Characterization of PM Using Multiple Site Data in a Heavily ...

Aerosol and Air Quality Research, 15: 11–27, 2015 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2014.02.0039

Characterization of PM Using Multiple Site Data in a Heavily Industrialized Region of Turkey Melik Kara1,2, Philip K. Hopke2*, Yetkin Dumanoglu1, Hasan Altiok1, Tolga Elbir1, Mustafa Odabasi1, Abdurrahman Bayram1 1 2

Department of Environmental Engineering, Dokuz Eylul University, Tinaztepe Campus, Buca-Izmir, 35160, Turkey Center for Air Resources Engineering and Science, Clarkson University, Box 5708, Potsdam, NY 13699, USA

ABSTRACT Source apportionment has most often been applied to a time series of data collected at a single site. However, in a complex airshed where there are multiple sources, it may be helpful to collect samples from multiple sites to ensure that some of them have low contributions from specific sources such that edges can be properly defined. In this study, samples were collected at multiple sites in the Aliaga region (38°40′–38°54′N and 26°50′–27°03′E) located in western Turkey on the coast of the Aegean Sea. This area contains a number of significant air pollution sources including five scrap iron-steel processing plants with electric arc furnaces (EAFs), several steel rolling mills, a petroleum refinery, a petrochemical complex, a natural gas-fired power plant, a fertilizer plant, ship breaking yards, coal storage and packaging, scrap storage and classification sites, large slag and scrap piles, heavy road traffic, very intense transportation activities including ferrous scrap trucks and busy ports used for product and raw material transportation. A total of 456 samples of PM10 at six sampling sites and 88 samples of PM2.5 at one site were collected for four seasons and the elemental composition was determined for 43 elements. The newest version of EPA PMF (V5.0) that has the capability of handling multiple site data was used for source apportionment. Eight factors were identified as iron-steel production from scrap (23.4%), resuspended and road dust (23.3%), crustal (20.5%), marine aerosol (14.4%), biomass and wood combustion (7.2%), salvage activities (4.7%), coal combustion (3.7%) and residual oil combustion (2.8%). The pattern of source contributions and conditional probability function analysis were consistent with the locations of the known sources. Thus, the multiple site data allowed for a comprehensive identification of the primary sources of PM in this region. Keywords: EPA PMF (V5.0); Trace elements; Source contribution; Iron-steel production; Aliaga.

INTRODUCTION Particulate matter (PM) may be emitted into the atmosphere from a variety of natural (i.e., soil erosion, sea spray, volcanic activities, natural forest fires) and anthropogenic sources (i.e., industrial activities, traffic emissions, residential heating, fossil fuel combustion including coal and biomass burning). In addition, they can be formed by the chemical transformation of organic compounds or inorganic gases in the atmosphere as secondary organic aerosol (SOA) (Kroll and Seinfeld, 2008) and secondary inorganic aerosol (SIA) (Belis et al., 2013). Atmospheric particulate matter with aerodynamic diameter smaller than 10 µm (PM10) has been identified as one of the most significant of air pollutants in terms of its environmental

*

Corresponding author. Tel.: 1-315 268 3861 E-mail address: [email protected]

and health impacts. Many studies indicated that PM10 can affect the climate (Tainio et al., 2013), reduce the visibility (Polissar et al., 2001; Chang et al., 2009), affect other ecosystems (sea, soil, or vegetation) (Leung and Jiao, 2006; Odabasi et al., 2010; Hofman et al., 2013; Im et al., 2013), and high PM10 concentrations may also play a role in the severity and incidence of respiratory diseases such as aggravated asthma, coughing and painful breathing, chronic bronchitis, and decreased lung functions (Ostro et al., 1999; Zheng, 2011; Chen et al., 2013). Thus, the characterization of particulate matter (PM) is important for regulators and researchers because of its potential impact on human health and long-range transport crossing international border (Stefan et al., 2010; Kim et al., 2012). In order to have an efficient air quality management system and its regulatory approaches, it is necessary to have reliable air quality data and understand the spatial and temporal variations of PM and its compositions and sources. Several studies have been carried out in recent years to ascertain particulate matter’s physical and chemical characteristics (Chen et al., 2003; Zheng et al., 2004;

Kara et al., Aerosol and Air Quality Research, 15: 11–27, 2015

Braga et al., 2005; Jones and Harrison, 2006; Adamo et al., 2008; Huang et al., 2011; Han et al., 2014). Source identification is an important step in air quality management. Therefore, receptor modeling has been widely applied to identify and apportion sources of particulate matter based on chemical species data collected at the receptor sites (Zabalza et al., 2006; Contini et al., 2012; Srimuruganandam and Shiva Nagendra, 2012; Tecer et al., 2012; Begum et al., 2013; Gianini et al., 2013). Positive Matrix Factorization (PMF) has become the most widely used receptor modeling approach (Paatero and Hopke, 2003). Air pollution has become a severe problem in many parts of the world because of the intense industrial and urban activities (Chan and Yao, 2008; Yue et al., 2008; Stone et al., 2010; Lee et al., 2011; Vienneau and Briggs, 2013; Xue et al., 2013). Following rapid social and economic development over the past several decades, particulate matter and its elemental composition in urban and industrial regions has also become significant in Turkey. Several studies related to monitoring of the PM10 have been performed at regional and national scales in Turkey (Karaca et al., 2005; Polat and Durduran, 2012; Kara et al., 2013), and the chemical composition and sources of atmospheric PM have also been studied in many parts of the country (Karakas et al., 2004; Yatin et al., 2000; Bayraktar et al., 2011; Durukan et al., 2013). The Anatolia located in the Eastern Mediterranean receives air masses from different directions (Ozturk et al., 2012). Major sources of atmospheric particles in the Mediterranean areas, including the area of the present study, are the long-range transport of mineral dust from deserts of North Africa (Herut et al., 2001), and industrial emissions from Eastern Europe (Sciare et al., 2003). Dogan et al. (2008) indicated that potential industrial source areas include the Balkan Countries, Ukraine, and regions located north of Ukraine. In addition, the local anthropogenic or industrial emissions, biogenic emissions, and sea salt are other significant PM sources for the region (Odabasi et al., 2002; Kocak et al., 2007; Turkum et al., 2008; Tecer et al., 2012). In some heavily industrialized regions of Turkey (i.e., Iskenderun, Gebze, and Aliaga), local sources are more dominant compared to other sources, and the PM concentrations can frequently exceed air quality standards (Karademir, 2006; Yatkin and Bayram, 2008; Odabasi et al., 2010; Pekey et al., 2010). Over the past 30 years, the Aliaga region has undergone a rapid transition from an agricultural region to a heavily industrialized region, and has formed a complex industrial structure arising from iron-steel production, petroleum refining, petrochemical plants, and other industries (i.e., fertilizer plants, steel rolling mills). The establishment of industrial activities without proper planning followed by the subsequent population increase resulted in adverse impacts on the local ecosystem and these poses a potential threat to the human health. Therefore, it is necessary to identify the PM sources and apportion PM to those sources to provide efficient air quality management for this region. The objectives of this study were (1) to determine the spatial and temporal variations of PM10 and PM2.5 concentrations, and (2) to identify the PM10 sources using

multiple sites data by EPA PMF (V5.0) in the Aliaga Region. PM samples (PM10 from six sites and PM2.5 from one site) were collected in the region during four sampling campaigns (summer, fall, winter and spring) performed between June 2009 and April 2010. PM samples were analyzed for trace elements using ICP-MS and the results were evaluated by PMF and CPF to identify their possible sources. MATERIALS and METHODS Study Area The Aliaga region (38°40′–38°54′N and 26°50′–27°03′E) is located in the western part of Turkey on the coast of the Aegean Sea. It contains a number of significant air pollution sources including five scrap iron-steel processing plants with electric arc furnaces (EAFs), several steel rolling mills, a large petroleum refinery, a petrochemical complex, a natural gas-fired power plant, a fertilizer plant, ship breaking yards, coal storage and packing, scrap storage and classification sites, large slag and scrap piles, heavy road traffic, very intense transportation activities including ferrous scrap trucks and busy ports used for product and raw material transportation. Aliaga town with a population of ~60 000, several villages, agricultural areas, and some resort sites are also located within the region. The locations of the sampling sites, industrial activities and settlements in the study area are illustrated in Fig. 1. Sampling and Analysis Particulate Matter (PM10) samples were collected concurrently at five sites [Aliaga town (38°47.2′N–26°58.5′E), Helvaci (38°42.1′N–27°01.6′E), Bozkoy (38°43.2′N– 26°57.9′E), Horozgedigi (38°43.9′N–26°55.5′E) and Cakmakli (38°44.8′N–26°54.7′E) villages]. Samplings were conducted for 24 h periods (10:00 am to 10:00 am) for 15 to 23 days at each station during each season (summer, fall, winter and spring) between June 2009 and April 2010 (Table 1). A sampling site (38°49.1′N–26°55.6′E) near the ship breaking yards was also added in the winter and spring seasons. Furthermore, PM2.5 samples were collected at just one site, Bozkoy, during the same periods. PM10 inlets that fulfill the US EPA specifications were used for PM10 as well as PM2.5 sampling (EPA, 2000). The particles enter the inlet at a flow rate 16.7 L/min by means of an air sampler pump supplying a constant volumetric flow rate. In this study, a Partisol™ 2025i-D Dichotomous air sampler (www.thermoscientific.com/AQI) which can simultaneously collect fine and coarse PM samples was used to collect PM2.5 and PM10 samples in Bozkoy site. Five PM10 sampler systems [PM10 inlet (Model PF 20630; Zambelli S.r.l.) with digital pump systems (ISODUST HF, PF 12000PE-01; Zambelli S.r.l.) (www.zszambelli.com)] were used at the remaining sampling sites. Teflon filters (PTFE) (47 mm, 2.0–µm pore size, Whatman) were used for sampling. The particulate masses were determined by weighing the filters before and after the exposure with a microbalance (Mettler-Toledo Model XP26, capable of weighing 1 µg). Quality assurance exercises were provided

Kara et al., Aerosol and Air Quality Research, 15: 11–27, 2015

Fig. 1. Locations of the sampling sites, industrial activities and settlements in the study area. Table 1. Summary of the sampling information. Sampling Period Summer Fall Winter Spring

Duration 30.06.2009 24.07.2009 30.09.2009 23.10.2009 04.01.2010 28.01.2010 01.04.2010 23.04.2010

Number of PM10 Samples Bozkoy Horozgedigi Cakmakli

Aliaga town

Helvaci

Ship breaking

Total

22

20

21

16

21

-

100

23

22

21

22

22

-

110

20

18

23

21

20

16

118

19

18

23

23

21

22

126

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by certified 20 mg weight with 1% acceptance criteria. Since the temperature and humidity in the weighing room may affect the moisture content of the filters, and thus its weight, filters were equilibrated in a conditioned room (50 ± 5% relative humidity and 20 ± 2°C temperature) for at least 24 hours before being weighed. The filters were digested in 8 mL HNO3 (Merck, Suprapur® ) and 5 ml HCl (Merck, Suprapur® ) mixture with a microwave digestion system (MARS 5, CEM Corp.). Then the samples were diluted to 50 mL with deionized water (18.2 MΩ/cm) and filtered through a 0.45 µm PTFE filter (Millipore) before analysis. Analysis of trace elements (Ag, Al, As, B, Ba, Bi, Ca, Cd, Ce, Co, Cr, Cu, Dy, Er, Fe, Ga, Gd, Hg, K, La, Li, Mg, Mn, Mo, Na, Nd, Ni, P, Pb, Pr, Rb, Sb, Se, Sm, Sn, Sr, Th, Tl, U, V, Y, Yb and Zn) were carried out using Inductively Coupled Plasma – Mass Spectrometry (ICP-MS) (Agilent 7700x, with HMI). Blanks were prepared simultaneously for a routine check for estimation of each metal in the reagents and blank filters. For quality assurance, NIST standard reference material SRM 1648a Urban Particulate Matter was analyzed. Quality control/quality assurance procedures were applied during the sample preparation and analysis. The continuing check verification (CCV-1) standard solution (High Purity Standards, Charleston, SC) was used to check the validity of calibration curve during analysis. The limit of detection (MDL) of the method was defined as the mean blank mass plus three standard deviations (MDL = mean blank+3 SD). Meteorological Conditions The region is mainly classified within the local climate of the Mediterranean Sea that is characterized by hot, dry summers and cool, wet winters and springs. The annual rainfall reaches to 688 mm with the highest monthly rainfall of 131 mm falling during December (MGM, 2013). Monthly average temperatures in sampling periods were measured as 28.2, 21.0, 10.6 and 16.1°C for July, October, January and April, respectively. The prevailing winds are northwest and southeasterly in the region. The wind rose plots were generated using WRPLOT View (Lakes Environmental, Canada) for the four sampling periods. They are shown in Fig. S1 in the supplementary material. The annual prevailing wind directions were: WNW, 26.5%; NW, 13.3%; N, 13.0 and S, 8.8% and mean wind speed was 3.1 m/s during the sampling period. Positive Matrix Factorization (PMF) Positive Matrix Factorization (PMF) in the form of EPA PMF V5.0 was used in this study. This version is described in greater detail by Paatero et al. (2013). Uncertainties as well as the BDL concentrations and missing values were treated by the approaches described by Polissar et al. (1998). The uncertainties (σij) for the ICP/MS data were estimated from the following:

2 3

 ij  xij  ( DL j ) for samples below limit of detection (1)

2 3 2  ij  0.1xij  ( DL j ); xij  3DL j 3 values

     

 ij  0.2 xij  ( DL j ); DL j  xij  3DL j 

for detected

(2)

where xij is the determined concentration for species j in the ith sample, and DLj is the detection limit for species j. The coefficients 0.2 and 0.1 in Eqs. (1) and (2) were empirically determined (Zabalza et al., 2006). PMF has most often been applied to a time series of data collected at a single site. However, PMF generally requires a large number of samples to make a stable and reliable source identification. Also, in a complex airshed having multiple sources, it may be helpful to collect samples from multiple sites to ensure that some of them have low contributions from specific sources. Therefore, the newest version of EPA PMF (V5.0) that can handle multiple site data was used in the present study. Conditional Probability Function To identify directionally of local point sources, a conditional probability function (CPF) (Ashbaugh et al., 1985; Kim et al., 2004) was calculated using source contribution estimates resolved by PMF analysis and wind speed and direction data measured at the site. The same daily fractional contribution was assigned to each hour of a given day to match to the hourly wind data. Specifically, the CPF is defined as; CPF 

m n

(3)

where mΔθ is the number of occurrences from wind sector Δθ that are upper 25 percentile of the fractional contributions, nΔθ is the total number of observations from the same wind sector. In this study, the size of the wind direction sector was set to 11.25°. Calm winds (< 1 m/s) were excluded from this analysis. The sources are likely to be located in the directions that have high conditional probability values. Kruskal-Wallis (K-W) Test To assess the spatial and temporal variability of the PM and elemental concentrations, Kruskal-Wallis (K-W) tests were performed. The K-W test is a non-parametric test to assess whether the samples originate from the same distribution. It can determine if more than two samples are independent. The null hypothesis is that all the samples come from the same distribution based on non-significant differences in the population medians. If the significance level is smaller than 0.01, the null hypothesis is rejected and the alternate hypothesis is accepted. RESULTS and DISCUSSIONS PM10 and PM2.5 Concentrations A total of 456 samples of PM10 from six sampling sites and 88 samples of PM2.5 from one site were collected over

Kara et al., Aerosol and Air Quality Research, 15: 11–27, 2015

four seasons. Thus, spatial and seasonal variability of PM10 and PM2.5 were evaluated in the region. During the study, the annual average values of PM10 concentrations were 39.9, 50.4, 53.6, 55.0, 54.1, and 49.8 µg/m3 for Aliaga town, Helvaci, Bozkoy, Horozgedigi, Cakmakli, and the ship breaking yards, respectively. These annual PM10 values exceeded the European Air Quality Directive, 2008/50/EC (EC, 2008) limit value of 40 μg/m3 (a PM10 limit value of 40 μg/m3 was also set by the Air Quality Assessment and Management Regulation of Turkey) at all sites except Aliaga town. These results indicate that local industrial sources significantly affect the PM mass concentrations at the sampling sites. The Horozgedigi, Cakmakli and Bozkoy sites were located in same area and were closest to the iron-steel plants with electric arc furnaces (EAFs) and steel rolling mills. In this area called the Nemrut area, many fugitive sources (i.e., large slag and scrap piles, unpaved roads, coal storage and packing, a very dense transportation activity of trucks) influence the entire region. The PM10 levels during the sampling days were highest in this area, exceeding 100 µg/m3. Helvaci and Aliaga town sites were more distant (~10 km) from the Nemrut area. Helvaci had the high PM values because of urban activities (i.e., traffic, heating) and industrial activities since it was downwind to the industrial region. The lowest concentrations were measured in Aliaga town. The ship breaking site reflected the ship breaking activities. Lower values were measured in the winter due to the reduced activity and weather conditions. High concentrations were again measured in the spring season. PM10 concentrations indicated similar seasonal patterns for all sampling sites. Regarding the spatial and seasonal variability, there were significant differences in PM10 concentrations among six sampling sites in the all seasons (p < 0.01) (Table 2). For the Bozkoy, Aliaga town, Cakmakli, and ship breaking sites, the K-W test indicated significant differences among sampling seasons (p < 0.001). Significant differences were not observed across seasons at Helvaci (p = 0.634) and Horozgedigi (p = 0.171). In summer, the PM10 concentrations were slightly higher than those in other seasons, probably

due to re-suspended PM from unpaved roads, storage piles, soil, and industrial facilities during this dry season. Regarding the attainment of the daily limit value in EU Directive 2008/50/EC (EC, 2008), the 90.4 percentile value exceeded the limit value of 50 μg/m3 at all sites, even in Aliaga town that had 17 exceedances (20% of samples). The exceedance ratio was approximately 50% for the rest of sampling sites during the sampling period. The annual average PM2.5 concentration was 28.3 µg/m3 at Bozkoy site (Table 3).This value is above the air quality limit (25 µg/m3) for PM2.5 (EC, 2008). The seasonal PM2.5 values varied between 12 to 48, 10 to 52, 11 to 77, and 12 to 55 µg/m3 during summer, fall, winter, and spring, respectively. Seasonal PM2.5 concentrations were not significantly different among the sampling seasons (p = 0.737). The highest PM2.5 was in the summer season followed by winter and spring. The lowest average PM2.5 concentration (27.1 µg/m3) was measured in fall. The PM2.5/PM10 ratios were calculated from PM2.5 and PM10 concentrations measured at Bozkoy site. The ratio of PM2.5/PM10 is used to assess whether PM10 is dominated by anthropogenic or crustal sources. High PM2.5/PM10 ratios (> 0.5) indicate that the anthropogenic sources contribute to PM10 to a greater extent. In this study, the average PM2.5/PM10 ratios showed a clear seasonal pattern ranging between 0.41 and 0.77. The ratio of PM2.5/PM10 was higher in winter (0.77) compared to other seasons (Table 3). The higher ratios in winter were due to lower PM10 values during the wet season when suspension and re-entrainment of soil were minimal. Winter rainfall was 119 mm compared with 48, 45, and 2 mm in spring, fall, and summer, respectively. Summer had the lowest PM2.5 to PM10 ratios since crustal sources (i.e., soil, wind entrainment, re-suspended dust) were the dominant sources in the region. The values in this area were slightly lower than the ratios measured in Alsasua, Spain (0.70 to 0.86) (Zabalza et al., 2006); Athens, Greece (0.45 to 0.78) (Pateraki et al., 2012); Agra, India (0.55 to 0.76) (Kulshrestha et al., 2009); and central Taiwan (0.56 to 0.72) (Fang et al., 1999). These results suggested that the coarse PM significantly contributed to total PM10 mass in the study area.

Table 2. Seasonal and annual PM10 concentrations (µg/m3) for the six sampling sites. Sites Aliaga town Helvaci Bozkoy Horozgedigi Cakmakli Ship Breaking

Summer 47.1 ±12.0 (46.6) 52.7 ± 13.1 (53.2) 70.6 ± 15.1 (74.0) 54.7 ± 13.1 (50.5) 56.2 ± 14.0 (51.2) -

Average ± Std. Dev. (µg/m3) (Median) Fall Winter Spring 39.5 ± 9.8 (39.7) 26.6 ± 7.6 (24.6) 46.2 ± 17.2 (40.3) 48.1 ± 17.4 (42.8) 52.0 ± 23.4 (47.3) 49.1 ± 16.6 (55.3) 54.2 ± 21.0 (47.0) 36.9 ± 19.9 (29.9) 54.3 ± 19.4 (52.9) 55.9 ± 17.4 (56.7) 47.1 ± 19.7 (47.0) 61.4 ± 25.8 (58.0) 53.2 ± 13.2 (51.2) 43.4 ± 20.6 (35.8) 63.0 ± 22.7 (63.8) 27.9 ± 8.4 (29.1) 65.8 ± 20.3 (64.1)

Annual 39.9 ± 14.3 (38.1) 50.4 ± 17.6 (49.0) 53.6 ± 22.2 (48.8) 55.0 ± 20.4 (52.5) 54.1 ± 19.0 (51.1) 49.8 ± 25.0 (45.2)

Table 3. Seasonal and annual PM2.5 concentrations (µg/m3) and PM2.5/PM10 ratios in four seasons.

PM2.5 (µg/m3) PM2.5/PM10

Summer 29.6 ± 10.1 (30.7) 0.41 ± 0.08 (0.42)

Average ± Std. Dev. (Median) Fall Winter Spring 27.1 ± 13.0 (23.2) 28.8 ± 16.2 (22.1) 27.6 ± 11.4 (24.8) 0.49 ± 0.09 (0.49) 0.77 ± 0.12 (0.79) 0.51 ± 0.11 (0.54)

Annual 28.3 ± 12.8 (25.0) 0.55 ± 0.17 (0.51)

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Elemental Concentrations Elements were determined in 229 PM10 and in 48 PM2.5 samples. Table 4 lists the average concentrations, standard deviations, and percent of missing and below detection limits observations of each element in PM10 samples used for PMF analysis. The elemental concentrations in PM10 and PM2.5 were dominated by crustal and sea salt elements (Ca, Mg, Al, K, and Na). These elements were followed by Fe, Zn, Mn and Pb emitted from scrap handling and ironsteel production. Their concentrations were also high at all

of the sampling sites, but the relative importance of these elements changed according to sampling site properties. The elemental concentrations for PM10 were usually higher in Bozkoy and at the ship breaking site compared to the other sites, especially for Fe, Zn, Pb, Mn, Cr, Cu, Ni, Sn, Sb, As, Cd, Ag, and Co that are related to scrap handling and iron-steel production. The Bozkoy site was mainly influenced from the activities located in the Nemrut area given its proximity and the prevailing wind direction. Cakmakli and Horozgedigi are also located within the

Table 4. PMF input data statistics for the elemental composition of PM10 samples. Species

S/N*

Average Ag 1.8 0.64 Al 3.4 1198 As 3.7 4.24 B 0.7 6.01 Ba 5.0 14.8 Bi 0.8 0.8 Ca 4.0 2241 Cd 5.5 3.13 Ce 5.1 0.87 Co 1.2 0.73 Cr 5.2 33.3 Cu 6.0 39.8 Dy 2.3 0.05 Er 1.6 0.03 Fe 5.1 1832 Ga 5.2 3.14 Gd 2.2 0.07 Hg 0.8 0.28 K 6.2 604 La 4.9 0.49 Li 0.1 0.83 Mg 4.1 508 Mn 7.1 75.5 Mo 2.7 4.81 Na 2.8 918 Nd 3.1 0.37 Ni 5.9 12.4 P 0.4 120 Pb 7.4 175 Pr 3.9 0.08 Rb 4.5 1.88 Sb 1.9 3.69 Se 1.2 0.59 Sm 1.9 0.07 Sn 4.2 4.89 Sr 3.4 7.39 Th 3.4 0.14 Tl 2.5 0.11 U 3.8 0.14 V 5.0 15.6 Y 4.2 0.24 Yb 1.5 0.02 Zn 3.9 929 * S/N donates signal to noise ratio.

Conc. (ng/m3) Std. Dev. 0.91 803 3.5 3.39 9.73 0.66 2034 4.11 0.67 0.52 21.1 40.8 0.04 0.02 1791 2.09 0.06 0.21 458 0.35 0.47 340 91.9 5.11 493 0.3 8.56 133 231 0.07 1.23 5.07 0.29 0.06 5.77 6 0.11 0.09 0.12 10.4 0.18 0.02 1091

Missing 1.3 0 0.9 0.9 0 0.9 0.4 0 0.4 0.9 1.3 0.9 0 0 0 0 0 0 0 0 0 0 0 3.9 0 0 1.3 0.9 0.4 0 0 1.7 0 0 0 0 0 0 0 0.9 0 0 0.4

Zero or < DL 37.6 1.3 0.4 39.7 0.4 41.5 9.6 0 0.4 24.9 0 0 7.9 17 0.4 0 11.8 63.8 0 0.4 86.5 2.6 0 33.2 2.2 2.6 0 65.9 0 2.2 0 27.9 17.5 15.7 0.9 1.3 4.8 7.4 0 1.3 0.9 18.3 6.1

% of xj DL < xj < 3DL 26.6 26.6 23.1 51.5 7 48.9 17.9 10 6.1 55 1.3 3.1 44.1 56.3 10 4.8 43.2 21.8 2.2 8.3 13.5 11.8 0.4 12.2 31.9 29.3 2.2 28.4 0 19.7 9.6 35.8 68.1 49.8 24.9 23.6 25.3 40.6 17.9 9.6 15.3 58.5 13.5

xj > 3DL 34.5 72.1 75.5 7.9 92.6 8.7 72.1 90 93 19.2 97.4 96.1 48 26.6 89.5 95.2 45 14.4 97.8 91.3 0 85.6 99.6 50.7 65.9 68.1 96.5 4.8 99.6 78.2 90.4 34.5 14.4 34.5 74.2 75.1 69.9 52 82.1 88.2 83.8 23.1 79.9

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Nemrut area. The ship breaking site reflected salvage facilities. The Cu concentrations were higher at this site since copper was emitted from recycling copper wires and electronic wastes (U.S. Geological Survey, 2013). K and Rb showed higher concentrations in Helvaci that may be attributed to the influence of biomass burning. The lowest concentrations were obtained in Aliaga town that is relatively far from the Nemrut area, but close to major roads, the refinery, and petrochemical complexes. Therefore, each site showed differences according to the influences of nearby sources. Fig. S2 depicts the average trace element concentrations for all sampling sites. While the K-W test (p < 0.01) indicated that there were significant differences in the concentrations of Ag, As, Ba, Ca, Cd, Co, Cr, Cu, Er, Fe, Ga, K, Mg, Mn, Mo, P, Pb, Rb, Sb, Se, Sn, U, Y, and Zn, there were no significant differences for Al, B, Bi, Ce, Dy, Gd, Hg, La, Li, Na, Nd, Ni, Pr, Sm, Sr, Th, Tl, V, and Yb among the sampling sites. The elemental concentrations (ng/m3) were generally higher in spring, followed by summer and fall and they were the lowest in winter. Some elements indicated different patterns depending on the sampling sites. While the mean concentrations of K, Cr, V, Ni, Mo, Sb, Ag, Se, and Y in PM10 were higher in summer, P, B, As, and Bi were higher in winter and fall. Table S1 lists the average seasonal elemental concentrations for PM10 in the study area. For PM2.5, the concentrations of Fe, Zn, Pb, Mn, Cu, V, Ni, Cd, Mo, Ba, Sb, and Se were higher in the summer and fall seasons, whereas As, B, Rb, Bi, Tl, U, Co, and Ga were higher in winter time. Some of the trace elements were found in concentrations below the detection limit in the PM2.5. Thus, seasonal comparisons are not possible for the fine fraction. The mean seasonal concentrations for PM2.5 in Bozkoy are given in Table S2. More detailed discussion on the elemental concentrations is provided in the Supplementary Material. Source Apportionment The elemental composition of sources of particulate matter was resolved by PMF. To determine the optimal number of sources, 5−8 factors were examined. The model was run 20 times with a random seed to determine the stability of Q values; Q values were stable and all runs converged. The Q (robust) and Q (true) values were 2444. The Q values, the resulting source profiles, and the scaled residuals distributions were studied and the eight factor solution was identified based on the scaled residuals distributions and the interpretability of the resulting profiles (Belis et al., 2013; Wang et al., 2013b). All of the scaled residual distributions were approximately symmetric within the range of −3 and +3 that represents good agreement between the observed and predicted values (Friend et al., 2012; Li et al., 2013). In addition, the marker elements belonging to different PM sources resolved by PMF in previous studies were summarized in Table S4. Eight factors were identified: iron-steel production from scrap (23.4%), re-suspended and road dust (23.3%), crustal (20.5%), marine aerosol (14.4%), biomass and wood

combustion (7.2%), salvage activities (4.7%), coal combustion (3.7%) and residual oil combustion (2.8%). These factors represented only primary PM10 sources since species such as sulfate and nitrate were not included. Also there were no measurements of carbonaceous species. The source profiles are depicted in Fig. 2, and Fig. 3 presents the time-series plots of the estimated daily contribution from each factor to the PM mass for different sampling sites. The first factor was ascribed to iron-steel production from scrap. The factor was associated with high contribution of Fe, Mn, Pb, Zn, Sn, Ag, Cd, and Cr and with high concentrations of Fe, Mn, Zn, and Pb. These elements are marker elements for steel production from scrap (Thurston et al., 2011; Yatkin and Bayram, 2008; Mansha et al., 2012). These results were consistent with PM stack samples collected from electric arc furnaces in the study area. The elemental analysis for PM10 and PM2.5 in stack gas indicated that the PM contained substantial amounts of Fe, Zn, Mn, K, Mg, Ca, Al, Pb, Cd, Cr, Cu, and Sn. The average percentage contributions were determined as 20–30% for Zn, 20–27% for Fe, 2–4% for Pb, and 1–2% for Mn. Steel production was a major source (23.4%) and dominated the sites near these plants. The highest contributions were in Bozkoy followed by Horozgedigi and Helvaci because of the prevailing northwesterly winds. The CPF plots (Fig. 4) showed the Nemrut area containing several steel production plants and steel rolling mills contributing to almost all of the sites. The second source profile has high contributions of Ca, Ba, Co, Cu, Ni, Se, Mg, Mn, Se, Sr, and Zn (Escrig et al., 2009; Fabretti et al., 2009), and high concentrations of Ca, Fe, Mg, and Zn. It was defined as re-suspended and road dust from the Nemrut area where there are significant fugitive sources (i.e., paved and unpaved roads, slag piles, EAFs filter dust piles, and coal piles). Uncontrolled PM emissions from roads and piles by resuspension from loading and dumping activities are produced through wind entrainment. Relatively low contributions of some crustal elements (i.e., Al, K, and some rare earth elements) to this factor may be due to the fact that resuspended material from road dust and other fugitive sources is highly contaminated. Peaks were observed in summer and fall and the highest contributions were found at Bozkoy and Horozgedigi. These factor contributions were also seen in Cakmakli and Aliaga town. Cakmakli is located on the route of trucks carrying scrap from ports to plants and the major arterial roads (Canakkale-Izmir) pass through Aliaga town. The CPF plots in sampling sites mainly point to the Nemrut area (Fig. 5). The third factor profile was dominated by high contributions of Al, Ca, Mg, Ba, Sr (Begum et al., 2005; Beuck et al., 2011) and lanthanides including La, Ce, Dy, Er, Nd, Pr, Sm, Th, Y, and Yb (Trapp et al., 2010; Gianini et al., 2012). The concentrations of Ca, Al, and Mg are high representing soil/crustal sources. It contributes 20.5% of the PM mass. Fig. 3 shows the seasonal variability. The peaks were observed during spring, when wind speeds were higher. Similar patterns are seen at all of the sites supporting an assignment of windblown soil. The relationships between soil species such as Al vs. Mg, Ca vs. Sr, Ba vs. Mg, Ca vs.

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Fig. 2. PMF source profiles for PM10 mass in Aliaga Region. Mg and lanthanides were strong (r2 = 0.60–0.99) and statistically significant (p < 0.01). CPF plots (Fig. S3) indicated different directions for each sampling site since the areas influencing them were different. The CPF plots point to rural areas (agricultural) and Nemrut industrial area.

Factor 4 is characterized by Na, Mg, Al, Sr, and P (Aldabe et al., 2011; Kocak et al., 2011). It is associated with marine aerosol. The factor showed high contributions (14.4%) in this region since the sampling region is located on the Turkish shore of the Aegean Sea. In the seawater

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Fig. 3. Time-series plots of the estimated daily contribution from each factor to the PM mass.

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Fig. 4. The CPF plot showing directions for iron-steel production from scrap. samples collected from the seashore in the region, the highest concentrations were measured for Na, Mg, and K, followed by Al and P (unpublished data). High P concentrations in seawater could be due to wastewater discharges from the fertilizer plant and contaminated stream waters in the area. The time series plot indicates relatively constant contributions during summer and fall in Horozgedigi and Helvaci and during winter and spring in Cakmakli and the ship breaking. The Cakmakli and ship breaking sites were located on a peninsula and they were affected by different wind directions. The highest contribution was obtained in Cakmakli site. The contribution of Cr and Hg also appeared in this factor. Similar to P concentrations, Cr was measured at high levels in Aliaga Bay seawater (Unpublished data). Refinery wastewaters contain high amounts of Cr (Gerhardt and Maroney, 1994). Therefore, source of Cr in seawater might

be the wastewater discharges from the refinery located in the area. On the other hand, Hg was frequently associated with chlorine-caustic soda production (SOLVAY, 2006) in petrochemical complexes. The petrochemical complex in the study area discontinued the mercury-cell process and utilized the membrane-cell process in July 2000, however sediment Hg concentrations remained high (unpublished data). Hg concentrations in seawater were not measured in the study area. However, they might be high due to sediment to seawater transfer of Hg. Marine aerosol did not represent clear directionality in the CPF plots owing to the sampling sites’ regional directionality relative to the sea (Fig. S4). The biomass and wood combustion factor (number 5) was clearly represented by the dominance of K and Rb in the profile (Santoso et al., 2008; Gianini et al., 2012; Wang

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Fig. 5. The CPF plot showing directions for re-suspended and road dust. et al., 2013a) as well as the high concentrations of Ca, Na and Mg. Crop residue burning on farmland especially wheat croplands is a common agricultural practice in this region. Crop residue burning is used to remove excess residue to facilitate planting and control pests and weeds prior to planting or reseeding (Dhammapala et al., 2006; Wulfhorst et al., 2006; Huang et al., 2012). Small-scale forest fires occur in the summer and contribute to this factor. In addition, wood is used as a fuel along with coal in residences in winter. Coal combustion represents another factor discussed below. Examining the time series plot, the summer peaks may be explained by crop residue burning, while small winter peaks indicate wood combustion for residential heating. This factor is high in Helvaci because it is surrounded by croplands (Fig. S5). The sixth factor is characterized by Cu, Sb, Sn and Cd.

These elements, especially Cu and Sb have been widely used for copper wire (ATSDR, 1992; Hileman, 2002; Beavington et al., 2004). Copper recycling by removing the plastic insulation from copper wire and electronic wastes are performed on the scrap from ship breaking. This procedure is conducted by uncontrolled burning of the plastic coating in open areas. Although this is illegal, it is used to reduce costs. The time series indicates that the high contributions for this factor appear in ship breaking yards. Therefore, this factor represents the salvage activities. In Fig. 6, the salvage factor show clear directionality in the CPF plots at the ship breaking and Aliaga town sites located near the source area. It is not observed at the other sites because it is a relatively weak source. The elemental composition for factor 7 is dominated by As, B, Hg, Se, Tl, U, and Zn as seen in Fig. 2. Se, As, and

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Fig. 6. The CPF plot showing directions for salvage activities. Tl are often used as a marker elements for coal combustion (Ogulei et al., 2006; Zhou et al., 2009) and As has also been identified with Sb and Zn as markers for coal-fired power plant emissions (Moreno et al., 2007). The factor profile and high contributions in winter is attributed to coal combustion for residential heating. The time series shows the same pattern at all of the sampling sites except the ship breaking site. These impacted sites were located within the villages and Aliaga town. The coal combustion CPF plots did not indicate clear directionality because the sites are surrounded by residences (Fig. S6). The last factor (factor 8) was associated with V and Ni that are signature elements for residual oil combustion emissions (Kim and Hopke, 2004; Mazzei et al., 2008; Kocak et al., 2009). This factor also contains Mo suggesting impacts from the refinery and petrochemical complexes

(Bosco et al., 2005; Bozlaker et al., 2013). In addition, the contributions of La and Ce appeared in this factor as related to refinery activities (Alleman et al., 2010). The time series plot did not show clear seasonal differences, but the contributions were lower in the winter at all of the sampling sites. The CPF plots point to peninsula where the refinery and petrochemical complexes are located (Fig. 7). CONCLUSIONS In this study, spatial and seasonal variability of PM10 and PM2.5 were evaluated for four seasons in the Aliaga region by the samples collected at six sites for PM10 and at one sites for PM2.5. The limit values for the protection of human health in European air quality Directive were exceeded for both PM10 and PM2.5. The establishment of industrial

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Fig. 7. The CPF plot showing directions for residual oil combustion. activities without proper planning and subsequent population increase has caused the increased PM concentrations in this region. The significant PM sources such as iron-steel production and fugitive sources (i.e., paved and unpaved roads, slag piles, EAFs filter dust piles, and coal piles) have also contributed to the PM mass. This made necessary to take precaution for PM concentrations and to improve an efficient air quality management system. The elemental concentrations were measured to be higher at sites located near industrial activities in Nemrut area and Fe, Zn, Pb, Mn, Cr, Cu, Ni, Sn, Sb, As, Cd, Ag, and Co that are related to scrap handling and iron-steel production has high concentrations compared with the rest of measured elements. PM10 sources using multiple sites data by EPA PMF (V5.0), having the capability of handling multiple site data,

were determined for region. Eight factors were identified as iron-steel production from scrap (23.4%), re-suspended and road dust (23.3%), crustal (20.5%), marine aerosol (14.4%), biomass and wood combustion (7.2%), salvage activities (4.7%), coal combustion (3.7%) and residual oil combustion (2.8%). The pattern of source contributions and conditional probability function analysis were consistent with the locations of the known sources. Thus, the multiple site data allowed for a comprehensive identification of the primary sources of PM in this region. ACKNOWLEDGMENTS This study was funded by the “Assessment of current status of Aliaga industrial region for air pollution” project conducted by Dokuz Eylul University and supported by

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the Turkish Ministry of Environment and Urbanism and by the industries located in Aliaga region. We would also like to thank the Scientific and Technological Research Council of Turkey (TUBITAK) for scholarship support for the first author. SUPPLEMENTARY MATERIAL Supplementary data associated with this article can be found in the online version at http://www.aaqr.org. REFERENCES Adamo, P., Giordano, S., Naimo, D. and Bargagli, R. (2008). Geochemical Properties of Airborne Particulate Matter (PM10) Collected by Automatic Device and Biomonitors in a Mediterranean Urban Environment. Atmos. Environ. 42: 346–357. Aldabe, J., Elustondo, D., Santamaria, C., Lasheras, E., Pandolfi, M., Alastuey, A., Querol, X. and Santamaria, J.M. (2011). Chemical Characterisation and Source Apportionment of PM2.5 and PM10 at Rural, Urban and Traffic Sites in Navarra (North of Spain). Atmos. Res. 102: 191–205. Alleman, L.Y., Lamaison, L., Perdrix, E., Robache, A. and Galloo, J.C. (2010). PM10 Metal Concentrations and Source Identification Using Positive Matrix Factorization and Wind Sectoring in a French Industrial Zone. Atmos. Res. 96: 612–625. Ashbaugh, L.L., Malm, W.C. and Sadeh, W.Z. (1985). A Residence Time Probability Analysis of Sulfur Concentrations at Grand Canyon National Park. Atmos. Environ. 19: 1263–1270. ATSDR (1992). Toxicological Profile for Antimony and Compounds. Agency for Toxic Substances and Disease Registry U.S. Public Health Service Altanta, GA. Bayraktar, H., Turalioglu, F.S., Tuncel, G. and Zararsiz, A. (2011). Elemental Composition of PM10 and PM2.5 in Erzurum Urban Atmosphere, Turkey. Fresenius Environ. Bull. 20: 1124–1134. Beavington, F., Cawse, P.A. and Wakenshaw, A. (2004). Comparative Studies of Atmospheric trace elements: Improvements in Air Quality near a Copper Smelter. Sci. Total Environ. 332: 39–49. Begum, B.A., Hopke, P.K. and Markwitz, A. (2013). Air Pollution by Fine Particulate Matter in Bangladesh. Atmos. Pollut. Res. 4: 75–86. Begum, B.A., Hopke, P.K. and Zhao, W.X. (2005). Source Identification of Fine Particles in Washington, DC, by Expanded Factor Analysis Modeling. Environ. Sci. Technol. 39: 1129–1137. Belis, C.A., Karagulian, F., Larsen, B.R. and Hopke, P.K. (2013). Critical Review and Meta-Analysis of Ambient Particulate Matter Source Apportionment Using Receptor Models in Europe. Atmos. Environ. 69: 94–108. Beuck, H., Quass, U., Klemm, O. and Kuhlbusch, T.A.J. (2011). Assessment of Sea Salt and Mineral Dust Contributions to PM10 in NW Germany Using Tracer Models and Positive Matrix Factorization. Atmos. Environ.

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Received for review, February 22, 2014 Revised, May 9, 2014 Accepted, July 1, 2014

Supplementary Material for

Characterization of PM Using Multiple Site Data in a Heavily Industrialized Region of Turkey Melik Kara1,2, Philip K. Hopke2, Yetkin Dumanoglu1, Hasan Altiok1, Tolga Elbir1, Mustafa Odabasi1, Abdurrahman Bayram1 1

Department of Environmental Engineering, Dokuz Eylul University, Tinaztepe Campus, Buca-Izmir, 35160 TURKEY 2 Center for Air Resources Engineering and Science, Clarkson University, Box 5708, Potsdam, NY 13699 USA

(a)

(c)

(b)

(d)

Fig. S1. Wind roses for four sampling periods a) summer, b) fall, c) winter, and d) spring.

Elemental Concentrations

In addition, PM2.5/PM10 ratios for the elements are listed in Table S2. The elements (i.e. Fe, Al, Ca, Na, Mg, Ba, Sr, and lanthanides) had low PM2.5/PM10 ratios and were mainly related to mineral matter. The elements represented in the fine fraction and with high PM2.5/PM10 ratios were mainly related to anthropogenic emissions (i.e. Zn, K, Pb, Cr, Cu, V, Sn, B, Mo, Sb, Cd, As, Rb, Ag, Bi, Se, Hg, and Tl). The elements related to coal and wood burning (As, K, B, Rb, Se, Hg, and Tl) for residential heating had also mainly higher PM2.5/PM10 in winter than in the other seasons. These results were consistent with findings in Switzerland by Minguillon et al. (2012) . The concentrations of some elements (i.e. Fe, Zn, Mn, Pb, Cr) emitted from scrap handling and iron-steel production were quite high since these plants and their activities are the dominant PM sources in the study region. The mean concentrations for Fe, Zn, Pb, Mn, and Cr in Bozkoy that are mostly affected from Nemrut area activities were 2959, 2043, 379, 159, and 60 ng/m3, respectively. Compared these values with previous studies (Alleman et al., 2010; Carvalho and Freitas, 2011; Gupta et al., 2007; Kim et al., 2002), these levels were higher than those measured in several industrial areas. Table S3 compared selected elemental concentrations in PM10 samples collected from different industrial areas. In the region, the lanthanides (La, Ce, Nd, Pr, Sm, Gd, Dy, Er, Yb, and Y) concentrations were measured as either low or below detection limits in several PM10 and PM2.5 samples. The REEs ratios were examined to determine whether these element concentrations were related to refinery emissions due to catalyst usage (Olmez and Gordon, 1985). The La/Ce ratios in PM10 ranged between 0.52 and 0.61 in all sampling sites excluding Cakmakli. The La/Ce ratio (0.68) was slightly higher in Cakmakli than other sites. The common lanthanide Ce is typically around

twice as abundant as lighter immediate neighbor La, producing natural La/Ce ratios of 0.4-0.6 in uncontaminated rocks and soils (Rudnick and Gao, 2014). On the other hand, the La/Sm ratios were ranged between 6.9 and 9.1 in all sampling sites. Similar to La/Ce, the ratio La/Sm were slightly higher in Cakmakli. In PM2.5, the mean La/Ce and La/Sm values observed 0.6 and 5.8, respectively. These values indicated that the ambient PM samples were not influenced by the catalysts in the refinery and lanthanides in particulate matter were of crustal origin. Whereas, La/Ce and La/Sm ratios in PM2.5 samples were found to be 2.9 and 53.7 in the vicinity of refinery complex located in Houston, TX (Kulkarni et al., 2006). 10000.0

1000.0

ng/m3

100.0

10.0

1.0 Ca

Fe

Zn

Al

Na

K

Mg

Pb

P

Mn

Cr

Cu

Ni

Ba

V

Sn

Sr

B

Sb

Mo

As

Sm

Gd

Dy

Er

Yb

10.00

ng/m3

1.00

0.10

0.01 Cd

Ga

Rb

Ag

Aliaga

Li

Bi

Co

Bozkoy

Ce

Se

Cakmakli

La

Nd

Hg

Helvaci

Y

U

Th

Horozgedigi

Tl

Pr

Ship Breaking

Fig. S2. Average trace element concentrations for six sampling sites.

Table S1. Average seasonal elemental concentrations for PM10 (ng/m3) Ca Fe Al Zn Na K Mg P Pb Mn Cu Cr V Ba Ni B Mo Sr Sn Ga As Cd Sb Rb Li Ce Bi Co Ag Se La Nd Y Hg U Th Tl Pr Sm Gd Dy Er Yb

Summer 2638 ± 1609 1878 ± 1728 880 ± 510 908 ± 863 1175 ± 278 709 ± 441 485 ± 221 128 ± 34 158 ± 227 88 ± 111 42 ± 38 45 ± 61 26 ± 12 15 ± 8.3 22 ± 22 6.3 ± 1.7 14 ± 18 8.6 ± 3.9 3.7 ± 4.5 3.2 ± 1.7 2.5 ± 1.3 2.2 ± 2.7 8.4 ± 15 1.8 ± 0.97 1.3 ± 0.19 0.89 ± 0.27 1.3 ± 0.9 0.95 ± 0.45 1.6 ± 1.9 0.87 ± 0.21 0.45 ± 0.13 0.34 ± 0.11 0.24 ± 0.08 0.25 ± 0.2 0.09 ± 0.04 0.14 ± 0.05 0.07 ± 0.02 0.08 ± 0.02 0.07 ± 0.02 0.06 ± 0.02 0.05 ± 0.02 0.03 ± 0.01 0.02 ± 0.01

Fall 2305 ± 1685 1791 ± 1844 1329 ± 816 988 ± 1188 976 ± 517 611 ± 399 550 ± 336 277 ± 237 138 ± 205 75 ± 94 31 ± 33 29 ± 29 16 ± 8.7 16 ± 9 14 ± 11 9.3 ± 4.3 6.9 ± 7 6.4 ± 2.6 3.5 ± 4.2 3.4 ± 2.1 3±2 2.7 ± 4.1 2.3 ± 1.8 1.7 ± 0.99 1.3 ± 0.23 0.85 ± 0.26 0.84 ± 0.45 0.78 ± 0.51 0.67 ± 0.88 0.64 ± 0.25 0.48 ± 0.14 0.35 ± 0.12 0.24 ± 0.1 0.19 ± 0.04 0.18 ± 0.14 0.14 ± 0.05 0.14 ± 0.14 0.08 ± 0.03 0.06 ± 0.02 0.06 ± 0.02 0.05 ± 0.02 0.03 ± 0.01 0.02 ± 0.01

Winter 1189 ± 1346 893 ± 961 776 ± 475 892 ± 1289 429 ± 257 476 ± 529 262 ± 196 470 ± 648 136 ± 229 39 ± 59 38 ± 43 27 ± 20 5.8 ± 5.4 7.9 ± 5.1 6.8 ± 6.4 7.5 ± 5.2 2.6 ± 2.9 3.3 ± 1.7 4.1 ± 5.2 1.7 ± 1.1 7.2 ± 4.8 3.1 ± 4.8 5.7 ± 5.8 1.5 ± 1.3 1.3 ± 0.34 0.35 ± 0.18 1.3 ± 0.93 0.7 ± 0.39 0.69 ± 0.81 0.45 ± 0.24 0.21 ± 0.11 0.16 ± 0.08 0.13 ± 0.12 0.29 ± 0.14 0.14 ± 0.16 0.06 ± 0.04 0.12 ± 0.06 0.03 ± 0.02 0.03 ± 0.01 0.03 ± 0.02 0.03 ± 0.02 0.02 ± 0.01 0.02 ± 0.01

Spring 3157 ± 2608 2626 ± 1961 1686 ± 907 1220 ± 1286 1077 ± 473 620 ± 445 704 ± 379 142 ± 76 275 ± 283 96 ± 88 57 ± 67 45 ± 19 17 ± 9.4 19 ± 11 12 ± 6.3 6.9 ± 2.6 6.6 ± 5.6 11 ± 9.8 8 ± 7.3 4 ± 2.4 5 ± 4.6 4.3 ± 4.3 6.1 ± 6.5 2.4 ± 1.4 1.5 ± 0.48 1.3 ± 1 1.2 ± 0.74 0.97 ± 0.62 1.2 ± 1.4 0.54 ± 0.18 0.76 ± 0.5 0.59 ± 0.46 0.33 ± 0.26 0.37 ± 0.21 0.13 ± 0.09 0.2 ± 0.16 0.14 ± 0.09 0.12 ± 0.1 0.11 ± 0.09 0.10 ± 0.08 0.08 ± 0.07 0.04 ± 0.03 0.03 ± 0.02

Table S2. The seasonal average concentrations for PM2.5 and PM2.5/PM10 ratios for elements Zn Fe Al Ca Na K Pb Mg P Mn Cr Cu V Sn Ni B Mo Ba Sb Cd As Sr Rb Ag Bi Ga Li Se Co Hg Ce La Nd Tl Y Th Sm U Gd Dy Pr Er Yb

Summer 1448 ± 422 1135 ± 531 206 ± 28 597 ± 412 432 ± 123 373 ± 102 350 ± 181 151 ± 63 BDL 106 ± 48 33 ± 22 58 ± 35 27 ± 18 7.6 ± 3.8 17 ± 11 5.3 ± 1.3 5.4 ± 3 5 ± 3.6 12 ± 19 4.1 ± 1.7 2.9 ± 1.2 2.2 ± 1 1.3 ± 0.42 1.5 ± 0.55 0.71 ± 0.19 1.3 ± 0.79 BDL 0.85 ± 0.19 0.49 ± 0.16 0.2 ± 0.05 0.15 ± 0.03 0.09 ± 0.02 0.07 ± 0 0.06 ± 0.01 0.04 ± 0.01 0.03 ± 0.01 BDL 0.03 ± 0.01 BDL 0.01 ± 0 0.01 ± 0 BDL BDL

PM2.5, ng/m3 Fall Winter 1538 ± 1547 1189 ± 1380 1182 ± 1329 500 ± 560 1267 ± 441 500 ± 166 457 ± 279 908 ± 1302 640 ± 402 414 ± 317 616 ± 384 486 ± 468 241 ± 294 191 ± 282 420 ± 190 233 ± 92 113 ± 23 92 ± 3.6 78 ± 91 35 ± 45 19 ± 11 14 ± 8.4 41 ± 48 22 ± 23 12 ± 7.5 2.2 ± 0.87 6.1 ± 6.3 4.3 ± 4.9 7.9 ± 6.7 2.4 ± 1.6 6.7 ± 2.9 6.7 ± 3.7 5.3 ± 1.8 2.2 ± 0.25 4.5 ± 3.4 2.3 ± 1.8 3.7 ± 2.2 3.2 ± 3.2 4.8 ± 6 3.6 ± 5.8 3.3 ± 2.7 7.5 ± 5.4 1.7 ± 0.54 1.7 ± 0.63 1.3 ± 1.1 1.8 ± 1.2 1.7 ± 1.2 0.83 ± 0.96 1 ± 0.55 1.3 ± 0.52 1.4 ± 1.1 0.68 ± 0.56 BDL 1.18 ± 0 0.74 ± 0.31 0.52 ± 0.33 0.58 ± 0.33 0.84 ± 0.84 BDL 0.24 ± 0.11 0.17 ± 0.04 0.09 ± 0.02 0.11 ± 0.03 0.06 ± 0.01 0.07 ± 0.01 BDL 0.12 ± 0.09 0.12 ± 0.06 0.05 ± 0.02 0.03 ± 0.01 0.02 ± 0 BDL BDL BDL 0.05 ± 0.02 0.05 ± 0.02 0.02 ± 0 BDL 0.01 ± 0 BDL 0.01 ± 0 BDL BDL BDL 0.01 ± 0 BDL

Spring 1214 ± 1051 959 ± 776 737 ± 379 734 ± 589 539 ± 312 381 ± 239 310 ± 283 281 ± 161 92 ± 0 75 ± 66 36 ± 9.4 35 ± 30 8.4 ± 5.7 8.3 ± 8.2 7 ± 5.5 6.4 ± 2 4.9 ± 4.6 4.4 ± 2.6 4.3 ± 3.3 3.9 ± 3.4 3.2 ± 1.7 2.8 ± 2.3 1.7 ± 0.99 1.7 ± 1.6 1.2 ± 0.63 1.1 ± 0.71 1.11 ± 0 0.56 ± 0.22 0.52 ± 0.15 0.32 ± 0.17 0.26 ± 0.22 0.13 ± 0.1 0.13 ± 0.1 0.1 ± 0.05 0.06 ± 0.06 0.06 ± 0.03 0.05 ± 0.01 0.04 ± 0.01 0.03 ± 0.02 0.03 ± 0.02 0.03 ± 0.02 0.02 ± 0.01 0.01 ± 0

Summer 0.65 0.24 0.22 0.14 0.33 0.52 0.68 0.20 NA 0.41 0.43 0.68 0.80 0.73 0.54 0.59 0.69 0.18 0.72 0.63 0.66 0.18 0.47 0.80 0.69 0.22 NA 0.77 0.35 0.53 0.14 0.16 0.13 0.67 0.13 0.11 NA 0.18 NA 0.18 0.11 NA NA

PM2.5/PM10 Fall Winter 0.58 0.62 0.26 0.34 0.42 0.51 0.13 0.46 0.42 0.63 0.60 0.93 0.59 0.76 0.36 0.51 0.43 0.29 0.36 0.50 0.43 0.56 0.55 0.72 0.69 0.62 0.69 0.78 0.52 0.60 0.68 0.80 0.88 0.88 0.20 0.29 0.73 0.91 0.62 0.77 0.59 0.84 0.20 0.35 0.46 0.80 0.83 0.85 0.75 0.75 0.24 0.36 NA 0.58 0.79 0.99 0.38 0.61 NA 0.81 0.18 0.21 0.21 0.26 0.14 NA 0.71 0.88 0.15 0.18 0.14 NA NA NA 0.24 0.31 0.19 NA 0.18 NA 0.15 NA NA NA 0.21 NA

Spring 0.66 0.32 0.43 0.16 0.46 0.69 0.72 0.35 0.54 0.48 0.55 0.67 0.69 0.74 0.59 0.70 0.92 0.25 0.78 0.71 0.75 0.23 0.62 0.82 0.73 0.30 0.55 0.96 0.51 0.67 0.24 0.23 0.24 0.76 0.20 0.19 0.23 0.31 0.21 0.23 0.20 0.27 0.21

Table S3. The elemental concentrations (ng/m3) in comparison with other industrial sites. Region

Fe

Zn

Pb

Mn

Cr

Cu

Ni

V

Sb

As

Cd

Reference

Aliaga, Turkey

1932

948

185

71.9

35.6

49.6

13.5

16

5.7

4.9

3.4

This study

Dunkirk, France

977

80

37.5

147

7.5

12.6

12.4

2.3

5.1

1.3

Alleman et al. (2010)

Kolkata, India

123

535

118

2.1

6.3

8.3

5.2

Gupta et al. (2007)

Tito Scalo, Italy

589

214

30

6

34

85

5

Ragosta et al. (2006)

Taejon city, Korea

1633

240

243

50.3

25.1

3.2

Bobadela, Lisbon

430

92

Kim et al. (2002) Carvalho and Freitas (2011)

Daejeon city, Korea

1393

146

204

48.2

17.3

30.7

Lim et al. (2010)

Dunkirk, France

250

50

17

60

13

8

Spain

692

269

Houston, Texas

228

23

2.6

5.1

5

Dhaka, Bangladesh

2242

652

456

48

26

13

37.9

13.1

20.3

7.8

5.5

3.6

4.9

6.8

3.1

3.7

16

12.2

7.7

6.1

3.8

0.6

12.3

3.2

2.4

1.1

2.5

0.5

0.6

0.6

0.1

Mbengue et al. (2014) Pandolfi et al. (2008) Bozlaker et al. (2013) Begum et al. (2005)

Table S4. The marker elements belong to different sources resolved by PMF. Place

Soil

Spain

Ca, Fe, K, Mg, Na

Bangladesh

Al, Ca, Fe, K, Si, Ti

USA Bangladesh Bangladesh Thailand

Al, Ca, Fe, Si, Ti Al, Mg, Na, P, S, Si Al, Ca, Fe, K, Si Al, Ca, Fe, La, Mn

Italy Spain

Al, Ca, Fe, K, Mg, Na, Ti

Switzerland

Al, Ca, La, Mg , Nd, Sr, Ti, Y

USA

Al, Ca, Fe, Si

India USA USA India USA

Al, Si

Indonesia Canada Spain

Al, Ca, Fe, K, Mg, Na, P, S, Si Ca, Fe, S, Si, Ti, Zn

Fe, Zn

Reference

Begum et al. (2004) EC, Fe, Zn

Begum et al. (2005)

Mg, Na, Pb, Si

BC, S

Begum et al. (2005)

K, Pb, S, Si

BC, K, S

Begum et al. (2010

Al, Ca, Fe, Mn, Zn

Chueinta et al. 2000) Cu, Cr, Fe, Pb, Zn

Al, Cu, Fe, Pb, Sb Cu, Fe, Mn, Pb, Sb, Sn, Zn Ba, Cr, Cu, Fe, Mn, Mo, Sb, Zn

Fabretti et al. (2009) Gianini et al. (2012a)

Cu, EC, Fe, OC, Zn

Gildemeister et al. (2007)

Br, Cu, Fe, Zn,

Heo et al. (2009)

EC, Fe, OC, Si OC, Pb, Zn

Kim et al. (2003) EC, Fe, S

Kim et al. (2004) Kothai et al. 2008) Lee and Hopke (2006)

Ca, Cu, Si

As, Cd, Co, Sb, Mansha et al. 2012) Se, V Mazzei et al. 2008) Cr, EC, Mn

EC, OC Al, Br, Fe, S, Si, Zn

Fe, Pb

Cd, Co, Cu, Ni, Khare and Baruah Te, Zn (2010)

Cu, Pb, Zn

Al, Ca, Fe, Si

Ogulei et al. 2006) Ramadan et al. (2000)

EC, Fe, S

Pb, S, Zn

Santoso et al. (2008) As, Pb, Sb, Se, V, Zn

Al, Ba, Ca, Fe, Zn

Contini et al. (2012) Escrig et al. (2009)

Fe, Cu, Mn Zn

Al, Ca, Fe, K, Si Al, Ca, La, Mn, Si, Sm, Ti Al, Ca, Fe, Sr, Ti

Industry

Aldabe et al. (2011)

Al, Ba, Ca, Mg Cd, Pb, Sb, Zn

USA USA

Diesel

Cu, Fe, Zn

Al, Ca, Fe, K, Al, Br, Pb, Si, Mg, Si Zn Al, Ca, Fe, Mn, Na, Si Al, Fe, K, Si, Ti Al, Ca, Fe, Si, Ti Ca, Fe, Sc, Si, Co, Sb, Sc, Zn Ti Al, Ca, Fe, Na, Si, Ti

Pakistan Italy

Motor vehicle

Pb, Sb, Zn

France

South Korea

Traffic- Road Dust

Xie et al. (1999) Zabalza et al. (2006)

Table S4. Continued. Place

Iron-Steel Processing

Wood and Biomass Burning

Fuel Oil

Spain Bangladesh

Fe, K, Mn, Pb Zn

USA

Ni, V

Bangladesh Bangladesh

Sb, V

Italy As, Cd, Cs, K, Pb, Tl, Zn

France

Cl, Mg, Na

Aldabe et al. (2011)

BC, K

Cl, Na

Begum et al. (2004)

K, OC, S

Na, S

K, Na, Sb

Cl, Na

Chueinta et al. 2000) Contini et al. (2012)

Ni, S, V, Zr

Escrig et al. (2009)

Co, Ni, V

Fabretti et al. (2009) K, Rb

K

USA

Br, Fe, Mn, Pb, Sn, Zn Cu, Cr, Fe, Mn Ni, S, V

India

Cr, Ni, Pb

USA

Al, Fe, Mn, Zn

Pakistan

Co, Cr, Fe, Mn, Mo, Ni, Sn

Heo et al. (2009) Fe, Mn, P, Te, V

Kim et al. (2003)

K, OC, S

Kim et al. (2004)

Ca, K

Indonesia Canada Mn, Pb, Zn

Kothai et al. 2008) Lee and Hopke (2006) Mansha et al. 2012)

Br, Cl, Na

Ni EC, K, Na, OC, Cl, K, Na S BC, K

USA

Khare and Baruah (2010)

K, Si

K, Na

Ni, V Cu, Fe, Pb

Gianini et al. (2012a) Gildemeister et al. (2007)

Ca, Cl, Na

India

Spain

Begum et al. (2005) Begum et al. (2010

Ca, Fe, K, Mn, Zn

Italy

Begum et al. (2005)

Br, Cl, Na

K, OC, Pb, Si, Zn

USA

OC, S, Se

BC, Fe, K, S

South Korea

USA

Reference

Ni, V

Switzerland USA

Coal

BC, K, S Cu, Cr, Fe, Pb, S Zn

Thailand

Spain

Sea salt

Mazzei et al. 2008) As, Mn, Se, Zn Ogulei et al. 2006) Ramadan et al. EC, La, OC, S (2000) Santoso et al. (2008)

Cl, K, Na

Xie et al. (1999)

Cl, Na

Zabalza et al. (2006)

Fig. S3. The CPF plot showing directions for crustal and soil.

Fig. S4. The CPF plot showing directions for marine aerosol.

Fig. S5. The CPF plot showing directions for biomass and wood combustion.

Fig. S6. The CPF plot showing directions for coal combustion.

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