Introduction
Shipping – a fast-growing sector
Shipping has always been an important mode of transportation throughout the
course of history. In contrast to the past, today ships almost
exclusively carry freight, with the exception of a small number of cruise
ships and ferries. Globalization of markets has lead to an enormous increase
in world trade and shipping traffic in the last decades, with growth rates
being typically about twice those of the world gross domestic product (GDP)
.
Shipping is generally the most energy-efficient transportation mode, with
the lowest greenhouse gas emissions per tonne per kilometer
(3–60 gCO2t-1km-1), followed by rail
(10–120 gCO2t-1km-1), road
(80–180 gCO2t-1km-1) and air transport
(435–1800 gCO2t-1km-1), which is by far the least
efficient . At the same time, with a volume of 9.84
billion tons in 2014, shipping accounts for four-fifths of the worldwide total
merchandise trade volume , as compared to, for example, the
total air cargo transport volume of 51.3 million tons in 2014
. As a result, shipping accounts for a significant part of
the emissions from the transportation sector .
Despite growth rates now being lower compared to those prior to the 2008
economic crisis, seaborne trade is growing faster than the rest of the
transportation sector, with an annual growth rate of 3–4 % in the
years 2010 to 2014, compared to 2.0–2.6 % for the global
merchandise volume . The number of ships larger
than 100 gross tonnes increased from around 31 000 in 1950 to over 52 000 in
1970 to 89 000 in 2001 and is estimated to increase to
about 150 000 in 2050 . At the same time, total fuel
consumption and emissions increased as well
.
predicted that future development of shipping emissions will depend more on
the usage of new technologies and imposed regulations than on the economic
growth rates.
Ship emission chemistry
The most important pollutants emitted by ships are carbon dioxide
(CO2), carbon monoxide (CO), nitrogen oxides (NOx =
NO + NO2), sulfur dioxide (SO2), black carbon (BC),
volatile organic compounds (VOCs) and particulate matter (PM) .
This study focuses on NO2 and SO2 because both are emitted
in considerable amounts and both absorb light in the UV–visible spectral
range and therefore can readily be measured by differential optical
absorption spectroscopy (DOAS), which is explained in Sect. .
In 2001, shipping emissions accounted for 15 % of all anthropogenic
NOx and 8 % of all anthropogenic SO2
emissions .
NOx is predominantly formed thermally from atmospheric molecular
nitrogen (N2) and oxygen (O2) during high-temperature
combustion processes in ship engines in an endothermic chain reaction called
the Zeldovich mechanism. The emitted NOx comprises mainly NO,
with less than 25 % of NOx being emitted as NO2
. measured emission factors for gaseous
and particulate pollutants onboard three Chinese vessels and found that more
than 80 % of the NOx was emitted as NO and that
emission factors were significantly different during different operation
modes.
In the ambient atmosphere, NO is rapidly converted to NO2 by
reaction with ozone (O3), leading to a lifetime of only a few
minutes. During the daytime NO2 is photolyzed by UV radiation (λ<420nm), releasing NO and ground-state oxygen radicals
(O(3P)). In a three-body-collision reaction involving N2 or
O2 the oxygen radical reacts with an oxygen molecule to reform ozone
. When daylight is available, these reactions form a
“null-cycle” and the transformation between NO and NO2 is very
fast, leading to a dynamic equilibrium. This is also known as the Leighton
photostationary state. Owing to the lack of photolysis, NO reacts
rapidly with O3 to form NO2 during the night. In addition,
the nitrate radical (NO3) is formed by reaction of NO2 with
O3. An equilibrium of NO2 with NO3, forming
N2O5, the acid anhydride of nitric acid HNO3, results
.
During the day OH reacts with NO2 in a three-body reaction to form
HNO3. An important sink for NO2 in the troposphere is wet
deposition of the resulting HNO3. The mean tropospheric lifetime of
NOx varies between a few hours in summer and a few days in winter
, depending on altitude. Inside ship plumes,
found a substantially reduced lifetime of NOx of
about 1.8 h compared to approximately 6.5 h in the background
marine boundary layer (around noon). This is attributed to enhanced levels of
OH radicals in the plume.
Unlike for NOx, ship emissions of SO2 are directly linked to
the fuel sulfur content. Around 86 % of the fuel sulfur content is
emitted as SO2 . found
a linear relationship between SO2 and sulfate particle emission and
that only around 4.8 % of the total sulfur content is either
directly emitted as or immediately transformed into particles after the
emission. An important sink for SO2 is wet deposition after oxidation
by OH radicals to the extremely hygroscopic sulfur trioxide
(SO3) reacting rapidly with liquid water to form sulfuric acid
(H2SO4) . Another important sink is dry deposition,
leading to a lifetime of approximately 1 day in the boundary layer, which
can be even shorter in the presence of clouds .
Influence on air quality and climate
Sulfate aerosols influence climate directly by scattering and absorbing
solar radiation and indirectly by increasing cloud condensation and changing
cloud reflectivity and lifetime . In
the presence of VOCs, nitrogen oxides are
important precursors for the formation of tropospheric ozone and therefore
photochemical smog. The release of both NO2 and SO2 leads to
an increase in acidification of 3–10 % in coastal regions,
contributing significantly to acid rain formation that damages ecosystems
. The deposition of reactive nitrogen
compounds causes eutrophication of ecosystems and decreases biodiversity
.
Around 70 % of shipping emissions occur within 400 km of
land , contributing substantially to air pollution in
coastal areas . Ship emissions were found to provide a
dominant source of air pollution in harbor cities . In
addition, transport of tropospheric ozone and aerosol precursors over
several hundreds of kilometers also affects air quality, human health and
vegetation further inland, far away from their emission point
.
NO2 and SO2 can cause a variety of respiratory problems.
Tropospheric ozone is harmful to animals and plants, causing various health
problems. The European Union (EU) legislation for O3 exposure to humans has set a
target limit of 120 µgm-3 (∼ 60 ppbv) for
maximum daily 8 h mean but allows exceedances on 25 days averaged over
3 years . As mentioned above, both NO2 and
SO2 play a role in the formation of particles. Fine particles are
associated with various health impacts like lung cancer, heart attacks,
asthma and allergies .
Attempts to decrease shipping emissions with stricter regulations
International ship traffic is subject to regulations of the International
Maritime Organization (IMO). Shipping emissions are regulated by the
International Convention for the Prevention of Pollution from Ships (MARPOL
73/78) Annex VI . This annex was added in 1997 and was enforced in 2005. A revision with more stringent emission limits was
adopted in 2008 and enforced 2010. With this, limits on sulfur content
in heavy fuel oils (HFOs) were globally set and local Sulfur Emission Control Areas (SECAs), later revised to general Emission Control Areas (ECAs), along the
North American coast and in the Baltic and North seas (including the English
Channel) were established with more stringent restrictions and controls.
MARPOL introduced a global fuel sulfur limit of 4.5 %, which was
reduced to 3.5 % in 2012 and will be further reduced in 2020 (or
2025 depending on a review in 2018) to 0.5 %. In the established
ECAs, from 2010 on, the limit was set to 1.5 % and was further
reduced in 2010 to 1.0 %. Carrying out airborne in situ measurements
in several flight campaigns in the English Channel and North and Baltic seas,
measured an 85 % compliance in 2011 and 2012 with
the 1 % fuel sulfur limit. In the Gulf of Finland and Neva Bay area,
found a 90 % compliance in 2011 and
97 % compliance in 2012 with the 1 % fuel sulfur limit from
ground-based, ship-based and helicopter-based in situ measurements.
Recently, from 1 January 2015 on, the allowed fuel sulfur content in SECAs
was further reduced to 0.1 %. Using in situ measurements in Wedel at
the bank of the river Elbe, a few kilometers downstream from Hamburg,
Germany, showed that in late 2014 more than 99 %
of the measured ships complied with the 1 % sulfur limit, and in
early 2015 95.4 % of the measured ships complied with the new
0.1 % sulfur limit. By analyzing 1.5 years of SO2
measurements at the English Channel, found a 3-fold
reduction in SO2 from 2014 to 2015. They estimated the lifetime of
SO2 in the marine boundary layer to be around half a day.
measured a substantial drop in SO2 emissions by
91 % when the investigated container ships entered the Californian
ECA and switched from HFO with 3.15 % fuel sulfur
content to marine gas oil with 0.07 % fuel sulfur content.
These estimates were obtained performing airborne in situ measurements.
MARPOL Annex VI also establishes limits dependent on engine power for the
emission of NOx from engines built after 2000 (Tier I), 2011 (Tier
II) and 2016 (Tier III), but due to the slow penetration of the full shipping
fleet, the impact on NOx emissions is not yet clear. Since 2010, a
NOx Emission Control Area (NECA) exists around the North American coast and
in the Caribbean, while for the North and Baltic seas the establishment of such a
NECA is planned and was recently agreed on, but the future enforcement date
is still unclear. The EU also established a sulfur content limit
of 0.1 % for inland waterway vessels and ships at berth in community
ports, which has been in force since 1 January 2010 .
The impact of shipping emissions on the North Sea for different regulation
scenarios was investigated in a model study by the Helmholtz-Zentrum
Geesthacht (HZG) within the scope of the Clean North Sea Shipping project.
For current emissions, a relative contribution of shipping emissions to air
pollution in coastal regions of up to 25 % in summer and
15 % in winter for NO2 and 30 % in summer and
12 % in winter for SO2 was found . For
the year 2030, the contribution of the continuously growing shipping sector
to the NO2 concentrations is predicted to decrease. The extent of
reduction depends on the date on which the stricter Tier III regulations
will enter into force and on the fraction of the fleet complying with these
regulations (i. e. the age of the fleet), with up to 80 % reduction
if all ships comply (in the improbable case of a new-ship-only fleet). For
SO2, the established fuel sulfur content limit of 0.1 %
(ECA) and 0.5 % (globally) will lead to significant reductions, a
further decrease is expected if the fraction of ships powered by liquid natural gas grows
.
DOAS measurements of shipping emissions – previous studies
Optical remote sensing using the DOAS technique to measure shipping emissions has been conducted before. For
example, performed airborne (from airplane and helicopter)
DOAS measurements of NO2 and SO2 in ship plumes by measuring
sea-scattered light. and measured
flow-rate emissions (mass per second) of NO2 and SO2 for
single ships with ground-based multi-axis DOAS (MAX-DOAS) measurements across the Giudecca
Channel in the Venice lagoon. measured nocturnal
NO2-to-SO2 ratios in ship plumes in the Strait of Georgia
with the active long-path DOAS technique. tested
and compared optical remote sensing methods (DOAS, lidar, UV camera) and
in situ (sniffer) methods for the measurement of shipping emissions in the
framework of the SIRENAS-R campaign in the harbor of Rotterdam in 2009.
showed that a UV (SO2) imaging camera can be used
to measure SO2 in ship plumes at the Kongsfjord at Ny Ålesund,
Svalbard, and the harbor of Rotterdam.
The global pathways of the ships can be seen in long time-averaged
NO2 measurements from various satellite instruments: from GOME over
the Indian Ocean , from SCIAMACHY onboard ENVISAT over the
Indian Ocean and the Red Sea , and in even more detail with many more visible ship tracks from GOME-2 onboard Metop-A
. The higher resolution of OMI yielded ship tracks in the
Baltic Sea and in all European seas .
The MESMART project
The current study is part of the project MESMART (Measurements of shipping
emissions in the marine troposphere), which is a cooperation between the
University of Bremen (Institute of Environmental Physics, IUP) and the
Federal Maritime and Hydrographic Agency (Bundesamt für Seeschifffahrt
und Hydrographie, BSH), supported by the Helmholtz Zentrum Geesthacht.
It aims to monitor background concentration as well as elevated signals of
gases and particles related to ship emissions with various methods to cover a
wide range of relevant pollutants and their spatial and seasonal distribution
to estimate the influence of ship emissions on the chemistry of the
atmospheric boundary layer (for further information visit
http://www.mesmart.de/).
Aims of this study
The objectives of this study are to assess whether measurements of individual
ship plumes are feasible with a ground-based MAX-DOAS instrument, to compare
MAX-DOAS with co-located in situ measurements, to estimate the contribution
of ships and land-based sources to air pollution in a North Sea coastal
region, to survey the effect of fuel sulfur content regulations on
SO2 concentrations in the marine boundary layer and to analyze the
SO2-to-NO2 ratio in plumes to gain information about plume
chemistry and the sulfur content in shipping fuels.
In the following, first the measurement site is described, followed by a
presentation of the wind statistics and data availability. After this, the
DOAS, the MAX-DOAS
instrumentation and measurement geometry, and the DOAS data analysis
approach used are briefly described. In the next section, selected results
from this study are presented: the measured differential slant column
densities (DSCDs), the retrieved path-averaged volume mixing ratios (VMRs), the
comparison to in situ measurements, the diurnal and weekly variability, the
contribution estimates for ships as well as land-based pollution sources, and
the analysis of SO2-to-NO2 ratios in ship plumes. Finally, a
summary is given and conclusions are drawn.
Measurement site
The measurements presented within this study were taken on Neuwerk, a small
island in the North Sea (German Bight) with the size of about 3 km2
and 33 inhabitants. It is located in the Wadden Sea northwest of Cuxhaven at
the mouth of the river Elbe, roughly 8–9 km off the coast, as can be
seen from the map in Fig. a.
(a) Location of the measurement site Neuwerk in the German
Bight, close to the mouth of the river Elbe. White numbers show the number of ship movements (data
from 2011/2012). Data source: German Federal
Waterways and Shipping Administration . Map source:
http://www.bing.com/maps/ (01.04.2014). (b) Azimuthal viewing
directions of the MAX-DOAS instrument towards the main shipping lane
(highlighted by the magenta line), passing the island in the north at a
distance of 6–7 km. Map source: http://www.freie-tonne.de
(16.07.2013)
The North Sea has one of the highest ship densities in the world
. The majority of ships that arrive in the port of
Hamburg sail through the German Bight and the river Elbe and therefore pass
Neuwerk. Hamburg is among the largest ports worldwide and is together with
Rotterdam and Antwerp one of the three largest ports in Europe, with a
4–5 % increase in container volume in the last years
. Hamburg has also experienced a large increase in
the number of cruise ships, with 176 ship calls in 2014 compared to 25 in
the year 2005 .
Neuwerk is relatively close to the main shipping lane from the North Sea into
the river Elbe. On this highly frequented waterway, nearly all ships to and
from the port of Hamburg and the Kiel canal (connection to the Baltic Sea)
pass the island at a distance of 6–7 km (as shown in
Fig. b). Still close but further away to the
west are the shipping lanes to the Weser River and the ports of Bremerhaven, Bremen and Wilhelmshaven (JadeWeserPort).
Neuwerk is surrounded by the Hamburg Wadden Sea National Park and there are
no significant sources of air pollution on the island itself, making it a
very suitable station for measurements of shipping emissions.
The ship emission measurements presented in this study were carried out with
a MAX-DOAS instrument (see Sect. ), which measures in
multiple azimuthal viewing directions (as shown in
Fig. b), pointing directly towards the
shipping lane while the different viewing azimuth angles cover a large part
of the region.
Several measurement devices, including the two-channel MAX-DOAS instrument
(for UV and visible spectral range), an Airpointer in situ measurement device
(measuring CO2, NOx, SO2 and O3), a high-volume filter sampler and passive samplers, and a weather station and
an AIS (automatic identification system) signal receiver, are positioned on
the main platform of a radar tower at a height of about 30 m (see
Fig. ).
Additional wind data are available from measurements by the Hamburg Port
Authority (HPA) on Neuwerk and the neighboring island Scharhörn. The
seasonal distribution of wind directions on Neuwerk is shown in
Fig. .
In spring and summer, on a high percentage of days the wind blows from the
open North Sea, where shipping emissions are the only significant source of
local air pollution. Consequently, the site provides an optimal opportunity
for measurements of ship emission plumes. In winter, southerly directions
prevail, bringing potentially polluted air masses from the land and blowing
the ship emission plumes away from the measurement site. In addition, as the
MAX-DOAS technique requires daylight and because of the short days and the
low sun resulting in less UV light reaching the surface, measurements are in
general sparse in winter months, especially for SO2, which has its
strong absorption features in the UVB range. This effect can be seen in winter gaps
in Fig. , which presents the data availability for more than 2 years of measurements on Neuwerk.
Measurement techniques, instruments and data analysis
Differential optical absorption spectroscopy (DOAS)
The principle of optical absorption spectroscopy is the attenuation of light
intensity while passing through an absorbing medium, described by the
well-known Lambert–Beer law (also known as the Beer–Lambert–Bouguer law). For the
general case of electromagnetic radiation passing through an anisotropic
medium with a number density n and a temperature- and pressure-dependent
absorption cross section σ of an absorbing species along the light
path s, the measured intensity at wavelength λ is given by
I(s,λ)=I0(λ)⋅exp-∫0sn(s′)⋅σλ,T(s′),p(s′)⋅ds′,
with the intensity of radiation entering the medium I0, temperature T
and pressure p. For measurements in the atmosphere, this simple model has
to be extended by considering multiple trace gases with different
absorption cross sections and light scattering on air molecules (Rayleigh
scattering), aerosol particles or water droplets (Mie scattering) as well as
inelastic scattering by air and trace-gas molecules (Raman scattering). The
latter is responsible for the Ring effect , another
important extinction process, which can be described by a pseudo
cross section.
Radar tower Neuwerk with MAX-DOAS and the in situ measurement
device.
Seasonal wind direction distribution for Neuwerk (data from 4 July
2013 to 27 June 2016). The colored sectors show directions with wind from the
coast (green) and from the open North Sea (blue).
Data availability in the analyzed measurement period between July
2013 and July 2016. From March 2014 on (hatched), there were instrumental
problems with the in situ SO2 instrument resulting in a strong
oscillation of ±0.5 ppb superimposing the data. However, these
data can still be used for the comparison of long-term
averages.
The key and original idea of the DOAS is to separate the optical depth and the absorption cross sections
σ(λ) into a slowly varying function σ0(λ)
that accounts for elastic scattering and broadband absorption structures. It
is described by a low-order polynomial and a rapidly varying part
σ′(λ), the differential cross section, considering the
narrow-band absorption structures . The absorption
cross sections are measured in the laboratory. Neglecting the temperature and
pressure dependence of the absorption cross section, polynomial and
differential cross sections are fitted to the measured optical depth lnI/I0 in the linearized so-called DOAS equation:
lnI(λ)I0(λ)=-∑i=1NSi⋅σi′(λ)-∑pcp⋅λp+r(λ).
The retrieved quantities are the coefficients of the polynomial cp and the
slant column densities Si of the absorbers, which are the integrated
number densities along the light path: Si=∫ni(s)ds. The
fit residual r(λ) contains the remaining optical depth.
MAX-DOAS instrument and viewing geometry
The MAX-DOAS technique
is a passive remote sensing method measuring scattered sunlight. The MAX-DOAS
instrument used in this study comprises of a telescope mounted on a pan-tilt
head, an optical fiber bundle, and two spectrometers (UV and visible spectral
ranges), each equipped with a charge-coupled device (CCD) camera. The telescope that is attached to the
outer sheathing of the circular platform of the Neuwerk radar tower is used
to collect the light from a specific viewing direction and to focus the light
onto the entrance of the optical fiber. The combination of converging lens
and light fiber leads to a field of view of approximately 1∘.
The pan-tilt head allows the instrument to point in different azimuth angles
(panning) as well as different elevation angles (tilting). Dark measurements,
which are needed for the determination of the CCD's dark signal, are
undertaken on a daily basis. Also on a daily basis, line lamp measurements are
taken using an internally mounted HgCd lamp for the wavelength calibration of
the spectra and the determination of the slit function of the instrument. The
spectral resolution, represented by the full width at half maximum (FWHM) of the slit function of the
instrument, is about 0.4 nm for the UV and 0.7 nm for the
visible channel.
The Y-shaped optical light fiber cable is a bundle of 2 × 38
cylindrical, thin and flexible quartz fibers, guiding the light from the
telescope to the two temperature-stabilized spectrometers with attached CCD
detectors inside the weatherproof platform building. Each single fiber has a
diameter of 150 µm and is 20 m long.
The UV and visible spectral range instruments consist of identical Andor Shamrock SR-303i
imaging spectrographs and a grating spectrometer in the Czerny–Turner design with
a focal length of 303 mm. The gratings in use are different: the UV
instrument is equipped with a 1200 groovesmm-1, 300 nm
blaze-angle grating and the visible instrument with a
600 groovesmm-1, 500 nm blaze-angle grating. The UV
instrument covers the wavelength range 304.6–371.7 nm; the visible
spectrometer covers 398.8–536.7 nm. For the UV range, a Princeton NTE/CCD
1340/400-EMB detector with a resolution of 1340 × 400 pixels
and a pixel size of 20 × 20 microns, cooled to
-35 ∘C is used. For the visible spectral range, an Andor
iDus DV420-BU back-illuminated CCD detector with a resolution of
1024 × 255 pixels and a pixel size of
26 × 26 microns, cooled as well to -35 ∘C,
is used.
The measurement geometry for the ground-based MAX-DOAS measurements on
Neuwerk is sketched in Fig. . To measure ship emissions, the
telescope is pointed towards the horizon, collecting light that passed
directly through the emitted ship plumes. A close-in-time zenith sky
measurement is used as a reference so that the retrieved tropospheric
DSCD S′ is the difference of the
slant column densities (SCDs) along paths 1 and 2 in Fig. : S′=S1-S2=Soff-axis-Sreference. The stratospheric light
path and trace-gas absorption is approximately the same for both measurements
and therefore cancels out, which is important for NO2, which is also
present in the stratosphere. This approach also minimizes possible
instrumental artifacts.
Measurement geometry for MAX-DOAS measurements on Neuwerk with
schematic light paths for off-axis (1) and zenith sky reference measurements
(2) for an exemplary solar zenith angle (SZA) of
55∘.
The assumption that the vertical part of the light path cancels out when
taking the difference between off-axis and zenith sky (reference)
measurements
off course is only valid if the NO2 in the air above the instrument,
which is of no interest to us here, is spatially homogeneously distributed.
This is usually the case for stratospheric NO2. If a spatially
limited pollution plume from point sources like ships or power plants is
blown above the radar tower and no plume is in the horizontal light path, the
mentioned assumption is violated, leading to an underestimation of the
derived DSCD. Also, clouds or fog can make the interpretation of the measured
DSCD more challenging due to multiple scattering.
DOAS data analysis and fit settings
The recorded spectra are spectrally calibrated using a daily acquired HgCd
line lamp spectrum, and the dark signal of the CCD detector is corrected using
daily nighttime dark measurements. The logarithm of the ratio of the measured
off-axis (viewing towards the horizon) spectrum and reference (zenith sky)
spectrum gives the optical thickness (also called optical depth). Multiple
(differential) trace-gas absorption cross sections obtained from laboratory
measurements, as well as a low-order polynomial, are then fitted
simultaneously to the optical depth. The retrieved fit parameters are the
SCDs of the various absorbers and the coefficients of the
polynomial. The fits were performed with the software NLIN_D
.
The settings and fitted absorbers vary according to the spectral range used.
For the retrieval of NO2 in the UV range, a fitting window of
338–370 nm was used and for NO2 in the visible range a fitting
window of 425–497 nm was used, both adapted from experiences during the CINDI
and MAD-CAT
(http://joseba.mpch-mainz.mpg.de/mad_cat.htm) intercomparison
campaigns. The oxygen-collision complex O2-O2, often denoted as
O4, is simultaneously retrieved from both NO2 fits. The fit
parameters for the DOAS fit of NO2 and SO2 are summarized in
detail in Table .
For the retrieval of SO2, several different fitting windows between
303 and 325 nm have been used in previous ground-based studies
. This results from
the need to find a compromise between the low light intensity caused by the
strong ozone absorption around 300 nm on the one hand and the rapid
decrease in the differential absorption of SO2 at higher wavelengths
on the other hand, limiting the choice of the fitting window. In this study,
a fitting window of 307.5–317.5 nm was found as the optimal range
for our instrument, which is similar to recommendations in .
The fit parameters for the DOAS fit of SO2 are summarized in detail
in Table .
Only SO2 measurements with a RMS lower than 2.5 × 10-3
have been taken into account for the statistics, filtering out bad fits with
ozone interferences in low light and bad weather conditions.
Under optimal conditions, the RMS of a typical DOAS fit is around 1×10-4 for
NO2 in the visible range, 2×10-4 for NO2 in the UV range and
5×10-4 for SO2. By assuming that an optical density of
twice the RMS can be detected , it is possible to estimate
the detection limit of our instrument regarding the different trace gases.
The differential absorption cross section of NO2 is of the order of
1×10-19 cm2molec-1 and of the order of
2×10-19 cm2molec-1 for SO2. Combining this yields a
NO2 detection limit of around 1×1015 moleccm-2, corresponding to 0.05 pbb in the
visible range and 2×1015 moleccm-2 corresponding to
0.1 pbb in the UV range. The SO2 detection limit lies around
2.5×1016 moleccm-2, corresponding to 0.2 ppb.
The typical absolute fit errors are 2–3×1014 moleccm-2 for NO2 in the visible range,
5–6×1014 moleccm-2 for NO2 in the UV range
and 2×1015 moleccm-2 for SO2, a factor of 5
to 10 smaller than the detection limit.
DOAS fit settings for the retrieval of NO2 and O4 in
the UV and visible spectral ranges.
Parameter
NO2 (UV)
NO2 (visible)
Fitting window
338–370 nm
425–497 nm
Polynomial degree
4
3
Intensity offset
Constant
Constant
Zenith reference
Coinciding zenith measurement*
Coinciding zenith measurement*
SZA range
Up to 85∘ SZA
Up to 85∘ SZA
O3
223 K & 243 K
223 K
NO2
298 K
298 K
O4
293 K
293 K
H2O
–
293 K
HCHO
297 K
–
Ring
SCIATRAN
SCIATRAN
* Interpolation in time between the zenith measurements
directly before and after the off-axis scan.
DOAS fit settings for the retrieval of NO2 and O4 in
the UV and visible spectral ranges.
Parameter
SO2 (UV)
Fitting window
307.5–317.5 nm
Polynomial degree
3
Intensity offset
Constant & slope
Zenith reference
Coinciding zenith measurement*
SZA range
Up to 75∘ SZA
O3
223 K & 243 K
NO2
298 K
SO2
293 K
Ring
SCIATRAN
* Interpolation in time between the zenith measurements directly before and after the off-axis scan.
Retrieval of path-averaged near-surface VMRs from MAX-DOAS SCDs
To measure shipping emissions at our measurement site, our MAX-DOAS telescope
is pointed towards the horizon, where the ships pass our site at a distance
of 6–7 km. Since our instrument has a field of view of approximately
1∘, the lowest usable elevation angle avoiding looking onto the
ground is 0.5∘, providing us with the highest sensitivity to
near-surface pollutants. This is the elevation at which
the highest slant columns are usually measured at our site. To convert a MAX-DOAS trace-gas
column, which is the concentration of the absorber integrated along the
effective light path, into concentrations or VMRs, the length
of this light path has to be known. This effective light path length depends
on the atmospheric visibility, which is limited by scattering on air
molecules as well as aerosols. As described in Sect. , trace-gas absorptions in the higher atmosphere like
stratospheric NO2 nearly cancel out using a close-in-time zenith-sky
reference spectrum. Following this, we can assume that the signal for our
horizontal line of sight is dominated by the horizontal part of the light
path after the last scattering event. As introduced by ,
the length L of this horizontal part of the light path can then be
estimated using the SCD S of the O4 molecule, which
has a well-known number density n=N/V in the atmosphere:
LO4=SO4,horiz-SO4,zenithnO4=SO4′nO4,
with the DSCD S′. The surface number density
of O4 is proportional to the square of the molecular oxygen
concentration and can be easily calculated
from the temperature T and pressure p measured from the radar tower:
nO4=nO22=0.20942⋅nair2withnair=pair⋅NATair⋅R,
with the Avogadro constant NA and the universal gas constant R.
Knowing the path length, it is then possible to calculate the average number
density of our trace-gas x along this horizontal path and the path-averaged
VMR ν:
nx=Sx,horiz-Sx,zenithLO4=Sx′LO4and thusνx=nxnair.
This O4 scaling in principle takes into account the actual light path
and its variation with aerosol loading and also needs no assumption on the
typical mixing layer height, therefore overcoming the disadvantages of a
simple geometric approximation.
However, when the atmospheric profile of the investigated trace-gas x has a
shape that differs from that of the proxy O4, systematic errors are
introduced, as has been shown by and in
extensive and comprehensive radiative transfer model (RTM) simulations.
Pollutants like NO2 and SO2 have a profile shape very
different from O4. They are emitted close to the ground (e.g., from
ships), have high concentrations in low-altitude layers and tend to decrease
very rapidly with height above the boundary layer. They are often
approximated as box profiles, while the O4 concentration simply
decreases exponentially with altitude. This difference in profile shapes
violates the basic assumption that the O4 DSCD is a good proxy for
the light path through the NO2 and SO2 layers. The resulting
near-surface VMRs will not be representative for the amount
of trace gases directly at the surface, but for some kind of average over a
certain height range in the boundary layer.
The studies like and use correction
factors from radiative transfer calculations to account for this. These
correction factors depend on the amount of aerosol present in the
atmosphere, often described by the aerosol optical density (AOD); the solar
zenith angle (SZA) as well as the relative solar azimuth angle; the
height of the pollutant box profile; and the extent and vertical position of
the aerosol layer in relation to this box profile . The
strong dependence of the correction factors on the height of the box profile
for trace-gas layer heights of less than 1 km makes it necessary for
the application of the suggested parameterization method to have additional
knowledge about the trace-gas layer height, ideally from measurements (e.g.,
lidar) or otherwise from estimations. The use of this method for low boundary
layer heights below 500 m without knowing the actual height is not
recommended by the authors .
At our measurement site, no additional knowledge (measurements) about the
height of the NO2 and SO2 layers is available and the trace-gas layer heights are typically around 200–300 m. A comparison of
the uncorrected MAX-DOAS VMRs retrieved with the upper equations to our
simultaneous in situ measurements (see Sect. )
confirms the need for a correction factor but also shows that the scaling
factor needed changes from day to day as well as during the course of the
day. This indicates that the NO2 and SO2 layer heights are
very variable, depending on wind speed, wind direction, atmospheric
conditions and chemistry. The lack of comparability between both measurement
techniques and geometries, which is further discussed in Sect. , prevents us from estimating diurnally varying
correction factors.
The non-consideration of these scaling factors will lead to a systematic
overestimation of the effective horizontal path length and therefore to a
systematic underestimation of MAX-DOAS VMRs, by up to a factor of 3
.
In summary, a detailed radiative transfer study for the determination of the
right correction factors is out of the scope of this study, which focuses on the
statistic evaluation of a 3-year dataset of shipping emission
measurements in the German Bight. Therefore, when the following MAX-DOAS
VMRs are shown, it has to be kept in mind that these are uncorrected VMRs
obtained by the formulas above.
This approach has been applied successfully by and
for measurements in urban polluted air masses over Mexico
City and the city of Hefei (China) using MAX-DOAS measurements at
1∘ and 3∘ elevation and only
at
1∘ elevation , respectively.
applied this approach to measurements at a high mountain site at the
Izaña Atmospheric Observatory on Tenerife (Canary Islands),
at Zugspitze (Germany) and at Pico Espejo (Venezuela). Due
to the low aerosol amounts at such heights, the latter two studies applied the
approach without using correction factors. The fact that our instrument is
located on a radar tower at a height of about 30 m above totally flat
surroundings (the German Wadden Sea) allows an unblocked view to the horizon
in all feasible azimuthal viewing directions. This led to the idea of trying
to apply this approach to our shipping emission measurements on Neuwerk.
Specifications of the Airpointer in situ
device: measured trace gases, corresponding measuring techniques, measuring
ranges and detection limits (source: MLU-Recordum (manufacturer),
http://mlu.eu/recordum-airpointer/).
Trace gas
CO2
O3
NO, NO2
SO2
Measuring
Nondispersive
UV absorption
NO chemi-
UV fluorescence
technique
IR spectroscopy
(EN 14625)
luminescence
(EN 14212)
LI-COR LI820
(EN 14211)
Detection limit
1 ppm
0.5 ppb
0.4 ppb
0.25 ppb
Measuring range
up to 20 000 ppm
up to 200 ppm
up to 20 ppm
up to 10 ppm
Time resolution
1 s
< 30 s
< 60 s
< 90 s
Since the O4 DSCD is retrieved simultaneously to NO2 in both
the UV and visible DOAS fits for NO2, this approach can be applied to
NO2 retrieved in both fitting ranges. The approach can also be
applied to SO2, although the difference of light paths due to the
different fitting windows in the UV for O4 (NO2) and
SO2 introduces an uncertainty that has to be accounted for.
derived an empirical formula from RTM calculations for a
variety of aerosol scenarios to convert the path length at 310 nm
from the path length at the O4 absorption at 360 nm:
L310=0.136+0.897×L360-0.023×L3602,
in which L310 and L360 are given in kilometers. This formula was also applied
to our measurements to correct the light path length for the SO2
fitting window. Although this formula has been calculated for polluted sites,
the authors state that the deviations for other sites with different
conditions are expected to be small .
Using Eqs. –, several problems can arise
from the division by the DSCD of O4. For
example, if the O4 DSCD is negative, which can happen at low
signal-to-noise-ratio DOAS fits (e.g., under bad weather conditions), the
resulting path length will be negative. If at the same time the trace-gas
DSCD is positive, then the trace-gas VMR will be negative as
well, a nonphysical result. However, even when there is no NO2 or
SO2, there is still some noise and therefore the retrieved VMRs are
not exactly zero but scatter around zero. Thus, slightly negative values have
to be included when averaging over time to avoid creating a systematic bias.
If, however, the O4 DSCD is close to zero, the path length
will be very small, leading to extremely high (positive or negative) mixing
ratios, which are also unrealistic. To address both problems, measurements
with negative or small retrieved horizontal path lengths are discarded. For
the measurements on Neuwerk, with respect to the characteristics of the
measurement site, a minimum path length of 5 km seems to be a
reasonable limit. This value provides the best compromise between the number
of rejected bad measurements and the total number of remaining measurements
for NO2 in the UV and visible ranges as well as for SO2. For statistics
on DSCDs, however, no such filtering
is applied since negative values are not unphysical in this case and just
mean that there is more trace-gas absorption in the reference measurement
than in the off-axis measurement.
In situ instrumentation
In addition to the MAX-DOAS instrument, in situ observations are also taken,
using the Airpointer, a commercially available system that combines four
different instruments in a compact, air-conditioned housing. The manufacturer
is Recordum (Austria), distributed by MLU
(http://mlu.eu/recordum-airpointer/). The Airpointer device measures
carbon dioxide (CO2), nitrogen oxides (NOx=NO+NO2), sulfur dioxide (SO2) and ozone (O3) using
standard procedures. Table shows more detailed
information about the different included instruments, their measurement
methods, precision, and time resolution.
In this study the in situ 1 min means of all compounds were used.
NO2 itself is not directly measured but calculated internally by
subtracting the measured NO from the measured NOx
concentration.
NO2 (UV and visible ranges) and SO2 differential slant
column densities measured at 0.5∘ elevation and the
-25∘ viewing azimuth angle (approximately NNW direction) on
Neuwerk on 23 July 2014. The excerpt on the right shows the NO2 (vis) measurements in different azimuth viewing
directions for one example peak.
Results
Measured slant column densities of NO2 and SO2
In this study, 3 years of continuous MAX-DOAS measurements on Neuwerk
have been evaluated. Figure shows for one example day in summer
2014 the measured DSCDs of NO2 in the UV
and visible spectral ranges as well as of SO2 for the
0.5∘ elevation angle (viewing to the horizon) and the
-25 ∘ azimuth angle (approximately NNW direction; see
Fig. ). Sharp peaks in the curves originate
from ship emission plumes passing the line of sight of the instrument. On
this day, elevated levels of NO2 were measured in the morning,
corresponding to a polluted air mass coming from land, which appears as an
enhanced, slowly varying NO2 background signal below the peaks. The
systematic difference between the NO2 in the UV range (red curve) and the
NO2 in the visible range (blue curve) emerges from the longer light path in
the visible range due to stronger Rayleigh scattering in the UV range (wavelength
dependence ∝λ-4). This is further investigated in Sect. below.
By comparing SO2 (green curve) with NO2 (red and blue
curves), it can be seen that for many of the NO2 peaks there is a
corresponding and simultaneous SO2 peak, but not for all of them.
This indicates a varying sulfur content in the fuel of the measured ships.
Fuel with a higher sulfur content leads to higher SO2 emissions (see
also Sect. ).
By comparing measurements in different azimuthal viewing directions, the
movement direction of the ship (and its plume) can be easily distinguished.
The zoom in on the right of Fig. shows the visible range
NO2
measurements in different azimuth directions for one example peak from the
time series shown on the left. The color-coded viewing directions (see also
Fig. ) are sketched schematically below. From
the measurements it can be seen that the emitted plume was consecutively
measured in all directions at different times. It was first measured in the
easternmost viewing directions and at last in the westernmost direction,
indicating that the ship and its plume moved from east to west.
For the identification of sources for air pollution on Neuwerk, the wind
direction distribution for the DSCDs of
NO2 and SO2 measured in 2013 and 2014 is plotted for four
different elevation angles (0.5, 2.5, 4.5 and 30.5∘) in
Fig. . When the wind comes from the open North Sea (blue
shaded sector) the measured NO2 and SO2 DSCDs are clearly
lower than for other directions, for which the wind comes from the coast
(green and yellow shaded sectors) and blows land-based air pollution to the
island. The wind direction dependence is more or less similar for both trace
gases but with a higher fraction of ship-related signals in the overall
SO2 columns. The values are especially high when the wind comes
from the cities of Cuxhaven (ESE direction) and Bremerhaven (SSE) for both
NO2 and SO2.
Overlayed wind roses for different elevation angles showing the wind
direction distribution of the UV NO2 (a) and SO2
(a) differential slant column densities measured in the main viewing
direction at 0.5, 2.5, 4.5 and 30.5∘ elevation in the years 2013
and 2014. The wind roses are plotted on top of each other, i. e. the highest
values were measured at the lowest elevation angle (blue bars). The colored
sectors show directions with wind from land (green), open North Sea (blue)
and mixed origin (yellow).
NO2 (UV and visible) and SO2 path-averaged volume
mixing ratios measured at an 0.5∘ elevation angle and
-25∘ viewing azimuth angle (approximately NNW direction) on
Neuwerk on 23 July 2014.
Elevation angle sequences of slant columns (i.e., vertical scanning) contain
information on the vertical distribution of trace gases. For lower-elevation
angles, the measured trace-gas slant columns for tropospheric absorbers are
usually higher because of the longer light path in the boundary layer.
As expected, higher elevations show on average lower DSCDs due to the shorter
light path in the boundary layer. The highest NO2 and SO2
DSCDs in the lowest elevation angle (0.5∘, blue bars) in relation
to DSCDs in higher elevations are measured especially for wind from all
northern directions, in a sector ranging from WSW to ESE. These directions
coincide with the course of the main shipping lane coming from the WSW
direction (the English Channel, the Netherlands, East Frisian Islands),
passing the island in the north and running close to the city of Cuxhaven
(ESE direction) into the river Elbe. This indicates that the enhanced columns
in the 0.5∘ elevation angle are pollution emitted from ships in a
near-surface layer.
For southerly wind directions no major shipping lane is in the direct
surroundings and land-based pollution sources dominate. The average DSCDs at
0.5 and 2.5∘ elevation are nearly the same for both NO2
and SO2, indicating that the pollution is located higher up in the
troposphere.
(a) Scatter plot: NO2 slant column density
retrieved in the visible vs. UV spectral ranges measured in all azimuth angles at
0.5∘ elevation for solar zenith angles smaller than
75∘. The parameters derived from the linear fit by orthogonal
distance regression (Deming regression) are also shown.
(b) Histogram of the ratio of the two NO2 slant column
densities (visible / UV). Panel (c) is the same as panel (a) but for volume mixing
ratios. (d) Histogram of the ratio of the two NO2 volume
mixing ratios (visible / UV).
Volume mixing ratios of NO2 and SO2
For the example day presented in Fig. , the path-averaged VMRs retrieved with the approach presented in Sect. are shown in Fig. .
From the mathematics of the approach, one would expect a good agreement
between the NO2 VMRs retrieved in the UV and visible ranges if
NO2 is well mixed in the boundary layer since averaging constant
values over different paths should give equal mean values. In the figure, in
fact, one can see a very good agreement between both NO2 VMRs, in particular for situations characterized by background pollution.
Although the light path in the visible spectral range is clearly longer than
in the UV range, for all the peaks shown here the UV instrument measured a higher
path-averaged VMR. The reason for that is spatial inhomogeneities along the
line of sight.
If NO2 is not distributed homogeneously along the light path, which
is the case in the presence of individual ship exhaust plumes, one can expect
different values for the means over the two light paths as they probe
different parts of the NO2 field. Such differences can be identified
in the figure by looking at the peaks.
The light path in the visible spectral range is longer than in the UV range because
of more intensive Rayleigh scattering in the UV range. The difference between UV
and visible peak values depends on the exact location of the plume within the
light paths.
A short distance of the plume to the instrument and its complete coverage by
the shorter UV path leads to higher values in the UV since the part of the
light path probing the higher NO2 values has a larger relative
contribution to the signal than for the longer visible path.
If the plume is further away from the instrument and only in the visible path
or close to the UV scattering point, one will retrieve a higher VMR in the visible range. This relationship contains information on the
horizontal distribution of the absorber and will be further investigated in a
second paper.
NO2 differential slant column densities, AIS and wind data
for Neuwerk on 9 July 2014. (a) NO2 DSCD in
0.5∘ elevation for the 35∘ azimuth viewing
direction. (b) Vertical bars indicating that a ship is in the
line of sight of the instrument. Solid bars: ship moves from left to right
(west to east); dashed bars: vice versa; colors represent ship length.
(c) Wind speed and direction measured on Scharhörn
(HPA).
Statistical evaluation of UV and visible NO2 data
To quantitatively investigate the relationship between the NO2 SCDs measured simultaneously in the UV and visible spectral
ranges, all single pairs of DSCD measurements with an RMS better than 1×10-3 are plotted onto a scatter plot, shown in Fig. a.
As can be seen from the figure, NO2 DSCDs in the UV and visible ranges are
strongly positively correlated with a Pearson correlation coefficient of
0.983. Because of the difference in the horizontal light path lengths in both
spectral regions (due to more intense Rayleigh scattering in the UV range), the
slope of the regression line is 1.30 corresponding to a 30 % longer
light path in the visible range. The intercept of the regression line is small.
Figure b shows a histogram of the ratios between both
SCDs. The distribution peaks for ratios of 1.3, in good
agreement with the retrieved slope from the scatter plot.
When converting the SCDs to mixing ratios using the
O4 scaling, the dependence on light path should be removed and
quantitative agreement is expected between the UV and visible VMRs. A scatter
plot for the horizontal path-averaged VMRs is shown in Fig. c. It is clearly visible that the points scatter
symmetrically along the 1, 1 identity line. Comparing this plot with the
plot in panel (a) shows that the difference in light path lengths is in fact
corrected for by the O4 scaling approach. The slope of the regression
line is close to unity and the intercept is very small. The Pearson
correlation coefficient has further increased to 0.984. The histogram
(Fig. d) peaks at 1.0.
As discussed above, differences are still expected not only as a result of
measurement uncertainties but also due to different averaging volumes in case
of inhomogeneous NO2 distributions (which is especially the case for
ship plumes under certain wind directions). For the horizontal light path
lengths, a mean value of 9.3 km with a standard deviation of
2.3 km was retrieved in the UV range, and a mean value of 12.9 km
with a standard deviation of 4.5 km was retrieved in the visible range. On
days with optimal measurement conditions (clear sky days), typical horizontal
light paths are around 10 km in the UV range and 15 km in the
visible spectral range.
Allocation of ship emission peaks to ships using wind and AIS data
The detailed information on passing ships transmitted via the
AIS and the
acquired weather and wind data can be used to allocate the measured pollutant
peaks to individual ships.
Measurements from 9 July 2014 are shown in Fig. .
Panel (a) shows the MAX-DOAS DSCD of
NO2. Panel (b) includes various information about passing ships: the
vertical bars indicate when a ship was in the line of sight of the MAX-DOAS
instrument. Solid bars represent ships coming from the left and going to the
right (from west to east, i.e., sailing into the river Elbe), dashed bars vice
versa. The colors of the bars indicate the ship length, with small ships
shown in blue and very large ships (> 350 m) in red. Panel (c)
displays the wind speed and direction.
On this day, the wind came from northern directions, directly from the
shipping lane, with moderate wind speeds of 10 to 35 kmh-1, resulting
in low background pollution values (1–2×1016 moleccm-2)
as well as sharp and distinct ship emission peaks (up to 1.2×1017 moleccm-2) of NO2. By comparing the ship emission
peak positions to the vertical bars (representing times when ships crossed
the MAX-DOAS line of sight) in the schematic representation below, it can be
seen that most of the peaks can be allocated to individual ships. In some
cases, when two or more ships simultaneously cross the line of sight, the
single contributions cannot be separated. Large ships (orange and red bars)
tend to exhaust more NO2, while the contribution of small ships
(length < 30 m) represented by the dark blue bars is usually not
measurable.
MAX-DOAS and in situ NO2 volume mixing ratio, AIS and wind
data on 9 July 2014: (a) MAX-DOAS (visible) and in situ
NO2 VMRs. (b) Vertical bars indicate that a ship is in the
line of sight of the instrument. Solid bars: ship moves from left to right
(west to east); dashed bars: vice versa; colors represent ship length.
(c) Wind speed and direction measured on Scharhörn
(HPA).
Comparison of MAX-DOAS VMR to in situ measurements
The fact that our measurement site is also equipped with an in situ device
(see Sect. for a description), makes it
possible to compare the MAX-DOAS VMRs of NO2 and SO2 to our
simultaneous in situ measurements. The differences of both measurement
techniques need to be considered for such a comparison: MAX-DOAS averages
over a long horizontal light path, while the in situ device measures at a
single location inside the plume. Since ship plumes usually never cover the
whole light path but rather a small fraction of it, very high concentration
peaks are usually underestimated in the MAX-DOAS VMR.
Figure shows the horizontal path-averaged NO2 VMR retrieved from the DSCDs shown in
Fig. as well as the in situ NO2 VMR
(panel a) in combination with ship data (panel b) and wind data (panel c).
Ship emission peaks measured by the in situ instrument are both higher and
broader than the corresponding MAX-DOAS peaks, leading to a considerably
larger integrated peak area, showing the systematic underestimation of the
NO2 concentrations inside ship plumes by the MAX-DOAS instrument due
to the averaging along the horizontal light path.
Normally, a time shift between MAX-DOAS and in situ peaks exists, which is
due to the long distance of about 6–7 km to the shipping lane that
the plumes have to travel until they reach the radar tower. This time shift
depends on the wind velocity and gets smaller for higher wind speeds. In the
figure, this dependency can be seen when comparing the magnitude of the time
delay for measurements in the morning (low wind speeds) and evening (higher
wind speeds) This travel time also explains the broader peaks in the in situ
measurements since the emitted plume spreads and dilutes on its way to the
radar tower.
However, if the pollution is horizontally well-mixed in the measured air
mass, which is approximately the case for background pollution coming from
the coast but not for ship plumes, MAX-DOAS and the in situ instrument should in
principle measure the same values. However, as discussed in Sect. , correction factors need to be applied to the MAX-DOAS VMRs
to account for the different profile shapes of O4 and the
investigated pollutants NO2 and SO2. However, in our case, correction factors cannot
be determined because no measurements of the height of the NO2 and
SO2 layers exist. The uncorrected VMRs shown here can be strongly
underestimated (up to a factor of 3) because they have been calculated with
an overestimated path length. This is the case for background pollution as
well as shipping emission measurements.
Because of the lack of comparability between both instruments for individual
measurements, for a meaningful comparison and the computation of a
correlation coefficient at this measurement site, an averaging over longer
time spans was applied to reduce the impact of the differences between both
measurement methods. The averaging over large horizontal distances in the MAX-DOAS
measurements should cancel out on temporal average when comparing to in situ measurements.
Figure in panel (a) shows 3 months of daily mean NO2
VMRs from the in situ and MAX-DOAS UV instruments in summer 2014. Due to instrumental problems with the in situ SO2
device in 2014
(see Fig. ), panel (b) shows 6 weeks of SO2 daily mean VMRs from summer
2013. To have comparable conditions, for the in situ instrument all
measurements between the start of the MAX-DOAS measurements in the morning
(with sunrise) and the end of measurements in the evening (with sunset) were averaged. The shaded areas show the corresponding standard deviation and
indicate the variability during the single days.
The long gap in the SO2 time series was caused by a power outage.
Comparison of MAX-DOAS (UV) and in situ daily mean VMRs of
NO2 (a) during summer 2014 and SO2
(b) during summer 2013. Shaded areas show the standard deviation for
each daily mean value.
It is clearly visible that the in situ NO2 VMRs are systematically
higher than the uncorrected MAX-DOAS VMRs. The scaling factors that would be
needed to bring both time series into agreement differ from day to day. A
closer look into the individual days shows that these scaling factors also
vary over the course of the day, even when wind direction and speed do not
change. The scatter plot for this time series of NO2 measurements in
Fig. a shows a good correlation between MAX-DOAS and
in situ daily means, but it also shows a slope strongly deviating from 1 and also some
scatter.
The most important reason for the systematic differences is certainly the
non-consideration of the correction factors arising from the different
profile shapes of O4 and NO2, leading to a systematic
underestimation of the VMRs from the MAX-DOAS instrument (see Sect. for a more detailed discussion). However, also light
dilution, i.e., light scattered into the line of sight between the
instrument and the trace-gas plume , might also play a role
in reducing the measured off-axis SCDs.
For SO2, the daily mean VMRs from MAX-DOAS and the in situ instrument in
Fig. b show a much better agreement. The scatter plot in
Fig. b confirms this with a slope much closer to unity, but
more scatter around the fitted line.
Scatter plot of (a) NO2 VMR and
(b) SO2 VMR from MAX-DOAS vs. in situ. For NO2, daily
means from summer 2014 are shown, and for SO2, daily means from summer 2013 are
shown. For the MAX-DOAS instrument, to get a better statistic, all
measurements in all azimuth viewing directions have been averaged. For the
in situ instrument, the mean of all measurements during the daily MAX-DOAS
measurement periods (sunrise till sunset) has been taken. The linear fits
were calculated with orthogonal distance regression (Deming regression);
parameters are shown in the figures.
The difference in scaling factors for NO2 and SO2 can be
attributed to plume chemistry. During combustion, mainly nitric oxide
(NO) is produced. This has to be converted to NO2 (through
reaction with tropospheric ozone) before it can be measured by the MAX-DOAS
instrument. Since the MAX-DOAS instrument sees the ship plumes in an earlier
state, the fraction of NO2 should be lower than in the in situ
measurements, explaining at least a part of the difference.
Although MAX-DOAS and in situ VMRs show systematic deviations in the absolute
values, a very good agreement of the shape (the course) of the curves is
found for NO2 as well as SO2. This illustrates that MAX-DOAS
can determine day-to-day trends as in situ measurements, even though no
correction factors have been applied.
Diurnal and weekly variability in NO2
Although our measurement station is located on a small island in the German
Bight close to the mouths of the Elbe and Weser rivers, our measurements are
strongly influenced by air pollution from traffic and industry on land,
depending on the prevailing wind direction. As can be seen from
Figs. a and , wind coming from
northeasterly, easterly, southerly and southwesterly directions will blow
polluted air masses from the German North Sea coast and hinterland to our
site. In Fig. the average diurnal variation in the measured
NO2 VMRs is shown as hourly mean values. Solid curves
show the respective curve for all measurements (with all wind directions);
dashed lines show the subset of measurements with wind coming only from the
open North Sea with no coastal background pollution. Looking at the diurnal
variation in all measurements, the typical daily cycle for
road-traffic-influenced air masses with enhanced values in the morning and in
the late afternoon during rush hour can be seen. If we restrain the data to
periods with wind from the open North Sea (dashed curves), this diurnal cycle
vanishes and values are more or less constant over the day and are also considerably
lower. This result is in accordance with the expectations that the amount of
ship traffic should be almost independent from the time of day.
Average diurnal cycle of MAX-DOAS (UV and visible) and in situ
NO2 volume mixing ratios for all measurements (solid lines) and for a
subset of measurements with wind from the open North Sea (dashed lines). For
a better visual comparability, the in situ values are scaled by a factor of
0.4.
The mean NO2 volume mixing ratios for each weekday shown in
Fig. again illustrate the influence of land-based road traffic.
If we consider the whole time series (solid lines), the lowest values are
measured on Sundays, when road traffic is less intense. There is only a small
weekly cycle for air masses coming from the open North Sea (dashed lines).
Measurements are more or less constant and again considerably lower. Such a
weekly cycle for NO2 in polluted regions has been observed and
discussed several times before, for example in ,
, and .
Average weekly cycle of MAX-DOAS (UV and visible) and in situ
NO2 volume mixing ratios for all measurements (solid lines) and for a
subset of measurements with wind from the open North Sea (dashed lines). For
a better visual comparability, the in situ values are scaled by a factor of
0.4.
It is also remarkable that except for a scaling factor of approximately 0.4,
the shape of the diurnal and weekly cycles retrieved from MAX-DOAS and in situ
measurements agrees very well for both instruments.
Dependence of NO2 and SO2 pollution levels on wind direction
As already mentioned in Sect. , on 1 January 2015 the sulfur content of marine fuels allowed inside the North and Baltic
seas ECAs was substantially decreased from 1.0 to
0.1 %. Therefore, one would expect lower sulfur dioxide
(SO2) values in 2015 compared to the years before, especially when
the wind is blowing from the open North Sea, where shipping emissions are the
only source of SO2. This expectation is confirmed by the
measurements. In the data since 2015, no distinct ship emission peaks have
been
visible anymore (for an example day see Sect. below). For a
more detailed analysis, mean values over the whole time series before and
after 1 January 2015 have been investigated, separated according to the
prevailing wind direction.
Excluded from the time series were 2 days of SO2 measurements (20 and 30 October 2014) that showed very
high values over several hours.
Comparisons with our simultaneous in situ measurements and measurements from
the German Umweltbundesamt (German Federal Environmental Agency) at the coast of the North Sea on Westerland/Sylt
and at the coast of the Baltic Sea on the island Zingst that show a similar
behavior, as well as HYSPLIT backward trajectories, suggest that on both days
SO2 plumes of the Icelandic volcano Bárðarbunga influenced
the measurements in northern Germany.
Figure shows the wind direction distribution of the mean
path-averaged VMRs of NO2 and SO2 for all
measurements before and after the change in fuel sulfur limit regulations.
Wind direction distribution of the measured NO2 (a)
and SO2 (b) volume mixing ratios at 0.5∘
elevation before and after the change in fuel sulfur limit regulations on
1 January 2015. The colored sectors show directions with wind mainly from
land (green), open North Sea (blue) and mixed origin (yellow).
For SO2, a significant decrease is found, particularly for wind
directions from west to north with wind from the open North Sea. For this
sector, values in 2015 are close to zero. This shows that the new and more
restrictive fuel sulfur content limits lead to a clear improvement in coastal
air quality. For wind directions with mainly land-based sources, no or only a
small decrease is observed.
The typical average SO2 concentrations measured by the German Federal
Environmental Agency in 2016 for rural stations in
northern Germany are around 0.5–1 µgm-3, corresponding to
0.2–0.4 ppb (conversion factor: 1 ppb =^
2.62 µgm-3 for SO2). Measurements in cities and
especially close to industrial areas show higher values. Bremerhaven, which
is the station closest to our instrument, has a mean concentration of
1.77 µgm-3, corresponding to 0.67 ppb. The reported
values for rural stations are in good agreement with our measurements of
0.3–0.4 ppb for wind directions with mainly land-based pollution
sources (green sector in Fig. b) since January 2015.
For NO2, however, both the directional distribution and the
absolute values are nearly identical for both time periods, implying no
considerable changes in NOx emissions. This result meets the
expectations since no NOx emission limits have been enforced
up to now for the North and Baltic seas ECA.
Contributions of ships vs. land-based pollution sources on coastal air quality on Neuwerk
The distribution of measured NO2 and SO2 VMRs
depending on the wind direction shown in Fig. can be used to
estimate the contributions of ships and land-based sources to coastal air
pollution levels. To trade ship emissions off against land-based emissions
(e.g., industry, road transport), two representative sectors of wind
directions have been chosen, both 90∘ wide: a northwesterly sector
(258.75∘ to 348.75∘) with wind from the open North
Sea and ships as the only local source of air pollution and a southeasterly
sector (123.75∘ to 213.75∘) with wind mainly coming
from land and almost no ship traffic. Air masses brought by wind from the
other directions, for example from the mouth of the river Elbe to the east of
Neuwerk, can contain emissions from land-based pollution sources as well as
ship emissions. These remaining directions will be referred to as “mixed”. It is now assumed that trace-gas concentrations measured during
periods with wind from one of these sectors have their source in the
according sector. For getting a good statistic, measurements in all azimuth
angles have been included. Figure shows the results in several
pie charts.
Contributions of ships and land-based pollution sources to measured
NO2 and SO2 levels on Neuwerk: (a1), (b1) and
(c1).
Percentage of measurements with wind coming mainly from land (green), only
from sea (blue) and from directions with mixed contributions (yellow) for all
NO2 data (a1) and SO2 data before (b1) and after the
change in fuel sulfur content limits (c1). (a2), (b2) and
(c2): contributions to the integrated volume mixing ratios of NO2
(a2) and SO2 (b2, c2) from the source regions as a percentage. (a3), (b3) and (c3): contributions to the integrated volume
mixing ratios when considering only the land and sea sectors. It can clearly
be seen that the lower fuel sulfur limit has led to a strong decrease in the
SO2 contribution from shipping since 2015.
For both NO2 and SO2, more than half (around
50–60 %) of all measurements have been taken while wind was coming
from either the assigned sea or land sector. This implies that not only a
small sample but also the majority of measurements can be used for the estimation
of source contributions, making the assumption of using these sectors as
representative samples for ships and land-based source regions a reasonable
approximation. There are differences in the time series of NO2 and
SO2 coming from the fact that the SO2 fit delivers realistic
values only up to 75∘ SZA and the NO2 was
fitted until 85∘ SZA, leading to less measurements for
SO2 than for NO2, which is especially pronounced in winter.
Despite this, the general distribution pattern of wind direction frequency
for NO2 and SO2 is quite similar, with wind coming from the
sea 32–42 % of the time and from the land sector 18–24 %
of the time.
For NO2 (upper row in Fig. ), more than half of the total
NO2 measured on Neuwerk can be attributed to wind from either of both
sectors, with 21 % coming from ships and 31 % coming from
land.
If we consider only the two sectors, for which we can identify the primary
sources and take theses as representative, we can say that 40 % of
the NO2 on Neuwerk comes from shipping emissions, but with
60 %, the majority, coming from land. One reason for this is that
the island Neuwerk is relatively close to the coastline (around
10 km) and is obviously still impacted by polluted air masses from
land, which has also been observed in the diurnal and weekly cycle analysis
shown in Figs. and . This might also show
that in coastal regions in Germany land-based sources like road traffic and
industry are, despite the heavy ship traffic, the strongest source of air
pollution and ship emissions come in second.
For SO2 the whole time series of measurements from 2013 to 2016 was
divided into two periods of nearly the same length: The first period is 2013
and 2014, which was before the introduction of stricter sulfur limits for
maritime fuels in the North Sea on 1 January 2015. The according statistics
for this period are shown in the middle row in Fig. . The second
time period, after the change in fuel sulfur limits, includes all
measurements from 2015 and 2016, with the corresponding pie plots in the
bottom row of Fig. .
Before the change, 32 % of the measurements were taken when the wind
was blowing from the sea sector and about 24 % when it was blowing
from the dedicated land sector. After the change, the wind came a bit
more often from the sea (42 %) and less often from land (18 %),
but in general the situation was quite similar.
The contributions of the three sectors (land, sea and mixed) to the total
integrated SO2 with 21 % coming from ships, 30 %
from land and 49 % from the mixed sector for the time before the
change in sulfur limits are very similar to those of NO2. After
the change, the contribution from the sea sector shrinks significantly from
21 to 7 %, while the relative contribution from the land sector
increased from 29 to 44 %, with the contribution from the mixed sector
staying the same at around 49 %. This increase for the land source
sector is only a relative increase while the absolute contributions slightly
decreased, as can be seen from Fig. . The relative contribution
from the sea sector (shipping-only source) decreased by a factor of 3, while
the absolute contribution from this sector decreased by a factor of 8, even
though the wind was coincidentally blowing more often from the open sea in
this time period.
The overall mean SO2 VMR before 2015 is
0.39 ± 0.45 ppb (mean ± standard deviation). For 2015 and
2016, the total mean value declined by two-thirds to
0.15 ± 0.34 ppb (mean ± standard deviation).
These results clearly show that the stricter limitations on the fuel sulfur
content are working and significantly improved air quality in the North Sea
coastal regions with respect to SO2. This is in good agreement with
other studies such as , who found that around
95 % of the ships are complying with the new limits. This implies that
the cheaper high-sulfur heavy oil fuel is no longer in use in the region of
measurement.
If again the two selected sectors are considered as representative for both
land and sea sources, the shares of the contributions in the sea / land ratio changed
from 42 % : 58 % (which is very similar to those of
NO2) to 14 % : 86 %. This again shows that since
2015, the vast majority of SO2 emissions can be attributed to land
sources and ships play only a negligible role. Prior to 2015, shipping
emissions were a significant source for SO2 in coastal regions.
One aspect that is neglected in the source allocation to wind sectors is
that in situations with good visibility and low wind speeds even for wind
coming from southern directions, the MAX-DOAS instrument can measure ship
emission peaks in the north of the island, but they are typically very small.
Compared to the often strongly enhanced background pollution in cases with
southerly winds, the contribution from these peaks is negligible (around
1–3 %) but certainly leads to a small overestimation of land
sources.
Determination of SO2-to-NO2 ratios in ship plumes
Monitoring of emissions from single ships requires the analysis of
individual plume peaks in the NO2 and SO2 datasets. It is
difficult to derive the absolute amounts (e.g., in mass units) of the emitted
gaseous pollutants using our MAX-DOAS remote sensing technique. The height and
width of the measured peaks does not only depend on the amount of emitted
pollutants but also strongly on the geometry, while getting the highest
values when measuring alongside the plumes and much smaller values when the
plume moves orthogonal to the line of sight of our instrument. In addition, the time span between emission and measurement also plays a role
in the height of the NO2 peaks because of NO-to-NO2 titration.
To determine the mixing ratio inside the plumes, additional information on
the length of the light path inside the plume would be needed, which cannot
be retrieved from our measurements. This means that without further
assumptions, we cannot determine emission factors for the emitted gases (e.g
for emission inventories, which are used as input for model simulations).
Although emission factors cannot be measured by MAX-DOAS directly, the
NO2 and SO2 signals yield the ratio of both. These ratios can
then be compared to ratios of emission factors reported in other studies as
well as measurements at other sites or with different instruments, bearing in
mind possible deviations due to NO-to-NO2 titration.
By comparing SO2-to-NO2 ratios from different ships, it is
possible to roughly distinguish whether a ship is using fuel with high or low
sulfur content (giving a high or low SO2-to-NO2 ratio).
Beecken and Mellqvist from Chalmers University of Technology (Sweden) use this relationship
for airborne DOAS measurements of ship exhaust plumes on an operational basis
in the CompMon project (compliance monitoring pilot for MARPOL Annex VI)
. Following the ships and measuring across the stack gas plume
they can discriminate between ships with a low (0.1 %) and high (1 %)
fuel sulfur content with a probability of 80–90 %
.
From the spectra measured by our MAX-DOAS UV instrument, both SO2 and
NO2 columns can be retrieved at once. The two columns are measured at
the exact same time along nearly the same light path. To calculate
SO2-to-NO2 ratios for the measured pollutant peaks, the
ratio of the measured DSCDs simply has to be computed.
In order to separate ship-related signals from smooth background pollution,
first a running median filter was applied to the time series of NO2
and SO2 measurements with a large kernel size (e.g., over 21 points).
If too many broad peaks are contained in the time series, this is not
sufficient and the resulting median might be systematically higher than the
actual baseline. In this case, a running median with a smaller kernel size (e.g., 5) was
applied to the values in the lower 50 %
quantile again, giving a good approximation of the real baseline.
In the next step, this baseline is subtracted from the raw signal. A simple
peak detection algorithm was used to identify the peaks in the
baseline-corrected NO2 signal. Then the corresponding peaks in the
SO2 were assigned, thus accounting for cases when no SO2
enhancement is measured. In a final manual checkup, all the identified peaks
were looked through, filtering out for example all the cases in which peaks are
too close together to be separated and fine-tuning the baseline detection
algorithm parameters if necessary.
To achieve a better signal-to-noise ratio, the integrals over both the
NO2 and SO2 peak are calculated and the ratio of both values
is computed in the last step.
Figure shows the approach as well as the results for an example
day in summer 2014, before the stricter fuel sulfur content limits were
introduced. Both the NO2 and SO2 signals show high and sharp
peaks, originating from ship plumes. Most of the peaks are of similar shape
in the NO2 signal as well as SO2 signal. The measured SO2-to-NO2 ratios lie in the range from 0.17 to 0.41. The SO2-to-NO2 ratio can vary strongly for different ships. For example, the
plume of the ship passing the line of sight around 12:00 UTC has a high
NO2 content, but is low in SO2, whereas the opposite is true
for the ship passing at 12:30 UTC, indicating that the second ship was using
fuel with a considerably higher sulfur content than the first one.
Calculation of SO2-to-NO2 ratios for ship emission
peaks for one example day (23 July 2014) before the change in sulfur emission
limits. Panel (a) shows the UV NO2 DSCD raw data for
0.5∘ elevation and -25∘ azimuth and the determined
baseline. Panel (b) shows the baseline-corrected NO2 data for which the
automatically identified peaks are highlighted with red triangles. Numbers
close to the peaks denote the peak integrals in
1016moleculescm-2 (marked in yellow) and the SO2-to-NO2 ratios (marked in blue). Panels (c) and (d)
show the corresponding plots for SO2.
As Fig. but for an example day (3 July 2015) after the
introduction of stricter fuel sulfur content limits. Measurements at
0.5∘ elevation and 65∘ azimuth are shown. Peak
integrals are given in 1016moleculescm-2.
Figure shows one example day in summer 2015, after the
establishment of stricter sulfur limits. For better comparison to
Fig. , the y-axis limits are the same. High NO2 peaks also occur
on this day. However, the SO2 signal shows no clearly distinguishable
peaks anymore, a result of much less sulfur in the fuel. Consequentially, the
measured SO2-to-NO2 ratios are much smaller on this day and
range from 0 to 0.09. There might be some small peaks in the SO2
signal, but for most of them it cannot be determined if these are real
enhancements or just noise fluctuations. The two peaks at 10:40 and 14:00 UTC, slightly above noise level but still very small, might be real
SO2 signals from ships with a higher-than-average fuel sulfur
content.
For a statistically meaningful comparison of both time periods, two
representative samples of ship emission peaks have been selected by hand for
days with good measurement conditions, which were identified by using the
solar radiation measurement data of our weather station. One sample of more
than 1000 peaks, measured in 2013 and 2014 and representing the state before
introduction of stricter fuel sulfur content limits, and another
equally sized sample of more than 1000 peaks, measured in 2015 and 2016
representing the situation afterwards, were analyzed in a semiautomatic way.
It cannot be ruled out that a certain fraction of
ships were measured repeatedly on different days. It is also highly probable
that the plumes from some individual ships were measured multiple times at
different locations in the different azimuth directions while the ship was
passing the island.
The distributions of the SO2-to-NO2 ratios derived from the
peak integrals for the two samples are shown in a histogram in
Fig. . It can be seen that SO2-to-NO2 ratios were
considerably higher before 2015, with a mean of 0.30, a standard deviation of
0.13 and a median value of 0.28. After the change in fuel sulfur content
limits, the SO2-to-NO2 ratios became much lower, with a mean
of 0.007, a standard deviation of 0.089 and a median value of 0.013, a
drastic reduction. A Welch's t test (unequal variances t test) shows that the
reduction is statistically highly significant. These results can be compared
to the overall average SO2-to-NO2 ratios on all days with
good measurement conditions from which the peaks have been selected: for the
time before 2015, this gives a mean value of 0.10 and a median of 0.17, and
for 2015 and 2016, this gives a mean value of 0.024 and a median of 0.058. As
expected, these values are significantly lower than the SO2-to-NO2 ratios obtained from the ship plumes that do not include
background pollution.
It is also interesting to compare our results with those from other studies,
bearing in mind possible systematic differences due to different measurement
geometries, techniques and sites and therefore different NO-to-NO2 titration in the plumes.
measured NO2-to-SO2 emission ratios in
marine vessel plumes in the Strait of Georgia in summer 2005. In a sample of
17 analyzed plumes, a median molar NO2 / SO2 ratio of 2.86 was
found. Translated into a SO2 / NO2 ratio, this yields a value of
0.35, which is, considering the small sample size, in good agreement with our
findings for the time before 2015.
Another study was carried out by measuring gaseous and
particulate emissions from various marine vessel types and a total of 139
ships on the banks of the river Elbe in 2011. SO2-to-NO2
emission ratios can also be derived from their reported SO2 and
NO2 emission factors: for small ships (< 5000 tons) a ratio 0.13
and an average fuel sulfur content of 0.22±0.21 % was
found; for medium size ships (5000–30 000 tons) a ratio of 0.24 and a fuel
sulfur content
of 0.46 ± 0.40 % was found; for large ships (> 30 000 tons) a ratio
of 0.28 and a fuel sulfur content of 0.55 ± 0.20 % was found. Especially the values for
medium and large ships fit quite well to our results, while plumes from
very small vessels (if measurable at all) have often not been taken into
account for the statistic because of the low signal-to-noise ratio.
When assuming that the dependency of SO2-to-NO2 ratio on fuel
sulfur content is also applicable to our dataset, we can roughly estimate
that the ships measured by us before 2015 used an average sulfur content of
0.5–0.7 %, in good agreement with the results of
, which since 2015 have decreased drastically, with
0.1 % as an upper limit.
Histogram showing the distribution SO2-to-NO2 ratios
in two samples (N=1055 for each) of ship emission peaks measured at
0.5∘ elevation and all azimuth angles for the time before (blue)
and after (green) the change in fuel sulfur content regulation on 1 January 2015.
Conclusions
In this study, 3 years of MAX-DOAS observations of NO2 and
SO2 taken on the island of Neuwerk close to the shipping lane towards
the harbor of Hamburg, Germany, were analyzed for pollution emitted from
ships. Using measurements taken at 0.5∘ elevation and different
azimuthal directions, both background pollution and plumes from individual
ships could be identified. Using simultaneously retrieved O4 columns,
path-averaged VMRs for NO2 and SO2 could be
determined. Comparison of NO2 measurements in the UV and visible
parts of the spectrum showed excellent agreement between mixing ratios
determined from the two retrievals, demonstrating consistency in the results.
MAX-DOAS measurements were also compared to co-located in situ observations.
High correlation was found between mixing ratios derived with the two methods
on average, with in situ measurements showing systematically larger values, in
particular during ship emission peaks. These deviations can be understood by
the difference in measurement volume, the MAX-DOAS measurements averaging
over light paths of several kilometers and a systematic underestimation of
MAX-DOAS VMRs due to different profile shapes of O4 and the
pollutants NO2 and SO2. For NO2, the difference is
larger than for SO2, probably because of conversion of NO to
NO2 during the transport from the ship where the signal is detected
by MAX-DOAS to the measurement site where the in situ instrument was located.
Although the measurement site is within a few kilometers of one of the main
shipping lanes, it is influenced by land-based pollution depending on wind
direction. Comparing measurements taken under wind direction from the
shipping lane and from land, systematic differences in the diurnal and weekly
cycles of NO2 are found. While NO2 from land shows high
values in the morning and evening and lower values around noon and on
weekends, NO2 levels from the sea are more or less constant over time as
expected from continuous shipping operations. These results are found in both
MAX-DOAS and in situ observations. Both NO2 and SO2 levels
are often higher when wind comes from land, indicating that land-based
sources contribute significantly to pollution levels on the island in spite
of its vicinity to the shipping lanes. Analyzing the wind dependence of the
signals in more detail, and excluding data with mixed air mass origin, the
contribution of shipping sources to pollution on Neuwerk could be estimated
to be 40 % for NO2 and 41 % for SO2 in the
years 2013 and 2014. As nearly half of the measurements were taken under wind
coming from mixed directions, this is only a rough estimate but still a
surprisingly small fraction.
Although the MAX-DOAS measurements cannot be used to directly determine
NOx or SO2 emissions from individual ships due to the
measurement geometry, the ratio of the SO2 column-averaged
mixing ratio to the NO2 column-averaged
mixing ratio gives a good estimate of the SO2-to-NOx
emission ratio. Using the data from Neuwerk, more than 2000 individual ship
emission plumes were identified and the ratio of SO2 to NO2
was computed after subtraction of the background values. The results varied
between ships but on average yielded values of about 0.3 for the years
2013/2014, in good agreement with results from other studies.
Since January 2015, much lower fuel sulfur content limits of 0.1 %
apply in the North and Baltic seas. This resulted in large changes in
SO2 levels in the MAX-DOAS measurements when the wind came from
the shipping lanes. In fact, ship-related SO2 peaks have rarely
been observed anymore since 2015. Applying the same analysis as for the period
before the change in legislation, no significant changes were found for
NO2 in terms of the ratio between ship and land contribution or absolute
levels. For SO2, in contrast, overall levels were reduced by
two-thirds, and the relative contribution of shipping sources was reduced
from 41 to 14 %. It is interesting to note that a
reduction in SO2 levels was also observed in most wind directions
coming from land, presumably because shipping emissions also contributed to
SO2 levels in coastal areas.
In summary, long-term measurements of NO2 and SO2 using a
MAX-DOAS instrument demonstrated the feasibility of remotely monitoring pollution
originating from ships. Pollution signals from individual ships can
be identified and path-averaged mixing ratios can be determined, which on
average correlate well with in situ observations, reproducing day-to-day
trends. MAX-DOAS measurements do not provide emission estimates for
individual ships but allow statistical analysis of signals from thousands of
ships at a distance and even under unfavorable wind conditions.
Implementation of stricter sulfur limits in shipping fuel led to a large
reduction in SO2-to-NOx ratios in shipping emissions and a
significant reduction in SO2 levels at the German coast. The amounts
of NO2 are as expected not significantly impacted by the change in
sulfur content in the fuel. This implies that combustion temperatures were
probably not significantly changed. The overall contribution of ship
emissions to pollution levels at the measurement site is large but land-based
sources still dominate, even in the immediate vicinity of shipping lanes.