1. science
2. ecology
3. pollution

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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]
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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.
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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
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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):
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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
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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
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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).
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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
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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.
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Rural Areas
Valencia Airport
Valencia Polytechnic
Industrial Zone
City center
Port
Rural Areas
629
630
(b)
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(c)
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(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.
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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
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713
714
715
716
717
718
719
720
721
722
723
724
725
726
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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
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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
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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
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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.
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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