Aerosol characteristics over urban Cairo: Seasonal variations as retrieved from Sun photometer measurements

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi: /2008jd009834, 2008 Aerosol characteristics over urban Cairo: Seasonal variations as retrieved from Sun photometer measurements M. El-Metwally, 1 S. C. Alfaro, 2,3 M. Abdel Wahab, 3 and B. Chatenet 2,3 Received 16 January 2008; revised 18 April 2008; accepted 3 June 2008; published 31 July [1] During the Cairo Aerosol Characterization Experiment an automated Sun photometer belonging to the NASA Aerosol Robotic Network has been implemented for the first time in the megacity of Cairo, Egypt. The inversion of the measurements performed by this instrument several times a day and over a duration of more than 1 year (from the end of October 2004 to the end of January 2006) provides a way of determining the temporal variability of aerosol characteristics such as size distribution, complex refractive index, single-scattering albedo, and asymmetry parameter. The analysis of the results reveals that Cairo s aerosol is a mixture of three individual components produced by different mechanisms: background pollution aerosol produced by local urban activities, pollution-like aerosol resulting from biomass burning in the Nile delta, and dust-like aerosol released by wind erosion in the Sahara. It is also shown that the variations in the overall aerosol properties are in fact due to changes in the proportions of this mixture. In particular, short-duration dust storms and biomass-burning episodes explain the largest observed aerosol optical depths (AOD) (AOD > 0.7) through the extreme enhancements of concentrations in dust-like aerosols characterized by low Ångström s exponent values (a < 0.5) and in biomass-burning aerosols (1.0 < a < 1.5). When averaged over longer (monthly and yearly) time periods, the effects of these high-frequency modifications are smoothed. In particular, an average mixed aerosol type is defined for the whole duration of the measurements period. The low single-scattering albedo (SSA) of this average aerosol and its marked spectral dependence clearly indicate that, at least on a yearly basis, the aerosol is dominated by its two light absorbing pollution components (background pollution and pollution-like) and to such an extent that it compares well with values obtained in other polluted megacities (e.g., Mexico City). This general dominance of the absorbing components can be challenged at shorter timescales. Indeed, the occurrence of several dust storms in springtime, and particularly in April, causes a significant increase in SSA and a parallel decrease in spectral dependence during this month. Conversely, the October biomass-burning events are not able to cause such important deviations from the yearly averaged mixed aerosol model that its optical properties can no longer be used for this month. Citation: El-Metwally, M., S. C. Alfaro, M. Abdel Wahab, and B. Chatenet (2008), Aerosol characteristics over urban Cairo: Seasonal variations as retrieved from Sun photometer measurements, J. Geophys. Res., 113,, doi: /2008jd Introduction [2] It is now recognized that besides altering air quality [Wilson and Spengler, 1996; Prospero, 1999] aerosols affect atmospheric radiation transfer directly by scattering and absorbing light, and indirectly by influencing cloud formation. Usually, the magnitude of this radiative impact is quantified at a given atmospheric level by means of the 1 Physics Department, Faculty of Science at Port Said, Suez Canal University, Port Said, Egypt. 2 LISA, UMR 7583, CNRS Université de Paris XII, Université de Paris VII, Créteil, France. 3 Astronomy and Meteorology Department, Faculty of Science, Cairo University, Giza, Egypt. Copyright 2008 by the American Geophysical Union /08/2008JD aerosol forcing [International Panel on Climate Change, 1996; Herman and Celarier, 1997; Haywood et al., 1999]. [3] Because of the variety of aerosol sources, their short atmospheric residence time, and the dynamic processes that may alter them after generation, the physical and chemical characteristics of airborne particles are highly inhomogeneous in space and time. Therefore, the accurate assessment of an aerosol impact on radiative transfer is a complex task. Indeed, a full description of all the characteristics that control its interaction with solar and terrestrial radiation is needed. Though in situ ground-level measurements traditionally considered as the most reliable for aerosol characterization must undoubtedly be performed, more global and continuous observations providing a better spatial and temporal coverage are also needed [Dubovik et al., 2002a]. In this respect, efforts are being made currently to derive aerosol properties directly 1of13

2 from satellite observations [Griggs, 1975; Holben et al., 1998; King et al., 1992]. Another approach also based on remote sensing consists in implementing a dense network of ground-based instruments from which key aerosol characteristics can be retrieved in quasi real time. This is the case of the Sun photometers deployed in the frame of the Aerosol Robotic Network (AERONET). These instruments provide direct derivation of the aerosol optical depth (AOD) at several wavelengths and the atmospheric columnar content in precipitable water (PW). Provided some assumptions are made regarding the particle shape (spherical or not), the Sun photometer data can also be inverted to retrieve columnar characteristics such as size distribution, complex refractive index, single-scattering albedo (SSA), and asymmetry parameter [Dubovik and King, 2000; Dubovik et al., 2006]. [4] In this work, we analyze the results of Sun photometer measurements performed between 29 October 2004 and 31 January 2006 in the frame of the Cairo Aerosol Characterization Experiment (CACHE). The over 1 year measurements allow us to characterize the seasonal variability of aerosol properties over Cairo, a megacity among the most polluted in the World and whose influence on the environment is thought to be felt at least at regional scale. This seasonal variability is expected to be important because, as shown by previous studies [e.g., El-Wakil et al., 2001; Tadros et al., 2002; Zakey et al., 2004] and by measurements performed at ground level during CACHE (manuscripts in preparation), Greater Cairo is a place where aerosols of different origins are bound to mix, and in proportions that depend on the activity of their sources some of which are highly seasonal. [5] The structure of the paper is as follows: after a presentation of the experimental setup and associated methods, the results obtained in Cairo are analyzed. More precisely, our first aim consists in defining and assessing the physical characteristics of the individual components whose mixture constitutes the overall aerosol. In a second step, we interpret the seasonal variability of the Cairo s aerosol properties as resulting from variations in proportions of the aerosol mixture. 2. Experimental Setup and Retrieval Methods Used in this Study 2.1. General Context and Site Description [6] Cairo is located at the limit of lower and upper Egypt and along the River Nile whose adjacent narrow cultivated belts have only a limited influence on the Egyptian climate. The general features of Cairo s climate have been described by El-Wakil et al. [2001]. All year round, predominant winds blow mainly from the north. However, there are some exceptions to this rule as is the case during the spring season when frequent chained depressions can be observed. These events, called Khamsin depressions in Arabic language, coincide with strong winds blowing from the south and raising dust on their way. Though the air masses associated with these events are warm and relatively dry, occasional clouds, rainfall and thunderstorms may also be observed during these periods [El-Fandy, 1940]. Regarding atmospheric stability, it must also be mentioned that frequent temperature inversions occurring over Egypt in summer and autumn favor development of subsidence inversion at about m above mean sea level [El-Fandy and El-Nisr, 1949; El-Wakil et al., 2001; Tadros et al., 2002; Zakey et al., 2004]. [7] With its 16 million inhabitants, Greater Cairo is one of the largest cities in the World. and is also considered as one of the most polluted. This is a direct result of the growth in population and associated activities that have been observed during the last decades. Motorized traffic and industries located within the city of Cairo itself or in the two neighboring areas of Helwan (south of Cairo) and Shoubra El-Kheima (in the north) constitute particle sources that are active all year round. In addition to these, particles produced by seasonal sources located outside Cairo can be transported to the city at certain times of the year. This is the case of mineral dust produced, mainly in spring though not exclusively, by the Khamsin events. In autumn, the plume produced by massive burning of agricultural waste in the Nile delta can also be transported to Cairo by the prevailing northern winds. This period also happens to coincide with the occurrence of a huge, dark pollution cloud named black cloud by local population. [8] Because of its potential effects on peoples health, the persistence of high levels of particulate concentration over Greater Cairo is a matter of great concern for its inhabitants and decision makers. By altering the atmospheric transfer of solar and terrestrial radiation the presence of a dense particle cloud over Cairo and its surroundings may have other, though not yet quantified, important effects such as the modification of the regional climate or the reduction of the amount of the photosynthetically active radiation able to reach the ground and necessary for the growth of plants in the Nile delta. [9] For quantifying these effects an accurate determination of the aerosol properties is necessary. Documenting these properties was one of the aims of CACHE that has been carried out mainly in 2004 and Among the many instruments implemented at various experimental sites, two Sun/sky radiometers (CIMEL) have been operated successively for more than 1 year (from 29 October 2004 to 31 January 2006). The first one was operated on the premises of Cairo University (Giza, N, E) until 14 April and was replaced afterward by another identical instrument operated at the Egyptian Meteorological Authority (EMA) headquarters located just a few kilometers away from the previous site (data collected at these two sites (Cairo_University and Cairo_EMA, respectively) are available online at Sun Photometer and its Direct Products [10] The CIMEL Sun/sky radiometers used during CACHE measured direct Sun radiance at five spectral channels (440, 670, 870, 940 and 1020 nm) and diffuse sky radiances in the solar almucantar at four wavelengths (441, 673, 873 and 1022 nm). After predeployment and postdeployment calibrations of the instruments, these solar extinction measurements were used to compute quality assured AOD (t a ) at each wavelength except for the 940-nm channel used to retrieve PW (in cm). The spectral dependence of AOD was used to compute the Ångström s exponent (a). Practically, a spectrally averaged value of this exponent, which contains information about the size of particles [Jung, 1955; Pandithurai et al., 1997; Remer et al., 1999], can be obtained by fitting Ångström s power law [Ångström, 1964]: t a / l a to the measured AODs. 2of13

3 Figure 1. Frequency of occurrences computed from the whole data set of instantaneous AOD at 440 nm (AOD 440 ) and a for the nm wavelength range (a ). [11] The value of a increases when the particle size decreases and a theoretical maximal value of 4 is obtained in the visible spectrum for scattering by molecules (Rayleigh scattering). For larger aerosols, the Ångström s exponent is smaller and may even become negative in the case of Saharan dust particles that are particularly coarse [Cerf, 1986]. However, because the exact value of a depends significantly on the spectral range used in its determination, the information contained in the Ångström s exponent versus AOD scatterplots may be difficult to interpret. Therefore, the more detailed spectral information provided by the determination of a in different, narrower, spectral ranges is currently favored to discriminate between different aerosol types [Cachorro et al., 2001]. In the case of Sun photometer measurements, the Angstrom exponent [Ångström, 1964], which quantifies the wavelength dependence of t a, may in the simple case of two spectral channels be computed from spectral values of t a using: a ¼ lnðt a1 =t a2 Þ= lnðl 2 =l 1 Þ ð1þ 2.3. Retrieved Aerosol Properties [12] A flexible inversion algorithm, developed by Dubovik and King [2000] and modified by Dubovik et al. [2002b] can also be used to retrieve columnar aerosol characteristics from direct Sun and diffuse sky radiance measurements. These characteristics include the vertically averaged aerosol volume size distribution in a range of radii between 0.05 and 15 mm. This distribution v(r) is represented by a sum of n lognormally distributed populations: vr ðþ¼ dvðþ r d ln r ¼ Xn C v;i s i ð2pþ exp ln r=r 2! m;i ; ð2þ 1=2 i¼1 in which v(r) and the amplitude of each population (C V,i )are volume concentrations per cross section for an atmospheric column, r is the aerosol radius, r m,i is the volume geometric 2s 2 i mean radius and s i is the geometric standard deviation for each mode. At each of the four wavelengths, the inversion procedure also provides the real and imaginary parts (n and k, respectively) of the aerosol complex refractive index (m), the scattering phase function which in turn allows computation of the asymmetry parameter (g), and the single SSA. Like g, the single-scattering albedo is an important input parameter for radiative transfer models and is thus required in aerosol radiative forcing studies. By definition, SSA represents the proportion of light extinction that is due to scattering alone, the rest being due to absorption. Its wavelength dependence can also be used as an indicator of the aerosol type. [13] The accuracy of the individual aerosol characteristics retrievals has been fully discussed by Dubovik et al. [2000, 2002a, 2002b, 2006]. It appears that one important source of uncertainty lies in the assumption made regarding the Figure 2. Scatterplot of AOD 440 versus a over Cairo for the period 29 October 2004 to 31 January of13

4 Table 1. Monthly Averages and Associated SD of AOD Measured at Four Sun Photometer Wavelengths for Cairo a AOD a 440 nm 670 nm 870 nm 1020 nm PW (cm) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean SD a Data are for the period from 29 October 2004 to 31 January The a values computed for several wavelength ranges and PW are also presented. SD, standard deviation; AOD, aerosol optical depths; PW, precipitable water. shape (spherical or spheroid) of the aerosol particles before running the inversion algorithm. For instance, in the presence of spheroid mineral dust particles the sphericity assumption leads to an overestimation of the finest classes in the inverted size distribution. It also creates an artificial spectral dependence of the real part of the refractive index and distortions in the retrieved phase function, especially at scattering angles larger than 80. A direct implication of the latter artifact is that g is also biased. 3. Results and Discussion 3.1. Aerosol Optical Depth and the Ångström s Exponent Statistical Distribution of AOD and a, and Their Correlation [14] Examination of the frequency of occurrence of AOD for the entire data set (e.g., AOD at 440 nm reported in Figure 1) shows a skewed distribution with the most probable value at 0.3 (±0.05). The cumulative probability density of observing an AOD larger than 0.5 is high (approximately 30%) and values substantially larger than 1 have also been recorded. This important frequency of occurrence of large AOD values confirms that the atmospheric load in particles over Cairo is usually particularly high. These large AOD are in the same order of magnitude as those measured at 550 nm by Kaskaoutis et al. [2007] in Athens (AOD varying from 0.21 ± 0.10 in winter to 0.44 ± 0.18 in summer) and as those reported by Masmoudi et al. [2003] for other Mediterranean AERONET stations (e.g., Tor Vergata in the vicinity of Rome and Thala in Tunisia). [15] Another important aspect is that the statistical distribution of the Ångström s exponent (e.g., a in Figure 1) reveals a great dispersion of the results thus denoting an important variability of the aerosol size distri- Figure 3. Mean monthly values of (full circles) AOD 440, (open circles) a , and (squares) PW for the whole measurement period in Cairo city. (The error bars are not represented for the sake of clarity.) 4of13

5 Figure 4. As in Figure 2 but for different seasons: (a) winter, (b) spring, (c) summer, and (d) autumn. bution over the measurements period. The most probable values (58% of occurrences between 1.0 and 1.5) are typical of particles located mostly in the submicron range whereas values smaller than 0.5 and typical of aerosols containing an important proportion of particles larger than 1 mm[ångström, 1964] are also not infrequent (15% of occurrences in the whole data set). [16] Possible correlations linking AOD and a can be sought by examining the scatterplot of the 6749 a instantaneous observations versus corresponding AOD values (Figure 2). As detailed by Kubilay et al. [2003] and by Xiang-Ao et al. [2005], this visual representation often allows one to define physically interpretable cluster regions for different types of aerosols with different optical properties. A striking feature of the scatterplot obtained with our data set is that a very wide range of a (0 to 1.8) is associated with rather low values of AOD. If an arbitrarily upper limit of 0.5 is chosen for defining these moderate AOD cases we find that 79% of the observations fall into this category and the wide range of a associated with them suggests (1) that when the atmospheric load in particles is not very high in Cairo the aerosol is probably a mixture of several components differing in size and (2) that the proportions of this mixture are not constant. This assumption that individual components of different origins and characteristics constitute Cairo aerosol is further supported by the fact that the largest AODs (e.g., AOD > 0.7) are associated with narrower ranges of a: one located approximately between 1 and 1.5, and the other between 0 and 0.5. More precisely, the largest AOD values measured during the year could possibly be explained by intensification of emissions by at least two different particle sources: a source releasing relatively fine particles associated with large Ångström s exponent and whose origin is probably anthropogenic (pollution-like source) and the other releasing coarser particles associated with very low a values. This second source might in fact correspond to the transport toward Cairo of large amounts of mineral dust generated by wind erosion in the deserts surrounding the city [Alfaro and Abdel Wahab, 2006]. Note that emissions by these two different sources are highly sporadic in time and that the particles they produce can be considered as added to what could be defined as a background aerosol whose composition is also certainly quite variable in time since the Ångström s exponent associated with the moderate AOD spans over an extremely large range of values, as mentioned above Temporal Variation of AOD and a [17] The monthly averages of AOD at 440, 670, 870, and 1020 nm, a in different wavelength ranges, and PW during cloud-free days are listed in Table 1. Figure 3 shows a 5of13

6 Figure 5. Air mass back trajectories computed with the HYSPLIT model for two particular days of the CACHE measurement period: (a) a biomass-burning case (29 October 2004) and (b) a particularly intense dust event (8 April 2005). selection of these data, namely AOD at 440 nm, a in the nm wavelength range, and PW. The standard deviation of these quantities is visualized by the vertical bar associated with each monthly average. Figure 3 reveals significant month-to-month variation in AOD even though the amplitude of seasonal variations is rather limited when compared to the well-defined bell-shaped seasonal trend with maximum in summer which is usually observed in temperate climates [e.g., Iqbal, 1983; Gueymard and Garrison, 1998]. In Cairo, there are two marked AOD maxima, one in spring and one in autumn centered in April and October, respectively. A secondary maximum in summer and a minimum in the three winter months (December, January, and February) are also observed. On the basis of this temporal pattern of AOD, which is in good agreement with previous results obtained by other authors [e.g., El-Wakil et al., 2001; Tadros et al., 2002; Zakey et al., 2004], four subperiods coinciding with the four seasons of the year can be defined for a more detailed temporal study of the aerosol characteristics in Cairo. For example, if equivalent of Figure 2 scatterplots are drawn for each season (Figure 4), they reveal that the largest optical depth peaks observed in spring and autumn can be explained by the predominance of two completely different kinds of aerosols. Indeed, the largest autumn AODs are clearly associated with large a values between 1 and 1.5 (Figure 4d), or in other words, with the previously defined pollution-like aerosols. A possible explanation for the presence of large amounts of this aerosol type in the atmospheric column in this period can lie in the fact that this is the time when farmers burn agricultural wastes (mostly rice residues) after harvest in the Nile delta and that the prevailing northern winds transport the biomass-burning plumes toward Cairo. This assumption is supported by examination of air mass back trajectories computed with the hybrid singleparticle Lagrangian integrated trajectory (HYSPLIT) model (HYSPLIT model data are available at gov/ready/hysplit4.html) showing that the largest autumnal AOD are associated with wind blowing from the north sector (Figure 5a). Moreover, the fact that a becomes less that 0.5 only rarely during this period shows that the influence of the dust-like component is pretty limited in autumn. Conversely, the a values vary between very low (approximately 0) and very high (more than 1.5) values during spring (Figure 4b). As already mentioned above, this very large range of a suggests that the ambient aerosol might be made of a mixture of the pollution-like and dust-like components in proportions that vary considerably. Note that the possibility of observing lower a in spring than in autumn shows that the importance of the mineral component is generally greater in spring. This is further confirmed by examining Figure 4b showing that only dust-like aerosols are responsible for the very large AOD (AOD > 1) values observed in this season. These peaks coincide with dust storm events that tend to occur more frequently, though not exclusively, during this period and that come from the southwest sector (Figure 5b), that is to say from the Saharan desert. The addition of this mineral dust component coming from outside Cairo to the background aerosol explains at the same time the maximum monthly averaged AOD and a minimum recorded in April (Figure 3). [18] During summer the situation is significantly different. AOD maxima can then be associated either with pollutionlike or, though to a somewhat lesser extent, with dust-like aerosols (Figure 4c). Thus, the respective influences of these two aerosol types are more balanced than during spring and 6of13

7 Table 2. Numerical Criteria Used for Separating Cases Dominated by the Three Aerosol Components (Dust-Like, Pollution- Like, and Background) a AOD 0.7 AOD < 0.7 Dust-Like Pollution-Like Background Mixed Frequency occurrences (%) a a 1.5 a >1.5 a < a For definition of criteria, see section A fourth category corresponding to cases not involved in the above three types is defined as mixed aerosol. For each case, the frequency of occurrence over the whole measurement period (Total = 1055) is also indicated. autumn. The exact origin of the increase in vertically integrated concentration of pollution-like aerosols in summer is not elucidated fully but could be the result of a combination of high PW values (Figure 3) and enhanced photochemistry via the production of fine-mode secondary aerosols that are some of its final byproducts (O. Favez et al., Significant formation of water-insoluble secondary organic aerosols in semiarid urban environment, submitted to Geophysical Research Letters, 2007). In addition to this, the frequently strong temperature inversions over Egypt during summer lead to the development of a stable boundary layer [Lasheen, 1970] which may trap a significant amount of particles causing a general increase in pollution, especially during night and early morning [Derimian et al., 2006]. [19] In winter, most measured AODs are less than 0.5. This could be explained by the Mediterranean depressions relatively frequent in this season and whose influence extends to Cairo causing a relative increase in wind speed and in precipitations over the city [Soliman, 1953]. The first effect promotes particle dispersion while the second one removes significant amounts of aerosols from the atmosphere by rain washout. However, it must also be noted that the largest AOD (nearly 3) in the whole data set has been recorded in this period of usually low aerosol concentrations. It was due to a rather isolated but very intense dust event that lasted from 28 January to 30 January Characterization of the Aerosol Individual Components [20] As detailed above, the a versus AOD scatterplot method has allowed identification of at least two aerosol types in Cairo, namely dust-like and pollution-like aerosols, whose occasional presence in large concentration explains the observed large AOD values. Therefore, it is important to determine the characteristics of these single components in order to understand the variability of the aerosol characteristics over the city. The methodology chosen for achieving this goal has consisted in isolating in the data set the observations corresponding to periods when either the dust-like or the pollution-like types were clearly dominant and in averaging the products of the inversion scheme for these two sub data sets. Practically, the arbitrary numerical criteria selected for the separation of the two aerosol types have been derived from Figure 2: a dust-like component is assumed to be clearly dominant when AOD is larger than 0.7 and a is less than 0.5. Similarly, the pollution-like component is considered as dominating the aerosol properties when AOD is larger than 0.7 and a between 1 and 1.5. However, these definitions for the dust-like and pollution-like aerosols leave out a significant number of observations. Among them a first aerosol category with a values larger than 1.5 and whose importance is especially apparent in winter (Figure 4a) can be defined. This particularly fine aerosol is also probably of anthropogenic origin and could be considered as constituting a background pollution aerosol type different from the pollution type previously defined. In summary, three main aerosol components have been identified: dust-like, pollution-like and background pollution. Table 2 summarizes the practical criteria used to separate the corresponding cases in the whole data set. It also provides the frequency of observations for each aerosol type. [21] At this stage, it must be acknowledged that many observations, for instance all those yielding a values between 0.5 and 1.0, have not been considered as a single aerosol category in the previous discussion. Indeed, these cases probably do not correspond to a single aerosol of unknown origin but rather to a mixing of the previously identified dust, pollution, and background pollution components. Theoretically, one should be able to derive the physical characteristics of the overall (mixed) aerosol simply from the characteristics of the individual components and from the proportions of their mixture Volume Size Distributions [22] The spherical model is probably better adapted to both the pollution-like and background pollution components on the one side and the spheroid model more suitable for non spherical desert particles on the other side [Dubovik et al., 2006], the results discussed in this section will be those obtained with these respective assumptions. Another important point is that the particle size distribution retrieved from the inversion scheme and expressed by equation (2) have amplitudes terms (C v ) that increase with the amount of particles present in the atmospheric column, that is to say the AOD. In consequence, the inverted size distributions have been normalized by the means of this AOD in order to facilitate comparison of the shapes of the three aerosol types size distributions (Figure 6). [23] The background pollution and pollution-like aerosols (Figures 6a and 6b, respectively) both show bimodal size distribution. Indeed, they both contain a fine particle mode centered on a radius of 0.15 mm (± 0.03mm, SD) and a coarse mode in the supermicron range (r = 3.58 ± 0.67 and 3.02 ± 0.40 mm, respectively). The main difference between the two cases lies in the relative importance of the coarse mode particles as compared to the fine mode ones. Indeed, the ratio of the amplitudes of these two modes is significantly larger (about 1.5) for the pollution-like aerosol than for the background pollution (amplitude ratio of 1.2) that thus appears to be finer. This also seems to indicate that the processes leading to the aerosol production are different in the two cases. More precisely, gas to particle conversion followed by coagulation on the one side and direct emission of particles in the accumulation mode on the other side must be the main processes explaining production of the background pollution aerosol. This hypothesis is consistent with the presence of a dense motorized traffic within Cairo that releases very fine black carbon particles found in the accumulation mode and of large industrial complexes 7of13

8 car traffic [Mahmoud et al., 2008], there is no significant temporal variation in these urban activities during the year could also explain that the background pollution aerosol is never associated with exceptionally large AOD values. This is not the case for the pollution-like aerosol whose emissions are particularly intense in autumn and whose origin has been assumed to be the biomass-burning process in the Nile delta. The size distribution obtained in this case is consistent with this assumption. Indeed, the burning of biomass is known to release at the same time particles in the accumulation mode (black carbon particles, mostly) but also coarser particles resulting from the condensation of organic compounds. [24] As expected for wind eroded mineral particles, the size distribution obtained in dust dominated conditions (Figure 6c) is characterized by the presence of a considerable amount of particles located in the supermicron mode. Contrary to what was observed with the two types of pollution aerosols, it clearly appears that the coarse mode itself is split in two different submodes: one being centered on a radius of about 1.5 mm and the other at 4 mm. This bimodal structure of the coarse mode is similar to the one found during dust events by Eck et al. [2005] in Beijing (r 1.5 and 4 mm) and by Masmoudi et al. [2003] in two African sites (r 1.3 and 3.8 mm). Also noteworthy is the fact that the routine used for fitting lognormal distributions to the inverted size distributions is not capable of separating these two coarse modes because it assumes from the start that particles in the supermicron range belong to a unique lognormally distributed particle population.. As a result, the parameters (C v, r m, s) of the retrieved aerosol size distribution must be considered with caution when a significant amount of dust-like aerosol is present in the atmospheric column. In particular, the radius of the coarse particle mode yielded by the automatic inversion procedure is expected to be somewhere in-between the values of the radii of the two real coarse particles submodes, that is between 1.5 and 4 mm. This prediction is confirmed by examining the inversion results presented in Table 3, which show that the coarse mode radius for the dust-like aerosol is only 2.33 mm, a value lower than the ones of the two pollution aerosols because of the presence of the finest coarse mode at r m =1.5mm characteristic of dust-like aerosols. As expected, the results in Table 3 also confirm the greater importance of Figure 6. Aerosol size distributions of the three aerosol individual components normalized by the AOD. The error bars correspond to the standard deviation of the inverted data. around the city that, besides black carbon, indirectly produce large amounts of sulfate aerosols laying in the same size range [Seinfeld and Pandis, 1998; Abu-Allaban et al., 2002; Favez et al., submitted manuscript, 2007]. In addition to this, the fact that, except at diurnal and weekly scales for Table 3. Parameters of the Volume Size Distribution for the Three Aerosol Components Over Cairo in the Period of the CACHE Campaign a Aerosol Background Pollution-Like Dust-Like Fine Mode C V 0.05 ± ± ± 0.09 r m 0.15 ± ± ± 0.04 s 0.41 ± ± ± 0.19 Coarse Mode C V 0.06 ± ± ± 0.57 r m 3.58 ± ± ± 0.59 s 0.68 ± ± ± 0.04 a C v is in mm 3 /mm 2, and r m and s are in mm. The corresponding uncertainty is shown by the standard deviation of the results. CACHE, Cairo Aerosol Characterization Experiment. 8of13

9 Table 4. Physical and Optical Characteristics (Real and Imaginary Parts (k, n) of the Complex Refractive Index, SSA and g) of the Three Main Components Present in Cairo Aerosol a Physical Property Aerosol Component 441 nm 673 nm 873 nm 1022 nm All Wavelengths k bgd-poll ± ± ± ± ± 0.08 poll-like 1.42 ± ± ± ± ± 0.10 dust-like 1.46 ± ± ± ± ± 0.05 n bgd-poll ± ± ± ± ± 0.01 poll.-like ± ± ± ± ± 0.01 dust-like ± ± ± ± ± SSA bgd-poll ± ± ± ± ± 0.06 poll-like ± ± ± ± ± 0.04 dust-like ± ± ± ± ± 0.02 g bgd-poll ± ± ± ± ± 0.02 poll-like ± ± ± ± ± 0.03 dust-like ± ± ± ± ± 0.02 a The three main components present in Cairo aerosol are background pollution, pollution-like, and dust-like. Note that mentioned uncertainties correspond to standard deviations. SSA, single-scattering albedo; bgd-poll., background pollution; poll-like, pollution-like. the coarse mode/fine mode in the dust-like case (amplitude ratio = 5.4) than in the two pollution cases Optical Properties of the Three Aerosol Components [25] The values of m, SSA, and g provided by the inversion method at the four Sun photometer wavelengths and for each aerosol component are summarized in Table 4. The real (k) and imaginary (n) parts of the refractive index are significantly different for the three aerosol components, a fact that reflects the differences in chemical or mineralogical composition. In good agreement with results obtained at other AERONET sites where dust is a dominating aerosol component (e.g., Solar Village in Saudi Arabia and Bahrain in the Persian Gulf [Dubovik et al., 2002a]), k is relatively large for the dust-like component (1.51 ± 0.05). This value is also consistent with the order of magnitude reported in the literature for the major mineral species present in mineral dust, namely quartz and clay minerals. These minerals do not absorb light, which explains that the imaginary part retrieved for the dust-like aerosol is low (0.002 ± 0.001) and mostly independent of wavelength, at least over the measurement range. For the background aerosol, the real part of the refractive index is found to be particularly low (1.42 ± 0.08), but large value for the imaginary part (0.012 ± 0.01) is consistent with the previous assumption of this aerosol type being mainly produced by combustion processes (industry and traffic) and, hence, particularly rich in fine and very absorbing black carbon particles. Finally, the refractive index of the pollution-like component is in-between the ones of the other two. The relatively large value of its imaginary part (0.008 ± 0.001) indicates that this aerosol type is also an efficient absorber, in good agreement with the assumption that it is produced by biomass-burning activities. It can also be noted that the refractive indices (k, n) obtained at 870 nm by Mukai et al. [2005] in Shirahama (Japan) during dust events (k = , n = 0.002) and in pollution dominated conditions (k = , n = ) are close to the values presented in Table 4. [26] Combined with the differences in size distribution already commented on above, these differences in composition explain the spectral behaviors of the SSA and g. Indeed, the finest aerosol component (background pollution) is the one for which the spectral dependence of scattering is expected to be the largest (see the magnitude of Ångström s exponent). This is reflected directly by the strong dependence of the asymmetry parameter on wavelength. The fact that this dependence is negative shows that, as predicted by electromagnetic theory, the importance of backscattering as compared to forward scattering increases with wavelength for particles in the submicron range. [27] From the definition of the SSA its value includes the effects of scattering and absorption at the same time. The spectral dependence of the latter being almost negligible, the effect of size on scattering will control in great part the spectral behavior of SSA. As with the asymmetry parameter, the SSA is found to be negatively correlated with l. The same explanation applies to the pollution-like and dust-like components. Indeed, the increasing importance of the supermicron particle population tends to smooth the spectral dependence of SSA and g out until it disappears almost completely for the dust-like aerosol. In this last case, it can be noted that the SSA is particularly large, thus denoting that the aerosol is not a very efficient absorber of solar light except maybe at the shorter wavelength (440 nm) where SSA is lower than at the other three l. This enhancement of absorption at short wavelengths has often been interpreted [e.g., Sokolik and Toon, 1999; Alfaro et al., 2004; Derimian et al., 2006] as resulting from the presence of iron oxides such as hematite and goethite in desert dust (note that by absorbing specifically in the UV and in the short band of the solar spectrum, these mineral species are responsible for the reddish or yellowish color of mineral dust). Should the Sun photometer be able to perform measurements at wavelengths shorter than 440 nm, it is probable that this effect would have been detected with more efficiency. More generally, the Cairo observations (lower absorption and SSA spectral dependence in presence of dust than during pollution episodes) are consistent with previous measurements performed at Bahrain, in the Persian Gulf [Smirnov et al., 2002] Monthly Variations of the Aerosol Properties Defining a Mixed Aerosol Component [28] The proportions reported in Table 2 show that the three pure aerosol components defined above represent only 14% of all instantaneous observations. These pure aerosol 9of13

10 Table 5. Seasonal Distribution of the Observation Frequency for the Four Different Aerosol Types a Cases Winter (%) Spring (%) Summer (%) Autumn (%) Background Pollution-like Dust-like Mixed a Aerosol types are defined in section cases are not distributed evenly during the year. Indeed, 58% of the pollution-like cases are observed in autumn, 67% of dust-like events in spring, and 57% of the background pollution in winter (Table 5). All the remaining situations that are characterized by a < 1.5 and an AOD < 0.7, and that probably correspond to situations in which the aerosol is a mixture of the three main components, are more evenly distributed over the year (Table 5). [29] Though the large range of instantaneous values covered by a suggests that the proportions of these mixtures can vary substantially and rapidly with time, the examination of Figure 3 reveals that the dependence of a on time is significantly smoothed when monthly averages are considered. This could mean that, in spite of the large variability of aerosol properties at short timescales, it should be possible to define a much less variable mixed aerosol at month scale. Practically, the properties of this mixed aerosol could be determined by averaging the instantaneous results satisfying the mixed aerosol conditions (a < 1.5 and AOD < 0.7) for each month. The results yielded by this method confirm that in spite of significant fluctuations in the size distribution of the mixed aerosol (the amplitudes of its two main modes can differ by up to 25% from their mean values) the monthly averaged optical properties (SSA and g) do not vary much from one month to the other. Thus, we can define a unique mixed aerosol component whose properties are obtained by averaging the monthly results. The values of these properties and their standard deviations are summarized in the first two lines of Table 6. [30] More generally, the single-scattering albedo of the mixed aerosol can be compared to those of the three pure aerosol components (Figure 7). The values and spectral dependence of SSA for this mixed aerosol fit relatively well between the ones of the background pollution and pollution-like components but are quite different from the ones of the dust-like component. This suggests that, though the mixed aerosol certainly contains at certain times a significant proportion of mineral dust (a is occasionally quite low), the influence of this component can be neglected when annual averages are considered. This is due to the fact that the definition of mixed aerosols excludes extreme events in which any one of the pure aerosol components would be neatly dominant. A consequence of this remark is that deviations of the monthly averaged optical properties from those of the mixed aerosol should be a good indicator of the influence of a particular component in a month scale. The next section is dedicated to the analysis of such deviations Deviations from the Mixed Aerosol Properties [31] In order to detect the aforementioned deviations, monthly averaged properties have been computed and included in Table 6. Those properties that differ by more than one standard deviation from the ones of the mixed aerosol are in the footnote of Table 6. It can thus be seen that except for some particular months that will be studied in more details below, most of the monthly data presented in Table 6 are in good agreement with the mixed aerosol characteristic values. This means that, at least on a month scale, the mixed aerosol defined previously can generally be considered as representative of the overall aerosol situation in Cairo. This rule suffers some exceptions, especially in (1) April, June, and October, when the amplitude of the coarse mode is unusually high for the different reasons already mentioned above (dust events, probable intensification of photochemistry combined with humidity effect, and biomass burning, respectively), and (2) in December when this amplitude is unusually low (presence of the background pollution aerosol only). This difference in the size distribution Table 6. Size Distribution Characteristics (C v,r m, and s), SSA, g, and Refractive Indices (k and n) of the Cairo Aerosol a Fine Mode Coarse Mode SSA All Wavelengths C V r m s C V r m s 441 nm 673 nm 873 nm 1022 nm g k n Mixed ± ± ± SD For All Events Month Jan b b b b ± ± ± Feb ± ± ± Mar ± ± ± b Apr b b b ± ± ± b May ± ± ± Jun ± ± ± Jul ± ± ± Aug ± ± ± Sep ± ± ± Oct ± ± ± Nov ± ± ± Dec ± ± ± b a The first two lines contain annual means and standard deviations of the mixed aerosol component defined in the text. The following lines report the monthly averaged the aerosol characteristics computed from the complete data set. b Note that these optical properties deviate by more than one standard deviation from the mixed aerosol values. 10 of 13

11 Figure 7. Spectral dependence of SSA for the three pure aerosol components (background, pollutionlike, and dust-like) and for the mixed aerosol defined in the text. The monthly averaged measurements for December, April, July, and October are also reported. In this last case, the error bar represents the standard deviation. combined with a variety of origins and compositions also has an impact on the aerosol optical properties. Indeed, the large April SSA values and their low spectral dependence can be considered as being the result of the several dust events that occurred during that particular month. Also in April, the influence of the mineral dust is visible on the imaginary part of the aerosol refractive index that is significantly lower than that of the average mixed aerosol. In January, the occurrence of a particularly intense dust event already mentioned in section was enough to alter the monthly averaged SSA whose values are larger than those of the mixed aerosol and independent of wavelength. However, this effect of the dust component is not detected on the asymmetry parameter and the refractive index (whose spectral dependence is not shown in Table 6 for lack of space) that appear to be less sensitive to the presence of mineral dust than SSA. In the winter months, and more particularly in December when no dust storm exist, the aerosol was mainly produced by combustion activities (industry and traffic) and thus particularly rich in its fine, light absorbing, black carbon component. This explains the large values of the imaginary part of the refractive index for this month. Though they are not strictly speaking outside the range of variability of the mixed aerosol values, the December SSAs are also clearly located in the most absorbing part of this range (Figure 7). The effect of the absorbing carbonaceous components on the SSA is also visible on the July and October data (Figure 7). For these two months, the SSA values at the four wavelengths are close to those of the background pollution and pollution-like components, respectively. This indicates that in July the aerosol is as absorbing as in December and thus also dominated by the black carbon fraction. On the contrary, the October biomass-burning activities seem to release a more complex mixture of carbonaceous species than industries and car engines do. In this mixture the absorbing role of the black carbon is moderated by the presence of at least another component, assumedly organic species, which does not absorb solar light at the Sun photometer wavelengths but only scatters it. 4. Summary and Conclusion [32] The present study has shown that the aerosol present over Cairo can be considered as a mixture of three individual components of differing origins composition, size distribution and optical properties: The first component is a background pollution aerosol produced locally and all year round by combustion activities (motorized traffic and industries). This component is characterized by the relative importance of its fine (accumulation) mode, by the fact that it strongly absorbs solar radiation, and that this absorption increases rapidly with wavelength. These three factors suggest that the background pollution component is particularly rich in black carbon, in good agreement with its assumed origin. The second component, or pollution-like aerosol, is produced by biomass-burning activities in the Nile delta. As compared to background pollution, it is characterized by the presence of a relatively important supermicron mode, probably less absorbing than black carbon itself. As a consequence, the SSA is somewhat larger and less spectrally dependent for this component than for the previous one. Finally, the third component (dust-like) is of mineral origin and produced either by wind erosion in the deserts or in the city itself by road works, construction sites, etc. This aerosol is much coarser than the other two and much less absorbing. In addition, the SSA slightly increases at the shorter wavelengths (between 441 and 673 nm) before becoming constant at larger wavelengths. 11 of 13