AIR POLLUTANT TRENS IN BARCELONA -...

REPORT 2 12/2016

AIR POLLUTANT

TRENS IN BARCELONA

AIRUSE LIFE 11 ENV/ES/584

Authors:

Report 2: Air pollutant trends in Barcelona


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INDEX

1. INTRODUCTION ................................................................................................................................ 3

2. METHODOLOGY ............................................................................................................................... 5

2.1. DATA COLLECTION AND ANALYSIS ................................................................................................... 5

2.2. SOURCE APPORTIONMENT ................................................................................................................. 5

3. RESULTS .............................................................................................................................................. 6

3.1. PM CONCENTRATION ......................................................................................................................... 6

3.2. MAJOR AND TRACE ELEMENTS ......................................................................................................... 7

3.3. SOURCE PROFILES AND CONTRIBUTIONS ....................................................................................... 10

3.4. SOURCE TEMPORAL TRENDS............................................................................................................ 16

4. CONCLUSIONS ................................................................................................................................. 21

REFERENCES ........................................................................................................................................... 23



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1. INTRODUCTION

In the last decades, the European Union has made large efforts for the improvement of air quality

(AQ) by means of the elaboration and implementation of both ambient air and emissions

directives. Additional measures to abate pollution have been applied by certain Member States,

as well as by regional and city governments. In these cases, the levels of ambition varied widely,

from most Northern and Central European countries where early measures were adopted before

the entrance in force of the 2005 limit values, up to specific Southern and Eastern European

countries where measures were much delayed and in most cases much less ambitious. Among

the national and city scale measures it is interesting to highlight the national German labelling

(S2 and S4) of vehicles for the Low Emission Zones (LEZs), and the setting up of a number of

LEZs in largest cities of Europe, such as London or Berlin (Minguillón et al., 2013).

Among the efficiently implemented standards in emissions abatement are the IPPC (Integrated

Prevention and Pollution Control) Directive (1996/61/EC, Industrial Emissions Directive

2010/75/EC), the Large Combustion Plants Directive (2001/80/EC), the National Emission

Ceilings (NEC) Directive (2001/81/EC), and the EURO standards on road traffic emission

(1998/69/EC, 2002/80/EC, 2007/715/EC). Currently, the EC is in a process for evaluating the

NEC Directive and generating a new Medium Combustion Plants Directive. Furthermore, during

the last decades, several regulations have been issued to control emissions from shipping

(Adamo et al., 2014). IMO (International Maritime Organization)/MARPOL and the EU have set

absolute limits on sulphur content in fuel, and SOx and NOx emissions from ships (IMO, 2011;

Directive 2005/33/EC).

In Spain a national AQ plan was approved by the Council of Ministers of the Government of

Spain, the last update being Plan Aire (April 2013). Furthermore, 45 regional and 3 local (city

scale) AQ plans have been implemented since 2004. Thus, most of the plans are designed and

approved by the Autonomous Regional Government but the territory where these apply tends to

be rather small, mostly focusing on improving AQ at major city centres or specific industrial

areas.

There are a large proportion of AQ zones across Europe meeting the AQ limit values and

objectives for CO, SO2, PM2.5 and metals. Conversely, and in spite of the above policy efforts, an

important proportion of the European population lives in areas exceeding the AQ standards for

the annual limit value of NO2, the daily limit value of PM10 and the health protection objective of

O3 (EEA, 2013). For PM10 and NO2, causes of these exceedances have already been discussed by

Harrison et al. (2008) and Williams and Carslaw (2011), among others. EEA (2013) shows that

PM10 and NO2 limit values are exceeded mostly in urban areas, and especially at traffic sites. In

specific countries such as Spain it has been reported that >90% of the NO2 exceedances are

attributed to road traffic emissions (Querol et al., 2012).

It has been evidenced that the real-life NO2 emissions of diesel passenger cars in urban driving

conditions are much higher than expected and in any case much higher than the emissions

produced for the EURO test driving cycles (Williams and Carslaw, 2011). EURO standards have

had a very clear benefit on a PM emissions from diesel passenger cars, especially since EURO 4

(2005) and EURO 5 (2009), but not on abating urban ambient PM concentrations (Fuller and

Green, 2006 even found evidences of an increasing trend for near road PM in London), neither

for reducing NO2 emissions (Carslaw and Rhys-Tyler, 2013). Furthermore, the proportion of

diesel cars has markedly increased in many European fleets due to the support given to the use of



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diesel fuel by climate policy. Taking these two issues into account, it is easy to understand the

large and widespread problem that we have in our cities to attain NO2 AQ standards. It becomes

also evident that non-technological measures focusing on diminishing the use of private cars in

cities are needed to attain these standards. The problem may be even more serious in specific

southern European cities due to the higher inhabitants and cars density and the widespread

occurrence of street canyons (Cyrys et al., 2012).

In spite of the above problems, there is a clear evidence for PM concentrations having decreased

markedly during the last decade in a number of European regions (Querol et al., 2014; EEA,

2013, Barmpadimos et al., 2012 and Cusack et al., 2012), as a result of: a) the EU policy for

reducing emissions; b) the national and numerous regional and local AQ plans implemented, and

c) the favourable 2008-2012 meteorology for Southern Europe as compared with 2005-2007

(Barmpadimos et al., 2012 and Cusack et al., 2012).

In this framework, the work carried out by the Institute for Environmental Assessment and Water

Research (IDAEA-CSIC) in Barcelona, Spain, focuses on the assessment of air quality

characterization and trends in the Barcelona region. To this end, the research group at IDAEA-

CSIC initiated in 1999 a continuous record of atmospheric pollutant concentrations (PM10, PM2.5,

PM1 and their chemical components) which is currently ongoing. During the period 1998-2014

the monitoring location underwent a number of changes (Figure 1):

1. The time series was initiated in 1999 at the monitoring station “Gornal”, a traffic-oriented

station.

2. In 2001 the monitoring location was moved to “Sagrera”, an urban background location

under the influence of a local unpaved playground.

3. Between 2002 and 2008 the measurements were carried out at an urban background

location (“Jaume Almera”), which in 2008 was influenced by construction works for a

new Metro line.

4. In 2009 the station was moved to a new urban background location, very close the

“Jaume Almera” site but without the influence of the construction works.

5. In 2010 the station was moved to its current location at “Palau Reial”, also an urban

background location located less than 500m from the previous location.

The successive location changes obviously had an impact on the pollutant concentrations

registered, as will be described below. However, results evidence that pollutant trends may be

detected and interpreted in spite of these changes.



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Figure 1. Location of the IDAEA-CSIC air quality monitoring stations between 1999 and 2014.

2. METHODOLOGY

2.1. Data collection and analysis

PM data were collected in Barcelona from 2003 to 2014 (PM10 fraction) and from 2005 to 2014

(PM1 fraction) at the background monitoring stations described in the introduction (Figure 1).

PM sampling was carried out by using MCV and DIGITEL high volume (30 m3h

-1) samplers

equipped with DIGITEL PM10 and PM1 inlets at a rate of one or two 24h filters per week per

PM size fraction, resulting in a total of 1020 PM10 samples and 890 PM1 samples. Particles were

collected on quartz-fibre filters and analysed following the procedures described by Querol et al.

(2001) for total carbon (TC) by elemental analysis or thermo-optical analysis, Al, Ca, K, Mg, Fe,

Ti, Mn, P, S, Na and 46 trace elements by inductively coupled plasma mass and atomic emission

spectrometry (ICP-AES) and mass spectrometry (ICP-MS), respectively, NO3- and Cl

- by ion

chromatography and NH4+ by specific ion electrode.

2.2. Source apportionment

Source apportionment studies are generally performed by means of receptor models that are

based on the mass conservation principle:

𝑋𝑖𝑗 = ∑ 𝑔𝑖𝑘𝑓𝑖𝑘𝑝𝑘=1 𝑖 = 1,2, … . , 𝑚 𝑗 = 1,2, … , 𝑛 (1)

where Xij is the jth species concentration measured in the i

th sample, gik is the contribution of the

kth source to i

th sample and fjk is the concentration of the j

th species in k

th source.

When both gik and fjk are unknown, factor analysis (FA) techniques such as Principal

Components Analysis (PCA) (Thurston and Spengler, 1985; Henry and Hidy, 1979) and Positive

Matrix Factorization (PMF) (Paatero and Tapper, 1994) are used for solving (1).

For this analysis we used the new version of PMF model provided by EPA (PMF version 5.0;



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http://www.epa.gov/heasd/research/pmf.html). The model was applied to PM (PM1 and PM10)

chemical speciated data collected in Barcelona during the periods 2003-2014 (PM10; 1020 daily

samples) and 2005-2014 (PM1; 890 daily samples). The PMF analysis was performed separately

for these two PM fractions.

PMF is a weighted least-squares method so that individual estimates of the uncertainty in each

data value are needed. There are several sources of error contributing to the overall uncertainty of

the ambient measurements but the one associated with the analytical procedure is likely to be one

of the most important sources of uncertainty. For this reason it was experimentally determined

using a similar methodology than the one described by Thompson and Howarth (1976). We

remind to the paper from Amato et al. (2009) where all the details related with the error

calculation were given.

The uncertainty estimate provides a criterion to separate the species which retain a significant

signal from the ones dominated by noise. This criterion is based on the signal-to-noise S/Nj ratio

defined by Paatero and Hopke (2003). Species with S/N < 2 are generally defined as weak

variables and downweighted by a factor of 3–5. Nevertheless, since S/N is very sensitive to

sporadic values much higher than the level of noise, the percentage of data above detection limit

was used as complementary criterion.

The combination of both criteria permitted to select 31 strong species in both PM1 and PM10 for

the PMF analyses.

3. RESULTS

3.1. PM concentration

The PM10 concentrations were relatively high and constant during 1999-2007, undergoing a

progressive decrease since 2008. The high concentrations registered at the beginning of 2008 are

attributed to the influence of the construction works of the new L9 metro line. From 2010 to

2014, the PM10 concentrations have been relatively low. When comparing the coarse (PM1-10)

and fine (PM1) fractions, it is observed that during 1999-2010 the variations of these two

fractions were not simultaneous, whereas from 2010 to 2014 the variation of PM1 concentrations

follows that of PM1-10 concentrations (Figure 2). There is a very clear descend on average PM10

levels, from 40-48 µgm-3

in the initial period to 30-22 µgm-3

at the end.

Figure 2. PM10, PM2.5, PM1 and PM1-10 average monthly concentrations in Barcelona from 1999

to 2014.

0

10

20

30

40

50

60

70

j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n j m m j s n

µg

m-3

PM10 PM2.5 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

5

10

15

20

25

30

35

40

45

50


µg

m-3

PM1-10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20142013



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3.2. Major and trace elements

Total carbon (TC) concentrations were relatively high from 1999 to 2007, and showed a seasonal

pattern with higher concentrations in winter than in summer. From 2008, TC concentrations

decreased and the seasonal variation was softened (Figure 3). However, there is a clear

decreasing trend for TC that can be driven by several reasons including: i) the effect of Euro 4

and 5, ii) the ban on using petcoke or fuel oil for power plants since the implementation of the

2007 Regional AQ Plan, iii) the economic crisis.

Nitrate concentrations underwent a decrease in 2008 to 2014 (Figure 3). This decrease is related

to a reduction in ambient NOx concentrations. The steady decrease of nitrate concentrations

observed since 2008 is attributed to the decrease of NOx emissions from the five power

generation plants around the city. During 2008-2012 the Regional AQ Plan has driven the

implementation of SCRT (continuously regenerating PM traps with selective catalytic reduction

for NO2), hybridization and shift to natural gas engines of the Barcelona´s bus fleet may have

had also an influence in NOx ambient concentrations (de Miguel et al., 2013). There is also a

slight decrease in traffic flow in Barcelona (c. 12% since 2007) that may have contributed to this.

Sulphate concentrations showed a marked decrease from 2007 to 2008, maintained all through

2009-2014 (Figure 3). This trend is similar to that followed by SO2 concentrations. Such

decrease may be attributed to the legislation that came into force in 2008, the EC Directive on

Large Combustion Plants, which resulted in the application of flue gas desulfurization (FGD)

systems in a number of large facilities in 2007-2008 in Spain. Moreover, from 2008 on, the use

of heavy oils and petroleum coke for power generation was forbidden around Barcelona. (only

natural gas is allowed to this end according to the 2007 Regional AQ Plan). Thus the decrease in

bulk PM concentrations can be mainly attributed to the decrease in sulphate concentrations

(Querol et al., 2014).

Figure 3. Non-mineral Carbon, and PM1 and PM10 nitrate and sulphate average monthly

concentrations in Barcelona from 1999 to 2014.

Sodium is present mainly in the coarse PM fraction being attributed to the sea salt influence and

hence its concentrations do not show any clear trend from 1999 to 2014. However, a seasonal

pattern is observed for most of the years, with higher concentrations in summer (Figure 4).

0

2

4

6

8

10

12

14

16

18

20


µg

m-3

Cnm (µg m-3)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

2

4

6

8

10

12

14


µg

m-3

NO3-

PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

1

2

3

4

5

6

7

8

9

10

11


µg

m-3

SO42-

PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014



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Calcium is also mainly concentrated in the coarse fraction and it is mostly attributed to the

influence of construction works. Its concentration started to decrease in 2009 and then relatively

low concentrations have been registered from 2011 to 2014 (

Figure 4 4). This can be related to the economic crisis, resulting in a decrease of the construction

activities, and therefore a decrease in the ambient Ca concentrations. As can also be the case for

the implementation of the 2007 Regional AQ Plan since 2008, with a change in the handling of

waste products generated in construction and demolition works. The change of the location of

the monitoring site in 2009 may have had an influence in this variation as well, but we have to

note that the changes of location from 1999 to 2001 and 2001 to 2003 did not influenced Ca

levels.

Aluminium and K concentrations in PM10 follow the same trend described for Ca. Nevertheless,

fine K concentrations, attributed mostly to biomass burning emissions, do not show any clear

trend, indicating that there were no significant changes in this activity (

Figure 4 4). Organic matter and elemental carbon (EC) were recorded in lower concentrations in

2010-2014 than in 2005-2009 period (

Figure 5 5), due to the same reasons are those described for TC above.

Figure 4. PM1 and PM10 Na, Ca, Al, and K average monthly concentrations in Barcelona from

1999 to 2014.

0,0

0,5

1,0

1,5

2,0

2,5

3,0


µg

m-3

Na PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

1

2

3

4

5

6

7


µg

m-3

Ca PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0


µg

m-3

Al PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0,0

0,2

0,4

0,6

0,8

1,0

1,2


µg

m-3

K PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014



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Figure 5. PM1 and PM10 OM and EC average monthly concentrations in Barcelona from 1999

to 2014.

Regarding trace elements, there is also a clear downward trend for Pb, Cu, Zn, Mn, Cd, As, Ti,

Sr, Sn, Sb, V, Ni and Cr. The decrease observed for Pb concentrations from 1999 to 2003 is

attributed to the ban of Pb-bearing gasoline. After 2003, the decrease observed for Cu, Sb, Mn,

Pb, Zn, Cr, and Cd may be attributed to a decrease in the emissions from industrial production

(smelters, Querol et al., 2007) at a regional scale around Barcelona. The implementation of the

IPPC Directive in 2008 is the most probable cause of this downward trend, which was evident

already before the financial crisis (

Figure 6 6). Besides the industrial source, Cu, Sb, Sn and Zn have been also partially attributed

to non-exhaust road dust emissions in the study area (Amato et al., 2009).

Vanadium, a typical tracer of fuel oil combustion, experienced a significant gradual reduction

starting in 2007-2008. The 2007 Regional AQ Plan ban on the use of heavy oils and petroleum

coke for power generation may account for this decrease (Cusack et al., 2012; Querol et al.,

2014).

0

2

4

6

8

10

12

14

16


µg

m-3

OM PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

4,5


µg

m-3

EC PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014



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Figure 6. PM1 and PM10 Cu, Sb, Pb, As, Mn, V and Sn average monthly concentrations in

Barcelona from 1999 to 2014.

3.3. Source profiles and contributions

Seven factors were identified in both PM10 (Figure 7) and PM1 (Figure 8) fractions as vehicle

exhaust, road dust, secondary nitrate, secondary sulphate, mineral (including the Saharan dust

contribution in PM10), heavy oil combustion and metallurgy processes. An eighth factor detected

in PM10 was an aged sea salt factor, not detected in PM1 given that this source mainly contributes

to the coarse PM mode (PM1-10).

0

20

40

60

80

100

120

140

160

180

200


ng

m-3

Cu PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

2

4

6

8

10

12

14

16


ng

m-3

Sb PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

50

100

150

200

250

300


ng

m-3

Pb PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

1

1

2

2

3

3

4


ng

m-3

As PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

5

10

15

20

25

30

35

40

45

50


ng

m-3

Mn PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

5

10

15

20

25


ng

m-3

V PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

2

4

6

8

10

12

14

16


ng

m-3

Sn PM10 PM1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014



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Figure 7. PM10 source profiles from PMF analysis (µg/m3) on the left vertical axis. Explained

variation (%) on the right vertical axis.

Aged sea salt

Metallurgy

Vehicle exhaust

Secondary sulfate

Fuel Oil

Secondary nitrate

Road dust

Mineral+Saharan dust

PM10 factor profiles



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Figure 8. PM1 source profiles from PMF analysis (µg/m3) on the left vertical axis. Explained

variation (%) on the right vertical axis.

Metallurgy

Vehicle exhaust

Secondary sulfate

Fuel Oil

Secondary nitrate

Road dust

Mineral

PM1 factor profiles



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The contributions of the detected factors to the measured mass of PM10 and PM1 are reported in

Figures 9 and 10, respectively, for the whole period considered and in Figure 11 for every couple

of years for more detail.

Figure 9. PM10 source contributions from PMF analysis (2003-2014)

Figure 10. PM1 source contributions from PMF analysis (2005-2014)

In Figure 9 the mean Saharan dust contribution in PM10 was calculated and subtracted from the

Mineral source contribution from PMF analysis. Moreover, a Sea salt contribution (fresh sea

salt) was calculated and subtracted from the Marine (aged sea salt) source contribution from

PMF. The difference represents secondary sulphate and nitrate in the form of sodium sulphate

and nitrates in the atmosphere (Nitrate + sulphate source).

The vehicle exhaust source represents the emissions from vehicle engines, excluding secondary

inorganic species and explains the 45-50% of the total variation of TC. This factor also includes

trace of Fe, Cr, Cu, Sn, Sb and Ba which are elements associated to the break wear. While no

marked differences were found between the profile concentrations in PM10 and PM1 solutions,

the relative contribution of this factor in PM10 (15%) was lower than in PM1 (25%).

The mineral factor (Figure 7) accounts for several sources of mineral matter, including

Vehicle exhaust5.1; 15%

Road dust3.0; 9%

Secondary nitrate5.4; 15%

nitrate + sulfate 3.7; 10%

Secondary sulfate 5.3; 15%

Fuel oil3.0; 8%

Metallurgy 0.9; 2%

Mineral4.8; 13%

Saharan dust1.6; 5%

Sea salt1.6; 5%

mg/m3; %

Vehicle exhaust3.8; 25%

Road dust0.1; 1%

Secondary nitrate

1.4; 10%

Secondary sulfate

5.3; 35%

Fuel oil1.9; 13%

Metallurgy0.4; 3%

Mineral1.3; 9%

mg/m3; %



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resuspension from urban working areas, gardens and unpaved parking areas. Dust resuspension

by traffic is mainly explained by the following road dust factor. The contribution of African dust

was subtracted from the PMF mineral factor. Thus, the calculated Mineral+Saharan dust

contribution was 13%+5% in PM10 and 9% in PM1. Very little Saharan dust impact is expected

to affect the PM1 fraction.

The secondary sulphate factor, traced by 𝑆𝑂4= and 𝑁𝐻4

+, is the result of the formation of

secondary sulphate in atmosphere from the photochemical oxidation of sulphur oxides initially

emitted as gases from local emissions and from long range transport. Ammonium sulphate is

mainly fine and as a consequence the absolute contributions to PM10 and PM1 mass were very

similar around 5.3 µgm-3

. However, the relative contribution was much higher in PM1 (35%)

compared to PM10 (15%). The secondary nitrate factor, traced by 𝑁𝑂3− and 𝑁𝐻4

+, is the result of

the formation of secondary nitrate in atmosphere from the photochemical oxidation of nitrogen

oxides initially emitted as gases from local emissions (mainly vehicles) and from industries. The

absolute contribution was higher in PM10 (5.4 µgm-3

) compared to PM1 (1.4 µgm-3

) due to the

formation of sodium and/or calcium nitrate in the coarse mode.

The amounts of organic material present in the secondary sulfate and nitrate factors can be in

part attributed to the semi-volatile organic compounds condensing into the high specific surface

area of ammonium sulfate and ammonium nitrate particles. As previously explained an

additional contribution (3.7 µgm-3

) from secondary sulphate and nitrate in PM10 was separated

from the marine (aged sea salt) contribution from PMF analysis and presented in Figure 9.

The metallurgy factor, with high concentrations of Pb, Zn, Fe, Mn, and Cd was related to the

mixed influence of industrial activities located in the area such as smelters and cement kilns. The

contribution of this source to PM10 (0.9 µgm-3

; 2%) and PM1 (0.4 µgm-3

; 3%) was relatively

lower compared with the other sources. Fuel oil combustion source is characterized by high

concentrations V and Ni reflecting the likely influence of ship emissions and industrial

combustion processes. The relative contributions of this source to PM10 and PM1 mass were 8%

and 13%, respectively.

The road dust factor was considerably enriched in Fe, Cu, and Sb. Enrichments in mineral

elements was also observed. Therefore, it was attributed to brake wear particles and, to a lesser

extent, to mineral matter resuspended by passing vehicles.

The marine factor detected in PM10 represents aged sea salt and it is characterized by Na+, Cl

-,

𝑁𝑂3− and 𝑆𝑂4

=. Aged sea salt particles are depleted in chloride and enriched in nitrate and

sulphate salts. To take into account for this and to apportion properly the sulphate and nitrate

particles, a “fresh” sea salt was calculated from the measurements of Na+ and Cl

- and subtracted

to the marine factor from PMF. The remaining mass was named as Nitrate + sulphate source

(Figure 9).



15 / 24

Figure 11a. Temporal evolution of PM10 source contributions from PMF analysis for every

couple of years of the period considered.

2003-2004 2005-2006

2007-2008 2009-2010

2011-2012 2013-2014

PM1048 mgm-3

PM1045 mgm-3

PM1043 mgm-3

PM1030 mgm-3

PM1028 mgm-3

PM1022 mgm-3

Traffic20.1; 42%


Secondary sulfate

11.5; 24%

Fuel oil2.9; 6%

Metallurgy1.4; 3%

Mineral4.9; 10%

Saharan dust

3.1; 6%

Sea salt1.9; 4% Traffic

20.6; 46%


Secondary sulfate

8.4; 19%

Fuel oil4.1; 9%

Metallurgy1.1; 2%

Mineral5.7; 13%

Saharan dust

2.1; 5%

Sea salt1.8; 4%

Traffic17.3; 40%


Secondary sulfate

7.6; 17%

Fuel oil3.2; 7%

Metallurgy0.8; 2%

Mineral9.7; 22%

Saharan dust

2.4; 6%

Sea salt1.9; 5%

Traffic10.1; 33%

Secondary nitrate0.9; 3%Secondary

sulfate6.2; 20%

Fuel oil3.1; 10%

Metallurgy0.8; 3%

Mineral3.2; 11%

Saharan dust

1.3; 4%

Sea salt1.9; 6%

Traffic10.9; 39%


Secondary sulfate

5.9; 21%

Fuel oil2.6; 9%

Metallurgy0.7; 2%

Mineral2.5; 9%

Saharan dust

0.9; 3%

Sea salt1.5; 5%

Traffic8.8; 40%


Secondary sulfate

4.7; 22%

Fuel oil1.6; 7%

Metallurgy0.9; 4%

Mineral2.2; 10%

Saharan dust

0.4; 2%

Sea salt1.4; 7%



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Figure 11b. Temporal evolution of PM1 source contributions from PMF analysis for every

couple of years of the period considered.

3.4. Source temporal trends

All source contributions showed statistically significant decreasing trends (Figures 12 and 13)

with the exception of the marine contribution to PM10 which did not show any significant trend

in agreement with the lack of trend observed for sodium. Temporal trends were analysed by

means of the Theil-Sen method (Theil, 1950; Sen, 1968), available in the Openair software

(Carslaw, 2012; Carslaw and Ropkins, 2012). The method was applied to the monthly averages

to calculate the regression parameters of the trends including slope, uncertainty in the slope and

the p-value. Data were deseasonalized. The applied method yields accurate confidence intervals

even with non-normal data and it is less sensitive to outliers and missing values (Hollander and

2005-2006 2007-2008

2009-2010 2011-2012

2013-2014

PM119 mgm-3

PM116 mgm-3

PM115 mgm-3

PM114 mgm-3

PM111 mgm-3

Traffic8.8; 47%


Secondary sulfate

7.0; 37%

Fuel oil2.7; 14%

Metallurgy0.6; 3%

Mineral1.0; 6%

Traffic6.9; 42%


Secondary sulfate

6.5; 40%

Fuel oil1.9; 12%

Metallurgy0.5; 3%

Mineral1.8; 11%

Traffic3.5; 23%


Secondary sulfate

4.8; 30%

Fuel oil2.0; 13%

Metallurgy0.3; 2%

Mineral1.8; 12%

Traffic4.0; 29%


Secondary sulfate

5.1; 36%

Fuel oil1.8; 13%

Metallurgy0.3; 2%

Mineral0.9; 7%

Traffic3.3; 29%


Secondary sulfate

3.9; 36%

Fuel oil1.2; 11%

Metallurgy0.3; 3%

Mineral0.8; 7%



17 / 24

Wolfe, 1999). All the regression parameters were estimated through bootstrap resampling. The

slopes indicate how concentrations have changed through time and are expressed in units (µgm-

3). The p-values show whether the calculated trends are statistically significant. A statistically

significant trend was assumed as p<0.05, meaning that there was a 95% chance that the slope

was not due to random chance. A highly statistically significant trend was assumed as p<0.001.

Exhaust emissions source contribution to PM10 and PM1 was relatively high up to 2007 showing

a decrease from 2008. The road dust source contribution also was higher until 2007 compared

with 2008-2014. A clear decreasing trend for this source was observed reflecting the decreasing

trends observed for the metals tracers of this source (Cu, Sn, Sb mainly).

The contributions of secondary sulphate and nitrate clearly decreased with time in both fractions

as seen in previous sections. This being related in the case of SO2 emissions to the

implementation of the EC Directive on Large Combustion Plants and the ban of the 2007

Regional AQ Plan on the use of heavy oils and petroleum coke for power generation; and in the

case of secondary nitrate to lower NOx emissions from power generation plants due to the

economic crisis, the implementation of a large combustion plants directive, and the modifications

in the Barcelona public transport (SCRT, hybridization and shift to natural gas engines).

The mineral source contribution was the highest during the period 2007-2008 showing a sharp

decrease from 2008 which can be attributed to the reduction of construction works (likely

coinciding with the economic crisis), change in the handling of construction and demolition

waste products, and change in the measuring location from 2009.



18 / 24

Figure 12. Temporal trends of PM10 source contributions from PMF analysis. P-value: degree of

statistical significance. ***: p<0.001. Variations expressed as mass (µgm-3

) per year,

including minimum and maximum ranges.

year

Tra

ffic

0

5

10

15

2004 2006 2008 2010 2012 2014

-0.14 [-0.23, -0.06] units/year ***

year

Se

co

nd

ary

su

lfa

te

0

5

10

15

2004 2006 2008 2010 2012 2014

-0.53 [-0.66, -0.4] units/year ***

year

Se

co

nd

ary

nitra

te

5

10

15

2004 2006 2008 2010 2012 2014

-0.47 [-0.6, -0.37] units/year ***

year

Me

tallu

rgy

0.0

0.5

1.0

1.5

2.0

2.5

2004 2006 2008 2010 2012 2014

-0.06 [-0.08, -0.04] units/year ***

year

Fu

elO

il

0

2

4

6

2004 2006 2008 2010 2012 2014

-0.14 [-0.21, -0.08] units/year ***

year

Ro

ad

du

st

0

5

10

15

2004 2006 2008 2010 2012 2014

-0.69 [-0.79, -0.59] units/year ***

year

Ma

rin

e

0

5

10

2004 2006 2008 2010 2012 2014

-0.02 [-0.13, 0.08] units/year

year

Min

era

lan

dS

ah

ara

nd

ust

0

5

10

15

20

25

2004 2006 2008 2010 2012 2014

-0.55 [-0.69, -0.42] units/year ***

Traf

fic

Min

eral

Seco

nd

ary

sulf

ate

Seco

nd

ary

nit

rate

Met

allu

rgy

Fuel

oil

Road

du

st

Mar

ine



19 / 24

Figure 13. Temporal trends of PM1 source contributions from PMF analysis. P-value: degree of

statistical significance. ***: p<0.001. Variations expressed as mass (µgm-3

) per year,

including minimum and maximum ranges.

year

Min

era

lan

dS

ah

ara

nd

ust

0

1

2

3

4

5

2006 2007 2008 2009 2010 2011 2012 2013 2014

-0.09 [-0.14, -0.03] units/year ***

year

Ro

ad

du

st

0.0

0.1

0.2

0.3

0.4

2006 2007 2008 2009 2010 2011 2012 2013 2014

-0.02 [-0.03, -0.01] units/year ***

year

Fu

elO

il

0

2

4

2006 2007 2008 2009 2010 2011 2012 2013 2014

-0.14 [-0.2, -0.07] units/year ***

year

Me

tallu

rgy

0.2

0.4

0.6

0.8

1.0

2006 2007 2008 2009 2010 2011 2012 2013 2014

-0.04 [-0.05, -0.03] units/year ***

year

Se

co

nd

ary

nitra

te

2

4

6

8

2006 2007 2008 2009 2010 2011 2012 2013 2014

-0.2 [-0.27, -0.12] units/year ***

year

Se

co

nd

ary

su

lfa

te

2

4

6

8

10

12

2006 2007 2008 2009 2010 2011 2012 2013 2014

-0.39 [-0.49, -0.3] units/year ***

year

Tra

ffic

0

5

10

2006 2007 2008 2009 2010 2011 2012 2013 2014

-0.43 [-0.56, -0.28] units/year ***

Traf

fic

Min

eral

Seco

nd

ary

sulfa

te

Seco

nd

ary

nit

rate

Met

allu

rgy

Fuel

oil

Road

du

st



20 / 24

The metallurgy source contribution strongly decreased up to 2008 showing a fairly constant, and

much lower, contribution from 2009. The decrease observed for this source was likely due to the

implementation of the IPPC Directive in 2008 which is the most probable cause of the

downward trend observed for this source contribution.

Finally, the fuel oil combustion source showed a significant decreasing trend from 2007-2008

reflecting the decreasing trends observed for V and Ni, as a consequence of the ban on using

petcoke or fuel oil for Power generation imposed by the 2007 Regional AQ Plan.

Figure 11a and b is similar to Figures 12 and 13, but a Traffic source was introduced which was

calculated as the sum of Vehicle exhaust + Road dust + 0.8·(Secondary nitrate) + 0.5·(nitrate +

sulfate). As reported in Figure 9, the nitrate + sulfate source was obtained by calculating and

subtracting the Sea salt source to the Aged sea salt source contribution. One half of the nitrate +

sulfate source contribution was attributed to the Traffic source and one half to the Secondary

sulfate source. The clear reduction of the Traffic source contribution with time is observed in

Figure 11. In PM10 the Traffic source contribution decreased from 20 µgm-3

(42% of PM10) in

2003-2004 to around 9 µgm-3

(40%) in 2013-2014. In PM1 this contribution decreased from

around 9 µgm-3

(47%) in 2005 – 2006 to around 3 µgm-3

(29%) in 2013 – 2014. Moreover,

clearly a decrease in the Secondary sulfate source contribution was observed in both PM10 and

PM1. The contribution of this sources decreased by around 45% from 2005-2006 to 2013-2014

in both fractions. The Mineral source contribution also decreased with time and this effect was

much higher in PM10 compared to PM1. The same was observed for the Fuel Oil and Metallurgy

source contributions.



21 / 24

4. CONCLUSIONS

Some clear points can be concluded after considering the results here described:

PM10 concentrations have been decreased progressively since 2008, showing relatively

low concentrations from 2010 to 2014. Varying from 40-48 µgm-3

in the initial period to

30-22 µgm-3

at the end.

The variations of PM1 did not follow those of PM1-10 during 1999-2010 whereas after

2010 both follow similar patterns. Thus PM1 increases from 2003 to 2007 and both PM1

and PM1-10 decreased from 2007 to 2014.

Total carbon (TC) concentrations have decreased since 2008. This can be driven by

several reasons including: i) the effect of Euro 4 and 5, ii) the ban on using petcoke or

fuel oil for power plants since the implementation of the 2007 Regional AQ Plan, iii) the

economic crisis.

Nitrate concentrations have been decreasing since 2008 until now, this being attributed to

the lower power generation due to the economic crisis, but also to the implementation of

SCRT, the hybridization and shift to natural gas engines of the Barcelona´s bus fleet and

the slight decrease in traffic flow in Barcelona (c. 12% since 2007).

Sulphate concentrations show a marked decrease during 2007 and 2008, which kept

falling through 2009-2014, attributed to the 2008 implementation of the LCP Directive

and the 2007 Regional AQ Plan on the use of heavy oils and petroleum coke in power

generation. This descend has influenced significantly the decrease of bulk PM

concentrations.

The lower construction activity due to the economic crisis together with the modification

on the handling protocols for waste products generated in construction and demolition

works may be responsible for the decrease in coarse Ca, Al and K concentrations.

Fine K concentrations, attributed to biomass burning emissions, do not show any clear

trend, indicating that there were no significant changes in this activity during the period

considered.

The decrease observed for Pb concentrations from 1999 to 2003 is attributed to the ban of

Pb-bearing gasoline.

After 2003, the decrease observed for Cu, Sb, Mn, Pb, Zn, Cr, and Cd may be related to a

descend on the emissions from industrial production (smelters). The implementation of

the IPPC Directive is the most probable cause of this downward trend.

Vanadium concentrations decreased from 2007-2008, being attributed to the 2007 ban on

the use of heavy oils and petroleum coke in power generation.

All source contributions from PMF analysis showed also statistically significant

decreasing trends with the exception of the marine contribution to PM10 which did not

show any significant trend in agreement with the lack of trend observed for sodium (from

sea salt).



22 / 24

The results demonstrate a clear beneficial effect of environmental policies on air quality in recent

years. However to meet target and limit values and WHO guide levels important actions are still

required for the future. We also would like to highlight that the interpretation of past air quality

trends may yield very relevant results for planning further cost-effective actions.

Xavier Querol

Andrés Alastuey

Maria Cruz Minguillón

Teresa Moreno

Marco Pandolfi

Mar Viana

Institute of Environmental Assessment and Water Research (IDAEA)

Consejo Superior de Investigaciones Científicas (CSIC)



23 / 24

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