Tareq Hussein This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Xinyang Li2, Qusay Al-Dulaimi2, Shatha Daour1, Nahid Atashi2,3, Mar Viana4, Andres Alastuey4, Larisa Sogacheva5, Sharif Arar6, Afnan Al-Hunaiti6, Tuukka Petäjä2

1 Department of Physics, The University of Jordan, Amman 11942, Jordan
2 Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki FI-00014, Finland
3 Faculty of Geographical science and Planning, University of Isfahan, Isfahan 8174673441, Iran
4 Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona 08034, Spain
5 Climate Change Programme, Finnish Meteorological Institute, Helsinki, Finland
6 Department of Chemistry, The University of Jordan, Amman 11942, Jordan


 

Received: May 4, 2020
Revised: July 23, 2020
Accepted: September 14, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.2020.05.0195  


Cite this article:

Hussein, T., Li, X., Al-Dulaimi, Q., Daour, S., Atashi, N., Viana, M., Alastuey, A., Sogacheva, L,. Arar, S., Al-Hunaiti, A. and Petäjä, T. (2020). Particulate Matter Concentrations in a Middle Eastern City – An Insight to Sand and Dust Storm Episodes. Aerosol Air Qual. Res. 20: 2780–2792. https://doi.org/10.4209/aaqr.2020.05.0195


HIGHLIGHTS

  • Annual PM10 was 64 ± 39 µg m3.
  • PM2.5/PM10 ratio was 0.8 ± 0.2.
  • SDS episodes were tracked back to North Africa, Arabian Peninsula, and Levant.
  • 24-hour PM10 during SDS episodes was 108–188 µg m–3.
 

ABSTRACT


In this study, the particulate matter mass (PM10 and PM2.5) concentrations we measured during May 2018–March 2019 in an urban atmosphere of Amman, Jordan. The results showed that the annual mean PM10 concentration was 64 ± 39 µg m–3 and the PM2.5/PM10 ratio was 0.8 ± 0.2. According to the Jordanian Air Quality standards (JS-1140/2006), the observed PM10 annual mean value was below the limit value but that of the PM2.5 was three times higher than the corresponding limit value. However, both exceeded the World Health Organization (WHO) air quality guideline values. In a larger perspective, the annual mean PM10 concentrations in Jordan were lower than what was reported in other cities in the Middle East but were higher when compared to other Mediterranean cities. During the measurement period, Jordan was affected by Sand and Dust Storm (SDS) episodes on 14 days. The source origins of these dust outbreaks were traced back to North Africa, the Arabian Peninsula, and the Levant. The 24-hour PM10 concentrations during these SDS episodes ranged between 108 and 188 µg m–3, which was about 3–6 times higher than the mean values during clean conditions (~33 µg m–3).


Keywords: Urban air quality; Particulate matter; Dust particles; Back-trajectory.


INTRODUCTION


Aerosols affect the Earth’s atmosphere directly, e.g., by the scattering of solar radiation, which results in the cooling of the Earth's surface, and indirectly, e.g., by participating in cloud formation. In urban areas, aerosols originate from a vast range of local sources (natural and anthropogenic) and long-range transport. Aerosols have adverse health effects (Pope and Dockery 2006). Cardiorespiratory and lung problems have been often associated with long-term exposure and inhalation of dust particles (Pope et al., 2002; Hoek et al., 2013).

In general, a sand and dust storm (SDS) is by definition an aeolian processes that occur wherever there is a supply of granular material (typically inorganic grains with diameter smaller than 70 µm) and atmospheric wind of sufficient strength to move the grains (Kok et al., 2012; Middleton, 2017). SDS episodes occur via three modes: saltation, when loose materials are removed from the surface and carried by the fluid, before being transported back to the surface, creep, and suspension (Gillette et al., 1974; Shao et al., 1993). During SDS episodes, dust particles are susceptible to turbulent fluctuations and can remain airborne for a short-term (diameter 20–70 µm) or long-term (diameter < 20 µm) (Natsagdorj et al., 2003; Thomas et al., 2005; Evan et al., 2011; Kok et al., 2012). Short-term suspended dust has a local effect whereas Long-term suspended dust stay airborne up to several weeks and be transported for thousands of kilometers from their source region (Gillette and Walker, 1977; Zender et al., 2003; Miller et al., 2006; Kok et al., 2012; Zoljoodi et al., 2013; Doronzo et al., 2015; Gherboudj et al., 2017).

The arid and semi-arid regions that have a supply of granular material are found in deserts, beaches, and etc. Airborne dust particles can be transported from their source area across thousands of kilometers affecting weather and climate, air quality, ecosystem productivity, hydrological cycle, and many components of the Earth system in addition to severe human health effects (Small et al., 2001; Menendez et al., 2007; McTainsh and Strong, 2007; Goudie, 2009; Goudie, 2009; Karanasiou et al., 2012; Rezazadeh et al., 2013; Almasi et al., 2014; Goudie, 2014; Diaz et al., 2017; Middleton, 2017). The dominant sources of SDS are originated in the Northern Hemisphere forming the so called “Afro-Asian belt”, which includes the west coast of Africa, Middle East, Iran, Afghanistan, Pakistan, Mongolia, and China (Middleton 1986a, b; Herman et al., 1997; Torres et al., 1998; Prospero et al., 2002; Furman et al., 2003; Léon and Legrand, 2003; Goudie, 2009).

Recently, Gherboudj et al. (2017) characterized the spatiotemporal variability of the Middle East and North African Dust Emission Potential (MENA-DEP) according to three scales: low dust emission areas, moderate dust emission areas, and high dust emission areas. As such, the high and moderate dust emission areas in north Africa were: Chad, Niger, Mauritania, Occidental Sahara, west and north Algeria, south Tunisia, north-west and central Libya, central Egypt, Sudan, and African horn. In the Middle East, the high and moderate dust emission areas were Jordan, Syria, east Iraq, and Arabian Peninsula. In addition to these areas, central Iran, west Afghanistan, south-west Pakistan, and west India. In spite of these reviews there is a need of investigating the impact on air quality and health of desert dust in different areas by applying comparable methodologies in order to characterize exposure (Querol et al., 2019).

The dust source areas in the Levant, Arabian Peninsula, and Iran were also identified and investigated in several previous studies (Rezazadeh et al., 2013; Alam et al., 2014; Nabavi et al., 2016; Naimabadi et al., 2016; Khaniabadi et al., 2017; Rashki et al., 2017). Al-Dousary et al. (2017) classified SDS episodes in the northeast of the Arabian Peninsula during 2000–2017; they distinguished three major types and twelve subtypes of dust storms trajectories based in the width and the shape of the dust outbreak.

The Eastern Mediterranean region also suffers from SDS episodes, which have been reported more frequently during the past decades (Furman et al., 2003; Keramat et al., 2011; Hussein et al., 2011; Hamidi et al., 2013; Hussein et al., 2014; Kchih et al., 2015; Hussein et al., 2017; Munir et al., 2017; Hussein et al., 2018; Bin Abdulwahed et al., 2019; Amarloei et al., 2019; Saeifar et al., 2019; Fountoukis et al., 2020). The increased frequency of SDS episodes in the Eastern Mediterranean region has been referred to the impact of climate change and its consequences by desertification, deforestation, wetland destruction, increased population growth and anthropogenic emissions, food insecurity, and water shortage (Amiraslani and Dragovich 2011; Rezazadeh et al., 2013; Notaro et al., 2015). Notaro et al. (2015) showed that the Eastern Mediterranean region has suffered of warming and a drying episode since the beginning of this century. This led to an increased potential to collapse the Fertile Crescent (namely Iraq and Syria). In the Middle East, and especially in the Arabian Peninsula, a pronounced variability in dust activity was reported with an abrupt regime shift from an inactive dust period (1998–2005) to an active dust period (2007–2013) (Aba et al., 2018), which was linked to climate change and global warming impacts in the 2000s (Notaro et al., 2015; Doronzo et al., 2016, 2018). The increased dust episodes and atmospheric dust concentrations on top of the atmosphere have a significant impact on the albedo and short-wave radiation over the African and Arabian regions leading to higher surface reflection (Satheesh et al., 2006), especially in Palestine (Singer et al., 2003), Iraq (Al-Hemoud et al., 2020), Kuwait (Al-Dousari 2009). This also has a socioeconomic impact on oil sector (Al-Hemoud et al., 2019), photovoltaic energy efficiency (Al-Dousari et al., 2019), and health (Al-Dousari et al., 2018). For example, aerosol dust cause direct and indirect adverse effects for fauna, flora and human health in the regional scale (Abd El-Wahab et al., 2018).

Besides climate change impacts, anthropogenic aerosols have had an increasing trend during the previous decades in the Middle East (Givati and Rosenfeld, 2007). These particles are anticipated to slow down the conversion of cloud drops into raindrops and snowflakes, thus decreasing precipitation from short-lived clouds such as form in moist air that crosses topographic barriers. This in turn, escalated the desertification process in the Middle East causing increased frequency of dust episodes and atmospheric dust particle concentrations.

While aerosol research has been given increased attention in the Eastern Mediterranean, it is still at an early stage in the Middle East (Hamad et al., 2015; Heo et al., 2017; Taheri et al., 2019), especially in Jordan (Hussein et al., 2018). Therefore, the main objective of this study was to investigate the particulate matter (PM10 and PM2.5) mass concentrations over a long-term period from May 2018 to March 2019 in the urban atmosphere of Amman, Jordan. The methods included aerosols sampling using high-volume samplers and gravimetric analysis combined with air mass back trajectories to identify the source origin of SDS episodes.

 
METHOD



Measurement Location

The long-term aerosol measurement campaign was performed during May 2018–March 2019 on the roof top (about 20 m above the ground) of the Department of Physics at the campus of the University of Jordan [32.0129N, 35.8738E]. The campus is situated at an urban background location in the northern part of Amman, which is the capital city of Jordan. The surrounding area of the campus is a mixture of residential area and road network with one of the main roads (Queen Rania street) passing parallel to the west side of the campus (Fig. S1). The downtown was about 10 km south of the campus area.

 
High-volume Sampling

Two high-volume samplers (model CAV-A/mb, MCV, S.A., Spain) were used; one for PM10 and another one for PM2.5. The high-volume samplers were operated at 30 m3 h1 and record the overall mean temperature and pressure during the sampling session. The cascade heads (model PM1025-CAV, MCV, S.A.) were used for sampling particles with aerodynamic diameter lower than 10 µm (PM10) and lower than 2.5 µm (PM2.5) respectively. These sampling heads can accommodate a 15-centimetre diameter round filters, which were quartz filters (Pallflex, PALLXQ250ETDS0150, TISSUQUARTZ 2500 QAT-UP).

Each sampling session (including PM10 and PM2.5) lasted for 24-hours every 6 days. In total, 51 PM10 samples and 48 PM2.5 samples were collected. Blank sampling was performed several times during the campaign by following the same procedure as for the active sampling except that the high-volume sampler was turned off (i.e., zero flow rate). The blank samples were necessary for the accuracy check of the sampling procedure and analysis (Querol et al., 2004).

 
Gravimetric Analysis

The gravimetric analysis was according to the European directive EN1234-1 (20°C and 50% HR) at the Institute of Environmental Assessment and Water Research (IDAEA-CSIC, Barcelona, Spain). The gravimetric analysis included pre-sampling and post-sampling weighing of each filter (including blanks). The pre-weighing and post-weighing were done with the same procedure: conditioning temperature 20°C and relative humidity 50%. The conditioning time was 2 days. Each sample was weighed twice (24 hours interval in between) and the average value was recorded. The weighing was made by using a microbalance (Mettler-Toledo, model: XP105 with electrostatic charge detection, Switzerland).

The 24-hour average particulate matter concentration (PMx [µg m–3]) was calculated

 

where mpost and mpost [µg] are the post sampling and the pre-sampling weight of the filter, Q [30 m3 h1] is the sampling flow rate, and Dt [24 hours] is the sampling time interval.

 
Meteorological Conditions

The weather conditions were monitored on-site with a weather station (WH-1080, Clas Ohlson: Art. no. 36-3242), which was set to record the reading with 5-minute interval. The weather data included: ambient temperature, pressure, relative humidity, wind speed and direction, and precipitation.

 
Back Trajectories

Air mass back trajectories were calculated by using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Hess, 1997; Draxler et al., 2012; Stein et al., 2015), which provides detailed information about the origin and path of air masses that arrived at the measurement site. Four-days back trajectories were calculated for each hour at arrival heights 100, 500, and 1500 meters above ground level.

Back trajectories crossing maps were generated in terms of the frequency of air mass crossing over each grid cell of the domain. Here the domain was taken to cover the west-south Asia, North Africa, and Europe (i.e., longitude –20°–60° and latitude 15°–55°). The crossing map resolution was set to 0.5° for the 4-day back trajectories calculated for each hour.


RESULTS AND DISCUSSION


 
Meteorological Conditions

The hourly, daily, and monthly means of the meteorological conditions (ambient temperature (T), relative humidity (RH), absolute pressure (P), wind speed (WS), and hourly precipitation) are presented in Fig. 1. The monthly mean, standard deviation, minimum, and maximum values are listed in Table S2.

Fig. 1. Time series of weather conditions during May 1, 2018–March 19, 2019 presented as hourly, daily, and monthly means for (a) ambient temperature, (b) relative humidity, (c) absolute pressure, and (d) wind speed. (e) The rainfall was presented as hourly cumulative precipitation.Fig. 1. Time series of weather conditions during May 1, 2018–March 19, 2019 presented as hourly, daily, and monthly means for (a) ambient temperature, (b) relative humidity, (c) absolute pressure, and (d) wind speed. (e) The rainfall was presented as hourly cumulative precipitation.

The ambient temperature (T) showed a clear seasonal variation with high values during the summer (June–August) and low values during the winter (December–February). During the summer, the monthly mean T was around 24°C and during the winter it was around 9°C (Fig. 1(a)). During May 2018–March 2019, the daily mean T was in the range 3–30°C (overall mean 17 ± 7°C).

The seasonal variation of the relative humidity (RH, Fig. 1(b)) and the absolute pressure (P, Fig. 1(c)) was opposite to that of T. For example, the monthly RH was about 55% and 82% during the summer and the winter; respectively. As for P, it was about 896 hPa and 901 hPa during the summer and the winter; respectively. During May 2018–March 2019, the daily mean RH was in the range 20–100% (overall mean 68 ± 21%) and the daily mean P was in the range 890–908 hPa (overall mean 899 ± 4 hPa).

The wind speed (WS) showed a different monthly variation than T, RH, and P. The monthly mean wind speed was minimum during the autumn (September–November) and maximum during the summer (Fig. 1(d)). The maximum monthly WS value was about 2.1 m s1 (August) and the minimum was about 0.8 m s1 (November). During May 2018–March 2019, the maximum daily mean WS was about 3.6 m s1 (March 1, 2019).

The rain season started in October 2018 with a small amount (cumulative ~13 mm) (Fig. 1(e)). During December 2018, the cumulative precipitation was about 180 mm. During January–February 2019, the cumulative precipitation was about 120 mm. By the end of the measurement campaign (i.e., March 2019), the cumulative precipitation was about 470 mm.

 
Back Trajectories

The 4-day back trajectories crossing maps are presented in Fig. S2 (Supplementary Material) for arrival heights 100 m and 1500 m. The spatial extent of the trajectories crossing for 1500 m arrival height was broader than that for 100 m arrival height. For back trajectories arrived at 1500 m height, they covered the whole Mediterranean Sea Basin and included north Africa, Red Sea, north and middle region of the Arabia Peninsula, the Levant with an extension to the Caspian Sea, and Europe. As for arrival height at 100 m, the back trajectories covered the middle and eastern parts of the Mediterranean Sea, northeast Africa, north Red Sea, north Arabian Peninsula, the Levant, and southeast Europe. Furthermore, trajectories arrived at 100 m showed a predominant crossing path over the eastern part of the Mediterranean Sea.

 
Particulate Matter (PM) Concentrations

Throughout the measurement period (Fig. 2 and Table S3), the PM10 daily concentration varied between 20 and 190 µg m–3 with an overall average 64 ± 39 µg m–3. The PM2.5 daily concentration varied between 15 and 190 µg m–3 with an overall average 47 ± 32 µg m–3 (i.e., annual). The ratio PM2.5/PM10 was more than 0.4 and the overall mean was 0.8 ± 0.2; i.e., about 80% of the PM10 was within the fine fraction (Fig. 2 and Table S3).

Fig. 2. Time series of PM10 and PM2.5 concentrations with markups for sand and dust episodes (SDS) and clean air periods (i.e., PM10 concentrations < 70 µg m–3).Fig. 2. Time series of PM10 and PM2.5 concentrations with markups for sand and dust episodes (SDS) and clean air periods (i.e., PM10 concentrations < 70 µg m–3).

Surprisingly, the PM concentrations did not show a clear seasonal variation. This is contrary to previous observation reported via on-line continuous long-term measurement of the particle number size distribution, which showed a clear seasonal variation for the coarse mode particle number concentration with higher concentrations during the winter than summer and showed specific peaks in spring and autumn (Hussein et al., 2018, 2019). A reason for not observing this seasonal variation in this study can be due to the sampling protocol conducted here; collecting a sample every 6 days. Therefore, it is recommended to perform the sampling every other day if not possible on daily basis; i.e., higher time resolution of the sampling sessions is recommended.

According to the Jordanian standards (JS-1140/2006) for ambient air quality, the annual mean the PM10 and PM2.5 must not exceed 70 µg m–3 and 15 µg m–3; respectively. This means that the observed PM10 annual mean value is below its annual limit value but the PM2.5 annual mean three times higher than its limit value. As for the 24 h mean limit value, PM10 and PM2.5 must not exceed 120 µg m–3 and 65 µg m–3; respectively. According to this, the exceedance of PM10 was 6 times and that of PM2.5 was 7 times. As will be shown in the next section, these exceedances were during the reported SDS episodes. Compared to the World Health Organization (WHO) air quality guidelines for PM10 (annual and 24h must not exceed 20 µg m–3 and 50 µg m–3; respectively) and PM2.5 (annual and 24h must not exceed 10 µg m–3 and 25 µg m–3; respectively), the observed annual concentrations here are exceeded both annual guidelines. According to the 24 h WHO guidelines, only 6 days did not exceed the PM2.5 limit value and 25 days did not exceed the PM10 limit value.

The WHO (2018) released an update for the global ambient air quality database that reported the annual mean PM10 and PM2.5 concentrations during 2008–2016. Recalling the data for three Jordanian cities (Al-Zarqa’, Amman, and Irbid) in 2017, the annual mean PM10 was 82, 68, and 53 µg m–3; respectively. This is consistent with our observation here with an annual mean PM10 ~64 µg m–3. The world overall annual mean PM10 was ~72 µg m–3 during 2008–2016, which is slightly higher than what was observed during our measurement campaign.

In general, the annual mean PM10 concentrations in Jordan were higher than what was reported by the WHO (2018) in urban, suburban, and residential sites in countries around the Mediterranean Sea in 2016. For example, the annual mean PM10 for Turkish cities (80 sites) was 52 ± 18 µg m–3 (range 17–91 µg m–3), Italian cities (231 sites) was about 25 ± 6 µg m–3 (range 10–43 µg m–3), Greek cities (12 sites) was 52 ± 18 µg m–3 (range 21–43 µg m–3), Cypriot cities (4 sites) was 37 ± 6 µg m–3 (range 29–41 µg m–3), and two Maltese cities 38 ± 8 µg m–3 (range 32–43 µg m–3).

Compared to other cities in the Middle East as reported by the WHO (2018), the annual mean PM10 concentrations in Jordan were lower than what was observed in Kuwait (130 ± 35 µg m–3; 9 sites), Palestine and Israel (90 µg m–3), Egypt (249–284 µg m–3; two sites), and the United Arab of Emirates (122–153 µg m–3; three sites).

 
Sand and Dust Storm (SDS) Episodes

As indicated in Fig. 2, SDS episodes were considered when PM10 > 70 µg m–3. This arbitrary threshold was selected based on the distribution of daily PM10 concentrations, which showed two distinct groups of samples below and above this threshold (Fig. S3 in Supporting Material). In practice, this threshold is slightly higher than the annual PM10 mean value and it separates two groups of the PM10 concentrations distributions. Based on this threshold, 14 days were identified and listed in Table 1. According to the air mass back trajectories analysis, the atmospheric SDS was transported from three main source regions: (1) long-range transport from north Africa (Sahara), (2) medium range transport from the Arabian Peninsula, and (3) short-range transported from the Levant. Sometimes, the transport was a combination of two or three regions. Accordingly, type identification was suggested: S-type originated from Sahara region, SL-type originated from Sahara region and the Levant region (i.e., SDS combined from these two regions), and SLA-type originated from all three regions. The SLA was the most common SDS type because the back trajectories originated from north Africa crosses or circulates over the northern part of the Arabian Peninsula and the Levant region.

Table 1. Sand and Dust Storm (SDS) episodes according to type and observation during the sampling period. The type of SDS is denoted as: Saharan (S); Saharan and Levant (SL); Saharan, Arabian, and Levant (SAL); Saharan, Arabian, Levant, and Ahvaz (SALA). The source region was verified according to the back trajectories analysis for crossing maps on the sampling day (+ following day). The date here indicates the start of the sampling day.

During the measurement period, a single S-type SDS episode (on July 25, 2018; PM10 ~121 µg m–3 and PM2.5 ~109 µg m–3) was identified, which was solely originated from the Sahara region (Fig. 3). Interestingly, during this episode the back trajectories arrived at 1500 m (Fig. 3(c)) were originated and crossed over North Africa but those arrived at 100 m and 500 m (Figs. 3(a)–3(b)) were from the Mediterranean Sea.

Fig. 3. Back trajectories (96 hours) crossing maps during S-type SDS-episodes (indicated on Fig. 2) at arrival heights (a) 100 meters, (b) 500 meters, and (c) 1500 meters. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day).Fig. 3. Back trajectories (96 hours) crossing maps during S-type SDS-episodes (indicated on Fig. 2) at arrival heights (a) 100 meters, (b) 500 meters, and (c) 1500 meters. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day).

Two SL-type SDS episodes were identified (Table 1) when the air masses originated from the Sahara region and circulated over the Levant region (Fig. 4). During these episodes, the back trajectories arrived at 100 m and 500 m originated from the Mediterranean Sea and circulated over the western parts of Syria, but the trajectories arrived at 1500 m circulated originated from the Sahara region and circulated over Syria, Iraq, and Jordan (i.e., Levant). During the first SL-type episode (May 26, 2018) the PM10 concentration was about 108 µg m–3 and during the second one (June 7, 2018) it was about 127 µg m–3. The average for SL-type episodes was 117 µg m–3 and 112 µg m–3 for PM10 and PM2.5, respectively, with a PM2.5/PM10 ratio of 0.95. The fine size distribution measured during the S and SL types, can be due to size segregation during transport with preferential deposition of coarser particles.

Fig. 4. Back trajectories (96 hours) crossing maps during SL-type SDS-episodes (indicated on Fig. 2) at arrival heights (a) 100 meters, (b) 500 meters, and (c) 1500 meters. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day).Fig. 4. Back trajectories (96 hours) crossing maps during SL-type SDS-episodes (indicated on Fig. 2) at arrival heights (a) 100 meters, (b) 500 meters, and (c) 1500 meters. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day).

During the autumn, winter, and spring, more intense SDS episodes were observed and they spanned over long time periods (Table 1). These episodes SLA-type SDS (Fig. 5). The back trajectories analysis at all arrival heights confirmed the origin of these SDS to be from the three regions dust sources: Sahara, Arabian Peninsula, and Levant. During these SLA-type episodes, the PM10 concentration was in the range 88–188 µg m–3. An intensive SLA-type episode was observed during several weeks in October, 2018 (Fig. 2). During this episode, the PM10 concentrations were higher than 100 µg m–3 and also recorded the highest concentration (as high as 188 µg m–3). The average for SLA-type episodes was 122 µg m–3 and 77 µg m–3 for PM10 and PM2.5, respectively, with a PM2.5/PM10 ratio of 0.63. The coarser size distribution of PM during the SLA types compared with the S and SL types, is probably due the proximity of the source area.

Fig. 5. Back trajectories (96 hours) crossing maps during SLA-type SDS-episodes (indicated on Fig. 2) at arrival heights (a) 100 meters, (b) 500 meters, and (c) 1500 meters. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day).Fig. 5. Back trajectories (96 hours) crossing maps during SLA-type SDS-episodes (indicated on Fig. 2) at arrival heights (a) 100 meters, (b) 500 meters, and (c) 1500 meters. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day).

Notice that S-type and SL-type episodes occurred during the summer. They indicate that some SDS episodes can be lifted up to the upper atmosphere while being transported from their source region to the receptor region, where they settle down. The SLA-type episodes occurred during the Autumn, winter, and spring and they recorded higher PM10 concentrations than the S-type and SL-type episodes, which occurred in the summer. Furthermore, SLA-type episodes transported dust at any arrival height and the back trajectories crossed over a larger spatial extent than that for the S-type and SL-type episodes.

As a comparison to the SDS days, the PM10 concentrations less than 50 µg m–3 occurred during rainy days or accompanied with air masses originating and crossing over the Mediterranean Sea and the eastern part of Europe (Table 1, Fig. 2, Fig. 6).

Fig. 6. Back trajectories (96 hours) crossing maps during low PM10 concentrations (indicated on Fig. 2) at arrival heights (a) 100 meters, (b) 500 meters, and (c) 1500 meters. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day).Fig. 6. Back trajectories (96 hours) crossing maps during low PM10 concentrations (indicated on Fig. 2) at arrival heights (a) 100 meters, (b) 500 meters, and (c) 1500 meters. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day).

Sahara SDS crossing over the Mediterranean Sea was reported in the literature (Gkikas et al., 2018; Solomos et al., 2018). Solomos et al. (2018) analyzed a record-breaking dust episode observed on Crete on March 22, 2018 that recorded 24h mean PM10 concentration as 206, 850, and 1125 µg m–3 in Chania, Finokalia, and Heraklion; respectively.

Gkikas et al. (2018) focused on the direct radiative effects of 20 intense and widespread dust outbreaks originated in North Africa and affected the Mediterranean basin during March 2000–February 2013. Similarly, Alam et al. (2014) and Nabavi et al. (2016) focused on the aerosol optical depth and climatology of some dust outbreaks originated in the Middle East and the Arabian Peninsula that affected west-south Asia. According to these studies, the dust transport in the Middle East and North Africa (MENA) region is transported from west to east (Alam et al., 2014; Nabavi et al., 2016; Naimabadi et al., 2016; Khaniabadi et al., 2017; Rashki et al., 2017; Gkikas et al., 2018). This consistent with previous observation in Jordan that most of the SDS was mainly started from North Africa and transported to the Middle East after crossing/circulating over the Arabian Peninsula and the Levant. Nevertheless, some episodes started within the Levant and the southern region of the Arabian Peninsula.

 
CONCLUSIONS 


Air quality issues related to sand and dust storms (SDS) in the Middle East are one of the critical issues that require more attention because the frequency and the intensity of SDS episodes have increased recently due to escalating climate change impacts and increased anthropogenic emissions in the region. Here, particulate matter (PM10 and PM2.5) concentrations were measured and investigated during May 2018–March 2019 in the urban atmosphere of Amman, Jordan. The methods included aerosols sampling using high-volume samplers and gravimetric analysis combined with air mass back trajectories.

The annual mean PM10 concentration was 64 ± 39 µg m–3 (20–190 µg m–3). The PM2.5/PM10 ratio was 0.8 ± 0.2, which means that about 80% of the PM10 was within the fine fraction. According to the Jordanian Air Quality standards (JS-1140/2006), the observed PM10 annual mean value was below its limit value but that of the PM2.5 was three times higher than its limit value. However, both exceeded the World Health Organization (WHO) air quality guideline. According to the WHO global ambient air quality database during 2008–2016, the annual mean PM10 concentrations in Jordan were lower than what was reported for other cities in the Middle East but were higher when compared to other Mediterranean cities.

During the measurement period extending over 11 months, Jordan was affected by SDS episodes on 14 days. The source origins of these dust outbreaks were traced back to North Africa, the Arabian Peninsula, and the Levant. The 24-hour PM10 concentrations during these SDS episodes ranged between 108 µg m–3 and 188 µg m–3, which is about 3–6 times higher than the mean values during clean conditions (~33 µg m–3).

The limitation of this study is the sampling protocol as collecting a 24-hour sample every 6 days. It is recommended to perform the sampling with higher time resolution. In the future, we continue monitoring with this method and also include on/line sampling with an optical particle sizer. Combining ground-based monitoring with satellite observation would provide an insight into the SDS episodes characteristics and calibration of the satellite observations.

 
ACKNOWLEDGMENTS


This research was part of a close collaboration between the University of Jordan, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), and the Institute for Atmospheric and Earth System Research (INAR / Physics, University of Helsinki). We acknowledge financial support provided by the Deanship of Academic Research (DAR, project No. 2015) at the University of Jordan, the Spanish Ministry of Science and Innovation (Project CEX2018-000794-S) and by AGAUR (project 2017 SGR41), and Academy of Finland Center of Excellence (project. No 272041), ERA-PLANET (www.era-planet.eu), trans-national project SMURBS (grant n. 689443 EU Horizon 2020 Framework Programme and Academy of Finland via the Center of Excellence in Atmospheric sciences and NanoBioMass (project number 1307537). The eCOST action (inDUST, project number CA16202) is acknowledged for supporting this research via the Short Term Scientific Mission (STSM) mobility grant. This manuscript was written and completed during the sabbatical leave of the first author (Tareq Hussein) that was spent at the University of Helsinki and supported by the University of Jordan during 2019.

 
DISCLAIMER


The authors declare no conflict of interest. Data will be available upon request.


REFERENCES


  1. Aba, A., Al-Dousari, A.M. and Ismaeel, A. (2018). Atmospheric deposition fluxes of 137Cs associated with dust fallout in the northeastern Arabian Gulf. J. Environ. Radioact. 192: 565–572. https://doi.org/10.1016/j.jenvrad.2018.05.010

  2. Abd El-Wahab, R.H., Al-Rashed, A.R. and Al-Dousari, A. (2018). Influences of physiographic factors, vegetation patterns and human impacts on aeolian landforms in arid environment. Arid Ecosyst. 8: 97–110. https://doi.org/10.1134/S2079096118020026

  3. Alam, K., Trautmann, T., Blaschke, T. and Subhan, F. (2014). Changes in aerosol optical properties due to dust storms in the Middle East and Southwest Asia. Remote Sens. Environ. 143: 216–227. https://doi.org/10.1016/j.rse.2013.12.021

  4. Al-Dousari, A.M. (2009). Section III: Recent studies on dust fallout within preserved and open areas in Kuwait. Desertification in arid lands: Causes, consequences and mitigation. KUWAIT. J. SCI. pp. 137–148.

  5. Al-Dousari, A., Doronzo, D. and Ahmed, M. (2017). Types, indications and impact evaluation of sand and dust storms trajectories in the Arabian Gulf. Sustainability 9: 1526. https://doi.org/10.3390/su9091526

  6. Al-Dousari, A.M., Ibrahim, M. I., Al-Dousari, N., Ahmed, M. and Al-Awadhi, S. (2018). Pollen in aeolian dust with relation to allergy and asthma in Kuwait. Aerobiologia 34: 325–336. https://doi.org/10.1007/s10453-018-9516-8

  7. Al-Dousari, A., Al-Nassar, W., Al-Hemoud, A., Alsaleh, A., Ramadan, A., Al-Dousari, N. and Ahmed, M. (2019). Solar and wind energy: Challenges and solutions in desert regions. Energy 176: 184–194. https://doi.org/10.1016/j.energy.2019.03.180

  8. Al-Hemoud, A., Al-Dousari, A., Misak, R., Al-Sudairawi, M., Naseeb, A., Al-Dashti, H. and Al-Dousari, N. (2019). Economic impact and risk assessment of sand and dust storms (SDS) on the oil and gas industry in Kuwait. Sustainability 11: 200. https://doi.org/10.3390/su11010200

  9. Al-Hemoud, A., Al-Dousari, A., Al-Dashti, H., Petrov, P., Alsaleh, A., Al-Khafaji, S., Behbehani, W., Li, J. and Koutrakis, P. (2020). Sand and dust storm trajectories from Iraq Mesopotamian flood plain to Kuwait. Sci. Total Environ. 710: 136291. https://doi.org/10.1016/j.scitotenv.2019.136291

  10. Almasi, A., Mousavi, A. R., Bakhshi, S. and Namdari, F. (2014). Dust storms and environmental health impacts. J. Middle East Appl. Sci. Technol. 8: 353–356. https://jmeast.webs.com/JMEAST-%20P001,%20Sup-issue%208,%202014.pdf

  11. Amarloei, A., Fazlzadeh, M., Jafari, A. J., Zarei, A. and Mazloomi, S. (2020). Particulate matters and bioaerosols during Middle East dust storms events in Ilam, Iran. Microchem. J. 152: 104280. https://doi.org/10.1016/j.microc.2019.104280

  12. Amiraslani, F. and Dragovich, D. (2011). Combating desertification in Iran over the last 50 years: An overview of changing approaches. J. Environ. Manage. 92: 1–13. https://doi.org/10.1016/j.jenvman.2010.08.012

  13. Bin Abdulwahed, A., Dash, J. and Roberts, G. (2019). An evaluation of satellite dust-detection algorithms in the Middle East region. Int. J. Remote Sens. 40: 1331–1356. https://doi.org/10.1080/01431161.2018.1524589

  14. Díaz, J., Linares, C., Carmona, R., Russo, A., Ortiz, C., Salvador, P. and Trigo, R.M. (2017). Saharan dust intrusions in Spain: Health impacts and associated synoptic conditions. Environ. Res. 156: 455–467. https://doi.org/10.1016/j.envres.2017.03.047

  15. Doronzo, D.M., Khalaf, E. A., Dellino, P., de Tullio, M. D., Dioguardi, F., Gurioli, L., Mele, D., Pascazio, G. and Sulpizio, R. (2015). Local impact of dust storms around a suburban building in arid and semi-arid regions: Numerical simulation examples from Dubai and Riyadh, Arabian Peninsula. Arab. J. Geosci. 8: 7359–7369. https://doi.org/10.1007/s12517-014-1730-2

  16. Doronzo, D.M., Al-Dousari, A., Folch, A. and Dagsson-Waldhauserova, P. (2016). Preface to the dust topical collection. Arab. J. Geosci. 9: 468. https://doi.org/10.1007/s12517-016-2504-9

  17. Doronzo, D.M. and Al-Dousari, A. (2019). Preface to dust events in the environment. Sustainability 11: 628. https://doi.org/10.3390/su11030628

  18. Draxler, R. and Hess, G.D. (1997). Description of the HYSPLIT_4 modeling system, NOAA Tech. Memo. ERL ARL-224: Maryland, MD, USA. https://www.arl.noaa.gov/wp_arl/wp-content/uploads/documents/reports/arl-224.pdf (accessed December 1, 2020).

  19. Draxler, R., Stunder, B., Rolph, G., Stein, A. and Taylor, A. (2020). HYSPLIT4 User’s Guide. http://www.arl.noaa.gov/documents/reports/hysplit_user_guide.pdf (accessed on 08.01.2020).

  20. Evan, A.T., Foltz, G.R., Zhang, D. and Vimont, D.J. (2011). Influence of African dust on ocean–atmosphere variability in the tropical Atlantic. Nat. Geosci. 4: 762–765. https://doi.org/10.1038/ngeo1276

  21. Fountoukis, C., Harshvardhan, H., Gladich, I., Ackermann, L. and Ayoub, M.A. (2020). Anatomy of a severe dust storm in the Middle East: Impacts on aerosol optical properties and radiation budget. Aerosol Air Qual. Res. 20: 155–165. https://doi.org/10.4209/aaqr.2019.04.0165

  22. Furman, H.K.H. (2003). Dust storms in the Middle East: Sources of origin and their temporal characteristics. Indoor Built Environ. 12: 419–426. https://doi.org/10.1177/1420326X03037110

  23. Gherboudj, I., Beegum, S.N. and Ghedira, H. (2017). Identifying natural dust source regions over the Middle-East and North-Africa: Estimation of dust emission potential. Earth Sci Rev. 165: 342–355. https://doi.org/10.1016/j.earscirev.2016.12.010

  24. Gillette, D.A., Blifford Jr, I.H. and Fryrear, D.W. (1974). The influence of wind velocity on the size distributions of aerosols generated by the wind erosion of soils. J. Geophys. Res. 79: 4068–4075. https://doi.org/10.1029/JC079i027p04068

  25. Gillette, D.A. and Walker, T.R. (1977). Characteristics of airborne particles produced by wind erosion of sandy soil, high plains of west Texas. Soil Sci. 123: 97–110.

  26. Givati, A. and Rosenfeld, D. (2007). Possible impacts of anthropogenic aerosols on water resources of the Jordan River and the Sea of Galilee. Water Resour. Res. 43: W10419. https://doi.org/10.1029/2006WR005771

  27. Gkikas, A., Obiso, V., Perez Garcia-Pando, C., Jorba, O., Hatzianastassiou, N., Vendrell, L., Basart, S., Solomos, S., Gassó, S. and Baldasano Recio, J.M. (2018). Direct radiative effects during intense Mediterranean desert dust outbreaks. Atmos. Chem. Phys. 18: 8757–8787. https://doi.org/10.5194/acp-18-8757-2018

  28. Goudie, A.S. (2009). Dust storms: Recent developments. J. Environ. Manage. 90: 89–94. https://doi.org/10.1016/j.jenvman.2008.07.007

  29. Goudie, A.S. (2014). Desert dust and human health disorders. Environ. Int. 63: 101–113. https://doi.org/10.1016/j.envint.2013.10.011

  30. Hamad, S.H., Schauer, J.J., Heo, J. and Kadhim, A.K. (2015). Source apportionment of PM2.5 carbonaceous aerosol in Baghdad, Iraq. Atmos. Res. 156: 80–90. https://doi.org/10.1016/j.atmosres.2014.12.017

  31. Hamidi, M., Kavianpour, M.R. and Shao, Y. (2013). Synoptic analysis of dust storms in the Middle East. Asia Pac. J. Atmos. Sci. 49: 279–286. https://doi.org/10.1007/s13143-013-0027-9

  32. Heo, J., Wu, B., Abdeen, Z., Qasrawi, R., Sarnat, J.A., Sharf, G., Shpund, K. and Schauer, J.J. (2017). Source apportionments of ambient fine particulate matter in Israeli, Jordanian, and Palestinian cities. Environ. Pollut. 225: 1–11. https://doi.org/10.1016/j.envpol.2017.01.081

  33. Herman, J.R., Bhartia, P.K., Torres, O., Hsu, C., Seftor, C. and Celarier, E. (1997). Global distribution of UV-absorbing aerosols from Nimbus 7/TOMS data. J. Geophys. Res. 102: 16911–16922. https://doi.org/10.1029/96JD03680

  34. Hoek, G., Krishnan, R.M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B. and Kaufman, J.D. (2013). Long-term air pollution exposure and cardio-respiratory mortality: A review. J. Environ. Health 12: 43. https://doi.org/10.1186/1476-069X-12-43

  35. Hussein, T., Al-Ruz, R. A., Petäjä, T., Junninen, H., Arafah, D. E., Hämeri, K. and Kulmala, M. (2011). Local air pollution versus short-range transported dust episodes: A comparative study for submicron particle number concentration. Aerosol Air Qual. Res. 11: 109–119. https://doi.org/10.4209/aaqr.2010.08.006

  36. Hussein, T., Alghamdi, M.A., Khoder, M., AbdelMaksoud, A.S., Al-Jeelani, H., Goknil, M.K., Shabbaj, I.I., Almehmadi, F.M., Hyvarinen, A., Lihavainen, H. and Hämeri, K. (2014). Particulate matter and number concentrations of particles larger than 0.25 µm in the urban atmosphere of Jeddah, Saudi Arabia. Aerosol Air Qual. Res. 14: 1383–1391. https://doi.org/10.4209/aaqr.2014.02.0027

  37. Hussein, T., Boor, B.E., dos Santos, V.N., Kangasluoma, J., Petäjä, T. and Lihavainen, H. (2017). Mobile aerosol measurement in the eastern Mediterranean – A Utilization of portable instruments. Aerosol Air Qual. Res. 17: 1875–1886. https://doi.org/10.4209/aaqr.2016.11.0479

  38. Hussein, T., Juwhari, H., Al Kuisi, M., Alkattan, H., Lahlouh, B. and Al-Hunaiti, A. (2018). Accumulation and coarse mode aerosol concentrations and carbonaceous contents in the urban background atmosphere in Amman, Jordan. Arab. J. Geosci. 11: 617. https://doi.org/10.1007/s12517-018-3970-z

  39. Hussein, T., Dada, L., Hakala, S., Petäjä, T. and Kulmala, M. (2019). Urban aerosol particle size characterization in eastern Mediterranean conditions. Atmosphere 10: 710. https://doi.org/10.3390/atmos10110710

  40. Karanasiou, A., Moreno, N., Moreno, T., Viana, M., De Leeuw, F. and Querol, X. (2012). Health effects from Sahara dust episodes in Europe: Literature review and research gaps. Environ. Int. 47: 107–114. https://doi.org/10.1016/j.envint.2012.06.012

  41. Kchih, H., Perrino, C. and Cherif, S. (2015). Investigation of desert dust contribution to source apportionment of PM10 and PM2.5 from a southern Mediterranean coast. Aerosol Air Qual. Res. 15: 454–464. https://doi.org/10.4209/aaqr.2014.10.0255

  42. Keramat, A., Marivani, B. and Samsami, M. (2011). Climatic change, drought and dust crisis in Iran. WASET J. 57: 10–13. https://doi.org/10.5281/zenodo.1058935

  43. Khaniabadi, Y. O., Daryanoosh, S. M., Amrane, A., Polosa, R., Hopke, P. K., Goudarzi, G., Mohammadi, M. J., Sicard, P. and Armin, H. (2017). Impact of Middle Eastern Dust storms on human health. Atmos. Pollut. Res. 8: 606–613. 

  44. Kok, J.F., Parteli, E.J., Michaels, T.I. and Karam, D.B. (2012). The physics of wind-blown sand and dust. Rep. Prog. Phys. 75: 106901. http://doi.org/10.1088/0034-4885/75/10/106901

  45. Léon, J.F. and Legrand, M. (2003). Mineral dust sources in the surroundings of the north Indian Ocean. Geophys. Res. Lett. 30: 1309. https://doi.org/10.1029/2002GL016690

  46. McTainsh, G. and Strong, C. (2007). The role of aeolian dust in ecosystems. Geomorphology 89: 39–54. https://doi.org/10.1016/j.geomorph.2006.07.028

  47. Menéndez, I., Diaz-Hernandez, J.L., Mangas, J., Alonso, I. and Sánchez-Soto, P.J. (2007). Airborne dust accumulation and soil development in the North-East sector of Gran Canaria (Canary Islands, Spain). J. Arid Environ. 71: 57–81. https://doi.org/10.1016/j.jaridenv.2007.03.011

  48. Middleton, N.J. (1986a). A geography of dust storms in South-west Asia. Int. J. Climatol. 6: 183–196. https://doi.org/10.1002/joc.3370060207

  49. Middleton, N.J. (1986b). Dust storms in the Middle East. J. Arid Environ. 10: 83–96. https://doi.org/10.1016/S0140-1963(18)31249-7

  50. Middleton, N.J. (2017). Desert dust hazards: A global review. Aeolian Res. 24: 53–63. https://doi.org/10.1016/j.aeolia.2016.12.001

  51. Miller, R.L., Cakmur, R.V., Perlwitz, J., Geogdzhayev, I.V., Ginoux, P., Koch, D., Prigent, C., Ruedy, R., Schmidt, G.A. and Tegen, I. (2006). Mineral dust aerosols in the NASA Goddard Institute for Space Sciences ModelE atmospheric general circulation model. J. Geophys. Res. 111: D06208. https://doi.org/10.1029/2005JD005796

  52. Munir, S., Habeebullah, T.M., Mohammed, A.M., Morsy, E.A., Rehan, M. and Ali, K. (2016). Analysing PM2.5 and its association with PM10 and meteorology in the arid climate of Makkah, Saudi Arabia. Aerosol Air Qual. Res. 17: 453–464. https://doi.org/10.4209/aaqr.2016.03.0117

  53. Nabavi, S.O., Haimberger, L. and Samimi, C. (2016). Climatology of dust distribution over West Asia from homogenized remote sensing data. Aeolian Res. 21: 93–107. https://doi.org/10.1016/j.aeolia.2016.04.002

  54. Naimabadi, A., Ghadiri, A., Idani, E., Babaei, A.A., Alavi, N., Shirmardi, M., Khodadadi, A., Marzouni, M.B., Ankali, K.A., Rouhizadeh, A. and Goudarzi, G. (2016). Chemical composition of PM10 and its in vitro toxicological impacts on lung cells during the Middle Eastern Dust (MED) storms in Ahvaz, Iran. Environ. Pollut. 211: 316–324. https://doi.org/10.1016/j.envpol.2016.01.006

  55. Natsagdorj, L., Jugder, D. and Chung, Y. S. (2003). Analysis of dust storms observed in Mongolia during 1937–1999. Atmos. Environ. 37: 1401–1411. https://doi.org/10.1016/S1352-2310(02)01023-3

  56. Notaro, M., Yu, Y. and Kalashnikova, O.V. (2015). Regime shift in Arabian dust activity, triggered by persistent Fertile Crescent drought. J. Geophys. Res. 120: 10–229. https://doi.org/10.1002/2015JD023855

  57. Pope III, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Ito, K. and Thurston, G.D. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 287: 1132–1141. http://doi.org/10.1001/jama.287.9.1132

  58. Pope III, C.A. and Dockery, D.W. (2006). Health effects of fine particulate air pollution: Lines that connect. J. Air. Waste. Manage. Assoc. 56: 709–742. https://doi.org/10.1080/10473289.2006.10464485

  59. Prospero, J.M., Ginoux, P., Torres, O., Nicholson, S.E. and Gill, T.E. (2002). Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev. Geophys. 40: 1002. https://doi.org/10.1029/2000RG000095

  60. Querol, X., Alastuey, A., Rodrıguez, S., Viana, M.M., Artınano, B., Salvador, P., Mantilla, E., Garcia do Santos, S., Fernandez Patier, R., De La Rosa, J., Sanchez de la Campa, A., Menéndez, M. and Gil, J.J. (2004). Levels of particulate matter in rural, urban and industrial sites in Spain. Sci. Total Environ. 334: 359–376. https://doi.org/10.1016/j.scitotenv.2004.04.036

  61. Querol, X., Tobías, A., Pérez, N., Karanasiou, A., Amato, F., Stafoggia, M., Pérez García-Pando, C., Ginoux, P., Forastiere, F., Gumy, S., Mudu, P. and Alastuey, A. (2019). Monitoring the impact of desert dust outbreaks for air quality for health studies. Environ. Int. 130: 104867. https://doi.org/10.1016/j.envint.2019.05.061

  62. Rashki, A., Kaskaoutis, D.G., Francois, P., Kosmopoulos, P.G. and Legrand, M. (2015). Dust-storm dynamics over Sistan region, Iran: Seasonality, transport characteristics and affected areas. Aeolian Res. 16: 35–48. https://doi.org/10.1016/j.aeolia.2014.10.003

  63. Rezazadeh, M., Irannejad, P. and Shao, Y. (2013). Climatology of the Middle East dust events. Aeolian Res. 10: 103–109. https://doi.org/10.1016/j.aeolia.2013.04.001

  64. Saeifar, M.H. and Alijani, B. (2019). Detection of dust storm origins in the middle east by remotely sensed data. J. Indian Soc. Remote Sens. 47: 1883–1893. https://doi.org/10.1007/s12524-019-01030-5

  65. Satheesh, S.K., Deepshikha, S. and Srinivasan, J. (2006). Impact of dust aerosols on Earth–atmosphere clear‐sky albedo and its short wave radiative forcing over African and Arabian regions. Int. J. Remote Sens. 27: 1691–1706. https://doi.org/10.1080/01431160500462162

  66. Shao, Y., Raupach, M.R. and Findlater, P.A. (1993). Effect of saltation bombardment on the entrainment of dust by wind. J. Geophys. Res. 98: 12719–12726. https://doi.org/10.1029/93JD00396

  67. Singer, A., Ganor, E., Dultz, S. and Fischer, W. (2003). Dust deposition over the Dead Sea. J. Arid Environ. 53: 41–59. https://doi.org/10.1006/jare.2002.1023

  68. Small, I., Van der Meer, J. and Upshur, R.E. (2001). Acting on an environmental health disaster: The case of the Aral Sea. Environ. Health Perspect. 109: 547–549. https://doi.org/10.1289/ehp.01109547

  69. Solomos, S., Kalivitis, N., Mihalopoulos, N., Amiridis, V., Kouvarakis, G., Gkikas, A., Binietoglou, I., Tsekeri, A., Kazadzis, S., Kottas, M., Pradhan, Y., Proestakis, E., Nastos, P.T. and Marenco, F. (2018). From tropospheric folding to Khamsin and Foehn winds: How atmospheric dynamics advanced a record-breaking dust episode in Crete. Atmosphere 9: 240. https://doi.org/10.3390/atmos9070240

  70. Stein, A.F., Draxler, R.R., Rolph, G.D., Stunder, B.J., Cohen, M.D. and Ngan, F. (2015). NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96: 2059–2077. https://doi.org/10.1175/BAMS-D-14-00110.1

  71. Taheri, A., Aliasghari, P. and Hosseini, V. (2019). Black carbon and PM2.5 monitoring campaign on the roadside and residential urban background sites in the city of Tehran. Atmos. Environ. 218: 116928. https://doi.org/10.1016/j.atmosenv.2019.116928

  72. Thomas, D.S., Knight, M. and Wiggs, G.F. (2005). Remobilization of southern African desert dune systems by twenty-first century global warming. Nature 435: 1218–1221. https://doi.org/10.1038/nature03717

  73. Torres, O., Bhartia, P.K., Herman, J.R., Ahmad, Z. and Gleason, J. (1998). Derivation of aerosol properties from satellite measurements of backscattered ultraviolet radiation: Theoretical basis. J. Geophys. Res. 103: 17099–17110. https://doi.org/10.1029/98JD00900

  74. World Health Organization (WHO) (2018). Global Ambient Air Quality Database (update 2018, accessed on March 20, 2020, https://www.who.int/airpollution/data/cities/en/). Ambient (outdoor) air quality database 2018 by country and city (https://www.who.int/airpollution/data/aap_air_uality_database_2018_v14.xlsx?ua=1,ap_air_quality_database_2018_v14.xlsx)

  75. Zender, C.S., Bian, H. and Newman, D. (2003). Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology. J. Geophys. Res. 08: 4416. https://doi.org/10.1029/2002JD002775

  76. Zoljoodi, M., Didevarasl, A. and Saadatabadi AR. (2013). Dust events in the western parts of Iran and the relationship with drought expansion over the dust-source areas in Iraq and Syria. Atmos. Clim. Sci. 3: 321–336. https://doi.org/10.4236/acs.2013.33034

Don't forget to share this article 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.