retrieval of aerosol properties over land and over sea

RETRIEVAL OF AEROSOL PROPERTIES OVER LAND AND OVER SEA∗

Gerrit de Leeuw(1), Cristina Robles Gonzalez(1), Pepijn Veefkind(1, 2), Rob Decae(1) and Jolanta KusmierczykMichulec(1) (1)

TNO Physics and Electronics Laboratory TNO P.O. Box 96864, 2509 JG The Hague, The Netherlands; email: [email protected];

(2)

current address: KNMI, P.O.Box 201, 3730 AE De Bilt, The Netherlands email: [email protected]

INTRODUCTION Aerosols affect, among others, climate [1], air quality [2], health [3], and electro-optical observations. Examples of the latter are the effect of aerosols on the retrievals of surface properties from satellite observations, or the degradation of electro-optical systems used, e.g., for military purposes. Both effects are due to the extinction of radiation at wavelengths from the UV, through the visible to the IR due to scattering and absorption. These processes affect the earth radiation budget through its influence on both incoming and out-going radiation. Aerosols have been identified as an important factor in the regulation of the Earth’ climate. The incoming solar radiation is scattered by aerosols, which induces a negative (cooling) effect on the atmospheric radiation balance. The magnitude of aerosol cooling is a major uncertainty in climate models [4], and may be similar to that of the warming effect of common greenhouse gases. Certain types of aerosols also absorb radiation. This property has often been used to identify aerosols by satellite remote sensing and providing an aerosol index. However, the absorption of aerosols that are most important for climate, e.g., ammonium sulphate and ammonium nitrate that are mainly produced due to anthropogenic activity, is negligible. One of the goals of the research presented here is to discriminate between different aerosol types. The concentrations and occurrence of aerosols are highly variable in both space and time, due to the occurrence of many direct sources with strongly different source strengths, atmospheric residence times of several days, and a variety of transport mechanisms that disperse the aerosol throughout the troposphere. The latter depends on the meteorological situation. In addition, many gaseous precursors are emitted which constitute secondary aerosol sources that are spread in space and time. To obtain information on the regional and global distributions of aerosols, and thus on their effects on climate and other important issues such as health and environmental effects, satellite remote sensing is a very promising tool. Different satellite instruments are nowadays (becoming) available that have been designed to derive specific information that can also be used for aerosol retrieval. The problem to derive aerosol properties from radiation received by satellite-based sensors is to separate the contribution due to reflection at the surface from the contribution due to scattering by aerosols, both of which are modified by atmospheric gases and depend on the sun-satellite geometry. AEROSOL RETRIEVAL FROM SATELLITE DATA Aerosol properties can be retrieved from satellite data obtained only for cloud-free scenes. In that case, the radiance received by satellites at the top of the atmosphere (TOA) is composed of contributions due to scattering by gases and aerosols, and reflection at the surface. Absorption by molecular species and aerosols further modify the TOA radiance. Hence the signal received by electro-optical instrumentation on satellites in principle contains information on the ∗

The work presented in this paper is supported by the Netherlands Space Research Organisation (SRON), contracts EO-008 and EO-037. ATSR-2 and GOME data are provided by the European Space Agency (ESA) through DLR and ESRIN.

surface properties and on the atmospheric composition. The problem is to separate the various contributions. Until recently it was thought that aerosol retrieval was only possible over water, i.e. dark surfaces with very low reflection. Over the oceans, aerosol optical depth (AOD) has been retrieved since more than two decades, thus providing a long time series of observations. Such time series is of great values for trends analysis. Over land no such data are available, except for the aerosol index provided by TOMS data, based on aerosol absorption. An algorithm that works over land, as evidenced from comparison of aerosol optical depths and their wavelength dependencies retrieved from ATSR-2 (ERS-2) data and from AERONET sunphotometer data [5], was developed by TNO Physics and Electronics Laboratory (TNO-FEL) [6]. This algorithm uses the dual view and the 4 spectral bands in the visible and near-IR offered by ATSR-2. A second algorithm for application over land was developed for use with GOME (ERS-2) data at wavelengths in the UV, where the surface reflection is very low [7]. An overview of algorithms presently available at TNO-FEL is presented in table 1 [8]. They are extensively described in [9]. The aerosol retrieval efforts at TNO-FEL started with algorithms developed for GOME, applied over the Southern Hemisphere, over the ocean [10]. The results are quite reasonable, but could not be tested by lack of independent observations. Furthermore, the large GOME pixel renders the amount of useful data very small, and therefore AVHRR (NOAA) and ATSR-2 algorithms were developed, in cooperation with the Professor Durkee, Naval Postgraduate School in Monterey (CA, USA), who had a wide experience with aerosol retrieval using AVHRR. The algorithms were successfully applied to data obtained during the TARFOX campaign off the United States east-coast in July 1997 [11]. During TARFOX, also data were obtained over land from sunphotometers, which allowed for testing of the newly developed dual view algorithm [6]. In view of the favourable results, the dual view algorithm was also applied over NW Europe, and more specific to data obtained over The Netherlands, Belgium, Northern France and NW Germany. The results compared favourably with sunphotometer data from the AERONET station in Lille, as well as with data obtained in Holland from sunphotometers in De Bilt and from UV measurements in Petten. Furthermore, the analysis showed a favourable comparison with aerosol measurements at ground level in Petten [7]. AEROSOL OPTICAL DEPTH OVER EUROPE Next, the single and dual view algorithms were applied to data over Europe, for all ATSR-2 overpasses in August 1997. The single view algorithm was applied over water because of problems encountered with the dual view algorithm caused by large differences in the surface reflectance for the two look-angles. The results over land were compared with time-synchronised and collocated observations from the AERONET sunphotometers in Lille, Aire-Adour (both in France) and Ispra (Italy) [12,13]. The individual data were averaged to produce a composite map of aerosol optical depth over Europe. The results are reproduced in Fig. 1 [13]. They clearly show the contributions of various industrial and urbanised areas [12], with AOD at 0.555 µm of up to 0.8, whereas in clean areas the AOD may be lower than 0.1. The results were further interpreted by using a chemistry transport model including ammonium sulphate and nitrate aerosols [13, 14]. The model results were evaluated with available surface measurements of aerosol chemical composition [13]. At most of the stations sulphate concentrations are underestimated by a factor of about 1.7 which can be explained by the absence of cloud chemistry in the model. Modelled and measured nitrate concentrations compare favourably in The Netherlands, both as regards the absolute concentrations and the daily variation. However, in other areas there is larger uncertainty in the calculated nitrate concentrations. Overall, the average nitrate and total nitrate concentrations in Europe are underestimated, frequently by more than 50 %. Comparisons with experimental data show that the model results are a factor of 2-3 lower [15]. On the other hand, model results and measurements show similar trends, which renders them useful for the interpretation of trends in the observed AOD fields. Table 1. Aerosol retrieval algorithms available at TNO-FEL. Future missions are in parenthesis. Wavelength

Ocean VIS/NIR

Dual-view VIS/NIR

GOME-UV 340-400 nm

Surface

Sea

Land/Sea

Land Sea

Sensors

Various

ATSR-2 (AATSR)

GOME (Sciamachy)

Refs.

[10,11]

[6,7]

[7]

Fig. 1. Composite map of the mean aerosol optical depth at 0.555 µm over Europe for August 1997. AOD has been retrieved by application of the dual view algorithm over land [6] and the single view algorithm over water [11] to ATSR-2 data [13]. The computed aerosol fields were converted to AOD using a simple mass-extinction relationship [16]. The results for sulphate, presented in Fig. 2, show similar trends as the observed AOD, with highest values of up to 0.2 over Central and SE Europe, and values of up to 0.1 over northern Italy, western Europe, and some other areas over the Iberian Peninsula, Italy and Great Britain. Calculated AOD for nitrate is generally low, with values smaller than 0.01 over most of Europe, except in a band from southern England, over western Europe to northern Italy, where values of up to 0.02 are computed, with maxima over the Po Valley (up to 0.03) and over The Netherlands (up to 0.08). Adding the calculated sulphate and nitrate AOD obviously should render values that are lower than observed, because also other types should be accounted for, such as organics, black carbon, dust and, over the oceans, sea salt. From comparison between the observed and modelled AOD, and keeping in mind the uncertainties in either of them, estimates can be made of the contributions of sulphate and nitrate to the total AOD. The results, shown as the ratio of the computed AOD to the values retrieved from the ATSR-2 observations, are presented in Fig. 3.

a b Fig. 2. Aerosol optical depth due to sulfate aerosols field computed with the LOTOS model. Fig. 2a shows the mean aerosol optical depth in August 1997 at 0.550 µm, Fig. 2b shows the variance [13].

a b Fig. 3. The ratio of sulfate (a) and nitrate (b) aerosol computed from the LOTOS model to the total aerosol optical depth retrieved from satellite data with the dual view algorithm [13]. The contribution of sulphate can be as high as 70%, observed, e.g., over Austria, Germany, Poland, the Czech Republic, Northern Italy and some areas of the Balkans, indicating a major influence of sulfate aerosols in these areas. Over The Netherlands, Belgium, France, Southern Italy and Spain, values of 0.1-0.3 were obtained, while for the rest of Europe sulfate contributes less than 0.1 to the total AOD. Note that, as indicated above, model uncertainties can lead to underestimation of these values by a factor of up to 1.7. The results in Fig. 3 show that contribution of nitrate to the total AOD is less than 5% over most Europe. In view of the uncertainties in the calculated nitrate concentrations discussed above, this figure can be a factor 2-3 low. Hence, nitrate may contribute up to about 10-15% in most of Europe. Values up to 30% are observed over The Netherlands and Northern Italy suggesting a major influence of the nitrate only in these areas. The relative contribution of sulfate and nitrate aerosols to the total aerosol light-scattering was measured in Petten, The Netherlands, for the summer of 1994 [17]. From these measurements was concluded that in the summer, the contribution of sulfate and nitrate aerosols are of similar magnitude and between 30 and 40% of the total aerosol lightscattering. Similarly, from measurements in Delft, The Netherlands, in 1985, the contribution of the sulfate was estimated around 38% [18]. These estimates for sulphate are higher than those derived from the model/satellite comparisons in Fig. 3, which show a contribution of 20% over The Netherlands. However, as indicated above, comparison of model results with direct measurements shows that the sulfate concentrations may be underestimated by a factor of up to 1.7. Hence, the actual contribution of sulphate aerosol to the total AOD over The Netherlands could be as high as 34%. Furthermore, the satellite and model calculations consider the sulphate concentrations integrated over the vertical column (3 km for the LOTOS model), whereas the measurements were made at ground level. Taking further into account that the SO2 emissions, the precursor for sulphate aerosol, have been significantly reduced (in The Netherlands 30% between 1980 and 1996 [19]), that the surface measurements were made in the 80’s and early 90’s whereas the data derived here apply to 1997, the present estimates do not contradict earlier results. For nitrate, relative contributions of about 30% were obtained over The Netherlands, a number which compares favorably with the indicated measurements. Thus, the results in regard of the contributions of sulfate and nitrate to the total AOD over The Netherlands, are in reasonable agreement with experimental data. Over the rest of Europe no experimental data is available for validation.

AOD WAVELENGTH DEPENDENCE: ÅNGSTRÖM COEFFICIENT Aerosol particle size distributions are often approximated by a power law function, generally referred to as the Junge [20] distribution:

 dN  −ν log  = C (log D ) dD  

(1)

where N is the number of particles per cm-3, D is the particle diameter, C is a constant referred to as the Junge constant, and ν is the Junge coefficient. The Junge constant C gives the number of particles for a wavelength of 1 µm. For very clean atmospheres C can have very small values, on the order of 1, whereas in a polluted atmosphere the value of C can be on the order of 103-104. Likewise, the value of the Junge coefficient ν can vary from about 3 to about 5. A low value of ν indicates the presence of very low concentrations of sub-micron particles and relatively high concentrations of particles larger than 1 µm. This situation may occur over the ocean, where large concentrations of sea spray particles may occur at elevated wind speeds in a further relatively clean atmosphere. Vice versa, large values of ν are indicative of very high concentrations of sub-micron aerosol particles. Over land, common values are ν≈4. The aerosol extinction coefficients, which can be calculated from an aerosol size distribution with the appropriate refractive index [21], vary with wavelength λ. For a Junge size distribution, the wavelength dependence follows a power law distribution, with a coefficient α = ν-2. For a situation where the aerosol is well-mixed over the vertical column, or alternatively, the aerosol extinction coefficient is invariable with height, a similar relation applies to the AOD (AOD is the product of extinction coefficient and optical path length). In the literature, the variation of the AOD with the wavelength is often referred to as the Ångström law:

AOD(λ ) ∝ λ−α

(2)

where α is the Ångström coefficient. ATSR-2 has four channels (0.555, 0.659, 0.865 and 1.6 µm) which are used for aerosol retrieval. The application of (2) to derive the Ångström coefficient is illustrated in Fig. 4, where the AOD is plotted versus the wavelength, on a log-log scale. The slope of the fitted line gives the Ångström coefficient α. The AOD was retrieved for all available cloud-free pixels over Europe in August 1997, for all available wavelengths. Ångström coefficients were derived from these data. The results, averaged over the whole month, are presented in Fig. 5 [13]. Since α = ν-2, small values of the Ångström coefficient indicate the dominance of large particles, such as sea salt or dust, whereas large Ångström coefficients are usually obtained when small particles such as sulfate or nitrate dominate. Hence, the large variations in the satellite retrieved Ångström coefficients in Fig. 5 indicate the variability of the aerosol size distributions. Very low Ångström coefficients in Fig. 5 are often associated with large uncertainties, as evidenced by high variances caused by low AOD values (see Fig. 1) which do not allow for an accurate fit. The Ångström coefficients and their variation across Europe and the European regional seas are discussed in [13]. The values presented in Fig. 5 are in reasonable agreement with other values mentioned in the literature, based on aerosol climatology [22], sunphotometer observations [23, 24], or retrievals from other satellites [25]. Observed discrepancies are discussed based on aerosol transport in relation to meteorological situations, as well as the fact that the data in Fig. 4 are averaged over a whole month, thus covering several different meteorological situations. Most other observations apply to single measurements, and thus a specific meteorological situation.

AOD

1

0.1

0.01 100

1000 Wavelength (nm)

10000

Fig. 4. AOD as function of wavelength. The line is a least squares fit to the data, the slope of which gives the Ångström coefficient α.

Fig. 5. Mean Ångström coefficient for August 1997 computed from linear squares fits to the Ångström relation (2) [13]. RELATION BETWEEN ÅNGSTRÖM COEFFICIENTS AND CHEMICAL COMPOSITION As discussed above, the Ångström coefficient gives information on the shape of the particle size distribution, which in turn is connected with the origin of the aerosols, and thus to a certain extent also with the chemical composition. The latter has been investigated using simultaneous measurements of the aerosol optical depth, with a sunphotometer, and aerosol chemical composition over the Baltic [26]. The measurements were made during two periods of two weeks each, the first one in the summer (July) of 1997, the second one in the winter (March) of 1998. An example of the results from the measurements in the winter period is presented in Fig. 6, showing the measured fraction of sodium (Na) of the total particulate matter (TPM), plotted versus the observed Ångström coefficients. The Na fraction is high for low Ångström coefficients, and decreases strongly with increasing Ångström coefficient, confirming the qualitative arguments presented in the previous section. A power law was fitted to the data points:

measurements winter '98 0.08 0.07

-1.5246

y = 0.0086x R 2 = 0.8997

Na/TPM

0.06 0.05 0.04 0.03 0.02 0.01 0 0

0.2

0.4

0.6

0.8

1

1.2

1.4

Angstrom coefficient

Fig. 6. Fraction of sodium to the total suspended matter, Na/TPM, as function of simultaneously measured Ångström coefficients. A power law relationship fitted to the data points is given in the legend. The measurements were made from a ship on the Baltic, during 2 weeks in the winter of 1998.

Na = 0.0086α −1.52 TPM

(3)

The correlation coefficient R2 is 0.9. Similar empirical relations were derived for other aerosol components, i.e. black carbon, organic carbon, water solubles, and dust, for both the winter and summer periods. The results for each season are quite similar. The relations derived for the winter were subsequently applied to estimate the contributions of various aerosol types from the measured Ångström coefficients, for both the summer and the winter measurements. The relations found from the winter measurements were applied to those from the summer period, as an independent check, for the same area but with a different data set. The results, Fig. 7, show that the calculated values of Na/TPM compare favourably with the experimental values. 0.08 March'98

calculated values Na/TPM

July'97

0.06

0.04

0.02

0.00 0.00

0.02

0.04

0.06

0.08

measured values Na/TPM

Fig. 7. Comparison of Na/TPM retrieved from observed Ångström coefficients versus experimental values. DISCUSSION AND CONCLUSIONS An overview has been presented of aerosol retrieval work at TNO-FEL, focused on data from the European satellites ERS-2 (ATSR-2 and GOME), but also using AVHRR (NOAA). A significant result is the retrieval of aerosol properties over land, using the possibilities offered by, especially, ATSR-2, but also from GOME data, when available. This has resulted in maps of AOD and Ångström coefficients over Europe for August 1997 (Fig. 1), yielding a wealth

of data for further interpretation. These maps are based on data obtained from ERS-2 overpasses in cloud free situations, i.e. at best every third day. The good agreement between the monthly averaged AOD values of the satellite data and the ground based sunphotometer data renders credibility to these values, within the limits of experimental uncertainty [12]. The results are interpreted using a chemical transport model, from which AOD field of sulphate and nitrate were calculated. Taking into account the various uncertainties, as well as model deficiencies, estimates can be made of the contributions of these aerosol types to the total AOD, which in turn can be used to derive conclusions as regards climate forcing. Absolute assessment of the contributions of the various aerosol types to the total AOD is not possible, except for areas for which reliable experimental data are available for validation, such as The Netherlands [17]. For most other areas only trends can be indicated and model uncertainties need to be accounted for. Therefore this study is regarded as a demonstration of the complementary use of aerosol retrievals and model results. This allows for the deduction of information on the contribution of the independent species to the total light scattering, which cannot be obtained from each of these methods separately. The AOD provides the column integrated value for the total aerosol attenuation. The Ångström coefficient is a good indicator of the shape of the aerosol size distribution. These parameters are also indicative of the types of aerosols present in the column: large Ångström coefficients indicate the presence of small particles from anthropogenic sources, whereas small Ångström coefficients indicate the dominance of larger particles, such as sea spray aerosol. Measurements over the Baltic were used to derive empirical relations between aerosol composition and Ångström coefficients. These relations were used in turn to retrieve the aerosol composition from Ångström coefficients, obtained from sunphotometer measurements, using an independent data set over the Baltic, for a different season. Experience with the algorithms developed thus far will be applied to future missions such as ENVISAT (AATSR and SCIAMACHY), MSG, and EOS-AURA, cf. Table 2. The algorithm for the OMI instrument on EOS AURA is presently developed and will consider 9 different aerosol types, at several relevant relative humidity’s, with specific properties as regards size distributions and refractive indexes, as well their respective scale heights. In addition, several other parameters will be used as a priori information, such as surface properties obtained from other sources. This algorithm is designed to provide global measurements of the AOD, for cloud-free situations over land and over sea, with a spatial resolution of 13x24 km2. Parallel, algorithms will be developed for AATSR, SCIAMACHY and AVIRIS, using the specific opportunities offered by each of these instruments. Thus, accuracy due to spectral resolution and, for AATSR, the dual view, and spatial resolution from sun synchronous satellites can be combined with frequent observations using MSG and other geo-stationary satellites. Table

2.

Characteristics

Sensor/Satellite

of

satellite

Channels (nm) in UV,VIS and NIR 640, 840

sensors

used

Spatial resolution (km) 1-4

for

atmospheric

Swath width (km) 2000

AVHRR NOAA ATSR-2 555, 659, 865, 1600 1-4 512 ERS-2 GOME Continuous in 340 x 40 or 960 or ERS-2 240-790 80 x 40 240 SCIAMACHY 30 x 240 to 960 or ENVISAT 30 x 27 120 AATSR 555, 659, 865, 1600 1 512 ENVISAT OMI Continuous in 13 x24 2600 EOS-AURA * 270-500 SEVIRI 635, 810, 1640 1-3 Full earth disc MSG image * TNO-FEL is developing the operational aerosol retrieval algorithm for OMI

aerosol

retrieval

at

TNO-FEL

comments No in-flight calibration Water vapour absorption In-flight calibration Dual view capability Spectrometer 3 viewing geometries and vertical information In-flight calibration Dual view capability Spectrometer Daily earth coverage Geo-stationary 15 minute interval

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