Introduction
Air pollution is one of the major environmental issues in metropolitan areas
because of its adverse effects on human health
e.g.,. Strong emissions, e.g.,
from traffic, industry or heating, can drastically decrease air quality, in
particular when the emitted pollutants are captured below an inversion and
when meteorological conditions prevent an exchange of polluted and clean air.
Without effective vertical mixing and advection pollutants can accumulate in
the lowermost atmospheric layers and concentration thresholds as defined
by the European Union Air Quality Standards (Directive 2008/50/EC) may be
exceeded. For this reason several trace gases and particle mass
concentrations (diameter below 10 µm, PM10) are continuously
monitored by air pollution monitoring networks near the surface implemented
by federal or state administrations. In the case of an exceedance of legally
binding thresholds, measures to reduce pollution are mandatory. This could
include restrictions of motorized individual traffic.
Surface concentrations of gaseous pollutants as nitrogen oxides (NOx),
ozone (O3), sulfur dioxide (SO2) or carbon monoxide (CO) as well as
particulate matter are routinely measured by in situ monitoring stations.
Gaps of in situ measurement networks can be filled by data from remote
sensing techniques e.g., or numerical models.
To better understand – or supplement – direct observations, air quality may
be linked to integral parameters such as the aerosol optical depth
e.g., or to meteorological
parameters such as the height of the mixing layer (henceforward referred to
as mixing layer height (MLH) or Hml). The MLH can be considered as a measure for the
vertical mixing within the atmospheric boundary layer and determines the
dilution of pollutants which are emitted near the ground. Therefore, the MLH
is frequently examined in evaluation studies of regional chemistry transport
models or serves as an
input parameter for chemical box models . Due to the close
relationship between turbulent vertical exchange and near-surface air
quality, several attempts have been made to establish correlations between
MLH and near-surface pollutant concentrations (examples will be given in
Sect. 2). The underlying assumption is that high concentrations close to the
surface may coincide with shallow mixing layers and vice versa. This
assumption, which is used although vertical mixing is certainly not the only
controlling process e.g.,, will
be examined in this paper. Our study is based on 2 months of data in summer
from the BAERLIN2014 campaign (Berlin air quality and ecosystem research:
local and long-range impact of anthropogenic and natural hydrocarbons) in
Berlin, Germany .
A frequently used approach to determine MLH is the implementation of so-called ceilometers, automated and eye-safe single-wavelength backscatter
lidars . As there is no strict definition of the technical
specifications of a “ceilometer”, recently the term ALC (automated
lidars and ceilometers) has been introduced and is often used synonymously.
Though originally designed for only determining cloud base heights,
ceilometers are now used for a variety of more sophisticated activities such
as the retrieval of the particle backscatter coefficient βp
and mixing layer height. Since ceilometers are commercially available,
including software providing “atmospheric products” (e.g., the MLH), we feel
that it is necessary to scrutinize the application of such products. This is
the main motivation and objective of our paper: to investigate the potential
of proprietary software to derive the MLH and the usefulness of correlations
between such derived MLHs and surface concentrations of pollutants in an
urban environment. The motivation of the latter is to increase the awareness
that such correlations might be prone to over-interpretation. A thorough
discussion of the meteorological reasons and atmospheric chemistry
responsible for the observed distribution of pollutants is beyond the goal of
the study.
For the determination of the MLH range, corrected signals of ALC can be
analyzed e.g.,, often using proprietary software
e.g.,. Recently ceilometer networks have been installed
by several national weather services (e.g., almost 100 instruments by the
German Weather Service), and it is expected that in the near future dense
networks providing data in real time will be available on a European scale.
Prospectively the implementation of urban networks for air quality
studies is also likely, at least for selected cities occasionally suffering from
pollution events – recently three ceilometers were set up in the greater
Paris are
for this purpose (OCAPI: Observation de la Composition Atmosphérique
Parisienne de l'IPSL).
However, the retrieval of MLH is an issue even though state-of-the-art
ceilometers provide a clear identification of aerosol layers; often several
atmospheric layers are detected but it remains ambiguous which one is the
mixing layer. This problem can be severe, especially in the case of automatic
retrievals optimized for specific atmospheric conditions. Retrievals might
fail or lead to under- or overestimates if the aerosol concentration is
extremely low or high or if the range of incomplete overlap of the
instrument is too large. Consequently any correlation between MLH and
pollution – and thus the potential to use the MLH in discussions of air
quality – might depend on the selected MLH retrieval technique. In this
paper we want to investigate this topic by applying different MLH retrievals
provided by the manufacturer of the ceilometer (in our case Vaisala) and a
novel scheme COBOLT . We have calculated correlations with
concentrations of pollutants at different locations in the metropolitan area
of Berlin to compare the effects due to the spatial inhomogeneity of
pollutants and due to uncertainties of the MLH retrieval. The results may
help to interpret possible links between air quality and MLH, even though
there was only one ceilometer available during BAERLIN2014.
A selection of studies dealing with the link between MLH and pollutants is
introduced in the next section. Then, we briefly describe the air quality
network of Berlin and the measurement campaign. Section 4 provides a detailed
description of different options to retrieve the MLH, including a comparison.
Correlations with concentrations of pollutants are discussed in view of their
dependence on the selected MLH retrieval and their location inside Berlin. A
summary concludes the paper.
Relation between mixing layer height and surface concentrations
In this section a brief overview of studies dealing with the retrieval of the
mixing layer height and its role with respect to air quality is given.
When discussing retrievals of the MLH it is important to note that it is
defined in different ways, depending on the availability of specific
measurement techniques and data sets. Most approaches are based on the
analysis of either the temperature profile e.g.,, the wind
field e.g., or concentration profiles of particles
e.g.,. With the establishment of active remote
sensing networks (e.g., the abovementioned ceilometer networks) the latter
approach is gaining importance; basically it is assumed that the
concentration of particles considerably decreases at the transition from the
mixing layer to the free troposphere. Thus, the analysis of particle
backscatter is a promising approach to determine the MLH.
A thorough review of approaches to determine the MLH was given by
. They emphasized the benefit of active remote sensing
techniques as they allow measurements of the vertical distribution of
particles. Intercomparisons have shown
e.g., that sodar and RASS can be
used to monitor MLH; however, they usually cannot provide the full diurnal
cycle of the MLH in central Europe, especially in summer
. Moreover, these techniques are less frequently applied
mainly because of their more complicated implementation and higher expenses
for investment and maintenance. The same is true for sophisticated
multi-wavelength lidars e.g.,, sodars
e.g.,, combinations of instruments
e.g., and combinations of models and measurements
e.g.,. A large number of studies relying on lidar
data have been published introducing different methodologies to determine
MLH: among others, and used
algorithms based on first derivatives of the backscatter signal;
used second derivatives; the temporal
variance; , and applied
wavelet covariance transforms; used graph theory,
cluster analysis; and statistical methods were used by
and . With recent upgrades of the
hardware those methodologies can also be applied to ALC, and with the
implementation of networks they have become more attractive as they provide
continuous monitoring and good spatial coverage.
The role of the mixing layer for pollution and its adverse effects on health
have been highlighted for more than 50 years
. Consequently the link between air quality
(in terms of particulate matter and concentrations) and MLH has been
investigated in many studies, primarily for urban areas. It should be
emphasized that a comparison of different studies is inherently difficult,
especially when only qualitative conclusions have been made. On the one hand,
different meteorological conditions and species are investigated, i.e.,
different gaseous pollutants and different sites in rural or urban
environments. On the other hand, there are conceptual differences, i.e.,
statistical analyses are based on hourly values, daily averages or diurnal
cycles averaged over several weeks or even seasons, and there are different
approaches to determine the MLH from measurements or numerical models.
Moreover, there are differences with respect to the selection of a suitable
MLH parameter used for correlation analysis: averages, medians, maximum values or certain
percentiles are used, or MLHs are grouped in intervals.
Studies relying on numerical parameterizations were conducted by
, who used reanalysis data, and , who used
routine meteorological observations to find an anticorrelation between
PM2.5 and MLH for Delhi, India, and Xi'an, China. found
a strong anticorrelation between PM10 and MLH (derived from radiosonde
data) for Stuttgart, Germany, with a coefficient of determination of
R2>0.95. The awareness of the potential of active remote sensing started
at the end of the last century, when the first generation of ceilometers was
deployed. These systems suffered from low pulse energies so that their use
was confined to winter measurements or clear atmospheric conditions when the
measurement range of the instrument was sufficient to cover the complete
vertical extent of the mixing layer. deployed CT25k and
LD40 ceilometers (Vaisala) but primarily relied on co-located sodar data when
they found a high anticorrelation between PM10 and MLH in Hanover and
greater Munich, Germany, for winter conditions. They also found negative
correlations for CO and NOx with quite variable R2, depending on the
site and the horizontal wind. Differences between summer and winter
measurements were also observed. These findings agree with results from a
campaign in Budapest, Hungary, with a similar set of instruments
. Examples with state-of-the-art ceilometers include
Beijing, China e.g.,, Essen, Germany
, and Paris, France , and rural sites
. Some of these studies also investigated correlations with
gaseous pollutants, e.g., for Houston, Texas. Significant
negative correlations between surface NO concentration and MLH were reported
for Beijing . However, surface NO2 concentrations are
only weakly affected by the MLH as they are mainly secondarily formed through
atmospheric processes. For Paris, investigated the
relationship between surface concentrations of NO2, column amount of
NO2 and the MLH. Their results suggest that the discrepancies between
NO2 surface concentrations and column amount can be explained by the
differences in the MLH. For seven cities in the North China Plain an
anticorrelation between near-surface O3 and MLH was found ;
however, this case study was confined to only 1 day.
investigated conditions near a major traffic road in Essen, Germany, and
found that correlations between several constituents and MLH are
significantly negative when the MLH from the ceilometer measurements is
grouped into intervals of 200 m.
Currently such investigations cannot ultimately demonstrate which
correlations between surface concentrations and atmospheric stratification
exist, how robust they are and how large their range of applicability is. One
prerequisite for progress is a critical review of standard methods for the
determination of the MLH. Then, the dependence of such correlations on
season, meteorological conditions or location can be investigated.
The BLUME network and the BAERLIN2014 campaign
Berlin is the capital of Germany with about 3 500 000 citizens. The terrain
is flat with altitude differences of not more than 25 m except some small
hills of up to about 85 m. A considerable part (about 40 %) of the area
of Berlin is covered by forests, agricultural areas, lakes and rivers.
Similar to many other metropolitan areas, Berlin suffers from episodes of poor
air quality, in particular when particulate matter (PM10) and NO2
concentrations exceed the EU limit values. Thus, measures have been
implemented such as restrictions of the traffic in the city center. Air
pollution in Berlin originates not only from anthropogenic emissions of urban
sources but also from long-range transport of particulate matter from
industrialized areas in Poland, biogenic emissions and formation of secondary
aerosol compounds; their relative contributions are not yet agreed upon in
detail .
The 16 automated air quality stations of the BLUME network.
Ceilometer measurements reported in this paper were conducted at station 42, one of the urban background sites (in grey). Traffic stations are
shown in red. The green flags are stations at the outskirts of Berlin and in
forests (rural background). More details are given in
Table . The three black stars indicate the wind
measurements in Tegel, Tempelhof and Schönefeld (from northwest to
southeast) mentioned in Sect. 5.1. Adaptation of a figure of the Senate of
Berlin.
Routine measurements of the air quality of Berlin are conducted at 16
automated stations of the so-called BLUME network (; see
Fig. ) under the responsibility of the Senate of Berlin by
European law. Their main purpose is the monitoring of surface concentrations
of trace gases and particle mass concentration. For this study hourly data
are available. BLUME distinguishes three categories of stations: five of the
stations are located at residential districts (labeled “urban background”,
grey flags), five at the outskirts of Berlin and forest areas (“rural
background”, green flags) and six at traffic hot spots (red flags). These
data are reported to the Federal Environment Agency (UBA) of Germany and
included into the European air quality database (AIR BASE).
A summary of the automatic stations of the BLUME network and the monitored
quantities used in this study are given in Table .
Particulate matter is measured with the automatic PMI (particulate monitoring
instrument), type FH 62 I-R (Thermo ESM Anderson), one of the standard
systems for Germany's air quality network. It is based on attenuation of
beta radiation from a Krypton gas cell. It performs real-time measurement of
the suspended particulate matter on a filter. For the gaseous species
discussed in this study Horiba's air pollution monitors (370-series) are
deployed, i.e., an APNA-370 (for NO, NO2 and NOx, chemiluminescence
method) and an APOA-370 (for ozone, absorption in the UV spectral range).
Automatic stations of the BLUME network of Berlin. Names of the
locations with the corresponding district are given in parentheses. Coordinates
are given as latitude (degree north) and longitude (degree east); d42 is
the distance (in km) from station 42 (Nansenstraße). Listed are only
measurements of pollutants discussed in this paper.
ID
Location
Coordinates
d42
Pollutants
Outskirts (rural background)
27
Schichauweg (Marienfelde)
52.3984∘, 13.3681∘
11.0
NOx
O3
32
Jagen (Grunewald)
52.4732∘, 13.2251∘
14.0
PM10
NOx
O3
77
Wiltbergstr. (Buch)
52.6435∘, 13.4895∘
17.6
PM10
NOx
O3
85
Müggelseedamm (Friedrichshagen)
52.4477∘, 13.6471∘
15.4
PM10
NOx
O3
145
Jägerstieg 1 (Frohnau)
52.6533∘, 13.2961∘
20.3
NOx
O3
Urban background
10
Amrumer Str. (Wedding)
52.5430∘, 13.3491∘
8.2
PM10
NOx
O3
18
Belziger Str. (Schöneberg)
52.4858∘, 13.3488∘
5.6
NOx
42
Nansenstr. (Neukölln)
52.4894∘, 13.4309∘
0
PM10
NOx
O3
171
Brückenstr. (Mitte)
52.5136∘, 13.4188∘
2.8
PM10
NOx
282
Rheingoldstr. (Karlshorst)
52.4853∘, 13.5295∘
6.7
NOx
Traffic
115
Hardenbergplatz (Charlottenburg)
52.5066∘, 13.3330∘
6.9
NOx
117
Schildhornstr. (Steglitz)
52.4636∘, 13.3183∘
8.2
PM10
NOx
124
Mariendorfer Damm (Mariendorf)
52.4381∘, 13.3877∘
6.4
PM10
NOx
143
Silbersteinstr. (Neukölln)
52.4675∘, 13.4417∘
2.5
PM10
NOx
174
Frankfurter Allee (Friedrichshain)
52.5141∘, 13.4699∘
3.8
PM10
NOx
220
Karl-Marx-Str. (Neukölln)
52.4817∘, 13.4340∘
0.9
PM10
NOx
During summer 2014 a dedicated field campaign, BAERLIN2014, was set up for 3
months (from 2 June until 29 August 2014), deploying several additional
measurements from mobile and airborne platforms focusing on ozone, secondary
organic aerosols and the effect of urban vegetation . One
Vaisala ceilometer was available at that time. It was installed at the BLUME
station 42 (Nansenstraße, at the corner of Framstraße;
52.4894∘ N, 13.4309∘ E; see Fig. ) on the
roof of a childcare
facility (5 m above street
level). This station is located in a residential neighborhood with trees and
bushes. It is categorized as an “urban background” site: in 2014 annual
averages were 27 and 21 µg m-3 of PM10 and PM2.5,
respectively, 41 µg m-3 of O3 and
37 µg m-3 of NOx. The PM10 threshold (daily average
of 50 µg m-3) was exceeded on 28 days, which is below the
limit of 35 days according to EU regulations
(http://ec.europa.eu/environment/air/quality/standards.htm).
The objective of the ceilometer measurements was the determination of the MLH
and thus the option to combine in situ measurements at the surface with data
concerning the vertical direction. Based on previous case studies for Munich
and Paris , as well as long-term observations for the
region Munich–Augsburg–Freising and for Vienna
, we assume that the derived MLH is representative
for Berlin. As can be seen from Table , all sites are within
20 km distance from the ceilometer with five stations being very close (less
than 6.4 km).
Note that all times are given in CET (central European time).
Mixing layer heights from ceilometer measurements
Ceilometer data
In the framework of BAERLIN2014 a Vaisala CL51 ceilometer
was deployed. The instrument is fully automated and eye-safe. It provides
backscatter signals at 910 nm. As this wavelength is influenced by water
vapor absorption, it is complicated to derive optical properties of particles
in a quantitative way , but the identification of
aerosol layers is not affected because strong changes of the aerosol backscatter
are not masked by the water vapor absorption. The height range of the
measurements is more than 4 km, thus covering the typical range of MLH over
a continental site like Berlin. Due to its optical design using the same lens
for the emitter and the receiver optical paths, the minimum range is on the
order of 50 m for the detection of aerosol layers and even lower for clouds.
The spatial and temporal resolution are 10 m and 16 s, respectively.
Ceilometer data (firmware version V1.032) are available for 67 days between
27 June and 2 September 2014 (except 15 July). The output signals are range
corrected consistently for the whole measurement range; i.e., the
“H2on” parameter was set to 1 as discussed by . To
improve the detection of aerosol layers close to the ground, an additional
overlap correction function, similar to a concept outlined by
, was applied.
Determination of the MLH
Virtually all retrievals of the MLH from ceilometer measurements are based on
the shape of the range-corrected signal (i.e., uncalibrated) or the vertical
profile of the attenuated backscatter coefficient (i.e., calibrated;
). Several methods to analyze the gradient of the
profile or its temporal variability are available, different thresholds can
be selected to distinguish between clouds and aerosol layers, and different
temporal and vertical averaging can be applied to reduce the influence of
noise.
The standard procedure for the MLH determination from Vaisala ceilometers
(Hml,v) is the MATLAB-based software package BL-VIEW developed by
the manufacturer. It provides up to three altitudes of aerosol layers
(referred to as candidate levels in the following); they are counted upward,
i.e., candidate level 1 is closest to the ground. They are determined from
local minima of the gradient of the backscatter profile considering data of a
14 min time period prior to the actual measurement; in the case of low
signal-to-noise ratios this time span is extended to 20 min. To improve the
retrieval, signals are smoothed along the line of sight, thresholds are
defined to identify cloud “contamination”, and unrealistic outliers are
deleted. In the case of rain, no Hml,v is provided. Each candidate
level is given with a quality flag based on the absolute value of the
gradient and the “width” of the local minimum .
Quality flags are 1, 2 or 3, with 3 meaning the highest reliability.
Candidate levels with quality flag 3 are not necessarily given for all times.
This information is stored in an ASCII-file, and it is left to the user to
find their own criteria to determine the MLH; i.e., different selection of
the candidate levels is possible and the quality flags might be considered
or not. The advantage of the provision of three candidate levels is that
different layers can be detected at the same time (e.g., stable layer,
convective layer, residual layer); the disadvantage is that the attribution
of the layers is more complicated . The details of BL-VIEW
are not disclosed to the user.
In this paper we use different criteria. To facilitate further reading we
introduce the acronym “L1” for the criterion of “lowest candidate level if
it has a quality flag of at least 1” (this is identical to the condition of
“lowest candidate level without considering the quality flag”). “L2” and
“L3” are defined accordingly. So in all cases the lowest candidate level
(1) is chosen if the quality flag fulfils the corresponding conditions;
otherwise no MLH is retrieved. “Q3” stands for the criterion of “lowermost
candidate level with quality flag 3”, meaning that any candidate level is
chosen as long as it has the best quality flag. If more than one candidate level fulfils this quality criterion,
the lowermost is selected. For reasons of clarity our nomenclature is
summarized in Table . It is obvious that L1 is more often
fulfilled than L3 and that any successful retrieval according to L3 is also a
successful Q3 retrieval.
An alternative approach to determine the MLH (Hml,c) has been
developed by , referred to as COBOLT (“continuous boundary
layer tracing”). The code is written in the open-source programming language
Python and can be used on Windows and Linux platforms. The algorithm is based
on a time- and height-dependent function A(t,z) that has been defined
according to Eq. ():
A(t,z)=ϵgMg(t,z)99th(ϵgMg(t,z))+ϵvMv(t,z)99th(ϵvMv(t,z)).
Top: time–height cross section of range-corrected ceilometer signals
(Vaisala CL51) at the BLUME network site 42, Nansenstraße, on
1 July 2014 (in arbitrary units). The MLH as determined from COBOLT
(Hml,c) is marked by dark green dots. Bottom: comparison of the
MLH retrievals: Hml,c as above (green line). Hml,v
with L1 criterion (blue triangles), the L3 criterion (red dots) and the
Q3 criterion (cyan squares); for the definition see
Table .
It depends on the magnitude and orientation of gradients of the range-corrected ceilometer signal (first term on the right hand side), and on the
temporal variability of the aerosol layering (second term). Both terms are
weighted according to ϵg and ϵv,
respectively, and are normalized by the 99th percentile of the function. By
applying the Sobel operator , in principle a two-dimensional
gradient method, to XΛ,
Overview over different approaches to determine MLH from BL-VIEW:
the conditions with respect to the quality flag and the number of the
candidate level.
Acronym
Selected candidate level
Quality flag
L1
1
1, 2 or 3
L2
1
2 or 3
L3
1
3
Q3
Lowermost of 1, 2 or 3
3
XΛ(t,z,a,b)=1a∫z0zmaxX(t,z)Λz-badz,
with X(t,z) as range-corrected ceilometer signal and a low-pass filter
Λz-ba defined as
Λz-ba=a2-z+bifb-a2≤z≤ba2+z-bifb≤z≤b+a20elsewhere,
the function Mg(t,z) and the direction of the gradients
Θ(t,z) are obtained. The application of the Sobel operators to a low-pass-filtered ceilometer signal is equivalent to the wavelet covariance
transform method using a Haar wavelet . Parameters a and
b in Eq. () are the wavelet dilation and translation,
respectively. The advantage of the Sobel operator is that both temporal and
spatial changes can be evaluated simultaneously. The weighting function
ϵg(t,z) is defined such that MLH that are unlikely in a
meteorological sense are suppressed:
ϵg(t,z)=0.1if0∘≤Θ≤185∘0.1if355∘≤Θ≤360∘1elsewhere.
With this definition, range-corrected ceilometer signals that increase
with height (Θ≈90∘) – and most likely do not
represent the top of the mixing layer – have a very low weight. In contrast,
negative gradients caused by decreasing aerosol backscattering with height
(Θ≈270∘) are emphasized. Mv(t,z) is the
temporal variance of XΛ(t,z) and the weighting factor
ϵv(t,z) is height dependent in order to account for the
decreasing signal to noise ratio with height. Specific gradient angles are
excluded:
ϵv(t,z)=0if-5∘≤Θ≤5∘0if175∘≤Θ≤185∘1-z3kmelsewhere,zin km.
The function A(t,z) was defined to especially determine the height of the
convective boundary layer. The empirical weights ϵg and
ϵv had undergone extensive testing to find solutions that
provide a reliable identification of the top of the mixing layer from the
maximum of A(t,z). For the determination of the diurnal cycle of the MLH,
the maximum of A(t,z) is traced in time. For the initialization of the
time–height tracking procedure Hml,c at a starting time t0 is
required. It is determined between 2.5 and 3.5 h after sunrise, when the
convective mixing layer is assumed to be existent .
Relying on the variance method, which is especially sensitive to the beginning
convection , the height of the maximum value of A(t,z) is
chosen as the initial Hml,c(t0). Starting with
Hml,c(t0), a search window with a vertical extent dependent on
the solar zenith angle is moved backward in time to cover the period before
sunrise and forward until sunset. In the case of rain Hml,c remains
unchanged but is flagged; consequently, observations during (long-lasting)
precipitation events can be excluded by the user if desired. In the presence
of convective clouds at the top of the boundary layer, the strongest decrease
of the signal in the cloud is used to determine Hml,c, which is
usually a few tens of meters above the cloud bottom. The analogue procedure
as for the convective daytime MLH is applied after sunset for the nocturnal
stable boundary layer. To account for the transition from decaying thermals
in the well-developed mixing layer to the establishment of a stable boundary
layer, a linear change of the Hml,c between both layers is assumed
to take place between 30 min before until 60 min after sunset
.
In COBOLT an ensemble of 40 potential tracks, Hml,c(t), is
calculated with different initial conditions and search criteria, e.g.,
different widths of the search window. The selection of the final result is
performed by means of the function Cj for each ensemble member j
(≤ 40) as defined in Eq. ():
Cj=∑i=0N-1(ti+1-ti)2+(Hml,c(ti+1)-Hml,c(ti))2∑i=0NA(ti,Hml,c),
with N being the number of time steps ti within 1 day, i.e., N=144
for COBOLT's temporal resolution of 10 min. The track j with the minimum
value of Cj is selected as the final result: the main idea behind this
selection is that the MLH is assumed to develop smoothly in time; i.e.,
sudden “jumps” (that would increase the length of the track) do not occur
in reality but are caused by wrong attribution of the mixing layer in the case of
multi-layered aerosol distributions. As a consequence, COBOLT retrievals do
not have any temporal gaps, and unrealistic growth rates of Hml,c
are suppressed. Otherwise, in particular in the case of the detection of two
layers, the retrieved Hml,c might “switch”
between those layers, resulting in very strong and rapid changes.
Comparison of MLH (in km) retrieved by COBOLT (Hml,c)
and different BL-VIEW approaches during the BAERLIN2014 campaign.
(a) Hml,v,L1 from L1 criterion,
(b) Hml,v,L3 from L3 criterion and
(c) Hml,v,Q3 from Q3 criterion. The number of occurrence
is color coded.
To make both approaches more comparable, time is assigned to the center of
the interval of the BL-VIEW retrieval. Note that a perfect temporal
coincidence is not possible because of the inherent properties of both
algorithms, e.g., the height-dependent temporal averaging in the case of COBOLT.
Comparison of MLH retrievals
A typical example of CL51 measurements and the MLH retrieval is shown in
Fig. . The attenuated backscatter signal (color-coded, in
arbitrary units) up to 7 km above ground is shown in the upper panel for
1 July 2014. Sunrise at 03:46 and sunset at 20:32 CET are highlighted by
the black lines. Visual inspection shows broken cloud fields from 09:00
to 16:00 CET at different altitudes, afterwards an almost continuous cloud
deck at 3 km, and inhomogeneous aerosol layers up to 2.0 km before sunrise
and up to 3.0 km after sunset. The MLH as identified by COBOLT
(Hml,c) is marked by dark green dots.
The results of all MLH retrievals are shown in a separate panel for reasons
of clarity (Fig. bottom): BL-VIEW (Hml,v) with
different selection criteria (L1, L3 and Q3) is shown as blue triangles, red
dots and cyan squares, respectively, whereas Hml,c is shown as
green line (same as in the upper panel). The temporal interval is 10 min. It
can be seen that the overall agreement between COBOLT and BL-VIEW L3 is very
good and coincides with what a human observer would have analyzed. Note that,
in general, cloud bottoms were not misinterpreted as MLH by either approach.
For L2 (not shown here) and L1 more cases of wrong assignments occur.
Disagreements between COBOLT and L3 are rare, mainly between 20:00 and
22:00 CET when Hml,v is significantly higher – here the
residual layer seems to be interpreted as the mixing layer by the Vaisala
retrieval. Disagreements are more frequent when L1 or L2 is applied instead
of L3, e.g., around noon, when BL-VIEW L1 selects the top of elevated aerosol
layers and occasionally clouds as the MLH, or after sunset, when L1 selects
the residual layer. It is obvious that Hml,v is often not
available during the daylight period, especially when L3 is considered. The
main reason is the high temporal variability of the distribution of aerosol
particles and clouds, e.g., under not well-mixed conditions with more than
one aerosol layer that prohibit an unambiguous determination of
Hml,v. Consequently, candidate levels are rapidly changing,
leading to lower quality flags
and a failure of the MLH assessment. So it can be
understood that the temporal coverage of Hml,v is quite low if L3
is applied. Figure confirms that even the application of L1
(and L2, not shown here) does not fill all temporal gaps. As all MLHs from L3
are by definition also fulfilling the Q3 criterion, these results do not
differ much. Only very few cases are added, e.g., before sunset, when the top
of the residual layer was identified as the second or third candidate level
and flagged with the highest quality.
These conclusions also hold for the whole period of BAERLIN2014. The
intercomparison of the different MLH retrievals is summarized in
Fig. . Figure a concerns BL-VIEW when
the “weak” constraint L1 is applied: for each {Hml,v,L1,
Hml,c} pair the number of occurrence is color-coded. As expected
from the example shown in Fig. , many cases with
Hml,v,L1>Hml,c exist. This is a consequence of multiple
aerosol layers and the different behavior of the algorithms in the presence
of a residual layer. The correlation coefficient according to Pearson is
R=0.653. The corresponding comparison if the stronger constraint L3 is
applied is shown in Fig. b. Here, the correlation is
obviously better with R=0.754. Again, the number of cases with
Hml,v,L3>Hml,c is much larger than the opposite case
but less frequent than before (Fig. a). Similar results
are found when Q3 is applied (Fig. c). It is the same
distribution as the L3 case, however, with some additional cases when the
lowest candidate level has a low quality flag, whereas one of the upper levels is considered to be quite
reliable. Consequently, the additional points concern primarily large
Hml,v and the correlation is lower than before (R=0.650). It is
clear that the application of more rigorous criteria leads to a drastic
reduction of successful Hml,v retrievals: with the L1 criterion
the total number is 8346, whereas it is only 2998 and 3331 for L3 and Q3,
respectively. Note that the largest possible number of MLH retrievals would
be 9648 (67 × 24 × 6).
To better understand the reasons for the discrepancies between the
approaches,
the difference (ΔH)
ΔH(t)=Hml,c(t)-Hml,v(t)
for each 10 min interval is calculated. Figure a concerns the
L3 criterion. Green bars show the range between the 25th and 75th percentiles
of ΔH at a given time. The red line illustrates the median value. For
comparison the corresponding median of the L1 approach (black line) is also
shown. It is obvious that the median is very small for both BL-VIEW
approaches and stays between +0.03 and -0.11 km before noon. Between
16:00 and 23:00 CET ΔH is clearly shifted to negative values with a
median reaching -0.33 and -0.56 km for L3 and L1, respectively. This is
a clear indication that with the establishment of the residual layer in the
late afternoon and after sunset, the BL-VIEW algorithm tends to select the
top of the residual layer as Hml,v, especially if the user
selects the L1 criterion. A similar effect is found in cases of complex
aerosol particle distributions with several layers. L3 gives a much better
agreement with COBOLT, but, as already mentioned, the stricter L3 criterion
leads to considerable temporal gaps in the Hml,v retrieval: in
Fig. c it can be seen (orange line) that the relative number of
10 min intervals that allows us to determine Hml,v is never
larger than 61 %. Between 10:00 and 20:00 CET it is typically only in
the 15–25 % range because in the majority of cases the lowest candidate
level does not have the highest quality flag (see Fig. ). The low number of successful
retrievals is also the reason for the rare cases (e.g., at 15:40 CET) when
the absolute value of ΔH for L3 is larger than for L1. If the weaker
L1 criterion is applied the availability of Hml,v is
significantly increased (see the red line) and reaches a relative frequency
of successful retrievals of more than 75 % throughout the day, but at the
expense of a in general good agreement between Hml,v and
Hml,c.
The corresponding comparison for the Q3 criterion is shown in
Fig. b. The findings are similar as before; however, the range
of differences ΔH(t) is extended towards larger negative values
(green lines) as expected. This concerns the whole diurnal cycle but the
effect is strongest after sunset. The number of successful
Hml,v retrievals is slightly larger than for the L3 criterion, as
can be seen in the lower panel (green line).
(a) Difference ΔH of the retrieved MLH from COBOLT
and BL-VIEW L3 during the BAERLIN2014 campaign: vertical lines indicate the
interval from the 25th to the 75th percentile. The red line is the median of
the distribution. For comparison the corresponding median from the
L1 criterion is shown (black line). (b) Same as top panel but
ΔH of the retrieved MLH from COBOLT and BL-VIEW Q3.
(c) Relative frequency of successful Hml,v-retrievals
(L3 in orange, Q3 in green, L1 in red) in percent in relation to the
COBOLT retrieval.
If we compare – as a consequence of these findings – only MLH retrievals
before sunset, the agreement between BL-VIEW and COBOLT is indeed improved. If
the L1 criterion is applied to the complete diurnal cycle, 23.4 % of the
intercomparisons show large negative differences (ΔH<-0.5 km). If
only measurements before sunset are considered the number is reduced to
19.1 %. The corresponding numbers for the Q3 criterion are 20.2 and
17.3 %, respectively. For the L3 criterion we find 12.3 and 9.5 %.
Retrievals when Hml,c is larger than Hml,v are quite
rare. A difference ΔH of more than 0.5 km occurs in less than
1.5 % of the cases for all three BL-VIEW approaches.
Figure shows the mean diurnal cycle of MLH from 67 days as
retrieved by BL-VIEW L3 and COBOLT. The dark blue line corresponds to
Hml,c, whereas the orange line is for Hml,v. The mean
maximum vertical extent is approximately 1.5 km, similar to results from
found for Vienna. The light blue lines indicate the
temporal variability as calculated from the standard deviation
σc (COBOLT approach). It is on the order of 100 m before
sunrise and up to 500–700 m in the afternoon. Though this finding is based
on COBOLT, which provides complete temporal coverage, it remains open whether
this is representative for summer months in Berlin. Similar values but less
variability were found for Barcelona, Spain . From summer
observation over 5 years in Paris, France, found larger
values (Hml=1.95±0.38 km), whereas maxima less than 0.8 km
were observed during 2 years at Vancouver, Canada
, and Santiago, Chile . The mean
Hml,c at night is in the range of 0.2 km, underlining the need of
ceilometers with a very low overlap (or a reliable overlap correction
function; see, e.g., for a CHM15k ceilometer) for
investigations of the mixing layer. The most prominent differences between
BL-VIEW and COBOLT are the larger Hml,v during the night and the
rapid changes of Hml,v around noon. The main reason for these
“fluctuations” is the low number of retrievals when L3 is applied: e.g., for
some of the 10 min intervals only in 5 out of 67 days
could Hml,v be found. Thus, the significance is limited, but
Hml,v is nevertheless within the range of Hml,c±σc.
The green line in Fig. shows the first derivative of the
COBOLT retrieval Hml,c. This quantity can be relevant in view of
temporal averaging, e.g., when MLH is correlated with concentration
measurements having a lower temporal resolution. This topic is briefly
discussed in the next section.
It is worthwhile to also determine a typical afternoon value of MLH.
Figure confirms that this period provides the maximum volume
for the mixing of emitted compounds and that the MLH is representative for
several hours. The latter has been the reason for including a measurement
around 2 h after local noon in the regular EARLINET schedule
. Based on the mean diurnal cycle we define this value
as the average over the 3 h time period starting 30 min after noon.
Figure shows the results from COBOLT (blue dots) and L3
(orange dots) for the whole period of 67 days. Note that BL-VIEW with the
strict L3 criterion fails to determine Hml,v in 21 days (shaded
areas) for the reasons mentioned above. If both values are available the
general agreement is, however, good; only a few cases exist when
Hml,v is much larger than the respective COBOLT result
Hml,c (e.g., 27 June, 1 July and 10 July).
We conclude that the main discrepancies between COBOLT and BL-VIEW origin
from the presence of the residual layer and elevated aerosol layers during
daytime whereas broken cloud fields cause less problems. The main drawback of
the present version of BL-VIEW is the limited temporal coverage, when only
retrievals with the highest quality
flag are considered.
Mean diurnal cycle of Hml,c (dark blue) and
Hml,c ± σc as retrieved with COBOLT and
Hml,v from BL-VIEW L3 (orange) at the urban background site 42,
in 10 min resolution, averaged over 67 days. The green line shows the growth
rate of Hml,c (in km h-1).
Temporal averaging of the mixing layer height
When evaluating ceilometer data a temporal resolution of MLH retrievals of
the order of 10 min can be achieved. This is typically better than the
resolution of air quality measurements of automated monitoring stations. To
make MLH retrievals comparable with the in situ measurements of the
BLUME network, 1 h averages have to be calculated. In this context the
growth rate of the mixing layer (dHml/dt) is
relevant; it is shown for the mean diurnal cycle derived from COBOLT as a
green line in Fig. . It can be seen that the mean
Hml,c rises with 150–200 m per hour between 08:00 and
12:00 CET with a maximum of 290 m. This is in good agreement with other
continental cities e.g.,. The mean diurnal cycle
of Hml,c shows its strongest decrease after sunset, reaching
rates of -450 m per hour. For individual days these rates can be exceeded.
However, in the case of L3 or Q3 the MLH cannot be retrieved for each 10 min
interval (see low values in Fig. c). As a consequence, hourly
averages of the MLH can be biased on the order of ±100 m due to the
rapid growth of the mixing layer during strong convection events before noon.
After sunset the uncertainty can be even larger (±200 m).
Daily afternoon value of Hml (averaged over 3 h
starting 30 min after noon) as derived from COBOLT (blue dots) and BL-VIEW
L3 (orange dots) between 27 June and 2 September 2014. The alignment of the
labels of the x axis (date) is defined by the position of the dots
separating day and month. The shaded areas highlight days when
Hml,v could not be retrieved by applying the
L3 criterion.
Medians of the MLH are derived from all available 10 min retrievals (up to
six, depending on the retrieval) of the corresponding hour, for all 67 days.
So, up to 402 values are considered. The resulting hourly values as they are
used in the following discussion (Sect. 5) are shown in
Fig. . In particular before sunrise averages are larger
than the medians of MLHs. This is expected from Fig. a and b,
which show negative values of ΔH(t); i.e., there are cases of much larger
MLH derived from the BL-VIEW retrievals.
Diurnal cycle of hourly values of the MLH (in km) as determined from
the different retrievals (see legend). Thick solid lines are for medians;
thin broken lines for averages.
Link to air quality
In the following we consider BLUME measurements of PM10 and
concentrations of O3 and NOx. These measurements are available with a
temporal resolution of 1 h. For the MLH we use the arithmetic mean of up to
six values from 10 min intervals.
Episodic mobile (bicycle) measurements during BAERLIN2014 have already shown
that there is significant horizontal heterogeneity in gas-phase pollutants
and particle number concentrations . In the following we
discuss the influence of different retrievals of the MLH on correlations with
surface measurements of PM10 and concentrations of gases (O3 and
NOx). Note that in situ measurements are available at different sites,
whereas only one ceilometer was deployed; consequently an inherent assumption
of the following discussion is that the MLH is the same over Berlin.
Correlation between MLH and PM10
For the discussion of correlations between MLH and PM10 we can use
measurements at the outskirts (32, 77, 85), at urban background
stations (10, 42, 171) and at five stations that are strongly
influenced by traffic (117, 124, 143, 174, 220; see
Fig. and Table ). The diurnal cycles of
PM10 (in µg m-3) at these 11 stations are shown in
Fig. , calculated as medians of all measurements of the
corresponding hour of each day of the measurement period (67 days). It can be
seen that the concentrations at the traffic sites (solid lines) are in
general slightly higher than at the urban background and the outskirts. The
amplitude of the mean diurnal cycle is quite small – between
4.4 µg m-3 at site 32 (red dotted line) and
10.6 µg m-3 at site 124 (green solid line) – whereas the day-to-day variations are comparably large at all sites and all times of a day.
On the one hand, the diurnal cycles have some common features; e.g., a
distinct increase during the morning rush hours at all traffic sites and some
of the urban background sites. This is plausible from vehicle emissions. At
the urban background site 171 and sites at the outskirts, however, the
strong increase occurs several hours later. The delay might be caused by the
transport time from the main sources to the site. On the other hand, changing
contributions of large-scale transport from variable directions, local
sources or particle removal by precipitation can lead to a quite different
development in the course of a day, including continuously
increasing/decreasing PM10, sporadic “peaks” or sudden drops at any
time. The combination of these effects complicates a meteorological
interpretation of mean diurnal cycles.
Diurnal cycle of PM10 in µg m-3 at 11 BLUME
stations, based on medians of measurements on 67 days. The temporal
resolution is 1 h. The locations at the outskirts of Berlin (dotted lines),
urban background sites (dashed) and traffic sites (solid) are indicated in
the legend; see also Table .
For the determination of the diurnal cycle of the MLH we have – as already
mentioned in Sect. 4.2 – four different MLH retrievals available. For the
correlations between the PM10 measurements and the MLH retrievals,
further options can be considered: either averages or medians of hourly
values (67 or less) as shown in Fig. can be used.
Figure illustrates how these correlations depend on the site
and the retrieval. Eleven blocks according to the 11 sites are separated
and labeled following Table . For each site four different
correlations are shown (from left to right): averages of MLH vs. averages of
PM10, medians of MLH vs. medians of PM10, averages of MLH vs.
median of PM10 and median of MLH vs. averages of PM10. The
different colors indicate which ceilometer retrieval is used to determine the
MLH: the COBOLT approach is shown in black and the BL-VIEW retrievals in red
(L1 criterion), green (L3) and blue (Q3).
Correlation coefficient R for mean diurnal cycles of MLH and
PM10 for 11 sites (from left to right): sites on the outskirts
(32, 77 and 85), urban background sites (10, 42 and 171), and
traffic sites (117, 124, 143, 174 and 220). The results of the
different retrievals are color coded as indicated in the legend. The four
vertical lines of each block correspond to different options of correlation:
MLH average vs. PM10 average (1), MLH median vs. PM10 median (2),
MLH average vs. PM10 median (3) and MLH median vs.
PM10 average (4).
The wide range of correlation coefficients for the different locations is
obvious: the strongest correlation between MLH and PM10 is found for the
traffic site 124 (R≈0.77) and the strongest anticorrelation for
site 143 (traffic, R≈ -0.79) and site 10 (urban background,
R≈-0.78). Thus only for two sites is a correlation found, as would
be
expected if vertical dilution were the dominant process for the surface
concentration of particulate matter. Compared to the large spatial
heterogeneity, the differences for different correlation options and MLH
retrievals are, with the exceptions of sites 32 (outskirts), 171
(urban background) and 174 (traffic), small: for a given MLH retrieval
(i.e., same color) the range of R (maximum minus minimum) for different
options is typically 0.08; for a given option (i.e., same vertical line) it is
0.11 on average. For the three sites mentioned the sensitivity to the
correlation option is, however, 0.25–0.35. The reason is that correlations
involving averages of PM10 (first and last vertical line of each block)
clearly differ for those based on medians. The latter are less effected by
short episodes of extreme concentrations, which are not unusual for
particulate matter.
As already mentioned, these correlations are based on ceilometer measurements
at one site and it was impossible in the framework of BAERLIN2014 to
verify that the diurnal cycle of the MLH within the 20 km range of the air
quality stations is identical. Large differences of the correlation
coefficients are, however, also found if we restrict ourselves to the five
BLUME stations (sites 220, 143, 171, 174 and 124) that are
closest to the ceilometer site (0.9≤d42≤6.4 km; see
Table ). Over this small area in the center of the city,
changes of the diurnal cycle of the MLH are very unlikely. Nevertheless our
previous conclusions are confirmed: as can be seen from Fig.
the correlations between MLH and PM10 are quite variable, ranging from
more than R=0.7 (site 124, d42=6.4 km) to less than R=-0.7
(site 143, d42=2.5 km).
At first glance it seems to be surprising that even within the same category
the correlations are quite different. The three stations at the margin of
Berlin (outskirts) show, however, different characteristics with respect to
their distance to major traffic sources. Station 32 (Grunewald) is only
0.8 km west of the AVUS motorway, whereas stations 77 and 85 are
more than 3.5 km from the next motorway. The latter station is close to a
large lake. Thus there is in principle sufficient time for mixing during the
transport from these sources towards the measurement site,
depending of course on the wind direction that certainly changes during to observation
period. The three urban background stations show even more pronounced
differences. For station 10 the distance to the next main road is larger
than for the other two sites; due to the east–west orientation and the
broad street, ventilation is more effective than for the reference site 42
(Neukölln), which has a lot of vegetation in a typical residential neighborhood
in the inner part of a big German city and a comparably large distance to
major roads. In contrast, station 171 is close to a main road but it
benefits from a good ventilation from the river Spree. For the traffic
stations technical conditions, e.g., the number of lanes, the presence of
traffic lights close to the monitoring station and height and distance of
the surrounding buildings, become especially relevant because of the short
distance between the emitters and the monitoring station. Consequently, the
vertical dilution in the mixing layer is less relevant for PM10
concentrations and correlations are rather governed by the diurnal cycle of
the traffic, which is not necessarily dominated only by the morning and
evening rush hours but could have a significant contribution from buses and
trucks throughout the day.
Correlation coefficients R between medians of hourly MLH (derived
from COBOLT) and PM10 for different subsets of data. “All” indicates diurnal
cycles based on 67 days as shown in Fig. (second vertical line
of each block). “v40”, “v30” and “v25” indicate only consideration of days with
average wind speed v‾ below 4.0, 3.0 and 2.5 m s-1,
respectively. “m–f” indicates Monday to Friday. “w–end” is the weekend only. The
station IDs are according to Table .
Station ID
All
v40
v30
v25
m–f
w–end
32
0.12
0.11
-0.05
-0.29
0.26
-0.46
77
-0.35
-0.41
-0.40
-0.44
-0.12
-0.80
85
-0.45
-0.50
-0.45
-0.42
-0.25
-0.74
10
-0.71
-0.77
-0.84
-0.83
-0.68
-0.76
42
0.62
0.54
0.20
-0.18
0.68
-0.14
171
-0.13
-0.25
-0.62
-0.74
0.13
-0.64
117
0.55
0.42
0.28
-0.10
0.52
-0.11
124
0.71
0.74
0.67
0.52
0.76
0.19
143
-0.80
-0.81
-0.80
-0.76
-0.63
-0.74
174
0.50
0.35
-0.29
-0.47
0.58
-0.68
220
-0.36
-0.42
-0.53
-0.55
-0.27
-0.53
We conclude that the completely different correlations between mean diurnal
cycles of MLH and PM10 at the different sites as shown in
Fig. clearly demonstrate that the surface concentration of
particulate matter is determined not only by the vertical stratification of the
mixing layer alone but also by local sources and sinks and the wind field
(see ). Moreover, the distance between the
main sources and the measurement site is relevant.
The lack of a unique correlation is confirmed if we consider subsets of data
with specific meteorological conditions. Two examples are briefly discussed:
the consideration of the wind field and the differences of working days and
weekends. If only days are considered when the average wind speed over Berlin
was below a certain threshold, a pronounced correlation is more likely because
the vertical exchange can dominate advection. Hourly wind measurements in
10 m altitude were available at three stations in Berlin, i.e., Tegel
(52.5644∘ N, 13.3088∘ E; d42=11.8 km), Tempelhof
(52.4675∘ N, 13.4021∘ E; d42=3.1 km) and
Schönefeld (52.3807∘ N, 13.5306∘ E; d42=13.9 km). They constitute a northwest-to-southeast transect through Berlin
(see black stars in Fig. ). For a simplified categorization
of the wind field we use the daily averages of the wind speed v‾.
We found 52 (out of 67) days where v‾ was below 4 m s-1 at
all three stations, 28 days with v‾<3 m s-1 and only
16 days with v‾<2.5 m s-1. In the latter case correlations
between PM10 and Hml,v,L3 or Hml,v,Q3,
respectively, suffer from the low number of successful retrievals (see
Sect. 4.3). Therefore the correlation coefficients shown in
Table (columns “v40”, “v30” and “v25”, respectively)
only refer to COBOLT retrievals of the MLH. Though the correlation
coefficients are in general more shifted to negative values compared to
Fig. (see also column labeled “all” in
Table ) and anticorrelations occur more frequently, the large
spatial variability remains.
If we distinguish – as the second example – working days and weekends
(columns “m–f” and “w-end” of Table , respectively), we
find very pronounced differences with a tendency to stronger
anticorrelation for weekends. This is plausible as the diurnal cycle of the
emissions is less pronounced. However, there were only 10 weekends with
ceilometer measurements during BAERLIN2014, so these findings should be
treated as preliminary.
As an additional example one may focus on day–night differences of the
correlation. For this purpose we use coincident hourly measurements
(depending on the ceilometer retrieval up to 1608 values) rather than the
mean diurnal cycle as before to overcome the small number of samples. We
define daytime as the period between 07:01 and 20:00 CET, and nighttime as times before 07:00 and after 21:00 CET. Then, for daytime
measurements we get very low correlation coefficients of -0.33<R<0.10, and for
nighttime the correlation is only slightly different (-0.27<R<-0.09).
The main result is that during nighttime R<0 for all sites, and only one
site with ‖R‖<0.1 was found. On the one hand, these values are plausible
as we expect an anticorrelation between MLH and PM10 in view of the
suppressed vertical mixing in particular during night when the mixing layer
is typically shallow (see Fig. ). On the other hand, the
absolute values of R are too small for supporting a strict scientific
interpretation.
We conclude that the heterogeneity of the city is obviously more relevant
than the selection of the MLH retrieval and the correlation option. The
introduction of only three classes of monitoring stations (traffic, urban
background, outskirts) cannot reflect the full complexity of pollution in the
metropolitan area, and a re-assignment might be advisable when traffic flows
have changed over years.
Correlation between MLH and gaseous pollutants
With respect to gaseous pollutants we restrict our discussion to O3 and
NOx. Ozone measurements on a hourly basis are available at seven sites and
NOx at all 16 sites (see Table ).
The mean diurnal cycle of O3 is shown in Fig. for the five
stations located at the outskirts of Berlin (dotted lines) and two urban
background sites (10 and 42, solid lines); medians considering
67 days of data are plotted. It exhibits the typical pronounced diurnal cycle
with a maximum of about 100 µg m-3 between 14:00 and
16:00 CET. The minimum occurs shortly after sunrise, which was between 04:00
and 05:00 CET during the BAERLIN2014 campaign. Note that the diurnal cycles
based on averages instead of medians are quite similar: during the afternoon
(largest concentrations) averages are about 5 µg m-3 larger
than medians. There is a close agreement between all stations, not only for
the mean diurnal cycle but also on a daily basis (not shown here), suggesting
that the spatial dependence of ozone concentration is less pronounced. This
can be expected as ozone is not emitted but formed in the atmosphere within
several hours after release of precursors. Thus, transport and mixing are key
driving forces.
The diurnal cycles for NOx concentrations are shown in
Fig. , again calculated as medians. The concentration at the
stations at the outskirts of Berlin (dotted lines) are the lowest with a
maximum during the morning rush hours of not more than
25 µg m-3. The urban background stations (solid lines) show
larger concentrations with a morning maximum of up to about
40 µg m-3. Significantly higher concentrations are observed
at the traffic stations (dashed lines), again with a maximum during the
morning rush hours. The absolute values and the development during the day
are, however, much more diverse than at the less polluted locations. One reason
can be that roadside NOx concentrations depend strongly on the distance
from the source e.g.,, which is similar as
for PM10. Due to the spatial variability of the mean diurnal cycles it
is clear that for the traffic sites the correlations must vary as well. If
averages instead of medians are considered, NOx concentrations are somewhat
larger (between 5 and 20 µg m-3) and the morning maxima are
slightly more pronounced.
Diurnal cycle of O3 concentrations (in µg m-3) at
five stations at the outskirts (dotted lines) and two urban background
stations (solid) as given in the legend (see also Table ).
Medians of the concentrations are plotted.
Correlations between MLH and concentrations are shown in
Fig. , separately for the three site categories. For the
outskirts of Berlin (leftmost block) and the urban background sites, very
strong positive correlations for ozone (circles) are derived. On average we
find R=0.94 for all sites and MLH retrievals. The differences between the
sites are virtually negligible. One of the reasons for the very high
correlations is that both MLH and O3 concentration strongly increase after
sunrise. The increase of the concentration is caused by the onset of
photochemical ozone production and by downward mixing of ozone from the
residual layer in the morning hours when the mixing layer grows because of
radiative heating of the ground and increasing convection. As shown by
downward mixing of ozone from aloft
can be a major source of near-surface ozone for polluted urban sites with
high NOx levels. In “green” areas of low NOx concentration ozone
production is also intensified. So, high correlations can be found, though
manifold and partly different physical reasons are responsible.
The correlations between MLH and NOx concentration at the sites on the
outskirts are strongly negative: on average R=-0.86 is found. Again the
spatial differences are almost negligible. An anticorrelation can be
expected from mixing during the transport from the city center to the
outskirts of Berlin. Negative correlations are also found for the urban
background stations with the exception of station 171; due to their
closer proximity to the main sources the absolute values on average are,
however, slightly smaller (R=-0.51). Though labeled as an “urban
background”,
site 171 resembles much more the traffic sites. As already mentioned in
Sect. 5.1, it is indeed close to a major road, but in contrast to the
PM10 concentration the presence of the nearby river does not counteract
the NOx distribution in a similar way. For sites dominated by traffic a
positive correlation is found, but with a wide spread of values from R≈0.16 for site 117 to R≈0.77 for site 115.
Additionally, there is a pronounced dependence on the MLH retrieval; e.g., for
site 174, R=0.36 (L1 retrieval) and R=0.59 (COBOLT).
Diurnal cycle of NOx concentration (in µg m-3)
at all BLUME stations: five at the outskirts of Berlin (dotted lines), five
urban background stations (solid) and six traffic stations (dashed) as
indicated in the legend (see also Table ). Medians of
concentrations are plotted.
These findings are confirmed by investigations on an hourly basis (up to 1608
cases; see Fig. ). Correlation between MLH and
O3 concentration (open circles) are again high and virtually independent
on the location. However, differences between the MLH retrievals are on the
order of 0.2: for BL-VIEW Q3, on average R=0.52 with a very small
variation with the location (standard deviation of 0.02) is found, whereas
the correlation is larger (R=0.71±0.01) if COBOLT is applied. With
respect to NOx concentrations the correlation coefficients are
approximately -0.36 and -0.25 for outskirts and urban background stations,
respectively, and much more dependent on the site. For the traffic sites the
correlation is weak, with a large spread of -0.1≤R≤0.3. These
results suggest that only in the case of secondary compounds and primary
pollutants in the absence of nearby traffic sources strong correlations
between MLH and gaseous pollutants can be found.
Correlation coefficient R of mean diurnal cycles of MLH and O3
(circles) and NOx concentrations (crosses), shown for the 16
sites as indicated by the ID number according to Table . The
four MLH retrievals are color-coded according to the legend. Correlations
based on MLH averages and O3 and NOx medians are plotted. Note that
at the traffic stations no O3-measurements are
available.
Summary and conclusions
The MLH is expected to have an influence on air
quality at the surface. It is assumed that extended mixing layers lead to
dilution of pollutants and thus tend to decrease surface concentrations.
Several publications have indeed reported such anticorrelations. However,
neither the representativeness of such correlations for metropolitan areas
nor the role of choice of the MLH retrieval has yet been investigated. This
paper is devoted to these topics by examining the relationship between MLH
and near-surface concentrations of particulate matter (PM10), NOx and
O3. It is based on 2 months of data from the field campaign BAERLIN2014
conducted in Berlin, Germany.
Frequently used tools to determine the MLH are automated lidars and
ceilometers (ALC). Especially commercial systems with their unattended
continuous operation are very promising since they are available as networks.
Here, we compare four different approaches to determine the MLH, three of
them based on proprietary software delivered by the manufacturer of the
instrument (Vaisala), and the recently developed approach COBOLT
. It was found that a complete diurnal cycle with a high
temporal resolution often cannot be derived from the proprietary software
and that there is a tendency to overestimate the MLH in the presence of the
residual layer.
Same as Fig. but the correlation coefficient is based on
1 h measurements.
It is obvious that the differences of the retrieved MLH influence the
correlation coefficients between MLH and pollutant concentrations. For mean
diurnal cycles correlation coefficients differ by approximately 0.1 if
different MLH retrievals are applied. These differences are smaller than the
differences found when different locations in the city are compared – even
if their distance is only a few kilometers from each other. In the case of
PM10 we found strong correlations as well as strong anticorrelations
even if the sites are assigned to the same category (e.g., urban
background or traffic stations). This clearly demonstrates that the MLH
is not the only parameter controlling the surface concentration and that
local emissions and transport play a dominant role. This is in agreement to
the pronounced heterogeneity over Berlin as reported by . In
the case of ozone as a secondary pollutant the correlations for different sites
show only small differences. The strong correlation was found due to the
similarity (although for different reasons) of the mean diurnal cycles of
ozone and MLH with maximum values in the afternoon. An anticorrelation for
near-surface concentrations of NOx, as suggested by several previous
studies, was only found in the absence of direct exposure to traffic sources.
We conclude that in the case of a large city as Berlin the MLH can be an
indicator for urban air quality only in a very limited sense and that any
correlation between MLH and concentrations of pollutants should be treated
with care: it is unlikely that they are representative for the entire
metropolitan area, in particular if the terrain is flat. At least for the
observed summer period in Berlin this was not the case. Consequently,
whenever links between MLH and near-surface concentrations are interpreted,
it is mandatory to carefully describe the location, i.e., meteorological
conditions and local sources, and the details of the MLH retrieval.
Compared to the heterogeneity of the former we think that the selection of a
certain MLH retrieval does not have the highest priority for correlation
studies. It would be interesting to study wintertime conditions when the
PM10 concentrations in Berlin are about 50 % higher than in summer.
We do not expect that in winter the MLH is the only controlling parameter,
but it is not clear if the correlation (and its variability) is more or less
pronounced. It remains open whether the situation is different for regions
without pronounced changes in land use, without significant local emissions
or in areas with pronounced orography.
To better understand the complex interactions between the MLH, wind field,
emissions, chemical processing, etc. for air quality, there is a need for
models down to a building-resolving scale as well as more extended data sets,
especially for heterogeneous areas. The specific setup of models and
experiments must be defined according to the scale of interest. Continuous
ceilometer measurements, including at least one complete annual cycle, can
provide a significant contribution and help to investigate the generality of
the results, e.g., to check for seasonal changes or for differences between
working days and weekends. It is obvious that ALC with a very low overlap
range are required for the observation of very shallow mixing layers typical
during nighttime and in winter. Moreover, it would be nice to have
coincident ceilometer measurements at different sites or to have one or more
mobile systems to check our hypothesis that the MLH does not change on a
scale of a large city.
It should be added that accurate retrievals of the MLH are beneficial for
several applications: they can be used for box-model calculations and for the
validation of meteorological models and the meteorological part of chemistry
transport models. As the MLH is not a prognostic variable it is important to
assess the accuracy of different parameterizations
e.g.,. In this context a high
accuracy of the MLH retrieval is crucial and a methodology that provides the
full diurnal cycle with high temporal resolution, and avoids wrong
allocations of aerosol layers, must be applied. Finally, we want to emphasize
that state-of-the-art ALC allow for the derivation of profiles of the
particle backscatter coefficient βp if the signals have been
calibrated e.g.,. In the case of ceilometers
emitting in the spectral range near 910 nm, the signal must however be
corrected for water vapor absorption . Profiles of
βp can be used for the validation of chemistry transport
models e.g., in a more direct way than the MLH as, e.g.,
mixing ratios or mass concentrations of aerosol particles (or different
aerosol components) are available as prognostic variables. By applying the
adequate scattering theory, βp can then be derived. From the
long-term perspective, this is the preferable strategy for validation.