Carbon species in PM10 particle fraction at different monitoring sites

Environmental Pollution xxx (2016) 1e11

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Carbon species in PM10 particle fraction at different monitoring sites*    Ranka Godec*, Ivana Jakovljevi
c, Kresimir Sega, Mirjana Ca ckovi
c, Ivan Besli
c, Silvije Davila, Gordana Pehnec Institute for Medical Research and Occupational Health, Ksaverska c. 2, Zagreb, Croatia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 March 2016 Received in revised form 16 June 2016 Accepted 16 June 2016 Available online xxx

The aim of this study was to determine and compare the levels of elemental carbon (EC), organic carbon (OC) and polycyclic aromatic hydrocarbons (PAHs) mass concentrations in PM10 particles (particles with aerodynamic diameter less than 10 mm) between seasons (winter and summer) and at different monitoring sites (urban background and rural industrial). Daily samples of airborne particles were collected on pre-fired quartz fibre filters. PM10 mass concentrations were determined gravimetrically. Samples were analysed for OC and EC with the thermal/ optical transmittance method (TOT) and for PAHs by high-performance liquid chromatography (HPLC) with a fluorescence detector. Measurements showed seasonal and spatial variations of mass concentrations for carbon species and for all of the measured PAHs (Flu, Pyr, Chry, BaA, BbF, BaP, BkF, BghiP and IP) in PM10 at the urban site and rural monitoring site described here. Diagnostic PAH ratios (Flu/(Flu þ Pyr), BaA/(BaA þ Cry), IP/ (IP þ BghiP), BaP/BghiP, IP/BghiP and BaP/(BaP þ Chry)) make it possible to assess the sources of pollution, and these showed that diesel vehicles accounted for most pollution at the rural-industrial (RI) site in the summer, whereas coal and wood combustion were the causes of winter pollution. This difference between winter and summer PAH ratios were more expressed at the RI site than at the UB site because at the UB site the predominant heating fuel was gas. The OC/EC ratio yielded the same conclusion. Factor analysis showed that EC and OC originated from traffic at both sites, PAHs with 5 or more benzene rings originated from wood pellets industry or biomass burning, while Pyr and Flu originated from diesel combustion or as a consequence of different atmospheric behaviour e evaporation and participation in oxidation and photo oxidation processes. © 2016 Elsevier Ltd. All rights reserved.

Keywords: EC OC OC/EC ratio PAHs PAHs diagnostic ratios

1. Introduction 1.1. Particulate matter Particles with an aerodynamic diameter smaller than 10 mm (PM10) remain in the air from few hours to several days, and in € schl, 2005). Larger particles are quickly some cases even weeks (Po removed from the atmosphere by sedimentation under gravity and deposit on horizontal surfaces as dust and dirt (Harrison, 2004). Atmospheric particles originate from primary and secondary sources caused by various natural or anthropogenic activities.

* This paper has been recommended for acceptance by Chen Da. * Corresponding author. E-mail addresses: [email protected] (R. Godec), [email protected] (I. Jakovljevi
c),    [email protected] (K. Sega), [email protected] (M. Ca ckovi
c), [email protected] (I. Besli
c), [email protected] (S. Davila), [email protected] (G. Pehnec).

Primary particles are those emitted directly from the source, while certain volatile organic compounds can form particles from photochemical reactions under the conditions of high ozone concentrations (secondary particles) (Kim et al., 1999; Ma and Birmili, 2015). Chow and Watson (1998) describe how dust from fields, farmland, roadways and construction sites is a primary contaminant which can participate in the formation of secondary particles. For example, some components of dust, such as ammonium nitrate fertilizer, may volatilize into ammonia and nitric acid gases, thereby contributing to secondary aerosol or calcium carbonate may react with nitric and hydrochloric acid gases while on the ground, in the atmosphere, or on filter samples to form coarse particle nitrates and chlorides. The main anthropogenic sources of particulate matter are factories, power plants, waste incinerators, biomass burning, burning farmland, forest fires, household space heating, and traffic exhaust fumes from motor vehicles, and construction (Chow and Watson, 1998; Fernandez et al., 2003; Kampa and

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R. Godec et al. / Environmental Pollution xxx (2016) 1e11

Castanas, 2008; Rohr and Wyzga, 2012). Particle size determines their deposition in the human respiratory tract. The coarse fraction of PM10 is deposited mainly in the upper respiratory tract, while fine particle fractions (PM2.5 and PM1) deposit in the pulmonary alveoli (Kampa and Castanas, 2008). Different composition of pollutants in the air, time of exposure and the fact that people are usually exposed to a mixture of pollutants, rather than individual substances, cause a variety of negative effects on human health. Health effects range from nausea and breathing difficulties, skin irritation, to cancer (Donaldson et al., 2005). 1.2. Carbon in particles Carbon is one of the most abundant elements in airborne particles. Particulate matter containing carbon is a serious problem influencing climate and peoples’ health. The term total carbon (TC) refers to all forms of carbon present in the samples of particulate matter in the air. In this paper, total carbon is referred to the sum of organic (OC) and elemental (EC) carbon (Chio and Liao, 2008; Pio et al., 2011). Elemental carbon is a primary pollutant, implying that it is directly emitted in the atmosphere from natural and anthropogenic sources, while the origin of organic carbons can be primary (emitted directly in particulate phase) or secondary (from gas-to-particle conversions in the atmosphere) (Kumagai et al., 2009; Park et al., 2012; Pio et al., 2011). Some sources of EC are vehicular exhausts and fossil-fuel combustion, biomass burning, residential space heating (Chio and Liao, 2008; Pio et al., 2011; Ram and Sarin, 2010; Zhang et al., 2012). Elemental carbon, which contains pure graphite carbon, but may include dark-coloured, non-volatile organic material such as coal tar and biogenic coal, is a visible component of particulate matter in ambient air. Particulate EC and OC are also defined by the methods of collection and analysis. Elemental carbon is black, and is often referred to as soot, but it is also known as black carbon (BC) and light absorbing carbon (LAC) (Chen et al., 2004; Chow et al., 2002; Wilson et al., 2002). The sources of primary OC can be natural (emissions plant spores and pollen, plant debris, forest fires, volcanic eruptions) and anthropogenic (incomplete combustion of fossil fuels, biomass burning, and mechanical processes) (Bice et al., 2009; Castro et al., 1999; Kim et al., 1999; Louie et al., 2005; Na and Cocker, 2005; Pio et al., 2011; Putaud et al., 2010). Secondary OC originates from low volatility oxidation products of reactive organic gases in the gas phase which are condensed or absorbed on the surface of pre-existing particles. Under certain circumstances, the products of atmospheric reactions result in the formation of new particles. Organic compounds can affect thermodynamic and chemical properties of particles and thus their role in the atmosphere (Turpin et al., 2000). 1.3. Polycyclic aromatic hydrocarbons Organic carbon includes several groups of compounds that can be analysed separately and whose properties such as water solubility, vapour pressure, the distribution coefficients between the aqueous and solid phases of the air and the solid or liquid phase and the half-life in air, soil and water are listed in databases (Chow and Watson, 1998; Harrison, 2004). These identified and measured organic compounds make up for less than 10% of the measured mass of particulate organic carbon in the air, and one group are polycyclic aromatic hydrocarbons (PAHs) which may have anthropogenic and/or natural sources of origin. PAHs represent a group of organic compounds containing two or more aromatic rings made of carbon and hydrogen atoms. Studies have shown that PAHs with two or three benzene rings exist in the vapour phase, and their concentration in the air increases with temperature, while PAHs with four or more benzene rings are observed primarily in the

particulate phase (Hanedar et al., 2014; Masiol et al., 2012). They are by-products of incomplete combustion or pyrolysis of organic substances. They are generated whenever fossil fuels or vegetation is burned, and they are one of the first pollutants identified as potential carcinogens (BjØrseth and Ramdahl, 1985). The quantity and characteristics of PAHs emitted from industrial plants depend on the type of fuel used. In cities, the major sources of PAHs are home furnaces especially if wood or coal is used as the fuel. Their concentrations in the air are expected to be higher in winter time than in summer. High concentrations of PAHs in the atmosphere of urban areas keep growing due to a constant rise in the number of motor vehicles and population density, so human exposure in these
n et al., 2010; area is higher than in rural surroundings (Calle Caricchia et al., 1999; Masiol et al., 2012; Ravindra et al., 2008; Wenger et al., 2009). Irrespective of the measures undertaken in the past years to reduce the levels of pollutants in the air, and replace fossil fuels with more environmentally friendly fuels such as natural gas or oil for house heating, pollutant concentrations keep increasing due to high population traffic density. Currently, the major PAH output comes from car exhausts. Manoli et al. (2002) found that the particles present in car exhaust gases are smaller than the particles found as a result of coal burning. Most PAHs are bound to smaller particles. These smaller particles are more dangerous to human health because they penetrate deeper into the respiratory system. Rural sites have minimum anthropogenic PAH emissions and the population is usually exposed to PAHs through domestic heating. The aim of this study was to simultaneously measure several carbon species in PM10 particles in Croatia. Furthermore, the first time measurements of EC and OC at a rural site in Croatia were carried out and the results are presented here. It was necessary to understand the origins and sources of EC, OC and PAHs and their harmful effect on the environment and humans’ health to compare their levels in PM10 across different seasons and sites. Diagnostic PAHs ratios and factor analyses were used to assess the sources of pollution and the origin of each pollutant. Estimation of secondary organic carbon is also presented in this study. To this date in Croatia the measurement of EC and OC has been conducted only at an urban background monitoring station (Godec et al., 2012), while PAHs were determined at urban and rural monitoring stations (Godec  sovi
et al., 2008; Jakovljevi
c et al., 2016, 2015; Si c and Fugas, 1991;  sovi
Si c et al., 2012, 2008), but never at the same time. 2. Materials and methods 2.1. Sampling sites and sampling The rural industrial (RI) site was situated at the north-eastern outskirt of Delnice (45 24014.1000 N, 14 480 28.3300 E), away from the town centre and close to economic zone, 685 m above the sea level in the hills of Gorski Kotar, Croatia (Fig. 1). The population of Delnice was 5952 at the time of measurement (Census, 2011). Air pollution at this site originated from wood used as fuel for cooking and space heating, as well as from the local wood pellet industry. Winters are long and harsh, with abundant snowfall, while summers are short and crisp in the morning and evening. The urban background (UB) site was situated in the yard of the Institute for Medical Research and Occupational Health (45 500 6.8300 N, 15 580 42.1200 E), in the northern residential part of Zagreb, 168 m above the sea level (Fig. 1). Zagreb had a population of 790017 inhabitants at the time of measurement (Census, 2011). Pollution at this site originates from domestic furnaces and moderate traffic. As these were the first measurements at rural stations, the results had to be compared with a previously established urban background monitoring station. The rural background monitoring station for

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the first weighing and reweighing after the subsequent 24 h. A microbalance Mettler TOLEDO MX5 with a resolution of 1 mg was used. 2.3. Carbon analyses

Fig. 1. Positions of urban background (UB) site and rural industrial (RI) site.

continuous measurements of air pollution levels was established five years after the preliminary measurements. Meteorological parameters (temperature, relative humidity, wind speed and direction, pressure and precipitation) for both monitoring sites are shown in Table 1. Weather conditions were monitored every 15 min using a Davis Vantage Pro2 Weather Station on both sites. Daily samples of PM10 particle fraction were collected from 15th January till 16th March 2010 (winter season) and from 24th June to 22nd August 2010 (summer season). They were collected on quartz fibre filters (Pall flex Tissue quartz 2500QAT-UP) pre-fired at 900  C for three hours, using a PM10 reference sampler (prescribed and described in EN 12341:1998) Sven Leckel LVS3 (urban site) and sequential sampler Sven Leckel Sequential Sampler SEQ47/50 (rural site, Sven LeckelInginierbüro, Berlin, Deutschland) without carbon denuder. The flow rates for both samplers were 38.3 l min1 (approximately 55 m3 per day). At the urban monitoring site, the samples were collected daily and brought back to laboratory, while at the rural monitoring site the samples were collected daily, but every two weeks samples were brought back to the laboratory. Quartz fibre filters were chosen because filter preparation and sample analysis require a high temperature. Before sampling, filters were prefired in order to reduce the blank level of carbon in the filter (Birch and Cary, 1996; Lin and Tai, 2001). After sampling and weighing, filters were kept in petri dishes in deep freeze at 18  C until analysis in order to prevent the loss of volatile components. 2.2. PM10 mass concentrations PM10 mass concentrations were determined gravimetrically according to the HRN EN 12341 standard (2005). Before and after sampling, the filters were conditioned at a constant temperature (20 ± 1  C) and relative air humidity (50 ± 5% RH) for 48 h, prior to Table 1 Meteorological parameters at RI and UB monitoring sites. Site

Season

T/ C

R.H./%

w.s./km h1

w.d.

p/hPa

pr./mm h1

RI

Summer Winter Summer Winter

18.6 1.7 22.3 1.2

14.5 91.7 68.3 77.9

2.9 2.5 1.0 0.9

W NE NE NE

942.7 1013.6 1000.1 998.7

0.29 0.02 11.54 5.89

UB

Te temperature, R.H. e relative humidity, w.s. e wind speed, w.d. e wind direction, p e pressure, pr. e precipitation, W e west, NE e north-east.

Organic carbon, elemental carbon, and total carbon (TC, a sum of EC and OC) in PM10 fraction were determined by the thermaloptical transmittance method (TOT), using a Carbon Aerosol Analyzer (Sunset Laboratory Inc.) with a flame ionization detector (FID, Birch and Carya, 1996; Godec et al., 2012) following a NIOSHlike protocol called Quartz. A portion of each sample (1.5 cm2) was used to determine OC and EC with the TOT method. To ensure QA/ QC so as to prove the consistent operation of the instrument, the inner standard, an external sucrose aqueous solution and a cross method procedure were used. For the evaluation of the efficiency of method recovery, two sets of filters (blank samples and real samples) were analysed after being spiked with a known concentration of carbon. The results of recovery were 96%e104% with a relative standard deviation RSD <5% (Godec et al., 2012). The detection limits were determined and calculated on the basis of an average (g ± 3s) drawn from ten repeated measurements of blank samples (unexposed filters). The detection limits were: 0.02 mg cm2 for EC, 0.82 mg cm2 for OC, and 0.83 mg cm2 for TC. The detection limits expressed in mg m3 in 55 m3 of air were 0.01 mg m3 for EC, 0.18 mg m3 for OC and 0.18 mg m3 for TC (Godec, 2013). 2.4. PAHs analyses PAH samples were analysed with high-performance liquid chromatography (HPLC, Pro Star, model 230, Varian) with a fluorescence detector and time-programmed change in excitation and emission wavelength (model 360, Varian), in order to optimize the selectivity and sensitivity for individual PAH species. PAHs were separated on a Varian stainless steel Pursuit 3 PAH column (100  4.6 mm). The mobile phase was a mixture of acetonitrile and water (60:40), and the flow rate was 0.5 mL min1 (Jakovljevi
c  sovi
 sovi
et al., 2015; Si c et al., 2012; Si c and Fugas, 1991). Before analysis filters were extracted with a mixture of toluene and cyclohexane (7:3) in an ultrasonic bath for 1 h. After this, they were separated from undissolved parts by centrifugation (10 min, 3000 rpm) and evaporated to dryness using a mild stream of nitrogen at 30  C. They were then re-dissolved in acetonitrile. To prepare the calibration curves, a commercial PAH standard was used (Supelco EPA 610 PAHs Mix). Standard working solutions were obtained by diluting certified solutions with Merck HPLC-grade acetonitrile. The method detection and quantification limits were calculated as concentration equivalents to three and ten times the signal-to-noise ratio. The quantification limit for BaP was 0.04 ng m3. The accuracy of the method was determined by analysing the Certificate Reference Material (CRM NIST 1649b, Urban dust). The accuracy of the method for BaP was more than 95%. Samples were analysed for the following PAHs: fluoranthene (Flu), pyrene (Pyr), chrysene (Chry), benzo(a)anthracene (BaA), benzo(b)fluoranthene (BbF), benzo(a)pyrene (BaP), benzo(k)fluoranthene (BkF), dibenzo(a,h)anthracene (DahA), benzo(ghi)perylene (BghiP), indeno(1,2,3-c,d)pyrene (IP). 2.5. Statistical analysis Statistical analyses like descriptive statistics, distribution testing, calculation of diagnostic PAHS ratios and factor analyses were performed using the STATISTICA 12.0 software.

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SPAHs in winter and summer were lower than the averages for each season confirming the log-normal concentration distribution. At the UB site, PM10 mass concentrations during winter and summer ranged from 8.5 mg m3 to 118.4 mg m3. During that period, PM10 mass concentrations were higher than the daily limit value (50 mg m3), 19 times during winter (2 times concentrations were higher than 100 mg m3) and not at all during summer (Fig. 3). In the winter period, the average mass concentrations of EC, OC and TC were approximately 2.5 times higher than in summer. The mass contribution of EC to the total PM10 mass in winter was also higher than in summer. The average mass contribution of OC to the total PM10 mass during winter and summer was similar, about 27%. During summer, the average BaP and SPAH mass concentrations were about 50 times lower than in winter. The medians of BaP and SPAHs in winter were smaller than their averages, while in summer these values were similar. Seasonal variations were tested and proved by the analysis of variance, which showed that the results of measurements of the mass concentration of all pollutants were statistically higher (p ¼ 0.05) in winter at both monitoring sites. Spatial variations were also tested by the analysis of variance, which showed that during winter there were statistically significant differences in the mass concentrations of OC and BaP between the RI and UB site. During summer statistical differences were found in the mass concentrations of EC and all of the measured PAHs between the RI and UB sites. PM10 mass concentrations did not differ significantly between the sites. Studies in which EC, OC and PAHs in PM10 are simultaneously measured are scarce. Jedynska et al. (2014) performed a study involving 10 European cities in which she compared the measured mass concentrations of EC, OC, BaP and S PAH in PM2.5. Some authors (Jakovljevi
c et al., 2015; Manoli et al., 2015; Schwarz et al.,  sovi
2008; Si c et al., 2012; Yttri et al., 2007) have conducted studies only on PAHs or EC and OC which were used to compare the results with those obtained in this paper (Table 4). EC during winter and summer at both sites in this study is similar to those date found in literature except at the UT site in Greece (Manoli et al., 2015). This is probably due to its position near seaport where the presence of carbonates in particles is expected. In Croatia, OC concentrations measured in winter at both sites were higher than that measured at urban sites in Prague (Schwarz et al., 2008) and at rural background sites in Austria, Slovakia and the Czech republic (Yttri et al., 2007). The values of BaP and S PAH at the RI site in winter are much more higher than the literature data (Jakovljevi
c et al., 2015; Manoli et al.,  sovi
c et al., 2012). These high values are a consequence of 2015; Si Delnice’s geographical location, where the RI monitoring site was, surrounded by mountains that prevent the circulation of air masses and favour the accumulation of pollutants. Nevertheless, other measurements of these pollutants are similar to those measured

3. Results and discussion 3.1. General characteristics of OC, EC and PAHs species To select the appropriate statistical methods for determining statistically significant differences in carbon levels in airborne particles at the measuring stations (spatial distribution) and at different sampling times (time distribution), it was necessary to determine the distribution of the measured mass concentrations of particulate matter and carbon content in the particles. Reports from the literature show that a variety of impurities present in the air and measured for a longer period are usually distributed according to a log-normal distribution. The reasons for such a distribution are: the pollution concentration in the air cannot be negative; the results are grouped around low values, while high concentrations are very rare. It is assumed that the PM10, EC, OC, BaP and ƩPAH (sum of all measured PAHs in this study including BaP) concentrations measured in airborne particles at the urban background (UB) site and rural industrial (RI) site burdened by pollution from domestic furnaces on wood and the local wood pellet industry also followed a log-normal distribution. This assumption was examined using the graphic empirical distribution versus theoretical distributions (aka. probability-probability plot). From each of the observed sequence data, using a parameter theory distribution, we calculated the mathematical expectation (m) and the variance (s2). The results showed good agreement with the empirical value of the assumed log-normal distribution of mass concentrations of all of the measured pollutants (PM10, EC, OC, BaP and ƩPAHs) at both measuring sites. During continuous measurements over two months in winter and summer at the RI and UB monitoring sites, the mass concentrations of airborne particles PM10, EC, OC, BaP and ƩPAHs (sum of all measured PAHs in this study including BaP) in these particles were determined as well as their temporal and spatial distribution. The proportions of carbon in particulate matter collected at the rural and urban area were also investigated. Table 2 summarizes the mass concentration of PM10, and content of EC, OC, TC, BaP and ƩPAHs in these particles, as well as mass contribution of EC, OC and TC to the overall PM mass at the RI monitoring site. In Table 3, the same statistical parameters for the UB monitoring site are shown. At the RI monitoring site, PM10 mass concentrations exceeding the daily limit value (50 mg m3) set by EU legislation (Directive, 2008/50/EC), were found 20 times during winter (on 3 occasions the concentrations were even higher than 100 mg m3) and only once during summer (Fig. 2). In the winter period, the average mass concentrations of EC, OC and TC were more than three times higher than in summer. Average mass contributions of EC and OC to the total PM10 mass were also higher in winter. The medians of BaP and

Table 2 Statistical parameters of measured pollutants at the RI monitoring site. Winter (N ¼ 61)

Statistical parameters

Mass concentrations

Mass contribution to the overall PM10 mass

PM10/mg m3 EC/mg m3 OC/mg m3 TC/mg m3 BaP/ng m3 ƩPAHs/ng m3 EC/% OC/% TC/%

Summer (N ¼ 60)

x

sx

xmin

x25

x50

x75

xmax

x

sx

xmin

x25

x50

x75

xmax

47.0 2.2 18.9 21.1 5.47 48.5 4.6 36.0 40.6

25.7 1.7 16.0 17.0 6.89 75.1 3.0 9.7 11.3

9.4 0.2 2.6 2.7 0.09 0.8 1.1 18.6 20.0

29.8 0.7 8.6 9.5 0.86 6.6 2.2 28.2 30.7

44.6 1.9 15.7 17.6 3.66 25.8 3.4 34.4 41.5

56.3 3.5 24.1 26.0 6.49 47.8 6.8 42.8 50.7

155.8 5.7 91.5 95.6 35.19 429.0 11.5 58.7 61.4

19.3 0.5 5.8 6.2 0.06 0.8 2.6 31.1 33.6

9.6 0.2 2.3 2.5 0.08 0.8 1.2 8.6 9.1

2.7 0.1 0.8 0.9 0.01 0.1 1.3 19.7 21.5

12.3 0.3 4.0 4.4 0.02 0.3 1.7 27.0 29.1

17.7 0.4 5.6 6.0 0.04 0.5 2.1 28.8 31.1

24.7 0.6 7.0 7.6 0.08 1.1 2.9 34.6 37.5

50.8 1.0 11.6 12.4 0.50 4.5 7.4 70.4 71.6

Ne number of samples, xe average, sxe standard deviation, xmin e minimum measured value, x25 e 1st quartile, x50 e median, x75 e 3rd quartile, xmax e maximum measured value, EC e elemental carbon, OCe organic carbon, TCe total carbon, BaPe benzo(a)pyrene, Ʃ PAHs e all measured PAHs.

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Table 3 Statistical parameters of measured pollutants at the UB monitoring site. Winter (N ¼ 61)

Statistical parameters

Mass concentrations

Mass contribution to the overall PM10 mass

3

PM10/mg m EC/mg m3 OC/mg m3 TC/mg m3 BaP/ng m3 ƩPAHs/ng m3 EC/% OC/% TC/%

Summer (N ¼ 60)

x

sx

xmin

x25

x50

x75

xmax

x

sx

xmin

x25

x50

x75

xmax

43.6 1.6 12.0 13.5 1.93 21.7 4.1 26.8 30.9

22.3 0.8 7.1 7.6 1.61 16.6 2.4 3.3 4.5

15.6 0.4 3.6 4.2 0.34 3.9 1.4 17.4 20.5

27.2 1.0 6.8 7.9 1.00 2.3 2.5 24.3 27.8

38.2 1.3 10.3 11.8 1.40 17.5 3.2 27.0 30.3

53.9 2.1 14.6 16.9 2.35 27.6 5.5 29.2 33.7

118.4 4.0 39.6 42.6 7.90 72.2 11.4 33.4 44.1

19.4 0.6 5.0 5.6 0.04 0.4 3.2 26.9 30.1

7.0 0.2 1.6 1.7 0.03 0.2 1.2 5.4 5.7

8.5 0.2 1.6 1.9 0.01 0.1 1.7 20.5 23.5

13.7 0.4 3.9 4.2 0.03 0.3 2.3 24.0 26.3

18.5 0.6 5.0 5.5 0.04 0.4 3.1 26.0 29.6

24.0 0.7 6.2 6.9 0.06 0.5 3.6 28.5 31.9

35.1 1.0 8.3 9.1 0.20 1.3 6.6 59.6 63.2

Ne number of samples, xe average, sxe standard deviation, xmin e minimum measured value, x25 e 1st quartile, x50 e median, x75 e 3rd quartile, xmax e maximum measured value, EC e elemental carbon, OCe organic carbon, TC- total carbon, BaPe benzo(a)pyrene, Ʃ PAHs e all measured PAHs.

Fig. 2. Temporal profiles of PM10 with DLV (daily limit value), OC and BaP during winter at the RI site.

Fig. 3. Temporal profiles of PM10 with DLV (daily limit value), OC and BaP during winter at the UB site.

 sovi
earlier in Croatia (Jakovljevi
c et al., 2015; Si c et al., 2012). 3.2. Investigating sources of PAH species using diagnostic PAH ratios Some PAH ratios can be used to identify their major sources (Cazier et al., 2016; Hanedar et al., 2014; Jakovljevi
c et al., 2016,  sovi
c et al., 2012; 2015; Jyethi et al., 2014; Ravindra et al., 2008; Si

Teixeira et al., 2012; Yunker et al., 2002). Table 5 shows some diagnostic PAHs ratios (Flu/(Flu þ Pyr), BaA/(BaA þ Cry), IP/ (IP þ BghiP), BaP/BghiP, IP/BghiP and BaP/(BaP þ Chry) during winter and summer at the UB and RI sites. At the RI site, Flu/(Flu þ Pyr) indicated gasoline and other liquid fossil fuels as the potential sources of PAHs in both winter and summer, while at the UB site in winter this ratio corresponded to

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Table 4 Comparison between the UB and RI sites in Croatia and other sites in Europe.

mg m3

Site/Season

UB

Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer

RI UB UT UB UT UB UT R UR UT UI RB (Ispra) RB (Illmitz) RB (Stara lesna) RB (Kosetice) UB (San pietro capofiume)

ng m3

Literature

PM10

EC

OC

BaP

SPAH

43.6 19.4 47.0 19.3 36.0 29.0 52.0 51.0 36.8 21.4 55.6 23.8 38.1 20 26 41 e e e e e e e e e e e e e e e e e e

1.6 0.6 2.2 0.5 1.0 0.8 5.4 7.6 e e e e 0.8 0.4 0.9 0.6 e e e e e e e e 2.7 1.0 1.2 0.8 1.0 0.6 1.1 0.9 1.9 1.0

12.0 5.0 18.9 5.8 9.8 4.3 15.0 9.2 e e e e 8.1 2.4 6.1 2.7 e e e e e e e e 11.6 4.2 6.3 4.8 3.7 5.0 4.9 4.0 7.4 4.5

1.93 0.04 5.47 0.06 0.70 0.002 0.86 0.12 1.15 0.04 2.76 0.07 e e e e 2.62 0.04 2.7 0.05 3.74 0.05 3.75 0.11 e e e e e e e e e e

21.7 0.4 48.5 0.8 9.4 0.64 12.0 4.0 7.4 0.3 18.8 0.5 e e e e 19.7 0.6 17.7 0.4 23.8 0.6 26.4 1.5 e e e e e e e e e e

This study

Manoli et al., 2015

 sovi
(Si c et al., 2012)

(Schwarz et al., 2008)

(Jakovljevi
c et al., 2015)

(Yttri et al., 2007)

UBe urban background site, RI e rural industrial site, UT e urban traffic site, R e rural site, UR e urban residential site, UI e urban industrial site, RB e rural background site.

coal and wood burning. During winter, the average BaA/ (BaA þ Chry) ratio suggested that PAHs mostly originated from combustion at both sites. Average IP/(IP þ BghiP) ratios were higher

at the RI site compared to the UB site. IP/(IP þ BghiP) values at the UB site indicated vehicle emissions (diesel) as a major source, while in winter at the RI site wood combustion was prevalent. The BaP/

Table 5 Average diagnostic PAH ratios during winter and summer at the urban background and rural industrial sites with corresponding sources from literature. This study RI

Value

Sources

References

<0.5 0.4

Gasoline combustion Liquid fossil fuel (crude oil) combustion Coal/wood burning Petroleum Combustion Gasoline Diesel Coal Wood burning Diesel emissions Vehicles Coal Traffic emission Brown coal Wood Gasoline Diesel coal/coke Vehicles with a catalyst Diesel Gasoline

(Cazier et al., 2016; Hanedar et al., 2014; Jakovljevi
c et al., 2016,  sovi
2015; Jyethi et al., 2014; Ravindra et al., 2008; Si c et al., 2012; Teixeira et al., 2012; Yunker et al., 2002)

UB

W

S

W

S

Flu/(Flu þ Pyr)

0.45

0.42

0.51

0.40

BaA/(BaA þ Chry)

0.50

0.34

0.40

0.31

IP/(IP þ BghiP)

0.62

0.48

0.37

0.39

BaP/BghiP

1.86

0.29

0.62

0.45

IP/BghiP

1.63

0.92

0.58

0.65

BaP/(BaP þ Chry)

0.49

0.60

0.46

0.53

>0.5 <0.2 >0.35 0.18 0.37 0.56 >0.62 0.35e0.70 0.3e0.78 0.9e6.6 0.5e0.6 >1.25 0.29 0.4 1 1.09 0.33 0.50 0.73

RI e rural industrial site, UBe urban background site, W e winter, Se summer.

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R. Godec et al. / Environmental Pollution xxx (2016) 1e11 Table 6 Average OC/EC ratios, secondary OC and average SOC contribution to total OC during winter and summer at the urban background and rural industrial sites. RI

N OC/EC (OC/EC)min SOC/mg m3

x

sx

SOC/OC

xmin x25 x50 x75 xmax %

UB

Winter

Summer

Winter

Summer

61 10.7 3.4 11.4 13.7 0 3.6 7.2 12.1 77.7 58

60 14.0 6.9 2.6 1.5 0 1.5 2.5 3.8 8.2 45

61 8.5 2.7 7.8 6.4 0 3.1 6.0 11.0 31.7 60

60 9.2 3.5 3.0 1.4 0 2.2 2.8 4.0 5.6 57

RI e rural industrial site and UBe urban background site Ne number of samples, xe average, sxe standard deviation, xmin e minimum measured value, x25 e 1st quartile, x50 e median, x75 e 3rd quartile, xmax e maximum measured value, OC/EC e organic/elemental carbon ratio, SOCe secondary organic carbon.

BghiP ratio in summer was characteristic for mobile sources at both sites. In winter at the RI site, BaP/BghiP and IP/BghiP ratios showed that PAHs mostly came from stationary source combustion emissions, i.e., from the use of coal and coke as fuels for domestic heating. The BaP/(BaP þ Chry) ratio at both sites during summer and winter suggested a higher contribution of diesel than gasoline, probably due to the influence of local wood industry (emissions from diesel engines during wood processing, transportation of wood by trucks, etc.). This difference between winter and summer PAH ratios was more expressed at the RI site than at the UB site because the heating fuels used at the RI site were coal, oil and wood while at the UB site the predominant heating fuel was gas. 3.3. Estimation of secondary OC using OC/EC ratio Due to the seasonal variation of photochemical activity, OC/EC ratios were lower in wintertime than in summertime at both sites (Table 6). Higher OC/EC ratios at the RI site suggest that there was more wood burning and less traffic than at the UB site where most of pollution came from traffic. In a previous research (Godec et al., 2012), high OC/EC ratios of unknown origin were observed at the UB site. The OC/EC ratio in this study at the UB site was higher

7

during summer than the OC/EC ratio found in a study in Prague (Schwarz et al., 2008), at both UB and UT sites. During winter at the RI site in this study and at the UB site in Prague the values of OC/EC ratio were similar (OC/EC ¼ 10). In other cities, at urban and in
nchez de la Campa dustrial sites described by Pio et al. (2011) and Sa et al. (2009), the OC/EC ratios were much lower than those observed in this study, while at rural stations these ratios were similar to this study. The OC/EC ratio can be used as an indirect evaluation of secondary OC (SOC) in particulate matter, in PM10. The high OC/EC ratio in this study at both sites in summer and winter suggested that there was a big amount of secondary organic carbon in the air (Table 6). Castro et al. (1999) described how the minimum OC/EC ratio can be used to calculate SOC by Equation (1). SOC originates not only from local emissions but also from chemical transformation during regional and long range transport (Kroll and Seinfeld, 2008; Ma and Birmili, 2015). Pio et al. (2011) describe how during winter at UB sites, SOC originates from the contribution of other OC primary sources like fossil fuel combustion, biomass burning, evaporation, oxidation and condensation processes. But long-range transport of OC and EC at the RI site in our study during summer, occurring because of the shifting air masses, can be considered to be very small due to this site is well protected by high mountain chains which reduce transboundary pollution. Although it was expected a higher contribution of the SOC to the total OC mass during the summer than in the winter at both measuring sites due to strong photochemical activity, in this study was noticed higher SOC contribution during winter then in summer. This can be due to favourable meteorological conditions and area surrounded by mountains which contribute higher production of SOC. Average SOC contribution to the total OC mass at the RI site during summer was lower than others which were higher and close to 60% (Table 6). These results were comparable with literature data (Castro et al., 1999).

 OCsec ¼ OCtotal 

OC EC

 $EC

[1]

minimum

Fig. 4. 3D plot of factor loadings (24-h averages of mass concentrations) during the summer period of measurements at the a) RI and b) UB sites.

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8

R. Godec et al. / Environmental Pollution xxx (2016) 1e11

Fig. 5. 2D plot of factor loadings (24-h averages of mass concentrations) during the winter period of measurements at the UB site.

Table 7 Results of MLRA based on the result of PCA for the RI site during summer. N ¼ 59

Coefficient

SE

t

p

Factor 1 Factor 2 Factor 3

0.31 8.40 0.69

0.668 0.693 0.667

0.47 12.12 1.03

0.642 0.000 0.306

R ¼ 85.4% R2 ¼ 73% R2ADJ ¼ 71.5%.

3.4. Identification of carbon and PAH species using factor analysis 3.4.1. Principal component analyses (PCA) PAHs and EC and OC mass concentrations were subjected to factor analysis. Factors were based on principal component extraction and rotated by the normalized varimax method. For the winter period at the RI site, only one factor was extracted. This factor described all of the PAHs and OC and established their common origin. EC was not included in this factor. For the summer period at the RI site, three factors were separated (Fig. 4a). The first factor separated PAHs with 5 or more benzene rings, suggesting their common origin from local wood pellet industry or biomass burning (household heating and cooking on wood, burning grass and branches due to agricultural activities etc., (Li, 2009; Oros and Simoneit, 2001a, 2001b). The second factor separated the EC and OC that originate from traffic (Viana et al., 2006). The third factor separated Flu and Pyr, PAHs with low molecular mass. Factor analysis probably separated Flu and Pyr according to their atmospheric behaviour. Smaller molecules are less stable and during summer they often evaporate or decompose through oxidation and photo oxidation. Pyr and Flu are the most volatile of the PAHs included in this study and due to gas/particle partitioning during summer they are in significant amounts present in the gaseous phase (Jakovljevi
c et al., 2016; Ma and Birmili, 2015). The second explanation could be that Flu and Pyr originate from a different pollution source compared to other PAHs. For the winter period at the UB site, two factors were extracted (Fig. 5). Factor analysis separated all of the PAHs (Factor 1) from EC and OC (Factor 2). Factor 1 stands for wood and biomass burning as

a source of pollution (Li, 2009; Oros and Simoneit, 2001a, 2001b) while Factor 2 stands for traffic. At the UB site during summer, three significant factors were also extracted (Fig. 4b). The first factor separated PAHs with 5 or more benzene rings except Chry. The second factor separated Flu, Pyr and Cry, while the third factor separated EC and OC. Factor 1 separated PAHs that originate from biomass burning. Factor 2 suggests that Pyr, Flu and Chry had different sources e they were probably emitted from diesel vehicles, trucks and machines during summer road repairs (Teixeira et al., 2012). EC and OC in Factor 3 originated from traffic, brake, tire wear and oil drips (Viana et al., 2006). At both sites during summer, DahA was not included in either of the factors.

Fig. 6. Correlation between the measured (PM10-measured) and the modelled PM10 (PM10-MLRA) at the RI site during summer.

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R. Godec et al. / Environmental Pollution xxx (2016) 1e11

9

Fig. 7. Contribution of factors to PM10 at UB site during winter.

3.4.2. Multi-linear regression analysis (MLRA) For each site MLRA is conducted to confirm the results of the PCA analysis using absolute score factors as source traces. Table 7 shows the results for the RI site during summer, and here Factor 2 (EC, OC) was the significant one. At the UB site during summer Factor 2 (EC, OC) and Factor 3 (Flu, Pyr) were significant with regard to Factor 1 (PAHs with 5 or more benzene rings). Both factors were significant at the UB site during winter. The daily measured PM10 concentrations were correlated with the modelled ones obtained from the MLRA analysis to test for the accuracy of the model (Fig. 6). Good agreement between the measured and the modelled values of PM10 was achieved for both sites during winter and summer. Contributions of factors to PM10 were calculated for each site and each season of measurements. At the UB site during winter, the contribution of Factor 2 (EC, OC) to PM10 is higher to those days when the mass concentration of PM10 exceeded the daily limit value (50 mg m3, Directive 2008/50/EC, 2008) as seen in Figs. 3 and 7.

this case these showed that, at the RI site during summer most of the pollution came from diesel vehicles and during winter from coal and wood combustion. Factor analysis showed that EC and OC originated from traffic at both sites, PAHs with 5 or more benzene rings originated from local wood pellet industry or biomass burning, while Pyr and Flu originated from diesel combustion or as a consequence of different atmospheric behaviour e evaporation and participation in oxidation and photo oxidation processes. Significant factors of PCA analysis were verified by MLRA; also MLRA was used to extract more information about sources and locations. Acknowledgments These preliminary measurements have been conducted within the IAEA TC Project RER/2/005 00 Characterizing Seasonal Variations in Elemental Particulate Matter Concentrations in European Urban and Rural Areas under Different Climatic Conditions” and Ministry of Science, Education and Sports (MSES), Croatia “Spatiotemporal distribution and origin of aerosols in urban surroundings” (0220222882-2271).

4. Conclusions

References

Measurements have shown seasonal and spatial variations of mass concentrations for carbon species and all of the measured PAHs (Flu, Pyr, Chry, BaA, BbF, BaP, BkF, BghiP, DahA and IP) in PM10 at an urban site and a rural monitoring site. EC during winter and summer at both sites in this study is similar to the data found in literature. In Croatia, OC measured in winter at both sites was higher than the values measured at urban sites and at rural background sites in other European countries. The values of BaP and S PAH at the RI site in winter are much higher than the literature data as a consequence of Delnice’s geographical location, surrounded by mountains that prevent the circulation of air masses and favour the accumulation of pollutants. SOC originate not only from local emissions but also from chemical transformation during regional and long range transport. However, the long-range transport of OC and EC at the RI site during summer due to the shifting of air masses can be considered to be very small because this site is well protected by high mountain chains which reduce transboundary pollution. Using the diagnostic PAHs ratios (Flu/(Flu þ Pyr), BaA/ (BaA þ Cry), IP/(IP þ BghiP), BaP/BghiP, IP/BghiP and BaP/ (BaP þ Chry)), it is possible to assess the sources of pollution, and in

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Please cite this article in press as: Godec, R., et al., Carbon species in PM10 particle fraction at different monitoring sites, Environmental Pollution (2016), http://dx.doi.org/10.1016/j.envpol.2016.06.034