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. 2019 Dec 10;17(2):1107–1120. doi: 10.1007/s40201-019-00425-3

Investigation of aerosols pollution across the eastern basin of Urmia lake using satellite remote sensing data and HYSPLIT model

Shokufeh Delfi 1, Mohammad Mosaferi 2,3,✉, Mohammad Sadegh Hassanvand 4, Shahram Maleki 5
PMCID: PMC6985422  PMID: 32030178

Abstract

Background

Desiccation of the world’s second hypersaline lake, Lake Urmia, has drawn attention toward the feasibility of particle emissions from the lakebed to downwind regions. Therefore, this research was conducted to study spatiotemporal variations of aerosols across the eastern basin of the lake during 2001 to 2016.

Methods

The Aerosol Optical Depth (AOD) products of Moderate-resolution Imaging Spectroradiometer (MODIS), extracted from Terra platform for 999 rural and urban areas and compared over adjacent (Shabestar, Osku, Azarshahr, Ajabshir, Bonab, Malekan), middle (Tabriz and Maragheh) and far (Bostanabad, Heris, and Sarab) counties. Number of dusty days and direction of high wind speeds (≥ 11 m/s) were acquired from the East Azerbaijan Meteorological Organization and analyzed. Moreover, performing the backward trajectory model, the origin and distribution of aerosols were determined at altitudes of 500, 1000 and 2000 m.

Results

The spatiotemporal variations of AOD provided statistically significant correlations (R2 ≥ 0.5 and p < 0.05) against the number of dusty days. AOD value was higher between 2009 and 2016 and estimated to be 0.36, 0.33 and 0.31 over adjacent, middle and far areas, respectively. Analysis of wind direction and trajectory plots implied that the particulate matter (PM) of study area was mainly transported from Iraq and Syria, especially in April, May and June months.

Conclusions

PM has followed an increasing trend, while the adjacent areas have experienced higher pollution compared to far counties. The southwestern winds can play an important role in transportation of aerosols from either lakebed or western countries to the study area.

Keywords: Saline lake, AOD, Dusty days, MODIS, Particulate matter

Introduction

Atmospheric PM arising from either natural or anthropogenic sources has been defined as one of the important concerns worldwide since they can bring about health problems [1], influence solar radiation [2] and lead to climate change [3].

PM along with other atmospheric pollutants are monitored by ground-based networks throughout the world, conventionally [4]. Despite the high temporally resolution of networks, sparse distribution and lack of stations in small towns or rural areas hinder them from providing wide spatial coverage of atmospheric pollution, particularly in developing countries [4, 5]. Fortunately, the development of remote sensing techniques during past decades has provided valuable opportunities for achieving comprehensive measurements of air pollutants from regional to global scales [5]. Relevantly, AOD is a measure of the scattering and absorption of sunlight by aerosols in the vertical atmospheric column. This parameter greatly represents the PM air quality over the surface of earth [6, 7]. Among the other satellite detectors such as Multi-angle Imaging Spectroradiometer (MISR) or Ozone Monitoring Instrument (OMI), MODIS is the most widely used instrument for studying AOD variations due to its proper resolution and helpful spectral bandwidth [8, 9]. Using satellite-derived retrievals in conjunction with ground- level measurements, back trajectory models [10] and geographical information system (GIS) [11], has successfully provided the opportunity of the study on aerosols variations and their properties particularly during atmospheric pollutions such as biomass burning events [12] or dust episodes [13, 14].

Saline dust storms as a special kind of dust storm, transport high concentrations of saline, alkaline and other potentially toxic PM from the dried lake bed to nearby regions [15] which in turn threaten the ecology security and public health [15]. This phenomenon has been observed in many arid and semi-arid regions; it is stated that nearly 1 × 106 and 4.6× 106 tons of saline particles are deflated annually from the Aral Sea in Kazakhstan and Uzbekistan and Ebi Nur Lake in northwestern China, respectively [15]. Similarly, the release of particles from the dried bed of Owens Lake in California has led to the increase in PM10 (particulate matter with aerodynamic diameter ≤ 10 μm) levels and caused different respiratory problems [16].

Urmia Lake (UL) is the second hypersaline Lake in the world and the largest inland wetland in the northwest of Iran. However, the lake Level has decreased about 6 m during past decades and 90% of its surface has diminished owing to climatic changes, dam construction and increase in agricultural activities, etc. [17, 18]. About 9.2% decline in mean precipitation has been reported over the years of 1964–2005 [19], while the annual trend line slope of temperature has changed from 0.02 to 0.14 °C per year in lake basin during past four decades until 2007 [20]. Drying progression is supposed to convert UL into a vast salt desert overlaid with 60 cm thick salt deposits which would pave the way for saline dust storms, influencing downwind areas as far as 300 km [21]. The previous measurements of PM in the vicinity of UL revealed that saline aerosols and crustal particles overall account for about 60% of PM10, which is estimated to be about 9 times higher than the World Health Organization (WHO) annual guideline value (20 μg/m3) [22, 23]. According to the previous study, the soil salinization is not only limited to the UL bed, but it has also extended to nearby farmlands [24]. Employing daily MODIS-derived AOD over northwestern Iran, Mardi et al. pointed out that the combination of salt and salty soil bodies have increased by two orders of magnitude around the UL during past decades [25]. Despite the valuable findings of previous researches, to the best of our knowledge, PM pollution and its dispersion pattern have not been particularly investigated over the eastern basin of UL. Thus, the present study was conducted to compare 16-year period (2001–2016) spatiotemporal distribution of PM over 11 counties in the eastern basin of UL, benefiting from MODIS AOD retrievals. Meanwhile, the origin and dispersion of aerosols were investigated through backward trajectories and some meteorological parameters.

Methods

Study area

UL is situated between East Azarbaijan and West Azarbaijan provinces, occupying an area of 5000 km2 (Fig. 1). The eastern basin of the lake is located in 45.7325 °E, 38.0045 °N , involving 11 counties with about 999 residential areas. To have a better comparison between affected areas, we categorized the counties into three groups based on their distance from the lake bed, as follows (Fig. 1):

  1. Adjacent counties: Shabestar, Azarshahr, Osku, Ajabsheer, Bonab, and Malekan

  2. Middle counties: Tabriz and Maragheh

  3. Far counties: Bostanabad, Heris, and Sarab

Fig. 1.

Fig. 1

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Study area

Data acquisition and analysis

AOD

The information on global coverage of AOD retrievals at 10 km spatial resolution for every 1–2 days were provided by MODIS sensors aboard Terra and Aqua satellites of U. S National Aeronautics and Space Administration (NASA). The local time of equatorial crossing for Terra (launched on December 18, 1999) and Aqua (launched on May 4, 2002) is 10:30 A.M. and 1:30 P.M., respectively [26].These satellites have 36 spectral channels with the capability to provide extensive datasets about oceanic, land and atmospheric conditions [6]. The uncertainties of MODIS measurements were estimated to be about ± 0.05± 0.15 (AOD) and 0.03 ± 0.05 (AOD) over land and ocean, respectively [26]. AOD values ranging from 0.1 to 1 are freely available on NASA website. Herein, the monthly global AOD retrievals from Terra were acquired from level 2 MODIS (MOD04) at 0.55 μw wavelength over the period of January 2001 to December 2016 [27]. Satellite imageries were downloaded in type of Geo Tiff raster with 0.1 degrees and were introduced in GIS program. Thereafter, annual mean AOD values were calculated for all intended rural and urban locations and were averaged within each county. To get valid AOD values, the unmeasured areas by MODIS which were appeared as black pixels were omitted from raster layers before AOD extraction in the GIS program.

Meteorological data

The datasets of high wind speed (≥11 m/s) and the number of dusty days were obtained from East Azerbaijan Meteorological Organization based on the observations of 9 weather stations, operating in Ajabsheer, Bonab, Maragheh, Bostanabad, Tabriz, Malekan, Heris, Shabestar and, Sarab cities. The correlation between the frequency of dusty days and AOD values was confirmed using the Pearson correlation test.

Origin and dispersion patterns of aerosols across eastern basin of UL

Origin and dispersion of PM investigated by Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model developed by the air resources laboratory of National Oceanic and Atmospheric Administration (NOAA). HYSPLIT model is a complete system for computing simple air parcel trajectories to complex dispersion and deposition [28]. The model calculation method is a combination between the Lagrangian and the Eulerian methodology; Lagrangian approach applies a moving frame of reference for the advection and diffusion calculations as the trajectories move from their initial location, while the Eulerian methodology employs a fixed three- dimensional grid as a frame of reference to compute pollutant air concentrations [28]. In this study, simple parcel trajectories were performed using the web-based real-time system [29] for the years of 2009–2016, during the period in which the lake level has been reported to decrease rapidly from 1271.80 to 1270.36 m [30]. However, due to a large number of trajectory outputs, the plots retrieved during 2016 were used as an example and presented in Figs. 7, 8, 9 and 10. Trajectories of air masses were calculated for 48 h at three altitudes of 500, 1000 and 2000 m by applying global data assimilation system (GDAS 1 degree) input data. Backward trajectories began from weather stations, Osku and Azarshahr cities for the April to August months, during the period in which dust events commonly occur [31].

Fig. 7.

Fig. 7

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Monthly backward trajectories of aerosols for Osku (1), Ajabshir (2) and Malekan (3)

Fig. 8.

Fig. 8

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Monthly backward trajectories of aerosols for Shabestar (1), Azarshahr (2) and Bonab (3)

Fig. 9.

Fig. 9

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Monthly backward trajectories of aerosols for Tabriz (1) and Maragheh (2)

Fig. 10.

Fig. 10

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Monthly backward trajectories of aerosols for Heris (1), Bostanabad (2) and Sarab (3)?

Results and discussion

Spatial and temporal analysis

The median values of AOD found to be 0.33, 0.30 and 0.31 over adjacent, middle and far areas, respectively (Fig. 2). The adjacent areas have included a wide range of AOD values rather than middle or far counties. However, the concentration of aerosols in adjacent and middle areas are higher than in far areas. Overall, the AOD rate of adjacent, middle and far counties varied between 0.18 to 0.72 (Fig. 3a), 0.18 to 0.61 (Fig. 3b) and 0.17 to 0.55 (Fig. 3c), respectively.

Fig. 2.

Fig. 2

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Box plots of annual mean AOD concentrations over the study area

Fig. 3.

Fig. 3

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Temporal trends of annual mean of aerosols over adjacent (a) Middle (b) and Far (c) areas

Fig. 3a indicates that the mean annual AOD in Osku, Azarshahr, Malekan, Shabestar, Ajabshir and Bonab has started from 0.20, 0.22, 0.23, 0.25, 0.25 and 0.27 in 2001, and reached the peak rates of 0.41, 0.46, 0.43, 0.40, 0.5 and 0.49 in 2003, respectively. Nevertheless, the average value of AOD decreased to 0.2, 0.2, 0.4, 0.3, 0.3 and 0.4 during 2004 to 2008 and reached the highest level of 0.5, 0.51, 0.72, 0.66, 0.61 and 0.52 between 2009 and 2012, and then decreased to 0.26, 0.30, 0.33, 0.35, 0.41 and 0.4 by 2016, respectively. According to Fig. 3b, AOD of Maragheh has started to increase since 2001, reaching the maximum level of 0.6 in 2012 and decreased to 0.38 in 2016. while, despite the significant peak rates of 0.42 observed in 2003 and 2009, the variations of PM were relatively steady in Tabriz. Figure 3c shows that the AOD values of Sarab, Heris and Bostanabad have increased from 0.25, 0.26 and 0.18 in 2001, to 0.41, 0.44 and 0.34 in 2003, peaked at 0.55, 0.49 and 0.41 between the years 2009–2012 and finally decreased to 0.37, 0.33 and 0.18 by 2016, respectively.

As can be concluded from Fig. 3, the AOD has peaked from 0.28, 0.24 and 0.24 in 2002 to 0.45, 0.42 and 0.40 in 2003 over adjacent, middle and far areas, respectively. Mardi et al. has confirmed this peak rate of AOD and found it changing from 0.26 in 2002 to 0.35 in 2003 over northwestern Iran due to local windblown dust aerosols during April and July of 2003 [25]. The minor differences of AOD values between present study and previous research may result from some differences in processing methods of satellite imageries in GIS. The rise in aerosols amount over the period of 2009–2012 (Fig. 3), can be attributed to the both high concentration of black carbon aerosols during biomass burning activities [25] and occurrence of dust events during 3–8 July in 2009 [32], 19 March in 2012 [33] and 22–25 May in 2012 [34]. Whereas, the decreasing trend in AOD rates from 2012 to 2016 (Fig. 3) was also observed by Namdari.et al. who attributed it to the reduction in dust events and meteorological variability [35]. Actually, the increase of temperature and decreasing of soil moisture in the Middle East [31], have led to increasing of aerosol concentration over Iran during 1979–2016 [36]. The increase of aerosols loading from both anthropogenic and natural sources in the country has caused 3% decline in solar radiation per decade since 2000 [37]. Furthermore, previous study revealed that AOD values have statistically increased in each February during 2009 to 2015 over northwestern Iran [25], while the similar event has been reported in southwestern United States, where the beginning of the spring season shifted to be 1 or 2 weeks earlier over a 20-year period since 1995 [38].

According to Fig. 4a, the dustiest days have occurred from 2009 to 2012 over adjacent areas. Figure 4b. indicates more than 50 dusty days during 2008–2012 and the number of days reached up to 70 in 2009 and 2012 across Tabriz and Maragheh, respectively. Frequency of dusty days in Heris and Sarab was comparatively low and varied between 1 to 28 (Fig. 4c). Satellite-derived AOD showed statistically significant relation (p< 0.05) against the number of dusty days and the correlation coefficients were provided as 0.5,0.6, 0.6, 0.7, 0.8 and 0.9 over Tabriz, Maragheh, Bonab, Sarab, Malekan and, Heris, respectively.

Fig. 4.

Fig. 4

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Number of dusty days over adjacent (a) Middle (b) and Far (c) areas

Topography and dust dispersion

The spatial variation of aerosols revealed that the adjacent areas experienced high AOD values compared to middle or far counties. Moreover, the recent studies in the UL basin found inverse relationships between topsoil salinity and distance from lake bed [39] and between PM flow and height of soil surface [40]. Overall, these results imply that the topographical altitudes can affect the dispersion of aerosols. Therefore, the topographical height of study counties and its relation with PM emission have been discussed in this section.

As seen in Fig. 5, the western regions of the basin include flat and plain lands, while moving to the eastern sides, the altitude gets higher as reaching the hillsides of Sahand Mountains. The Mishoo Mountain has surrounded the northern part of Shabestar, where both western winds and the humidity of the lake affect the region. Bonab, the closest area, is totally under the influence of the lake. Malekan as the low-altitude area is mainly under the impact of winds blowing from Siberia, the Atlantic Ocean, the Black Sea, the Mediterranean Sea, and the Caspian Sea. Despite the northern, southern and eastern parts of Tabriz which are surrounded by Sahand Mountains, western regions are covered with salty plains (Fig. 5). The Sahand Highlands have covered the northern part of Maragheh but the central and southern areas include plain lands (Fig. 5). Compared to the adjacent and middle areas, Sarab, Bostanabad and, Heris with average elevations of about 1650, 1790 and 1825 m are the highest areas, respectively. The northern parts of Heris and Sarab are surrounded by Aghdagh highlands, while Bostanabad, as a mountainous region, is surrounded by Bozghush Mountains in the north and Morodagh in the west (Fig. 5).

Fig. 5.

Fig. 5

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Topographic altitudes in the eastern basin of Urmia lake

Indeed, Sahand Mountain with 3707 m height can act as a natural barrier and prevent dispersion of low-altitude aerosols to eastern sides of the basin. In particular, the findings of the most recent study on dust samples in Tabriz is a piece of good evidence for this fact, which revealed that anthropogenic sources have more contribution to dust emissions rather than natural sources such as UL bed [41]. The negative relationship between topography altitude and spatial distribution of aerosols has been confirmed by Farajzadeh et al. who found maximum concentrations of aerosols in the southwest and east regions of Iran and showed low rates of AOD in high mountainous areas of Zagros and northwest areas [42]. Another study conducted in central and southwest Asia indicated considerable annual and seasonal variety of dust frequency between the stations owing to differences in topographic features and dust-plumes pathways [43]. Similarly, Zhang et al. observed high amount of aerosols in plain lands of northeastern and southwestern China, while they reported low AOD values over highland areas such as Tibetan Plateau [44].

Wind analysis and trajectory of air masses over the study area

As can be seen in Fig. 6, the most prevailing winds blew from southwest and south, respectively. Southwestern winds are more likely to influence the most number of nearby areas around the lake through salt particle dispersion [45]. However, in Ajabshir, Bostanabad, Heris, and Shabestar, the wind direction is from the west. Figure 6 shows that the mean wind speed ranged between 10.5 to 14.5 m/s across adjacent areas and getting higher over the middle and eastern parts, reaching the maximum level of 19 m/s over Bostanabad. The fine PM can be released under common wind conditions, while coarse particles, making up more than 70% of dust grains, can be transported by winds speed of ≥15 m/s to several thousand kilometers [15]. Due to a recent study conducted in UL basin, the wind speed considered as the main factor of dust occurrence when soil moisture is low, meanwhile the high wind speeds are so determinant for transportation of sandy particles [46].

Fig. 6.

Fig. 6

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Distribution of winds speed and direction over the eastern basin of Urmia lake (2001–2016)

According to the trajectories outputs (Figs. 7, 8, 9 and 10), Saudi Arabia, Iraq, Syria, the Caspian Sea, and Turkey determined as dominant origins of dust aerosols. In general, PM has followed a similar dispersion pattern during specific months; the dust aerosols originated from Iraq and Syria were often transported during April, May, and June, respectively. While in July and August they were mainly originated from the Caspian Sea or central parts of the country. The comparison between PM transportation at different heights showed that the air masses at altitudes of 500 and 1000 m were mostly transported over the northern parts of Iran whereas the PM of higher altitudes (2000 m) originated from western countries, particularly Iraq and Syria. The biomass burning activities in northern countries such as Ukraine, Russia, Kazakhstan, and the Republic of Azerbaijan [47] can express the emission of dust plumes from the Caspian sea [43], during July and August months in trajectory outputs. On the other hand, the increase of frequency and intensity of Middle Eastern dust storms [48], has considerably affected the western and central parts of Iran during past decades [49]. Besides the Sahara Desert, which releases about 700 million tons of dust into the atmosphere annually, the desert areas in Iraq, Syria, and the northern parts of the Arabian Peninsula play a crucial role in dust transportation to Iran [50]. Furthermore, the progress of desertification in the UL basin is supposed to make another source of saline dust storms in the northwest of Iran [24] exacerbating the windblown PM pollution originated from western countries. Relevantly, Arhami et al. predicted that the release of UL bed particles could cause 30 to 60% increase in PM10 levels over nearby cities during dust episodes [51].

Conclusion

Disastrous shrinkage of UL over the past decades is supposed to cause a source of saline dust storms in northwestern Iran. In this regard, this study carried out to investigate and compare PM pollution across the adjacent (Shabestar, Azarshahr, Ajabshir, Osku, Bonab, Malekan), middle (Tabriz and Maragheh) and far (Sarab, Bostanabad, Heris) areas in the eastern basin of the lake between the years 2001–2016.

According to the MODIS AOD retrievals, adjacent counties are much prone to PM rather than farther areas and this is presumably attributed to topographical features; meaning that the western parts of study region are covered with plain lands, whereas, the central and eastern areas are surrounded by the mountain ranges influencing the distribution of aerosols. Compared to the first half of the study period, the mean AOD during 2009–2016 has increased by 25%, 24.14% and 21.43% over adjacent, middle and far counties, respectively. The variations of AOD values were in good agreement with the number of dusty days, recorded by weather stations, and provided statistically significant correlations (p< 0.05) through the Pearson Correlation test. Studying the path of air masses along with wind analysis implies that the intensive winds blowing from southwest can play an important role in transportation of PM originating from either western countries or UL bed particularly during April, May and June months.

Regardless of the anthropogenic sources, the present study aimed at investigating the spatiotemporal trend of aerosols over the eastern counties of UL. However, studying of synergistic interactions between industrial activities and natural sources of PM can be considered as another topic for the next research in this region. Although the results of the study showed an increasing trend in PM, there is no certain evidence for blaming UL since the dust of western countries also affects the area. Thus, it is recommended to compare AOD rates of lake basin between the years with low (such as 2008–2009) and high Precipitations (such as2018–2019).

Acknowledgements

This research was funded by Vice Chancellor for Research of Tabriz University of Medical Sciences, Tabriz, Iran [grant number: 96-6-7-510696]. The authors would like to appreciate the NASA research center and NOAA Air resources laboratory for providing MODIS satellite data and HYSPLT model, respectively. we also acknowledge East Azerbaijan Meteorological Organization for provision of required meteorological datasets.

Conflict of interest

The authors declare that they have no Conflict of Interest.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Shokufeh Delfi, Email: delfishokufeh@yahoo.com.

Mohammad Mosaferi, Email: mosaferim@tbzmed.ac.ir.

Mohammad Sadegh Hassanvand, Email: hassanvand@sina.tums.ac.ir.

Shahram Maleki, Email: malekishahram59@yahoo.com.

References

  • 1.Beelen R, Raaschou-Nielsen O, Stafoggia M, Andersen ZJ, Weinmayr G, Hoffmann B, Wolf K, Samoli E, Fischer P, Nieuwenhuijsen M, Vineis P, Xun WW, Katsouyanni K, Dimakopoulou K, Oudin A, Forsberg B, Modig L, Havulinna AS, Lanki T, Turunen A, Oftedal B, Nystad W, Nafstad P, de Faire U, Pedersen NL, Östenson CG, Fratiglioni L, Penell J, Korek M, Pershagen G, Eriksen KT, Overvad K, Ellermann T, Eeftens M, Peeters PH, Meliefste K, Wang M, Bueno-de-Mesquita B, Sugiri D, Krämer U, Heinrich J, de Hoogh K, Key T, Peters A, Hampel R, Concin H, Nagel G, Ineichen A, Schaffner E, Probst-Hensch N, Künzli N, Schindler C, Schikowski T, Adam M, Phuleria H, Vilier A, Clavel-Chapelon F, Declercq C, Grioni S, Krogh V, Tsai MY, Ricceri F, Sacerdote C, Galassi C, Migliore E, Ranzi A, Cesaroni G, Badaloni C, Forastiere F, Tamayo I, Amiano P, Dorronsoro M, Katsoulis M, Trichopoulou A, Brunekreef B, Hoek G. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet. 2014;383(9919):785–795. doi: 10.1016/S0140-6736(13)62158-3. [DOI] [PubMed] [Google Scholar]
  • 2.Alam K, Qureshi S, Blaschke T. Monitoring spatio-temporal aerosol patterns over Pakistan based on MODIS, TOMS and MISR satellite data and a HYSPLIT model. Atmos Environ. 2011;45(27):4641–4651. doi: 10.1016/j.atmosenv.2011.05.055. [DOI] [Google Scholar]
  • 3.Rosenfeld D, Sherwood S, Wood R, Donner L. Climate effects of aerosol-cloud interactions. Science. 2014;343(6169):379–380. doi: 10.1126/science.1247490. [DOI] [PubMed] [Google Scholar]
  • 4.Sohrabinia M, Khorshiddoust AM. Application of satellite data and GIS in studying air pollutants in Tehran. Habitat Int. 2007;31(2):268–275. doi: 10.1016/j.habitatint.2007.02.003. [DOI] [Google Scholar]
  • 5.Kloog I, Koutrakis P, Coull BA, Lee HJ, Schwartz J. Assessing temporally and spatially resolved PM2. 5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmos Environ. 2011;45(35):6267–6275. doi: 10.1016/j.atmosenv.2011.08.066. [DOI] [Google Scholar]
  • 6.Gupta P, Christopher SA, Wang J, Gehrig R, Lee Y, Kumar N. Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmos Environ. 2006;40(30):5880–5892. doi: 10.1016/j.atmosenv.2006.03.016. [DOI] [Google Scholar]
  • 7.Guo Y, Feng N, Christopher SA, Kang P, Zhan FB, Hong S. Satellite remote sensing of fine particulate matter (PM2. 5) air quality over Beijing using MODIS. Int J Remote Sens. 2014;35(17):6522–6544. doi: 10.1080/01431161.2014.958245. [DOI] [Google Scholar]
  • 8.Kaskaoutis D, Kalapureddy M, Krishna Moorthy K, Devara P, Nastos P, Kosmopoulos P, et al. Heterogeneity in pre-monsoon aerosol types over the Arabian Sea deduced from ship-borne measurements of spectral AODs. Atmos Chem Phys. 2010;10(10):4893–4908. doi: 10.5194/acp-10-4893-2010. [DOI] [Google Scholar]
  • 9.Bullard J, Baddock M, McTainsh G, Leys J. Sub-basin scale dust source geomorphology detected using MODIS. Geophys Res Lett. 2008;35:L15404. 10.1029/2008GL033928.
  • 10.Engel-Cox JA, Hoff RM, Haymet A. Recommendations on the use of satellite remote-sensing data for urban air quality. J Air Waste Manage Assoc. 2004;54(11):1360–1371. doi: 10.1080/10473289.2004.10471005. [DOI] [PubMed] [Google Scholar]
  • 11.Gulliver J, Briggs D. STEMS-Air: a simple GIS-based air pollution dispersion model for city-wide exposure assessment. Sci Total Environ. 2011;409(12):2419–2429. doi: 10.1016/j.scitotenv.2011.03.004. [DOI] [PubMed] [Google Scholar]
  • 12.Van Donkelaar A, Martin RV, Levy RC, da Silva AM, Krzyzanowski M, Chubarova NE, et al. Satellite-based estimates of ground-level fine particulate matter during extreme events: a case study of the Moscow fires in 2010. Atmos Environ. 2011;45(34):6225–6232. doi: 10.1016/j.atmosenv.2011.07.068. [DOI] [Google Scholar]
  • 13.Rashki A, Kaskaoutis DG, Eriksson PG. Rautenbach CdW, Flamant C, Vishkaee FA. Spatio-temporal variability of dust aerosols over the Sistan region in Iran based on satellite observations. Nat Hazards. 2014;71(1):563–585. doi: 10.1007/s11069-013-0927-0. [DOI] [Google Scholar]
  • 14.Sinyuk A, Torres O, Dubovik O. Combined use of satellite and surface observations to infer the imaginary part of refractive index of Saharan dust. Geophys Res Lett. 2003;30:1081. 10.1029/2002GL016189.
  • 15.Abuduwaili J, Liu D, Wu G. Saline dust storms and their ecological impacts in arid regions. J Arid Land. 2010;2(2):144–150. doi: 10.3724/SP.J.1227.2010.00144. [DOI] [Google Scholar]
  • 16.Wurtsbaugh WA, Miller C, Null SE, DeRose RJ, Wilcock P, Hahnenberger M, et al. Decline of the world's saline lakes. Nat Geosci. 2017;10(11):816. doi: 10.1038/ngeo3052. [DOI] [Google Scholar]
  • 17.Abbaspour M, Javid AH, Mirbagheri SA, Ahmadi Givi F, Moghimi P. Investigation of lake drying attributed to climate change. Int J Environ Sci Technol. 2012;9(2):257–266. doi: 10.1007/s13762-012-0031-0. [DOI] [Google Scholar]
  • 18.Ghalibaf MB, Moussavi Z. Development and environment in Urmia lake of Iran. Eur J Sustain Dev. 2014;3(3):219–226. doi: 10.14207/ejsd.2014.v3n3p219. [DOI] [Google Scholar]
  • 19.Delju A, Ceylan A, Piguet E, Rebetez M. Observed climate variability and change in Urmia Lake Basin, Iran. Theor Appl Climatol. 2013;111(1–2):285–296. doi: 10.1007/s00704-012-0651-9. [DOI] [Google Scholar]
  • 20.Fathian F, Morid S, Kahya E. Identification of trends in hydrological and climatic variables in Urmia Lake basin. Iran Theor Appl Climatol. 2015;119(3–4):443–464. doi: 10.1007/s00704-014-1120-4. [DOI] [Google Scholar]
  • 21.Hoseinpour M, Fakheri Fard A, Naghili R, editors. Death of Urmia Lake, a silent disaster investigating causes, results and solutions of Urmia Lake drying. 1st International Applied Geological Congress, Department of Geology, Islamic Azad University, Islamic Azad University-Mashad Branch, Iran; 2010.
  • 22.Gholampour A, Nabizadeh R, Hassanvand MS, Nazmara S, Mahvi AH. Elemental composition of particulate matters around Urmia Lake, Iran. Toxicol Environ Chem. 2017;99(1):17–31. 10.1080/02772248.2016.1166226.
  • 23.Gholampour A, Nabizadeh R, Hassanvand MS, Taghipour H, Nazmara S, Mahvi AH. Characterization of saline dust emission resulted from Urmia Lake drying. J Environ Health Sci Eng. 2015;13(1):82. doi: 10.1186/s40201-015-0238-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Alizade Govarchin Ghale Y, Baykara M, Unal A. Analysis of decadal land cover changes and salinization in Urmia Lake Basin using remote sensing techniques. Nat Hazards Earth Syst Sci Discuss. 2017. 10.5194/nhess-2017-212.
  • 25.Mardi AH, Khaghani A, MacDonald AB, Nguyen P, Karimi N, Heidary P, et al. The Lake Urmia environmental disaster in Iran: a look at aerosol pollution. Sci Total Environ. 2018;633:42–49. doi: 10.1016/j.scitotenv.2018.03.148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Remer LA, Kaufman Y, Tanré D, Mattoo S, Chu D, Martins JV, et al. The MODIS aerosol algorithm, products, and validation. J Atmos Sci. 2005;62(4):947–973. doi: 10.1175/JAS3385.1. [DOI] [Google Scholar]
  • 27.https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_AER_OD [database on the Internet]. Accessed: 2019.
  • 28.Stein A, Draxler RR, Rolph GD, Stunder BJ, Cohen M, Ngan F. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull Am Meteorol Soc. 2015;96(12):2059–2077. doi: 10.1175/BAMS-D-14-00110.1. [DOI] [Google Scholar]
  • 29.https://ready.arl.noaa.gov/HYSPLIT.php [database on the Internet]. Accessed: 2019.
  • 30.http://www.ulrp.ir/en/category/reports/ [database on the Internet]. Accessed: 2019.
  • 31.Sabetghadam S, Khoshsima M, Alizadeh-Choobari O. Spatial and temporal variations of satellite-based aerosol optical depth over Iran in Southwest Asia: identification of a regional aerosol hot spot. Atmos Pollut Res. 2018;9(5):849–856. doi: 10.1016/j.apr.2018.01.013. [DOI] [Google Scholar]
  • 32.Hamidi M, Kavianpour MR, Shao Y. A quantitative evaluation of the 3–8 July 2009 Shamal dust storm. Aeolian Res. 2017;24:133–143. doi: 10.1016/j.aeolia.2016.12.004. [DOI] [Google Scholar]
  • 33.Alam K, Trautmann T, Blaschke T, Subhan F. Changes in aerosol optical properties due to dust storms in the Middle East and Southwest Asia. Remote Sens Environ. 2014;143:216–227. doi: 10.1016/j.rse.2013.12.021. [DOI] [Google Scholar]
  • 34.Ashrafi K, Motlagh MS, Neyestani SE. Dust storms modeling and their impacts on air quality and radiation budget over Iran using WRF-Chem. Air Qual Atmos Health. 2017;10(9):1059–1076. doi: 10.1007/s11869-017-0494-8. [DOI] [Google Scholar]
  • 35.Namdari S, Valizade K, Rasuly A, Sarraf BS. Spatio-temporal analysis of MODIS AOD over western part of Iran. Arab J Geosci. 2016;9(3):191. doi: 10.1007/s12517-015-2029-7. [DOI] [Google Scholar]
  • 36.Arkian F, Nicholson S. Long-term variations of aerosol optical depth and aerosol radiative forcing over Iran based on satellite and AERONET data. Environ Monit Assess. 2018;190(1):1. doi: 10.1007/s10661-017-6336-1. [DOI] [PubMed] [Google Scholar]
  • 37.Jahani B, Dinpashoh Y, Wild M. Dimming in Iran since the 2000s and the potential underlying causes. Int J Climatol. 2018;38(3):1543–1559. doi: 10.1002/joc.5265. [DOI] [Google Scholar]
  • 38.Hand J, White W, Gebhart K, Hyslop N, Gill T, Schichtel B. Earlier onset of the spring fine dust season in the southwestern United States. Geophys Res Lett. 2016;43(8):4001–4009. doi: 10.1002/2016GL068519. [DOI] [Google Scholar]
  • 39.Hamzehpour N, Eghbal M, Bogaert P, Toomanian N. Top soil salinity prediction in South-Western part of Urmia Lake with ground water data. Int J Agric Res Innov Technol. 2014;4(1):57–63. doi: 10.3329/ijarit.v4i1.21093. [DOI] [Google Scholar]
  • 40.Zabihi F, Esfandiari M, Dalalian M, Moeini A. Horizontal flux of suspended particles sampling by big spring number eight (BSNE) sampler in lake Urmia area. Appl Ecol Environ Res. 2018;16(2):1313–1327. doi: 10.15666/aeer/1602_13131327. [DOI] [Google Scholar]
  • 41.Eivazzadeh M, Yadeghari A, Gholampour A. Temporal and spatial variations of deposition and elemental composition of dust fall and its source identification around Tabriz, Iran. J Environ Health Sci Eng. 2019;17(1):29–40. doi: 10.1007/s40201-018-00323-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Rezaei M, Farajzadeh M, Ghavidel Y, Alam K. Spatio-temporal variability of aerosol characteristics in Iran using remotely sensed datasets. Pollution. 2018;4(1):53–67. [Google Scholar]
  • 43.Rashki A, Kaskaoutis D, Sepehr A. Statistical evaluation of the dust events at selected stations in Southwest Asia: from the Caspian Sea to the Arabian Sea. Catena. 2018;165:590–603. doi: 10.1016/j.catena.2018.03.011. [DOI] [Google Scholar]
  • 44.He Q, Gu Y, Zhang M. Spatiotemporal patterns of aerosol optical depth throughout China from 2003 to 2016. Sci Total Environ. 2019;653:23–35. doi: 10.1016/j.scitotenv.2018.10.307. [DOI] [PubMed] [Google Scholar]
  • 45.Nasiri M, Ashrafi K, Ghazban F. The use of HYSPLIT model to determine the affected areas of Dispersed Sea-salt particles of dried Urmia Lake. Int J Eng Res Appl. 2014;4(1):272–279. [Google Scholar]
  • 46.Effati M, Bahrami H-A, Gohardoust M, Babaeian E, Tuller M. Application of satellite remote sensing for estimation of dust emission probability in the Urmia Lake Basin in Iran. Soil Sci Soc Am J. 2019;83(4):993–1002. doi: 10.2136/sssaj2019.01.0018. [DOI] [Google Scholar]
  • 47.Crosbie E, Sorooshian A, Monfared NA, Shingler T, Esmaili O. A multi-year aerosol characterization for the greater Tehran area using satellite, surface, and modeling data. Atmosphere. 2014;5(2):178–197. doi: 10.3390/atmos5020178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Soleimani Z, Parhizgari N, Rad HD, Akhoond MR, Kermani M, Marzouni MB, et al. Normal and dusty days comparison of culturable indoor airborne bacteria in Ahvaz, Iran. Aerobiologia. 2015;31(2):127–141. doi: 10.1007/s10453-014-9352-4. [DOI] [Google Scholar]
  • 49.Naimabadi A, Ghadiri A, Idani E, Babaei AA, Alavi N, Shirmardi M, et al. 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. 2016;211:316–324. doi: 10.1016/j.envpol.2016.01.006. [DOI] [PubMed] [Google Scholar]
  • 50.Goudarzi G, Shirmardi M, Naimabadi A, Ghadiri A, Sajedifar J. Chemical and organic characteristics of PM2. 5 particles and their in-vitro cytotoxic effects on lung cells: the Middle East dust storms in Ahvaz, Iran. Sci Total Environ. 2019;655:434–445. doi: 10.1016/j.scitotenv.2018.11.153. [DOI] [PubMed] [Google Scholar]
  • 51.Sotoudeheian S, Salim R, Arhami M. Impact of Middle Eastern dust sources on PM10 in Iran: Highlighting the impact of Tigris-Euphrates basin sources and Lake Urmia desiccation. J Geophys Res Atmos. 2016;121:14,018-34.

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