The Aura–OMI Aerosol Index distribution over Greece

Atmospheric Research 98 (2010) 28–39

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Atmospheric Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / a t m o s

The Aura–OMI Aerosol Index distribution over Greece D.G. Kaskaoutis a, P.T. Nastos b,⁎, P.G. Kosmopoulos b, H.D. Kambezidis a, S.K. Kharol c, K.V.S. Badarinath c a

Atmospheric Research Team, Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Lofos Nymphon, P.O. Box 20048, GR-11810 Athens, Greece University of Athens, Department of Geology and Geoenvironment, University Campus GR-15784 Athens, Greece c Atmospheric Science Section, Oceanography Division, National Remote Sensing Centre (Dept. of Space-Govt. of India) Balanagar, Hyderabad-500 037, India b

a r t i c l e

i n f o

Article history: Accepted 16 March 2010 Keywords: Aerosol Index OMI Absorbing aerosols Spatial distribution Greece

a b s t r a c t The Aerosol Index (AI) observations derived from the Ozone Monitoring Instrument (OMI) on board the Dutch–Finnish Aura satellite are analyzed over Greece covering the period from September 2004 to August 2008. The AI data cover the whole Greek territory (34°–42°N, 20°–28°E) with a spatial resolution of 0.25°× 0.25°. The results show significant spatial and temporal variabilities of the seasonal and monthly-mean AI, with higher values at the southern parts and lower values over northern Greece. On the other hand, the AI values do not show significant differences between the western and eastern parts and, therefore, the longitude-averaged AI values can be utilized to reveal the strong south-to-north gradient. This gradient significantly changes from season to season being more intense in spring and summer, while it is minimized in winter. Another significant remark is the dominance of negative AI values over northern Greece in the summer months, indicating the presence of non-UV-absorbing aerosols, such as sulfate and sea-salt particles. The great geographical extent of the negative AI values in the summer months is indicative of long-range transport of such aerosols. In contrast, the high positive AI values over southern Greece, mainly in spring, clearly reveal the UV-absorbing nature of desert-dust particles affecting the area during Saharan dust events. The annual variation of spatial-averaged AI values shows a predominant spring maximum (0.424 ± 0.329, in April) due to dust particles, which dominate this average and a summer minimum due to the negative AI values observed over northern Greece. In the cold period of the year (November to February) the AI values are higher over northern Greece compared to those in south, while in the rest of the year the opposite is true. © 2010 Elsevier B.V. All rights reserved.

1. Introduction The physical and optical properties of the aerosol particles are of great importance for the attenuating processes of solar radiation, even in cases with similar ozone and aerosol amounts (Balis et al., 2004). To this respect, Balis et al. (2004) have shown from a co-located Raman lidar system and spectral UV-B irradiance measurements that for the same aerosol optical depth (AOD) and for the same ozone column, surface UV-B irradiances may differ by up to 10%, which was attributed to differences in the aerosol type. On the other hand, apart from ⁎ Corresponding author. Tel.: + 30 210 7274191; fax: + 30 210 7274191. E-mail address: [email protected] (P.T. Nastos). 0169-8095/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.atmosres.2010.03.018

the ozone variations that mainly affect the UV-B (280–315 nm) spectrum, atmospheric aerosols have a great importance in attenuating (by scattering and absorption) irradiance in the UV-A (315–400 nm) spectrum (Badarinath et al., 2008). Thus, Badarinath et al. (2008) showed that the soot aerosols attenuate more the UV radiation than the desert particles over Hyderabad. Since the aerosol effect on atmospheric processes is more intense in UV band, the Aerosol Index (AI) is defined, which is an indicator of the presence of UV-absorbing aerosols, such as soot particles and desert dust (Torres et al., 1998). The AI is positive for absorbing aerosols and negative for non-absorbing ones (pure scattering). Nevertheless, negative AI can be caused by features other than non-absorbing aerosols. Among these features are elevated clouds and spectral slopes in

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the surface albedo between the two wavelengths used for the AI detection (Baddock et al., 2009). Moreover AIb 0 indicates the absence of elevated absorbing aerosols, since the AI increases with altitude for the same aerosol load (Prospero et al., 2002). An AI N 1.0 is typical of absorbing aerosols such as smoke or dust (Gassó and Stein, 2007; Kubilay et al., 2005; Washington et al., 2003). The presence of clouds results in near-zero values for the UV-AI (Hsu et al., 1999). The discrimination between UVabsorbing and non-absorbing aerosols via satellites can find application in the identification of such particles as well as their source regions. Thus, the detection and mapping of dust events and dust transport pathways have benefited greatly from the use of remote sensing; at global scale major dust source regions have been identified using AI values (Prospero et al., 2002; Washington et al., 2003; Alpert et al., 2004; Engelstaedter et al., 2006). Sun synchronous satellites provide the aerosol properties on a regional and/or global scale with varying spatial resolution depending on the application and topic of interest and a temporal resolution of once per day. The AI is the only continuous aerosol database for about 30 years via various satellites (e.g. Nimbus-7, Meteor 3, Earth-Probe, Aura) aiming at studying the UV-absorbing aerosols from biomass burning (Eck et al., 2001, 2003) and dust exposures (Alpert et al., 2004; Badarinath et al., 2007; Kaskaoutis et al., 2008). The AI is not yet sufficiently described over eastern Mediterranean and Greece. Among some recent publications we can refer those conducted by Kubilay et al. (2005) over eastern Mediterranean, by Koukouli et al. (2006) at Thessaloniki in northern Greece, by Kalivitis et al. (2007) at Heraklion in southern Greece, and by Kaskaoutis et al. (2008) over the whole of Greece. Those studies mainly used the AI as an additional indicator for the presence of dust (Kaskaoutis et al., 2008) or correlating it with surface PM10 concentrations and AOD measurements (Kalivitis et al., 2007). On the other hand, Koukouli et al. (2006) provided a climatology of the AI at Thessaloniki using 6 years of TOMS observations and associated the AI values with back trajectories from different sectors. However, all the above studies used AI values obtained by TOMS; only Kaskaoutis et al. (2008) used both TOMS and OMI-AI, but for a specific case of an intense dust storm. The main goal of the present study is to assess the use of OMI/Aura AI in a long-term period over a region that is affected by a variety of aerosol types and loadings. Till now no study has been conducted for analyzing the spatio-temporal OMI/Aura AI distribution over Greece and investigating the presence of UV-absorbing or non-absorbing aerosols. The present study analyzes OMI/Aura AI over whole Greece covering the geographical area 34°–42°N and 20°–28°E for the period September 2004 to August 2008. 2. OMI-AI retrievals The OMI (http://aura.gsfc.nasa.gov/instruments/omi/ index.html) is a contribution of the Netherlands's Agency for Aerospace Programs in collaboration with the Finnish Meteorological Institute to the EOS Aura mission (Schoeberl et al., 2006). The OMI instrument employs hyperspectral imaging in a push-broom mode to observe solar backscatter radiation in the ultraviolet and visible. It is a nadir-viewing spectrometer, which measures solar reflected and back-

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scattered radiation in the ultraviolet–visible spectrum, 270 to 500 nm (Levelt et al., 2006a,b). The OMI multi-wavelength algorithm has been developed to retrieve several aerosol parameters (such as AOD, AI, absorption and extinction optical depth, single-scattering albedo) using the measured reflectance at the top of the atmosphere (TOA). Extensive description of the multi-wavelength algorithm, the two models used (i.e. the inversion and forward models), the geographical aerosol distribution, the surface albedo input data and the cloud screening algorithm, are given in Curier et al. (2008). For the OMI wavelengths in both UV and visible almost all surfaces (except ice/snow, desert and salt lakes) have low surface albedo. Therefore, under clear skies, the TOA radiances are mainly dominated by the aerosol and molecular attenuation (scattering and absorption). Despite the relatively short-term operation of OMI (∼ 4 years), great efforts have been made focusing on validating the OMI algorithms, and comparing the OMI products with ground-based retrievals (e.g. Veihelmann et al., 2007; Curier et al., 2008; Kazadzis et al., 2008). In the present study the AI is obtained via the OMI/Aura Level-2G Total Column Ozone Data Product OMTO3G (Version 003), which is now available (http://disc.gsfc.nasa.gov/Aura/ OMI/omto3g_v003.shtml) from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The AI is calculated as the residue between the measured radiance and the calculated one using the Lambert Equivalent Reflectivity (LER) assumption. Assuming a Rayleigh scattering atmosphere above a Lambertian surface, LER is defined as the value of the Lambertian spectral surface albedo for which the modeled and measured TOA reflectances, I, are equal (Herman and Celarier, 1997). The AI is given from the following expression:  AI = −100log

Iλ1 Iλ2



 + 100log

meas

   Iλ1 ðALERλ1 Þ : ð1Þ Iλ2 ðALERλ2 Þ calc

ALER is the wavelength-dependent surface Lambertequivalent albedo. The surface albedo is fitted to the observed radiance at the longer wavelength assuming a pure molecular atmosphere. The surface albedo at the shorter wavelength is determined on the basis of the fitted surface albedo at the longer wavelength. The AI at 388 nm in the present study is calculated using the wavelengths λ 1 = 342.5 nm and λ2 = 388 nm. Therefore, the absorbing AI is defined as the difference between the measured (including aerosol effects) spectral contrast at the 342.5- and 388-nm wavelength radiances and the contrast calculated from the radiative transfer theory for a pure molecular (Rayleigh particles) atmosphere. The method for the AI retrieval via Eq. (1) is based on the principle that for a fixed 388-nm radiance the I342.5/I388 spectral contrast is larger for non-absorbing aerosols and clouds and decreases with increasing absorption. Thus, UV-absorbing aerosols produce smaller contrast than predicted by the pure Rayleigh scattering atmospheric model; consequently they yield positive values. On the other hand, the non-absorbing aerosols produce greater contrast and negative values. Thus, the AI, as a measure of the wavelengthdependent change in Rayleigh-scattered radiance from aerosol absorption, is especially suitable for detecting the presence of elevated absorbing aerosols above high reflecting surfaces, such

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as desert, and snow/ice over land. Desert-dust and biomassburning aerosols have similar effects on the UV radiation by attenuating the Rayleigh reflected radiation below the aerosol layer (Torres et al., 1998; De Graaf et al., 2005). In addition, and unlike the Moderate Resolution Imaging Spectroradiometer (MODIS), the OMI-AI is sensitive to aerosol absorption even when the particles are above clouds; therefore, AI is derived successfully in both cloudless and cloudy conditions (Ahn et al., 2008). 3. Results and discussion 3.1. Spatial distribution of AI The 4-year (2004–2008) OMI-AI mean spatial distribution over Greece is shown in Fig. 1a. The atmosphere over the Greek territory, except areas with local industries and anthropogenic activities, is affected by aerosols from other pollution sources such as Saharan dust outbreaks, biomass-burning smoke and maritime particles. All these aerosols are characterized by large

differences in both physicochemical and optical properties. Seaspray and sulfate aerosols are the most common non-absorbing aerosols (Torres et al., 1998), while smoke particles with significant amount of black or organic carbon can have both absorbing and non-absorbing characteristics (Anderson et al., 1996; Torres et al., 1998). Finally, mineral or dust aerosols, transported from Sahara, represent the clearest possible absorbing signature (Israelevich et al., 2002), which has a direct effect on AI because of its strongly wavelengthdependent imaginary part of the refractive index in the UV (Sinyuk et al., 2003; De Graaf et al., 2005). Relatively high AI values (0.5–0.6) are found over the south Greek sea regions (Fig. 1a), directly affected by the presence of dust particles still suspended in the air at the upper atmospheric levels. Over north Greece and Balkan countries the AI is quite low, even negative, associated with non-UV-absorbing aerosols, such as air pollutants transported from the industrialized areas in the Balkans and eastern Europe. These pollutants mainly consist of SO2 as has been clearly established by Zerefos et al. (2000). Moreover, the pollution transport from eastern Europe and

Fig. 1. 4-year (September 2004–August 2008) average spatial distribution of AI over Greece based on monthly OMI data (a), average spatial distribution of accumulated precipitation in the same period based on monthly TRMM data (b) their correlation as scatter density plot of the annual mean values over the region 34°–42°N, 20°–28°E (c).

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Balkan countries towards the eastern Mediterranean takes place in the lower atmospheric levels (Kambezidis et al., 1995; Lelieveld et al., 2002; Kallos et al., 2007); thus these aerosols within the boundary layer give negative signal in the AI values. The results indicate that regions like Turkey, central Greece and urban/industrialized areas (e.g. Athens) are strongly influenced by UV-absorbing aerosols from anthropogenic emissions, mainly soot and carbonaceous particles that constitute different kinds of aerosols from those over north Greece and the Balkans. In general, larger AI values occur over marine rather than continental regions of Greece. However, since both positive and negative AI values are considered in this study, due to the averaging, it is not obvious whether the larger AI values over marine areas are due to sea salt or whether the lower AI values are due to the negative AI over the continent. In marine regions of south Greece, the main dependence of AI values is on elevated desert-dust aerosols still suspended over oceanic regions, since dust is the main aerosol type over oceans adjoining the desert areas over the globe. From the lower AI values over continental areas (mainly in northern and northwestern Greece and Peloponnese), it is concluded that the continental aerosols above these areas mainly consist of non-UV-absorbing particles. In Fig. 1a some unrealistic slant ridges and dips of AI values are observed despite the 4-yearaveraged data. These diagonal stripes are more intense over the Aegean Sea and seem to be along the satellite orbit paths. This seems to be problematic for revealing a realistic aerosol spatial distribution since these larger AI values do not present actual aerosol fields. However, their influence on the mean AI values over the study region is rather negligible. In any case a more detailed analysis about it is needed as well as validation of the OMI results with other satellite sensors. However, the AI values considered in the present study do not present a realistic measure of the aerosol loading, since they strongly depend on the aerosol type and altitude of the aerosol plumes, in order to be compared with other aerosol measures, like AOD. The computed mean annual value of AI over the study region is equal to 0.273 ± 0.129, where the standard deviation indicates significant spatio-temporal variability. This value is rather larger than the annual mean AI observed over Thessaloniki in northern Greece (Koukouli et al., 2006), but significantly lower than that presented for Heraklion, Crete (Kalivitis et al., 2007). It is well known that wet deposition is the most effective process for the atmospheric depletion of the aerosol load. Thus, it is a real challenge to investigate the role of precipitation in the AI values as well as in their spatial distribution. Such a study has never been conducted over Greece, while Habib et al. (2006) correlated AI over India with rainfall data over that region. They found that, in general, rainfall decreased the AI values. An in-depth investigation (analysis of the AI vs rainfall time series, monthly and seasonal spatial distributions, correlations above areas with specific meteorological characteristics, etc.) is beyond the scope of the present study and constitutes a challenge for further research. However, a first indication of the association between AI and rainfall amounts over Greece is given in Fig. 1b, c. The rainfall data were obtained by the Tropical Rainfall Measurement Mission (TRMM) for the same period with spatial distribution of AI of 0.25° × 0.25° and correspond to monthly-accumulated precipitation. Fig. 1b shows the annual mean spatial distribution of the accumulated precipitation over Greece. It is

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shown that the precipitation pattern follows the Greek mountainous topography with higher values over Pindos Mountains and northern Greece and lower values in the south and over the central Aegean Sea. The precipitation is of large intensity especially in west and northwest Greece, with the appearance of thunderstorms (Nastos et al., 2002; Nastos and Zerefos, 2008). Comparing Fig. 1a and b it is worth to observe the larger AI values over southern Greece associated with the least precipitation. Additionally, there are clear inverse correlations between AI and precipitation values over Pindos Mountain Range and central Peloponnese. The correlation between annual mean AI and accumulated precipitation over Greece is shown in Fig. 1c as a scatter density plot, with a 0.1 step for AI and 10 mm for precipitation. The white gaps correspond to missing data. Despite the relative large scatter (R2 = 0.21), a remarkable decreasing trend is revealed in the density maximum area, showing that precipitation has a clear effect on AI. The remarkable decrease is spatially limited where precipitation is lower than 500 mm. From Fig. 1b it is shown that the lower precipitation is almost over the sea area. This fact means that the precipitation affects mainly the ocean AI. Although the annual mean values of both parameters tend to smooth out their spatio-temporal variability leading to lower correlation, the decreasing trend seems to be significant. Aerosol plumes, mainly dust outbreaks and biomass burning have a highly episodal nature. Therefore, correlations with precipitation as made in the present study may be much higher when daily data are used rather than monthly means. The analysis of AI and precipitation on monthly or daily basis constitutes an issue for further research. In Fig. 2a, b the spatial distribution of AI in the cold (October to March) and in the warm (April to September) periods is shown. Papadimas et al. (2008) based on 6-year MODIS observations found larger aerosol amounts over whole Greece in the warm period and lower and more smoothly spatialdistributed AODs in the cold period. As revealed from the seasonal figures, the AI spatial distribution is more intense in the warm rather than in the cold period. However, in both periods, and with higher intensity in the cold one, there seem to be diagonal stripes in Fig 2a, b as in Fig. 1a. These stripes have a north–northwestern direction concluding to an unrealistic AI spatial distribution in the cold period of the year. Despite the fact that these stripes affect the AI spatial distribution, they do not significantly affect the monthly-averaged AI values over the region or their annual variation, since this is a systematic feature in the Aura–OMI-AI values. On the other hand, in the warm period, when AI is larger, the influence of these stripes is much lower. The tendencies in AI are expressed as terms of differences ΔAI and are calculated from the linear regression (ΔAI = a* Δt) to the time series for warm and cold periods individually; the monthly AI values in the 4-year period were used for the trend calculations. ΔAI actually represents the slope of the AI regression line in each pixel starting in September 2004 and ending in August 2008. For comparison the colored scale in Fig. 2c, d is the same, while blue colors correspond to negative tendency. In the cold period the negative values, indicating a decreasing trend of AI, cover a larger geographical extent (453 out of 1024 pixels) against the warm period (405 pixels). Nevertheless, in the majority of the pixels an increasing trend in AI values is revealed. This leads to

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Fig. 2. Spatial distribution of mean AI over Greece in the cold (October to March) (a) and in the warm (April to September) periods; tendencies in AI values in absolute terms for the cold (c) and the warm period (d).

an overall mean increasing AI of 0.015 ± 0.148 and 0.032 ± 0.165 for the cold and the warm period, respectively. The ΔAI varies between −0.57 and 0.68 in the cold period and between −0.65 and 0.54 in the warm. The increasing or decreasing tendency in AI is not uniform over the region, exhibiting large spatial distribution. In the cold period a decreasing tendency appears mainly in northwestern Greece, in the south and over central Aegean, while an increase in AI exists in central continental Greece and Turkey. In the warm period, the increasing trend of AI mainly occurs over central Greece and Aegean Sea, while over northern Greece a decreasing tendency seems to dominate. In contrast, Papadimas et al. (2008) found a decreasing trend in AOD550 values especially over continental Greece and a lower decreasing trend in the southernmost marine regions.

The AI spatial distributions over Greece show a pronounced south-to-north gradient. In order to analyze it we focus on the latitudinal variation of the AI values, spatially averaged over the longitudinal area 20° to 28°E. The AI calculations were performed from the daily AI values in the study period. For each latitude the averaged-longitudinal (20°–28°E) AI value was used. The large number of longitudinal-averaged values in the 4-year period (∼ 120 in maximum) seems to be sufficient for this scope and missing data were not observed. The monthly-mean longitudeaveraged AI values are plotted against latitude in Fig. 3 for each season. Significant seasonal differences are revealed in both AI values and their spatial distribution over Greece. In winter, the spatio-temporal AI distribution is rather low, as observed from the smoothness in the colored graph.

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Fig. 3. Monthly variability of the longitude-averaged AI values over Greece for winter (a), spring (b), summer (c) and autumn (d). Each AI data point on any plot is the mean value over the period September 2004–August 2008. Any single 3D plot corresponds to a specific month.

December exhibits higher values over all parts of Greece with emphasis in the north. AI decreases in January and February, while values equal to those of December are observed only on certain days. Note the absence of negative AI values in the winter period, which is not the case in the other seasons. Furthermore, there is an evidence for a little larger AI in northern Greece compared to that in the south, which would be verified in the following. In spring, the aforementioned situation strongly changes. The remarkable finding in this season is the highest AI values as well as the strong south-to-north gradient. The high AI values over southern Greece are mainly attributed to the more frequent and intense Saharan dust events over the eastern Mediterranean (Moulin et al., 1998; Fotiadi et al., 2006; Kalivitis et al., 2007; Meloni et al., 2007). The intensity of these events is mainly driven by the Mediterranean cyclones (Kaskaoutis et al., 2008; Meloni et al., 2008). The intense dust events on a specific day can also affect the 4-year-averaged AI value, as clearly observed in March, 16 to 21 and 28, on 11–12, 16–17 and 20–22 April and on 6, 12 and 18 May. On days not affected by dust, the AI values are relatively low, below 0.3 even in southern Greece; thus the AI variability between the south and north parts of

Greece is mainly depicted during the dust events for the spring season. The spatio-temporal variability is larger in March and April, while it drops in May when negative AI values start to be present in northern Greece. Negative AI values are also found on specific days in March and April, but without preference over northern Greece. In summer, the situation is further modified. The strong south-to-north gradient continues to exist, but now is mainly driven by the negative AI values in the north, since the AIs in the south are lower than those in the spring. The negative AI values clearly differentiate the UV-absorbing nature between the aerosols over northern and southern Greece, or between summer and the rest of the year. An extensive analysis of air-mass trajectories in the Mediterranean during the MINOS campaign (August 2001) presented by Lelieveld et al. (2002) revealed a dominant northerly flow below 4 km, further confirming our results and those of Stohl et al. (2002) and Duncan and Bey (2004) for a summer southward flow of European pollution over the Aegean Sea and eastern Mediterranean. All these studies support our assertions that the polluted European aerosols are transported over northern Greece within the boundary layer,

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where their identification by AI is rather difficult. This fact, in combination with higher presence of non-UV-absorbing sulfate aerosols, and the absence of elevated dust particles in such long distances from Sahara, gives negative AIs over northern Greece in the summer. The spatio-temporal variability of AI is not as intense as in spring, while moderate-tohigh AI values are observed over south and central Greece. The three summer months do not exhibit significant differences either in AI values or in their spatial distribution. Autumn presents large variability in AI spatio-temporal distribution as well as significant differences among the three months. Thus, on certain days of September Saharan dust events can be intense as has also been established by Papayannis et al. (2005). On the other hand, September is also influenced by the northerly Etesian winds carrying sulfate pollution or sea-salt aerosols over the Aegean Sea. In October the AI values are smoothly distributed over the study region, with the exception of negative values on the first and large values indicative of dust exposure on the last days. November exhibits moderate AI values over the whole Greek area, the spatial variability is further decreased and the situation is somewhat similar to that observed in December. Absorption by UV-absorbing aerosols is strongly altitude dependent and increases with increasing altitude because these aerosols strongly absorb the radiation coming from below, i.e., the higher the aerosol altitude the greater the fraction of affected radiation (Torres et al., 1998). Therefore, the sensitivity of the AI to aerosol amount increases more or less proportionally with the aerosol layer height, while any aerosol below about 1000 m is unlikely to be detected (de Graaf et al., 2005; Hsu et al., 1999). Thus, for AI values less than 0.5 the interpretation of AI in terms of aerosol amount is very uncertain. African dust is transported over Mediterranean and Greece at higher atmospheric levels (Alpert et al., 2004), while pollution transport is mainly detected within the boundary layer (Kallos et al., 2007). The first case has a direct effect on the large AI values, while the second gives AIs near to zero or even negative. The possible errors and uncertainties in the AI retrievals through the OMI algorithms may be of a great importance on certain days. The sources of uncertainties are the UV refractive index and the presence of clouds. Thus, as mentioned by Torres et al. (1998), weakly absorbing aerosols at low altitudes yield negative signals and cannot be distinguished from non-absorbing aerosols. Furthermore, sub-pixel cloud contamination can underestimate aerosols even though clouds themselves are much brighter than them (Torres et al., 1998). The AI will vary with several factors including differences between modeled and true land or ocean color and reflectivity, changes in the solar zenith angle, changes in the aerosol layer height and cloud reflectivity and pressure. However, the uncertainty and the variability are minimized on monthlymean basis or in longitude-averaged values. In Fig. 4 the mean longitudinal AI values for each month of the examined period are plotted against latitude. The blue color corresponds to December, March, June and September, the red to January, April, July and October, and the green to February, May, August and November. Significant differences in both AI values as well as in their latitudinal variability are observed in each season. More specifically, in the winter there is negligible difference (despite a slight increase towards north) in the AI values according to latitude, while in the

autumn this difference is somewhat more intense, with larger values at lower latitudes and also a possibility for negative values at the higher ones. In contrast, in the spring and summer a strong latitudinal variation exists, with larger AI values in spring and negative AI values in summer at northern latitudes. From this figure one can also note the large variability in the AI values in the same month. This variability is more intense in April (red) caused by a large spatiotemporal fluctuation in the occurrence and the intensity of the dust events over the eastern Mediterranean (Antoine and Nobileau, 2006) and Greece (Kosmopoulos et al., 2008). It was found that the trend in the monthly-mean longitudeaveraged AI values for each month of the study is better simulated with a linear fit of the form AI = a*Lat + b. The a and b values associated with the R2 coefficient are given in Table 1. For each month four values of the above parameters are given for each year. The main conclusions drawn from Table 1 can be summarized as follows: 1) In winter the slope (a) is positive in the majority of the months showing a slight increase of the AI values towards northern Greece. Note also the very low R2 values in some months indicative of large scatter. 2) In spring the slope is always negative, indicating a decreasing AI trend to the northern latitudes. The slope in absolute values is larger than that in the winter months, while R2 is now much larger, reaching even above 0.85. 3) In the summer months the scatter in the linear fits is even less, while the slope even higher showing the large southto-north gradient in AI values. 4) In autumn the slope of the linear fit drops significantly, while there are months in which it becomes positive. Furthermore, the scatter becomes more significant and in late autumn the situation approaches that of the winter. 3.2. Monthly variation of AI Fig. 5 shows the time series for the OMI-AI monthly-mean values above Greece with their corresponding standard deviations. The AI values lie in a wide range, as low as −0.8 up to 3.0 in certain cases. The highest values are systematically observed between March and April (see the max values in Table 2), while the lowest in the summer months. This wide range of values results from the co-existence of urban, continental, marine and dust aerosols over Greece, a fact that makes it complicated to segregate optical effects on the observed radiation because of the dependence on different aerosol types. Fig. 5 presents a significant month-to-month variation as regards the mean AI values. Even in the same month, the AI variability from year-to-year is large. Despite the significant monthly variability the trend in AI values in the examined period is absent (ΔAI = 0.35%) as is clearly revealed from the linear fit. Such negligible trend in AOD and fine-mode values obtained from MODIS was also observed over Athens (Kaskaoutis et al., 2007) for the period 2000– 2005. Koukouli et al. (2006) also found negligible tendency in TOMS-AI values over Thessaloniki in the period 1997–2001. In contrast, Papadimas et al. (2008) found a statistically significant decreasing trend (− 20% to − 30%) of AOD550 derived by MODIS over Mediterranean and Greece in the period 2000–2006. The fact that AI does not show a trend,

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Fig. 4. Latitudinal variation of the monthly-mean longitude-averaged AI values over Greece for each season in the period September 2004–August 2008. Blue for December, March, June and September; red for January, April, July and October and green for February, May, August and November.

Table 1 Values of the a and b coefficients in the equation AI = a*Lat + b applied to the monthly-mean values averaged over the longitudinal region 20°–28°E. The coefficient of determination R2 shows the scatter of the data points from the fitting linear form. The given values correspond to each month in the period September 2004–August 2008. The values of a, b and R2 at each specific position correspond to the same equation. The bold numbers of R2 are those values above 0.6; the underlined bold ones indicate R2 values above the 90% statistical significance level. Month

Year

January

2005 2007 2005 2007 2005 2007 2005 2007 2005 2007 2005 2007 2005 2007 2005 2007 2004 2006 2004 2006 2004 2006 2004 2006

February March April May June July August September October November December

a 2006 2008 2006 2008 2006 2008 2006 2008 2006 2008 2006 2008 2006 2008 2006 2008 2005 2007 2005 2007 2005 2007 2005 2007

− 0.0123 0.0361 0.0183 − 0.0277 − 0.0243 − 0.0239 − 0.0307 − 0.0486 − 0.0705 − 0.0583 − 0.0609 − 0.0914 − 0.0857 − 0.0582 − 0.0581 − 0.0502 − 0.0334 − 0.0491 − 0.0210 − 0.0082 0.0073 0.0245 0.0023 0.0181

R2

b 0.0088 0.0144 0.0013 0.0239 − 0.0204 − 0.045 − 0.0730 − 0.0643 − 0.0472 − 0.0812 − 0.0654 − 0.1065 − 0.0665 − 0.0644 − 0.0832 − 0.0640 −.00565 − 0.0345 0.0095 − 0.0227 0.0164 − 0.0143 − 0.0063 0.0120

0.8787 − 0.9896 − 0.4322 1.3805 1.1559 1.2905 1.6591 2.0320 2.9318 2.4773 2.3136 3.8265 3.3130 2.4040 2.3211 2.0910 1.7895 1.9976 1.0432 0.4040 0.1300 − 0.6787 0.4521 − 0.3158

− 0.0235 0.2575 0.2629 − 0.7364 1.064 2.1635 3.1367 3.0970 1.9164 3.5193 2.7100 4.2152 2.4938 4.2152 3.3427 2.5515 2.2544 1.4748 − 0.2457 1.2197 − 0.3460 0.9126 0.6666 − 0.0310

0.2207 0.7718 0.3536 0.6013 0.5604 0.5178 0.6637 0.8591 0.9122 0.8894 0.9156 0.9414 0.9264 0.8740 0.8820 0.7856 0.7702 0.8795 0.5805 0.2058 0.1207 0.7104 0.0197 0.7356

0.2776 0.5326 0.0041 0.8671 0.3475 0.8531 0.8708 0.8331 0.8354 0.9244 0.9092 0.9599 0.8775 0.7633 0.9147 0.8725 0.8806 0.7599 0.1370 0.6666 0.4877 0.5916 0.2002 0.4032

36

D.G. Kaskaoutis et al. / Atmospheric Research 98 (2010) 28–39

Fig. 5. Inter-annual variation of the monthly-mean AI values associated with the standard deviation of the mean monthly value. The linear fit shows the trend in the AI variation.

whereas AOD does, might signify that even though the atmospheric loading itself is decreasing over the region, this type of aerosols remains unaltered. The monthly spatial distribution of AI presented in Section 3.1 showed a significant south-to-north gradient. For this reason, we define two sub-regions, south and north, focusing on analyzing the differences in the AI values. The two sub-regions are equal in spatial coverage (south: 34°–37°N, 20°–28°E and, north: 39°–41°N, 20°–28°E); the number of the monthly-mean OMI pixels is 4608.

The mean monthly variability associated with the standard deviations in the examined period over whole Greece and in the two sub-regions is shown in Fig. 6. The computed results, which consist of the mean, standard deviation, maximum and minimum values in the three regions, are presented in Table 2. Focusing on the whole Greek area, the annual AI variability seems to be bi-modal with two peaks presented in April and December, while lower values are presented in the summer closely associated with the negative values in these months. This double peak in the monthly variation of the AI values above Greece is consistent with the variability found in Thessaloniki (Koukouli et al., 2006) and in eastern Mediterranean (Israelevich et al., 2002). The monthly-mean AI values range from 0.102 (July) to 0.424 in April and 0.441 in December, with a mean of 0.273 ± 0.104. This positive mean value corresponds to the mixture of absorbing aerosols (dust or soot particles), which are expected to affect Greece. The highest AI values (mainly in spring) are found to be associated with trajectories that transport air from north Africa over Greece. However, many studies (Fotiadi et al., 2006; Kaskaoutis et al., 2007; Kosmopoulos et al., 2008; Papadimas et al., 2008) have shown more transparent atmospheric conditions over Greece in the winter as was also found by the OMI-AOD values for the examined period (not shown here). However, the AI values in the winter are relatively high, especially over northern Greece. This also shows the limitation of AI to present the aerosol amount, since it depends on several parameters, as noted before. Concerning the monthly AI variability in southern Greece the bi-modal pattern, in general, remains. The main difference is depicted in the larger monthly variability that mainly consists of the larger spring AI values. A secondary peak is

Table 2 Monthly mean, standard deviation, maximum and minimum AI values above Greece and its southern and northern parts in the period September 2004–August 2008. Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Whole Greece

Southern Greece (34°–37°N)

Northern Greece (39°–42°)

Mean (St dev)

Max

Min

Mean (St dev)

Max

Min

Mean (St dev)

Max

Min

0.348 (0.208) 0.268 (0.218) 0.326 (0.249) 0.424 (0.329) 0.269 (0.277) 0.187 (0.311) 0.102 (0.289) 0.149 (0.259) 0.232 (0.333) 0.202 (0.226) 0.325 (0.209) 0.441 (0.216)

1.569

− 0.346

1.569

− 0.28

− 0.289

− 0.489

1.834

− 0.489

1.444

− 0.375

3.127

− 0.317

3.127

− 0.248

1.752

− 0.265

3.533

− 0.628

3.533

− 0.381

2.533

− 0.628

1.463

− 0.788

1.463

− 0.741

1.211

− 0.628

1.483

− 1.331

1.483

− 0.397

0.886

− 1.331

1.855

− 0.993

1.54

− 0.416

1.198

− 0.993

2.226

− 1.006

2.226

− 0.294

0.829

− 1.006

2.855

− 0.7

2.855

− 0.352

2.235

− 0.64

1.473

− 0.589

1.232

− 0.4

1.473

− 0.516

1.345

− 0.343

1.085

− 0.256

1.345

− 0.247

1.382

− 0.289

1.332

− 0.214

0.377 (0.203) 0.283 (0.196) 0.268 (0.209) 0.299 (0.304) 0.112 (0.229) − 0.012 (0.262) − 0.069 (0.279) − 0.008 (0.238) 0.132 (0.329) 0.182 (0.212) 0.349 (0.212) 0.459 (0.229)

1.558

1.834

0.320 (0.215) 0.263 (0.235) 0.409 (0.272) 0.568 (0.307) 0.434 (0.245) 0.391 (0.256) 0.272 (0.204) 0.309 (0.198) 0.344 (0.294) 0.234 (0.223) 0.299 (0.200) 0.423 (0.201)

1.382

− 0.186

D.G. Kaskaoutis et al. / Atmospheric Research 98 (2010) 28–39

Fig. 6. Monthly variability of the mean AI values over whole Greece, southern Greece and northern Greece. The vertical bars correspond to one standard deviation from the monthly mean.

also revealed in September, since Saharan dust events may be frequent as already has been established from previous studies in Athens and Thessaloniki (Papayannis et al., 2005). The strong seasonality observed in Saharan dust exposures (Antoine and Nobileau, 2006), also associated with great spatial and temporal variability in aerosol load (Kaskaoutis et al., 2008), has a clear evidence in the larger standard deviations of the AI values in the spring, and mainly in April. In contrast, in winter, the AI spatial distribution is smoother (Fig. 3) and this is also depicted in the lower standard deviations. The mean AI over south Greece (0.356 ± 0.094) is larger than the value computed over the whole Greek territory. On the other hand, the AI monthly variability in northern Greece presents a different pattern, with higher values in the winter (November to January), a plateau of medium values (∼ 0.28) in February–April and low AIs in the summer. It is very characteristic that the monthly-mean AI is negative in the summer. The mean AI value (0.198 ± 0.169) over northern Greece is about half of that observed over the south. However, these values as well as the monthly variability cannot be compared with those presented by Koukouli et al. (2006) at Thessaloniki, since they used only positive AI values from TOMS. The daily values of the AI at Thessaloniki ranged from − 1.67 to 3.06, with a mean of 0.22 ± 0.73 in the period 1997–2001. However, assuming only positive AI values they obtained a mean of 0.65 ± 0.54. Koukouli et al. (2006) also found a mean AI value of 0.97 ± 0.77 for biomass-burning events that affected Thessaloniki and a respective mean AI of 1.25 ± 0.72 for desert-dust events at the same site.

37

In the previous analysis it was established that the OMI-AI values over Greece consist of a significant fraction of negative values, which strongly affect the monthly AI variability. However, an open question still exists. How significant is this fraction and, how much would the monthly AI variability change ignoring the negative AI values? To this respect, Fig. 7 shows the monthly % fraction of the negative AI values over whole, southern and northern Greece. Regarding the entire Greek region, the larger fraction of negative AI values is presented in the summer months reaching up to 30% in July. In the period November–February the fraction of negative AI values is similar in the three regions, while in the summer months the negative values over northern Greece consist more than 50%, reaching 61% in July. The pattern over northern Greece affects more the general pattern over the whole Greek territory, since that over southern Greece seems to be more complicated, exhibiting larger fraction of negative AIs in October (13%) and February (9%). The south Greek area is mainly covered by sea and the stronger winds in late autumn and winter may produce larger amounts of non-absorbing seasalt aerosols as was also observed over Bay of Bengal (Ramachandran, 2004; Satheesh et al., 2006). The lower fraction of negative AI values occurs in spring, the period with the higher presence of UV-absorbing desert-dust particles over southern Greece. In the whole period the fraction of negative AI values is 14.5% on average above Greece, becoming 24.4% over north and only 4.8% over south Greece. Ignoring the negative AI values, as in de Graaf et al. (2005) and Koukouli et al. (2006), the monthly-mean AI variability is re-analyzed above the three regions. The monthly-mean AI values with their standard deviations are summarized in Table 3, and for a better presentation they are shown in Fig. 8. Regarding southern Greece, the AI monthly variability remains nearly the same, since the fraction of the ignored negative AI values in each month is too small to strongly affect the results. However, this is not the case over whole and northern Greece, where the monthly-mean variability and, especially the AI values, significantly changes. Thus, the monthly variability becomes now smoother, since the large increase in the summer AI reduces the previous monthly differences. This is more pronounced in northern Greece, where a rather weak monthly variability is now observed,

Fig. 7. Monthly variation of the fraction (%) of the negative AI values over whole Greece, southern Greece and northern Greece.

38

D.G. Kaskaoutis et al. / Atmospheric Research 98 (2010) 28–39

Table 3 Monthly mean and standard deviation of the AI values above whole Greece, southern Greece and northern Greece assuming positive AI values only. Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Whole Greece

Southern Greece (34°–37°N)

Northern Greece (39°–42°)

Mean

St dev

Mean

St dev

Mean

St dev

0.360 0.299 0.354 0.466 0.346 0.329 0.262 0.267 0.339 0.267 0.341 0.446

0.198 0.199 0.231 0.302 0.229 0.227 0.193 0.182 0.298 0.192 0.198 0.211

0.333 0.295 0.425 0.574 0.446 0.415 0.301 0.328 0.366 0.281 0.314 0.426

0.205 0.219 0.262 0.301 0.234 0.237 0.183 0.183 0.285 0.199 0.189 0.199

0.389 0.307 0.299 0.364 0.227 0.209 0.201 0.182 0.305 0.248 0.366 0.467

0.191 0.176 0.187 0.269 0.170 0.168 0.193 0.144 0.302 0.175 0.198 0.221

which approaches that presented by Koukouli et al. (2006). However, Koukouli et al. (2006) found higher TOMS-AI values in July and August than those obtained in the present study. The different spatial coverage, time period and satellite sensor used are significant reasons and can justify these differences. In all regions the exclusion of the negative AI values has a clear evidence in the lower standard deviations and, on average, increases the AI values at 24.5% in whole Greece, 5.6% in the south and 50.2% in the north.

4. Conclusions The objective of this study was to analyze spatial, seasonal and interannual variabilities of AI over Greece, as detected by

the OMI instrument during the years 2004 to 2008 inclusive with an evaluation of potential contributing factors, including precipitation and long-range transport. The spatial distribution in OMI-AI values was related to the Saharan dust events mainly over southern Greece and to the trans-boundarypollution transport, consisting mainly of sulfate particles, in northern Greece. The presence of UV-absorbing desert-dust aerosols, mainly in the upper atmospheric levels, had a clear signal in AI values, enhancing them over southern Greece and causing large spatial and temporal variabilities during the days associated with dust exposure, mainly in spring. On the other hand, the prevailing northerly flow in the summer is responsible for the transport of anthropogenic pollution from eastern Europe above northern Greece within the boundary layer, thus leading to negative AI values, characteristics of the presence of non-absorbing aerosols. The monthly AI variability over Greece presented maximum values in spring and December and lower, even negative, in the summer. Focusing on south and north Greece, this annual variation was driven by the stronger monthly variability consisted of the higher AI in spring over south Greece and the lower (negative) AI in summer over north Greece. The annually averaged AI values were found to be 0.273 ± 0.104, 0.356 ± 0.094 and 0.198 ± 0.169, over whole, southern and northern Greece, respectively. On the other hand, in the 4 years examined there was no trend in the AI values above Greece, while the spatial distribution of the tendency in AI showed a rather complicated pattern. The negative AI values above Greece corresponded to a significant fraction, also exhibiting large monthly variability, with larger fraction in the summer, which can be above 50% over northern Greece. On average, the negative AI values corresponded to 14.46%, 4.81% and 24.39%, over whole, southern and northern Greece, respectively. Ignoring the negative AIs, it was found that the monthly AI variability remained unaltered, having larger values, especially over northern Greece in the summer. Overall, it can be concluded that satellite-remote sensing was successfully applied in deducing the origin, temporal and spatial distribution of absorbing aerosols; the positively constrained spectral contrast in the UV range derived from OMI-AI was successfully used to detect dust transport over Greece. The large differences in the AI values between northern and southern Greece need further in-depth analysis and association with local emissions, regional meteorology and long-range transport. However, this study is the first of its kind utilizing OMI-AI data and taking into account its spatial and temporal distribution over Greece; this study can be the basis for further research work in the future.

Acknowledgements The authors would like to thank the OMI and TRMM science data support teams for processing data via Giovanni website (http://giovanni.gsfc.nasa.gov/).

References

Fig. 8. Same as Fig. 6, but ignoring the negative AI values.

Ahn, C., Torres, O., Bhartia, P.K., 2008. Comparison of ozone monitoring instrument UV aerosol products with AQUA/Moderate Resolution Imaging Spectroradiometer and Multiangle Imaging Spectroradiometer observations in 2006. J. Geophys. Res. 113 D16S27.

D.G. Kaskaoutis et al. / Atmospheric Research 98 (2010) 28–39 Alpert, P., Kishcha, P., Shtivelman, A., Krichak, S.O., Joseph, J.H., 2004. Vertical distribution of Saharan dust based on 2.5-year model predictions. Atmos. Res. 70, 109–130. Anderson, B.E., Grant, W.B., Gregory, G.L., et al., 1996. Aerosols from biomass burning over the tropical South Atlantic region: distributions and impacts. J. Geophys. Res. 101 (D19), 24117–24138. Antoine, D., Nobileau, D., 2006. Recent increase of Saharan dust transport over the Meditterenean Sea, as revealed from ocean color satellite (SeaWiFS) observations. J. Geophys. Res. 111, D12214. doi:10.1029/2005JD006795. Badarinath, K.V.S., Kharol, Shailesh Kumar, Kaskaoutis, D.G., Kambezidis, H.D., 2007. Dust storm over Indian region and its impact on the ground reaching solar radiation — a case study using multi-satellite data and ground measurements. Sci. Total Environ. 384, 316–332. Badarinath, K.V.S., Kharol, Shailesh Kumar, Krishna Prasad, V., Reddi, E.U.B., Kambezidis, H.D., Kaskaoutis, D.G., 2008. Influence of anthropogenic activities on UV Index variations — a study over tropical urban region using ground based observations and satellite data. J. Atmos. Chem. 59, 219–236. Baddock, M.C., Bullard, J.E., Bryant, R.G., 2009. Dust source identification using MODIS: a comparison of techniques applied to the Lake Eyre Basin, Australia. Remote Sens. Environ. 113, 1511–1528. Balis, D.S., Amiridis, V., Zerefos, C., Kazantzidis, A., Kazadzis, S., Bais, A.F., Meleti, C., Gerasopoulos, E., Papayannis, A., Matthias, V., Dier, H., Andreae, M.O., 2004. Study of the effect of different type of aerosols on UV-B radiation from measurements during EARLINET. Atmos. Chem. Phys. 4, 307–321. Curier, R.L., Veefkind, J.P., Braak, R., Veihelmann, B., Torres, O., de Leeuw, G., 2008. Retrieval of aerosol optical properties from OMI radiances using a multiwavelength algorithm: application to western Europe. J. Geophys. Res. 113. doi:10.1029/2007JD008738 D17S90. De Graaf, M., Stammes, P., Torres, O., Koelemeijer, R.B.A., 2005. Absorbing Aerosol Index: sensitivity analysis, application to GOME and comparison with TOMS. J. Geophys. Res. 110, D01201. doi:10.1029/2004JD005178. Duncan, B.N., Bey, I., 2004. A modeling study of the export pathways of pollution from Europe: seasonal and interannual variations (1987– 1997). J. Geophys. Res. 109, D08301. doi:10.1029/JD004079. Eck, T.F., Holben, B.N., Ward, D.E., Dubovic, O., Reid, J.S., Smirnov, A., Mukelabai, M.M., Hsu, N.C., O’ Neil, N.T., Slutsker, I., 2001. Characterization of the optical properties of biomass burning aerosols in Zambia during the 1997 ZIBBEE field campaign. J. Geophys. Res. 106 (D4), 3425–3448. Eck, T.F., Holben, B.N., Ward, D.E., et al., 2003. Variability of biomass burning aerosol optical characteristics in southern Africa during SAFARI 2000 dry season campaign and a comparison of single scattering albedo estimates from radiometric measurements. J. Geophys. Res. 108 (D13), 8477. Engelstaedter, S., Tegen, I., Washington, R., 2006. North African dust emissions and transport. Earth-Sci. Rev. 79, 73–100. Fotiadi, A., Hatzianastassiou, N., Drakakis, E., Matsoukas, C., Pavlakis, K.G., Hatzidimitriou, D., Gerasopoulos, E., Mihalopoulos, N., Vardavas, I., 2006. Aerosol physical and optical properties in the Eastern Mediterranean Basin, Crete, from Aerosol Robotic Network data. Atmos. Chem. Phys. 6, 5399–5413. Gassó, S., Stein, A.F., 2007. Does dust from Patagonia reach the sub-Antarctic Atlantic Ocean. Geophys. Res. Lett. 34, L01801. doi:10.1029/2006GL027693. Habib, G., Venkataraman, C., Chiapello, I., Ramachadran, S., Bopucher, O., Reddy, M.S., 2006. Seasonal and interannual variability in absorbing aerosols over India derived from TOMS: relationship to regional meteorology and emissions. Atmos. Environ. 40, 1909–1921. Herman, B.M., Celarier, E., 1997. Earth surface climatology at 340 nm and 380 nm from TOMS data. J. Geophys. Res. 102, 12059–12076. Hsu, N.C., Herman, J.R., Torres, O., Holben, N.B., Tanre, D., Eck, T.F., Smirnov, A., Chatenet, B., Lavenu, F., 1999. Comparisons of the TOMS aerosol index with sunphotometer aerosol optical thickness: results and applications. J. Geophys. Res. 104, 6269–6279. Israelevich, P.L., Levin, Z., Joseph, J.H., Ganor, E., 2002. Desert aerosol transport in the Mediterranean region as inferred from the TOMS aerosol index. J. Geophys. Res.. doi:10.1029/2001JD002011 Kalivitis, N., Gerasopoulos, E., Vrekousis, M., Kouvarakis, G., Kubilay, N., Hatzianastassiou, N., Vardavas, I., Mihalopoulos, N., 2007. Dust transport over the Eastern Mediterranean from TOMS, AERONET and surface measurements. J. Geophys. Res. 112, D03202. doi:10.1029/2006JD007510. Kallos, G., Astitha, M., Katsafados, P., Spyrou, C., 2007. Long-range transport of anthropogenically and naturally produced particulate matter in the Mediterranean and North Atlantic: current state of knowledge. J. Appl. Meteorol. Climatol. 46, 1230–1251. Kambezidis, H.D., Peppes, A., Melas, D., 1995. An experimental study in Athens area under the sea breeze conditions. Atmos. Res. 36, 139–156. Kaskaoutis, D.G., Kosmopoulos, P., Kambezidis, H.D., Nastos, P.T., 2007. Aerosol climatology and discrimination of different types over Athens, Greece based on MODIS data. Atmos. Environ. 41, 7315–7329. Kaskaoutis, D.G., Kambezidis, H.D., Nastos, P.T., Kosmopoulos, P.G., 2008. Study on an intense dust storm over Greece. Atmos. Environ. 42, 6884–6896.

39

Kazadzis, S., Bais, A., Arola, A., Krotkov, N., Kouremeti, N., Meleti, C., 2008. Ozone Monitoring Instrument spectral UV irradiance products: comparison with ground based measurements at an urban environment. Atmos. Chem. Phys. Discuss. 8, 17467–17493. Kosmopoulos, P.G., Kaskaoutis, D.G., Nastos, P.T., Kambezidis, H.D., 2008. Seasonal variation of columnar aerosol optical properties over Athens, Greece, based on MODIS data. Remote Sens. Environ. 112, 2354–2366. Koukouli, M.E., Balis, D.S., Amiridis, V., Kazadsis, S., Bais, A., Nickovic, S., Torres, O., 2006. Aerosol variability over Thessaloniki using ground based remote sensing observations and the TOMS aerosol index. Atmos. Environ. 40, 5367–5378. Kubilay, N., Oguz, T., Kocak, M., 2005. Ground-based assessment of Total Ozone Mapping Spectrometer (TOMS) data for dust transport over the northeastern Mediterranean. Global Biogeochem. Cycles 19. doi:10.1029/ 2004GB002370 GB1022. Lelieveld, J., et al., 2002. Global air pollution crossroads over the Mediterranean. Science 298, 794–999. Levelt, P.F., van den Oord, G.H.J., Dobber, M.R., Mälkki, A., Visser, H., de Vries, J., Stammes, P., Lundell, J., Saari, H., 2006a. The Ozone Monitoring Instrument. IEEE Trans. Geosci. Remote Sens. 44 (5), 1093. Levelt, P.F., Hilsenrath, E., Leppelmeier, G.W., van den Oord, G.B.J., Bhartia, P.K., Tamminen, J., de Haan, J.F., Veefkind, J.P., 2006b. Science objectives of the Ozone Monitoring Instrument. IEEE Trans. Geosci. Remote Sens. 44 (5), 1199. Meloni, D., di Sarra, A., Biavati, G., DeLuisi, J.J., Monteleone, F., Pace, G., Piacentino, S., Sferlazzo, D.M., 2007. Seasonal behavior of Saharan dust events at the Mediterranean island of Lampedusa in the period 1999–2005. Atmos. Environ. 41, 3041–3056. Meloni, D., di Sarra, A., Monteleone, F., Pace, G., Piacentino, S., Sferlazzo, D.M., 2008. Seasonal transport patterns of intense Saharan dust events at the Mediterranean island of Lampedusa. Atmos. Res. 88, 134–148. Moulin, C., Lambert, C.E., Dayan, U., et al., 1998. Satellite climatology of African dust transport in Mediterranean atmosphere. J. Geophys. Res. 103, 13137–13144. Nastos, P.T., Zerefos, C.S., 2008. Decadal changes in extreme daily precipitation totals in Greece. Adv. Geosci. 16, 55–62. Nastos, P.T., Philandras, C.M., Repapis, C.C., 2002. Application of canonical analysis to air temperature and precipitation regimes over Greece. Fresenius Environ. Bull. 11 (8), 488–493. Papadimas, C.D., Hatzianastassiou, N., Mihalopoulos, N., Querol, X., Vardavas, I., 2008. Spatial and temporal variability of aerosol properties over Mediterranean basin based on 6 years (2000–2006) MODIS data. J. Geophys. Res. 113, D11205. doi:10.1029/2007JD 009189. Papayannis, A., Balis, D., Amiridis, V., Chourdakis, G., Tsaknakis, G., Zerefos, C.S., Castanho, A.D.A., Nickovic, S., Kazadzis, S., Grabowski, J., 2005. Measurements of Saharan dust aerosols over the Eastern Mediterranean using elastic backscatter-Raman lidar, spectrophotometric and satellite observations in the frame of the EARLINET project. Atmos. Chem. Phys. 5, 2065–2079. Prospero, J.M., Ginoux, P., Torres, O., Nicholson, S.E., Gill, T., 2002. Environmental characterization of global sources of atmospheric soil dust identified with Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev. Geophys. 40. doi:10.1029/ 2000RG000095. Ramachandran, S., 2004. Spectral aerosol optical characteristics during the northeast monsoon over the Arabian Sea and the tropical Indian Ocean: 2. Angstrom and anthropogenic influence. J. Geophys. Res. 109, D19208. doi:10.1029/2003JD004483. Satheesh, S.K., Srinivasan, J., Moorthy, K.K., 2006. Contribution of sea-salt to aerosol optical depth over the Arabian Sea derived from MODIS observations. Geophys. Res. Lett. 33, L03809. doi:10.1029/2005GL024856. Schoeberl, M.R., et al., 2006. Overview of the EOS Aura mission. IEEE Trans. Geosci. Remote Sens. 44, 1066–1074. Sinyuk, A., Torres, O., Dubovik, O., 2003. Combined use of satellite and surface observations to infer the imaginary part of the refractive index of Saharan dust. Geophys. Res. Lett. 30. doi:10.1029/2002GL016189. Stohl, A., Eckhardt, S., Forster, C., James, P., Spichtinger, N., 2002. On the pathways and timescales of intercontinental air pollution transport. J. Geophys. Res. 107, D4684. doi:10.1029/2001JD001396. Torres, O., Bhartia, P.K., Herman, J.R., Ahmad, Z., Gleason, J., 1998. Derivation of aerosol properties from satellite measurements of backscattered ultraviolet radiation: theoretical basis. J. Geophys. Res. 103, 17099–17110. Veihelmann, B., Levelt, P.F., Stamnes, P., Veefkind, J.P., 2007. Simulation study of the aerosol information content in OMI spectral reflectance measurements. Atmos. Chem. Phys. Discuss. 7, 1785–1821. Washington, R., Todd, M., Middleton, N.J., Goudie, A.S., 2003. Dust storm source areas determined by the Total Ozone Monitoring Spectrometer and surface observations. Ann. Assoc. Amer. Geogr. 93, 297–313. Zerefos, C., Ganev, K., Kourtidis, K., Tzortziou, M., Vasaras, A., Syrakov, E., 2000. On the origin of SO2 above northern Greece. Geophys. Res. Lett. 27, 365–368.