Particles size and composition in Mediterranean countries:
geographical variability and short-term health effects
MED-PARTICLES Project 2011-2013
Under the Grant Agreement EU LIFE+ ENV/IT/327
Particles size and composition in Mediterranean countries:
geographical variability and short-term health effects
MED-PARTICLES
ACTION 8.
Report on: geographical comparison of different PM sizes and components across cities and
countries, and according to Saharan dust/no dust days and forest fires days.
Summary: Report on the methodology adopted in each city and the results of the speciation process.
1
PM chemical components
1
Introduction
Atmospheric aerosols are generally composed of variable amounts of sulphate (SO42-), ammonium
(NH4+), nitrate (NO3-), sodium (Na), chloride (Cl), trace metals (e.g: Ni, V, Cu, Zn, Mn) crustal
elements (Al, Si, Ca, Fe, Mg, K) water and carbonaceous material. The sulphate component is derived
predominantly from the atmospheric oxidation of anthropogenic and natural sulphur-containing
compounds such as sulphur dioxide (SO2). Nitrate is formed mainly from the oxidation of atmospheric
nitrogen dioxide (NO2). Sulphate and nitrate are initially formed as sulphuric (H2SO4) and nitric acids
(HNO3), but are progressively neutralised by atmospheric ammonia forming the corresponding
ammonium salts. Chlorides also enter atmospheric particles as a result of ammonia neutralisation of
hydrochloric acid (HCl) vapour, which is emitted from sources such as incinerators and power
stations. But the main source of chlorides is sea spray even at locations hundreds of miles from the
coast. Crustal materials include soil dust and windblown minerals. They vary in composition
according to local geology and surface conditions and reside mainly in the coarse particle fraction. The
carbonaceous fraction of the aerosols consists of both elemental and organic carbon. Elemental carbon
(EC), that might also called black carbon (BC), is emitted directly into the atmosphere, predominantly
from combustion processes.
2
Sampling sites
Barcelona, Spain: The site is called Palau Reial, 41°23'14"N, 2°6'56" E, 77 a.s.l.). It is an urban
background site, but highly influenced by emissions from the Diagonal Avenue located at around
300m with a traffic flow of around 100000 cars/working day. The Barcelona metropolitan area is
characterized by high road traffic density and by a wide range of industrial activities. Facilities
comprise ferrous and non-ferrous smelters, cement and asphalt production industries, which are spread
between the two river basins in the North and South of the Metropolitan area. Furthermore, two power
2
stations and two city waste incinerators are also based in the considered area. Traffic density is a
consequence of the high population concentration in the city of Barcelona (101 km2 with 1.6 million
inhabitants, 4.5 million in the greater metropolitan area) leading to one of the highest car densities in
Europe. Jointly, the urban architecture, characterized by square-blocks with narrow streets, reduces the
dispersion of pollutants and the scarce precipitation favors the accumulation of air pollutants.
Madrid, Spain: The site is called Escuelas Aguirre (40º25’32’’N, 03º40’52’’W, 672 m a.s.l.), situated
in an urban location in the city centre, significantly influenced by vehicular traffic. Madrid (40º25’N,
03º42’W) is a South-European city with specific characteristics from the point of view of air pollution
and climate. The population of the city of is approximately 3.2 million inhabitants (6 million in the
metropolitan area), with a car fleet of nearly 1.9 million vehicles in the city (4.5 in the metropolitan
area). More than a half (53 %) of the vehicle fleet in 2012 is diesel powered, confirming an increasing
trend towards a dieselization in the last ten years. Since industrial activity in the surrounding area
consists essentially of light factories, Madrid urban area is characterised as the typical urban plume,
fed by road traffic emissions and heating devices in the winter months. These devices are mainly
natural gas fuelled, and in a minor extent fuel-oil, although there are a number of coal combustion
devices still operating in the city.
Huelva, Spain: The site is called Campus del Carmen, 37° 15' 57"N, 6° 55'26" W, 10 a.s.l), an urban
background site influenced by industrial emissions. The city of Huelva (160,000 inhabitants) lies on
the Atlantic coast of southwestern Spain. An industrial state with several chemical factories (a copper
smelter among them) was built in the 1960s, located 1.5 km to the south of the city.
Montelibretti is a peri-urban site, located inside the CNR Research Area RM1, about 25 km from the
centre of Rome. The nearest village, Monterotondo Scalo, is at about 5 km, but the site is also
influenced by the traffic caused by the CNR staff and the Salaria National road, at about 500 meters.
Rome, Italy: The site is called Villa Ada, is an urban background station located inside the main green
area in the city of Rome (Villa Ada Park). The sampling point is about 200 m from the nearest street.
Bologna, Italy: an urban background site located in Bologna in the Po Valley. PM2.5 samples were
collected on a daily basis during the period 2011-2012.
3
3
Instrumentation and chemical analysis
In the three Spanish cities, Huelva, Barcelona and Madrid high volume samplers (MCV, and Andersen
samplers) were used for PM sampling with a flow rate of 30 m3 h−1 equipped with PM10 and PM2.5
inlets for the determination of PM10 and PM2.5 mass concentrations. PM sampling had 24h duration.
Approximately 2-3 samples were collected every week using quartz-fiber filters ( 15 cm), previously
baked at 200 °C. The filters were conditioned at constant temperature and humidity before and after
sampling. They were then weighed at least three times (in 24h) to obtain constant values. PM 10 and
PM2.5 concentrations were determined gravimetrically. Subsequently, the collected filters were
analyzed for water-soluble ions (NH4+, Cl-, SO42-, NO3-), major elements (Al, Ca, K, Mg, Fe, Na) and
46 trace elements by inductively coupled plasma atomic emission, ICP-AES and mass spectrometry,
ICP-MS respectively following the procedure described by Querol et al. (2001). Briefly, ½ of each
filter was acid digested (HF:HNO3:HClO4, with a mixture of 2.5:1.25:1.25 ml, kept at 90°C in a
Teflon reactor during 6h, driven to dryness and re-dissolved with 1.25 ml HNO3 and then diluted with
water up to 25 ml) for the chemical analysis using ICP-AES and ICP-MS. To assure the quality of the
analytical procedure a small amount (5 mg) of the NIST-1633b (fly ash) reference material loaded on
a ¼ quartz micro-fibre filter was also analysed. Detection limit and accuracy of the techniques were
estimated as 0.18 ng/m3 and 1-3% respectively for ICP-AES, and 0.007 ng/m3 and 0-7% respectively
for ICP-MS.
Another ¼ of each filter was water leached (6h at 60ºC, preceded by incubation in an
ultrasound bath for 10 minutes, in 50 ml sealed PVC bottles) for the determination of soluble ion
concentrations (Cl-, SO42-, NO3-) by ion chromatography and ion selective electrode (NH4+), allowing
an average detection limit for the analysed components of 25-30 ng/m3. In the remaining ¼ of each
filter total carbon was determined by means of the ECOSC-144DR instrument. In Barcelona, for the
samples collected in the period 2007-2010 a (1.5 cm2) portion of each filter was also used for the
determination of organic and elemental carbon (OC and EC, respectively) by a thermal–optical
transmission technique (Birch and Cary 1996) using the Sunset Laboratory OCEC Analyser and the
NIOSH protocol. The sum of OC+EC was expressed as total carbon, Ctotal.
PM10 and PM2.5 samples were collected in Barcelona, during the period 2003-2010. A total of
680 and 737 valid samples of PM10 and PM2.5 respectively, were used for source apportionment
analysis. PM sampling was conducted in Huelva, Campus El Carmen during 2003-2010 and a total of
4
412 and 405 valid samples of PM10 and PM2.5 respectively, were used for further analysis. In
Madrid, PM10 and PM2.5 samples were collected during the period 2007-2008. A total of 94 and 92
valid samples of PM10 and PM2.5 respectively, were used for source apportionment analysis.
In Montelibretti site and at the Villa Ada site PM2.5 and PM10 sampling was conducted by
both the beta attenuation monitor SM200 (Opsis AB Furulund-S) operating at flow rate of 1 m3 h-1 and
teflon filters as a substrate and the MICRODUST sequential sampler (AQUARIA, Lacchiarella, MI-I)
operating at a flow rate of 2 l min-1 equipped with quartz filters. The Teflon filters were subjected to
energy-dispersion X-ray fluorescence analysis (X-Lab2000, Spectro Analytical Instruments, Kleve-D)
for the determination of major and minor elements: Al, Si, Fe, K, Ca, As, Cr, Cu, Mn, Ni, Pb, Ti, V,
Zn. Then the filters were extracted in deionized water and analyzed by ion chromatography (ICS90
Dionex Co) for the determination of Cl-, NO3-, SO4=, Na+, NH4+, K+, Mg++ and Ca++. Elemental EC,
and organic carbon OC were analysed in the quartz filters by thermo-optical analysis (OCEC Sunset
Analyser, Sunset Laboratory) using the NIOSH temperature protocol. PM10 samples were collected
in Montelibretti and in Villa Ada sites on a daily basis during the period 2005-2010. A total of 1940
and 860 valid PM10 samples in Montelibretti and Villa Ada respectively were used for source
apportionment analysis.
In Bologna site, two Dual Channel Monitors (SWAM, Fai Instrument) were used to sample PM
on quartz filters. After sampling EC and OC were determined by thermo-optical analysis (OCEC
Sunset Analyser, Sunset Laboratory) using the EUSAAR_2 protocol. Ion chromatography (Dionex)
was used for the determination of Cl-, NO3-, SO4=, Na+, NH4+, K+, Mg++ and Ca++. PM2.5 samples
were collected in Bologna on a daily basis during the period 2012-2013.
4
Average concentrations of PM sizes and chemical components
Tables 1-6 provide the average concentrations and the standard deviations of PM chemical
components in the sampling sites. Only species with more than 75% of their concentration values >
LOD are given in the tables. Some atmospheric components had a preference for the coarse sizes such
as mineral matter, sea spray and nitrate. Other components such as caronaceous aerosol, ammonium
and in less proportion sulphate are mostly found in the fine fraction. It is important to remark that the
amount of coarse components (PM2.5-10) in the atmosphere of Barcelona was significant. In general the
5
concentrations were higher in Barcelona, Bologna and Madrid where the influence of traffic enhanced
significantly the levels of all chemical components.
In Figure 1 the long-term trends of four representative components in PM10 are shown. The
cities of Barcelona and Huelva are presented as they cover longer periods of PM sampling than the
other sites. The secondary aerosol components such as SO42-, NO3-, and typical traffic tracers Cu, Zn
emitted from the abrasion of tyres and breaks show a decreasing trend from 2003 to 2010, especially
evident after 2006. These trends are probably related to the mitigation strategies taken the last decade
and a proportion of these reductions may be related to the economic crisis and favorable meteorology.
Table 1. Average concentrations and standard deviation of PM10 and PM2.5 chemical components in
Huelva, Spain
Componen
t
µg/m3
Ctotal
Al2O3
Ca
K
Na
Mg
Fe
PO43SO42NO3Cl
NH4+
ng/m3
Ti
V
Cr
Mn
Ni
Cu
Zn
As
Se
Sb
PM1
0
Mea
n
5.42
1.66
1.17
0.42
1.10
0.30
0.67
0.29
3.78
2.67
1.15
1.04
56.83
5.40
2.73
12.89
3.46
47.95
50.98
6.80
1.78
0.87
PM2.5
SD
Mean
SD
3.43
1.72
0.97
0.33
0.89
0.23
0.58
0.37
3.26
1.81
1.61
1.11
3.91
0.48
0.33
0.21
0.34
0.08
0.18
0.11
3.31
1.15
0.31
1.15
2.26
0.70
0.52
0.20
0.47
0.09
0.20
0.12
2.74
1.21
0.43
1.22
86.33
4.39
3.80
24.04
3.45
78.23
83.67
10.18
3.14
1.74
19.55
3.83
1.54
3.50
2.42
29.02
45.56
5.70
1.44
0.69
38.69
3.41
3.00
4.29
2.98
33.29
83.94
9.31
4.38
0.17
6
Ba
Pb
17.57
14.34
30.23
17.65
12.40
12.98
26.52
16.25
Table 2. Average concentrations and standard deviation (SD) of PM10 and PM2.5 chemical
components in Barcelona, Spain
Component
µg/m3
Ctotal
OC
EC
Al2O3
Ca
K
Na
Mg
Fe
SO42NO3Cl
NH4
PM
10
Me
an
8.77
3.36
1.84
1.28
2.14
0.38
1.01
0.28
0.86
3.79
4.35
0.90
1.25
PM2.5
SD
Mean
SD
3.47
1.96
1.18
1.21
1.64
0.29
0.72
0.17
0.54
2.62
3.72
0.86
1.14
8.05
2.86
1.53
0.47
0.60
0.20
0.26
0.08
0.28
3.40
2.43
0.36
1.58
3.25
1.82
0.98
0.60
0.62
0.23
0.18
0.07
0.21
2.42
3.17
0.22
1.51
33.41
13.74
13.41
7.13
3.24
7.91
2.55
6.11
2.30
10.70
3.35
8.59
3.58
5.78
2.53
51.39
27.05
35.89
80.61
0.65
0.49
3.72
58.72
0.61
0.49
1.88
67.65
0.41
0.38
1.50
31.46
15.76
34.17
22.06
13.53
21.08
ng/m3
Ti
V
Cr
Mn
Ni
Cu
Zn
As
Se
Sb
Ba
Pb
40.0
6
10.4
4
5.21
17.5
5
4.81
56.5
1
85.4
8
0.84
0.70
4.94
25.9
2
17.4
9
Table 3. Average concentrations and standard deviation (SD) of PM10 and PM2.5 chemical
components in Madrid, Spain
7
Componen
t
µg/m3
PM10
Mean
PM2.5
SD
Mean
SD
Ctotal
10.44
5.59
7.59
3.86
Al2O3
0.72
0.59
0.36
0.29
Ca
2.09
1.35
0.26
0.15
K
0.33
0.22
0.20
0.41
Na
0.38
0.33
0.16
0.10
Mg
0.22
0.15
0.08
0.08
Fe
1.67
0.87
0.20
0.15
SO42NO3-
3.15
1.40
2.35
1.13
2.46
2.09
1.49
1.72
Cl
0.75
0.46
0.44
0.31
NH4+
1.51
0.83
1.48
0.47
Ti
39.92
30.91
8.27
13.97
V
1.86
1.25
0.89
0.67
Cr
6.73
3.85
4.79
4.73
Mn
20.40
11.42
5.40
3.02
Ni
58.98
43.54
3.91
2.89
Cu
1.72
1.00
18.51
9.98
Zn
112.75
68.86
50.16
26.22
As
1.74
ng/m3
1.97
1.76
1.75
Se
0.43
0.25
0.33
0.22
Sb
14.98
8.82
4.80
14.52
Ba
38.25
35.82
43.69
32.56
Pb
15.09
11.01
9.75
7.90
Table 4. Average concentrations and standard deviation (SD) of PM10 chemical components in
Montelibretti, Italy
Component
µg/m3
Ctotal
OC
EC
Si
Al
PM10
Mean
8.58
7.37
1.21
0.89
0.25
SD
4.40
3.77
0.64
1.08
0.28
8
Ca
K
Na
Mg
Fe
Ca2+
Mg2+
SO42NO3Cl
NH4+
ng/m3
Ti
V
Cr
Mn
Ni
Cu
Zn
As
Pb
1.35
0.38
0.52
0.09
0.24
1.10
0.09
2.89
1.73
0.35
0.91
1.09
0.24
0.62
0.09
0.22
0.72
0.09
1.95
1.35
0.73
0.61
17.00
3.66
5.08
7.27
3.50
17.24
23.64
1.23
4.18
27.42
2.98
4.34
4.92
3.22
16.03
25.28
1.01
5.39
Table 5. Average concentrations and standard deviation (SD) of PM10 chemical components in Villa
Ada, Rome, Italy
Component PM10
µg/m3
Mean
Ctotal
10.72
OC
8.47
EC
2.25
Si
1.00
Al
0.26
Ca
1.26
K
0.38
Na
0.54
Mg
0.10
Fe
0.46
2+
Ca
1.15
2+
Mg
0.10
K+
0.29
2SO4
3.37
NO3
1.99
Cl
0.36
SD
5.52
4.25
1.37
0.91
0.24
0.71
0.32
0.57
0.10
0.28
0.71
0.10
0.29
2.07
1.40
0.70
9
NH4+
ng/m3
Ti
V
Cr
Mn
Ni
Cu
Zn
As
Pb
0.87
0.63
22.92
5.56
9.93
12.10
4.64
40.37
38.04
3.11
10.40
24.74
3.77
6.03
7.73
2.69
19.70
34.97
2.13
8.81
Table 6. Average concentrations and standard deviation (SD) of PM2.5 chemical components in
Bologna, Italy
Component PM2.5
µg/m3
OC
1.6
1.5
SiO2
2.4
2.2
SO42-
5.1
2.1
NO3-
0.1
0.1
Fe
3.2
3.5
Mn
2.4
2.6
Ni
6.2
5.9
Ti
10.8
11.7
ng/m3
10
Figure 1. Long term trends of SO42-, NO3-, Cu and Zn in PM10 in Barcelona and Huelva
11
5
Average concentrations of PM sizes and chemical components by presence of
dust or forest fires events
Barcelona
Table 1. Descriptive characteristics of the PM10 components by presence of dust at
the ground level, Barcelona, Spain, (study period 2003-2010)
PM components
PM10
Ctotal
SiO2
Al2O3
Ca
K
Na
Mg
Fe
SO42NO3Cl
NH4+
P
Ti
V
Cr
Mn
Ni
Cu
Zn
As
Se
Sr
Cd
Sn
Sb
Ba
La
Pb
Mean
#No Dust
days
# Dust days
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
557
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
No Dust
Dust
37.512
6.664
3.098
1.033
1.972
0.346
0.924
0.244
0.785
3.267
4.145
0.859
1.188
0.025
0.033
0.009
0.005
0.016
0.004
0.055
0.086
0.001
0.001
0.005
0.000
0.006
0.005
0.024
0.000
0.017
52.097
7.793
6.840
2.280
2.843
0.542
1.298
0.429
1.176
6.023
4.526
0.958
1.438
0.035
0.070
0.014
0.006
0.022
0.006
0.060
0.082
0.001
0.001
0.008
0.000
0.007
0.006
0.034
0.001
0.020
12
Table 2. Descriptive characteristics of the PM10 components by presence of forest
fires events, Barcelona, Spain, ( study period 2003-2010)
PM components
PM10
Ctotal
SiO2
Al2O3
Ca
K
Na
Mg
Fe
SO42NO3Cl
NH4+
P
Ti
V
Cr
Mn
Ni
Cu
Zn
As
Se
Sr
Cd
Sn
Sb
Ba
La
Pb
#No Fire
days
# Fire days
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
663
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
Mean
No Fire
Fire
39.885
6.845
3.773
1.258
2.122
0.377
0.997
0.275
0.848
3.718
4.203
0.868
1.212
0.027
0.039
0.010
0.005
0.017
0.005
0.057
0.085
0.001
0.001
0.005
0.000
0.006
0.005
0.026
0.000
0.017
55.454
7.941
6.157
2.052
2.639
0.546
1.083
0.415
1.123
5.916
6.525
1.161
1.981
0.032
0.066
0.013
0.006
0.024
0.006
0.051
0.103
0.001
0.001
0.008
0.000
0.006
0.005
0.036
0.001
0.019
13
Rome- Villa Ada
Table 3. Descriptive characteristics of the PM10 components in Villa
Ada monitoring station by presence of dust at the ground level, Rome,
Italy, ( study period 2005-2007)
PM components
PM10
Si
Al
Ca
K
Na
Mg++
Fe
SO42NO3Cl
NH4+
Ti
V
Cr
Mn
Ni
Cu
Zn
As
Pb
K+
Ca++
OC
EC
#No Dust
days
# Dust
days
843
711
711
705
711
711
711
711
711
711
711
711
710
711
711
711
710
711
711
710
705
556
711
860
860
173
144
144
140
144
144
144
144
144
144
144
144
144
144
144
144
144
144
144
144
144
114
144
170
170
Mean
No Dust
Dust
29.022
0.773
0.205
1.150
0.366
0.544
0.093
0.424
3.094
1.928
0.391
0.812
15.428
4.874
9.952
11.374
4.574
40.589
38.316
2.402
9.703
0.294
1.015
8.557
2.194
34.346
1.997
0.532
1.816
0.471
0.533
0.108
0.620
4.538
2.220
0.242
1.075
46.477
8.386
9.735
15.260
4.666
39.667
33.481
2.450
11.715
0.302
1.639
8.200
2.340
14
Table 4. Descriptive characteristics of the PM10 components in Villa
Ada monitoring station by presence of forest fires events, Rome, Italy,
( study period 2005-2007)
PM components
PM10
Si
Al
Ca
K
Na
Mg++
Fe
SO42NO3Cl
NH4+
Ti
V
Cr
Mn
Ni
Cu
Zn
As
Pb
K+
Ca++
OC
EC
#No Fire
days
# Fire
days
993
834
834
824
834
834
834
834
834
834
834
834
833
834
834
834
833
834
834
833
828
644
834
1007
1007
40
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
35
37
40
40
Mean
No Fire
29.907
0.956
0.254
1.234
0.378
0.539
0.094
0.456
3.318
1.972
0.366
0.863
20.234
5.417
9.940
11.907
4.626
40.596
38.108
2.460
10.088
0.292
1.098
8.564
2.236
Fire
32.720
1.892
0.511
1.959
0.528
0.578
0.122
0.542
4.614
2.412
0.245
0.999
41.057
8.070
9.857
17.808
4.262
35.730
21.892
1.276
10.003
0.345
1.745
6.575
1.688
15
Rome - Montelibretti
Table 5. Descriptive characteristics of the PM10 components in
Montelibretti monitoring station by presence of dust at the ground
level, Rome, Italy, ( study period 2005-2010)
PM components
PM10
Si
Al
Ca
K
Na
Mg++
Fe
SO42NO3Cl
NH4+
Ti
V
Cr
Mn
Ni
Cu
Zn
As
Pb
Ca++
OC
EC
#No Dust
days
# Dust
days
1711
1594
1595
1595
1595
1610
1610
1594
1610
1610
1610
1610
1589
1589
1589
1589
1589
1589
1589
1590
1589
1610
1760
1760
353
331
331
331
331
334
334
331
334
334
334
334
331
331
331
331
331
331
331
331
331
334
351
351
Mean
No Dust
27.297
0.636
0.179
1.178
0.360
0.507
0.087
0.189
2.660
1.719
0.361
0.882
10.580
3.123
4.936
6.359
3.409
17.013
23.908
1.123
4.079
1.003
7.538
1.187
Dust
36.159
2.056
0.548
2.148
0.496
0.606
0.111
0.462
3.941
1.836
0.331
1.029
45.955
5.905
5.596
11.405
3.754
17.952
22.103
1.279
4.102
1.616
6.919
1.309
16
Table 6. Descriptive characteristics of the PM10 components in
Montelibretti monitoring station by presence of forest fires events,
Rome, Italy, ( study period 2005-2010)
PM components
PM10
Si
Al
Ca
K
Na
Mg++
Fe
SO42NO3Cl
NH4+
Ti
V
Cr
Mn
Ni
Cu
Zn
As
Pb
Ca++
OC
EC
#No Fire
days
# Fire days
2032
1898
1899
1899
1899
1916
1916
1898
1916
1916
1916
1916
1893
1893
1893
1893
1893
1893
1893
1894
1893
1916
2080
2080
54
49
49
49
49
50
50
49
50
50
50
50
49
49
49
49
49
49
49
49
49
50
53
53
Mean
No Fire
28.701
0.862
0.238
1.322
0.381
0.520
0.091
0.232
2.853
1.745
0.354
0.913
16.106
3.574
5.074
7.152
3.499
17.250
23.805
1.145
4.122
1.096
7.465
1.210
Fire
35.215
2.003
0.538
2.455
0.524
0.609
0.109
0.470
4.466
1.604
0.292
0.875
47.451
5.820
5.153
11.731
3.120
16.510
16.653
1.349
2.900
1.648
6.121
1.193
17
6
Source apportionment analysis
Source apportionment analysis was conducted in the datasets of PM chemical components of Huelva,
Barcelona, Madrid, Montelibretti and Villa Ada. The dataset from Bologna was not appropriate for
such an analysis since only 8 components had 75% of the measured concentrations above the detection
limit, LOD. The investigation of the emission sources was carried out by means of a Positive Matrix
Factorization (PMF, Paatero and Tapper, 1994). PMF is a least squares factor analysis based on mass
conservation principle
(1)
to assist in identification of sources and their contributions to observed pollutant loadings. In equation
(1), xij is the jth species loading measured in the ith sample, gik is the contribution of the kth source to
the ith sample, fkj is the loading of the jth species in the kth source and eij is the residual associated
with the jth species loading measured in the ith sample.
The number of elements to use within the PMF model and their uncertainties were determined
taking into account the possible sources of uncertainty as the method analytical uncertainty, the error
introduced and the blank values Karanasiou et al., (2009). The calculated uncertainty of these
components was in the range of 5-25% of the measured concentrations.
The robust mode was used to reduce the influence of extreme values. After several trials with
different Fpeak values (-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2) the Fpeak value was set to zero. To
determine the number of sources, different numbers of factors were tested. Preliminary runs of PMF
were performed to investigate if the resulting factors make sense with respect to known source profiles
provided in the literature. Key parameters given in the PMF analysis output, such as the Explained
Variation of the matrix F were used to select the number of factors and assisted in interpreting the
solution. The global optima of the PMF solution was also tested using multiple starting values. Finally,
we selected the number of factors that both adequately fit the data and provided the most physically
meaningful results. The resolved solution of PMF produced a good fit to the data as indicated by the
Q-value and the scaled residuals. Furthermore, the comparison of the reconstructed PM10 mass
contributions from all sources with the observed PM10 mass concentrations provided a squared
18
correlation coefficient of >0.75 (Figure 2) with the only exception PM2.5 in Huelva where the
correlation between measured and predicted mass was lower (0.54). This indicates that the resolved
sources effectively account for most of the variation in the PM mass concentration.
PM10 determined gravimetrically
100
Huelva
90
80
70
60
y = 0.96x - 0.23
R² = 0.75
50
40
30
20
10
0
0
20
40
60
80
100
PM10 predicted
50
45
PM2.5 determined gravimetrically
y = 0.74x + 3.03
R² = 0.54
Huelva
40
35
30
25
20
15
10
5
0
0
10
20
30
40
50
60
PM2.5 predicted
19
PM10 detrmined gravimetrically
140
y = 0.99x + 0.339
R² = 0.98
Madrid
120
100
80
60
40
20
0
0
20
40
60
80
100
120
140
PM10 predicted
PM2.5 determined gravimetrically
60
Madrid
50
40
y = 0.99x - 0.37
R² = 0.89
30
20
10
0
0
10
20
30
40
50
60
PM2.5 predicted
20
PM10 detrmined gravimetrically
140
y = 0.97x + 0.10
R² = 0.85
Barcelona
120
100
80
60
40
20
0
0
20
40
60
80
100
120
140
PM2.5 detrmined gravimetrically
PM10 predicted
100
90
80
70
60
50
40
30
20
10
0
y = 0.95x - 0.05
R² = 0.82
Barcelona
0
20
40
60
80
100
PM2.5 predicted
PM10 determined gravimetrically
140
Montelibretti
120
y = 0.83x + 4.75
R² = 0.83
100
80
60
40
20
0
0
50
100
150
200
PM10 predicted
21
PM10 determined gravimetrically
100
Villa Ada
90
80
70
60
50
y = 0.88x + 3.78
R² = 0.89
40
30
20
10
0
0
20
40
60
80
100
PM10 predicted
Figure 2. Regression of the PM mass determined gravimetrically and the PM mass predicted by PMF
model for the four cities.
6.1 Emission sources and mass contribution in Huelva, Spain
The chemical profiles of the emission sources in Huelva determined are given in Figure 3 with the
most reasonable results obtained when 6 factors were selected. The first source is secondary aerosol as
it mainly consists of the secondary components, sulphate, nitrate and ammonium. The phosphate
production source was recognized by the high contribution of phosphate ion while the mineral source
was made up of typical soil components (Al, Ca, Mn, Fe, K, and Mg). A mixed source related to
traffic was characterized by elements emitted by vehicles exhaust (carbonaceous components, Ctotal)
and road dust elements like Fe, Cu, Zn produced during tyre and break abrasion. The marine aerosol
source was consisted of the typical marine species Cl, Na, and a proportion of Mg. Finally, a source
called metallurgical industry was identified, as it explained the most of Cu variance, probably
characterizing the emissions of the Cu plant situated in the proximity of Huelva city. Figure 4 provides
the mass contribution of the emission sources in PM10 and PM2.5. The highest contributors to PM
were the mineral source (28% for PM10 and 21% PM2.5), the traffic source (30% and 22% of PM10
and PM2.5 mass, respectively) and the secondary aerosol (19% of the PM10 and 23% of the PM2.5
mass). The two industrial sources when summed contributed around 13% of PM levels.
22
6.2 Emission sources and mass contribution in Barcelona, Spain
The chemical profiles of emission sources in Barcelona are given in Figure 5 with the most reasonable
results obtained when seven factors were selected. The first source is vehicles emissions representing
the emissions from the vehicle engine and exhaust. It explains most of total carbon variation. The
second source is nitrate due to the high contribution of nitrate. The two natural sources, marine aerosol
and the mineral source were resolved by the presence of typical sea salt and crustal components. The
secondary sulphate source is the result of the formation of secondary sulfate in atmosphere from the
photochemical oxidation of sulfur oxides initially emitted as gases from local emissions (power plants,
port) and from long range transport. The fact that this source explains the variation of V and Ni, two
typical fuel oil tracers indicates the presence of shipping emissions in this factor. The industrial factor,
with high concentrations of TC, Zn, Pb, Fe, NH4+, Cl- and Mn was related to the mixed influence of
industrial activities located in the area such as smelters and cement kilns. The road dust source was
traced by the high contribution of TC, NO3-, Ca, Fe, Cu, components produced mechanically from the
abrasion of the road, tyres and brakes. These species are deposited on the road surface and
subsequently are resuspended by vehicles circulation. In Barcelona urban area the highest contributors
to PM were the road dust source (21% for PM10 and 17% PM2.5), the vehicles emissions (16% in
both PM10 and PM2.5) and secondary sulphate (14% of the PM10 and 20% of the PM2.5), Figure 6.
6.3 Emission sources and mass contribution in Madrid, Spain
Figure 7 shows the chemical profile of the sources resolved in Madrid. Six sources were identified
including coal combustion, road dust, mineral, marine aerosol, sulhate and nitrate/vehicles emissions.
Coal combustion was identified by the high contribution of total carbon, secondary components and
arsenic. Coal combustion is still used for heating purposes in Madrid urban although in a small
proportion (Artiñano et al., 2003). The road dust source was recognized by the presence of typical road
dust tracers (TC, Fe, Cu) and was clearly separated by the mineral source. The vehicles emissions
source was characterized by elements emitted by vehicles (Ctotal) and nitrate, a secondary component
which is formed by the primary emissions of NOx by the vehicle exhaust. A sulphate source was also
identified. Finally the marine aerosol source was consisted of the typical marine species Cl, Na, and a
23
proportion of Mg. Figure 8 provides the mass source contribution of the emission sources in PM10 and
PM2.5. The mineral source was the highest contributors to PM10 (31%) while the nitrate/vehicles
source had significant contribution in each size fraction, 18% for PM10 and 29% PM2.5, respectively.
6.4 Emission sources and mass contribution in Montelibretti, Italy
Figure 9 shows the chemical profile of the sources resolved in Montelibretti. Six sources were
identified: organic aerosol/biomass burning, marine aerosol, mineral, sulhate, nitrate and vehicles
emissions. The first source is a mixed source representing the organic aerosol (probably secondary
organic aerosol, SOA) since organic carbon has the highest levels in this profile and also biomass
burning due to the presence of potassium. Again, this source had a strong seasonal variation with the
highest concentrations during the cold period when biomass burning is used for residential heating. As
it was expected, the mineral source was also resolved. A marine aerosol was identified by the presence
of typical sea salt components. Two secondary aerosol sources were resolved, named sulphate and
nitrate. Finally, the vehicle emissions source was recognized by the presence of carbonaceous
components, OC, EC and some typical road dust tracers (Ca, Fe). As Montelibretti site is a regional
background site it is not directly influenced by the primary vehicles emissions but rather by the
mixture of both primary vehicle emissions and resuspened dust. The organic aerosol/biomass burning
and the mineral source were the highest contributors to PM10 mass levles 35% and 24% respectively,
Figure 10.
6.5 Emission sources in Villa Ada, Rome, Italy
The chemical profiles of emission sources in Villa Ada, Rome are given in Figure 11 with the most
reasonable results obtained when 6 factors were selected. Similarly to all other sites the two natural
sources, marine aerosol and the mineral source were easily identified. Also the secondary components
sulphate and nitrate were resolved in two separate factors. The source related to vehicles emissions
was characterized by high contribution of OC but also EC a typical tracer for diesel emissions. The
road dust source was characterized by high concentrations of the carbonaceous components and the
metals Fe, Ca, K and Cu typical tracers of brake and tyre wear. Vehicles emissions contributed 33%
24
to PM10 mass levels, followed by the sulphate contribution 18% of PM10 mass concentrations, Figure
12.
Figure 3. Chemical profile of emission sources in Huelva resolved by PMF
25
Figure 4. Mass contribution (%) of emission sources in Huelva for PM10 and PM2.5
26
Figure 5. Chemical profile of emission sources in Barcelona resolved by PMF
27
Figure 6. Mass contribution (%) of emission sources in Barcelona for PM10 and PM2.5
28
Figure 7. Chemical profile of emission sources in Madrid resolved by PMF
29
Figure 8. Mass contribution (%) of emission sources in Madrid for PM10 and PM2.5
30
1
Organic aerosol/biomass burning
0.1
0.01
0.001
1
Al
Si
Fe
K
Ca
As
Cr
Cu
Mn
Ni
Pb
Ti
V
Zn
Cl-
NO3-
SO4
Na+
NH4+
Mg
OC
EC
Marine
0.1
0.01
0.001
1
Al
Si
Fe
K
Ca
As
Cr
Cu
Mn
Ni
Pb
Ti
V
Zn
Cl-
NO3-
SO4
Na+
NH4+
Mg
OC
EC
Mineral
0.1
0.01
0.001
1
Al
Si
Fe
K
Ca
As
Cr
Cu
Mn
Ni
Pb
Ti
V
Zn
Cl-
NO3-
SO4
Na+
NH4+
Mg
OC
EC
Mg
OC
EC
Sulfate
0.1
0.01
0.001
1
Al
Si
Fe
K
Ca
As
Cr
Cu
Mn
Ni
Pb
Ti
V
Zn
Cl-
NO3-
SO4
Na+
NH4+
Nitrate
0.1
0.01
0.001
1
Al
Si
Fe
K
Ca
As
Cr
Cu
Mn
Ni
Pb
Ti
V
Zn
Cl-
NO3-
SO4
Na+
NH4+
Mg
OC
EC
NH4+
Mg
OC
EC
Vehicles
0.1
0.01
0.001
Al
Si
Fe
K
Ca
As
Cr
Cu
Mn
Ni
Pb
Ti
V
Zn
Cl-
NO3-
SO4
Na+
Figure 9. Chemical profile of emission sources in Montelibretti resolved by PMF
31
Figure 10. Mass contribution (%) of emission sources in Montelibretti for PM10
32
Figure 11. Chemical profile of emission sources in Villa Ada resolved by PMF
33
Figure 12. Mass contribution (%) of emission sources in Villa Ada for PM10
Conclusions: All the six sampling site where described with reference to their location and type. The
instrumentation used and the chemical analyses are different among cities. The average concentration of
PM sizes and chemical components are provided for all the location sampled and only species with more
than 75% of their concentration values are visualized. The trend of four representative components shows
that the secondary aerosol components such as SO42-, NO3-, and typical traffic tracers Cu, Zn emitted
from the abrasion of tyres and breaks have a decreasing trend from 2003 to 2010, especially evident after
2006. The cities of Barcelona and Huelva are presented as they cover longer periods of PM sampling than
the other sites. The source apportionment analysis identifies the vehicles emissions as the first source of
PM10 in Barcelona, Madrid and Rome Villa Ada. In Huelva the first source is secondary aerosol, while in
Madrid the mineral source and in Montelibretti organic aerosol, respectively, are the highest contributors
to PM10.
34
References:
1. Artíñano B., Salvador P., Alonso D.G., Alastuey A., Querol X., 2003. Characterization of
PM10 and PM2.5 in Madrid (Spain): An analysis of the main sources and high concentration
episodes. Environmental Pollution 125, 453-465.
2. Querol X., Alastuey A., Rodrıguez S., Plana F., Ruiz C.R., Cots, N., Massague G., Puig O.,
2001. PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia,
Spain. Atmospheric Environment 35, 6407–6419.
3. Paatero P., Tapper U., 1994. Positive matrix factorization: a non-negative factor model with
optimal utilization of error estimates of data values. Environmetrics 5, 111–126.
4. Karanasiou A.A., Siskos P.A, Eleftheriadis K., 2009. Assessment of source apportionment by
positive matrix factorization analysis on fine and coarse urban aerosol size fraction,
Atmospheric Environment 43, 3385-3395.
35