Indicators reflecting local and transboundary sources of PM2.5 and PMCOARSE in Rome – Impacts in air quality

Atmospheric Environment 96 (2014) 154e162

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Indicators reflecting local and transboundary sources of PM2.5 and PMCOARSE in Rome e Impacts in air quality Konstantinos Dimitriou*, Pavlos Kassomenos Laboratory of Meteorology, Department of Physics, University of Ioannina, University Campus, GR-45110, Ioannina, Greece

h i g h l i g h t s
PM10 transportation in Rome along with slow moving air masses is suggested.
Intrusions of PM2.5 were originated from Balkan Peninsula due to combustion.
Exogenous sources of PMCOARSE are scattered across Mediterranean and North Africa.
PCA strongly associated local PM2.5 emissions with vehicular combustion.
Secondary local sources of PMCOARSE (natural, dust resuspension) were indicated.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 April 2014 Received in revised form 8 July 2014 Accepted 10 July 2014 Available online 11 July 2014

The keystone of this paper was to calculate and interpret indicators reflecting sources and air quality impacts of PM2.5 and PMCOARSE (PM10ePM2.5) in Rome (Italy), focusing on potential exogenous influences. A backward atmospheric trajectory cluster analysis was implemented. The likelihood of daily PM10 exceedances was studied in conjunction with atmospheric patterns, whereas a Potential Source Contribution Function (PSCF) based on air mass residence time was deployed on a grid of a 0.5  0.5 resolution. Higher PM2.5 concentrations were associated with short/medium range airflows originated from Balkan Peninsula, whereas potential PMCOARSE sources were localized across the Mediterranean and coastal North Africa, due to dust and sea spray transportation. According to the outcome of a daily Pollution Index (PI), a slightly increased degradation of air quality is induced due to the additional quantity of exogenous PM but nevertheless, average levels of PI in all trajectory clusters belong in the low pollution category. Gaseous and particulate pollutants were also elaborated by a Principal Component Analysis (PCA), which produced 4 components: [Traffic], [photochemical], [residential] and [Secondary Coarse Aerosol], reflecting local sources of air pollution. PM2.5 levels were strongly associated with traffic, whereas PMCOARSE were produced autonomously by secondary sources. © 2014 Elsevier Ltd. All rights reserved.

Keywords: PM10 PM2.5 Air mass trajectories PSCF Rome Air quality

1. Introduction The inhalable fraction of airborne Particulate Matter (PM) includes aerosols with diameter less than 10 um (PM10). Increased mortality and morbidity levels were associated in the past with elevated PM10 concentrations (Tao et al., 2014; Curtis et al., 2006). PM10 and primarily PM2.5 (diameter less than 2.5 um), are able to penetrate in the human respiratory system (Cachon et al., 2014; Delgado-Buenrostro et al., 2013) inducing respiratory, cardiovascular and pulmonary diseases (Zhou et al., 2014; Guo et al., 2010). If the chemical composition of PM10 includes metalloid elements, these chemical properties are

* Corresponding author. E-mail addresses: [email protected], [email protected] (K. Dimitriou). http://dx.doi.org/10.1016/j.atmosenv.2014.07.029 1352-2310/© 2014 Elsevier Ltd. All rights reserved.

considered as responsible for contingent carcinogenic impacts (Garcia-Aleix et al., 2014, Fang et al., 2013). In addition, the demonstrated solubility of PM metal traces in lung fluids indicates an enhanced pulmonary toxic potential upon their inhalation (Wiseman and Zereini, 2014). In order to protect the European population, the European Union (EU) established a 50 ug/m3 average daily concentration limit for PM10 that should not be exceeded more than 35 times per year, whereas the annual limit is set at 40 ug/m3. For PM2.5, only a 25 ug/m3 annual average concentration limit is recommended by EU regulations. Principal Component Analysis (PCA) is a statistical method widely applied, in order to organize the air pollution mixture to main components and reduce the dimension of the input data (Minguillon et al., 2013; Singh and Sharma, 2012). High contributions of gaseous pollutants in specific PCA components are

K. Dimitriou, P. Kassomenos / Atmospheric Environment 96 (2014) 154e162

indicators of distinct emission sources affecting air quality (Yoo et al., 2011; Slezakova et al., 2013). Generally CO, NO, NO2 and Benzene (C6H6), are pollutants which are indicative of gasoline combustion (Traffic), SO2 is attributed to oil/natural gas combustion (households, industries etc), whereas O3 is an indicator of photochemical reactions (Statheropoulos et al., 1998; Vardoulakis and Kassomenos, 2008). Increased correlations between gaseous and particulate air pollutants are commonly used as markers for the identification of PM sources (Juda-Rezler et al., 2011; Dimitriou and Kassomenos, 2013). Hence, high loadings for PM in PCA components, along with enriched gaseous influences, suggest the origin of the emissions (Yoo et al., 2011; Kassomenos et al., 2014). Long range transportation of PM in urban areas is usually studied by air mass trajectories (Makra et al., 2011; Kassomenos et al., 2012). In the Mediterranean basin, Saharan dust outbreaks and Mediterranean sea spray are the principal exogenous sources of dust and maritime aerosols in urban areas (Valenzuela et al., 2012; Remoundaki et al., 2011; Kocak et al., 2007; Almeida et al., 2005), whereas other external sources as power plant combustion and biomass burning are also indicated (Argyropoulos et al., 2012; Gerasopoulos et al., 2011). Slow moving air masses can more effectively absorb and transfer particulates in urban areas, due to their longer residence time over regions where natural and anthropogenic PM emissions occur (Karaca et al., 2009; Fleming et al., 2012; Dimitriou and Kassomenos, 2014). Thus, the amount of time air spends over a region is linearly related to that region's contribution to pollutants measured at the receptor site (Xu et al., 2006; Kavouras et al., 2013; Chalbot et al., 2013). Consequently, short range trajectories describing the course of slow moving air parcels are associated with increased PM concentrations in urban areas (Borge et al., 2007; Karaca and Camci, 2010; Dimitriou and Kassomenos, 2013). The keystone of this paper was to calculate and interpret indicators reflecting sources and air quality impacts of PM2.5 and PMCOARSE (PM10ePM2.5) in Rome (Italy), focusing on potential exogenous influences. Initially, PM10 concentrations corresponding to backward atmospheric trajectory clusters produced at 500 m Above Ground Level (AGL) were analyzed by two statistical indices (Borge et al., 2007; Murena, 2004), reflecting the frequency of occurrence of daily PM10 exceedances and air quality degradation respectively. Subsequently, the residing time of airflows suspicious for PM transportation was analyzed on a 0.5  0.5 resolution grid, in order to produce a Potential Source Contribution Function (PSCF) indicating potential source areas (Kong et al., 2013; Polissar et al., 2001; Karaca et al., 2009; Kocak et al., 2011). PSCF was independently implemented for PM2.5 and PMCOARSE, aiming to reveal possible different transboundary sources of fine and coarse particles affecting air quality in Rome. Additionally, daily concentrations of gaseous (NO2, SO2, CO, O3 and C6H6) and particulate (PM2.5 and PMCOARSE) air pollutants were also elaborated by a PCA. Gaseous air pollutants were considered as indicators of local PM2.5 and PMCOARSE emissions. 2. Data and methodology 2.1. Data and sampling sites For this study, PM10 and PM2.5 levels measured at the historical urban/background sampling site “Villa Ada” (IT0953A) [lon: 12.506.945, lat: 41.932.777] in Rome (Italy) were analyzed. Due to the absence of hourly data for PM2.5, daily concentrations of PM were used. In addition, daily concentrations of gaseous pollutants (CO, NO2, SO2, O3 and C6H6) were also studied. Statistics and deficiencies (%) of PM10, PM2.5, CO, NO2, SO2, O3 and C6H6 daily mean concentration data series during cold periods (CP: 1 Octobere31 March) and warm periods (WP: 1 Aprile30 September), are included in Table 1. A seven years dataset was studied, extending through the time interval

155

2006e2012. All data were downloaded from the website of EU Air Quality Database (Airbase) in ugr/m3, except of CO (mg/m3). IT0953A station is equipped with Beta Ray Attenuation analyzers for the monitoring of PM concentrations, whereas CO, NO2, SO2, O3 and C6H6 levels are measured with infrared absorption, chemiluminescence, UV fluorescence, UV absorption and chromatographic analyzers respectively. All appliances are expected to operate within 15% of uncertainty bounds, according to EU guidelines. The selection of IT0953A station for this study prevailed, due to the long available series of PM10 and PM2.5 concentrations in its archives, and also due to the geographical position of the station which is suitable for the identification of long range transport impacts. The sampling site is situated inside Rome's major green park (Villa Ada), and it is not directly influenced by local emission sources but it's characterized by a homogeneous pollution that can be considered a background for Rome (Avino and Brocco, 2004). The station is located approximately 500 m away from main traffic arteries (Gobbi et al., 2007), neighboring to the northern (“Via del Foro Italico”) and eastern (“Via Salaria”) boundaries of the park. The area that surrounds the park is mainly residential. 2.2. Methodology 2.2.1. PCA Analysis The principal purpose of this paper, is to define atmospheric pathways possibly contributing to increased PMCOARSE ¼ PM10ePM2.5 and PM2.5 levels in Rome. Nevertheless, local PM sources also had to be studied, in order to complement and support the findings of long range transport analysis. Daily concentrations of particulate (PMCOARSE and PM2.5) and gaseous (CO, NO2, SO2, O3 and C6H6) air pollutants were elaborated by a PCA, in order to define local factors related to PM emissions (Yoo et al., 2011). CO and C6H6 were considered as indicators of traffic (Avino and Manigrasso, 2008; Deacon et al., 1997), whereas SO2 and O3 were markers of domestic/industrial and photochemical air pollution respectively (Vardoulakis and Kassomenos, 2008). NO2 is produced by various combustion sources. PCA was implemented separately in CP and WP, aiming to reveal possible seasonal trends of local PM emissions (Statheropoulos et al., 1998). 2.2.2. Air mass trajectory classification Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) of the NOAA Air Resources Laboratory was used, in order to produce 4-day backward air mass trajectories approaching Rome during the 7-years period 2006e2012 (Obrist et al., 2008). The trajectories reached the city at 500 m Above Ground Level (AGL), an altitude suitable for the identification of long range transport impacts, ensuring that the trajectory starts in the near ground atmospheric boundary layer (Karaca et al., 2009; Makra et al., 2011). The time of every air parcel's arrival in the two cities was set at 12:00 UTC. Only days with available data of daily mean PM10 and PM2.5 concentrations at the IT0953A station's archives were used as Table 1 Statistics and deficiencies (%) of PM10, PM2.5, CO, NO2, SO2, O3 and C6H6 daily mean concentration data series of IT0953A station, during CP and WP of the time interval 2006e2012.

CP

WP

a

3

Average (ug/m ) Stan-dev (ug/m3) Deficiencies (%) Average (ug/m3) Stan-dev (ug/m3) Deficiencies (%)

PM2.5

PM10

SO2

O3

COa

NO2

C6H6

23.0 12.9 6.7 15.5 5.8 5.0

30.5 15.3 3.8 24.6 9.1 5.6

1.4 1.2 13.9 1.2 0.9 14.0

24.2 15.8 0.9 55.5 16.2 2.0

0.6 0.2 0.5 0.4 0.1 1.0

47.6 15.3 1.3 31.8 11.3 0.7

2.1 1.2 8.1 0.9 0.5 11.7

CO concentrations are presented in mg/m.3.

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starting points for the computation of backward air mass trajectories, thus 2276 trajectories were calculated. The trajectories were then divided in a small number of groups, according to their origin and length (Dorling et al., 1992; Dorling and Davis, 1995), by using a K-means cluster analysis based on the Euclidean distance (Borge et al., 2007; Markou and Kassomenos, 2010). The longitude and latitude of the trajectories over consecutive 1-h intervals were used as clustering variables. Clusters including less than 3% of the total trajectories were excluded from the procedure, due to the insufficient fraction of trajectories (McGregor, 1993). The definition of each cluster's length and direction was achieved by the computation of a Centroid Trajectory (CT). The length (D) of CT was calculated as the sum of the 96 hourly consecutive distances (Di) between neighboring points along the CT. The hourly distances Di were obtained by the implementation of Haversine formula of the great circle distance between two points. This technique ensured a better accuracy in our results, in comparison with the calculation of D as the distance of the first and the end point of each CT. A classification of trajectory clusters into four main categories based on their centroid trajectory length was adopted for this study, according to the hierarchical boundaries proposed by Dimitriou and Kassomenos (2013).





Short range cluster 0 < D < 1000 (km) Medium range cluster 1000.1 < D < 1800 (km) Long range cluster 1800.1 < D < 3000 (km) Very long range cluster 3000.1 < D (km)

2.2.3. Indices expounding atmospheric transport patterns Three indexes [INDEX1, Pollution Index (PI) and Potential Source Contribution Function (PSCF)] were combined in this paper, in order to localize potential transboundary sources of PM, downgrading air quality and health in Rome. INDEX1 and PI refer to the complete inhalable fraction of airborne aerosols (PM10 ¼ PM2.5 þ PMCOARSE), whereas PSCF was implemented separately for PM2.5 and PMCOARSE.
INDEX1: Introduced by Borge et al., 2007, INDEX1 reflects the (%) likelihood of daily PM10 exceedances (average daily concentration>50 ug/m3) in each trajectory cluster (Makra et al., 2013). INDEX1 values are produced by Equation (1),

! INDEX1i %

¼

Dði > 50Þ *100 DðiÞ

(1)

where Di is the number of trajectories in Cluster i, and D(i > 50) is the number of exceedances of EU daily PM10 limit that correspond in Cluster i.
Pollution Index (PI): Introduced by Murena, 2004, PI defines air quality categories based on daily reference concentrations of individual air pollutants (Dimitriou et al., 2013; Buchholz et al., 2010; Kyrkilis et al., 2007). For the needs of this paper, PI values for PM10 are calculated by Equation (2),

PI  PIlo PI ¼ hi ðC  BPlo Þ þ PIlo BPhi  BPlo

(2)

where C is the daily mean PM10 concentration, BPhi the lowest PM10 concentration breakpoint that is greater than or equal to C, BPlo the

highest PM10 concentration breakpoint that is less than or equal to C, PIhi the PI value corresponding to BPhi and PIlo the PI value corresponding to BPlo. BP and PI values are included in Table 2. Daily PI values were calculated in each trajectory cluster separately and were used as markers of air quality degradation in Rome, due to exogenous PM intrusions.
Potential Source Contribution Function (PSCF): The structure of PSCF is based on air mass residence time allocation over specific regions, in order to localize potential sources of exogenous aerosols affecting air quality in urban areas (Karaca et al., 2009; Polissar et al., 2001). The amount of time air spends over a region is linearly related to that region's contribution to pollutants measured at the receptor site (Xu et al., 2006; Kavouras et al., 2013; Chalbot et al., 2013). Thus, slow moving air parcels having short range trajectories are more severely enriched by PM emission sources (Karaca and Camci, 2010; Salvador et al., 2010; Borge et al., 2007; Makra et al., 2011; Fleming et al., 2012; Kassomenos et al., 2012). On the contrary, in long range transport where various exchange/mixing processes (e.g. deposition and advection) and physical/chemical losses occur, the airflow pathway is less influenced (Fleming et al., 2012). According to the previous analysis, at short range clusters in which contingent PM2.5 and/or PMCOARSE transportation from transboundary sources is indicated by elevated PM2.5 and/or PMCOARSE concentrations, air mass residence time was analyzed on a (i, j) grid of a 0.5  0.5 resolution, as the sum of the number of trajectory points within each cell (Kavouras et al., 2013; Dimitriou and Kassomenos, 2014). Air mass residence density data were then inserted to PSCF, presented at Equation (3),

PSCFði;jÞ ¼

mði;jÞ nði;jÞ

(3)

where n(i, j) is the number of endpoints included in the ijth cell, and m(i, j) is the number of endpoints contained in the ijth cell belonging to trajectories corresponding to PM2.5 or PMCOARSE concentrations higher than a threshold value (Kocak et al., 2011). Here the threshold value was set at the 75th percentile (Kong et al., 2013). No official daily limit for PM2.5 or PMCOARSE is recommended by EU regulations. The procedure was performed separately for PM2.5 and PMCOARSE, in order to reveal possible different transboundary sources of fine and coarse particles. Sparse trajectory coverage of the more distant grid cells may result in highly uncertain extreme values of PSCF (Polissar et al., 2001). For large values of n(i, j), there is more statistical stability in the calculated value. In order to take into account the number of trajectory points that fall in a grid cell and minimize uncertainties, PSCF was multiplied with an arbitrary W(i, j) weight function (Kong et al., 2013; Polissar et al., 2001; Karaca et al., 2009; Kocak et al., 2011). W(i, j) is presented at Equation (4),

Wði;jÞ

8 > 1:0 > > < 0:7 ¼ > 0:4 > > : 0:2

  3nave < ni;j   1:5nave < ni;j < 3nave   nave < ni;j < 1:5nave   ni;j < nave

(4)

where nave is the average number of the endpoints of all the cells which present non-zero values of PSCF. The coordinates of the center of each 0.5  0.5 grid cell were used as mapping points. Density maps of air mass residence time and PSCF values were created, in order to isolate and determine more efficiently potential external sources of particulates,

K. Dimitriou, P. Kassomenos / Atmospheric Environment 96 (2014) 154e162 Table 2 Daily mean PM10 concentration breakpoints (ugr/m3) and Pollution Index (PI) breakpoints, defining air pollution categories. Pollution category

PI

PM10 24 h mean (ugr/m3)

Unhealthy Unhealthy for sensitive groups Moderate pollution Low pollution Good quality

100 85 70 50 25

500 238 144 50 20

influencing PM2.5 and PMCOARSE concentrations in Rome. All the data processing, statistical analysis and graphical presentation were performed with SPSS (v.20), Microsoft Excel (v.2007) and R code. 3. Results 3.1. Interpretation of PCA outcome
Component 1: High loadings for CO and C6H6 mark this component as vehicular (Table 3). Increased PM2.5 and NO2 loadings indicate that the production of fine particles and nitrogen oxides primarily comes from traffic. Low burden for PMCOARSE signifies different sources of fine and coarse particles in the area.
Component 2: NO2 typically arises via the oxidation reaction of Nitric Oxide (NO) with O3, thus maximum values of O3 correspond to minimum values of NO2 and vice-versa (Avino and Manigrasso, 2008). Hence, very high loadings for O3 along with enhanced loadings for NO2, but with opposite sign, characterize this component as strongly photochemical. Component 2 is also highly enriched with CO and C6H6, which are simultaneously emitted with NO from vehicular combustion (Table 3).
Component 3: This component is strongly influenced by SO2 emissions (Table 3), whereas low loadings are observed for all the other pollutants. Since no industrial activity exists in the station's area, sulphur emissions were attributed to household combustion and thus, this component is declared as domestic.
Component 4: This component contains increased loadings for PMCOARSE, along with reduced loadings for PM2.5 and gaseous pollutants (Table 3). Thus, the existence of coarse aerosols from secondary sources (e.g. dust resuspension, natural emissions, etc) is deduced.

3.2. Long range transport 3.2.1. Cluster analysis and PM10 profile Atmospheric pathways of air masses approaching Rome at 500 m AGL were divided to 9 clusters, according to the outcome of

157

the implemented Cluster Analysis (Fig. 1). Cluster 2 was excluded from the procedure because it contained less than 3% of total trajectories. Very long range Cluster 1 (5.3%) and long range Cluster 8 (8.6%), are consisted by trajectories originated from North Atlantic and present low PM10 concentrations and INDEX1 values (Table 4a). Medium range Cluster 3 (10.8%) and short range Cluster 6 (19.9%), describe East-North East (E/NE) airflows to Rome from Central Europe and Balkan Peninsula, through the Adriatic (Fig. 1). Raised average daily mean concentrations of PM10 were calculated in Cluster 3 and Cluster 6 (Table 4a), whereas increased values of INDEX1 indicate associations among E/NE circulation and exceedances of the established PM10 daily EU limit. Short range Cluster 4 (26.7%), gathered a large fraction of very short all around trajectories which reached Rome mainly through the Italian Peninsula and Tyrrhenian Sea. Cluster 4 is characterized by intermediate average daily mean PM10 concentrations and INDEX1 values, in comparison with other clusters. Medium range Cluster 5 (9.4%) includes trajectories that approached Rome from Northern directions, sliding over the mountain range of the Alps (Fig. 1). Low PM10 levels and INDEX1 values were detected at Cluster 5. Finally, medium range Cluster 7 (7.6%) and short-medium range Cluster 9 (9.7%), are consisted by trajectories of air parcels that arrived in Rome overflying across North Africa and the Mediterranean. Elevated average daily mean PM10 concentrations and INDEX1 values were observed mainly at Cluster 9. Slightly higher average levels of daily PI values were detected primarily at Clusters 3, 6 and 9 (Table 4a), thus a downgrading of air quality is associated with these atmospheric circulations, due to the additional mass of exogenous PM10. Nevertheless, average levels of PI in all clusters belong in the low pollution category, according to PI breakpoints (Table 2). Only during daily PM10 exceedances, PI values exceed the low pollution category. 3.3. PSCF values for PM2.5 and PMCOARSE Density maps of air mass residence time and PSCF values were deployed (Fig. 2), aiming to identify which of the residing areas of the incoming to Rome air parcels are potential external sources of PM. The procedure was implemented separately for short-medium trajectory clusters characterized by increased average daily mean concentrations of PM2.5 and/or PMCOARSE, in order to reveal the geographical distribution of fine and coarse exogenous PM sources. Increased levels of PM2.5 are observed particularly at Cluster 3 and Cluster 6 (Table 4b). Air mass dwelling time in those clusters is allocated over Balkan Peninsula, Eastern Central Europe and the Adriatic (Fig. 2a). Several urban and industrial combustion sources of PM2.5 exist in Balkan Peninsula and Eastern Central Europe (Houthuijs et al., 2001; Rajsic et al., 2008; Gerasopoulos et al., 2011; Manigrasso et al., 2012), whereas the influence of air masses enriched with sulfate particulates emitted from these regions was

Table 3 PCA analysis loadings and variance explanation at IT0953A station, during CP and WP separately. Station

Cold period (CP) Component

IT0953A(Villa Ada)

PM2.5 PMCOARSE SO2 O3 CO NO2 C6H6 Variance (%)

Warm period (WP)

1

2

3

4

1

2

3

4

0.928 0.110 0.107 0.267 0.532 0.654 0.735 31.5

0.136 0.044 0.062 0.914 0.622 0.436 0.472 23.7

0.102 0.051 0.985 0.027 0.153 0.036 0.177 14.8

0.051 0.984 0.052 0.039 0.255 0.126 0.084 15.2

0.879 0.099 0.130 0.015 0.512 0.819 0.404 27.1

0.060 0.047 0.006 0.914 0.551 0.261 0.613 22.7

0.165 0.113 0.959 0.138 0.250 0.023 0.359 16.7

0.060 0.975 0.111 0.061 0.307 0.075 0.239 16.1

Extraction method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

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Fig. 1. Trajectory clusters approaching Rome at 500 m AGL. Centroid trajectories are highlighted with red color. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Table 4 a) Centroid trajectory length, INDEX1 values, average PI values and PM10 concentration statistics, b) PM2.5 concentration statistics and c) PMCOARSE concentration statistics, at 500 m AGL trajectory clusters.

(a) PM10

(b) PM2.5

(c) PMCOARSE

a b c

Clusters 500 m AGL

1

Centroid length (km) Average (ug/m3) Maximum (ug/m3) Stan-dev (ug/m3) PM10 exceedancesa INDEX1 (%) Average PI Trajectories (%) Average (ug/m3) Maximum (ug/m3) Stan-dev (ug/m3) PM2.5 episodesb Trajectories (%) Average (ug/m3) Maximum (ug/m3) Stan-dev (ug/m3) PMCOARSE episodesb Trajectories (%)

3312 24.3 54 9.3 1 0.8 28.0 5.3 13.6 38 7.4 17 5.3 10.7 27 5.2 58 5.3

2c

2.0

2.0

2.0

3

4

5

6

7

8

9

1445 31.3 78 13.5 27 10.9 33.4 10.8 24.2 60 11.3 113 10.8 7.2 24 4.7 39 10.8

407 28.5 86 12.4 41 6.8 31.3 26.7 19.7 66 10.5 160 26.7 8.9 43 5.7 166 26.7

1570 23.0 60 10.2 4 1.9 26.5 9.4 15.8 50 8.7 35 9.4 7.2 19 4.1 30 9.4

665 31.2 80 12.8 45 10.0 33.5 19.9 22.6 74 10.9 162 19.9 8.5 33 5.3 111 19.9

1571 27.1 105 14.1 10 5.8 29.7 7.6 17.2 64 10.0 43 7.6 9.9 60 7.5 47 7.6

2059 24.4 90 11.8 7 3.6 27.5 8.6 14.8 72 10.0 33 8.6 9.6 23 5.0 68 8.6

1020 30.6 80 14.0 23 10.4 32.7 9.7 20.1 63 10.5 67 9.7 10.4 41 7.6 76 9.7

PM10 exceedances are referring to the excess of the established daily EU limit (50 ug/m3). PM2.5 and PMCOARSE episodes are referring to daily concentrations higher than the 75% percentile. Blank boxes correspond to Cluster 2 that includes less than 3% of total trajectories and was excluded from the procedure.

associated with a severe PM2.5 episode in Rome (June 2006), due to long range transport (Manigrasso et al., 2012). Elevated PSCF values in Cluster 3 are detected above Bulgaria, Romania, Serbia, Albania and the Adriatic, whereas peak PSCF values in Cluster 6 are observed over Croatia, Slovenia, Bosnia Herzegovina, Italian Peninsula and the Adriatic. Higher PSCF values and average daily mean PM2.5 concentrations are calculated at Cluster 3 (Fig. 2a, Table 4b). PM10 are dominated by PM2.5 fraction at IT0953A station (average daily PM2.5/PM10 ¼ 67.9%), thus slightly elevated average daily mean levels of PMCOARSE were calculated in short-medium range Clusters 9 and 7, and long-very long range Clusters 1 and 8 (Fig. 1, Table 4c). Clusters 1 and 8 include trajectories of rapidly moving air masses, not typically associated with long range transport of PM, and in addition present low maximum daily mean concentrations of PMCOARSE (Table 4c) and reduced values of INDEX1 and PI (Table 4a). Hence, long range transport impacts are not suggested in Clusters 1 and 8. Air mass residence time in Cluster 9 covers areas of Northern Africa and the Mediterranean, whereas air mass dwelling time in Cluster 7 is distributed above North-West Africa, the Mediterranean and Iberian Peninsula. Maximum PSCF values in Cluster 9 are scattered over the Mediterranean and coastal areas of Tunisia and Libya, whereas in Cluster 7 peak PSCF values were detected in the southern domain of the cluster, above Tyrrhenian Sea and Algeria (Fig. 2b). These findings suggest dust and sea spray intrusions in Rome from the Sahara desert and the Mediterranean respectively (Manigrasso et al., 2012; Dimitriou and Kassomenos, 2013). Dust and maritime aerosols are generally coarser than those generated from anthropogenic combustion (Almeida et al., 2005; Gerasopoulos et al., 2011), thus raised average daily mean PMCOARSE concentrations in Clusters 9 and 7 are justified. 4. Conclusions The main goal of this paper was to define atmospheric pathways possibly influencing PMCOARSE (PM10ePM2.5) and PM2.5 levels in Rome. Backward trajectory clusters were produced at 500 m AGL, whereas air mass residence time was analyzed on a grid of a 0.5  0.5 resolution. Potential exogenous PM sources were identified through a combination of statistical indicators and

methods. Nevertheless, local sources of PM2.5 and PMCOARSE were also studied, in order to supplement and support the outcome of long range transport analysis. Air pollution daily concentration data were elaborated by a PCA. Four components corresponding to main local sources of air pollution were created and interpreted as: Component 1 [Traffic], Component 2 [Photochemical], Component 3 [Residential] and Component 4 [Secondary Coarse Aerosol]. PM2.5 emissions were strongly associated to Component 1, reflecting vehicular combustion, whereas PMCOARSE production was associated with secondary sources (e.g. dust resuspension, natural emissions, long range transport etc) described from Component 4. In general, PM10 are dominated by PM2.5 fraction, as it was deduced from PM2.5/PM10 daily mean ratio values. In order to reveal possible associations among distinct atmospheric circulations and adverse effects for the population of Rome, the complete inhalable fraction of airborne aerosols (PM10 ¼ PM2.5 þ PMCOARSE) was initially studied, in conjunction with trajectory clusters. Raised average daily mean concentrations of PM10 were calculated at short-medium range clusters describing E/NE airflows to Rome from Central Europe and Balkan Peninsula through the Adriatic, and also in short-medium range clusters containing trajectories of air parcels overflying across North Africa and the Mediterranean. In addition, E/NE and North AfricanMediterranean clusters presented increased values of INDEX1, reflecting a higher (%) likelihood of daily PM10 exceedances. Thus, PM10 transportation in Rome along with slow moving air masses is suggested. Average values of PI daily air quality index were also slightly increased in E/NE and North African-Mediterranean clusters, thus a downgrading of air quality is provoked due to the additional quantity of exogenous PM10. However, average levels of PI in all clusters belong in the low pollution category. A more profound study of the suspicious clusters, possibly associated with long range transport impacts, was performed by the implementation of PSCF function. This technique was applied separately in clusters presenting elevated PM2.5 and PMCOARSE concentrations. The residing areas of incoming in Rome slow moving air masses are potential transboundary sources of PM. According to PSCF, for PM2.5 those areas are isolated mainly above Balkan Peninsula and Eastern Central Europe (E/NE clusters), where major combustion aerosol sources exist. Potential transboundary

Fig. 2. Air mass residence time and PSCF density maps of 4-day backward atmospheric trajectory clusters, associated with increased a) PM2.5 and b) PMCOARSE concentrations in Rome.

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sources of PMCOARSE were localized over North African coastal areas and the Mediterranean, indicating Sahara dust and sea spray intrusions respectively. Dust and maritime particles are generally categorized in the coarse fraction of PM10, thus the above conclusions are solidified. This paper aimed to calculate and interpret indicators reflecting sources and air quality impacts of PM2.5 and PMCOARSE in Rome, focusing on potential exogenous influences. The authors believe that the main goal of their attempt was achieved. Key findings can be used for the development of national and transnational policies on the abatement of air pollution. However further research is required on this field, due to the complexity of the aerosol mixture in the Mediterranean basin. Measurements of PM1 ultrafine fraction (diameter<1 um) in EU airbase, can provide beneficial information and upgrade the conclusions of this work. Acknowledgments The authors would like to thank the European Union (EU) Air Quality Database (Airbase), for the free concession of air pollution data. We would also like to acknowledge the contribution of the NOAA Air Resources Laboratory, for the kind and unrestricted provision of the HYSPLIT trajectory model. References 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, 3127e3138. Argyropoulos, G., Manoli, E., Kouras, A., Samara, C., 2012. Concentrations and source apportionment of PM10 and associated major and trace elements in the Rhodes Island, Greece. Sci. Totol Environ. 432, 12e22. Avino, P., Brocco, D., 2004. Carbonaceous aerosol in the breathable particulate matter (PM10) in urban area. Ann. Chim. 94, 647e653. http://dx.doi.org/ 10.1002/adic.200490082. Avino, P., Manigrasso, M., 2008. Ten-year measurements of gaseous pollutants in urban air by an open-path analyzer. Atmos. Environ. 42, 4138e4148. Borge, R., Lumbreras, J., Vardoulakis, S., Kassomenos, P., Rodriguez, E., 2007. Analysis of long range transport influences on urban PM10 using two-stage atmospheric trajectory clusters. Atmos. Environ. 41, 4434e4450. Buchholz, S., Junk, J., Krein, A., Heinemann, G., Hoffmann, L., 2010. Air pollution characteristics associated with mesoscale atmospheric patterns in northwest continental Europe. Atmos. Environ. 44, 5183e5190. Cachon, B.F., Firmin, S., Verdin, A., Ayi-Fanou, L., Billet, S., Cazier, F., Martin, P.J., Aissi, F., Courcot, D., Sanni, A., Shirali, P., 2014. Proinflammatory effects and oxidative stress within human bronchial epithelial cells exposed to atmospheric particulate matter (PM2.5 and PM>2.5) collected from Cotonou, Benin. Environ. Pollut. 185, 340e351. 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, 346e353. Curtis, L., Rea, W., Smith-Willis, P., Fenyves, E., Pan, Y., 2006. Adverse health effects of outdoor air pollutants. Environ. Int. 32, 815e830. Deacon, A.R., Derwent, R.G., Harrison, R.M., Middleton, D.R., Moorcroft, S., 1997. Analysis and interpretation of measurements of suspended particulate matter at urban background sites in the United Kingdom. Sci. Total Environ. 203, 17e36. Delgado-Buenrostro, N.L., Freyre-Fonseca, V., Cuellar, C.M.G., Sanchez-Perez, Y., Gutierrez-Cirlos, E.B., Cabellos-Avelar, T., Orozco-Ibarra, M., Pedraza-Chaverri, J., Chirino, Y.I., 2013. Decrease in respiratory function and electron transport chain induced by airborne particulate matter (PM10) exposure in lung mitochondria. Toxicol. Pathol. 41, 628e638. Dimitriou, K., Kassomenos, P.A., 2013. The fine and coarse particulate matter at four major Mediterranean cities: local and regional sources. Theor. Appl. Clim. 114 (3e4), 375e391. Dimitriou, K., Kassomenos, P.A., 2014. Decomposing the profile of PM in two low polluted German cities e mapping of air mass residence time, focusing on potential long range transport impacts. Environ. Pollut. 190, 91e100. Dimitriou, K., Paschalidou, A.K., Kassomenos, P.A., 2013. Assessing air quality with regards to its effects on human health in the European Union through air quality indices. Ecol. Indic. 27, 108e115. Dorling, S.R., Davies, T.D., Pierce, C.E., 1992. Cluster Analysis: a technique for estimating the synoptic meteorological controls on air and precipitation chemistrymethod and applications. Atmos. Environ. 26, 2575e2581. Dorling, S.R., Davies, T.D., 1995. Extending cluster analysis-synoptic meteorology links to characterise chemical climates at six northwest European monitoring stations. Atmos. Environ. 29, 145e167.

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