ACCEPTED MANUSCRIPT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 The dependence of PM size distribution from meteorology and local-regional contributions, in Valencia (Spain) – A CWT model approach Konstantinos Dimitriou* Laboratory of Meteorology, Department of Physics, University of Ioannina, Greece Abstract This paper combines an analysis of hourly air pollution measurements and daily meteorological data with backward air mass trajectories, in order to elucidate local/regional sources and processes (e.g. atmospheric dispersion/stagnation, dust resuspension, etc) defining PM levels and size distribution in Valencia (Spain). Four size fractions of PM (PM10, PMCOARSE=PM10-PM2.5, PM2.5 and PM1) were independently studied. No chemical/physical interactions among the four different size categories were assumed. Wind dispersion of PM2.5 and PM1 was indicated, whereas atmospheric stagnation conditions triggered the accumulation of fine particles, mainly produced from local combustion. Wind blown dust enhanced PMCOARSE concentrations, particularly throughout warm periods when dry land facilitates dust resuspension. Hourly air mass trajectory points were analyzed by Concentration Weighted Trajectory (CWT) model and Potential Source Contribution Function (PSCF) on a 0.5°×0.5° resolution grid. The outcome of CWT model and PSCF identified Iberian Peninsula, France, North-West Africa and the Mediterranean as potential exogenous PM source areas. Extreme events of all PM fractions were primarily associated with the prevalence of South-South West airflows, whereas Saharan dust PMCOARSE intrusions also emerged. The availability of hourly meteorological data and the analysis of the chemical species included in PM mass could further clarify the findings of this paper and remove uncertainties. Keywords: PM10, PM2.5, PM1, Potential Source Contribution Function, Concentration Weighted Trajectory model. *Corresponding Author: Dr. Konstantinos Dimitriou. University of Ioannina GR45110, Greece. Tel: +30-6976117441, Email: [email protected] 1 ACCEPTED MANUSCRIPT 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 1. Introduction Airborne Particulate Matter (PM) is a major environmental problem in various urban areas of East-South East (E-SE) Spain, due to mineral, maritime and combustion emission sources (Santacatalina et al, 2010, Querol et al, 2007). High PM10 (diameter less than or equal to 10μm) episodes in the area of Castello, were attributed variously to local, regional, and African dust intrusion events (Minguillon et al, 2007), whereas PM2.5 (diameter less than or equal to 2.5μm), PMCOARSE (PM10PM2.5) and PM1 (diameter less than or equal to 1μm) levels in the city of Elche, were strongly associated with traffic mainly during winter (Galindo et al, 2011). For the city of Valencia, an analysis conducted by Esteve et al, 2012, showed a moderate polluted atmosphere characterized by fine particles, whereas daily variations were attributed to traffic and the evolution of the boundary layer throughout the day. In addition, extreme particle episodes in Valencia can be induced from stagnant atmospheric conditions, due to the influence of anticyclonic systems (Segura et al, 2013). Possible associations among PM and gaseous air pollutants or meteorological parameters, can reveal sources and processes affecting aerosol levels (Beckerman et al, 2008, Yoo et al, 2011, Vardoulakis and Kassomenos, 2008). In the Mediterranean basin, this approach was used in various publications: Poor correlations of PM with gaseous emissions (SO2, NO2) in Brindisi (Italy) during summer, in conjunction with positive correlations with temperature, were interpreted as markers of Sahara dust intrusions (Mangia et al, 2011). In Thessaloniki (Greece), positive correlation between particles and NOx provided evidence about higher combustion-related emissions during the cold season, whereas increased contribution of secondary particles was suggested during the warm season (Kassomenos et al, 2011). However, in order to distinguish local from transboundary PM contributions, air mass trajectories signifying the origin of the incoming air parcels are usually deployed. Riccio et al, 2007 and Borge et al, 2007, identified days with significant exogenous PM10 contributions from North African countries in Naples (Italy) and Madrid (Spain) respectively. Major PM10 transport in Thessaloniki can be clearly associated with air masses arriving from Central and Southern Europe (Makra et al, 2011). This study combines an analysis of air pollution and meteorological data, along with air mass trajectories, in order to provide a detailed overview of sources and factors affecting levels and size distribution of PM in the urban area of Valencia (Spain), during the years 2010-2012. Four size fractions of PM (PM10, PMCOARSE, PM2.5 and PM1) were studied. Hourly air mass trajectory points were analyzed by Concentration Weighted Trajectory (CWT) model and Potential Source Contribution Function (PSCF) on a grid of 0.5°×0.5° resolution. The grid extends within the coordinate boundaries: 50.0° W – 40.0° E / 20.0° N – 60.0° N. Positive correlations among PM, NO2 and SO2, revealed local combustion emissions of particles, whereas regional sources of particulate air pollution were successfully localized by the implementation of CWT and PSCF. Atmospheric circulations favoring the accumulation of particles and the provocation of extreme events were also identified. 2 ACCEPTED MANUSCRIPT 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 2. Data and Methodology 2.1. Data and sampling sites The city of Valencia (Figure 1a and 1b) is located on the South East (SE) coast of Iberian Peninsula in West Mediterranean. The city center is mainly residential and commercial, whereas industrial facilities are situated particularly to the West-South West (W-SW) suburbs (Figure 1a). In addition, extensive rural areas exist around the city, whereas to the East lies the Mediterranean Sea. Valencia’s port is the largest in Spain and one of the busiest in the Mediterranean basin. For the needs of this paper, three years (2010-2012) of hourly concentration data of PM10, PM2.5, PM1, SO2 and NO2 (μgr/m3), were obtained from a background monitoring station sited at the city’s Polytechnic University Campus. The station’s European code is ES1885A and all data were downloaded from the website of the European Union (EU) air quality database (http://www.eea.europa.eu/themes/air/airquality/map/airbase). The sampling site is located at the North East (NE) boundaries of the city center (Figure 1, Table 1), away from main congested traffic arteries, and thus was selected in order to facilitate the identification of both local and regional contributions. PM, SO2 and NO2 concentrations were recorded by the use of Differential Optical Absorption Spectroscopy (DOAS), Ultra Violet (UV) fluorescence and chemiluminescence analyzers respectively, expected to work within 15% of uncertainty bounds, according to EU regulations. Daily values of Temperature (°C), Wind Speed (m/sec) and Atmospheric Pressure (hPa), covering the three year time interval 2010-2012, were also acquired from the European Climate Assessment & Dataset (ECAD) webpage (http://eca.knmi.nl/). These meteorological parameters were monitored at the International Airport of Valencia, localized 8 km to the West outside the city center (Figure 1, Table 1). A short statistical description of meteorological and air pollution data is included in Table 2. 2.2. Methodology 2.2.1. Pearson correlations Pearson correlation coefficients were calculated, among hourly concentrations of distinct size fractions of PM (PM10, PMCOARSE=PM10-PM2.5, PM2.5 and PM1) and gaseous air pollutants (SO2, NO2), aiming to identify local sources of PM production. NO2 was primarily considered as a marker of vehicular combustion (Juda-Rezler et al, 2011, Vardoulakis and Kassomenos, 2008), whereas SO2 was used as an indicator of industrial and household emissions (Vardoulakis and Kassomenos, 2008). However, the existence of Valencia’s large port, can also enrich the levels of PM, NO2 and SO2 (Beecken et al, 2014, Song and Shon, 2014), due to diesel burning in ship engines. The impact of ship emissions in air quality was previously reported in coastal Spanish cities as Barcelona and Algeciras (Viana et al, 2014). In order to elucidate the influence of meteorological parameters on PM size distribution in Valencia, Pearson correlations were also computed between average daily concentrations of PM fractions and average daily temperature, wind speed and atmospheric pressure levels. This procedure was conducted on a daily basis, due to the absence of hourly meteorological data, and thus the results must be further clarified in 3 ACCEPTED MANUSCRIPT 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 the future. Average daily concentrations of PM were calculated only if at least 18 of each day’s 24 hourly concentration values were available, thus only 1019 out of 1096 days (approximately 93%) were studied. This restriction was set for reliability reasons. The computation of all Pearson correlation coefficients, was performed separately during warm (April-September) and cold (October-March) periods of the selected years. This approach was preferred in order to identify potential seasonal alterations in the status of PM, due to the changes in human activities (e.g. traffic, domestic heating, etc) and meteorological conditions (e.g. temperature). 2.2.2. Air mass residence time Moving air parcels, have the ability to absorb and transport airborne particulates, generated from natural and anthropogenic emissions (Dimitriou and Kassomenos, 2014a). Air mass residence time over a specific source region is linearly related to that region’s contribution at the receptor site (Kavouras et al, 2013, Dimitriou and Kassomenos, 2014b, Xu et al, 2006). Three day backward air mass trajectories, approaching Valencia (Spain) at 500m Above Ground Level (AGL), were produced by Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (http://www.arl.noaa.gov/HYSPLIT.php) of the National Oceanic and Atmospheric Administration (NOAA), based on NCEP/NCAR global reanalysis meteorological data. The 500m AGL altitude was considered as 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, Dimitriou and Kassomenos, 2014c). A group of 24 trajectories, ending in Valencia at 00:00 – 23:00 UTC, were deployed for each day with available PM10, PM2.5 and PM1 daily concentration data (Chalbot et al, 2013, Dimitriou and Kassomenos, 2014b), thus 1785288 trajectory points [1019 days × 24 trajectories per day × 73 hourly trajectory points per trajectory (3 days backward per trajectory × 24 hours per trajectory day + 1 arrival point)] were finally studied. Air mass residence time was allocated within the cells of a 0.5°×0.5° resolution grid, as the sum of the number of trajectory points. The grid spreads within the longitude/latitude coordinate boundaries: 50.0° W – 40.0° E / 20.0° N – 60.0° N (Figure 1c). Few trajectory points, exceeding the grid boundaries, were highly distant from the sampling site and were not taken into account. 2.2.3. Concentration Weighted Trajectory (CWT) model Gridded air mass residence time data were then elaborated by Concentration Weighted Trajectory (CWT) model (Gogoi et al, 2011, Hidemori et al, 2014), aiming to produce a geographical overview of emission source areas, enriching PM levels in Valencia. CWT formula (Equation 1) yields a weighted concentration for each grid cell (i, j), based on the average daily PM levels measured at the sampling site, corresponding to the trajectories overflying across this grid cell (i, j): 196 197 4 ACCEPTED MANUSCRIPT 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 Cij is the Weighted Average Concentration (WAC) in a grid cell (i, j), Ck is the daily average concentration at the sampling site during day (k), and τijk is the number of trajectory points in grid cell (i, j), belonging to the 24 backward trajectories corresponding to day (k). CWT algorithm was applied independently for each one of the studied PM fractions (PM10, PMCOARSE, PM2.5 and PM1), in order to distinguish the origin of air masses influencing PM size distribution in Valencia. Grid cells presenting raised Cij values indicate potential source areas of PM, contributing to the total aerosol burden of Valencia (Salamalikis et al, 2015), depending on atmospheric circulation. Sporadic trajectory points, isolated in distant grid cells, may induce highly uncertain extreme Cij values (Karaca et al, 2009, Kocak et al, 2011). For that reason, as the final step of CWT model, Cij values were multiplied with an arbitrary W(i,j) weight function (Wang et al, 2006, Hsu et al, 2003). The applied weight function (Equation 2), is based on the relationship among the average number (nave) of trajectory points of all grid cells which contain at least one trajectory point and the number (nij) of trajectory points in the (i, j) cell (Dimitriou and Kassomenos 2014c). In this paper, CWT model was applied in order to indicate the potential source areas and atmospheric pathways influencing PM levels and size distribution in Valencia. However, the determination of the chemical species contained in PM mass, could upgrade the results and specify the emission origin of the aerosols. 2.2.4. Potential Source Contribution Function (PSCF) model The findings of CWT model were supplemented with the outcome of Potential Source Contribution Function (PSCF), reflecting the likelihood of extreme PM events, in conjunction with air mass dwelling time above specific regions. PSCF values are provided by the following expression (Kong et al, 2013, Dimitriou and Kassomenos, 2015, Karaca et al, 2009, Polissar et al, 2001): (3) In Equation 3, n(i,j) is the total number of trajectory points contained in the (i,j) cell, and m(i,j,) is the number of trajectory points in the ijth cell which belong to trajectories corresponding to exceedances of a daily threshold PM concentration value. Various threshold levels have been used in recent publications, extending from 60th 5 ACCEPTED MANUSCRIPT 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 percentile (Polissar et al, 2001) to 75th percentile (Kong et al, 2013, Karaca et al, 2009). In this study, a more strict definition of episodic extreme concentrations was adopted and thus the threshold criterion was set at the 85th percentile. PSCF model was separately implemented for PM10, PMCOARSE, PM2.5 and PM1, in order to identify sources and pathways possibly associated with extreme contributions of different size fractions of PM. The results of PSCF, were also multiplied with the same W(i,j) weight function (Equation 2), in order to enhance statistical stability. 3. Results 3.1. Local PM sources and meteorology A Pearson correlation analysis was implemented among different size fractions of PM and gaseous air pollutants. Moderate positive associations were computed between PM and NO2, whereas the relationship among PM and SO2 was lower (Table 3). All correlations were statistically significant at the 0.01 level and thus the importance of combustion in the generation of particles was highlighted (Galindo et al, 2011, Juda-Rezler et al, 2011). In addition, higher PM-NO2 correlations reflect the increased contribution of traffic (Beckerman et al, 2008) in the total aerosol burden in Valencia in comparison with household/industrial PM emissions. During warm periods of the years 2010-2012, the correlation among all fractions of PM and gaseous pollutants were decreased (Table 3), probably due to the reduction of fuel combustion for domestic heating and transportation purposes. Increased values of Pearson correlation coefficients were calculated among hourly concentrations of different PM fractions and the results were also significant at the 0.01 level (Table 3). Thus, common local sources of PM10, PMCOARSE, PM2.5 and PM1 in Valencia are suggested. However, the relationship among PMCOARSE and fine particles (PM2.5 and PM1) was weaker hence the influence of secondary natural sources of coarse particulates was emerged (Dimitriou and Kassomenos, 2014c). During warm periods, PM10-PM2.5 and primarily PM10-PM1 associations were decreased (Table 3), whereas on the contrary PM10-PMCOARSE correlations were amplified (Table 3). These findings were attributed to the drop of combustion emissions and also to the existence of more airborne coarse particulates from natural sources during summer and spring (e.g. dust resuspension, pollen, etc). The influence of meteorological conditions (Wind speed, Temperature, Atmospheric pressure) on PM size distribution was also studied with a Pearson correlation procedure. Wind speed clearly anti-correlated with PM10 and principally with fine particles (PM2.5 and PM1) suggesting atmospheric dispersion (Table 4) of combustion emitted PM fractions. In addition, positive associations of atmospheric pressure with PM10, PM2.5 and PM1, during cold seasons, signify the increment of fine PM concentrations due to the recirculation of polluted air: The influence of anticyclonic high pressure systems provokes atmospheric stagnation (Perez et al, 2008, Cheng et al, 2014). Yet, the relationship of PMCOARSE with wind speed during cold periods was non significant, while in warm seasons this correlation was significantly positive (Table 4), indicating the impact of wind blown dust (Buchholz et al, 2014, Vardoulakis and Kassomenos, 2008). This interpretation of the results is also supported by the enhancement of the positive correlation between PMCOARSE and temperature levels during warm seasons: High temperatures prevailing throughout dry and sunny days, remove the soil’s moisture, favoring dust resuspension (Vardoulakis 6 ACCEPTED MANUSCRIPT 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 and Kassomenos, 2008, Galindo et al, 2011, Mangia et al, 2011, Buchholz et al, 2014). Finally, the more frequent occurrence of Saharan dust intrusions during the warm seasons of the time interval 2010-2012 (Bilbao et al, 2014), may induce PMCOARSE episodes combined with highly elevated temperatures, justifying positive correlations (Galindo et al, 2011, Mangia et al, 2011). 3.2. Regional contributions Air mass residence time was analyzed by CWT and PSCF model, along with average daily concentrations of all the available PM fractions, in order to isolate regional sources of particulate air pollution affecting the city of Valencia. The produced WAC and PSCF values were plotted on surface maps, presented in Figure 2 and Figure 3 respectively. 3.2.1. Concentration Weighted Trajectory (CWT) model outcome According to the results of the model, exogenous sources of total PM10 were mainly localized within Southern Iberian Peninsula, France, coastal North West (NW) Africa and the Mediterranean (Figure 2a). Peak WAC levels for PM10 were associated with South-South West (S-SW) airflows, suggesting Saharan dust and Mediterranean spray transportation. In addition, areas of semi-arid land (Figure 1b) and urban/industrial emission sources, existing in South-South East (S-SE) Spain, could also enrich PM10 levels in Valencia (Santacatalina et al, 2010). Incoming air parcels, overflying across Iberian Peninsula, France, NW Africa and the Mediterranean, were also associated with the inflow of PMCOARSE. However, maximum contributions of PMCOARSE, were attributed to Northern airflows from Western France through NE Iberian Peninsula (Figure 1b), where various anthropogenic and natural sources (e.g. semi-arid lands, forests) exist. Severe Saharan dust intrusions from areas located deeper in NW Africa [Mauritania, Mali and Algeria (Engelbrecht et al, 2014)] were also clearly revealed (Figure 2b). Natural-non combustion particulates are generally coarser and thus these findings are justified. Clear similarities were observed among the spatial distribution and the relative contribution of regional PM10, PM2.5 and PM1 sources (Figure 2a, 2c and 2d). Fine particles represent a large proportion of PM10 (average daily PM2.5/PM10=0.64) in Valencia, whereas a high percentage of PM2.5 is consisted from PM1 (average daily PM1/PM2.5=0.76). Thus, the homogeneity of the surface maps was expected. Transferring of PM2.5 and PM1 from inside of the Iberian Peninsula, France, coastal North West (NW) Africa and the Mediterranean was indicated. Increased contribution of fine PM from S-SW directions was also emerged (Figure 2 c, Figure 2 d). Soil and sea spray particles more likely belong in the coarse fraction of PM (Almeida et al, 2005, Dimitirou and Kassomenos, 2014c, Flament et al, 2011), yet many previous studies which analyzed the chemical composition of fine PM, have revealed a substantial participation of dust and marine aerosols in PM2.5 mass (Mantas et al, 2014, Engelbrecht et al, 2014, Remoundaki et al, 2013, Rodriguez et al, 2011). In addition, Saharan desert dust is very frequently mixed with particulate pollutants deriving from industrial activities in Northern Algeria, Eastern Algeria, Tunisia and Morocco (Rodriguez et al, 2011). 7 ACCEPTED MANUSCRIPT 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 3.2.2. Potential Source Contribution Function (PSCF) outcome PSCF model was used aiming to identify PM source areas associated with daily episodes (15% highest) of fine and coarse particulate air pollution in Valencia. PSCF algorithm was also applied in order to verify the results of CWT model. Extreme events of all PM fractions were primarily associated with the prevalence of S-SW atmospheric circulation (Figure 3). Episodic levels of PMCOARSE were also connected with the arrival of air mass trajectories traveling through Western France and NE Spain, whereas the impact of Sahara dust outbreaks and Mediterranean spray was also emerged (Figure 3b). A PSCF maximum for PM2.5 and PM1 was observed in central France (Figures 3c and 3d), where major urban areas (e.g. Paris) and industrial facilities exist. In addition, extreme events of PM2.5 and PM1 corresponded to shorter trajectories, in comparison with PM10 and PMCOARSE episodes (Figure 3), suggesting the arrival of slow moving air masses and thus atmospheric stagnation. Conclusively, the results of PSCF and CWT models were consistent (Figure 2, Figure 3), revealing similar PM source areas. 4. Conclusions This paper combines an analysis of air pollution and meteorological data with air mass trajectories, in order to distinguish local and regional PM contributions, degrading air quality in Valencia (Spain). Air mass dwelling time was allocated on a grid of a 0.5°×0.5° resolution, in order to be used as input to CWT and PSCF models, providing a more detailed localization and quantification of exogenous contributions. Four size fractions of PM (PM10, PMCOARSE=PM10-PM2.5, PM2.5 and PM1) were studied, aiming to identify possible differences in sources and parameters favoring the increment of fine and coarse aerosol levels. Each one of these PM categories was studied independently, without considering aerosol size evolution. Strong Pearson correlations, calculated among different PM fractions, were considered as markers of common emission sources. However, secondary natural sources of PMCOARSE were indicated, primarily during warm seasons. In addition, positive associations of PM, primarily with NO2 and secondarily with SO2, respectively reflect the impact of vehicular and domestic/industrial combustion in aerosol production, whereas CO concentration data were not available. Yet, during warm seasons, PM-NO2 and PM-SO2 correlations were reduced, due to the drop of combustion emissions for domestic heating and transportation purposes. Wind dispersion of PM2.5 and PM1 was indicated, whereas atmospheric stagnation conditions triggered the accumulation of fine particles. On the contrary, PMCOARSE concentrations correlated positively with wind speed, due to the effect of wind blown dust, particularly throughout warm periods when dry land facilitates dust resuspension. The outcome of CWT model identified Iberian Peninsula, France, NW Africa and the Mediterranean as potential PM source areas, for all size fractions of aerosols, thus the incoming of dust, sea spray and combustion particulates is deduced. Peak transboundary contributions of PM2.5 and PM1 were associated with S-SW atmospheric circulation, whereas major contributions of PMCOARSE were attributed to dust intrusions from the Sahara desert and also to Northern airflows through Western France and NE Spain. The results of PSCF, verified the findings of CWT model, as 8 ACCEPTED MANUSCRIPT 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 the highlighted potential source areas associated with extreme PM events were coherent. In addition, elevated PSCF values for PM2.5 and PM1 were isolated in central France, where major cities (e.g. Paris) are situated. Episodic concentrations of PM2.5 and primarily PM1 were matched (from numerical calculations) with the prevalence of shorter slow moving all around trajectories, reflecting atmospheric stability. The main objective of this paper was to identify sources and meteorological parameters, defining the levels and size distribution of airborne PM in Valencia. Moreover, regional contributions were studied separately from local emissions, by the use of CWT and PSCF models, in order to indicate distinct atmospheric patterns associated with the addendum of particulates from exogenous sources. The impact of local PM emissions and meteorological factors was clearly revealed from a Pearson correlation analysis. Similar transboundary PM source areas were indicated by CWT and PSCF, and thus the results were solidified. The main conclusions of this work can be used for the determination of emission reduction policies, whereas different states could collaborate for the containment of transboundary air pollution. The availability of hourly meteorological data could improve the findings of this paper and remove uncertainties. An additional step forward would also be the analysis of the chemical species included in PM mass, which would elucidate further the origin of PM emissions. Acknowledgements The author would like to thank the European Union (EU) Air Quality Database (Airbase), for the provision of air pollution data, and also the European Climate Assessment & Dataset (ECA&D) project, for the concession of meteorological data. The author gratefully acknowledges the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model used in this publication. Finally I would like to thank professor Pavlos Kassomenos for his guidance and support. 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, 3127-3138. Beckerman, B., Jerrett, M., Brook, J.R., Verma, D.K., Arain, M.A., Finkelstein, M.M., 2008. 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Analysis of black carbon, particulate matter, and gaseous pollutants in an industrial area in Korea. Atm. Env. 45, 7698-7704. 13 ACCEPTED MANUSCRIPT Rural Areas Valencia Airport Valencia Polytechnic Industrial Zone City center Port Rural Areas 629 630 (b) 631 632 633 634 635 636 637 638 639 640 641 642 643 14 ACCEPTED MANUSCRIPT (c) 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 (50.0° W, 60.0° N) (40.0° E, 60.0° N) (50.0° W, 20.0° N) (40.0° E, 20.0° N) Figure 1: a) Map of Valencia. The blue dots mark the exact positions of the meteorological (Valencia Airport) and the air pollution (Valencia Polytechnic) stations. b) Map of Iberian Peninsula. The red dot marks the city of Valencia. c) Map of the studied grid area. The grid is surrounded by the black line. The red dot marks the city of Valencia. 15 ACCEPTED MANUSCRIPT 680 681 Table 1: Geographical characteristics of the Meteorological (METE) and the Environmental (ENVI) station Station Name (EU Code) Type Valencia Polytechnic (ES1885A) ENVI-Background Valencia Airport METE * 682 AMSL: Above Mean Sea Level Area Urban Suburban Longitude / Latitude -0.3363° 39.4803° -0.4778° 39.4896° Altitude (AMSL)* 7m 69m 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 16 ACCEPTED MANUSCRIPT 727 728 Table 2: Statistical description of hourly concentrations of airborne pollutants and daily meteorological parameters, during the time interval 2010-2012 PM10 (μgr/m3) PMCOARSE (μgr/m3) PM2.5 (μgr/m3) PM1 (μgr/m3) SO2 (μgr/m3) NO2 (μgr/m3) Wind Speed (m/sec) Temperature (°C) Pressure (hPa) Mean 20.3 7.3 13.0 10.1 3.1 25.4 3.4 17.4 1017.0 St. Deviation 14.8 7.7 10.0 8.8 1.7 22.0 1.7 6.4 6.6 Data Availability (%) 94.5 94.5 94.5 94.4 81.1 93.8 100.0 97.6 100.0 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 17 ACCEPTED MANUSCRIPT 768 769 770 a) Table 3: Pearson correlation coefficients among hourly concentrations of different PM fractions (PM10, PMCOARSE, PM2.5 and PM1) and gaseous air pollutants (SO2, NO2) during a) Cold periods and b) Warm periods, of the time interval 2010-2012 PM10 PMCOARSE PM2.5 PM1 SO2 NO2 PM10 1 0.755 0.892 0.813 0.213 0.446 PMCOARSE 1 0.377 0.248 0.177 0.351 PM2.5 1 0.976 0.177 0.388 PM1 1 0.171 0.377 771 b) 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 PM10 PMCOARSE PM2.5 PM1 SO2 NO2 PM10 1 0.816 0.852 0.696 0.101 0.273 PMCOARSE 1 0.393 0.178 0.139 0.227 PM2.5 1 0.946 0.033 0.228 PM1 1 0.034 0.274 * All correlations are significant at the 0.01 level 18 ACCEPTED MANUSCRIPT 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 Table 4: Pearson correlation coefficients among daily average concentrations of different PM fractions (PM10, PMCOARSE, PM2.5 and PM1) and daily meteorological parameters (Wind Speed, Temperature and Atmospheric Pressure) during a) Cold periods and b) Warm periods, of the years 2010-2012 Wind Speed a) PM10 ‐0.326 Temperature 0.025 Pressure a 0.204 PMCOARSE ‐0.057 0.163 0.050a PM2.5 ‐0.406 ‐0.056a 0.246 PM1 ‐0.424 ‐0.127 0.265 Wind Speed Temperature Pressure b) PM10 a ‐0.087 a 0.234 ‐0.002a PMCOARSE 0.138 0.314 0.040a PM2.5 ‐0.255 0.099b ‐0.037a PM1 ‐0.319 ‐0.036a ‐0.049a * All correlations are significant at the 0.01 level, except where noted: a b Correlation non significant , Correlation significant at the 0.05 level 19 ACCEPTED MANUSCRIPT 855 a) b) c) d) 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 Figure 2: Maps of gridded Weighted Average Concentration (WAC) values (μgr/m3) for a) PM10, b) PMCOARSE, c) PM2.5, and d) PM1. A different WAC scale was used for each map. 20 ACCEPTED MANUSCRIPT 879 a) b) c) d) 880 881 882 883 884 885 886 Figure 3: Maps of gridded Potential Source Contribution Function (PSCF) values for a) PM10, b) PMCOARSE, c) PM2.5, and d) PM1. A different PSCF scale was used for each map. 21
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