Aerosol contributions at an urban background site in Eastern Mediterranean – Potential source regions of PAHs in PM10 mass

Science of the Total Environment 598 (2017) 563–571

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Aerosol contributions at an urban background site in Eastern Mediterranean – Potential source regions of PAHs in PM10 mass Konstantinos Dimitriou ⁎, Pavlos Kassomenos Laboratory of Meteorology, Department of Physics, University of Ioannina, Greece

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• During cold seasons, regional airflows entrapped locally produced PM2.5 in Cyprus. • The impact of dust from NE Africa and the Middle East was apparent on PMCOARSE. • Continental airflows increased particulate-phase PAHs in Limassol. • Clean marine air masses dropped the levels of PAH compounds in all seasons. • Possible PAH intrusions from fire events in Central Turkey were indicated.

a r t i c l e

i n f o

Article history: Received 23 March 2017 Received in revised form 20 April 2017 Accepted 21 April 2017 Available online xxxx Editor: D. Barcelo Keywords: PM2.5 PM10 Polycyclic Aromatic Hydrocarbons Air mass trajectories Cyprus Potential Source Contribution Function

a b s t r a c t In this paper, two backward air mass trajectory-based models (Potential Source Contribution Function [PSCF] and Concentration Weighted Trajectory [CWT]) were combined, aiming to identify sources and factors defining the load of PM in the city of Limassol (Cyprus). The study also focused on the determination of atmospheric pathways enriching the aerosol phase of four carcinogenic Polycyclic Aromatic Hydrocarbons (PAHs): Benzo(a)pyrene (BaP), Benzo(a)anthracene (BaA), Benzo(b)fluoranthene (BbF) and Benzo(k)fluoranthene (BkF), in PM10 mass. The analysis was performed on a 0.5° · 0.5° resolution grid for the two-year period 2011–2012. During cold seasons, regional airflows triggered the accumulation of locally produced PM2.5, while the impact of dust plumes originated from deserts in NE Africa, Syria and the Middle East, was apparent on PM2.5 and principally on PMCOARSE levels. On the contrary, within warm seasons, weaker dust PMCOARSE contributions were detected in Limassol from areas in Egypt and Libya. Raised particulate-phase PAH concentrations in Limassol were clearly related to air parcels reaching Cyprus via continental areas. The use of outdated technologies for heating and transportation in Turkey and Syria, and fire events in central Turkey, are possible sources of exogenous PAHs throughout cold and warm period respectively. The influence of clean marine air masses dropped the levels of PAH compounds in all seasons. © 2017 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: University of Ioannina, GR-45110, Greece. E-mail address: [email protected] (K. Dimitriou).

http://dx.doi.org/10.1016/j.scitotenv.2017.04.164 0048-9697/© 2017 Elsevier B.V. All rights reserved.

564

K. Dimitriou, P. KassomenosScience of the Total Environment 598 (2017) 563–571

1. Introduction

2. Materials and methods

Air quality in Cyprus is influenced by both local and transported pollution including desert dust storms (Achilleos et al., 2014). A characteristic record-breaking dust outbreak originating from desert regions in northern Syria and Iraq occurred over the Eastern Mediterranean in September 2015, causing hourly mean PM10 (particles with an aerodynamic diameter smaller than 10 μm) values close to 8000 μg/m3 at Larnaca and Limassol (Mamouri et al., 2016). Yet, despite the large impact of dust intrusions and their increasing frequency over time, dust storms were responsible only for a small fraction of the exceedances of the daily PM10 concentration limit (50 μg/m3 [Council Directive 2008/50/EC]) in Nicosia (Achilleos et al., 2014), whereas the main particulate source for the city was traffic. Seven sources of PM2.5 (particles with an aerodynamic diameter smaller than 2.5 μm) including: regional sulfur, traffic emissions, biomass, re-suspended soil, oil combustion, road dust, and sea salt, were indicated in four cities of Cyprus, namely: Limassol, Nicosia, Larnaca and Paphos, while road dust, soil, and sea salt were the main sources of PMCOARSE (=PM10 − PM2.5) in the same areas (Achilleos et al., 2016). In the same study (Achilleos et al., 2016), all dust storm samples, except one, had PM levels below the daily standard. The results of Bari et al. (2009), also revealed that mineral soil, sea salt, road dust, oil combustion and gasoline vehicles, are major emission sources affecting the PM10 load in Cyprus. Polycyclic Aromatic Hydrocarbons (PAHs) are chemical compounds well known for their toxicity. The composition of suspended PM may comprise particulate-phase PAHs associated with carcinogenicmutagenic effects (Slezakova et al., 2010; Saarnio et al., 2008). Sarigiannis et al. (2015), analyzed the chemical content of three fractions of particulates (PM1 [particles with an aerodynamic diameter smaller than 1 μm], PM2.5 and PM10) in Athens (Greece) and estimated the PAH-induced lung cancer risk, attributed to the increased use of biomass for space heating in the winter of 2012–2013. According to the results of Sarigiannis et al. (2015), higher risk was estimated for infants and children, due to the higher body weight normalized dose and the human respiratory tract physiology. In Zhengzhou (China), lifetime inhalation cancer risks were approximately 8.9 · 10− 7 and 6.3 · 10−7, for industry and residential sites respectively, according to Incremental Lifetime Cancer Risk (ILCR) model applied on PAH concentrations in PM2.5 (Wang et al., 2014). In addition, the estimated value of lifetime lung cancer risks, associated with exposure to particulate PAHs in a traffic influenced urban site in Oporto (Portugal), exceeded the healthbased guideline levels (Slezakova et al., 2013). PAHs are by-products of incomplete combustion or pyrolysis of organic substances and they are generated whenever fossil fuels or vegetation is burnt (Godec et al., 2016). Therefore, coal combustion (Wang et al., 2014; Mikuska et al., 2015), biomass burning (Godec et al., 2016; Alves et al., 2011; Lu et al., 2016) and traffic (Slezakova et al., 2013; Slezakova et al., 2010) are major sources of particulate PAHs. Long-distance transport of air masses, from regions with industrialurban pollution and biomass burning, may transfer aerosols containing PAH constituents to other areas (Lu et al., 2016). The main ambition of this paper was to find the geographical location of PM and particulate-phase PAH sources, enriching the atmosphere of Limassol with exogenous aerosols. Gridded air mass residence time, produced from backward air mass trajectories, was the main variable used in two statistical procedures which were applied to identify the origin of air parcels, inducing advection of PM in Limassol. Particulate air pollution has been previously associated with adverse health effects in Cyprus. Higher values of PM10 are related to higher levels of morbidity in Cyprus, for cardiovascular and respiratory diseases (Tsangari et al., 2016), while all-cause and cardiovascular hospital admissions in Nicosia were 4.8% and 10.4% higher on dust storm days respectively (Middleton et al., 2008). Thus, the findings of this paper can be used to protect the island's population from being exposed to dangerous concentrations of airborne substances.

2.1. Air pollution data Limassol is a medium sized coastal city located in the Akrotiri bay at the Southern part of the island of Cyprus in Eastern Mediterranean (Fig. 1a). The city has a total population of approximately 170,000 inhabitants. For the purposes of this paper daily PM10 and PM2.5 concentrations (μg/m3), measured in the city of Limassol (Cyprus) through the two year period 2011–2012, were obtained from the air quality database of the Environment Agency (http://www.eea.europa.eu) of the European Union (EU). The PM10 and PM2.5 concentrations were derived from the “Limassol Residential” (Longitude: 33.0°, Latitude: 34.7°, Altitude: 22 m, EU code: CY0005A) urban background air pollution station (Fig. 1) and were determined by Digitel HVS and Sven Leckel SEQ47/50 gravimetric analyzers respectively. In addition, the content (ng/m3) of four particulate-phase PAHs (Benzo(a)pyrene (BaP), Benzo(a)anthracene (BaA), Benzo(b)fluoranthene (BbF) and Benzo(k)fluoranthene (BkF)) in PM10 mass was also specified by analyzing the collected aerosol specimens with a high performance Digitel HVS liquid chromatography - fluorescence detector. It should be mentioned that in order to increase the traceability of PAHs in PM10, the collected PM10 specimens of each pair of consecutive days were unified, aiming to increase the quantity of PM10 mass. Thus, the used PAH concentrations in this paper correspond to two-day time intervals. Simple statistics describing the monitoring station's data are presented in Table 1. The selected air pollution station is operated by the Department of Labour Inspection of the Ministry of Labour, Welfare and Social Insurance of the Republic of Cyprus (http://www.airquality.dli.mlsi.gov.cy), which is the authority responsible for the assessment, monitoring and reporting of air quality in Cyprus. The sampling site is located at an urban residential area to the North West (NW) of the city's commercial center and port (Fig. 1a). The station is situated in a small street garden (Fig. 1b) which is neighboring with the crossroad of two narrow streets (Agias Sofias Street (Fig. 1b) and Seychelles Street), thus some influence from traffic is anticipated. Nevertheless no major roads, associated with dense traffic flows and possible congestion, exist near the sampling site, hence the emergence of long range transport effects on PM concentration and composition data is expected. 2.2. PSCF and CWT algorithms The dwelling time of air parcels over PM emission source areas is a critical parameter related to potential atmospheric transport impacts (Salvador et al., 2010; Xu et al., 2006; Kavouras et al., 2013; Chalbot et al., 2013). Slow moving airflows, corresponding to short range trajectories, can more effectively incorporate airborne particulates and transfer them to distant regions, whereas rapid air streams are associated with atmospheric dispersion and physical losses of PM due to various exchange and mixing processes (Fleming et al., 2012). The Potential Source Contribution Function (PSCF) is an algorithm which couples backward air mass trajectories with air pollution levels at a receptor site, in order to identify source regions responsible for extreme intrusions of specific pollutants (Karaca et al., 2009; Polissar et al., 2001; Liu et al., 2016; Dimitriou and Kassomenos, 2016; Jeong et al., 2017; Zhu et al., 2017). In this paper, Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (http://www.arl.noaa.gov/ HYSPLIT.php) of the National Oceanic and Atmospheric Administration (NOAA) was applied, in order to produce 3-day (72 h) backward air mass trajectories approaching Limassol (Cyprus) at 500 m Above Ground Level (AGL). For each day, 24 trajectories reaching Limassol at 00:00–23:00 UTC were computed (Dimitriou, 2015). Air mass residence time was distributed on a 0.5° · 0.5° resolution grid, extending within the coordinate boundaries: 30°W-60°E and 17°N-60°N, as the sum of the number of hourly trajectory points within each cell (Kavouras

K. Dimitriou, P. KassomenosScience of the Total Environment 598 (2017) 563–571

565

Fig. 1. Limassol Residential station: a) exact position on map (red dot) b) visual depiction of the sampling site. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

et al., 2013). Few highly distant trajectory points, which were located outside of the grid's limits, were not included in the present study. PSCF is expressed by Eq. (1): PSCFij ¼

et al., 2016; Dimitriou et al., 2015; Salamalikis et al., 2015; Brereton and Johnson, 2012): ν

mij nij

ð1Þ

In Eq. (1), nij is the number of endpoints that fall in the ijth cell and mij is the number of endpoints that fall in the ijth cell and correspond to trajectories with pollutant concentrations higher than an arbitrary criterion value (Kong et al., 2013). Hence, each grid cell takes a value varying from 0 to 1 (dimensionless number), reflecting the likelihood for exceedances of the arbitrary concentration threshold, when the incoming air parcels overfly across this cell. Here the criterion was set to 85th percentile (Dimitriou, 2015) and PSCF algorithm was implemented separately for PM2.5, PMCOARSE and each one of the four previously mentioned PAHs. 24 backward trajectories corresponded to each daily concentration of PM2.5 and PMCOARSE, while 48 backward trajectories corresponded to each two-day average concentration of PAHs. A problem in the structure of PSCF method is that slight and extreme exceedances of the threshold concentration value are all treated in the same way, thus limitations in distinguishing moderate sources from strong ones may occur (Wang et al., 2006; Hsu et al., 2003). Thus, Concentration Weighted Trajectory (CWT) model, which is a technique of weighting trajectories with associated concentrations (Hsu et al., 2003; Squizzato et al., 2017), was also used for PM2.5, PMCOARSE and PAHs. CWT model is described by Eq. (2) (Saxena et al., 2017; Liu

Table 1 Statistics for PM2.5 (μg/m3), PMCOARSE (μg/m3) and particulate-phase PAHs in PM10 (ng/ m3), at Limassol Residential station during a) cold period and b) warm period. PM2.5

PMCOARSE

BaP in PM10

BaA in PM10

BbF in PM10

BkF in PM10

(a) Average 85th percentile Standard deviation

20.86 26.80 6.17

11.80 18.90 14.15

0.44 0.71 0.33

0.20 0.34 0.17

0.42 0.71 0.27

0.18 0.31 0.12

(b) Average 85th percentile Standard deviation

24.69 30.77 6.65

14.46 22.01 11.30

0.10 0.12 0.14

0.05 0.07 0.05

0.13 0.18 0.09

0.05 0.07 0.05

Cij ¼

∑k¼1 Ck τijk ν

∑k¼1 τijk

ð2Þ

In Eq. (2), Cij (μg/m3 [for PM2.5 and PMCOARSE] – ng/m3 [for PAHs]) is the Weighted Average Concentration (WAC) for the ijth cell. For the PM2.5 and PMCOARSE analysis, τijk is the number of trajectory points allocated in the ijth cell on day (k) and Ck is the pollutant's average concentration on day (k). For each PAH compound's analysis, τijk and Ck refer to the two-day time interval (k). According to the theory of concentration weighted trajectories high/low values of Cij imply that air parcels traveling over the ijth grid cell will have, on average, high/low pollutant concentrations at the receptor site (Salamalikis et al., 2015). The rarity of trajectory points in outlying grid cells may trigger highly uncertain extreme values of PSCFij and Cij. Therefore, in order to blunt those misleading extreme values and enhance the statistical balance of our methodology, PSCFij and Cij values were multiplied with an arbitrary Wij weight function (Eq. (3)). 8
 1:0 3nave ≤nij  > >
< 0:7 1:5n
ave ≤nij b3nave  Wij ¼ nave
≤nij b1:5n > 0:4 >  ave : 0:2 nij bnave

ð3Þ

In Eq. (3), Wij appears in the form proposed by Liu et al. (2016) and Dimitriou et al. (2015), which is based on the relationship between the average number (nave) of trajectory points of all grid cells which contain non-zero values of air mass residence time and the number (nij) of total trajectory points in the ijth cell. According to Eq. (3), the initial PSCFij and Cij values are preserved, only if nij is at least triple than nave. Finally, in order to identify possible seasonal changes at the geographical distribution of sources enriching the load of PM in Limassol with exogenous material, PSCF and CWT algorithms were applied separately during cold (16 October–15 April) and warm period (16 April–15 October). Only days with available concentration data of PM10, PM2.5 and of the four PAHs in PM10 mass, were included in the study, thus 302 and 322 days from the total 2-year interval were finally used for cold and warm period respectively.

566

K. Dimitriou, P. KassomenosScience of the Total Environment 598 (2017) 563–571

Fig. 2. a–b) Gridded WAC values (μg/m3) during cold period for PM2.5 and PMCOARSE c–d) gridded PSCF values during cold period for PM2.5 and PMCOARSE.

3. Results and discussion 3.1. Cold period During cold seasons, increased concentrations of PM2.5 in Limassol were associated with regional airflows (Fig. 2a), implying atmospheric stagnation (Zawadzka et al., 2013), thus the influence of local PM2.5 sources (e.g. traffic, heating) was suggested. However, maximum WAC and PSCF values for PM2.5 are mainly isolated in grid cells located to the east of the island of Cyprus (Fig. 2a, c), in coastal areas of Syria and Lebanon, thus the dust footprint in PM2.5 was indicated (Caggiano et al., 2011; Engelbrecht et al., 2014). Raised PMCOARSE levels (Fig. 2b)

and extreme PMCOARSE intrusions (Fig. 2d) in Limassol were clearly related to the arrival of air parcels approaching from Southern and Eastern directions. Therefore the strong impact of desert dust transportation from Egypt, Libya, Syria and the Arabian Peninsula, in the coarse aerosol fraction (Achilleos et al., 2016; Kocak et al., 2007; Almeida et al., 2005), was highlighted. Under the influence of dust intrusions, particulate loadings in Cyprus by far exceed the healthy levels recommended by EU guidelines (Achilleos et al., 2016; Mamouri et al., 2016). Among the human health effects of dust storms are respiratory disorders, cardiovascular disorders (including stroke) and skin irritations (Goudie, 2014). Ambient PM10 concentrations in Western Iran, during Middle East dust storm events, led to excessive hospital admissions for

Fig. 3. a) Backward air mass trajectories arriving in Limassol on 19/2/2011 b) dust outbreak over Eastern Mediterranean recorded from MODIS sensor on 19/2/2011.

K. Dimitriou, P. KassomenosScience of the Total Environment 598 (2017) 563–571

respiratory and cardiovascular diseases and have also been incriminated for the increment of respiratory and cardiovascular mortality (Marzouni et al., 2016; Khaniabadi et al., 2016). Increased WAC values for PM2.5 and primarily for PMCOARSE, indicated over North West (NW) Africa and South West (SW) Iberian

567

Peninsula (Fig. 2a, b), were associated with a severe dust intrusion which occurred in Limassol on 19/2/2011. During this event, average daily PM2.5 and PMCOARSE concentrations at Limassol Residential station were 55.4 μg/m3 and 186.9 μg/m3 respectively. In addition, the Aerosol Optical Depth (AOD) recorded over Limassol region from the Moderate

Fig. 4. a) Gridded WAC values (ng/m3) during cold period for PAHs b) gridded PSCF values during cold period for PAHs.

568

K. Dimitriou, P. KassomenosScience of the Total Environment 598 (2017) 563–571

Resolution Imaging Spectroradiometer (MODIS) system mounted on NASA's Aqua satellite reached 0.778. This episode was induced by rapidly moving air masses which crossed Northern Africa (Fig. 3a) provoking a major Saharan dust outbreak in the broader Eastern Mediterranean basin (Fig. 3b). However, the origin of the dust plume which affected Limassol were the desert areas in Egypt and Libya, thus the intensified WAC values over NW Africa and SW Iberian Peninsula were misleading. The CWT model's instability, in this specific case, was not effectively corrected by the Wij weight function, due to the scarceness of trajectory points in NW Africa in conjunction with the extreme intensity of this particular episode. Low PSCF values also verified that NW Africa is not a primary source of aerosols for Cyprus (Fig. 2c, d). Regarding PAH levels in PM10 mass, higher concentrations were detected for BaP and BbF in comparison with BaA and BkF (Fig. 4a, Table 1). Augmented concentrations of all four studied PAHs were associated with the prevalence of East-Northeast (E-NE) airflows via Eastern and Central Turkey and Syria (Fig. 4a). Increased PSCF values were also observed in the same areas (Fig. 4b) while in central Turkey, a characteristic PSCF maximum was isolated over the region of the city of Ankara. During the last 30 years in Turkey, the number of motor vehicles has increased dramatically and new fossil fuel power plants have been added (Kara et al., 2013), whereas approximately 67% of the residences in Ankara in 2009 was still being heated by burning coal (Genc et al., 2010). In addition, Alkurdi et al. (2014) and Alkurdi et al. (2013) reported respectively that heating systems with emphasis on wood stoves, and old in-service vehicles not equipped with catalytic converters, are major emitters of particulate-phase PAHs in Damascus (Syria). Therefore, urban-industrial combustion in the previously mentioned countries was underlined as a possible source of PAH compounds which may affect air quality in Cyprus during cold seasons, depending on synoptic circulation. In general, significantly higher concentrations of all four studied PAHs were recorded within cold period (Table 1), due to enhanced vehicular combustion and domestic heating. Similar findings have also

been previously reported in other Mediterranean sites. On average, the winter-summer concentrations of total PAHs in PM2.5 were 7.75–3.02 and 3.44–0.658 ng/m3 at an urban background site in Florence (Italy) and at a suburban site in Athens (Greece) respectively (Alves et al., 2017). In addition, high concentrations of all PAH congeners contained in PM2.5 samples collected in Rome (Italy) were observed during winter colder periods, e.g. BaP weekly average concentration reached 3.0 ng/m3, while much lower values were recorded in summer (b 0.1 ng/m3), with seasonal variability (winter-tosummer ratios) exceeding 10 (Finardi et al., 2015). According to Council Directive 2004/107/EC, the maximum permissible average concentration of BaP in PM10 throughout a calendar year is 1 ng/m3. In this study, the annual average BaP concentrations of 2011 (0.19 ng/m3) and 2012 (0.33 ng/m3) at Limassol Residential station were below the EU limit, however in 12 two-day PM10 specimens, collected during cold period, the determined BaP concentrations exceeded 1 ng/m3. 3.2. Warm period According to CWT results, increased PM2.5 concentrations in Limassol during warm seasons were connected to North-Northwest (N-NW) air mass trajectories traveling across western and central Turkey, and Southeast (SE) Greece (Fig. 5a). The intensification of tourism in the Greek islands and coastal Turkey during summer increases the population and generates possible additional PM2.5 emissions due to enhanced traffic, shipping, etc. Fires in Turkey (wild fires and controlled biomass burning) during summer have also been previously identified as a source of a mixture of fine dust and smoke aerosols for Limassol (Nisantzi et al., 2014). Significantly reduced WAC values were detected for PMCOARSE in warm period, in comparison with cold period (Figs. 2b, 5b), due to the lack of extreme dust intrusions from eastern directions. Desert dust events in Cyprus occurred more frequently during late winter and early spring (Achilleos et al., 2014) and thus most of them belonged in

Fig. 5. a–b) Gridded WAC values (μg/m3) during warm period for PM2.5 and PMCOARSE c–d) gridded PSCF values during warm period for PM2.5 and PMCOARSE.

K. Dimitriou, P. KassomenosScience of the Total Environment 598 (2017) 563–571

the cold period. Only a weak importation of dust PMCOARSE particles from deserts in Libya and Egypt was indicated during warm seasons (Fig. 5b), whereas WAC maxima for PMCOARSE corresponded to grid cells located over western Turkey and SE Greece.

569

PSCF results for PM2.5 and PMCOARSE were generally in good agreement with the outcome of CWT algorithm (Fig. 5), marking areas in Turkey (for both PM2.5 and PMCOARSE) and NE Africa (only for PMCOARSE) as origins of exogenous PM.

Fig. 6. a) Gridded WAC values (ng/m3) during warm period for PAHs b) gridded PSCF values during warm period for PAHs.

570

K. Dimitriou, P. KassomenosScience of the Total Environment 598 (2017) 563–571

Western and central Turkey and South East (SE) Greece were also marked by CWT formula as sources of particulate-phase PAHs, for the atmosphere of Limassol (Fig. 6a). Combustion emissions, related to the summer's touristic industry in those areas, might contribute substantially to the production of PAH particulates. In addition, wild fires and biomass burning are widely acknowledged as major PAH generators (Godec et al., 2016; Alves et al., 2011; Lu et al., 2016), thus the inserted PM2.5 in Limassol's atmosphere from Turkish fires (Nisantzi et al., 2014) probably comprised PAH substances. PSCF model also revealed more frequent occurrences of raised concentrations of all four studied PAHs, corresponding to NorthNortheast (N-NE) airflows across central Turkey (Fig. 6b), a finding that can be directly attributed to the fire events which took place, according to MODIS sensor, in this specific area during the studied period (Nisantzi et al., 2014).

Acknowledgments The authors would like to thank the Environment Agency (http:// www.eea.europa.eu) of the European Union (EU) for the free concession of air pollution data and also the MODIS science data support team for providing data via the Giovanni website (http://giovanni. sci.gsfc.nasa.gov). In addition, we would like to recognize the contribution of the Department of Labour Inspection of the Ministry of Labour, Welfare and Social Insurance of the Republic of Cyprus, and principally the head of air quality section, Dr. Chrysanthos Savvides, for the provision of valuable information concerning the Limassol Residential air pollution station. Finally, the authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model used in this publication. References

4. Conclusions The existing international literature, concerning particulate air pollution in Cyprus and especially in the city of Limassol, is limited. In this paper, two backward air mass trajectory-based models (PSCF and CWT) were combined, aiming to identify sources and factors defining the load of PM in the city of Limassol. The study also focused on the determination of atmospheric pathways enriching the aerosol phase of four carcinogenic PAHs (BaP, BaA, BbF and BkF) in PM10. Substantial alterations of circulation patterns resulting in elevated PM and PAH concentrations were observed between cold and warm periods. More specifically, during cold seasons, regional airflows triggered the accumulation of PM2.5 probably produced from local vehicular combustion and domestic heating, while the impact of dust plumes originated from deserts in NE Africa, Syria and the Middle East, was apparent on PM2.5 and principally on PMCOARSE levels. On the contrary, within warm seasons, weaker dust PMCOARSE contributions were detected in Limassol from areas in Egypt and Libya, while Turkey and SE Greece were the main transboundary aerosol reservoirs degrading the city's air quality. These findings were attributed to emissions related to the highly developed touristic industry in the Greek islands and coastal Turkey during summer, and also to the fire events which took place in central Turkey in the studied period. Raised particulate-phase PAH concentrations in Limassol were clearly connected with air parcels reaching Cyprus via continental areas, whereas the influence of clean marine air masses dropped the levels of PAH constituents. During cold period, the incoming of PAH compounds was associated with air masses overflying across Turkey and Syria, due to the use of outdated technologies in the fields of heating and transportation in those areas. Lower concentrations of PAHs were monitored in Limassol during warm period due to less combustion however the impact of airflows crossing central Turkey was evident, probably due to the absorption of particles generated from fire events. To our knowledge, this is the first paper which analyzed PAH levels in Cyprus in terms of long range transport. In addition, the use of gridded air mass residence time allowed a more precise localization of transboundary PM2.5 and PMCOARSE sources affecting air quality in Limassol, in comparison with previous publications. It must also be mentioned that the outcomes of the two models were in good agreement and the interpretation of all results was supported with references to the available literature on air pollution in Eastern Mediterranean. For all these reasons, key elements of this paper can be used as arguments pointing at the reduction of anthropogenic PM emissions in the studied region. Finally, the elucidation of atmospheric pathways favoring the increment of PM and PAH levels in Cyprus may contribute to the creation of a warning system which will prevent human exposure to unhealthy respiratory conditions.

Achilleos, S., Evans, J.S., Yiallouros, P.K., Kleanthous, S., Schwartz, J., Koutrakis, P., 2014. PM 10 concentration levels at an urban and background site in Cyprus: the impact of urban sources and dust storms. J. Air Waste Manage. Assoc. 64 (12), 1352–1360. Achilleos, S., Wolfson, J.M., Ferguson, S.T., Kang, C.M., Hadjimitsis, D.G., Hadjicharalambous, M., Achilleos, C., Christodoulou, A., Nisanzti, A., Papoutsa, C., Themistocleous, K., Athanasatos, S., Perdikou, S., Koutrakis, P., 2016. Spatial variability of fine and coarse particle composition and sources in Cyprus. Atmos. Res. 169, 255–270. Alkurdi, F., Karabet, F., Dimashki, M., 2013. Characterization, concentrations and emission rates of polycyclic aromatic hydrocarbons in the exhaust emissions from in-service vehicles in Damascus. Atmos. Res. 120-121, 68–77. Alkurdi, F., Karabet, F., Dimashki, M., 2014. Characterization and concentrations of polycyclic aromatic hydrocarbons in emissions from different heating systems in Damascus, Syria. Environ. Sci. Pollut. Res. 21, 5747–5759. Almeida, S.M., Pio, C.A., Freitas, M.C., Reis, M.A., Trancoso, M.A., 2005. Source apportionment of fine and coarse particulate matter in a sub-urban area at the Western European Coast. Atmos. Environ. 39, 3127–3138. Alves, C.A., Vicente, A., Monteiro, C., Goncalves, C., Evtyugina, M., Pio, C., 2011. Emission of trace gases and organic components in smoke particles from a wildfire in a mixedevergreen forest in Portugal. Sci. Total Environ. 409, 1466–1475. Alves, C.A., Vicente, A.M., Custódio, D., Cerqueira, M., Nunes, T., Pio, C., Lucarelli, F., Calzolai, G., Nava, S., Diapouli, E., Eleftheriadis, K., Querol, X., Musa Bandowe, B.A., 2017. Polycyclic aromatic hydrocarbons and their derivatives (nitro-PAHs, oxygenated PAHs, and azaarenes) in PM2.5 from Southern European cities. Sci. Total Environ. 595, 494–504. Bari, Md.A., Baumbach, G., Sarachage-Ruiz, L., Kleanthous, S., 2009. Identification of PM10 sources in a Mediterranean island. Water Air Soil Pollut. Focus 9, 39–53. Brereton, C.A., Johnson, M.R., 2012. Identifying sources of fugitive emissions in industrial facilities using trajectory statistical methods. Atmos. Environ. 51, 46–55. Caggiano, R., Fiore, S., Lettino, A., Macchiato, M., Sabia, S., Trippetta, S., 2011. PM2.5 measurements in a Mediterranean site: two typical cases. Atmos. Res. 102, 157–166. Chalbot, M.-C., Lianou, M., Vei, I.-C., Kotronarou, A., Kavouras, I.G., 2013. Spatial attribution of sulfate and dust aerosol sources in an urban area using receptor modeling coupled with Lagrangian trajectories. Atmos. Pollut. Res. 4, 346–353. Dimitriou, K., 2015. The dependence of PM size distribution from meteorology and localregional contributions, in Valencia (Spain) – a CWT model approach. Aerosol Air Qual. Res. 15, 1979–1989. Dimitriou, K., Kassomenos, P.A., 2016. Combining AOT, angstrom exponent and PM concentration data, with PSCF model, to distinguish fine and coarse aerosol intrusions in Southern France. Atmos. Res. 172–173, 74–82. Dimitriou, K., Remoundaki, E., Mantas, E., Kassomenos, P., 2015. Spatial distribution of source areas of PM2.5 by concentration weighted trajectory (CWT) model applied in PM2.5 concentration and composition data. Atmos. Environ. 116, 138–145. Engelbrecht, J.P., Menéndez, I., Derbyshire, E., 2014. Sources of PM2.5 impacting on Gran Canaria, Spain. Catena 117, 119–132. Finardi, S., Radice, P., Cecinato, A., Gariazzo, C., Gherardi, M., Romagnoli, P., 2015. Seasonal variation of PAHs concentration and source attribution through diagnostic ratios analysis. Urban Clim. http://dx.doi.org/10.1016/j.uclim.2015.12.001 (Article in Press). Fleming, Z.L., Monks, P.S., Manning, A.J., 2012. Review: untangling the influence of airmass history in interpreting observed atmospheric composition. Atmos. Res. 104–105, 1–39. Genc, D.D., Yesilyurt, C., Tuncel, G., 2010. Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere. Environ. Monit. Assess. 166, 11–27. Godec, R., Jakovljevic, I., Sega, K., Cackovic, M., Beslic, I., Davila, S., Pehnec, G., 2016. Carbon species in PM10 particle fraction at different monitoring sites. Environ. Pollut. 216, 700–710. Goudie, A.S., 2014. Desert dust and human health disorders. Environ. Int. 63, 101–113. Hsu, Y.-K., Holsen, T.M., Hopke, P.K., 2003. Comparison of hybrid receptor models to locate PCB sources in Chicago. Atmos. Environ. 37, 545–562. Jeong, U., Kim, J., Lee, H., Lee, Y.G., 2017. Assessing the effect of long-range pollutant transportation on air quality in Seoul using the conditional potential source contribution function method. Atmos. Environ. 150, 33–44. Kara, E., Özdilek, H.G., Kara, E.E., 2013. Ambient air quality and asthma cases in Niğde, Turkey. Environ. Sci. Pollut. Res. 20, 4225–4234.

K. Dimitriou, P. KassomenosScience of the Total Environment 598 (2017) 563–571 Karaca, F., Anil, I., Alagha, O., 2009. Long-range potential source contributions of episodic aerosol events to PM10 profile of a megacity. Atmos. Environ. 43, 5713–5722. Kavouras, I.G., Lianou, M., Chalbot, M.-C., Vei, I.C., Kotronarou, A., Hoek, G., Hameri, K., Harrison, R.M., 2013. Quantitative determination of regional contributions to fine and coarse particle mass in urban receptor sites. Environ. Pollut. 176, 1–9. Khaniabadi, Y.O., Daryanoosh, S.M., Amrane, A., Polosa, R., Hopke, P.K., Goudarzi, G., Mohammadi, M.J., Sicard, P., Armin, H., 2016. Impact of Middle Eastern dust storms on human health. Atmos. Pollut. Res. http://dx.doi.org/10.1016/j.apr.2016.11.005 (Article in press). Kocak, M., Mihalopoulos, N., Kubilay, N., 2007. Chemical composition of the fine and coarse fraction of aerosols in the northeastern Mediterranean. Atmos. Environ. 41, 7351–7368. Kong, X., He, W., Qin, N., He, Q., Yang, B., Ouyang, H., Wang, Q., Xu, F., 2013. Comparison of transport pathways and potential sources of PM10 in two cities around a large Chinese lake using the modified trajectory analysis. Atmos. Res. 122, 284–297. Liu, B., Liang, D., Yang, J., Dai, Q., Bi, X., Feng, Y., Yuan, J., Xiao, Z., Zhang, Y., Xu, H., 2016. Characterization and source apportionment of volatile organic compounds based on 1-year of observational data in Tianjin, China. Environ. Pollut. 218, 757–769. Lu, W., Yang, L., Chen, J., Wang, X., Li, H., Zhu, Y., Wen, L., Xu, C., Zhang, J., Zhu, T., Wang, W., 2016. Identification of concentrations and sources of PM2.5-bound PAHs in North China during haze episodes in 2013. Air Qual. Atmos. Health 9, 823–833. Mamouri, R.E., Ansmann, A., Nisantzi, A., Solomos, S., Kallos, G., Hadjimitsis, D.G., 2016. Extreme dust storm over the eastern Mediterranean in September 2015: satellite, lidar, and surface observations in the Cyprus region. Atmos. Chem. Phys. 16, 13711–13724. Marzouni, M.B., Alizadeh, T., Banafsheh, M.R., Khorshiddoust, A.M., Ghozikali, M.G., Akbaripoor, S., Sharifi, R., Goudarzi, G., 2016. A comparison of health impacts assessment for PM10 during two successive years in the ambient air of Kermanshah, Iran. Atmos. Pollut. Res. 7, 768–774. Middleton, N., Yiallouros, P., Kleanthous, S., Kolokotroni, O., Schwartz, J., Dockery, D.W., Demokritou, P., Koutrakis, P., 2008. A 10-year time-series analysis of respiratory and cardiovascular morbidity in Nicosia, Cyprus: the effect of short-term changes in air pollution and dust storms. Environ. Health 7, 39. Mikuska, P., Krumal, K., Vecera, Z., 2015. Characterization of organic compounds in the PM2.5 aerosols in winter in an industrial urban area. Atmos. Environ. 105, 97–108. Nisantzi, A., Mamouri, R.E., Ansmann, A., Hadjimitsis, D., 2014. Injection of mineral dust into the free troposphere during fire events observed with polarization lidar at Limassol, Cyprus. Atmos. Chem. Phys. 14, 12155–12165. Polissar, A.V., Hopke, P.K., Poirot, R.L., 2001. Atmospheric aerosol over Vermont: chemical composition and sources. Environ. Sci. Technol. 35, 4604–4621. Saarnio, K., Sillanpaa, M., Hillamo, R., Sandell, E., Pennanen, A.S., Salonen, R.O., 2008. Polycyclic aromatic hydrocarbons in size-segregated particulate matter from six urban sites in Europe. Atmos. Environ. 42, 9087–9097.

571

Salamalikis, V., Argiriou, A.A., Dotsika, E., 2015. Stable isotopic composition of atmospheric water vapor in Patras, Greece: a concentration weighted trajectory approach. Atmos. Res. 152, 93–104. Salvador, P., Artinano, B., Pio, C., Afonso, J., Legrand, M., Puxbaum, H., Hammer, S., 2010. Evaluation of aerosol sources at European high altitude background sites with trajectory statistical methods. Atmos. Environ. 44, 2316–2329. Sarigiannis, D.A., Karakitsios, S.P., Zikopoulos, D., Nikolaki, S., Kermenidou, M., 2015. Lung cancer risk from PAHs emitted from biomass combustion. Environ. Res. 137, 147–156. Saxena, M., Sharma, A., Sen, A., Saxena, P., Saraswati, Mandal, T.K., Sharma, S.K., Sharma, C., 2017. Water soluble inorganic species of PM10 and PM2.5 at an urban site of Delhi, India: seasonal variability and sources. Atmos. Res. 184, 112–125. Slezakova, K., Castro, D., Pereira, M.C., Morais, S., Delerue-Matos, C., Alvim-Ferraz, M.C., 2010. Influence of traffic emissions on the carcinogenic polycyclic aromatic hydrocarbons in outdoor breathable particles. J. Air Waste Manage. Assoc. 60 (4), 393–401. Slezakova, K., Castro, D., Delerue–Matos, C., Alvim–Ferraz, M.C., Morais, S., Pereira, M.C., 2013. Impact of vehicular traffic emissions on particulate-bound PAHs: levels and associated health risks. Atmos. Res. 127, 141–147. Squizzato, S., Cazzaro, M., Innocente, E., Visin, F., Hopke, P.K., Rampazzo, G., 2017. Urban air quality in a mid-size city — PM2.5 composition, sources and identification of impact areas: from local to long range contributions. Atmos. Res. 186, 51–62. Tsangari, H., Paschalidou, A.K., Kassomenos, A.P., Vardoulakis, S., Heaviside, C., Georgiou, K.E., Yamasaki, E.N., 2016. Extreme weather and air pollution effects on cardiovascular and respiratory hospital admissions in Cyprus. Sci. Total Environ. 542, 247–253. Wang, Y.Q., Zhang, X.Y., Arimoto, R., 2006. The contribution from distant dust sources to the atmospheric particulate matter loadings at XiAn, China during spring. Sci. Total Environ. 368, 875–883. Wang, J., Geng, N.B., Xu, Y.F., Zhang, W.D., Tang, X.Y., Zhang, R.Q., 2014. PAHs in PM2.5 in Zhengzhou: concentration, carcinogenic risk analysis, and source apportionment. Environ. Monit. Assess. 186, 7461–7473. Xu, J., DuBois, D., Pitchford, M., Green, M., Etyemezian, V., 2006. Attribution of sulfate aerosols in Federal Class I areas of the western United States based on trajectory regression analysis. Atmos. Environ. 40, 3433–3447. Zawadzka, O., Markowicz, K.M., Pietruczuk, A., Zielinski, T., Jaroslawski, J., 2013. Impact of urban pollution emitted in Warsaw on aerosol properties. Atmos. Environ. 69, 15–28. Zhu, Y., Yang, L., Kawamura, K., Chen, J., Ono, K., Wang, X., Xue, L., Wang, W., 2017. Contributions and source identification of biogenic and anthropogenic hydrocarbons to secondary organic aerosols at Mt. Tai in 2014. Environ. Pollut. 220, 863–872.