08-Gilas Hosseini

JRHS 2015; 15(3): 182-188

Copyright© Journal of Research in Health Sciences

Determination of the Concentration and Composition of PM10 during the Middle Eastern Dust Storms in Sanandaj, Iran

Gilas Hosseini (MSc)a, Pari Teymouri (MSc)a, Behzad Shahmoradi (PhD)b, and Afshin Maleki (PhD)b*

a Environmental Health Research Center and Student Research Committee, Kurdistan University of Medical Sciences, Sanandaj, Iran

b Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran

* Correspondence: Afshin Maleki (PhD), E-mail: maleki43@yahoo.com

Received: 22 June 2015, Revised: 10 August 2015, Accepted: 02 September 2015, Available online: 09 September 2015

Abstract

Background: The present study investigated the effect of the Middle East dust storm episodes on the concentration and composition of PM10 during April to September 2013 in Sanandaj City, western Iran.

Methods: Sampling was once every six days, and on dusty days using an Omni air sampler. The PM10 sample was collected on polytetrafluoroethylene filters. Average of 24 h values of PM10 mass concentrations was determined. Half of each sample filter and blank filter was analyzed for water -soluble ions and the other half was digested for metal analysis.

Results: The average PM10 concentration was 160.63 μg/m3. The lowest and highest concentrations of PM10 were in May and June respectively. The average PM10 concentration during the non-dusty days was 96.88 (μg /m3). Nevertheless, it increased by 4.8 times during the dusty days. Ca2+, Cl-, NO3-, and Na+ accounted for 71% of total water-soluble ions on the dusty days. During the dusty days, the dominant elements in PM10 were Na, Ca, Mg, Al, and Fe contributing to 95.72% of total measured metals.  The correlation coefficient and enrichment factor analysis have shown that on dusty days, Al, Ca, Fe, K, Mg, Na, Sr, and V were the elements with the crustal sources.

Conclusions: Concentrations of PM10 during dusty days were considerably higher than that during non-dusty days. In addition, concentrations of water-soluble ions and metals were also higher during dusty days.

Keywords: Water-soluble ions, Metal, PM10, Air pollution

Introduction

As a meteorological phenomenon, dust event usually occurs in arid and semi-arid areas1 after strong winds, and carries large amounts of dust and sand from sparsely vegetated dry deserts2. Areas known as dust prone locations are those with the annual average rainfall of 100 mm. On a global scale, the main source of dust emissions have been reported from Sahara, Middle East, Taklamakan, South west Asia, Central Australia, the Etosha and Mkgadikgadi parts of Southern Africa, the Salar de Uyuni (Bolivia), and Great Basin of the USA.

Dust events in the atmosphere have direct and indirect impacts on climate change. The direct effects include the absorption and scattering of sunlight and this condition affects the Earth's radiation budget3. Indirectly it can affect clouds lifetime by changing their properties4. Reduction in visibility due to atmospheric dust is another main problem that causes an increased challenge in aviation industry5. A large amount of particles are suspended in the air can also affect human health. Many epidemiological studies have shown a relationship between daily changes in the levels of particulate matter (PM) and health consequences such as cardiovascular and respiratory diseases and hospital admissions6. Each 10 microgram per millimeter cubed (µg/m3) increase in the mass concentration of PM10 causes a 5% increase in the total number of premature deaths7.

The range of environmental and health effects of PM depends on its chemical and physical nature.  Therefore, exploring the physical and chemical properties of PM is of great importance8. Water-soluble ions are composed up to about 30% of the particulate mass in the outer atmosphere9. Ionic composition of the particulates is important due to several reasons. First, water-soluble ions can determine the contribution of each source of particulate emissions; second, they can show the health effects of particulates; and third, they can change pollution control strategies from general control mode into specialized mode10. In addition, deposition of ions such as K+, NH+, PO43-, NO-3, and Fe2+ may improve the biogenic fertility of the oceans and cause changes in the environment11. However, the dissolved ions in water could be attributed to several factors including formation, growth, and evolution processes of the particulates. Thus, they could be better indicators of reactions occurring on the particulate surface compared with their elemental counterparts12. PM contains various metallic elements, which can be absorbed by lung through inhalation13 and can cause harm.

Studies have shown the occurrence of huge dust storms with high concentrations of PM10 in the Middle East. Severity and frequency of these storms were higher especially during the spring14,15. The major sources of Middle Eastern Dust (MED) storms include the Arabic Peninsula, Iraq, Kuwait, and some parts of Iran16. According to WHO, Sanandaj was ranked the third polluted city in the world in term of PM1017. The existence of PM10 pollution in Sanandaj City on one hand the lack of data on its quantitative and qualitative characteristics on the other hand bold necessity of carrying a scientific research work on this issue. Moreover, preparing a comprehensive database is crucial for the authorities concerning with control, planning, and increasing peoples knowledge in order to contribute to their protection against the hazardous effects of pollutants. The chemical composition of dust storms has impact on the environment and human health; however, few studies have been conducted in this regard. Since 2009, dust event phenomenon has frequently been occurring in Sanandaj and there have been an increased mortality and morbidity attributable to PM10 exposure18. Schools, airports, and offices have also been closed. In addition, there is no report on qualitative analysis of PM10 in Sanandaj City. Therefore, this study aimed to determine the atmospheric PM10 concentration of Sanandaj City and its ionic and metallic contents during April to September of 2013.

Methods

Study area

Sanandaj is a developing and non-industrialized city located in northwestern Iran, with a population of around 450,000 people. Its longitude and latitude are 47°00 E and 35°32 N respectively and its elevation is about 1500 meters above sea level. The city is influenced by dust storms coming from several countries, such as Iraq, Kuwait, and Saudi Arabia11,14 (Figure 1).

Meteorology in Sanandaj

Meteorological parameters including temperature (°C), wind speed (m/s), relative humidity (RH) (%), rainfall (mm) and visibility were obtained from Kurdistan Province Meteorological Organization and were used to show the climatic characteristics of Sanandaj City. With an average temperature of 28.54±1.6°C and humidity of 21.82 ± 1.69% RH, August was the hottest and driest month in Sanandaj City. April was the coolest (14.06 ±1.56°C) month while May was the most humid (55.27 ±16.84% RH) month. During the study period, the lowest and the highest reported wind speeds were 1.88 ±0.41 m/s and 2.66 ±1.24 m/s, respectively.

Instruments and measurement schedule

The concentration of PM10 was measured using a low-volume air sampler (FRM OMNITM Air Sampler, multi-cut inlet; BGI, Inc., USA) operating at a flow rate of 5 l min-1. This instrument is small and light (<10 kg), so that it can be mounted on power poles, fence posts, rooftops, and tripods in areas that are inaccessible to the high volume and low volume devices. Moreover, it is inexpensive and can be used to assess air quality in areas with high concentrations of pollutants. During April to September 2013, 28 PM10 samples were collected once in every six days in a 24 hours (h) period. Besides, 25 dusty days (DDs) samples were collected on days reported dusty by the Kurdistan Province Meteorological Organization. Out of the total 53 samples collected, 44 samples belonged to non-dusty days (NDDs) with concentrations of PM10 <250 µg/m3 and the rest nine were samples represented concentration of PM10>250 µg/m3 (DDs samples)15.

Filter analysis and chemical determination

The PM10 samples were collected on polytetrafluoroethylene (PTFE, Teflon) filters with 47 mm diameter and 2 mm pore size, from SKC. Before sampling, the filter was kept at normal room temperature and relative humidity for 24 h. It was weighed three times before and after sampling by an analytical balance (Sartorius 2004 MP). The average 24 h values of PM10 mass concentrations were obtained by subtracting the initial mass of the blank filter from the final mass of the sampled filter and dividing the difference by the total volume of air passing through the filter19. After gravimetric analysis, all filter samples were stored in a 20°C freezer before subsequent analysis of water-soluble ions and metals.

Analysis of Water-Soluble Ions

Half of each sample filter and blank filter was cut and shredded into a glass vial. Since PTFE filters are hydrophobic and direct dissolution of the samples in water is not possible, to overcome this problem 0.1ml of isopropanol was added in the glass vial20. After 15 minutes, about 15ml doubledistilled water was added to it. The vial was then shaken for at least 60 min and subsequently ultrasonicated for 30 min to complete the extraction. All the extracts were then filtered through a 0.2 micrometre (μm) pore size membrane (Schleicher and Schuell) and the filtrates were stored at 4 °C in clean tubes until chemical analysis was done21. A total of nine species of water-soluble ions in the aqueous extracts of the PM10 samples including ,, ,,,and were analyzed using a Metrohm 850 Professional ion chromatography (IC), Switzerland.

Metal Analysis

The other half of each sample filter was digested at 170 °C for 4 h in high-pressure Teflon digestion container using a mixture of 3 mL HNO3, 1 mL HClO4, and 0.1 mL HF. After elapsed time, each solution was dried at 95-100°C, and then diluted to 10 ml by adding hydrochloric acid and ultrapure water (18 M cm-1 of specific resistivity) at a ratio of 1:9 V%20. The obtained solution was filtered through a Whatman-42 filter paper. Twenty-one (21) elements (Al, As, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Sn, Sr, Te, Tl, V, Zn) were determined by inductively coupled plasma atomic emission spectroscopy (ICP-AES, Arcous, Germany).

Enrichment Factors of trace metals

Enrichment Factor (EF) was used to determine and evaluate the source of trace elements in ambient aerosols. Al is commonly used as crustal source indicator element22,23. EFcrust value for element (X) was calculated according to the Equation (1):

EFcrust= (CX-aerosol/CAl-aerosol)/(CX-crust /CAl-crust)        (Equation1)

Where, CX-aerosol and CAl-aerosol are concentrations of elements X and Al in aerosol respectively and CXcrust and CAl-crust are their concentrations in average crustal material24. Based on the values​​ of their EFcrust, elements are classified into two groups. EFcrust <10 indicates that the element in the aerosol has crustal source. These are known as non-enriched elements (NEE). In contrast, the value of EFcrust >10 indicates a significant share of an element has a non-crustal source, and these are referred to the anomalously enriched elements (AEE)25.

Composition of PM10

Their concentrations were estimated using concentration of measued ions and elements and calculated as: 1) crust=Al/0.08, because Al accounts for about 8% by weight of average crust and composion of mineral dust is assumed to be similar to average crust, 2) seconary = NH4++NO3-+SO42-, 3) sea salt = 2.54×(Na-0.3Al), in whitch Na-0.3 Al stands for the seasalt originated Na, 4) smoke = K-0.25 Al, here smoke is in fact nonecrustal K, 5) metals = is the total mass of all non-crustal/non seasalt elements measured by ICP-AES, and 6) micellaneous, which is the remaining content of PM10 that does not belong to the above mentioned groups.

Data analysis

The statistical package for social sciences (SPSS) software version 16.0 (Chicago, IL, USA) was used for statistical evaluation. All graphs were plotted using Microsoft Excel 2010.

Results

Concentration of PM10­

Table 1 shows PM10 concentrations during different months of the study period. The overall mean value of PM10 was 160.63 µg/m3. The highest and lowest concentrations of PM10 were 837.12 and 31.14 μg/m3 and were recorded in June and May respectively. The Iranian national PM10 standard is the same as WHO guideline, which is 50 µg/m3 for daily average. The findings revealed that the daily mean PM10 concentrations exceeded the WHO guidelines in 77% of the days sampled. Figure 2 shows the temporal trends for mean values ​​of PM10 concentrations. Figure 3 shows the comparison between PM10 concentration and meteorological parameters. Figure 3a indicates that the PM10 concentration decreased with increasing wind speed. In Figure 3b and 3d with increasing relative humidity (RH) (%) and rainfall, the PM10 concentrations decreased. Figure 3c shows that PM10 concentration decreased with increasing temperature. In Figure 3e, as PM10 concentration increased, the visibility decreased.

Concentration of chemical composition in PM10

Table 2 presents the mass concentration and chemical composition of PM10 in the collected samples during the DDs and NDDs. Ions contributed in 21.69% and 32.334% of PM10 mass during the DDs and NDDs respectively. On the DDs, Ca2+, Cl-, NO3- and Na+ were the highest concentrations in the PM10, , which accounted for 71% of total water-soluble ions. All water-soluble ions had their highest concentrations during the DDs. The ratio DDs to NDDs ions content of sampled PM10 are shown in Figure 4a. The highest increase in ion on the DDs was for  Ca2+ (CDD/ CNDD =3.87) compared to other ions, in which CDDs and CNDDs were the concentrations of the specific ion on the DDs and NDDs periods respectively. The highest ion concentrations next to Ca2+ were for  Cl-, SO42- and NO3- (3.66, 3.39 and 3.35) respectiviely.

NO3-/ SO42- ratio

Figure 5 shows the average NO3-/ SO42- ratio in the studied months and seasons. As shown in Figure 5, sulfur had higher concentration during summer, especially in June, due to higher formation of SO42- 2.

Chemical forms of major ionic species

Bivariate correlation was used to identify the chemical forms of studied anions and cations. Table 3 depicts the correlation coefficients among the major ions. Based on the correlation coefficients, NaCl, KCl, NH4Cl, CaCl2, MgCl2 NaNO3, KNO3, and Ca(NO3)2 on the DDs and K2SO41 on both the DDs and NDDs were the major ionic species.

Metal concentration of PM10

The results of metals concentration analysis in PM10 are listed in Table 2. The sums of percentages of metals in PM10 were 14.64% and 24.73% on the DDs and NDDs respectively. The dominant elements in PM10 were Na, Ca, Mg, Al, and Fe during the DDs (contributing for 95.72%) and NDDs (contributing for 92.73%) of the total measured metals. The ratio of DDs to the NDDs of metal contents of the studied PM10 samples is displayed in Figure 4b. Accordingly, all metal elements have increased on the DDs compared to the NDDs. Cruscal elements of Ca, Al, Mg, and Na (3.97, 3.20, 2.97 and 2.89, respectively) had the highest increase on the DDs compared to the NDDs.

Enrichment Factors of trace metals

Figure 6 shows the EFcrust distribution of elements over the periods of the DDs and NDDs. EFcrust values ​​for all elements in PM10 were lower on the DDs. It is noteworthy that the long-range transport particles of PM10 were diluted by anthropogenic heavy metals, relative to locally suspended particles. In addition, as shown in Figure 6, Al, Ca, Fe, K, Mg, Na, Sr, and V on the DDs had lower EFcrust values than 10. That means, there were the elements with the crustal sources. The other elements were of anthropogenic sources.

Composition of PM10

Chemical  species in PM10 were groupt into six classes including crust, secondary, sea sult, smoke heavy metals and micellaneous. Figure 7 presents the composition of PM10 on the DDs and NDDs. It shows that there was no non-crustal K in the studied PM10 samples. During the DDs concentration of all groups inceased about 3 times, except for the sixth group, which had a 9 fold increase. This constituent of PM10 contributed to 57.35% of the total PM10 concentratin. It could be comprised of carbonaceous components, H2O and/or other unmeasured ions and elements in the PM10.

Figure 1: PM10 Sampling site, Kurdistan University of Medical Sciences, Sanandaj, Iran

Figure 2: Temporal trends in daily average PM10 concentrations over the study period in Sanandaj City, western Iran

Figure 3: Comparison between PM10 concentration and meteorological parameters: a: Wind speed, b: RH, c: Temperature, d: Rainfall, e: Visibility

Figure 4: Ionic (a) and Metal (b) components in PM10 on dusty days and non-dusty days

Figure 5: The average NO3-/ SO42- ratio in the studied months and seasons

Figure 6: EFcrust values for analyzed elements in PM10 during dusty days and non-dusty days

Figure 7: Composition of PM10 during the dusty days and non-dusty days

Table 1: Comparison of PM10 concentrations (µg/m3)

Table 2: Mass Concentrations of PM10 and its chemical composition during the dusty days and non-dusty days

Table 3: The correlation coefficients among major ions in the PM10

Discussion

Previous report by Shahsavani et al.15 showed that the highest concentrations of PM10 (5337.6 μg/m3) during June. Similarly, Draxler et al.14 reported the highest concentrations of PM10 in June from Kuwait, Iraq, and Saudi Arabia. Since the current study area was nearby to the dust-producing countries in the Middle East such as Iraq, Kuwait, and Saudi Arabia, it is possible that the changes in PM10 concentrations follow the same trends as in those regions14. Most dust events in the Middle East occur in late spring and early summer. This event can be caused by the Shamal wind, a hot northwesterly wind that can carry large amounts of dust from southern areas of Iraq and increases the concentration of particulates26.

The decrease in PM10 concentration with increase in wind speed can be attributed to the fact that the main source of dust in Sanandaj is Iraq. The city is not surrounded by desert. In the direction of the winds, dust source did not exist. Therefore, increasing the wind speed does not increase the concentration of PM10, rather an increase in wind speed caused the dispersion of the particulates in the city and thus, the PM10 concentration decreased. The decrease in PM10 concentration was associated with an increase in temperature. As temperature increased, the airborne particulates expanded, resulting in a well vertically mixed up particles. This research showed no strong correlation between PM10 and meteorological parameters, except for PM10 and visibility (negative correlation of -0.502). As shown in Figure 3e, visibility was down on the DDs. The average visibility on the NDDs (9993 meters) was 1.7 times higher than in the DDs.

In a study conducted by Yadav and Rajamani27, PM10 concentrations during dust events in summer was 2907 µg/m3, which is 10-25 times higher than in the non-dust events. The results of another study showed that during the Asian dust events in 2000, the PM10 concentration in Beijing was higher than 1500 μg/m3. The concentration was more than 5-10 times higher during DDs compared to NDDs28. Rodriguez et al29 reported that daily average of PM10 concentrations in Sahara during dust events might be 10-23 times higher than the standard increase in Southern Spain. Another study conducted in Lanzhon (China) found that the average PM10 concentration in April and the average concentration of PM2.5 and PM1 in December to be the highest values. Furthermore, sand dust events in spring were found to have carried greater amount of coarse particles than fine particles30.

Anions such as SO42-, NO3-, Cl-, and carboxylates were responsible for acidic atmosphere while cations such as NH4+, Ca2+, and Mg2+ were reported to be the causes of basic atmosphere. The ratio of the summation of equivalent concentration of cations (µeq/m3) to summation of equivalent concentration of anions (µeq/m3) (C/A), which is known as ionic balance, can be used to study the acidity of the atmosphere1. In this study the ratio of DDs/NDDs (C/A) was 1.07/0.96.  The ratio is almost close to one and indicates that there were some other ions that should be measured. Slope of the regression line of A/C (reverse C/A ratio) plot for the NDDs was slightly higher than unity. This might be due to the attribution of uncalculated H+ or vaporization of NH4+ into gas phase. For the DDs, the slope was lower than unity, which implies the probable existence of carbonate or bicarbonate anions15 that have not been considered in this study.

To study the relative importance of mobile versus stationary sources of nitrogen and sulfur in the atmosphere, the ratio of NO3-/ SO42- was used because NO3-and SO42-(µeq/m3) are indicators of mobile and stationary emissions respectively. This ratio of DDs and NDDs are found to be 0.78 and 0.79 respectively. This indicates that stationary sources had higher contribution in atmospheric pollution than mobile ones.

As shown in figure 4b, all metal elements have increased on the DDs compared to the NDDs. Cruscal elements of Ca, Al, Mg, and Na had the highest increase on the DDs compared to that of the NDDs. These findings are in agreement with the results reported by Tsai et al.22 and Wang et al.1, in which Ca2+, Ca, and Al were the species with the highest increase in atmospheric concentration during Asian dust storms. The long-range transport of particles of PM10 was diluted by anthropogenic heavy metals, relative to locally suspended particles. Therefore, EFcrust was reduced on the DDs. However, this does not mean that the absolute concentrations of these metals in the air on the DDs were lower than those on the NDDs.

The strong correlation between Al and Fe on DDs (r = 0.98) shows the crustal origin of Fe. The average ratio of Fe/Al was 0.59 on the DDs, which is closer to the ratio of Fe/Al (0.68) in crust24. This shows that the bulk Fe could be due to crustal source. Besides the strong correlation between Al and V on the DDs (0.81), the very low EFcrust (2.70) for this element implies that V has a crustal source31. On the other hand, the mean ratio of V/Al (0.004) is close to what is stated in the crust (0.001). This in turn can confirm the crustal source of this element. Again the strong correlation between Al and Ca (r=0.78), Al and K (r=0.67), Al and Mg (r=0.64) Al and Mn (r=0.64), Al and Mg (r=0.58), Al and Sr (r=0.53) confirms that all these elements have crustal source.

The Al on the DDs has a correlation coefficient of less than 0.5 with As, Cd, Cr, Li, Mo, and Ni and their EFcrust is above 10 can indicate that these elements have anthropogenic sources. In both DDs and NDDs, Al and Cu have low correlation coefficient (< 0.5). This may indicate that Cu is a metal with anthropogenic source, which probably has local sources of pollution because on the NDDs it has a higher EFcrust than the DDs. The high correlation (0.5 to 0.98) of Cu with trace elements, such as As, Cd, Cr, Li, Mo, and Ni, imply non-crustal source, having identical source for all of them.

The crustal source of Al, Fe, Mn, and Cr elements and trace metals such as Pb, Cd, and Zn were derived from non-crustal sources with EF> 1027. Correlation coefficient between Al and Co, Sn, Tl, and Zn elements were 0.81, 0.75, 0.94, and 0.68, respectively. EFcrust over 10 for Co, Sn, Tl, and Zn refers to their dominant pollution sources. However, high correlation of these elements with Al refers to the fact that portion of these elements could be from the crustal source or the resuspended polluted crustal dust. Tahir et al.25 and Hsu et al.32 reported that Al, Na, Mg, K, Ca, Sr, Ba, Ti, Mn, and Co in PM10 of Taipei atmosphere were the elements with the crustal origin. It should be mentioned that on the NDDs, all elements with a correlation coefficient higher than 0.5, could be derived from an identical source.

Conclusions

During the study period, average PM10 concentration was 160.63 μg/m3. The highest and lowest concentrations of PM10 were found in June and May with the values of 837.12 and 31.14 μg/m3 respectively. Moreover, the average PM10 concentrations on the NDDs and DDs were 96.88 and 472.28 μg /m3, respectively. Ca2+, Cl-, NO3-, and Na+ accounted for 71% of the total water-soluble ions. The ratio of NO3-/SO42- revealed that stationary sources had higher contribution in atmospheric pollution than mobile ones. During the DDs, the dominant elements in PM10 were Na, Ca, Mg, Al, and Fe contributing to 95.72% of the total measured metals. The correlation coefficient and enrichment factor analyses revealed that Al, Ca, Fe, K, Mg, Na, Sr, and V on the DDs were the elements with the crustal sources. The classification the PM10 composition into six groups revealed that the concentration of all the classified groups including crustal, secondary, sea salt, metals and other, increased during the DDs. However, non-crustal K did not exist on the DDs or NDDs. Finally, it is suggested that other components of PM10, especially by considering their health effects and carbonaceous contents should be studied in future.

Acknowledgments

The present study is a part of the M.Sc. dissertation work of the first author. The authors would like to thank the Vice Chancellor of Research and Technology, Kurdistan University of Medical Sciences for providing the grant of this research study.

Conflict of interest statement

The authors have no competing interest.

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E-mail: jrhs@umsha.ac.ir