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. Chatenet2,3

Received 16 January 2008; revised 18 April 2008; accepted 3 June 2008; published 31 July 2008.

[1] During the Cairo Aerosol Characterization Experiment an automated Sun photometerbelonging to the NASAAerosol Robotic Network has been implemented for the first time inthe megacity of Cairo, Egypt. The inversion of the measurements performed by thisinstrument several times a day and over a duration of more than 1 year (from the end ofOctober 2004 to the end of January 2006) provides a way of determining the temporalvariability of aerosol characteristics such as size distribution, complex refractive index,single-scattering albedo, and asymmetry parameter. The analysis of the results reveals thatCairo’s aerosol is a mixture of three individual components produced by differentmechanisms: ‘‘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 inthe overall aerosol properties are in fact due to changes in the proportions of thismixture. In particular, short-duration dust storms and biomass-burning episodes explainthe largest observed aerosol optical depths (AOD) (AOD > 0.7) through the extremeenhancements of concentrations in dust-like aerosols characterized by low Angstrom’sexponent values (a < 0.5) and in ‘‘biomass-burning’’ aerosols (1.0 < a < 1.5). Whenaveraged over longer (monthly and yearly) time periods, the effects of these high-frequencymodifications are smoothed. In particular, an average ‘‘mixed aerosol’’ type is definedfor 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 pollutioncomponents (background pollution and pollution-like) and to such an extent that itcompares well with values obtained in other polluted megacities (e.g., Mexico City).This general dominance of the absorbing components can be challenged at shortertimescales. Indeed, the occurrence of several dust storms in springtime, and particularlyin April, causes a significant increase in SSA and a parallel decrease in spectraldependence during this month. Conversely, the October biomass-burning events are notable to cause such important deviations from the yearly averaged mixed aerosol modelthat 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, D14219, doi:10.1029/2008JD009834.

1. Introduction

[2] It is now recognized that besides altering air quality[Wilson and Spengler, 1996; Prospero, 1999] aerosols affectatmospheric radiation transfer directly by scattering andabsorbing light, and indirectly by influencing cloud forma-tion. Usually, the magnitude of this radiative impact isquantified at a given atmospheric level by means of the

‘‘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 thatmay alter them after generation, the physical and chemicalcharacteristics of airborne particles are highly inhomoge-neous in space and time. Therefore, the accurate assessmentof an aerosol impact on radiative transfer is a complex task.Indeed, a full description of all the characteristics that controlits interaction with solar and terrestrial radiation is needed.Though in situ ground-level measurements traditionallyconsidered as the most reliable for aerosol characterizationmust undoubtedly be performed, more global and continuousobservations providing a better spatial and temporal coverageare also needed [Dubovik et al., 2002a]. In this respect, effortsare being made currently to derive aerosol properties directly

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1Physics Department, Faculty of Science at Port Said, Suez CanalUniversity, Port Said, Egypt.

2LISA, UMR 7583, CNRS Universite de Paris XII, Universite de ParisVII, Creteil, France.

3Astronomy and Meteorology Department, Faculty of Science, CairoUniversity, Giza, Egypt.

Copyright 2008 by the American Geophysical Union.0148-0227/08/2008JD009834$09.00

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from satellite observations [Griggs, 1975; Holben et al.,1998; King et al., 1992]. Another approach also based onremote sensing consists in implementing a dense network ofground-based instruments from which key aerosol character-istics can be retrieved in quasi real time. This is the case of theSun photometers deployed in the frame of the AerosolRobotic Network (AERONET). These instruments providedirect derivation of the aerosol optical depth (AOD) at severalwavelengths and the atmospheric columnar content in pre-cipitable water (PW). Provided some assumptions are maderegarding the particle shape (spherical or not), the Sunphotometer data can also be inverted to retrieve columnarcharacteristics such as size distribution, complex refractiveindex, single-scattering albedo (SSA), and asymmetryparameter [Dubovik and King, 2000; Dubovik et al., 2006].[4] In this work, we analyze the results of Sun photom-

eter measurements performed between 29 October 2004 and31 January 2006 in the frame of the Cairo Aerosol Char-acterization Experiment (CACHE). The over 1 year mea-surements allow us to characterize the seasonal variabilityof aerosol properties over Cairo, a megacity among the mostpolluted in the World and whose influence on the environ-ment is thought to be felt at least at regional scale. Thisseasonal variability is expected to be important because, asshown by previous studies [e.g., El-Wakil et al., 2001;Tadros et al., 2002; Zakey et al., 2004] and by measure-ments performed at ground level during CACHE (manu-scripts in preparation), Greater Cairo is a place whereaerosols of different origins are bound to mix, and inproportions that depend on the activity of their sourcessome of which are highly seasonal.[5] The structure of the paper is as follows: after a

presentation of the experimental setup and associated meth-ods, the results obtained in Cairo are analyzed. Moreprecisely, our first aim consists in defining and assessingthe physical characteristics of the individual componentswhose mixture constitutes the overall aerosol. In a secondstep, we interpret the seasonal variability of the Cairo’saerosol properties as resulting from variations in proportionsof the aerosol mixture.

2. Experimental Setup and Retrieval MethodsUsed in this Study

2.1. General Context and Site Description

[6] Cairo is located at the limit of lower and upper Egyptand along the River Nile whose adjacent narrow cultivatedbelts have only a limited influence on the Egyptian climate.The general features of Cairo’s climate have been describedby El-Wakil et al. [2001]. All year round, predominantwinds blow mainly from the north. However, there aresome exceptions to this rule as is the case during the springseason when frequent chained depressions can be observed.These events, called ‘‘Khamsin’’ depressions in Arabiclanguage, coincide with strong winds blowing from thesouth and raising dust on their way. Though the air massesassociated with these events are warm and relatively dry,occasional clouds, rainfall and thunderstorms may also beobserved during these periods [El-Fandy, 1940]. Regardingatmospheric stability, it must also be mentioned that fre-quent temperature inversions occurring over Egypt in sum-mer and autumn favor development of subsidence inversion

at about 300–1000m above mean sea level [El-Fandy andEl-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 ofthe most polluted. This is a direct result of the growth in popu-lation and associated activities that have been observed duringthe last decades. Motorized traffic and industries locatedwithin the city of Cairo itself or in the two neighboring areasof Helwan (south of Cairo) and Shoubra El-Kheima (in thenorth) constitute particle sources that are active all year round.In addition to these, particles produced by seasonal sourceslocated outside Cairo can be transported to the city at certaintimes of the year. This is the case of mineral dust produced,mainly in spring though not exclusively, by the Khamsinevents. In autumn, the plume produced by massive burningof agricultural waste in the Nile delta can also be transported toCairo by the prevailing northern winds. This period alsohappens to coincide with the occurrence of a huge, darkpollution cloud named ‘‘black cloud’’ by local population.[8] Because of its potential effects on peoples’ health, the

persistence of high levels of particulate concentration overGreater Cairo is a matter of great concern for its inhabitantsand decision makers. By altering the atmospheric transfer ofsolar and terrestrial radiation the presence of a dense particlecloud over Cairo and its surroundingsmay have other, thoughnot yet quantified, important effects such as the modificationof the regional climate or the reduction of the amount of thephotosynthetically active radiation able to reach the groundand necessary for the growth of plants in the Nile delta.[9] For quantifying these effects an accurate determina-

tion of the aerosol properties is necessary. Documentingthese properties was one of the aims of CACHE that hasbeen carried out mainly in 2004 and 2005. Among themany instruments implemented at various experimentalsites, two Sun/sky radiometers (CIMEL) have been operat-ed successively for more than 1 year (from 29 October2004 to 31 January 2006). The first one was operated on thepremises of Cairo University (Giza, 30.01�N, 31.12�E)until 14 April and was replaced afterward by another iden-tical instrument operated at the Egyptian MeteorologicalAuthority (EMA) headquarters located just a few kilometersaway from the previous site (data collected at these two sites(Cairo_University and Cairo_EMA, respectively) are avail-able online at http://aeronet.gsfc.nasa.gov/).

2.2. Sun Photometer and its Direct Products

[10] The CIMEL Sun/sky radiometers used duringCACHE measured direct Sun radiance at five spectral chan-nels (440, 670, 870, 940 and 1020 nm) and diffuse skyradiances in the solar almucantar at four wavelengths (441,673, 873 and 1022 nm). After predeployment and postde-ployment calibrations of the instruments, these solar extinc-tion measurements were used to compute quality assuredAOD (ta) at each wavelength except for the 940-nm channelused to retrieve PW (in cm). The spectral dependence ofAOD was used to compute the Angstrom’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 beobtained by fitting Angstrom’s power law [Angstrom, 1964]:ta / l�a to the measured AODs.

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[11] The value of a increases when the particle sizedecreases and a theoretical maximal value of 4 is obtainedin the visible spectrum for scattering by molecules (Rayleighscattering). For larger aerosols, the Angstrom’s exponent issmaller andmay even become negative in the case of Saharandust particles that are particularly coarse [Cerf, 1986].However, because the exact value of a depends significantlyon the spectral range used in its determination, the informa-tion contained in the Angstrom’s exponent versus AODscatterplots may be difficult to interpret. Therefore, the moredetailed spectral information provided by the determinationof a in different, narrower, spectral ranges is currentlyfavored to discriminate between different aerosol types[Cachorro et al., 2001]. In the case of Sun photometermeasurements, the Angstrom exponent [Angstrom, 1964],which quantifies the wavelength dependence of ta, may inthe simple case of two spectral channels be computed fromspectral values of ta using:

a ¼ ln ta1=ta2ð Þ= ln l2=l1ð Þ ð1Þ

2.3. Retrieved Aerosol Properties

[12] A flexible inversion algorithm, developed byDubovikand King [2000] and modified by Dubovik et al. [2002b]can also be used to retrieve columnar aerosol characteristicsfrom direct Sun and diffuse sky radiance measurements.These characteristics include the vertically averaged aerosolvolume size distribution in a range of radii between 0.05 and15 mm. This distribution v(r) is represented by a sum of nlognormally distributed populations:

v rð Þ ¼ dV rð Þd ln r

¼Xni¼1

Cv;i

si 2pð Þ1=2exp �

ln r=rm;i� �� �22s2

i

!; ð2Þ

in which v(r) and the amplitude of each population (CV,i) arevolume concentrations per cross section for an atmosphericcolumn, r is the aerosol radius, rm,i is the volume geometric

mean radius and si is the geometric standard deviation foreach mode. At each of the four wavelengths, the inversionprocedure also provides the real and imaginary parts (n and k,respectively) of the aerosol complex refractive index (m), thescattering phase function which in turn allows computationof the asymmetry parameter (g), and the single SSA. Like g,the single-scattering albedo is an important input parameterfor radiative transfer models and is thus required in aerosolradiative forcing studies. By definition, SSA represents theproportion of light extinction that is due to scattering alone,the rest being due to absorption. Its wavelength dependencecan 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 sourceof uncertainty lies in the assumption made regarding the

Figure 1. Frequency of occurrences computed from the whole data set of instantaneous AOD at 440 nm(AOD440) and a for the 440–870 nm wavelength range (a440–870).

Figure 2. Scatterplot of AOD440 versus a440–870 overCairo for the period 29 October 2004 to 31 January 2006.

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shape (spherical or spheroid) of the aerosol particles beforerunning the inversion algorithm. For instance, in the pres-ence of spheroid mineral dust particles the sphericityassumption leads to an overestimation of the finest classesin the inverted size distribution. It also creates an artificialspectral dependence of the real part of the refractive indexand distortions in the retrieved phase function, especially atscattering angles larger than 80�. A direct implication of thelatter artifact is that g is also biased.

3. Results and Discussion

3.1. Aerosol Optical Depth and the Angstrom’sExponent

3.1.1. Statistical Distribution of AOD and a, and TheirCorrelation[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 mostprobable value at 0.3 (±0.05). The cumulative probabilitydensity of observing an AOD larger than 0.5 is high(approximately 30%) and values substantially larger than1 have also been recorded. This important frequency ofoccurrence of large AOD values confirms that the atmo-spheric load in particles over Cairo is usually particularlyhigh. These large AOD are in the same order of magnitudeas those measured at 550 nm by Kaskaoutis et al. [2007] inAthens (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 Angstrom’s exponent (e.g., a440–870

in Figure 1) reveals a great dispersion of the results thusdenoting an important variability of the aerosol size distri-

Figure 3. Mean monthly values of (full circles) AOD440, (open circles) a440–870, and (squares) PW forthe whole measurement period in Cairo city. (The error bars are not represented for the sake of clarity.)

Table 1. Monthly Averages and Associated SD of AOD Measured at Four Sun Photometer Wavelengths for Cairoa

AOD a

PW (cm)440 nm 670 nm 870 nm 1020 nm 440–870 440–675 500–870

Jan 0.345 0.226 0.190 0.177 1.0 1.2 0.8 1.2Feb 0.260 0.179 0.160 0.155 0.8 0.9 0.4 1.2Mar 0.358 0.226 0.190 0.175 1.0 1.2 0.8 1.0Apr 0.488 0.383 0.348 0.329 0.6 0.7 0.5 1.4May 0.374 0.243 0.199 0.182 1.0 1.1 0.8 1.8Jun 0.416 0.274 0.223 0.204 1.0 1.0 0.9 2.4Jul 0.457 0.264 0.195 0.170 1.3 1.3 1.2 2.3Aug 0.437 0.254 0.186 0.161 1.2 1.3 1.1 2.6Sep 0.427 0.273 0.218 0.199 1.0 1.1 0.9 2.6Oct 0.517 0.311 0.246 0.221 1.1 1.1 0.9 2.0Nov 0.364 0.217 0.172 0.156 1.1 1.2 0.9 1.8Dec 0.327 0.193 0.151 0.134 1.2 1.3 1.0 1.4

Mean 0.387 0.250 0.206 0.189 1.0 1.1 0.8 1.7SD 0.20 0.16 0.15 0.14 0.39 0.41 0.40 0.62

aData are for the period from 29 October 2004 to 31 January 2006. The a values computed for several wavelength ranges and PWare also presented. SD,standard deviation; AOD, aerosol optical depths; PW, precipitable water.

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bution over the measurements period. The most probablevalues (58% of occurrences between 1.0 and 1.5) are typicalof particles located mostly in the submicron range whereasvalues smaller than 0.5 and typical of aerosols containing animportant proportion of particles larger than 1 mm [Angstrom,1964] are also not infrequent (15% of occurrences in thewhole data set).[16] Possible correlations linking AOD and a can be

sought by examining the scatterplot of the 6749 a instan-taneous observations versus corresponding AOD values(Figure 2). As detailed by Kubilay et al. [2003] and byXiang-Ao et al. [2005], this visual representation oftenallows one to define physically interpretable cluster regionsfor different types of aerosols with different optical proper-ties. A striking feature of the scatterplot obtained with ourdata set is that a very wide range of a (0 to 1.8) is associatedwith rather low values of AOD. If an arbitrarily upper limitof 0.5 is chosen for defining these ‘‘moderate AOD’’ caseswe find that 79% of the observations fall into this categoryand the wide range of a associated with them suggests(1) that when the atmospheric load in particles is not veryhigh in Cairo the aerosol is probably a mixture of severalcomponents differing in size and (2) that the proportions ofthis mixture are not constant. This assumption that individ-ual components of different origins and characteristics

constitute Cairo aerosol is further supported by the fact thatthe largest AODs (e.g., AOD > 0.7) are associated withnarrower ranges of a: one located approximately between 1and 1.5, and the other between 0 and 0.5. More precisely,the largest AOD values measured during the year couldpossibly be explained by intensification of emissions by atleast two different particle sources: a source releasingrelatively fine particles associated with large Angstrom’sexponent and whose origin is probably anthropogenic(pollution-like source) and the other releasing coarser par-ticles associated with very low a values. This second sourcemight in fact correspond to the transport toward Cairo oflarge amounts of mineral dust generated by wind erosion inthe deserts surrounding the city [Alfaro and Abdel Wahab,2006]. Note that emissions by these two different sourcesare highly sporadic in time and that the particles theyproduce can be considered as added to what could bedefined as a background aerosol whose composition is alsocertainly quite variable in time since the Angstrom’s expo-nent associated with the moderate AOD spans over anextremely large range of values, as mentioned above.3.1.2. 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 duringcloud-free days are listed in Table 1. Figure 3 shows a

Figure 4. As in Figure 2 but for different seasons: (a) winter, (b) spring, (c) summer, and (d) autumn.

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selection of these data, namely AOD at 440 nm, a in the440–870 nm wavelength range, and PW. The standarddeviation of these quantities is visualized by the verticalbar associated with each monthly average. Figure 3 revealssignificant month-to-month variation in AOD even thoughthe amplitude of seasonal variations is rather limited whencompared to the well-defined bell-shaped seasonal trend withmaximum in summer which is usually observed in temperateclimates [e.g., Iqbal, 1983; Gueymard and Garrison, 1998].In Cairo, there are two marked AOD maxima, one in springand one in autumn centered in April and October, respec-tively. A secondary maximum in summer and a minimum inthe three winter months (December, January, and February)are also observed. On the basis of this temporal pattern ofAOD, which is in good agreement with previous resultsobtained by other authors [e.g., El-Wakil et al., 2001; Tadroset al., 2002; Zakey et al., 2004], four subperiods coincidingwith the four seasons of the year can be defined for a moredetailed temporal study of the aerosol characteristics inCairo. For example, if equivalent of Figure 2 scatterplotsare drawn for each season (Figure 4), they reveal that thelargest optical depth peaks observed in spring and autumncan be explained by the predominance of two completelydifferent kinds of aerosols. Indeed, the largest autumnAODs are clearly associated with large a values between1 and 1.5 (Figure 4d), or in other words, with the previouslydefined pollution-like aerosols. A possible explanation forthe presence of large amounts of this aerosol type in theatmospheric column in this period can lie in the fact that thisis the time when farmers burn agricultural wastes (mostly riceresidues) after harvest in the Nile delta and that the prevailingnorthern winds transport the biomass-burning plumes toward

Cairo. This assumption is supported by examination of airmass back trajectories computed with the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model(HYSPLIT model data are available at http://www.arl.noaa.gov/ready/hysplit4.html) showing that the largest autumnalAOD are associated with wind blowing from the north sector(Figure 5a). Moreover, the fact that a becomes less that 0.5only rarely during this period shows that the influence of thedust-like component is pretty limited in autumn. Conversely,the a values vary between very low (approximately 0) andvery high (more than 1.5) values during spring (Figure 4b).As already mentioned above, this very large range of asuggests that the ambient aerosol might be made of a mixtureof the pollution-like and dust-like components in proportionsthat vary considerably. Note that the possibility of observinglower a in spring than in autumn shows that the importanceof the mineral component is generally greater in spring. Thisis further confirmed by examining Figure 4b showing thatonly dust-like aerosols are responsible for the very largeAOD (AOD > 1) values observed in this season. These peakscoincide with dust storm events that tend to occur morefrequently, though not exclusively, during this period and thatcome from the southwest sector (Figure 5b), that is to sayfrom the Saharan desert. The addition of this mineral dustcomponent coming from outside Cairo to the backgroundaerosol explains at the same time the maximum monthlyaveraged AOD and aminimum recorded in April (Figure 3).[18] During summer the situation is significantly different.

AOD maxima can then be associated either with pollution-like or, though to a somewhat lesser extent, with dust-likeaerosols (Figure 4c). Thus, the respective influences of thesetwo aerosol types are more balanced than during spring and

Figure 5. Air mass back trajectories computed with the HYSPLIT model for two particular days of theCACHE measurement period: (a) a biomass-burning case (29 October 2004) and (b) a particularly intensedust event (8 April 2005).

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autumn. The exact origin of the increase in vertically inte-grated concentration of pollution-like aerosols in summer isnot elucidated fully but could be the result of a combinationof high PW values (Figure 3) and enhanced photochemistryvia the production of fine-mode secondary aerosols that aresome of its final byproducts (O. Favez et al., Significantformation of water-insoluble secondary organic aerosols insemiarid urban environment, submitted to GeophysicalResearch Letters, 2007). In addition to this, the frequentlystrong temperature inversions over Egypt during summerlead to the development of a stable boundary layer [Lasheen,1970] which may trap a significant amount of particlescausing a general increase in pollution, especially duringnight 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 depressionsrelatively frequent in this season and whose influenceextends to Cairo causing a relative increase in wind speedand in precipitations over the city [Soliman, 1953]. Thefirst effect promotes particle dispersion while the secondone removes significant amounts of aerosols from theatmosphere by rain washout. However, it must also benoted that the largest AOD (nearly 3) in the whole dataset has been recorded in this period of usually lowaerosol concentrations. It was due to a rather isolatedbut very intense dust event that lasted from 28 January to30 January 2006.

3.2. Characterization of the Aerosol IndividualComponents

[20] As detailed above, the a versus AOD scatterplotmethod has allowed identification of at least two aerosoltypes in Cairo, namely dust-like and pollution-like aerosols,whose occasional presence in large concentration explainsthe observed large AOD values. Therefore, it is important todetermine the characteristics of these single components inorder to understand the variability of the aerosol character-istics over the city. The methodology chosen for achievingthis goal has consisted in isolating in the data set theobservations corresponding to periods when either thedust-like or the pollution-like types were clearly dominantand in averaging the products of the inversion scheme forthese two sub data sets. Practically, the arbitrary numericalcriteria selected for the separation of the two aerosol typeshave been derived from Figure 2: a dust-like component isassumed to be clearly dominant when AOD is larger than0.7 and a is less than 0.5. Similarly, the pollution-likecomponent is considered as dominating the aerosol prop-

erties when AOD is larger than 0.7 and a between 1 and1.5. However, these definitions for the dust-like and pollu-tion-like aerosols leave out a significant number of obser-vations. Among them a first aerosol category with a valueslarger than 1.5 and whose importance is especially apparentin winter (Figure 4a) can be defined. This particularly fineaerosol is also probably of anthropogenic origin and could beconsidered as constituting a background pollution aerosoltype different from the ‘‘pollution type’’ previously defined.In summary, three main aerosol components have beenidentified: dust-like, pollution-like and background pollu-tion. Table 2 summarizes the practical criteria used toseparate the corresponding cases in the whole data set. Italso provides the frequency of observations for each aerosoltype.[21] At this stage, it must be acknowledged that many

observations, for instance all those yielding a values be-tween 0.5 and 1.0, have not been considered as a singleaerosol category in the previous discussion. Indeed, thesecases probably do not correspond to a single aerosol ofunknown origin but rather to a mixing of the previouslyidentified dust, pollution, and background pollution compo-nents. Theoretically, one should be able to derive thephysical characteristics of the overall (mixed) aerosol simplyfrom the characteristics of the individual components andfrom the proportions of their mixture.3.2.1. Volume Size Distributions[22] The spherical model is probably better adapted to

both the pollution-like and background pollution compo-nents on the one side and the spheroid model more suitablefor non spherical desert particles on the other side [Duboviket al., 2006], the results discussed in this section will bethose obtained with these respective assumptions. Anotherimportant point is that the particle size distribution retrievedfrom the inversion scheme and expressed by equation (2)have amplitudes terms (Cv) that increase with the amount ofparticles present in the atmospheric column, that is to saythe AOD. In consequence, the inverted size distributionshave been normalized by the means of this AOD in order tofacilitate comparison of the shapes of the three aerosoltypes’ size distributions (Figure 6).[23] The background pollution and pollution-like aerosols

(Figures 6a and 6b, respectively) both show bimodal sizedistribution. Indeed, they both contain a fine particle modecentered on a radius of 0.15 mm (± 0.03mm, SD) and acoarse mode in the supermicron range (r = 3.58 ± 0.67 and3.02 ± 0.40 mm, respectively). The main difference betweenthe two cases lies in the relative importance of the coarsemode particles as compared to the fine mode ones. Indeed,the ratio of the amplitudes of these two modes is signifi-cantly larger (about 1.5) for the pollution-like aerosol thanfor the background pollution (amplitude ratio of 1.2) thatthus appears to be finer. This also seems to indicate that theprocesses leading to the aerosol production are different inthe two cases. More precisely, gas to particle conversionfollowed by coagulation on the one side and direct emissionof particles in the accumulation mode on the other side mustbe the main processes explaining production of the back-ground pollution aerosol. This hypothesis is consistent withthe presence of a dense motorized traffic within Cairo thatreleases very fine black carbon particles found in theaccumulation mode and of large industrial complexes

Table 2. Numerical Criteria Used for Separating Cases Domi-

nated by the Three Aerosol Components (Dust-Like, Pollution-

Like, and Background)a

AOD � 0.7 AOD < 0.7

Dust-Like Pollution-Like Background Mixed

a � 0.5 1 � a � 1.5 a > 1.5 a < 1.5Frequency

occurrences (%)3 6 5 85

aFor definition of criteria, see section 3.2.2. A fourth categorycorresponding to cases not involved in the above three types is definedas mixed aerosol. For each case, the frequency of occurrence over the wholemeasurement period (Total = 1055) is also indicated.

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around the city that, besides black carbon, indirectly pro-duce large amounts of sulfate aerosols laying in the samesize range [Seinfeld and Pandis, 1998; Abu-Allaban et al.,2002; Favez et al., submitted manuscript, 2007]. In additionto this, the fact that, except at diurnal and weekly scales for

car traffic [Mahmoud et al., 2008], there is no significanttemporal variation in these urban activities during the yearcould also explain that the background pollution aerosol isnever associated with exceptionally large AOD values. Thisis not the case for the pollution-like aerosol whose emis-sions are particularly intense in autumn and whose originhas been assumed to be the biomass-burning process in theNile delta. The size distribution obtained in this case isconsistent with this assumption. Indeed, the burning ofbiomass is known to release at the same time particles inthe accumulation mode (black carbon particles, mostly) butalso coarser particles resulting from the condensation oforganic 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 consid-erable amount of particles located in the supermicronmode. Contrary to what was observed with the two typesof ‘‘pollution’’ aerosols, it clearly appears that the coarsemode itself is split in two different submodes: one beingcentered on a radius of about 1.5 mm and the other at4 mm. This bimodal structure of the coarse mode is similarto the one found during dust events by Eck et al. [2005] inBeijing (r � 1.5 and 4 mm) and by Masmoudi et al. [2003]in two African sites (r � 1.3 and 3.8 mm). Also noteworthyis the fact that the routine used for fitting lognormaldistributions to the inverted size distributions is not capableof separating these two coarse modes because it assumesfrom the start that particles in the supermicron range belongto a unique lognormally distributed particle population.. Asa result, the parameters (Cv, rm, s) of the retrieved aerosolsize distribution must be considered with caution when asignificant amount of dust-like aerosol is present in theatmospheric column. In particular, the radius of the coarseparticle mode yielded by the automatic inversion procedureis expected to be somewhere in-between the values of theradii of the two real coarse particles submodes, that isbetween 1.5 and 4 mm. This prediction is confirmed byexamining the inversion results presented in Table 3, whichshow that the coarse mode radius for the dust-like aerosol isonly 2.33mm, a value lower than the ones of the two pollutionaerosols because of the presence of the finest coarse mode atrm = 1.5 mm 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 aerosolindividual components normalized by the AOD. The errorbars correspond to the standard deviation of the inverteddata.

Table 3. Parameters of the Volume Size Distribution for the Three

Aerosol Components Over Cairo in the Period of the CACHE

Campaigna

Aerosol Background Pollution-Like Dust-Like

Fine ModeCV 0.05 ± 0.03 0.13 ± 0.05 0.13 ± 0.09rm 0.15 ± 0.02 0.15 ± 0.03 0.12 ± 0.04s 0.41 ± 0.04 0.48 ± 0.07 0.67 ± 0.19

Coarse ModeCV 0.06 ± 0.04 0.19 ± 0.06 0.70 ± 0.57rm 3.58 ± 0.67 3.02 ± 0.40 2.33 ± 0.59s 0.68 ± 0.05 0.68 ± 0.07 0.60 ± 0.04aCv is in mm3/mm2, and rm and s are in mm. The corresponding

uncertainty is shown by the standard deviation of the results. CACHE,Cairo Aerosol Characterization Experiment.

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the coarse mode/fine mode in the dust-like case (amplituderatio = 5.4) than in the two pollution cases.3.2.2. Optical Properties of the Three AerosolComponents[25] The values of m, SSA, and g provided by the

inversion method at the four Sun photometer wavelengthsand for each aerosol component are summarized in Table 4.The real (k) and imaginary (n) parts of the refractive indexare significantly different for the three aerosol components,a fact that reflects the differences in chemical or mineral-ogical composition. In good agreement with resultsobtained at other AERONET sites where dust is a dominat-ing aerosol component (e.g., Solar Village in Saudi Arabiaand Bahrain in the Persian Gulf [Dubovik et al., 2002a]), kis relatively large for the dust-like component (1.51 ± 0.05).This value is also consistent with the order of magnitudereported in the literature for the major mineral speciespresent in mineral dust, namely quartz and clay minerals.These minerals do not absorb light, which explains that theimaginary part retrieved for the dust-like aerosol is low(0.002 ± 0.001) and mostly independent of wavelength, atleast over the measurement range. For the backgroundaerosol, the real part of the refractive index is found tobe particularly low (1.42 ± 0.08), but large value for theimaginary part (0.012 ± 0.01) is consistent with theprevious assumption of this aerosol type being mainlyproduced by combustion processes (industry and traffic)and, hence, particularly rich in fine and very absorbingblack carbon particles. Finally, the refractive index of thepollution-like component is in-between the ones of theother two. The relatively large value of its imaginary part(0.008 ± 0.001) indicates that this aerosol type is also anefficient absorber, in good agreement with the assumptionthat it is produced by biomass-burning activities. It canalso be noted that the refractive indices (k, n) obtained at870 nm by Mukai et al. [2005] in Shirahama (Japan)during dust events (k = 1.51–1.52, n = 0.002) and inpollution dominated conditions (k = 1.42–1.52, n = 0.006–0.01) are close to the values presented in Table 4.[26] Combined with the differences in size distribution

already commented on above, these differences in composi-tion explain the spectral behaviors of the SSA and g. Indeed,the finest aerosol component (background pollution) is theone for which the spectral dependence of scattering is

expected to be the largest (see the magnitude of Angstrom’sexponent). This is reflected directly by the strong dependenceof the asymmetry parameter on wavelength. The fact that thisdependence is negative shows that, as predicted by electro-magnetic theory, the importance of backscattering as com-pared to forward scattering increases with wavelength forparticles 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 negli-gible, the effect of size on scattering will control in greatpart the spectral behavior of SSA. As with the asymmetryparameter, the SSA is found to be negatively correlatedwith l. The same explanation applies to the pollution-likeand dust-like components. Indeed, the increasing impor-tance of the supermicron particle population tends tosmooth the spectral dependence of SSA and g out untilit disappears almost completely for the dust-like aerosol.In this last case, it can be noted that the SSA is particu-larly large, thus denoting that the aerosol is not a veryefficient absorber of solar light except maybe at the shorterwavelength (440 nm) where SSA is lower than at the otherthree l. This enhancement of absorption at short wave-lengths has often been interpreted [e.g., Sokolik and Toon,1999; Alfaro et al., 2004; Derimian et al., 2006] asresulting from the presence of iron oxides such as hematiteand goethite in desert dust (note that by absorbing specif-ically in the UV and in the short band of the solarspectrum, these mineral species are responsible for thereddish or yellowish color of mineral dust). Should theSun photometer be able to perform measurements atwavelengths shorter than 440 nm, it is probable that thiseffect would have been detected with more efficiency.More generally, the Cairo observations (lower absorptionand SSA spectral dependence in presence of dust thanduring pollution episodes) are consistent with previousmeasurements performed at Bahrain, in the Persian Gulf[Smirnov et al., 2002].

3.3. Monthly Variations of the Aerosol Properties

3.3.1. Defining a Mixed Aerosol Component[28] The proportions reported in Table 2 show that the

three pure aerosol components defined above represent only14% of all instantaneous observations. These pure aerosol

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 Aerosola

Physical Property Aerosol Component 441 nm 673 nm 873 nm 1022 nm All Wavelengths

k bgd-poll. 1.41 ± 0.09 1.41 ± 0.08 1.42 ± 0.08 1.44 ± 0.08 1.42 ± 0.08poll-like 1.42 ± 0.11 1.44 ± 0.10 1.46 ± 0.09 1.48 ± 0.09 1.45 ± 0.10dust-like 1.46 ± 0.10 1.52 ± 0.06 1.54 ± 0.04 1.52 ± 0.04 1.51 ± 0.05

n bgd-poll. 0.011 ± 0.006 0.011 ± 0.005 0.012 ± 0.006 0.013 ± 0.007 0.012 ± 0.01poll.-like 0.008 ± 0.007 0.008 ± 0.006 0.009 ± 0.006 0.009 ± 0.006 0.008 ± 0.01dust-like 0.002 ± 0.001 0.002 ± 0.001 0.002 ± 0.001 0.002 ± 0.001 0.002 ± 0.001

SSA bgd-poll. 0.903 ± 0.04 0.873 ± 0.05 0.834 ± 0.07 0.812 ± 0.08 0.856 ± 0.06poll-like 0.914 ± 0.04 0.904 ± 0.04 0.882 ± 0.04 0.872 ± 0.05 0.893 ± 0.04dust-like 0.934 ± 0.03 0.958 ± 0.02 0.962 ± 0.02 0.965 ± 0.02 0.954 ± 0.02

g bgd-poll. 0.695 ± 0.04 0.613 ± 0.03 0.582 ± 0.02 0.576 ± 0.02 0.617 ± 0.02poll-like 0.694 ± 0.04 0.636 ± 0.04 0.615 ± 0.03 0.612 ± 0.03 0.639 ± 0.03dust-like 0.681 ± 0.04 0.661 ± 0.03 0.667 ± 0.02 0.679 ± 0.03 0.672 ± 0.02

aThe three main components present in Cairo aerosol are background pollution, pollution-like, and dust-like. Note that mentioned uncertaintiescorrespond to standard deviations. SSA, single-scattering albedo; bgd-poll., background pollution; poll-like, pollution-like.

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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 back-ground pollution in winter (Table 5). All the remainingsituations that are characterized by a < 1.5 and an AOD <0.7, and that probably correspond to situations in which theaerosol is a mixture of the three main components, are moreevenly distributed over the year (Table 5).[29] Though the large range of instantaneous values

covered by a suggests that the proportions of these mixturescan vary substantially and rapidly with time, the examina-tion of Figure 3 reveals that the dependence of a on time issignificantly smoothed when monthly averages are consid-ered. This could mean that, in spite of the large variability ofaerosol properties at short timescales, it should be possibleto define a much less variable mixed aerosol at month scale.Practically, the properties of this mixed aerosol could bedetermined by averaging the instantaneous results satisfyingthe mixed aerosol conditions (a < 1.5 and AOD < 0.7) foreach month. The results yielded by this method confirm thatin spite of significant fluctuations in the size distribution ofthe mixed aerosol (the amplitudes of its two main modescan differ by up to 25% from their mean values) themonthly averaged optical properties (SSA and g) do notvary much from one month to the other. Thus, we can definea unique mixed aerosol component whose properties areobtained by averaging the monthly results. The values ofthese properties and their standard deviations are summa-rized in the first two lines of Table 6.

[30] More generally, the single-scattering albedo of themixed aerosol can be compared to those of the three pureaerosol components (Figure 7). The values and spectraldependence of SSA for this mixed aerosol fit relatively wellbetween the ones of the background pollution and pollu-tion-like components but are quite different from the onesof the dust-like component. This suggests that, though themixed aerosol certainly contains at certain times a signif-icant proportion of mineral dust (a is occasionally quitelow), the influence of this component can be neglectedwhen annual averages are considered. This is due to the factthat the definition of ‘mixed’ aerosols excludes extremeevents in which any one of the pure aerosol componentswould be neatly dominant. A consequence of this remark isthat deviations of the monthly averaged optical propertiesfrom those of the mixed aerosol should be a good indicatorof the influence of a particular component in a month scale.The next section is dedicated to the analysis of suchdeviations.3.3.2. Deviations from the Mixed Aerosol Properties[31] In order to detect the aforementioned deviations,

monthly averaged properties have been computed andincluded in Table 6. Those properties that differ by morethan one standard deviation from the ones of the mixedaerosol are in the footnote of Table 6. It can thus be seenthat except for some particular months that will be studiedin more details below, most of the monthly data presented inTable 6 are in good agreement with the mixed aerosolcharacteristic values. This means that, at least on a monthscale, the mixed aerosol defined previously can generally beconsidered as representative of the overall aerosol situationin Cairo. This rule suffers some exceptions, especially in(1) April, June, and October, when the amplitude of thecoarse mode is unusually high for the different reasonsalready mentioned above (dust events, probable intensifica-tion of photochemistry combined with humidity effect, andbiomass burning, respectively), and (2) in December whenthis amplitude is unusually low (presence of the backgroundpollution aerosol only). This difference in the size distribution

Table 5. Seasonal Distribution of the Observation Frequency for

the Four Different Aerosol Typesa

Cases Winter (%) Spring (%) Summer (%) Autumn (%)

Background 57 25 2 16Pollution-like 17 5 20 58Dust-like 11 67 19 4Mixed 28 30 16 26

aAerosol types are defined in section 3.2.2.

Table 6. Size Distribution Characteristics (Cv, rm, and s), SSA, g, and Refractive Indices (k and n) of the Cairo Aerosola

Fine Mode Coarse Mode SSA All Wavelengths

CV rm s CV rm s 441 nm 673 nm 873 nm 1022 nm g k n

Mixed 0.055 0.129 0.476 0.136 2.930 0.668 0.884 0.874 0.855 0.849 0.648 ± 0.010 1.49 ± 0.033 0.010 ± 0.002SD 0.011 0.012 0.044 0.033 0.293 0.015 0.014 0.018 0.024 0.026

For All EventsMonth

Jan 0.05 0.14 0.53 0.16 2.89 0.68 0.913b 0.905b 0.887b 0.879b 0.655 ± 0.038 1.49 ± 0.072 0.008 ± 0.006Feb 0.03 0.13 0.57 0.11 2.83 0.68 0.891 0.887 0.876 0.870 0.665 ± 0.026 1.53 ± 0.070 0.009 ± 0.008Mar 0.06 0.12 0.52 0.12 3.05 0.68 0.865 0.859 0.839 0.830 0.643 ± 0.035 1.50 ± 0.075 0.013 ± 0.008b

Apr 0.08 0.12 0.59 0.25 2.46 0.65 0.891 0.911b 0.908b 0.908b 0.669 ± 0.030 1.51 ± 0.070 0.006 ± 0.004b

May 0.04 0.13 0.53 0.16 3.41 0.67 0.882 0.870 0.854 0.850 0.645 ± 0.020 1.56 ± 0.064 0.011 ± 0.008Jun 0.04 0.13 0.53 0.20 3.52 0.65 0.894 0.885 0.872 0.869 0.653 ± 0.027 1.55 ± 0.063 0.008 ± 0.006Jul 0.06 0.14 0.47 0.15 4.02 0.66 0.889 0.865 0.839 0.830 0.634 ± 0.028 1.53 ± 0.082 0.012 ± 0.008Aug 0.05 0.16 0.47 0.11 3.20 0.68 0.899 0.878 0.858 0.851 0.652 ± 0.027 1.51 ± 0.086 0.011 ± 0.008Sep 0.05 0.13 0.53 0.17 3.29 0.65 0.895 0.886 0.872 0.870 0.652 ± 0.026 1.53 ± 0.085 0.008 ± 0.006Oct 0.05 0.14 0.54 0.19 3.20 0.65 0.900 0.891 0.873 0.868 0.640 ± 0.030 1.56 ± 0.043 0.009 ± 0.006Nov 0.05 0.13 0.52 0.14 3.78 0.69 0.872 0.857 0.838 0.831 0.646 ± 0.032 1.53 ± 0.078 0.012 ± 0.008Dec 0.05 0.14 0.51 0.09 3.58 0.70 0.886 0.866 0.838 0.824 0.646 ± 0.035 1.49 ± 0.087 0.013 ± 0.009b

aThe first two lines contain annual means and standard deviations of the mixed aerosol component defined in the text. The following lines report themonthly averaged the aerosol characteristics computed from the complete data set.

bNote that these optical properties deviate by more than one standard deviation from the mixed aerosol values.

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combined with a variety of origins and compositions also hasan impact on the aerosol optical properties. Indeed, the largeApril SSA values and their low spectral dependence can beconsidered as being the result of the several dust events thatoccurred during that particular month. Also in April, theinfluence of the mineral dust is visible on the imaginary partof the aerosol refractive index that is significantly lower thanthat of the average mixed aerosol. In January, the occurrenceof a particularly intense dust event already mentioned insection 3.1.2 was enough to alter the monthly averagedSSAwhose values are larger than those of the mixed aerosoland independent of wavelength. However, this effect of thedust component is not detected on the asymmetry parameterand the refractive index (whose spectral dependence is notshown in Table 6 for lack of space) that appear to be lesssensitive to the presence of mineral dust than SSA. In thewinter months, and more particularly in December when nodust storm exist, the aerosol was mainly produced by com-bustion activities (industry and traffic) and thus particularlyrich in its fine, light absorbing, black carbon component. Thisexplains the large values of the imaginary part of the refractiveindex for this month. Though they are not strictly speakingoutside the range of variability of the mixed aerosol values,the December SSAs are also clearly located in the mostabsorbing part of this range (Figure 7). The effect of theabsorbing carbonaceous components on the SSA is alsovisible on the July and October data (Figure 7). For thesetwo months, the SSAvalues at the four wavelengths are closeto those of the background pollution and pollution-likecomponents, respectively. This indicates that in July theaerosol is as absorbing as in December and thus also domi-nated by the black carbon fraction. On the contrary, theOctober biomass-burning activities seem to release a morecomplex mixture of carbonaceous species than industries and

car engines do. In this mixture the absorbing role of the blackcarbon is moderated by the presence of at least anothercomponent, assumedly organic species, which does notabsorb solar light at the Sun photometer wavelengths butonly scatters it.

4. Summary and Conclusion

[32] The present study has shown that the aerosol presentover Cairo can be considered as a mixture of three individualcomponents of differing origins composition, size distribu-tion and optical properties: The first component is a back-ground pollution aerosol produced locally and all year roundby combustion activities (motorized traffic and industries).This component is characterized by the relative importance ofits fine (accumulation) mode, by the fact that it stronglyabsorbs solar radiation, and that this absorption increasesrapidly with wavelength. These three factors suggest that thebackground pollution component is particularly rich in blackcarbon, in good agreement with its assumed origin. Thesecond component, or pollution-like aerosol, is producedby biomass-burning activities in the Nile delta. As comparedto background pollution, it is characterized by the presence ofa relatively important supermicron mode, probably lessabsorbing than black carbon itself. As a consequence, theSSA is somewhat larger and less spectrally dependent for thiscomponent than for the previous one. Finally, the thirdcomponent (dust-like) is of mineral origin and producedeither by wind erosion in the deserts or in the city itself byroad works, construction sites, etc. This aerosol is muchcoarser than the other two and much less absorbing. Inaddition, the SSA slightly increases at the shorter wave-lengths (between 441 and 673 nm) before becoming constantat larger wavelengths.

Figure 7. Spectral dependence of SSA for the three pure aerosol components (background, pollution-like, and dust-like) and for the mixed aerosol defined in the text. The monthly averaged measurements forDecember, April, July, and October are also reported. In this last case, the error bar represents thestandard deviation.

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[33] The temporal variability of the aerosol characteristicscan be attributed to changes in the proportions of thismixture. These changes can be dramatic and occur withina few hours, for instance during a dust storm or a biomass-burning event. On these occasions, the AOD can peak atunusually large values because of the increase in theproportion of a unique component and the aerosol proper-ties are temporarily dominated by this single component.However, such occasions that only represent 15% of allobservations are rather scarce and short-lived. When theseextreme events are not taken into consideration, an averagemixed aerosol can be defined at an annual scale. Theinterest of this definition is that, except during monthswhen extreme events are quite frequent, it should bepossible to use the unique set of optical characteristics ofthis reference mixed aerosol instead of those of the overallaerosol for computing the aerosol radiative effects.[34] Practically, the comparison of the monthly averaged

SSAs for the overall aerosol with the one of the mixedaerosol shows that the agreement is good for all months ofthe year except April and, to a lesser extent, January. InApril, the cumulative effect of several dust outbreaks is toostrong for the mixed aerosol values to remain a goodapproximation to the SSA of the overall aerosol. Note thatthis result, namely that the influence of mineral dust on theaerosol optical properties is particularly felt in the springperiod, is in good agreement with estimations by Derimianet al. [2006] showing that the radiative effect of mineral

dust in Israel is significant only during this particular periodof the year. Also noteworthy is the fact that the opticalcharacteristics obtained for the mixed aerosol, and morespecially the SSA whose values lie between 0.85 and 0.88,are typical of strongly absorbing aerosols. This suggests thatthe background pollution and the pollution-like componentsmust play a much more important role in the composition ofthe average mixed aerosol than the dust-like component.This conclusion that the Cairo optical properties are con-trolled by its two absorbing components when averagedover sufficiently long periods, is confirmed by the compar-ison of the SSA yearly averages at several locations in theworld. Indeed, it appears clearly in Figure 8 that theabsorbing character of the aerosol in Cairo is (1) as markedas in Mexico City, a megacity also famous for its highpollution level, and (2) more pronounced than in Pariswhere regulations aiming at mitigating particulate pollutionseem to be more efficient or in the Arabian Peninsula wherethe effect of dust is dominating all year round.

[35] Acknowledgments. This work has been partly funded by theFrench Ministry of Foreign Affairs in the frame of the Imhotep program(contract 446538E) and the Institut National des Sciences de l’Univers(INSU). The authors are also grateful to the ADEME and PNCA (project:‘‘Especes a courte duree de vie: interactions avec le climat’’) for theirfinancial support; to Cairo University at Giza and the Egyptian Meteoro-logical Authority (EMA), which have provided the two experimental sites;and to the PHOTONS/LOA and AERONET/NASA teams for calibratingthe Sun photometer and processing its data. Finally, the authors acknowl-edge the NOAA Air Resources Laboratory (ARL) for the provision of the

Figure 8. Comparison of the annual SSA values at some AERONET sites dominated by differentaerosol types (Creteil-Paris, France, 1999; Solar-Village, Saudi Arabia, 1998–2000; Bahrain, PersianGulf, 1998–2000; Mexico City, 1999–2000). The data have been obtained from Dubovik et al. [2002a].

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HYSPLIT transport and dispersion model and/or READY website (data areavailable at http://www.arl.noaa.gov/ready.html) used in this publication.

ReferencesAbu-Allaban, M., A. W. Gertler, and D. H. Lowenthal (2002), A preliminaryapportionment of the sources of ambient PM10, PM2.5, and VOCs in Cairo,Atmos. Environ., 36, 5549–5557, doi:10.1016/S1352-2310 (02)00662-3.

Alfaro, S. C., and M. Abdel Wahab (2006), Extreme variability of aerosoloptical properties: The Cairo aerosol characterization experiment casestudy, in Remote Sensing of the Atmosphere for Environment Security,edited by A. Perrin, N. Ben Sari-zizi, and J. Demaison, pp. 285–299,Springer, New York.

Alfaro, S. C., S. Lafon, J. L. Rajot, P. Formenti, A. Gaudichet, and M.Maille (2004), Influence of iron oxides on light absorption by pure desertdust: An experimental study, J. Geophys. Res., 109, D08208,doi:10.1029/2003JD004374.

Angstrom, A. (1964), The parameters of atmospheric turbidity, Tellus, 16,64–75.

Cachorro, V. E., R. Vergaz, and A. M. de Frutos (2001), A quantitativecomparison of Angstrom turbidity parameter retrieved in different spec-tral ranges based on spectroradiometer solar radiation measurements,Atmos. Environ., 35, 5117–5124, doi:10.1016/S1352-2310 (01)00321-1.

Cerf, A. (1986), Atmospheric turbidity over West-Africa, Contrib. Atmos.Phys., 53, 414–429.

Derimian, Y., A. Karnieli, Y. J. Kaufman, M. O. Andreae, T. W. Andreae,O. Dubovik, W. Maenhaut, I. Koren, and B. N. Holben (2006), Dust andpollution aerosols over the Negev desert, Israel: Properties, transport, andradiative effect, J. Geophys. Res., 111, D05205, doi:10.1029/2005JD006549.

Dubovik, O., and M. D. King (2000), A flexible inversion algorithm forretrieval of aerosol optical properties from Sun and sky radiance mea-surements, J. Geophys. Res., 105, 20,673 – 20,696, doi:10.1029/2000JD900282.

Dubovik, O., A. Smirnov, B. N. Holben, M. D. King, Y. J. Kaufman, T. F.Eck, and I. Slutsker (2000), Accuracy assessments of aerosol opticalproperties retrieved from Aerosol Robotic Network (AERONET) Sunand sky radiance measurements, J. Geophys. Res., 105, 9791–9806,doi:10.1029/2000JD900040.

Dubovik, O., B. N. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D.King, D. Tanre, and I. Slutsker (2002a), Variability of absorption andoptical properties of key aerosol types observed in worldwide locations,J. Atmos. Sci., 59, 590 – 608, doi:10.1175/1520-0469 (2002)059<0590:VOAAOP>2.0.CO;2.

Dubovik, O., B. N. Holben, T. Lapyonok, A. Sinyuk, M. I. Mishchenko, P.Yang, and I. Slutsker (2002b), Non-spherical aerosol retrieval methodemploying light scattering by spheroids, Geophys. Res. Lett., 29(10),1415, doi:10.1029/2001GL014506.

Dubovik, O., et al. (2006), Application of spheroid models to account foraerosol particle nonsphericity in remote sensing of desert dust, J. Geo-phys. Res., 111, D11208, doi:10.1029/2005JD006619.

Eck, T. F., et al. (2005), Columnar aerosol optical properties at AERONETsites in central eastern Asia and aerosol transport to the tropical mid-Pacific, J. Geophys. Res., 110, D06202, doi:10.1029/2004JD005274.

El-Fandy, M. G. (1940), The formation of depressions of the Khamsin type,Q. J. R. Meteorol. Soc., 66, 323–335.

El-Fandy, M. G., and M. K. El-Nisr (1949), On the conditions favoring lowstratus formation over the Middle East in summer, Bull. Am. Meteorol.Soc., 30, 357–359.

El-Wakil, S. A., M. El-Metwally, and C. Gueymard (2001), Atmosphericturbidity of urban and desertic areas of the Nile basin in the aftermath ofMt. Pinatubo’s eruption, Theor. Appl. Climatol., 68, 89 – 108,doi:10.1007/s007040170056.

Favez, O., H. Cachier, J. Sciare, T. M. El-Araby, S. C. Alfaro, M. A.Harhash, and M. M. Abdel Wahab (2007), Seasonality of major aerosolspecies in Greater Cairo (Egypt), Atmos. Environ., 42, 1503–1516,doi:10.1016/j.atmosenv.2007.10.081.

Griggs, M. (1975), Measurement of atmospheric aerosol optical thick-ness over water using ERTS-1 data, J. Air Pollut. Control Assoc., 25,622–626.

Gueymard, C., and J. D. Garrison (1998), Critical evaluation of precipitablewater and atmospheric turbidity in Canada using measured hourly solarirradiance, Sol. Energy, 62, 291 – 307, doi:10.1016/S0038-092X(98)00005-X.

Haywood, J. M., V. Ramaswamy, and B. J. Soden (1999), Troposphericaerosol climate forcing in clear-sky satellite observations over the oceans,Science, 283, 1299–1303, doi:10.1126/science.283.5406.1299.

Herman, J. R., and E. Celarier (1997), Earth’s surface reflectivity clima-tology at 340–380 nm from TOMS data, J. Geophys. Res., 102,28,003–28,012, doi:10.1029/97JD02074.

Holben, B. N., et al. (1998), AERONET-A federated instrument networkand data archive for aerosol characterization, Remote Sens. Environ., 66,1–16.

International Panel on Climate Change (1996), Climate Change: TheScience of Climate Change, edited by J. T. Houghton et al., 584 pp.,Cambridge Univ. Press, New York.

Iqbal, M. (1983), An Introduction to Solar Radiation, 390 pp., Academic,New York.

Jung, C. E. (1955), The size distribution and aging of natural aero-sols as determined from electrical and optical data on the atmosphere,J. Meteorol., 12, 13–25.

Kaskaoutis, D. G., P. Kosmopoulos, H. D. Kambezidis, and P. T. Nastos(2007), Aerosol climatology and discrimination of different types overAthens, Greece, based on MODIS data, Atmos. Environ., 41, 7315–7329,doi:10.1016/j.atmosenv.2007.05.017.

King, M. D., Y. J. Kaufman, W. P. Menzel, and D. Tanre (1992), Remotesensing of cloud, aerosol, and water vapor properties from the ModerateResolution Imagine Spectrometer (MODIS), IEEE Trans. Geosci. RemoteSens., 30, 2–27, doi:10.1109/36.124212.

Kubilay, N., T. Cokacar, and T. Oguz (2003), Optical properties of mineraldust outbreaks over the northeastern Mediterranean, J. Geophys. Res.,108(D21), 4666, doi:10.1029/2003JD003798.

Lasheen, A. M. (1970), A further study in weather forecasting in the Med-iterranean: Contribution to the problem of instability in the area, M.S.thesis, Cairo Univ., Cairo.

Mahmoud, K. F., S. C. Alfaro, O. Favez, M. M. Abdel Wahab, and J. Sciare(2008), Origin of black carbon concentration peaks in Cairo (Egypt),Atmos. Res., 89, 161–169, doi:10.1016/j.atmosres.2008.01.004.

Masmoudi, M., M. Chaabane, D. Tanre, P. Gouloub, L. Blarel, andF. Elleuch (2003), Spatial and temporal variability of aerosol: Size dis-tribution and optical properties, Atmos. Res., 66, 1 –19, doi:10.1016/S0169-8095 (02)00174-6.

Mukai, S., I. Sano, and B. N. Holben (2005), Aerosol properties overJapan determined by Sun/sky photometry, Water Air Soil Pollut., 5,133–143.

Pandithurai, G., P. C. S. Devera, P. E. Raj, and S. Sharma (1997), Retrievalof aerosol size index from high-resolution spectroradiomter observations,Aerosp. Sci. Technol., 26, 154–162, doi:10.1080/02786829708965421.

Prospero, J. M. (1999), Long-term measurements of the transport of Africanmineral dust to the southeastern United States: Implications for regionalair quality, J. Geophys. Res., 104, 15,917 – 15,927, doi:10.1029/1999JD900072.

Remer, L. A., Y. J. Kaufman, and B. N. Holben (1999), Internationalvariation of ambient aerosol characteristics on the east coast of the UnitedStates, J. Geophys. Res., 104, 2223–2231, doi:10.1029/1998JD200037.

Seinfeld, J. H., and S. N. Pandis (1998),Atmospheric Chemistry and Physics:From Air Pollution to Climate Change, 1326 pp., John Wiley, New York.

Smirnov, A., B. N. Holben, N. T. O’Neill, T. F. Eck, D. L. Westphal, A. K.Goroch, and I. Slutsker (2002), Atmospheric aerosol optical propertiesin the Persian Gulf region, J. Atmos. Sci., 59, 620–634, doi:10.1175/1520-0469 (2002)059<620:3AAAOPIT>2.0.CO;2.

Sokolik, I. N., and O. B. Toon (1999), Incorporation of mineralogicalcomposition into models of the radiative properties of mineral aerosolfrom UV to IR wavelengths, J. Geophys. Res., 104, 9423 – 9444,doi:10.1029/1998JD200048.

Soliman, K. H. (1953), Rainfall over Egypt, Q. J. R. Meteorol. Soc., 79,389–397, doi:10.1002/qj.49707934106.

Tadros, M. T. Y., M. El-Metwally, and A. B. Hamed (2002), Determinationof Angstrom coefficients from spectral aerosol optical depth at two sitesin Egypt, Renewable Energy, 27, 621–645, doi:10.1016/S0960-1481(01)00156-2.

Wilson, R., and J. Spengler (Eds.) (1996), Particles in Our Air: Concen-trations and Health Effects, 254 pp., Harvard Univ. Press, Cambridge,Mass.

Xiang-Ao, X., C. Hong-Bin, W. Pu-Cai, Z. Xue-Mei, Q. Jin-Huan, and P.Gouloub (2005), Aerosol properties and their spatial and temporal varia-tions over North China in spring 2001, Tellus, Ser. B, 57, 28–39.

Zakey, A. S., M. M. Abdel Wahab, and P. A. Makar (2004), Atmosphericturbidity over Egypt, Atmos. Environ., 38, 1579–1591, doi:10.1016/j.atmosenv.2003.12.017.

�����������������������M. Abdel Wahab, Astronomy and Meteorology Department, Faculty of

Science, Cairo University, Giza, Egypt.S. C. Alfaro and B. Chatenet, LISA, UMR 7583, CNRS, Universite de

Paris XII, Universite de Paris VII, Creteil F-94010, France.M. El-Metwally, Physics Department, Faculty of Sciences at Port Said,

Suez Canal University, Port Said, Egypt. (melmetwally@yahoo.com)

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