Elemental composition of particulate matters around

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Elemental composition of particulate matters around Urmia Lake, Iran Article in Toxicological and Environmental Chemistry · March 2016 DOI: 10.1080/02772248.2016.1166226

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Elemental composition of particulate matters around Urmia Lake, Iran Akbar Gholampour, Ramin Nabizadeh, Mohammad Sadegh Hassanvand, Shahrokh Nazmara & Amir Hossein Mahvi To cite this article: Akbar Gholampour, Ramin Nabizadeh, Mohammad Sadegh Hassanvand, Shahrokh Nazmara & Amir Hossein Mahvi (2016): Elemental composition of particulate matters around Urmia Lake, Iran, Toxicological & Environmental Chemistry, DOI: 10.1080/02772248.2016.1166226 To link to this article: http://dx.doi.org/10.1080/02772248.2016.1166226

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Date: 01 April 2016, At: 03:33

Publisher: Taylor & Francis Journal: Toxicological & Environmental Chemistry DOI: http://dx.doi.org/10.1080/02772248.2016.1166226

Elemental composition of particulate matters around Urmia Lake, Iran Akbar Gholampoura, Ramin Nabizadehb, Mohammad Sadegh Hassanvandc, Shahrokh Nazmarab,

Downloaded by [University of Sussex Library] at 03:33 01 April 2016

Amir Hossein Mahvib, c, d *

a

Department of Environmental Health Engineering, School of Public Health, Tabriz University

of Medical Sciences, Tabriz , Iran b

Department of Environmental Health Engineering, School of Public Health, Tehran University

of Medical Sciences, Tehran, Iran. c

Center for Air Pollution Research, Institute for Environmental Research, Tehran University of

Medical Sciences, Tehran, Iran d

Center for Solid Waste Research, Institute for Environmental Research, Tehran University of

Medical Sciences, Tehran, Iran

*Corresponding author: Amir Hossein Mahvi; E-mail: [email protected] Tel: 0098-21- 88 95 49 14; Fax: 0098-21-66 46 22 67

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Abstract:


Atmospheric particulate matters and their elements were concurrently measured at two sites located in the north and southeast parts of Urmia Lake from January to September 2013. At both sampling sites, average concentrations of total suspended particulate, particles with the aerodynamic diameter of smaller than 10 μm, smaller than 2.5 μm, and smaller than 1 μm were 260 ± 106, 180 ± 73, 30 ± 8, and 25 ± 7 μg m-3, respectively. The analyzed water soluble ions accounted for approximately 11 - 13 % mass concentrations of total suspended particulate and 8 - 9 % of particles smaller than 10 μm, and the sum of the concentrations of the analyzed elements associated with both ranged from 9 to 41 μg m-3 (6.5 9.6 % in mass) and 7 to 26 μg m-3 (5.5 - 11.3 % in mass), respectively. Thus, particulate matter was composed of a complex mixture of minerals such as halite, quartz, gypsum, hexahydrite, and Bassanite.

Keywords: Saline Dust; Particulate Matter; Chemical Composition; Urmia Lake; Iran

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1. Introduction Atmospheric particulate matters (PM) originate from a variety of natural or anthropogenic sources and contain a range of physical, chemical, and morphological properties (Natusch, Wallace, and Evans 1974). PM is a complex mixture of elemental and organic carbon, ammonium, nitrates, sulphates, trace elements, and water (Qin and Oduyemi 2003). High concentrations of trace metals in the aerosols of coastal areas would lead to high deposition flux


to the nearby areas. These metals can be also transported to remote areas and thus affect the air and water quality of remote urban and rural regions (Paode et al. 1999). Moreover, it has been observed that fine aerosol particles readily accumulate many elements such as Pb, Cd, Zn, Cr, Ni, Mn, and Cu and cause seriously adverse threats for human health (Alipour 2006). Therefore, it is critical to examine the composition of trace metals in airborne particles. Dust storm is a weather phenomenon in which strong winds blow up a large amounts of dust and sand and cause decreased local visibility to less than 1 km (Kim, Alleman, and Church 1999). Saline dust storms differ from common dust storms, especially considering the sources of suspended PM, chemical composition, and grain size. Saline dust storms are defined as "a kind of environmental disaster in arid and semiarid regions that is caused by dust deflated from the salt-rich sediments of dried lake beds and strongly salinized soils on the margins of lake floors" (Abuduwaili, Liu, and Wu 2010). This phenomenon has been reported in many parts of the world including the Aral Sea region in Kazakhstan and Uzbekistan, Lake Balkhash region in Kazakhstan, the Inner Mongolian region in China, southeastern Australia, and many other regions with arid and semiarid climate (Gill 1996). Compared with common dust storms, saline dust storms transport high concentrations of fine-grain saline and alkaline material, such as sodium sulfate, sodium chloride, and other potentially toxic elements which could be threatening to the ecological security and human health in arid regions ( Abuduwaili, Liu, and Wu 2010). 3

Determination of the sources and estimation of their contribution to PM have been done using several receptor models such as principal component analysis (PCA), positive matrix factorization (PMF), and chemical mass balance model (CMB). Physical properties and chemical compositions of atmospheric PM are the main data used as input to receptor models. The essential principle of these models is that mass conservation can be assumed and a mass balance analysis can be used to apportion sources of PM in the atmosphere (Cheng et al. 2008). Thus,


selection of an appropriate model depends on prior knowledge on sources and their profiles. If the probable sources are known and detailed information on source profiles are available, CMB model would be useful (Sun et al. 2004). However, if the probable sources are unknown or lack of information on source profiles, PCA and PMF methods are preferred. Urmia Lake, located in the northwest of Iran, has been designated as an international park and protected biosphere region by the United Nations (Wang et al. 2005). It is the second great saline lake in the world and the largest lake in Iran. In the recent decade, because of the considerable decreasing of the precipitation, the numerous dam building, the drilling of the innumerable wells around the lake region, and Non-optimized consumption of water in the agricultural activities, water level of Urmia lake has fallen by more than 5 m and the salt concentration of this lake has increased from 185 to 220 g L-1(Hoseinpour, Fakheri Fard, and Naghili 2010). Parallel with decrease in water level and increase in the concentration of elements, the unique ecosystem of this lake is being destroyed. As a result, a salt desert would be created with the area of more than 5000 km2, overlaid with 50-60 cm thick salt and element deposits. In the warm season, wind could carry and transport these elements to adjacent areas as far as 300 km. The transported PM could damage agricultural lands, pollute the ecosystem, and cause a variety of diseases in the nearby urban and rural areas (Hoseinpour, Fakheri Fard, and Naghili 2010). Therefore, the

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Iranian Environmental Protection Organization is strongly attempting to protect the lake's environment. Thus far, no study has been conducted about the elemental characterization of atmospheric PM around Urmia Lake. Chemical composition of dust, resulted from Urmia Lake drying, is related to the characterization of soil properties in the region and this redistribution leads to dual effects


depending on the nature of transported materials (Liu et al. 2011). In order to obtain the most actual and efficient information about the dust of Urmia Lake, speciation of various elements in fine and coarse particles should be carried out using the same samples. The present study was therefore performed to determine the mass levels of total suspended particulate (TSP), particles with the aerodynamic diameter of smaller than 10 μm (PM10), particles with the aerodynamic diameter of smaller than 2.5 μm (PM2.5), and particles with the aerodynamic diameter of smaller than 1 μm (PM1) along with the variations of elemental and ionic species associated with TSP and PM10 in the Urmia Lake region in the period of January to September 2013. Since completely drying of Urmia Lake is possible in the coming years, the information presented in this article can be useful in comparing changes of PM mass levels and their chemical and elemental compositions at the studied sites.

2. Material and method 2.1. Studied area, sampling sites, and schedule Based on the region's wind direction, two sites were selected (Fig.1): 1) Tasuj site, located in the north of Urmia Lake (38° 13' 17.8" N - 45° 24' 38.5" E), and 2) Ajabshir site, located in the southeast of Urmia Lake (37° 31' 12.9" N - 45° 47' 53.0" E). Both PM sampling sites ware located on soil near the bed of Urmia Lake (with distance of 1 km from lake bed). Levels of TSP,

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PM10, PM2.5, and PM1.0 were measured 8 times every season from January to September 2013. In total, 48 samples of TSP and PM10 were collected and analyzed. Totally 30 composite samples (approximately 0.2 – 0.5 kg) were collected from surface soils and Urmia lake bed (0 – 10 cm), i.e. 13 samples from Tasuj and 17 samples from Ajabshir. Samples were taken from various locations in the area within a radius of 5 km from the PM sampling


sites. Samples were then homogenized by passing through a 2 mm metal sieve and stored in refrigerators at 4 °C until analysis. 2.2. Measuring PMs TSP and PM10 samples were collected by two high-volume samplers manufactured by Graseby– Andersen (Smyrna, Georgia, USA) at flow rates of 1.13–1.41 m3 min-1 for 24 h. Both TSP and PM10 were collected on a 20.3 cm

×

25.4 cm glass micro-fiber filter (Whatman Inc., USA).

Before and after the sampling, the filters were maintained first under 40 % relative humidity (RH) and 25 °C for over 48 h and later at ambient conditions for 2 h; then, they were weighed three times using an electronic balance with the reading precision of 0.1 mg (Model GR-300, San Jose, California, USA). After weighing, the filters were packed in aluminum foils and stored at – 20 ºC until extraction and chemical analysis. PM2.5 and PM1.0 were measured using two portable particulate air monitors (HAZ-DUST EPAM-5000, New Hampshire, USA).

Fig.1. here 2.3. Chemical analyses 2.3.1. Analysis of water soluble ions One quarter of each filter was placed in a glass vial and 40 mL ultrapure water (specific resistance ≥18 Ω cm) was added. The vials were shaken for 2 h and subsequently were ultra-

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sonicated for 30 min. The extracted solutions were filtered through a micro-porous membrane (pore size 0.45 μm) (Cheng et al. 2008). An ion chromatograph (850 Professional IC, Metrohm, Herisau, Switzerland) at an operating flow rate of 0.7 mL min-1 was used. The column oven was equipped with a separation column for anions (Metrosep A Supp 5 100/4.0) or cations (Metrosep C 4 150/4.0). The eluant was 3.2 mmol L−1 Na2CO3 + 1.0 mmol L−1 NaHCO3 for anions, and 2 mmol L−1 HNO3 + 0.7 mmol L−1 dipicolinic acid for cations. Column temperature was


maintained at 30 °C. Field and laboratory blanks and spiked samples were analyzed along with the PM samples. For all the ions, method detection limits (MDLs) were calculated as three times of the standard deviations of the blank values (five replicates of the blank). The obtained MDLs and the recovery efficiencies for the water soluble ions are presented in Table S1 in Supplementary Materials. 2.3.2. Analysis of elements Another quarter of each filter was digested at 170 °C for 4 h in a high-pressure Teflon digestion vessel (Applied Plastics Technology, Inc., Bristol, USA) with 10 mL HNO3 (69 %), 3 mL HCLO4 (70 %), and 1 mL HF (48 %) (All chemicals supplied from Merck, Germany). After cooling, the solutions were dried on the electric heater and 1 mL concentrated HNO3 was added. Then, it was diluted to 25 mL using distilled-deionized water. The obtained solutions were filtered through a micro-porous membrane with the pore size of 0.45 μm (Ho et al. 2006; Goudie and Middleton 2006). Al, As, Ba, Cd, Cu, Fe, La, Mn, Ni, P, Pb, Sr, Ti, Zr, V, and Si were determined by inductively coupled plasma-atomic emission spectroscopy (ICP-AES, Model ULTIMA, JOBIN-YVON Company, Paris, France); Be, Co, Cr, Li, Mo, Sb, Se, Sn, Te, Tl, Y, Zn, Pt, Rh, and Hg were below MDLs. MDLs were determined as three times of the standard deviations of the blank

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values (five replicates of the blank). Efficiency of recovery was measured by spiking one quarter of a particle-laden filter with known amounts of elements. Detection limits, MDLs, and recoveries are presented in Table S2 in the Supplementary Materials. Among these elements, For analysis of the crustal soil, 1 g was digested as previously mentioned ( Melaku, Dams and Moens 2005). Results of elemental measurements were used for calculating enrichment factor


(EF). To assess the impact of Urmia Lake drying on the dust emissions around the lake, it is essential, in a first step, to differentiate between the particles emitted from the earth's crust and those from the lake bed deposited salts. The enrichment factor (EF) method proposed by Zoller et al. (1974) has been widely used as the first step to evaluate the potential strength of pollution emitting sources (Wang, Sato, and Xing 2006). EF is the ratio of chemical concentration of an element in the aerosol samples to that in the crust and can be calculated as follows (Yin et al. 2012):

where CX is the concentration of element X and CFe is concentration of Fe as reference, the element with highest concentration determined in the soil crust (Table 4). Subscripts of particulate matter and crust refer to PM samples and crustal materials, respectively. EF values of less than one indicate that local crust was the main source of elements, EF of 1 to 5 means that these elements are emitted from other sources beside crustal sources (sea salts), EF of more than 5 suggests that other emission sources are predominant, and finally, if EF ≥ 10, significant fractions of the elements are contributed by non-crust sources (Yin et al. 2012). 8

2.4. Morphology and elemental composition To determine the morphology and elemental composition of the collected particles, TSP and PM10 samples were analyzed separately using the SEM-EDX system (Vega\\Tescan-XMU, Tescan Orsay holding, Brno, Czech Republic) at Razi Metallurgical Research Center.


2.5. Data analysis Data were analyzed by SPSS20 statistical software, (SPSS Inc., New York, USA) by means of linear regression for correlation coefficients among the elements, bivariate correlations to quantify the relations between the elemental concentrations, and multivariate test to quantify significance differences between concentrations of ions at the Tasuj site vs. the Ajabshir site. Differences and correlations were considered significant at p < 0.05 level. Meteorological data were obtained from the National Climatic Data Center (NCDC 2013) and the East Azerbaijan Meteorological Organization. The obtained data were examined for missing values and outliers and were entered into WRPLOT View Freeware 7.0.0 to plot the wind-rose. Concentrations of PMs were analyzed using Microsoft Excel 2010. 3. Results and Discussion 3-1. Meteorological data Based on the collected meteorological date, in both sampling regions, February was the coldest month with a monthly mean temperature of –1.0 °C, while August was the warmest month with a mean temperature of 28.0 °C. RH varied from 25 to 72 %. At Ajabshir Township, the seasonal mean wind speeds were 2.8, 2.7, 2.5, and 2.1 m s-1, at Tasuj Township, they were 2.4, 3.3, 3.3,

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and 2.0 m s-1 in winter, spring, summer, and autumn, respectively. Annual wind rose plots for both sampling region are shown in Fig. S1 in Supplementary Materials. At Tasuj Township, wind speeds were 0.5 - 13.6 m s-1, with an annual mean wind speed of 2.6 m s-1. At Ajabshir Township, wind speeds varied from 0.5 to 11.1 m s-1 and annual mean wind speed was 2.6 m s-1. At both sites, annual prevailing wind blew from south and southeast. It is known that a drought associated with strong wind is regarded as the climatic background for


the formation of dust storms (Fu et al. 2008). With respect to the meteorological data, June to September had the greatest potential for dust storms; therefore, in order to predict the direction of a probable saline dust storm, a wind rose was plotted for the relevant months and PM samples were collected until September (Fig. S1 in Supplementary Materials). At the Tasuj sampling site, prevailing wind blew from northeast and southwest during May to September and, in Ajabshir Township, it was from south to north and from west to east. According to the dynamic characteristics of dust, grains with diameters smaller than 10 μm can be transported by wind for several thousand kilometers under common wind power conditions, those with diameters of 10 - 20 μm for hundreds or up to several thousands of kilometers at wind speeds higher than 15 m s-1. 3-2. PM mass concentrations and relations Average concentrations of TSP, PM10, PM2.5, and PM1 were 260 ± 106, 180 ± 73, 30 ± 8, and 25 ± 7.0 μg m-3, respectively (Table 1). The highest concentration of PM was observed during August followed by June. There were no significant differences between PM ratios at Tasuj and Ajabshir sampling sites (Table 1) (Wilks' Lambda value = 0.73, F = 0.65, and P-value = 0.65). Averages of PM10/TSP, PM2.5/PM10, and PM1/PM2.5 ratios were 0.70, 0.19, and 0.84, respectively. Compared to previous

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studies (Hassanvand et al. 2014; Gholampour et al. 2014) on PM ratios in Tabriz urban (0.48) and industrial suburban regions (0.38), the PM2.5/PM10 at the Urmia Lake region were smaller, which could be due to PM emission from physical and wind erosion at Urmia Lake rather than around combustion sources. Due to significant differences in particle sources and also in geographical and meteorological conditions, the obtained results were not directly comparable.


The present results revealed that concentrations of TSP and PM10 were far higher than those of fine PMs. So, it could be concluded that dust resulting from Urmia Lake could not affect remote areas; nevertheless, further studies and modeling are necessary to clarify this hypothesis. Table 1: here 3.3. Chemical constituents of PM Despite higher PM and water soluble ion concentrations during the warm season, mass percentages of ions in the ambient air's PM at the two sampling sites during cold and warm seasons (Table 2) were not significantly different (Wilks' Lambda value=0.4, F=1.64, and Pvalue=0.2). Analyzed water soluble ions accounted for approximately 11 - 13 % of the TSP and 8 - 9 % of PM10 mass concentrations. Abuduwaili, Liu, and Wu (2010) stated that the soluble salts represented 10 - 25 % of the total mass of the saline dust in the Ebinur region; also, Gholampour et al. (2014) reported that in the Tabriz urban and suburban regions - which are near Urmia Lake - water soluble ions accounted for approximately 20 ± 10 % of total TSP mass and 25 ± 12 % of total PM10 mass. Low percentage of total ions in the PM mass could be due to the small amount of secondary ions, especially non-sea-salt sulfate and ammonium in the PM of Urmia Lake bed. Among all the detected ions (Table 2), sulfate was the dominant constituent followed by nitrate, chloride, and calcium, in agreement with the study by Hassanzadeh, Zarghami, and Hassanzadeh 11

(2012) in which Na+, K+, Ca2+, Li+, and Mg2+ were the main cations, while Cl–, SO42–, and HCO3– were the main anions in the Urmia lake water. Urmia Lake is hyper-saline and the concentrations of

Na+ and Cl– are roughly 4 times the concentration of seawater. High

concentrations of Na+ and Cl– (especially for TSP) could be caused by higher and persistent onshore winds creating abundant lake water droplets and aerosols (Hassanzadeh, Zarghami, and


Hassanzadeh 2012). Considering Na+ as a trace element of Urmia Lake salt and assuming all Na to have marine origins, excess sulfate (as non-sea-salt sulfates, nss- SO42–), excess potassium (as non-sea-salt potassium, nss-K+), and excess calcium (as non-sea-salt calcium, nss- Ca2+) can be calculated as (Ho et al. 2006):

Calculated excess ion in Table 3 shows that about 25 - 35 % of sulfate, 25 - 40 % of potassium, and only 3 - 5% of calcium originated from sea salt. These findings were in good agreement with those by Theodosi et al. (2011) that revealed 10 - 30 % of SO42– in Athens resulted from sea salts. These findings were approved by the calculated enrichment factor represented below. While nss-Ca2+ is considered an effective tracer of crustal sources, significantly higher levels of nss-Ca2+ in TSP and PM10 could demonstrate that one of the major source of this ion was the resuspension of PM from the region's crustal soil; despite the vicinity of the sampling sites to Urmia Lake (less than 500 m), the effect of Urmia Lake on the Ca2+ ion concentration was negligible.

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Table 2: here 3.4. Elemental constituents of PM Mean and standard deviation contents of elements in Urmia Lake (TSP and PM10) during the two cold and warm seasons (based on μg m-3 and mg g-1) are summarized in Table 3. The sum of the concentrations of the analyzed elements ranged from 9 to 41 μg m-3 for TSP and 7 to 26 μg m-3 for PM10. Also, they accounted for about 6.5 - 9.6 % of TSP mass and 5.5 - 11.3 % of PM10 Downloaded by [University of Sussex Library] at 03:33 01 April 2016

mass. No appreciable differences in the mean concentrations of most elements was observed between the two sampling sites (P-value = 0.25). The most abundant detected metals in the two sampling sites were Al (1.9 - 14.5 μg m-3), Fe (2.3 - 6.3 μg m-3), Cu (0.2 - 2.8 μg m-3), Ti (0.3 - 1.4 μg m-3), and P (0.01 - 1.82 μg m-3) for TSP and Fe (1.6 - 10.4 μg m-3), Al (0.5 - 8.5 μg m-3) , Cu (0.2 - 2.7 μg m-3), Ti (0.15 - 0.89 μg m-3), and P (0.01 - 0.59 μg m-3) for PM10. In several studies has been reported that the presence of Al and Fe is mainly the result of local and regional soil resuspension (Celo and Dabek-Zlotorzynska 2011; Hasheminassab et al. 2014). While some industrial sources could release elements such as Si, Fe, and Al, due to the lack of such industries in the studied region these elements may be due to natural dust resuspension. Since Cu may be from combustion sources, high levels of Cu in the PM samples could be caused by the combustion of fossil fuels in the existing residential areas and from the roads surrounding the sampling sites. The IARC has classified Pb, Ni, As, and Cd as carcinogenic to humans (Group 1) (WHO 2000). For ambient air, standard levels have been prescribed by the European Commission (EU) for Pb, Ni, As, and Cd as 0.5, 0.02, 0.006, and 0.005 μg m-3, respectively (EC 2014). Mean concentrations of Pb, As, and Cd in the majority of samples did not exceed the EU limits, but in some cases the average Ni bound to TSP in both seasons exceeded the EU standards. 13

Table 3: here 3.5. Elemental constituents of soil crust and deposited salts For calculating enrichment factor of elements in PM and also characterizing the elemental constituents of deposited salts in the bed of Urmia Lake, the concentrations of 31 elements in soil and salt samples were determined by ICP-AES (Table 4). The most abundant elements detected in the soil crust were Fe (25850 - 27200 mg kg-1), Al (6690 - 7790 mg kg-1), P (3960 - 11140 mg


kg-1), Ti (1760 - 8980 mg kg-1), and Mn (1050 - 3720 mg kg-1) and, in the deposited salts, the abundant elements were Ca (1360 - 1580 mg kg-1), P (250 - 1840 mg kg-1), Mg (330 - 340 mg kg-1), Fe (35 - 75 mg kg-1), and K (4 - 79 mg kg-1). The concentrations of all metals in the soil crust were much higher than in the deposited salts; so it could be concluded that, if the saline dust accrue from Urmia Lake, release of metals to other regions is not important. Table 4: here

3.6. Sources of elements in airborne particles The estimated enrichment factors for each element in the different seasons are given in Figs. 2 and 3. EFs for Al, Ti, Ca, P, Mn, K, F, and Si in both TSP and PM10 were near unity; so, it could be concluded that these elements originated from Urmia Lake's crustal soil and resuspension of crustal materials (Wang, Sato, and Xing 2006). EFs of Pb, V, Ni, La, Ba, Na, Zr, As, and Cl up to 10 could indicate that sources other than crustal soil had a major role in the emission of these elements. Concomitantly increased EFs of Na, Ba, and Cl could reveal that the emission of NaCl and BaCl2 from deposited sea salts affected PM ionic characterization. Despite higher EFs for Cu and Cd, low concentrations of these elements in the deposited sea salts represented that, aside from crustal sources and

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deposited sea salts, anthropogenic activities such as the combustion of fossil fuels in the residential areas near-by and roads surrounding the sampling sites contributed a substantial fraction to these elements in aerosols. These results were in good agreement with those of studies in other countries in saline lands and near riverbeds: EFs for Ca, Fe, Na, Li, Mn, and Sr were near 1, which reflected the importance of


dust contribution by crustal soils, and the enrichment of trace elements such as Cu, V, Cd, and Pb in these areas might be related to the incineration of various fuels and woods in residential areas (Chen et al. 2009). Observations of enhanced EF values for a few elements during the warm season indicated the relevance of elevated environmental temperature for their higher concentrations in PM.

Fig.2. here Fig.3. here 3.7. Size and morphology of aerosol particles Urmia Lake is classified as oceanic, being of the sodium- chloride- sulfate type (Eimanifar and Mohebbi 2007). Alipour et al. concluded that salts, except for halite, are generally lower in Urmia Lake compared to its sister, the Great Salt Lake in the USA. Results from the XRD and SEM-EDX photographs of collected samples from the two sampling sites showed that PMs were composed of a complex mixture of salt minerals such as halite (NaCl), quartz (SiO2), gypsum (CaSO4. 2H2O), hexahydrite (MgSO4.6H2O), and Bassanite (2CaSO4.H2O). EDX analyses of TSP and PM10 (Figure 4) showed that oxygen and silicon were the major components in TSP. Other elements, such as iron, magnesium, calcium, chlorine, sodium, and potassium were also of great importance.

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Comparison of TSP and PM10 elemental compositions with the world's average values revealed that silicon in the dust of Urmia Lake was less abundant (20 % for TSP and 14 % for PM10 vs. the world average of 60 %) (Gao et al. 2002). On the other hand, TSP resulting from the dust of Urmia Lake was richer in Fe (8.7 % vs. the world average of 6.9 %), Ca (6.6 % vs. the world average of 3.9 %), Mg (6.9 % vs. the world average of 2.6 %), and sodium (1.2 % vs. the world average of 0.5 - 1 %) than Saharan, Harmattan, Chinese, and North American dusts (Gao et al.


2002). It is important to note that the concentration of chloride in the world's average values is negligible, whereas the present results indicated chloride as being one of the main ions in the dust of Urmia Lake. Fig.4. here 4. Conclusion: The water level of Urmia Lake has decreased up to 5 m during the last decade. Along with it, a salt desert has been created with an area of more than 5000 km2. The implemented prevention and control measures mainly include the diversion or conservation of water for recovering dried lake beds in order to reduce wind erosion. However, water-saving potential is limited due to continuing social and economic development in arid regions. Local authorities have considered harvesting deposited salts from the Urmia Lake bed as one of the solutions for preventing saline dust emission. The present results, however, revealed that deposited salts had small roles in the emission of PM around the Urmia Lake and border crustal soil was the dominant source of PM. Based on these observations, there is concern that harvesting deposited salts from the bed of Urmia Lake not only cannot decrease PM emissions, but also that dust emission phenomena could become more severe and widespread because of

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uncovering and drying of sludge existing under the salt layers. Further studies are required to clarify the potential of deposited salts and under-layer sludge for transferring and emitting saline dust to the surrounding area using wind tunnels or other appropriate equipment. 5. Acknowledgments The authors would like to acknowledge General Administration of Education in East Azarbayejan Province for providing the sampling locations. This work was funded by Institute


for Environmental Research (IER), Tehran University of Medical Sciences (Grant number: 9201-46-21258).

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Ho, K. F., S. C. Lee, , J. J. Cao, J. C. Chow, J. G. Watson, and C. K. Chan. 2006. "Seasonal Variations and Mass Closure Analysis of Particulate Matter in Hong Kong." Science of The Total Environment 355(1-3): 276-287. doi:http://dx.doi.org/10.1016/j.scitotenv.2005.03.013. Hoseinpour, M., A. Fakheri Fard, and R. Naghili. 2010. "Death of Urmia Lake, a Silent Disaster Investigating Causes, Results and Solutions of Urmia Lake Drying." In 1st International Applied Geological Congress, Department of Geology, Islamic Azad University, Islamic Azad University-Mashad Branch, Iran, April 26-28, 700-4 Kim, G., L.Y. Alleman, and T.M. Church. 1999. "Atmospheric Depositional Fluxes of Trace Elements, 210Pb, and 7Be to the Sargasso Sea." Global Biogeochemical Cycles 13(4): 1183-1192. Liu, D., J. Abuduwaili, J. Lei, and G. Wu. 2011. Deposition Rate and Chemical Composition of the Aeolian Bust from a Bare Saline Playa, Ebinur Lake, Xinjiang, China. Water, Air, & Soil Pollution, 218(1-4): 175-184. Melaku, S., R. Dams and L. Moens. 2005. "Determination of Trace Elements in Agricultural Soil Samples by Inductively Coupled Plasma-Mass Spectrometry: Microwave Acid Digestion Versus Aqua Regia Extraction." Analytica Chimica Acta 543(1): 117-123. Natusch, D., J. Wallace, and C. Evans. 1974. "Toxic Trace Elements: Preferential Concentration in Respirable Particles." Science 183(4121): 202-204. NCDC (2013). http://www7.ncdc.noaa.gov/CDO/cdoselect.cmd. Paode, R. D., U.M. Shahin, J. Sivadechathep, T.M. Holsen, and W.J. Franek. 1999. "Source Apportionment of Dry Deposited and Airborne Coarse Particles Collected in the Chicago Area." Aerosol Science & Technology 31(6): 473-486. Qin, Y., and K.. Oduyemi. 2003. "Chemical Composition of Atmospheric Aerosol in Dundee, UK". Atmospheric Environment 37(1): 93-104. Sun, Y., G. Zhuang, Y. Wang, L. Han, J. Guo, M. Dan, W. Zhang, Z. Wang, and Z. Hao. 2004. "The Air-Borne Particulate Pollution in Beijing—Concentration, Composition, Distribution and Sources". Atmospheric Environment 38(1): 5991-6004. Theodosi, C., G. Grivas, P. Zarmpas, A. Chaloulakou, and N. Mihalopoulos. 2011. "Mass and Chemical Composition of Size-Segregated Aerosols (PM1, PM2.5, PM10) over Athens, Greece: Local versus Regional Sources." Atmospheric Chemistry and Physics 11(22): 11895-11911. Wang, X., T. Sato, and B. Xing. 2006. "Size Distribution and Anthropogenic Sources Apportionment of Airborne Trace Metals in Kanazawa, Japan." Chemosphere 65(11): 2440-2448. Wang, Y., G. Zhuang, Y. Sun, and Z. An. 2005. "Water-Soluble Part of the Aerosol in the Dust Storm Season—Evidence of the Mixing between Mineral and Pollution Aerosols." Atmospheric Environment 39(37): 7020-7029. WHO 2000. "Air Quality Guidelines for Europe, 2nd ed." European Series (91): 288 Copenhagen. Yin, L., Z. Niu, X. Chen, J. Chen, L. Xu, and F. Zhang. 2012. "Chemical Compositions of PM2.5 Aerosol During Haze Periods in the Mountainous City of Yong’an, China." Journal of Environmental Sciences 24(7): 1225–1233. Zoller, H., E. William, S. Gladney, and A. Robert Duce. 1974. "Atmospheric Concentrations and Sources of Trace Metals at the South Pole." Science 183(4121): 198-200.

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Table S1: MDLs and recovery efficiencies for water- soluble ions Water-soluble ion MDLs* (ng/mL ) 3

MDLs*(μg/m ) Recovery efficiencies (%)

95

6.3

4.4

12.5

5.3

3.7

6.5

3.6

52

45

6.1

0.0025

0.0002

0.0001 109122

0.0001 105107

0.0001 100117

0.0014

0.0012 102104

0.0002

78-106

0.0003 112133

0.0002

97-103

0.0001 101103

99-103

98-104


* Based on 3σ blank filters (n= 5)

Table S2: MDLs and recovery efficiencies for elements Method Method Detection detection Recovery detection Recovery limit* Element limit (%) limit (%) (ng/L) (ng/m3) (ng/m3) Al 300 3.8 76-93 Sb 100 1.25 63-68 As 100 1.3 92-97 Se 20 0.25 80-84 Ba 9 0.1 92-94 Sn 100 1.25 75-86 Be 8 0.1 75-91 Sr 3 52-68 Cd 100 1.3 71-89 Te 100 1.25 92-99 Co 100 1.3 61-83 Ti 40 0.5 61-77 Cr 200 2.5 96-120 Tl 100 1.25 Cu 40 0.5 71-92 Y 30 0.375 Fe 200 2.5 95-97 Zn 100 1.25 92-97 La 100 1.3 57-73 Zr 0 Li 30 0.4 86-106 Pt 100 1.25 Mn 40 0.5 79-88 Rh 100 1.25 67-73 Mo 30 0.4 89-97 V 50 0.625 64-79 Ni 500 6.3 88-93 Si 300 3.75 87-95 P 300 3.8 Hg 100 1.25 52-68 Pb 100 1.3 93-103 * Based on 3σ blank filters (n= 5) Detection limit* Element (ng/L)

20

95-106


a)

b)

c)

d)

Fig. S1.Annual wind rose plots during the year of 2013 for a) Tasuj and b) Ajabshir and wind rose plots during June to January for c) Tasuj and d) Aajabshir

21


Fig.1. Location of the studied area and sampling sites

22

TSP PM10 EF=1

Enrichment Factor Value


1000

100

EF=5 EF=10

10

1

0.1 Al

Cl

As

Cd

Pb

Cu

Ba

F

Zr

P

Ni

Mg

V

Mn

Ti

Na

Ca

La

Fe

Sr

K

Si

Elements

Fig.2. Enrichment factors for elements in TSP and PM10 (sampling during cold season); Error bars show standard deviations.

23

10000 TSP

Enrichment Factor Value


PM10 EF=1

1000

EF=5 EF=10 100

10

1

0.1 Al

Cd

Cl

Pb

Cu

As

Ba

Zr

P

Sr

F

V

Ni

Na Mg La Mn

Ti

Fe

K

Ca

Si

Elements

Fig.3. Enrichment factors for elements in TSP and PM10 (sampling during warm seasons); Error bars show standard deviations.

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halite halite

alcite

Hexahydrite Bassanite or gypsum


Quartz or other ilicate minerals

Quartz or other ilicate minerals

Quartz and Bassanite

Bassanite or gypsum

Quartz or other silicate

Bassanite or gypsum

Quartz or other silicate minerals

halite

Fig.4. SEM photograph of TSP and PM10 samples

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Table 1: Descriptive statistics for 24-h PM mass concentrations (μg m-3) and ratio of PM species in Urmia Lake sampling sites the ratio of PMs’ species PM10/TSP PM2.5/PM10 PM1/PM10 PM1/PM2.5 0.38 0.1 0.1 0.72 0.90 0.24 0.19 0.89 0.70 0.19 0.16 0.84 0.18 0.05 0.04 0.06 0.69 0.18 0.15 0.84

PM1 16.1 36.5 25.2 6.6 25.5


Min Max Average SD Median

PM concentrations (μg m-3) TSP PM10 PM2.5 151 96 22.3 522 329 42.6 261 180 30.1 106 73 7.5 236 180 28.6

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Table 2: Mineral constituents of TSP and PM10 based on μg m-3 and mg of mineral constituent per g of PM mass (mg g-1) in Urmia Lake during the two meteorological periods (n = 48) cold season warm seasons conc.( μg m-3) Contribute(mg g-1) conc. ( μg m-3) contribute(mg g-1) TSP PM10 TSP PM10 TSP PM10 TSP PM10 PM 241 ± 140 157 ± 77 280 ± 81 187 ± 81 Inorganic ions Na+ 2.6 ± 1.0 1.7 ± 0.4 13.8 ± 7.9 12.5 ± 5.3 5.2 ± 2.5 2.3 ± 1.2 19.1 ± 8.5 13.2 ± 8.8 NH4+ 0.9 ± 0.6 0.7 ± 0.5 4.6 ± 3.3 4.8 ± 3.9 2.7 ± 1.2 1.1 ± 0.5 10.1 ± 4.4 5.9 ± 4.1 + K 0.8 ± 0.5 0.3 ± 0.2 4.4 ± 3.4 2.5 ± 1.5 1.4 ± 0.2 0.6 ± 0.2 5.2 ± 1.6 3.7 ± 2. 8 nss-K+ 0.6 ± 0.5 0.2 ± 0.2 3.1 ± 3.2 1.4 ± 1.5 1.1 ± 0.1 0.5 ± 0.2 4.1 ± 1.1 2.8 ± 2.3 ss-K+ 0.2 ± 0.1 0.1 ± 0.0 1.2 ± 0.5 1.1 ± 0.3 0.3 ± 0.1 0.2 ± 0.1 1.1 ± 0.1 0.9 ± 0.3 Mg2+ 0.2 ± 0.1 0.2 ± 0.0 1.1 ± 0.4 1.1 ± 0.3 0.3 ± 0.2 0.2 ± 0.1 1.2 ± 0.4 1.0 ± 0.6 Ca2+ 2.9 ± 0.4 2.1 ± 0.8 14.2 ± 5.6 14.1 ± 3.7 2.6 ± 1.2 2.1 ± 0.6 9.8 ± 3.7 11.4 ± 6.2 nss-Ca2+ 2.8 ± 0.4 2.1 ± 0.8 13.8 ± 4.6 13.6 ± 3.5 2.4 ± 1.3 1.9 ± 0.7 9.4 ± 3.0 10.9 ± 5.3 ss-Ca2+ 0.1 ± 0.0 0.1 ± 0.0 0.6 ± 0.1 0.5 ± 0.1 0.1 ± 0.0 0.1 ± 0.0 0.4 ± 0.1 0.4 ± 0.1 ¯ F 0.2 ± 0.1 0.1 ± 0.3 0.1 ± 0.2 0.8 ± 1.9 0.3 ± 0.2 0.2 ± 0.2 1.0 ± 0.9 0.9 ± 1.4 Cl¯ 3.0 ± 1.1 1.7 ± 0.6 15.3 ± 8.4 11.8 ± 4.2 4.1 ± 1.2 2.0 ± 1.2 15.3 ± 5.3 11.2 ± 7.7 NO2¯ 0.1 ± 0.0 0.0 ± 0.0 0.2 ± 0.2 0.1 ± 0.1 0.1 ± 0.0 0.0 ± 0.0 0.2 ± 0.1 0.3 ± 0.0 ¯ NO3 4.1 ± 1.7 2.4 ± 1.8 21.0 ± 12.3 17.1 ± 14.2 8.4 ± 5.1 3.3 ± 2.5 30.1 ± 19.8 16.2 ± 14.8 SO425.2 ± 1.5 3.2 ± 1.4 26.7 ± 13.7 21.3 ± 8.0 12.2 ± 7.6 5.2 ± 3.6 42.9 ± 26.3 25.0 ± 20.4 nss-SO42- 3.8 ± 1.7 2.3 ± 1.4 18.2 ± 12.3 14.1 ± 7.9 11.7 ± 6.5 4.7 ± 2.9 33.5 ± 22.7 19.3 ± 17.5 ss-SO421.4 ± 0.5 0.9 ± 0.2 8.6 ± 1.3 7.3 ± 1.9 2.2 ± 0.7 1.2 ± 0.7 9.5 ± 2.8 5.8 ± 2.3 2PO4 0.4 ± 0.3 0.0 ± 0.0 1.8 ± 1.5 0.1 ± 0.1 0.3 ± 0.4 0.0 ± 0.0 0.7 ± 1.2 0.0 ± 0.0 0.6 ± 0.3 0.7 ± 0.3 0.4 ± 0.3 0.6 ± 0.3 Total 20.2 ± 3.8 12.5 ± 4.1 113 ± 46 86 ± 31 37 ± 13 17 ± 9 126 ± 50 89 ± 58 ions

27


Table 3: Mean concentration and standard deviation (SD) of elements based on μg m-3 and mg of mineral constituent per g of PM mass (mg g-1) for TSP and PM10 in Urmia Lake in cold and warm seasons (n=48)

PM Elements Al As Ba Cd Cu Fe Mn Ni P Pb Sr Ti Zr V Si Total Elements

cold season conc.( μg m-3) Contribute(mg g-1) TSP PM10 TSP PM10 241±140 157±77

warm seasons conc. ( μg m-3) contribute(mg g-1) TSP PM10 TSP PM10 280±81 187±81

7.8 ± 5.2 0.0 ± 0.0 0.2 ± 0.2 0.0 ± 0.0 1.1 ± 0.8 4.3 ± 1.6 0.1 ± 0.1 0.0 ± 0.0 0.3 ± 0.3 0.1 ± 0.1 0.0 ± 0.0 0.7 ± 0.3 0.1 ± 0.1 0.0 ± 0.0 0.2 ± 0.2

3.3 ± 2.8 0.0 ± 0.0 0.1 ± 0.1 0.0 ± 0.0 1.2 ± 0.7 4.2 ± 2.1 0.1 ± 0.0 0.0 ± 0.0 0.3 ± 0.2 0.0 ± 0.0 0.0 ± 0.0 0.4 ± 0.3 0.1 ± 0.1 0.0 ± 0.0 0.3 ± 0.5

40.1 ± 27.7 0.0 ± 0.0 1.3 ± 1.5 0.0 ± 0.0 5.1 ± 3.8 21.5 ± 10.1 0.6 ± 0.4 0.1 ± 0.1 1.3 ± 1.5 0.3 ± 0.5 0.1 ± 0.1 3.2 ± 0.8 0.3 ± 0.2 0.1 ± 0.0 0.6 ± 1.1

24.0 ± 24.1 0.0 ± 0.0 0.6 ± 0.7 0.0 ± 0.0 6.2 ± 4.5 29.0 ± 13.8 0.4 ± 0.2 0.1 ± 0.1 1.6 ± 0.9 0.1 ± 0.0 0.1 ± 0.1 2.7 ± 0.9 0.3 ± 0.3 0.1 ± 0.1 1.7 ± 3.2

8.8 ± 3.6 0.0 ± 0.0 0.3 ± 0.3 0.0 ± 0.0 1.2 ± 0.7 4.3 ± 1.6 0.1 ± 0.1 0.1 ± 0.0 0.7 ± 0.6 0.1 ± 0.1 0.2 ± 0.1 0.7 ± 0.4 0.1 ± 0.2 0.1 ± 0.0 0.3 ± 0.3

4.8 ± 0.7 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 1.0 ± 0.9 4.4 ± 1.7 0.0 ± 0.0 0.0 ± 0.0 0.3 ± 0.2 0.1 ± 0.0 0.2 ± 0.2 0.3 ± 0.1 0.1 ± 0.1 0.0 ± 0.0 0.5 ± 0.4

31.3 ± 10.1 0.0 ± 0.0 1.0 ± 1.0 0.0 ± 0.0 4.8 ± 2.9 15.6 ± 3.9 0.4 ± 0.1 0.1 ± 0.1 2.7 ± 2.5 0.5 ± 0.5 0.7 ± 0.9 2.6 ± 1.3 0.5 ± 0.9 0.1 ± 0.1 0.7 ± 0.9

15.0 ± 9.0 0.0 ± 0.0 0.1 ± 0.1 0.1 ± 0.0 5.7 ± 4.4 14.1 ± 9.3 0.2 ± 0.1 0.1 ± 0.1 2.1 ± 2.1 0.4 ± 0.4 1.3 ± 1.4 1.6 ± 1.1 0.6 ± 0.4 0.1 ± 0.1 1.3 ± 1.1

15.0 ± 9.0

9.9±6.8

74.6 ± 48.1

66.8 ± 49.0

16.9 ± 8.3

10.8 ± 4.5

61.1±25.4

42.7 ± 29.5

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Table 4: Mean concentration and standard deviation (SD) for elements (mg kg-1) in the crustal soil around Urmia Lake and lake bed deposited salts (n = 30) Sample(mg kg-1) Al As Ba Be Ca Cd Co Cr Cu Fe K La Li Mg Mn Mo Ni P Pb Sb Se Si Sn Sr Te Ti Tl V Y Zn Zr

crustal soil of Urmia Lake Average ± SD 7590 ± 440 4.6 ± 4.1 138 ± 40 1.9 ± 0.7 280 ± 113 0.0 ± 0.0 19.8 ± 7.3 49.7 ± 15.2 25.6 ± 18.1 26160 ± 460 500 ± 30 2.8 ± 0.6 16.1 ± 4.2 403 ± 1 1710 ± 1170 0.8 ± 0.4 46.7 ± 12.8 6330 ± 2870 50.3 ± 19.3 0.7 ± 0.2 0.1 ± 0.0 3570 ± 950 0.1 ± 0.0 220 ± 145 0.8 ± 0.0 5360 ± 2995 3.6 ± 1.3 45.0 ± 12.2 3.7 ± 1.2 9.5 ± 3.0 41.9 ± 9.7

lake bed deposited salts Average ± SD 42.6 ± 3.6 2.7 ± 2.8 1.9 ± 2.4 0.0 ± 0.0 1500 ± 130 0.0 ± 0.0 0.3 ± 0.3 0.5 ± 0.4 0.4 ± 0.3 60.3 ± 22.3 47.1 ± 38.1 0.1 ± 0.0 0.5 ± 0.3 365 ± 25 19.7 ± 19.4 0.2 ± 0.1 2.2 ± 2.0 1130 ± 810 0.5 ± 0.0 0.4 ± 0.0 0.1 ± 0.0 185 ± 35 0.1 ± 0.0 34.9 ± 26.8 0.8 ± 0.0 24.7 ± 20.9 0.2 ± 0.1 0.8 ± 0.7 0.0 ± 0.0 0.4 ± 0.1 0.6 ± 0.2

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