the identified sulfate layers. Altitudes are given in kilometers above ground level. Local time is UTC+2.
For 2 April, from 00:00 to 12:00, sulfate layers mixed with dust are well defined
between 2 and 3
For the layers identified above, the back trajectories of the aerosols were
computed with FLEXTRA, starting from Pillersdorf at the time corresponding to
the aerosol profiles for a backward period of 12
For 2 April, they are shown in Fig.
Pattern of back trajectories (upper plot of each panel) and their altitude profile,
including overpassed lidar stations (lower plot of each panel) for Pillersdorf, 2 April 2014 at
00:00
The aerosol layers identified at Pillersdorf were transported further. Some of the
layers pass over the lidar station from Bucharest. Their trajectories were
analyzed running FLEXTRA in forward mode for 3 d, starting from
Pillersdorf. Figure
Pattern of forward trajectories
The lidar measurements for the stations overpassed by the trajectories
determined from the backward and forward analyses are presented as range-corrected signal time series (RCS) in Figs.
Logarithm of the range-corrected signal at 1064
Range-corrected signal at 1064
Aerosol layers, their optical properties, and the concentration were determined
from the lidar measurements following the methodology described in Sect.
The association of the layers identified from lidar measurements with the altitude
of the backward or forward trajectories over the stations corresponding to the
layers identified in Pillersdorf was performed for all eight
concentration profiles measured (see Fig.
Association of layers from lidar measurements with layers and trajectories computed for Pillersdorf, 02 April 2014, 06:00.
The source–receptor sensitivity was computed for each layer identified in the
sulfate profiles at Pillersdorf; the column-integrated source–receptor sensitivity was also computed.
Figure
Source–receptor sensitivity for layers L1
For each layer, the relative distribution of the
Relative distributions of
Relative distributions of
Relative distributions of
For the lidar stations, a comparison of concentrations computed from the lidar
measurements with the sulfate concentrations computed from CAMS values for the
lidar station location and the concentrations computed from the modeled SRS are
given in Table
Comparison of sulfate concentration computed from lidar measurements, CAMS products, and FLEXPART for layers at lidar stations associated with layers from Pillersdorf, 2 April 2014, 06:00.
The optical properties, the sulfate fraction, and the aerosol types for the
aerosol layers identified for Pillersdorf, 2 April, 06:00 and the associated
layers at the lidar stations are given in
Table
Optical properties, sulfate fraction, and aerosol types for aerosol layers corresponding to Pillersdorf, 2 April 2014, 06:00.
The peak on 4 April was also analyzed similarly to the peak on 2 April. The
corresponding vertical profiles of sulfate, dust, and total aerosol
concentrations are shown in Fig.
CAMS aerosol, sulfate, and dust profiles for 4 April 2014, Pillersdorf. The gray area represents the identified sulfate layers. Altitudes are given in kilometers above ground level.
Range-corrected signal at 1064
Source–receptor sensitivity for layers L1
Association of layers from lidar measurements with layers and trajectories computed for Pillersdorf, 4 April 2014, 12:00.
Comparison of sulfate concentration computed from lidar measurements, CAMS data and FLEXPART for layers at lidar stations associated with layers from Pillersdorf, 4 April 2014, 12:00.
Optical properties, sulfate fraction, and aerosol types for aerosol layers corresponding to Pillersdorf, 4 April 2014, 12:00.
The daily variations in the in situ measurements of
Figure
From a qualitative analysis of in situ concentrations for PM
On 2 April, one observes from the concentration profiles
(Fig.
The back trajectories for 2 April (Fig.
The SRS patterns, shown in Fig.
For the lower layers, central Europe, including industrial centers from the “Black Triangle” (eastern Germany, southwest Poland, and Czech Republic), was the main source contributing to sulfate transported over northern Austria, where the Pillersdorf station is situated. Medium to smaller contributions come from sources in eastern Europe, northwest Africa, and the eastern US.
For the middle-altitude layers, sources from central Europe (northern Italy, Serbia, Hungary) contribute with similar emissions. Northwest Africa and the eastern US also have important contributions.
For the high-altitude layers, the main contributions come from northwest Africa, but sources from the southern and eastern US also contribute significantly. No contributions from Europe are seen for these layers.
For the peak on 4 April, having only one lidar station associated with aerosol trajectories, the analysis is more difficult. From the existing information, we can conclude that the pattern is similar to layers L2 and L3 from 2 April, with contributions from northern Italy, northwest Africa, and the southern US.
The AEs for the event have values between 0.67 and 0.79, which correspond to a
mixture of fine and coarse particles, with size distribution centered on 0.75
The aerosol type is determined from the optical properties for the layers
identified in this event, at the in situ station and the lidar stations.
A consistent aerosol type was found between the in situ station and the lidar
stations along the trajectories. The changes in the values of the aerosol LR,
AE,
and linear PDepR along the trajectories can be explained by
the mixing of dust with secondary sulfate from anthropogenic sources
during the transport paths to Leipzig, Munich, Garmisch-Partenkirchen,
Pillersdorf, and Bucharest and the adsorption of the
The excess of
The spring period studied in this paper is characterized by low, if any, deep convection. For the summer period, one expects, however, to have strong convective activity over central Europe. A study of the summer periods for the years 2014–2017 for the same region was also performed; the results will be presented in a separate paper.
The methodology developed in this paper allows us to obtain a better understanding of the effects of aerosol transport on the in situ measurements. It can be used as a general tool to correlate measurements at in situ stations with ground-based remote-sensing stations located around these in situ stations. A dedicated paper for the methodology, extended to trace gases and other aerosols, with analysis of more case studies is under preparation.
The lidar and the ceilometer data used in this study are available publicly upon registration at
The supplement related to this article is available online at:
CT collected and processed all data, developed the methodology, and performed the data analysis. Both authors contributed to the optimization of the analysis and the interpretation of the results. PS provided the pre-release of FLEXPART version 10, with a better wet deposition and other improvements. The paper was prepared by CT with contributions from PS.
The authors declare that they have no conflict of interest.
This article is part of the special issue “EARLINET aerosol profiling: contributions to atmospheric and climate research”. It is not associated with a conference.
We thank the principal investigators and their staff for establishing and maintaining the EARLINET lidar sites, the DWD ceilometers, and the AERONET stations. We thank the staff from the Environment Agency Austria, who provided the in situ data. We acknowledge ECCAD and CAMS for making data accessible and providing tools for data analysis.
This research has been supported by the Austrian Science Fund (FWF): project number M-2031, Meitner-Programm).
This paper was edited by Eduardo Landulfo and reviewed by two anonymous referees.