This study describes a novel application of an “onion-peeling” approach to multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of shipping emissions aiming at investigating the strong horizontal inhomogeneities in
For validation, a comparison to airborne imaging DOAS measurements during the NOSE campaign in July 2013 is performed, showing good agreement between the approximate plume position derived from the onion-peeling MAX-DOAS and the airborne measurements as well as between the derived in-plume
Over the last decades, there has been a strong increase in ship traffic and shipping emissions of gas-phase pollutants but a reduction in their land sources in much of Europe. This has lead to an increasing contribution of shipping emissions to air pollution in coastal regions. Consequently, emission reduction measures have been enacted by the International Maritime Organization (IMO) in the International Convention for the Prevention of Pollution from Ships (MARPOL 73/78 Annex VI) globally as well as, more stringently, locally in so-called emission control areas (ECAs) like the North and Baltic seas
Most measurements of air pollution are performed with in situ instrumentation, and this includes monitoring of the effect of ship emissions, which is usually performed with either land-based or shipborne in situ measurements. As shown in
MAX-DOAS measurements pointing at the horizon probe a long horizontal light path and are thus very sensitive to absorbers located close to the ground. The strong wavelength dependence of Rayleigh scattering (
The present study focuses on measurements in a relatively clean coastal region where ships passing by the island are often the only dominant source of air pollution
This study uses measurements in both the UV (
As can be seen from Fig.
This publication is a follow up to an earlier study entitled “Monitoring shipping emissions in the German Bight using MAX-DOAS measurements”
The present study is part of the project MESMART (Measurements of Shipping Emissions in the Marine Troposphere), a cooperation between the University of Bremen (Institute of Environmental Physics, IUP) and the German Federal Maritime and Hydrographic Agency (Bundesamt für Seeschifffahrt und Hydrographie, BSH), supported by the Helmholtz Zentrum Geesthacht. For further information visit
Multi-axis differential optical absorption spectroscopy (MAX-DOAS)
A detailed description of the MAX-DOAS instrument and its components as well as the general measurement geometry for ship emission measurements is given in
DOAS fit settings for the retrieval of
Neuwerk is a small island in the German Bight, northwest of the city of Cuxhaven at the mouth of the river Elbe, around 9 km off the coast. An overview of the area is shown in Fig.
To sample a larger region, the MAX-DOAS instrument was set up to have five different azimuthal viewing directions: 310, 335, 5, 35, and 65
For further information on the measurement site and instrumentation see
The quantity retrieved from DOAS measurements is the so-called slant column density (SCD), the integrated concentration of an absorber along the atmospheric light path. To measure the
For the comparison with in situ measurements the MAX-DOAS horizontal trace gas columns are converted to horizontal path-averaged volume mixing ratios (VMRs) by using the
The oxygen collision complex
Knowing the horizontal light path length
This
For a homogeneous, well-mixed
In addition, the different shapes of the atmospheric profiles of
Clouds can decrease or increase the light path length (and
Plume–light path geometry and the resulting path-averaged
As mentioned above, the wavelength dependence of Rayleigh scattering results in a wavelength dependence of the light path lengths after the last scattering point. This can be utilized to probe different air masses in the atmosphere by measuring both in the UV and visible spectral ranges.
The aforementioned
This yields the average volume mixing ratio VMR
As each ship is a moving point source for
Depending on the position of the plume in relation to the UV and visible light path, the path-averaged mixing ratios can differ substantially. Figure
In case (a) the plume is close to the instrument and is completely covered by the shorter UV path
Case (b) shows the opposite situation, when the plume is further away from the instrument than the UV scattering point and only covered by the visible path
In case (c) the plume is close to the UV scattering point. All three light paths see enhanced
As already discussed in
The time span between plume emission and measurement is important for the measured
The lifetime of
For a more quantitative treatment of the ship emissions, the exhaust plumes and their movement over time need to be considered. Here, ship plume trajectories have been calculated as simple forward trajectories combined with a Gaussian plume model. On a 10 s time grid, at each time step, each point-shaped plume air parcel is moved from its old position to a new position, which depends on wind direction and speed. Each ship emits a new plume air parcel per time step at the respective ship position, thus creating a chain-line-like string of plume air parcels. By starting with an initialization period of 3 h before the respective measurement time, old plumes from ships that passed by the island before and already left the region of interest can be included in maps as those shown in Fig.
Atmospheric stability classification scheme
Plume broadening and dispersion over time is accounted for by modeling the width and height of the plumes with a Gaussian plume model
The dispersion coefficients
Empirical stability parameters for the computation of the horizontal and
vertical dispersion coefficients
As the ships are moving point sources, the course of the plume does not only depend on the wind direction but also on the previous pathway of the ship. The ships move with a certain direction and speed, thus creating an apparent wind
For the method for deriving in plume
Figure
As a result of the longer light path, the
Differential slant column densities of
Figure
Figure
For northerly winds, the pollution plumes emitted from the ships are blown towards the radar tower, resulting in enhanced
In Fig.
Sequence of maps showing 15 consecutive measurements in 0.5
The sequence of maps shows two ships (magenta triangles) on the shipping lane, moving in opposite directions. The larger ship (length 351 m) moves westward, and the smaller ship (length 151 m) moves eastward. The locations of the two plumes (gray shaded stripes) differ considerably due to the different movement directions of the ships and the curved shape of the shipping lane around the island.
For the plume modeling, the stability class C representing slightly unstable conditions has been chosen based on the wind speed and the strong solar insolation on this clear-sky day.
In Fig.
Figure
Figure
In Fig.
Starting with Fig.
In Fig.
In Panel 15 the larger ship has moved further away from the instrument, leading for the first time in this sequence to a higher concentration along
Figure
Map showing a zoom in onto panel 10 of Fig.
The second selected case study shows a diametrically opposite situation: for southerly winds the emitted pollution plumes are blown to the north of the shipping lane (compare Fig.
Figure
Sequence of maps showing 15 consecutive measurements in 0.5
In the map sequence, three ships can be seen in the shipping lane, two large ones (336 and 365 m) and a smaller one (100 m). As all ships move in the same, eastward, direction, the plume trajectories are almost parallel. Apart from the ship emission plumes, another plume crosses the area of interest, originating from the two directly adjacent coal-fired power plants in Wilhelmshaven, located at 53.57
For the plume modeling, the stability class C representing slightly unstable conditions has been selected based on the wind speed and the strong solar insolation on this clear-sky day.
Figure
The next measurement in Fig.
In Fig.
Panel 4 (35
In panel 5 (65
Panels 6 and 7 are similar to panels 1 and 2, showing that the situation in these viewing directions has not changed 4 min later.
In panel 8, 4 min after panel 3, the plumes of the two big ships traveled a bit further northward, making the gradient between
Panels 10 to 12 are similar to panels 5 to 7.
In panels 13 to 15, the plumes of the two big ships are now clearly only probed by the visible light path giving enhanced
In all 15 consecutive measurements shown in the map sequence the in situ instrument measured constantly low values. This indicates that for southerly winds it cannot detect ship emission plumes at this site. Measured
Figure
Map showing a zoom in onto panel 15 of Fig.
In addition to visualizing the two-dimensional
The
Map showing the MAX-DOAS path-averaged VMRs (colored lines) and AirMAP vertical columns of
Figure
The Gaussian plume model was run for a stability class of B–C, which was selected due to the moderate insolation (cloudy in the morning, later clearing up) and wind speeds between 3 and 4 m s
Along
For the computation of the MAX-DOAS average in-plume
MAX-DOAS differential slant column densities of
At 09:53 UTC a
The average VMR inside the plume is given by
As already indicated above, a comparison to on-site in situ trace gas analyzers is well suited to validate the MAX-DOAS ambient
Airborne imaging DOAS measurements, as have been performed in the region of interest during the NOSE (for German “Nord-Ost-See-Experiment” meaning “North and Baltic seas experiment”) campaign
Delivering high-resolution
The Airborne imaging Differential Optical Absorption Spectroscopy instrument for Measurements of Atmospheric Pollution (AirMAP), installed on a Cessna research aircraft of the Freie Universität Berlin for the measurements, is a push-broom imaging DOAS instrument. Scattered sunlight from below the aircraft is collected by a wide-angle objective and coupled into a bundle of 35 sorted optical fibers. The image of the vertically stacked fibers is then dispersed by an imaging grating spectrometer and mapped onto a frame transfer CCD. The total field of view of around 52
In the AirMAP data analysis, differential slant column densities of
Figure
The time difference between both measurements of less than 20 s is very small, especially considering the integration time of the MAX-DOAS instrument of 10 s. The position of the plume (calculated with a forward trajectory) and the horizontal extent of the plume (computed with the Gaussian plume model) match the real plume positions measured by AirMAP very well. The plumes further north were measured by AirMAP around 1 min later, enough time for the wind to blow the plumes northward so that the positions do not fully coincide with the plume forward trajectories, which have been computed for the MAX-DOAS measurement time. Inspecting the AirMAP measurements in detail reveals that the real plumes are not as smooth as the modeled plumes and show some irregularities and random fluctuations caused by turbulence. This deviation is expected, as the Gaussian plume model used here assumes a steady state and describes a (long) time-averaged picture of a plume. Nevertheless, the modeled plume widths fit quite well. These results provide confidence in the modeled plume trajectories as well as in the onion-peeling approach to detect locally enhanced
For the validation of the in-plume
Sketch of the different measurement geometries of ground-based MAX-DOAS and airborne imaging DOAS instruments when measuring a ship plume. While the MAX-DOAS instrument scans the plume vertically, the AirMAP instrument measures in the nadir direction. Distances, heights, and sizes are not to scale.
The Gaussian plume model delivers a height of (
For the estimation from the MAX-DOAS measurements, we need to reconsider Fig.
Comparing Fig.
AirMAP vertical columns of
Figure
The measured vertical columns are total columns between flight altitude and ground level. To retrieve the local enhancement of
Possible error sources for the AirMAP measurements are fitting uncertainties on the retrieved DSCDs, uncertainties on the surface reflectance, the assumed profile shape, and aerosols, while uncertainties on the
The
This result is in reasonably good agreement with the average in-plume VMR of
Another possible explanation for the lower MAX-DOAS values could be the underestimation of the VMR due to overestimation of path lengths because of negligence of correction factors as mentioned in Sect.
Figure
Map showing the MAX-DOAS path-averaged VMRs (colored lines) and AirMAP vertical columns of
The present study describes a novel application of the onion-peeling MAX-DOAS approach to measurements of shipping emissions to estimate the two-dimensional pollutant distribution in the strongly inhomogeneous
To determine the horizontal light path lengths for the onion peeling, a simple approach using the trace gas column of the oxygen collision complex,
It is shown that for northerly wind directions, the onion-peeling MAX-DOAS method can detect enhanced
A combination of simple forward trajectories and a Gaussian plume model has been implemented to model the ship plumes, allowing us to compute in-plume
For validation of both the plume modeling and the MAX-DOAS results, airborne imaging DOAS measurements taken by the AirMAP instrument during the NOSE campaign on this very same day have been used. AirMAP's measured plume positions agree well with the ones estimated by using the onion-peeling MAX-DOAS approach showing that MAX-DOAS measurements can be used to derive the approximate position of ship emission plumes. The good agreement of modeled plume positions and shapes with AirMAP measurements shows that simple forward trajectories combined with a Gaussian plume model look-up table approach provide sufficient accuracy to model the two-dimensional
By incorporating information about the vertical plume extent from either plume model or MAX-DOAS vertical scan measurements, an in-plume
To conclude, the presented measurements provide a real world demonstration that the onion-peeling approach works for MAX-DOAS measurements and can successfully be applied to investigate air pollution by ships and to derive in-plume
The data used in this study are available from the cited references and directly from the authors upon request.
ASe and EP built the MAX-DOAS instrument; ASe, FW, EP, LK and BMÜ set up and operated the MAX-DOAS and in situ instruments on the radar tower. AR developed the DOAS retrieval code and measurement software and provided technical support. ASe performed the analysis of the MAX-DOAS data, provided the figures and wrote the manuscript. ASc and AM set up and operated the AirMAP imaging DOAS instrument during the NOSE campaign and AM provided the AirMAP level 2 data. TR is operator for the Cessna Research Aircraft from the FU Berlin and assisted preparing the flights. AR, FW, SS and JB supported data interpretation. All authors contributed to the writing of the paper.
The authors declare that they have no conflict of interest.
The research project which facilitated the reported study was funded in part by the German Federal Maritime and Hydrographic Agency (Bundesamt für Seeschifffahrt und Hydrographie, BSH) and the University of Bremen. The authors thank the Waterways and Shipping Office Cuxhaven (Wasser- und Schifffahrtsamt, WSA), the Hamburg Port Authority (HPA), the AirMAP and NOSE teams, and the FU Berlin for their help and support. Many thanks to the editor, Michel Van Roozendael, and to the two anonymous referees for their valuable comments and suggestions, which helped to improve this publication.
The article processing charges for this open-access publication were covered by the University of Bremen.
This paper was edited by Michel Van Roozendael and reviewed by two anonymous referees.