Spatial and Temporal Variation of the Extreme Saharan Dust Event Over Turkey in March 2016

Home , Dikmen, Elbistan, Melikgazi, Siirt Province

atmosphere

Article Spatial and Temporal Variation of the Extreme Saharan Dust Event over Turkey in March 2016

Hakki Baltaci

Turkish State Meteorological Service, Kütükçü Alibey Caddesi No.4, Ankara 06120, Turkey; [email protected]; Tel.: +90-2164-573-400; Fax: +90-2164-573-403

Academic Editor: Robert W. Talbot Received: 27 December 2016; Accepted: 14 February 2017; Published: 17 February 2017

Abstract: In this study, the inﬂuence of an extraordinary Saharan dust episode over Turkey on 23–24 March 2016 and the atmospheric conditions that triggered this event were evaluated in detail. PM10 (particulate matter less than 10 µm) observations from 97 air quality stations, METAR (Meteorological Terminal Aviation Routine Weather Report) observations at 64 airports, atmospheric soundings, and satellite products were used for the analysis. To determine the surface and upper levels of atmospheric circulation, National Centers of Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis data were applied to the extreme dust episodes. On 23 March 2016, high southwesterly winds due to the interaction between surface low- and high-pressure centers over Italy and Levant basin brought thick dust particles from Libya to Turkey. The daily PM10 data from 43 stations exceeded their long-term spring means over Turkey (especially at the northern and western stations). As a consequence of the longitudinal movement of the surface low from Italy to the Balkan Peninsula, and the quasi-stationary conditions of the surface high-pressure center allowed for the penetration of strong south and southwesterly winds to inner parts of the country on the following day. As a consequence, 100%, 90%, 88%, and 87% of the monitoring stations in Marmara (NW Turkey), central Anatolia, western (Aegean) and northern (Black Sea) regions of Turkey, respectively, exhibited above-normal daily PM10 values. In addition, while strong subsidence at the low levels of the atmosphere plays a signiﬁcant role in having excessive daily PM10 values in Black Sea, dry atmospheric conditions and thick inversion level near the ground surface of Marmara ensured this region to have peak PM10 values ~00 Local Time (LT).

Keywords: Saharan dust; air quality; synoptic analysis; METAR; Turkey

1. Introduction Dust outbreaks are considered to be one of the major natural hazards that affect the daily life. As a consequence of the intense dust storm, mineral aerosols of the desert dust are transported up to thousands of kilometers away from the source regions by atmospheric circulation. From the desert dusts, Saharan dust is known as the largest source of aeolian soil dust [1]. Previous studies showed that dust originating from the Sahara is mainly transported to the Caribbean and South America [2,3], the Mediterranean and Europe [4], the eastern Mediterranean (EM hereafter), and the Middle East [5]. Due to the transfer of the Saharan dust to distant places of the world, numerous studies investigated its impacts on health problems (e.g., [6–11]), atmospheric heating and stability [12], cloud formation and radiation [13], and visibility [14], from regional perspectives. In the EM basin (extending from Turkey to northern Africa and eastward to Iran), dust loadings originating from Sahara are shown mostly in spring months [15] due to the occurrence of intense Sharav cyclones, which are generated by the thermal contrast between cold Atlantic air and warm continental air, over the south of the Atlas Mountains (Morocco). As a consequence of these high energetic Sharav cyclones, mobilized dust is

Atmosphere 2017, 8, 41; doi:10.3390/atmos8020041 www.mdpi.com/journal/atmosphere Atmosphere 2017, 8, 41 2 of 16 transported to eastward directions with a typical speed of 7–8 degrees of longitude (~700–800 km) per day [5]. Therefore, many researchers investigated the relationship between synoptic-scale circulation patterns and dust particles by focusing on extreme case events in this EM region. Tsidulko [16] investigated the significant variation of the dust layers within the cyclone centers using the Eta weather and dust prediction model. They found that the dust layer extends up to 8–10 km in the warm sector of the cyclone over Mediterranean Sea, and this condition enables low dust concentration values. On the other hand, high values of the dust concentrations were found in the area of a cold front over Africa. Papayannis [17] emphasized the importance of the southerly air masses from the Sahara Desert on the high PM10 levels over Eastern Mediterranean Basin. Kaskaoutis [18], using satellite observations and ground-based measurements, presented Saharan dust outbreaks affecting Greece. It was found that a thick dust layer transported from Libya dramatically increased the PM10 concentrations (reaching up to 200 µg·m−3) over Athens on 17 April 2005. In Turkey, Kubilay [5] first pointed out the origins of the dust storms affecting Erdemli, a rural site located on the coast of the EM. They presented that the region is influenced by strong dust storms originating from the central Sahara in spring, the eastern Sahara in summer, and the Middle East/Arabian Peninsula in autumn. Griffin [19] studied the relationship between Saharan sourced dust days and microorganisms over the Turkish Mediterranean coastline. They found that a statistically significant correlation during the spring months was observed between an increase in the prevalence of microorganisms recovered from atmospheric samples on dust days. Later, Koçak [20] analyzed the contributions of natural sources to high PM10 events for the same area. They explained that mineral dust which is transported from North Africa causes highest levels of PM10 during the transition period (March, April and May). Kabatas [21] investigated the possible effect of Saharan dust on high concentration levels of PM10 over Turkey. For this purpose, they firstly applied RAQMS (Real-Time Air Quality Modeling System) model to the intense dust storm event on April 2008 and then, they compared simulated results with the measured ones at 118-air quality stations distributed throughout the country. According to their results, RAQMS model outputs and in situ PM10 observation time series show similar patterns with a correlation of 0.87. Although there are several studies concerning the origin of Saharan dust and its contributions to PM10 levels over Turkey, the influence of atmospheric circulation on dust concentrations has not previously been studied in detail with high spatial and temporal resolution. Therefore, the goal of the current study is to contribute to a better understanding the impacts of the synoptic mechanisms on PM10 concentration levels and horizontal visibility conditions over Turkey during the Saharan dust outbreaks of 23–24 March 2016.

2. Data Sources

To investigate the activity of dust plumes over Turkey, hourly and daily averages of PM10 data belonging to the 97 air quality monitoring stations, which were provided by the Turkish Ministry of Environment and Urbanization (National Air Quality Observation Network), were used in the study for the period 23–24 March 2016 (Figure1). For the climatological approach, seasonal mean PM 10 variations of these air quality stations were extracted from their daily averages for the period between 2010 and 2015. To illustrate the possible spatial and temporal variations in the plume concentrations of stations and their regional distributions, Turkey was divided into the seven geographical regions, which were described by Erinç [22], as follows: The Marmara region (MR), The Aegean region (AR), The Mediterranean region (MeR), The central Anatolian region (CAR), The Black Sea region (BSR), The eastern Anatolian region (EAR) and the southeastern Anatolian region (SEAR). To represent MR, AR, MeR, CAR, BSR, EAR, and SEAR; arithmetic averages of the PM10 data belonging to 9, 17, 12, 22, 15,14, and 8 air quality stations, respectively, were also used in the study. The detailed explanations of the numbered 97 air quality stations (Figure1) such as geographic coordinates and the daily average PM10 concentrations belonging to 23 and 24 March dust episodes are described in Table1. Atmosphere 2017, 8, 41 3 of 16

−3 Table 1. Geographic coordinates of the 97 air quality stations and their measured daily PM10 values (µg·m ) during dust storm days.

23 March 24 March 23 March 24 March No. Region Longitude Latitude Station Name No. Region Longitude Latitude Station Name 2016 2016 2016 2016 1 26.59 41.66 Edirne 65 156 50 34.7 38.62 Nevsehir 49 212 2 28.89 41.04 Esenler 72 321 51 34.01 38.37 Aksaray 45 85 3 28.95 41.01 Aksaray 54 85 52 34.68 37.97 Nigde 80 188 4 29.01 41.05 Besiktas 64 274 53 35.38 38.74 Osb 69 186 5 Marmara (MR) 29.03 40.99 Kadikoy 72 275 54 35.47 38.72 Hurriyet 95 189 Central Anatolia 6 29.21 40.89 Kartal 57 259 55 35.52 38.72 Melikgazi 51 126 (CAR) 7 29.98 40.14 Bilecik 44 116 56 32.51 37.94 Selcuklu 52 91 8 26.41 40.14 Canakkale 46 99 57 32.48 37.86 Meram 33 88 9 27.89 39.63 Balikesir 68 155 58 33.13 37.12 Karaman 47 121 10 29.99 39.42 Kutahya 64 86 59 37 39.74 Sivas 86 136 11 30.54 38.75 Afyon 33 58 60 33.62 40.6 Cankiri 26 28 12 29.41 38.67 Usak 87 77 61 31.15 40.85 Duzce 111 117 13 27.4 38.62 Manisa 128 223 62 31.6 40.73 Bolu 30 29 14 29.09 37.78 Merkezefendi 92 136 63 32.36 41.62 Bartin 98 88 15 29.1 37.77 Bayramyeri 92 120 64 32.62 41.2 Karabuk 95 80 16 27.84 37.84 Aydin 51 141 65 33.76 41.37 Kastamonu 94 147 17 28.13 37.34 Yatagan 142 115 66 35.15 42.03 Sinop 50 211 Aegean (AR) 18 28.36 37.22 Musluhittin 85 114 67 36.34 41.28 Ilkadim 81 214 19 27.07 38.5 Cigli 30 85 68 Black Sea (BSR) 36.46 41.22 Tekkekoy 55 158 20 27.11 38.45 Karsiyaka 16 34 69 34.96 40.56 Corum 76 86 21 27.17 38.46 Bayrakli 42 95 70 37.88 40.98 Ordu 50 86 22 27.08 38.4 Guzelyali 41 106 71 38.36 40.91 Giresun 66 100 23 27.22 38.47 Bornova 55 111 72 39.48 40.46 Gumushane 87 89 24 27.14 38.43 Alsancak 25 63 73 40.22 40.26 Bayburt 83 99 25 27.15 38.38 Sirinyer 44 83 74 40.53 41.02 Rize 40 71 26 27.14 38.31 Gaziemir 52 64 75 41.82 41.18 Artvin 9 13 Atmosphere 2017, 8, 41 4 of 16

Table 1. Cont.

23 March 24 March 23 March 24 March No. Region Longitude Latitude Station Name No. Region Longitude Latitude Station Name 2016 2016 2016 2016 27 30.29 37.72 Burdur 28 54 76 38.34 38.35 Malatya 39 81 28 30.7 36.89 Antalya 37 103 77 39.21 38.67 Elazig 33 37 29 34.64 36.81 Icel 60 66 78 39.55 39.1 Tunceli 20 25 30 35.26 37.19 Catalan 26 33 79 39.5 39.74 Erzincan 109 145 31 35.34 37 Meteoroloji 48 49 80 40.5 38.88 Bingol 16 27 32 Mediterranean 35.31 37 Valilik 51 59 81 41.51 38.75 Mus 161 203 33 (MeR) 35.35 36.85 Dogankent 12 15 82 Eastern Anatolia 41.27 39.9 Erzurum 26 39 34 36.24 37.07 Osmaniye 52 65 83 (EAR) 42.11 38.41 Bitlis 34 42 35 36.15 36.21 Antakya 68 72 84 43.74 37.57 Hakkari 64 84 36 36.9 37.58 Kahramanmaras 35 61 85 43.37 38.51 Van 37 43 37 37.2 38.2 Elbistan 53 59 86 43.04 39.72 Agri 26 36 38 30.55 37.78 Isparta 61 65 87 44.05 39.93 Igdir 81 100 39 30.5 39.78 Eskisehir 24 47 88 43.1 40.61 Kars 60 60 40 32.59 39.97 Sincan 44 59 89 42.7 41.11 Ardahan 22 20 41 32.8 39.97 Demetevler 57 77 90 37.13 36.72 Kilis 49 56 42 32.86 39.97 Kecioren 61 71 91 37.35 37.06 Gaziantep 43 72 43 Central Anatolia 32.88 39.94 Cebeci 70 70 92 38.28 37.76 Adiyaman 59 102 44 (CAR) 32.86 39.93 Sihhiye 64 77 93 Southeastern 38.79 37.16 Sanliurfa 25 75 45 32.84 39.9 Dikmen 42 79 94 Anatolia (SEAR) 40.22 37.92 Diyarbakir 44 46 46 32.93 39.93 Kayas 109 134 95 40.72 37.32 Mardin 34 62 47 33.52 39.84 Kirikkale 21 44 96 41.13 37.9 Batman 57 65 48 34.81 39.82 Yozgat 42 76 97 41.94 37.93 Siirt 89 75 49 34.16 39.15 Kirsehir 30 98 Atmosphere 2016, 7, x FOR PEER REVIEW 5 of 17

The influence of these two-day extraordinary dust events on the horizontal visibility across AtmosphereTurkey2017 was, 8, 41also investigated using the hourly observations of METAR reports at 64 airports (blue5 of 16 squares in Figure 1). This dataset was taken from Turkish State Meteorological Service (TSMS).

Black Sea

BSR MR EAR CAR AR SEAR MeR Aegean Sea Mediterranean Sea

Figure 1. The distribution of PM10 observations at 97 air quality monitoring stations (brown points) Figure 1. The distribution of PM10 observations at 97 air quality monitoring stations (brown points) and visibility observations (METARs, blue square) at 64 airports in Turkey. The detail explanations and visibility observations (METARs, blue square) at 64 airports in Turkey. The detail explanations (i.e., (i.e., geographic coordinates and daily averaged PM10 values of the stations during the dust storm geographic coordinates and daily averaged PM values of the stations during the dust storm days) days) of the numbers of the stations are described10 in Table 1. The borders of the seven geographic of theregions numbers with of its the abbreviated stations arenames described are also inshow Tablen in1 the. The figure. borders MR: ofMarmara the seven Region, geographic AR: Aegean regions withRegion, its abbreviated MeR: Mediterranean names are alsoRegion, shown CAR: in Central the figure. Anatolian MR: Region, Marmara BSR: Region, Black Sea AR: Region, Aegean EAR: Region, MeR:Eastern Mediterranean Anatolian Region, SEAR: CAR: Southeastern Central Anatolian Anatolian Region, Region. BSR: Black Sea Region, EAR: Eastern Anatolian Region, SEAR: Southeastern Anatolian Region. To determine the surface and upper levels of the atmospheric circulation, six- hourly values of NCEP/NCAR Reanalysis data [23] between 5° W–55° E and 30° N–60° N were applied on the The influence of these two-day extraordinary dust events on the horizontal visibility across Turkey extreme dust episodes. In this study, the inverse distance weighting methodology was applied for was also investigated using the hourly observations of METAR reports at 64 airports (blue squares in mapping METAR observations and total cloud cover data. Figure1).In This order dataset to characterize was taken fromqualitatively Turkish the State dust Meteorological activity over ServiceSahara (TSMS).and Turkey, satellite Toobservations determine (Cloud-Aerosol the surface and Li upperdar and levels Infrared of the Pathfinder atmospheric Satellite circulation, Observation six- hourly(CALIPSO, values of ◦ ◦ ◦ ◦ NCEP/NCARprovided by Reanalysis ICARE Data data and [23 Services] between Center, 5 W–55 availableE and at 30http://www.icare.univ-lille1.fr/calipso)N–60 N were applied on the extreme dustand episodes. Meteosat In Second this study, Generation the inverse (MSG) distance Spinning weighting Enhanced methodologyVisible and Infrared was appliedImager (SEVIRI)) for mapping METARat high observations spatiotemporal and totalresolution cloud were cover used. data. Information about the vertical structure of the dust Inepisode order and to vertical characterize behaviors qualitatively of the atmospheric the dust activityconditions over is provided Sahara andfrom Turkey, attenuated satellite observationsbackscatter (Cloud-Aerosol profiles at 532 Lidarnm retrieved and Infrared from th Pathfindere spaceborne Satellite Cloud-Aerosol Observation Lidar (CALIPSO,with Orthogonal provided Polarization (CALIOP) on board the CALIPSO together with its 60 m and 12 km vertical and by ICARE Data and Services Center, available at http://www.icare.univ-lille1.fr/calipso) and horizontal resolutions, respectively. From the literature, numerous studies investigated the vertical Meteosatstructure Second of the Generation Saharan dust (MSG) outbreaks Spinning by usin Enhancedg CALIPSO Visible observations and Infrared for Mediterranean Imager (SEVIRI)) Basin at high(e.g., spatiotemporal [24–26]) and resolution for Turkey were [21]. used. In this Information study, CALIPSO about theobservations vertical structure for 23 March. of the and dust two episode and verticalradiosonde behaviors observations of the (Izmir atmospheric and Kartal), conditions which were is provided located fromin western attenuated Turkey, backscatter at 00:00 UTC profiles of at 532 nmMarch retrieved 24 fromwere theused spaceborne in the Cloud-Aerosol analysis (The Lidar data with Orthogonalcan be accessed Polarization online (CALIOP) at on boardhttp://weather.uwyo.edu/u the CALIPSO together withpperair/sounding.html). its 60 m and 12 km vertical and horizontal resolutions, respectively. From theThe literature, horizontal numerous distribution studies of dust investigated was describe thed verticalconsidering structure the combination of the Saharan of three dust infrared outbreaks by usingchannels, CALIPSO namely observations channel 10 (12 for µm), Mediterranean channel 9 (10.8 Basin µm), (e.g.,and channel [24–26 7]) (8.7 and µm). for TurkeyMSG-SEVIRI [21]. is In this study,located CALIPSO geostationary observations at 0° W for over 23 the March. equator and an twod provides radiosonde 15 min observations temporal resolution (Izmir images. and Kartal), False-color images are created using an algorithm developed by EUMETSAT, which colors the which were located in western Turkey, at 00:00 UTC of March 24 were used in the analysis (The data difference between the 12.0 and 10.8 µm channels as red, the difference between the 10.8 and 8.7 µm can bechannels accessed as onlinegreen and at http://weather.uwyo.edu/upperair/sounding.html the 10.8 µm channel as blue (e.g., [27]). From this RGB). combination, dust Theappears horizontal pink or magenta. distribution of dust was described considering the combination of three infrared µ µ µ channels,Atmosphere namely 2016, 7,channel 41; doi:10.3390/atmos8020041 10 (12 m), channel 9 (10.8 m), and channel www.md 7 (8.7pi.com/journal/atmospherem). MSG-SEVIRI is located geostationary at 0◦ W over the equator and provides 15 min temporal resolution images. False-color images are created using an algorithm developed by EUMETSAT, which colors the difference between the 12.0 and 10.8 µm channels as red, the difference between the 10.8 and 8.7 µm channels as green and the 10.8 µm channel as blue (e.g., [27]). From this RGB combination, dust appears pink or magenta. Atmosphere 2017, 8, 41 6 of 16

3. ResultsAtmosphere and 2016 Discussion, 7, 41 6 of 16

3.1. Seasonal3. Results Averages and Discussion of PM10 Concentrations over Turkey

Before analyzing the extreme PM10 concentration levels over Turkey on March 2016, long-term 3.1. Seasonal Averages of PM10 Concentrations over Turkey seasonal averages of the 97 stations were extracted from the daily averages for the period from 2010 to Before analyzing the extreme PM10 concentration levels over Turkey on March 2016, long-term 2015. Arithmetic averages of the PM10 concentrations related to these 97 stations indicate that highest seasonal averages of the 97 stations were extracted from the daily averages for the period from 2010 seasonal average PM10 concentrations are shown in winter, fall, spring, and summer seasons over to 2015. Arithmetic averages of the PM10 concentrations related to these 97 stations indicate that Turkey with the average values of 78, 65, 54, and 49 µg·m−3, respectively. highest seasonal average PM10 concentrations are shown in winter, fall, spring, and summer seasons Inover winter, Turkey while with the highest average mean values PM of10 78,values 65, 54, and occur 49 inμg·m the−3, southeasternrespectively. parts of the Anatolian µ · −3 µ · −3 PeninsulaIn (i.e., winter, SEAR: while 96 highestg m mean), MR PM has10 thevalues lowest occur concentration in the southeastern levels parts (61 ofg mthe Anatolianin Figure 2a). The otherPeninsula regions (i.e., presentSEAR: 96 their μg·m averaged−3), MR has PMthe10 lowestconcentrations concentration within levels these(61 μg·m two−3 in limits Figure (i.e., 2a). EAR: −3 −3 −3 −3 84 µgThe·m other, AR: regions 83 µg· mpresent, MeAR: their averaged 82 µg·m PM,10 and concentrations CAR: 75 µg ·withinm ). these As shown two limits in Figure (i.e., 2EAR:a, while 84 all of theμ stationsg·m−3, AR: in 83 SEAR μg·m exceed−3, MeAR: 50 µ82g μ·mg·m−3−3on, and average, CAR: 75 four μg·m of−3 them). As shown exhibit in above Figure 100 2a, µwhileg·m− all3. of Moreover, the 70% ofstations the winter in SEAR days exceed exceed 50 μ EUg·m− (European3 on average, Union) four of dailythem exhibit limits inabove this 100 region μg·m (not−3. Moreover, shown). 70% InEAR, of the winter days exceed EU (European Union) daily limits in this− region3 (not shown). In EAR, whereas two stations (i.e., Bingol and Tunceli) have below 50 µg·m , average PM10 concentration μ −3 reachedwhereas to 185 twoµg ·stationsm−3 in (i.e., Igdir Bing andol is and shown Tunceli) as the have most below polluted 50 g·m city, overaverage Turkey. PM10 Practically,concentration 55% of reached to 185 μg·m−3 in Igdir and is shown as the most polluted city over Turkey. Practically, 55% of the winter days exceed the daily EU threshold value in this region. In the Aegean region (AR), 30% of the winter days exceed the daily EU threshold value in this region. In the Aegean region (AR), 30% all stations that were close to the seaside showed high PM values (above 100 µg·m−3). As stated by of all stations that were close to the seaside showed high PM10 10 values (above 100 μg·m−3). As stated Koçakby [ 20Koçak] this [20] condition this condition can be can explained be explained by the by the transportation transportation of of sea sea spray. spray.

(a) (b)(b)

Figure 2. The long-term (2010–2015) mean of the seasonal PM10 concentrations (μg·m−3) related−3 to 97 Figure 2. The long-term (2010–2015) mean of the seasonal PM10 concentrations (µg·m ) related to 97 air qualityair quality stations. stations. (a) Winter,(a) Winter, (b )(b Spring,) Spring, (c ()c) Summer, Summer, andand ( (d)) Fall Fall seasons, seasons, respectively. respectively.

In the transition period (spring), many Saharan dust outbreaks are transported to the EM basin Inand the have transition a significant period role (spring), for the excessive many Saharan daily PM dust10 concentrations outbreaks areover transported Turkey. The totwo the highest EM basin μ −3 and havelevels a of significant PM10 are roleshown for in the the excessive regions of daily SEAR PM and10 concentrations AR with the values over Turkey.of 68 and The 56 twog·m highest, −3 levelsrespectively. of PM10 are This shown condition in the can regions be explained of SEAR by the and positions AR with of the Sharav values cyclones, of 68 and and 56 thus;µg· m , respectively.southerly Thiscomponents condition of the can flows be (i.e., explained SE, S, an byd SW) the transport positions intense of the dust Sharav particles cyclones, without and any thus; southerlyobstacle components such as mountains. of the flows When (i.e., compared SE, S, andwith SW)AR, geographical transport intense proximity dust to particles the source without region any and abundant dust particles in flat areas of the Levant basin facilitated the transportation of denser, obstacle such as mountains. When compared with AR, geographical proximity to the source region and dusty particles to SEAR. It is also known that the Tigris-Euphrates alluvial plain has been recognized abundant dust particles in flat areas of the Levant basin facilitated the transportation of denser, dusty as the main dust source in the Middle East [28]. Due to transferring of these intense dust particles by particles to SEAR. It is also known that the Tigris-Euphrates alluvial plain has been recognized as the suitable synoptic systems, average PM10 values of all stations show high particulate matter mainconcentrations dust source in (between the Middle 50 and East 100 [28 μ].g·m Due−3) toin transferringthis basin (Figure of these 2b). intenseOn the other dust particleshand, while by suitablethe synopticdust systems,particles pass average over the PM Aegean10 values Sea ofthey all take stations up moisture show highresulting particulate in precipitation matter over concentrations the AR −3 (betweenduring 50 spring and 100 days,µg· mtherefore) in this removing basin (Figurethe PM2 b).via Onwet the deposition, other hand, resulting while in the lower dust PM particles10. As a pass μ −3 over theresult, Aegean average Sea PM they10 levels take at up about moisture half of resulting the stations in precipitationwere below 50 overg·m the. In AR regard during to MeR, spring thedays, barrier effect of the Taurus Mountains, a great chain running parallel to the Mediterranean coast, therefore removing the PM via wet deposition, resulting in lower PM10. As a result, average PM10 levels at about half of the stations were below 50 µg·m−3. In regard to MeR, the barrier effect of the Atmosphere 2017, 8, 41 7 of 16

Taurus Mountains, a great chain running parallel to the Mediterranean coast, attenuates penetrations of SaharanAtmosphere dust 2016 particles, 7, 41 to the inner parts, and thus, dust minerals are transferred only to the7 of eastern16 portions of the region (Figure2b). −3 Figureattenuates2c presentspenetrations 57% of of Sahara all stations’n dust averageparticles PMto the10 summerinner parts, data and that thus, are dust below minerals 50 µg ·mare for the areatransferred over Turkey only to (Figure the eastern2c). portions The three of the highest region PM (Figure10 concentrations 2b). are observed in the SEAR, μ −3 MeR, andFigure AR basins 2c presents with 65,57% 52, of andall stations’ 50 µg· maverage−3 averages, PM10 summer respectively. data that Also, are below highest 50 summerg·m for mean the area over Turkey (Figure 2c). The three highest PM10− 3concentrations are observed in the SEAR, PM10 level is measured in Siirt province with 103 µgm (Figure2c). In fall, average PM 10 data of MeR, and AR basins with 65, 52, and 50 μg·m−3 averages, respectively. Also, highest summer mean eight air quality stations over the whole territory exceed 100 µg·m−3 with inhomogeneous spatial PM10 level is measured in Siirt province with 103 μgm-3 (Figure 2c). In fall, average PM10 data of eight distributionair quality (Figure stations2d) over the whole territory exceed 100 μg·m−3 with inhomogeneous spatial distribution (Figure 2d) 3.2. Spatio-Temporal Variations of Extreme Dust Episodes on 23–24 March 2016 3.2. Spatio-Temporal Variations of Extreme Dust Episodes on 23–24 March 2016 In this section, spatial and temporal variations of the PM10 concentrations of air quality stations were evaluatedIn this section, for every spatial two and dust temporal storm days. variations of the PM10 concentrations of air quality stations Thewere data evaluated collected for every for 23 two March dust storm 2016 showdays. that almost 45% of all available stations’ daily PM10 concentrationsThe data exceeded collected the for long-term23 March 2016 spring show mean that almost (Figure 45%3a). of In all comparison available stations’ with thedaily daily PM10 limit thresholdconcentrations value, 54% exceeded of the the stations long-term exceed spring 50 meanµgm (Figure−3 daily 3a). average In comparison and 6% with ofthem the daily display limit over μ -3 100 µthresholdg·m−3 (Table value,1). 54% In regional of the stations perspective, exceed 80%,50 gm 67%, daily and average 47% of theand stations6% of them in the display BSR, MR,over and 100 μg·m−3 (Table 1). In regional perspective, 80%, 67%, and 47% of the stations in the BSR, MR, and AR regions, respectively, exceed their long-term spring mean PM10 levels. On the other hand, daily AR regions, respectively, exceed their long-term spring mean PM10 levels. On the other hand, daily PM10 data at most stations over the SEAR, EAR, and the eastern parts of the MeR exhibit below-normal PM10 data at most stations over the SEAR, EAR, and the eastern parts of the MeR exhibit values.below-normal For the CAR, values. mostly For the the CAR, inner mostly parts the of theinner region parts indicateof the region low dailyindicate PM low10 concentrations.daily PM10 In addition,concentrations. the positive In addition, or negative the positive anomaly or magnitudesnegative anomaly of the magnitudes particulate of matter the particulate (PM) concentrations matter − ranged(PM) from concentrations 0 to 50 µg· mranged3 at from many 0 to of 50 the μg·m air− quality3 at many stations. of the air quality stations.

(a)

(b)

Figure 3. Anomaly values of the (a) March 23, 2016 and (b) March 24, 2016 daily PM10 observations Figure 3. Anomaly values of the (a) March 23, 2016 and (b) March 24, 2016 daily PM10 observations μ −3 ( −g·m3 ) of the stations when compared with its long-term spring mean PM10 values. (µg·m ) of the stations when compared with its long-term spring mean PM10 values.

On 24 March 2016, the higher daily PM10 values were recorded at many stations of the country On(Table 24 March1 and 2016,Figure the 3b). higher In this daily day, PM 1074%values of all were stations recorded show at above-normal many stations daily of the PM country10 (Tableconcentrations.1 and Figure3 b).In the In thisregional day, 74%perspective, of all stations all stations show located above-normal in MR present daily PMhigher10 dailyconcentrations. PM10 anomalies, and followed by CAR (90%), AR (88%), and BSR (87%) regions, respectively. When In the regional perspective, all stations located in MR present higher daily PM10 anomalies, and followedcompared by CAR with (90%),measurements AR (88%), from andthe previous BSR (87%) day, regions,daily PM10 respectively. values for certain When stations compared turned with from negative to positive anomalies over MR, AR and BSR; maximum positive variation was measurements from the previous day, daily PM10 values for certain stations turned from negative to observed in the central part of the Anatolian Peninsula. It has also been shown that only the limited positive anomalies over MR, AR and BSR; maximum positive variation was observed in the central

Atmosphere 2017, 8, 41 8 of 16

part ofAtmosphere the Anatolian 2016, 7, 41 Peninsula. It has also been shown that only the limited regions (i.e., SEAR8 of 16 and eastern portions of the MeR) illustrate the below-normal daily PM10 values. However, although all the regions (i.e., SEAR and eastern portions of the MeR) illustrate the below-normal daily PM10 values. stations in the SEAR have negative daily PM10 anomalies, all of them also exceed the EU daily PM10 thresholdHowever, level although (Table1 all). Inthe addition, stations in most the SEAR of the have stations negative in Istanbul,daily PM10 (NW anomalies, Turkey, all mostof them populated also exceed the EU daily PM10 threshold level (Table 1). In addition, most of the stations in Istanbul, (NW city in Turkey), show four times higher daily PM10 concentrations when compared with the long-term Turkey, most populated city in Turkey), show four times higher daily PM10 concentrations when climatologicalcompared with spring the means long-term (i.e., climatological stations numbered spring means as 4, 5, (i.e., and stations 6 in Table numbered1 and Figure as 4, 5,1 ).and 6 in TableTo determine 1 and Figure the starting,1). ending, and peak times for PM10 concentration levels, we analyzed the temporalTo behavior determine of the starting, PM10 values ending, for and each peak region times and for thePM10 selected concentration air quality levels, stations we analyzed (Figure 4). −3 As shownthe temporal in Figure behavior4a, Aegean of the PM Region10 values reached for each to region peak and value the withselected 104 airµ qualityg·m stationsat 12 LT (Figure (Local 4). Time: UTCAs + 2-hours)shown in of Figure the 23 4a, March, Aegean and Region followed reached by to decreasing peak value trend with to104 the μg·m 18−3 LT, at 12 and LT started (Local Time: to increase thereafter.UTC + The2-hours) other of six the regions 23 March, show and increasing followed trendby decreasing between trend 12 and to 20the LT18 andLT, startedand started to decrease to increase thereafter. The other six regions show increasing trend between 12 and 20 LT and started to except Marmara Region. The increasing trend of the PM10 levels over Marmara continued in the first decrease except Marmara Region. The increasing trend of the PM10 levels over Marmara continued hours of the next day (Figure4b) and reached to the maximum level at 03 LT with 413 µg·m−3. After in the first hours of the next day (Figure 4b) and reached to the maximum level at 03 LT−3 with 413 06 LT, rapid decreasing trend of the PM10 concentrations concluded with 24 µg·m at the end of μg·m−3. After 06 LT, rapid decreasing trend of the PM10 concentrations concluded with 24 μg·m−3 at the day. The continued transportation of dust particles resulted with increased PM levels observed the end of the day. The continued transportation of dust particles resulted with increased10 PM10 levels duringobserved the morning. during the Thereafter, morning. PM Thereafter,10 levels PM started10 levels to decrease started to in decrease AR. According in AR. According to the results, to the MR is shownresults, as the MR most is shown influenced as the regionmost influenced of the country region fromof the thiscountry extraordinary from this extraordinary event. From event. this point of view,From it this was point shown of view, that Istanbul it was shown and itsthat surroundings Istanbul and haveits surroundings the highest have PM 10thelevels, highest which PM10 have not measuredlevels, which before. have Therefore,not measured the before. temporal Therefor behaviore, the oftemporal the four behavior urban settlementsof the four ofurban Istanbul was evaluatedsettlements betweenof Istanbul 23 was and evaluated 24 March, between 2016. As23 itand can 24 be March, shown 2016. from As Figureit can 4bec thatshown three from urban stationsFigure (i.e., 4c Esenler,that three Kartal, urban stations and Kadikoy) (i.e., Esenler, indicated Kartal, similar and Kadikoy) increasing indicated or decreasing similar increasing tendencies or in decreasing tendencies in the selected time. While Esenler and Kartal air quality stations have the selected time. While Esenler and Kartal air quality stations have maximum PM10 values at 18 LT maximum PM10 values at 18 LT with 139, 126 μg·m−3, respectively, Kadikoy area reached to its peak with 139, 126 µg·m−3, respectively, Kadikoy area reached to its peak value at 19 LT with 153 µg·m−3. value at 19 LT with 153 μg·m−3. At the first hours of the next day (Figure 4d), highest concentration At the first hours of the next day (Figure4d), highest concentration level of PM over Esenler, Kartal, level of PM10 over Esenler, Kartal, Kadikoy and Besiktas were recorded with 10636, 574, 564, 555 −3 Kadikoyμg·m and−3, respectively. Besiktas were recorded with 636, 574, 564, 555 µg·m , respectively.

(a) (b) concentration 10 Hourly PM

Local Time (UTC+2 hours) Local Time (UTC+2 hours) μ −3 Figure 4. (a) Temporal behavior of the hourly PM10 values ( g·m )− of3 the seven geographical regions Figure 4. (a) Temporal behavior of the hourly PM10 values (µg·m ) of the seven geographical regions of Turkey for 23 March, 2016. (b) same as (a) but for 24 March, 2016. (c) same as (a), and (d) same as of Turkey for 23 March, 2016. (b) same as (a) but for 24 March, 2016. (c) same as (a), and (d) same as (b) but denote the four urban settlements of Istanbul. (b) but denote the four urban settlements of Istanbul.

Atmosphere 2017, 8, 41 9 of 16 Atmosphere 2016, 7, 41 9 of 16

3.3. Association between Visibility and PM1010 ConcentrationsConcentrations ItIt is well known that there is a negative relationrelation between visibilityvisibility andand atmosphericatmospheric PMs.PMs. In this section, it was analyzedanalyzed the influenceinfluence of dust events on horizontal visibility conditions over Turkey waswas analyzed.analyzed. For this reason, hourly observations at 64 METAR stations werewere usedused forfor the period of 23–24 March 2016.2016. As a consequenceconsequence of the examiningexamining horizontal visibility conditions related to the PM10 concentration,concentration, low low visibility visibility conditions conditions were were found between 06:00 UTC and 12:00 UTC of thethe 24 March 2016. The The results results are are coherent coherent with with the the previous previous studies studies that that show show that that midday midday visibility visibility is isless less likely likely to be to beinfluenced influenced by the by thediurnal diurnal variation variation in humidity in humidity (e.g., (e.g., [29]). [ 29Herewith,]). Herewith, we used we 06:00, used 06:00,09:00 and 09:00 12:00 and UTC 12:00 METAR UTC METAR observations observations in order in to order investigate to investigate the spatial the distribution spatial distribution of the low of thevisibility low visibility over Turkey over (Figure Turkey (Figure5). At 06:005). At UTC 06:00 on UTC 24 March, on 24 March,horizontal horizontal visibility visibility less than less 9 km than is 9shown km is over shown the over whole the Marmara whole Marmara and western and western parts of parts the ofAegean the Aegean regions. regions. In Marmara, In Marmara, the north the northareas areasshow showthe lowest the lowest visibility visibility conditions conditions (betw (betweeneen 3 and 3 and 4 km) 4 km) over over the the airports airports owing owing to high particleparticle concentrationsconcentrations (Figure(Figure5 a).5a). Three Three hours hours later late (09:00r (09:00 UTC), UTC), while while horizontal horizontal visibility visibility started started to increaseto increase between between 8 and 8 9and km 9 in km AR, in north AR, of north MR regionof MR still region shows still low shows visibility low conditions visibility (Figureconditions5b). At(Figure midday, 5b). althoughAt midday, most although parts of most the countryparts of havethe country clear sky have conditions, clear sky theconditions, impacts the of suspended impacts of particlessuspended on particles visibility on are visibility substantial are substantial in the limited in the areas limited of the areas north of Marmara the north (between Marmara 6 (between and 9 km) 6 and southern9 km) and Aegean southern (Figure Aegean5c). After (Figure 12:00 5c). UTC, After all stations 12:00 inUTC, Turkey all stations have high in visibility Turkey conditionshave high (notvisibility shown). conditions According (not toshown). all these According results, onlyto all western these results, parts ofonly the western country parts (i.e., MARof the andcountry AR) showed(i.e., MAR low and visibility AR) showed conditions low visibility owing toconditions the atmospheric owing to particulates the atmospheric and theseparticulates conclusions and these are coherentconclusions with are high coherent PM10 concentrationswith high PM10(Figure concentrations3b). However, (Figure we 3b). cannot However, see the we inverse cannot relation see the betweeninverse relation high PM between10 values high and PM horizontal10 values visibility and horizontal over the visibility BSR and CARover regions.the BSR Thisand CAR condition regions. can beThis explained condition by can different be explained physical andby different chemical physical reactions and of the chemical particulate reactions matters of with the ambientparticulate air conditionsmatters with (e.g., ambient different air conditions local temperature, (e.g., differen humidityt local or temperature, wind profile). humidity or wind profile).

(a) Black Sea

Mediterranean Sea (b) Black Sea

Mediterranean Sea (c) Black Sea

Mediterranean Sea

Figure 5.5. ((a) SpatialSpatial distributiondistribution of thethe horizontalhorizontal visibilityvisibility conditionsconditions of thethe METARMETAR observationsobservations belonging toto 6464 airportsairports at at 06:00 06:00 UTC UTC of of 24 24 March. March. (b ()b Same) Same as as (a) ( buta) but for for 09:00 09:00 UTC. UTC. (c) same(c) same as ( aas) but (a) forbut 12:00for 12:00 UTC. UTC.

3.4. A Review of the Synoptic Pattern of 23–24 March 2016 In this section, synoptic conditions that trigger the transportation of the pollutants over Turkey were analyzed in detail. Before the extraordinary event in Turkey, the Sharav cyclone over the lee of

Atmosphere 2017, 8, 41 10 of 16

3.4. A Review of the Synoptic Pattern of 23–24 March 2016

AtmosphereIn this 2016 section,, 7, 41 synoptic conditions that trigger the transportation of the pollutants over Turkey10 of 16 were analyzed in detail. Before the extraordinary event in Turkey, the Sharav cyclone over the lee of the Moroccan Atlas mountains, and high-pressure ce centernter (HPC) over Egypt settled in the area over latitude of 30–35◦° N. As a result, dense dust particles were transported to the central Mediterranean basin on 22 March,March, 20162016 (not(not shown).shown). It was shownshown 24-hours later that Sharav cyclonecyclone developeddeveloped (985-hpa inin the the core) core) and and moved moved to Italyto Italy in the in direction the direction of NE. of Simultaneously, NE. Simultaneously, surface highsurface pressure high movedpressure more moved east more and settled east and over settled Levant over basin Leva (Figurent basin6a). On (Figure the other 6a). hand,On the cut other off-surface hand, lowcut overoff-surface the western low over Russia the expandedwestern Russia towards expanded to Black towards Sea. Ultimately, to Black Sea. the mobilizationUltimately, the of Saharanmobilization dust plumesof Saharan turned dust to plumes the eastern turned Mediterranean to the eastern basin.Mediterranean In terms basin. of the In low terms level of of the the low atmosphere level of the at thatatmosphere time (Figure at that6b), time the (Figure positions 6b), of the the positions low- and of high-pressure the low- and centers high-pressure were similar centers to were those similar of the centersto those of of the the surface, centers and of most the regionssurface, of and Turkey most were regions inﬂuenced of Turkey by strong were southwesterly influenced windby strong ﬂows (oversouthwesterly 30 m·s−1 ).wind While flows positions (over 30 of m·s the− surface-1). While and positions 850-hPa- of the low surface- centers and remain 850-hPa- almost low stationary centers atremain 12:00 UTC,almost expanding stationary of surface-at 12:00 and UTC, low-level expanding highs aboveof surface- southeastern and low-level Turkey leaded highs to above more strongsoutheastern southerly Turkey winds leaded (i.e., highto more pressure strong gradient southerl situation)y winds (i.e., over high the westernpressure parts gradient of the situation) country (Figureover the6 c,d).western Hence, parts rapid of the increment country (Figure in aerosol 6c,d). load Hence, over therapid Aegean increment region in isaerosol more distinctload over than the theAegean other region regions is ofmore Turkey distinct at the than midday the other of the re 23gions March of Turkey (Figure at4a). the Differently midday of from the the23 March other regions,(Figure 4a). the anticyclonicDifferently from conditions the other over regions, SEAR prevented the anticyclonic transferring conditions of particles, over whichSEAR originatedprevented fromtransferring Sahara, of to particles, this region which (Figure originated3a). from Sahara, to this region (Figure 3a).

(a) (b)

Figure 6. The evolution of the first ﬁrst dust storm eventevent over Turkey at 00:00 UTC 23 March 2016. (a) sea level pressurespressures (solid (solid lines, lines, every every 5-hPa), 5-hPa), and and total total cloud cloud cover cover (shaded, (shaded, %) at %) surface. at surface. (b) Height (b) Height (solid −1 lines,(solid dam)lines, anddam) winds and winds (m·s−1 (m·s) at 850-hPa.) at 850-hPa. (c) same (c) same as (a), as and (a), (d and) same (d) assame (b) as but (b for) but 12:00 for UTC12:00 of UTC the 23of the March. 23 March.

The temporal movements of dust layers can be analyzed using satellite images. Therefore, we The temporal movements of dust layers can be analyzed using satellite images. Therefore, we extracted the RGB product of the three SEVIRI infrared channels for 12:00 and 18:00 UTC of 23 March. extracted the RGB product of the three SEVIRI infrared channels for 12:00 and 18:00 UTC of 23 March. The dust appears pink and is concentrated into several plumes in Figure 7. As seen in Figure 7a, a thick The dust appears pink and is concentrated into several plumes in Figure7. As seen in Figure7a, dust layer from Libya is transported to the Aegean coasts of Greece, and relatively high PM10 values a thick dust layer from Libya is transported to the Aegean coasts of Greece, and relatively high PM were observed in the AR regions of Turkey. At 18:00 UTC, ongoing movement of the particles to east10 longitudes resulted with highest PM10 values over the large areas of the country (Figure 7b).

Atmosphere 2017, 8, 41 11 of 16 values were observed in the AR regions of Turkey. At 18:00 UTC, ongoing movement of the particles toAtmosphere east longitudes 2016, 7, 41 resulted with highest PM10 values over the large areas of the country (Figure117b). of 16

(a) (b)

Figure 7. Dust product for (a) 12:00 UTC and (b) 18:00 UTC on 23 March 2016, derived from three infrared Figure 7. Dust product for (a) 12:00 UTC and (b) 18:00 UTC on 23 March 2016, derived from three channels of the SEVIRI imager on Meteosat-10, with center wavelengths at 12.0, 10.8, and 8.7 µm. This infrared channels of the SEVIRI imager on Meteosat-10, with center wavelengths at 12.0, 10.8, and false-color image was created using an algorithm from EUMETSAT, which colors red the difference 8.7 µm. This false-color image was created using an algorithm from EUMETSAT, which colors red between the 12.0 and 10.8 µm channels, green the difference between the 10.8 and 8.7 µm channels and the difference between the 12.0 and 10.8 µm channels, green the difference between the 10.8 and blue the 10.8 µm channel. Dust appears pink or magenta, water vapor dark blue, thick high-level clouds 8.7 µm channels and blue the 10.8 µm channel. Dust appears pink or magenta, water vapor dark blue, red-brown, thin high-level clouds almost black and surface features pale blue or purple. thick high-level clouds red-brown, thin high-level clouds almost black and surface features pale blue or purple. On March 24 at 00:00 UTC, only the zonal movement of surface- and 850-hpa low- centers from 15° E to 20° E (from Italy to Balkan Peninsula) resulted with penetration of strong south and On March 24 at 00:00 UTC, only the zonal movement of surface- and 850-hpa low- centers southwesterly winds over the most regions of the Anatolian Peninsula (Figure 8a,b). That day, 74% from 15◦ E to 20◦ E (from Italy to Balkan Peninsula) resulted with penetration of strong south and of air quality stations showed above-normal PM10 concentrations (Figure 3b). In regional southwesterly winds over the most regions of the Anatolian Peninsula (Figure8a,b). That day, 74% of perspective, coarse PM increment was more remarkable in MR than the other parts of the country air quality stations showed above-normal PM concentrations (Figure3b). In regional perspective, and over 600 μg·m−3 concentration values were10 measured over the urban settlements of Istanbul coarse PM increment was more remarkable in MR than the other parts of the country and over (e.g., Esenler) in these hours (Figure 4b,d). South and southwesterly winds turned into the westerly 600 µg·m−3 concentration values were measured over the urban settlements of Istanbul (e.g., Esenler) winds owing to the rapid movement of this cyclone to more northeast (over Black Sea) at 12-hours in these hours (Figure4b,d). South and southwesterly winds turned into the westerly winds owing to later (Figure 8c,d) and thus; PM10 concentration levels started to decrease in the whole county from the rapid movement of this cyclone to more northeast (over Black Sea) at 12-hours later (Figure8c,d) the highest points (Figure 4b). and thus; PM concentration levels started to decrease in the whole county from the highest points It is well10 known from the previous studies that, omega winds in the context of the synoptic (Figure4b). perspective show the upward and downward movements of the dust particles (e.g., [14,30,31]). It is well known from the previous studies that, omega winds in the context of the synoptic From this viewpoint, the omega winds at 1000-hPa synoptic map were extracted for 00:00 UTC of perspective show the upward and downward movements of the dust particles (e.g., [14,30,31]). From March 24, available time for the observed dense particles over Turkey. As shown in Figure 9, sink this viewpoint, the omega winds at 1000-hPa synoptic map were extracted for 00:00 UTC of March 24, regions (positive values) owing to the strong downward movements of the air are shown over the available time for the observed dense particles over Turkey. As shown in Figure9, sink regions Black Sea and thus; transported dust can easily be subside in this region of the country. (positive values) owing to the strong downward movements of the air are shown over the Black Sea and thus; transported dust can easily be subside in this region of the country.

Atmosphere 2017, 8, 41 12 of 16 Atmosphere 2016, 7, 41 12 of 16 Atmosphere 2016, 7, 41 12 of 16 (a) (b) (a) (b)

Figure 8.8. TheThe evolutionevolutionof of the the second second dust dust storm storm event event over over Turkey Turkey at 00:00at 00:00 UTC UTC 24 March24 March 2016. 2016. (a) sea(a) Figurelevelsea level pressures 8. Thepressures evolution (solid (solid lines, of lines, the every second every 5-hPa), 5-hPa),dust and storm totaland ev cloudtotalent cloudover cover Turkeycover (shaded, (shaded, at %)00:00 at surface.%)UTC at 24surface. (Marchb) Height (b 2016.) Height (solid (a) − −1 sealines,(solid level dam)lines, pressures anddam) winds and (solid winds (m lines,·s 1 (m·s) atevery 850-hPa.) at 5-hPa), 850-hPa. (c) an samed (c) total same as (clouda) as and ( acover ()d and) same (shaded, (d) assame (b )%) as but at(b for surface.) but 12:00 for UTC(12:00b) Height of UTC the (solid24of the March. lines,24 March. dam) and winds (m·s−1) at 850-hPa. (c) same as (a) and (d) same as (b) but for 12:00 UTC of the 24 March.

Figure 9. Omega map (Pa·s−1) at 1000-hPa for 24 March 00:00 UTC. Red-to-yellow area corresponds to a sink area, while negative− 1values show upward movement of air. FigureFigure 9. 9. OmegaOmega map map (Pa·s (Pa·s−)1 at) at 1000-hPa 1000-hPa for for 24 24 March March 00:00 00:00 UTC. UTC. Red-to-yellow Red-to-yellow area area corresponds corresponds to to aa sink sink area, area, while while negative values show upward movement of air. 3.5 Vertical Profiles of Saharan Dust 3.5 VerticalFigure Profiles 10a shows of Saharan the ground Dust track of the CALIPSO orbit and the altitude orbit cross-section measurementFigure 10a of shows ß532 during the ground the southward track of the descent CALI ofPSO the orbit night and flight the of altitu the CALIPSOde orbit cross-section on 23 March measurement of ß532 during the southward descent of the night flight of the CALIPSO on 23 March

Atmosphere 2017, 8, 41 13 of 16

3.5. Vertical Profiles of Saharan Dust Figure 10a shows the ground track of the CALIPSO orbit and the altitude orbit cross-section measurement of ß532 during the southward descent of the night flight of the CALIPSO on 23 March Atmosphere 2016, 7, 41 13 of 16 2016. It was chosen this time and day because the CALIPSO profiles for the other times and days are unavailable2016. It duewas chosen to its transport this time routeand day (flies because only oncethe CALIPSO every 16 profiles days over for the a defined other times site) and and days unfavorable are weatherunavailable conditions due such to its as transport presence route of thick (flies clouds. only once The attenuatedevery 16 days backscatter over a defined quick-looks site) and derived fromunfavorable CALIOP on weather board CALIPSO conditions satellite such as indicate presence that of aerosol-loadedthick clouds. The plumes attenuated were distributedbackscatter from the near-groundquick-looks derived surface from to anCALIOP altitude on board of 8 km CALIPSO between satellite 24.5 ◦indicateN and that 26.5 aerosol-loaded◦ N. Most of plumes the aerosol ° ° loadwere is shown distributed between from 2 andthe near-ground 4 km in these surface source to an regions. altitude Toof illustrate8 km between the vertical24.5 N and behavior 26.5 N. of the suspendedMost of dust the particlesaerosol load during is shown transportation, between 2 and we analyzed4 km in these the atmosphericsource regions. sounding To illustrate observations the vertical behavior of the suspended dust particles during transportation, we analyzed the of Izmir (WMO number 17220, as a representative Aegean station, 38.43◦ N, 27.16◦ E) and Kartal atmospheric sounding observations of Izmir (WMO number 17220, as a representative Aegean (WMO number 17064, as a representative station of Marmara, 40.90◦ N, 29.15◦ E) for the 00:00 UTC of station, 38.43° N, 27.16° E) and Kartal (WMO number 17064, as a representative station of Marmara, March40.90 24.° FromN, 29.15 Figure° E) for 10 theb, a 00:00 thin UTC inversion of March layer 24. at From 700-hPa Figure is 10b, shown a thin in Izmir.inversion This layer level at 700-hPa is important for Saharanis shown originated in Izmir. dust This particles, level is which important are transported for Saharan to originated the eastern dust Mediterranean particles, which and generallyare enrichedtransported with important to the eastern aerosol Mediterranean loads [17]. and When genera welly compared enriched with with important the previous aerosol day’s loads radiosonde [17]. profileWhen (23 Marchwe compared 00:00 UTC,with the not previous shown), day’s the atmosphere radiosonde profile is fully (23 saturated March 00:00 from UTC, ground not shown), to this level. Basedthe on atmosphere METARs reportsis fully saturated numerous from convective ground to precipitation this level. Based events on METARs between reports 00:00 UTCnumerous and12:00 UTCconvective were observed precipitation at stations events located between in 00:00 AR (notUTC shown).and 12:00 InUTC conclusion, were observed both at thin stations inversion located layer at mid-levelin AR (not of theshown). atmosphere In conclusion, and removal both thin mechanism inversion layer of the at precipitationmid-level of the prevented atmosphere us showingand removal mechanism of the precipitation prevented us showing highest PM concentration levels in highest PM concentration levels in the AR. For Marmara, thick inversion level near the ground surface the AR. For Marmara, thick inversion level near the ground surface on the previous day shows on the previous day shows parallelism on 24 March and restricts the escape of the dust particles to parallelism on 24 March and restricts the escape of the dust particles to upper levels. In addition to upper levels. In addition to this, dry atmospheric conditions enable us to observe the highest PM this, dry atmospheric conditions enable us to observe the highest PM10 observations at this date, 10 observationswhich had at never this date, been whichrecorded had (Figure never 10c). been recorded (Figure 10c).

(a) NIGHTTIME OVERPASS 23/03/2016 532 nm Total Attenuated Backscatter (km-1sr-1) Begin UTC:00:38:13 End: 00:39:43

1.0x10-1

1.0x10-2

1.0x10-3 Altitude (km)

1.0x10-4 Lat 29.88 28.81 27.73 26.66 25.58 24.51 Lon 21.37 21.10 20.82 20.55 20.29 20.02 (b) (c)

Figure 10. (a) Attenuated backscatter quicklook derived from CALIOP (CALIPSO satellite on 23 Figure 10. (a) Attenuated backscatter quicklook derived from CALIOP (CALIPSO satellite on 23 March March 2016 at night during the over passes over the dust source region. (b) Skew T-logp diagram of 2016 at night during the over passes over the dust source region. (b) Skew T-logp diagram of Izmir Izmir (western station, WMO number: 17220) at 00:00 UTC of 24 March, 2016. The solid thick lines (western station, WMO number: 17220) at 00:00 UTC of 24 March, 2016. The solid thick lines represent represent temperature and dewpoint observations. Temperature in °C and pressure levels in hPa. (c) ◦ temperaturesame as ( andb) but dewpoint for Kartal observations.(northern station, Temperature WMO number: in 17064)C and pressure levels in hPa. (c) same as (b) but for Kartal (northern station, WMO number: 17064) 4. Summary and Conclusions It is well known that the Saharan dust outbreaks are frequently transported to western Turkey in spring months. In one of the latest Saharan dust episodes, high particulate matter concentrations

Atmosphere 2017, 8, 41 14 of 16

4. Summary and Conclusions It is well known that the Saharan dust outbreaks are frequently transported to western Turkey in spring months. In one of the latest Saharan dust episodes, high particulate matter concentrations were observed in the larger area of the Anatolian Peninsula between 23 and 24 March of 2016. Therefore, the spread of dust over Turkey and its effect on horizontal visibility have been investigated using PM10 observations from 97 air quality stations, and METAR observations from 64 airports. These two-day synoptic considerations of high dust events were analyzed using synoptic charts, and satellite images were also used in the analyses. A severe dust storm event occurred during the late quarter of March 2016. On 23 March 2016, Sharav cyclone developed and moved from its origin to Italy in NE direction. On the other side, surface high pressure simultaneously was settled over the Levant basin. Maintaining similar positions of the low (high) centers at 850-hPa with its surface pressure centers enabled us to observe strong southwesterly wind flows over Turkey. As a consequence, 45% of all stations exceeded their long-term means (54% showed daily limit values above those proposed by the EU). In a regional approach, northern and western parts of the country (i.e., Black Sea, Marmara, and Aegean regions) are most influenced from these strong dust plumes. Anticyclonic conditions over southeastern Anatolia prevented loading of the dust particles to this region. During the beginning of the 24 March 2016, zonal shifting of the low (pressure) centers from Italy to the Balkan Peninsula and quasi-stationary conditions of the high (pressure) centers facilitate rapid and dense transport of a thick Saharan dust layer (between 2 and 4 km) to Anatolian Peninsula under favor of strong south-southwestern winds. As a consequence, 74% of the 97 air quality stations showed above-normal PM10 concentrations. In a regional perspective, 100%, 90%, 88%, and 87% of the stations in Marmara (NW Turkey), central Anatolia, western (Aegean) and northern (Black Sea) portions of Turkey, respectively, exhibited above-normal daily PM10 values. For northern Turkey (BSR), subsidence at low-levels plays a significant role in observing above-normal PM10 concentrations. While occurring of some convective precipitation events over Aegean Region and thin inversion level at mid-level of the atmosphere (700-hPa) prevent excessive increase of PM10 levels in this region. When particles moved more north under the same synoptic conditions, a thick inversion layer near the ground surface restricted the escape of the suspended air particles from Marmara. In conclusion, −3 the highest PM10 levels are shown in MR (on average, 413 µg·m at 03 LT); peak values reached over 550 µg·m−3 in the four urban settlements of Istanbul (Esenler, Kartal, Kadikoy, and Besiktas) at 03 LT. When examined on a diurnal basis, the PM10 levels of these four stations were almost four times higher than their long-term spring means, and almost 1.5-fold higher than the previous spring's peak daily PM10 values. It was also shown that visibility decreased to 3–4 km in the north of Marmara, especially in the metropolitan city of Istanbul. Thus, there were significant negative effects of Saharan dust outbreaks on aviation activities. Combination of large dust storms coupled with cyclonic activities should definitely be a point of consideration in forecasting low visibility and above-normal PM10 values over the particular or whole terrain of Turkey.

Conﬂicts of Interest: The author declares no conﬂict of interest.

References

1. Goudie, A.S.; Middleton, N.J. Saharan dust storms: Nature and consequences. Earth-Sci. Rev. 2001, 56, 179–204. [CrossRef] 2. Chiapello, I.; Bergametti, G.; Gomes, L.; Chatenet, B.; Dulac, F.; Pimenta, J.; Santos Suares, E. An Additional Low Layer Transport of Sahelian and Saharan Dust over the North-Eastern Tropical Atlantic. Geophys. Res. Lett. 1995, 22, 3191–3194. [CrossRef] 3. Kellogg, C.A.; Grifﬁn, D.W.; Garrison, V.H.; Peak, K.K.; Royall, N.; Smith, R.R.; Shinn, E.A. Characterization of aerosolized bacteria and fungi from desert events in Mali, West Africa. Aerobiologia 2004, 20, 99–110. [CrossRef] Atmosphere 2017, 8, 41 15 of 16

4. Perez, L.; Tobias, A.; Querol, X.; Künzli, N.; Pey, J.; Alastuey, A.; Viana, M.; Valero, N.; Gonzales-Cabre, M.; Sunyer, J. Coarse particles from Saharan dust and daily mortality. Epidemiology 2008, 19, 800–807. [CrossRef] [PubMed] 5. Kubilay, N.; Cokacar, T.; Oguz, T. Optical properties of mineral dust outbreaks over the northeastern Mediterranean. J. Geophys. Res. 2003, 108, 4666. [CrossRef] 6. Akpinar-Elci, M.; Martin, F.E.; Behr, J.G.; Diaz, R. Saharan dust, climate variability, and asthma in Grenada, the Caribbean. Int. J. Biometeorol. 2015, 59, 1667–1671. [CrossRef][PubMed] 7. Jimenez, E.; Linares, C.; Martinez, D.J. Role of Saharan dust in the relationship between particulate matter and short-term daily mortality among the elderly in Madrip (Spain). Sci. Total Environ. 2010, 408, 5729–5736. [CrossRef][PubMed]

8. Mate, T.; Guaita, R.; Pichiule, M.; Linares, C.; Diaz, J. Short-term effect of ﬁne particulate matter (PM2.5) on daily mortality due to diseases of the circulatory system in Madrid (Spain). Sci. Total Environ. 2010, 408, 5750–5757. [CrossRef][PubMed] 9. Middleton, N.; Yiallouros, P.; Kleanthous, S.; Kolokotroni, O.; Schwartz, J.; Dockery, D.W.; Demokritou, P.; Koutrakis, P. A 10-year time-series analysis of respiratory and cardiovascular morbidity in Nicosia, Cyprus: The effect of short-term changes in air pollution and dust storms. Environ. Health 2008, 7, 39. [CrossRef] [PubMed] 10. Samoli, E.; Kougea, E.; Kassomenos, P.; Analitis, A.; Katsouyanni, K. Does the presence of desert dust modify

the effect of PM10 on mortality in Athens, Greece? Sci. Total Environ. 2011, 409, 2049–2054. [CrossRef] [PubMed] 11. Zauli Sajani, S.; Miglio, R.; Bonasoni, P.; Cristofanelli, P.; Marinoni, A.; Sartini, C.; Goldoni, C.A.; de Girolamo, G.; Lauriola, P. Saharan dust and daily mortality in Emilia-Romagna (Italy). Occup. Environ. Med. 2011, 68, 446–451. [CrossRef][PubMed] 12. Alpert, P.; Kishcha, P.; Shtivelman, A.; Krichak, S.O.; Joseph, J.H. Vertical distribution of Saharan dust based on 2.5-year model predictions. Atmos. Res. 2004, 70, 109–130. [CrossRef] 13. Bangert, M.; Nenes, A.; Vogel, B.; Vogel, H.; Barahona, D.; Karydis, V.A.; Kumar, P.; Kottmeier, C.; Blahak, U. Saharan dust event impacts on cloud formation and radiation over Western Europe. Atmos. Chem. Phys. 2012, 12, 4045–4063. [CrossRef] 14. Cabello, M.; Orza, J.A.G.; Barrero, M.A.; Gordo, E.; Berasaluce, A.; Canton, L.; Duenas, C.; Fernandes, M.C.; Perez, M. Spatial and temporal variation of the impact of an extreme Saharan dust event. J. Geophys. Res. 2012, 117, D11204. [CrossRef] 15. Moulin, C.; Lambert, C.E.; Dayan, U.; Masson, V.; Ramonet, M.; Bousquet, P.; Legrand, M.; Blakanski, Y.J.; Guella, W.; Marticorena, B.; Bergametti, G.; Dulac, F. Satellite climatology of African dust transport in the Mediterranean atmosphere. J. Geophys. Res. 1998, 103, 13137–13144. [CrossRef] 16. Tsidulko, M.; Krichak, S.O.; Alpert, P.; Kakaliagou, O.; Kallos, G.; Papadopoulos, A. Numerical study of a very intensive eastern Mediterranean dust storm, 13–16 March 1998. J. Geophys. Res. 2002, 107, 4581. [CrossRef] 17. Papayannis, A.; Balis, D.; Amiridis, V.; Chourdakis, G.; Tsaknakis, G.; Zerefos, C.; Castanho, A.D.A.; Nickovic, S.; Kazadzis, S.; Grabowski, J. Measurements of Saharan dust aerosols over the Eastern Mediterranean using elastic backscatter-Raman lidar, spectrophotometric and satellite observations in the frame of the EARLINET project. Atmos. Chem. Phys. 2005, 5, 2065–2079. [CrossRef] 18. Kaskaoutis, D.G.; Kambezidis, H.D.; Nastos, P.T.; Kosmopoulos, P.G. Study on an intense dust storm over Greece. Atmos. Environ. 2008, 42, 6884–6896. [CrossRef] 19. Grifﬁn, D.W.; Kubilay, N.; Koçak, M.; Gray, M.A.; Borden, T.C.; Shinn, E.A. Airborne desert dust and aeromicrobiology over the Turkish Mediterranean coastline. Atmos. Environ. 2007, 41, 4050–4062. [CrossRef]

20. Koçak, M.; Mihalopoulos, N.; Kubilay, N. Contributions of natural sources to high PM10 and PM2.5 events in the eastern Mediterranean. Atmos. Environ. 2007, 41, 3806–3818. [CrossRef]

21. Kabatas, B.; Unal, A.; Pierce, R.B.; Kindap, T.; Pozzoli, L. The contribution of Saharan dust in PM10 concentration levels in Anatolian Peninsula of Turkey. Sci. Total Environ. 2014, 488–489, 413–421. [CrossRef] [PubMed] 22. Erinç, S. Climatology and Its Methods, 3rd ed.; Istanbul, Gür-ay Pres Inc.: Istanbul, Turkey, 1984. (in Turkish) Atmosphere 2017, 8, 41 16 of 16

23. Kalnay, E.; Kanamitsu, M.; Kistler, R.; Collins, W.; Deaven, D.; Gandin, L.; Iredell, M.; Saha, S.; White, G.; Wollen, J.; et al. The NCEP/NCAR 40 year reanalysis project. Bull. Am. Meteorol. Soc. 1996, 77, 437–471. [CrossRef] 24. De la Paz, D.; Vedrenne, M.; Borge, R.; Lumbreras, J.; de Andres, J.M.; Perez, J.; Rodriguez, E.; Karanasiou, A.; Moreno, T.; Boldo, E.; Linares, C. Modelling Saharan dust transport into the Mediterranean basin with CMAQ. Atmos. Res. 2013, 70, 337–350. [CrossRef] 25. Guerrero-Rascado, J.L.; Olmo, F.J.; Aviles-Rodriguez, I.; Navas-Guzman, F.; Perez-Ramirez, D.; Lyamani, H.; Arboledas, L.A. Extreme Saharan dust event over the southern Iberian Peninsula in September 2007: Active and passive remote sensing from surface and satellite. Atmos. Chem. Phys. 2009, 9, 8453–8469. [CrossRef] 26. Mona, L.; Amodeo, A.; Pandolfi, M.; Pappalardo, G. Saharan dust intrusions in the Mediterranean area: Three years of Raman lidar measurements. J. Geophys. Res. 2006, 111, D16203. [CrossRef] 27. Schepanski, K.; Tegen, I.; Laurent, B.; Heinold, B.; Macke, A. A new Saharan dust source activation frequency map derived from MSG-SEVIRI IR-channels. Geophys. Res. Lett. 2007, 34, L18803. [CrossRef] 28. Hamidi, M.; Kavianpour, M.R.; Shao, Y. Synoptic analysis of dust storms in the Middle East. Asia-Pac. J. Atmos. Sci. 2013, 49, 279–286. [CrossRef] 29. Vajanapoom, N.; Shy, C.M.; Neas, L.M.; Loomis, D. Estimation of particulate matter from visibility in Bangkok, Thailand. J. Expo. Sci. Environ. Epidemiol. 2001, 11, 97–102. [CrossRef] 30. Ganor, E.; Stupp, A.; Osetinsky, I.; Alpert, P. Synoptic classification of lower troposphere profiles for dust days. J. Geophys. Res. 2010, 115, D11201. [CrossRef] 31. Prasad, A.K.; El-Askary, H.; Kafatos, M. Implications of high altitude desert dust transport from Western Sahara to Nile Delta during biomass burning season. Environ. Pollut. 2010, 158, 3385–3391. [CrossRef] [PubMed]

© 2017 by the author; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).