Impact of the global economic crisis on metal levels in particulate matter (PM) at an urban area in the Cantabria Region (Northern Spain).

Abstract

Air pollution by particulate matter is well linked with anthropogenic activities; the global economic crisis that broke out in the last year may be a proper indicator of this close relationship. Some economic indicators show the regional effects of the crisis on the Cantabria Region. The present work aims to evaluate the impact of the economic crisis on PM10 levels and composition at the major city of the region, Santander. Some metals linked to anthropogenic activities were measured at Santander and studied by Positive Matrix Factorization; this statistical analysis allowed to identify three main factors: urban background, industrial and molybdenum-related factor. The main results show that the temporal trend of the levels of the industrial tracers found in the present study are well agree with the evolution of the studied economic indicators; nevertheless, the urban background tracers and PM10 concentration levels are not well correlated with the studied economic indicators.

Full text

Page 1

Impact of the global economic crisis on metal levels in particulate matter
(PM) at an urban area in the Cantabria Region (Northern Spain)
A. Arruti, I. Fernández-Olmo*, A. Irabien
Universidad de Cantabria, Dep. Ingeniería Química y Química Inorgánica, Avda. Los Castros s/n, 39005 Santander, Cantabria, Spain
The study presents an evaluation of the economic crisis impact on PM levels and composition at a coastal urban area
in the Region of Cantabria (Northern Spain).
a r t i c l e i n f o
Article history:
Received 30 July 2010
Received in revised form
5 November 2010
Accepted 8 February 2011
Keywords:
Urban air pollution
Trace metals
Particulate matter
Economic crisis
a b s t r a c t
Air pollution by particulate matter is well linked with anthropogenic activities; the global economic crisis
that broke out in the last year may be a proper indicator of this close relationship. Some economic
indicators show the regional effects of the crisis on the Cantabria Region. The present work aims to
evaluate the impact of the economic crisis on PM10 levels and composition at the major city of the
region, Santander. Some metals linked to anthropogenic activities were measured at Santander and
studied by Positive Matrix Factorization; this statistical analysis allowed to identify three main factors:
urban background, industrial and molybdenum-related factor. The main results show that the temporal
trend of the levels of the industrial tracers found in the present study are well agree with the evolution of
the studied economic indicators; nevertheless, the urban background tracers and PM10 concentration
levels are not well correlated with the studied economic indicators.
? 2011 Elsevier Ltd. All rights reserved.
1. Introduction
A number of natural activities (such as volcanoes or fire) may
release different pollutants in the environment; nevertheless, the
anthropogenic activities are the major cause of environmental air
pollution. Particulate matter (PM) can be defined as a complex
mixture of suspended particles with different physical, chemical
and biological characteristics (Viana et al., 2006). A number of
epidemiological studies have demonstrated evidences about the
stronger associations between PM concentrations and respiratory
diseases; however, recent studies have also proven that the adverse
health effects can not be determined only by the study of PM levels,
the chemical, physical and biological properties must also be taken
into account (Künzli et al., 2005).
The particulate matter may be the carrier of hazardous species,
such as heavy metals or polycyclic aromatic compounds; on
account of these species the PM may have different effects on
human health and ecosystems (Karar and Gupta, 2006). Hence, the
nature of PM can be inorganic, organic ora mixture of them. Among
the inorganic elements constituting the PM, heavy metals are an
important group to be considered (López et al., 2005); some heavy
metals presented in particulate matter, such as Pb, As, Ni, V, Mn, or
Cu, are interesting due to their toxic character (WHO, 2000). Some
heavy metals are usually used as tracers of different emission
sources; for example, Mn is an important trace metal in areas
affected by ferromanganese alloy productionplants (Boudissa et al.,
2006), Mo is a traffic related tracer (Dongarrá et al., 2007) and Ni is
usually derived from combustion processes (Almeida et al., 2005).
The levels of Pb, As, Ni and Cd in PM10 are regulated through
the European Commission (EC) air quality directives, Directive
2004/107/EC and Directive 1999/30/EC.
The knowledge of the main tracers of the different emission
sources is a preliminary step in order to identify their contribution
in the PM levels and composition; receptor modelling is one of the
most commonly used tool to identify the emission sources contri-
bution (Querol et al., 2007). There are a wide variety of receptor
models. Three of the most widespread models are Principal
Component Analysis (PCA), Positive Matrix Factorization (PMF) and
Chemical Mass Balance (CMB). PMF and PCA require relative little
quantitative knowledge of the sources (Viana et al., 2008). PMF was
used in the present work as receptor model.
As mentioned above, the PM levels and composition are related
to some anthropogenic activities such as industrial activities and
traffic; hence, the global financial and economic crisis should have
an effect on the PM. The economic crisis that broke out in the last
trimester of 2008 has had devasting consequences for national
economies, enterprises and workers in industrialized and devel-
oping countries; the global economy slowed down and contraction
* Corresponding author.
E-mail address: fernandi@unican.es (I. Fernández-Olmo).
Contents lists available at ScienceDirect
Environmental Pollution
journal homepage: www.elsevier.com/locate/envpol
0269-7491/$ e see front matter ? 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.envpol.2011.02.008
Environmental Pollution 159 (2011) 1129e1135

Page 2

was announced in a numberof national economies. The economyof
the Cantabria Region also slowed down at the end of 2008;
nevertheless, the evolution of the industry in the region is char-
acterized by a temporal deviation with respect to the Spanish
evolution, so the economic crisis affected few months later to the
Cantabrian industry (Gobierno de Cantabria, 2010).
The present work aims to evaluate the economic crisis impact
on selected trace metal (Pb, Mn, Cu, As, Ni, V, Mo, Cr and Ti) levels in
PM at an urban area in the Cantabria Region, Northern Spain.
According to Positive Matrix Factorization (PMF) results the studied
metals were divided into four groups, which are based on their
main emission sources. The temporal evolution (2008 and 2009) of
the metals identified in each group is performed and compared
with the trend of some economic indicators.
2. Materials and methods
2.1. The study area
The Cantabria Region (Northern Spain) covers an area of 5321 km2between the
mountains of the Cordillera Cantábrica and the Cantabrian Sea. In the Cantabria
Region the service sector is the most important contributor to the regional Gross
Domestic Product (GDP), 56.6%; the second contributor is the industry, 16.5%.
Metallurgy is the most important industrial activity in the Cantabria Region; the
contribution to the regional GDP is 5.6% (ICANE, 2010). Santander is the most
important urban area of the region; this city and some surrounding towns make up
an agglomeration area (106 km2) with about 250,000 inhabitants (almost the half of
the population of Cantabria).
Santander (182,700 inhabitants, 2009) extends over a wide bay with views of
the Cantabrian Sea. The sampling site “SANT” is an urban background site located on
the rooftop of the building “E.T.S de Ingenieros Industriales y de Tele-
comunicaciones” (SANT; 43?2802600N, 3?4704700W, 23 m.a.s.l), which is close to the
Sardinero beaches zone. The sampling site is located close to a park; an industrial
area mostly related to iron, steel and ferroalloys manufacturing plants is located at
city suburbs, the distance from the sampling site is approximately 5e10 km SW.
Hence, the selected sampling site is not located in the vicinity of pollution sources;
the nearby roads are on the other side of the park. SANTsampling site is not directly
influenced by the principal anthropogenic emission sources (such as traffic or
industrial).
Additionally, some data from the Cantabria Air Pollution Network monitoring
stations were also used. The air monitoring stations in Santander are: Tetúan (urban
background) and Santander-centro (urban with traffic influence). Fig. 1 shows the
location of the three sites selected by the present study.
2.2. Sampling methodology and chemical analysis
PM10 was sampled on glass fibre filters (150 mm of diameter) using a EN-UNE
12341 equivalent high volume sampler (MCV, operation conditions: 30 m3/h and
24 h of sampling period) while the sampling of PM2.5 was carried out on glass fibre
filters (150 mm of diameter) by a EN-UNE 14907 high volume sampler (MCV,
operation conditions: 30 m3/h and 24 h of sampling period).
PM10 and PM2.5 concentrations were determined by gravimetry; before and
after the weighting process the filters were equilibrated under the same tempera-
ture and humidity conditions. The sample treatment for trace metal (Pb, Mn, Cu, As,
Ni, V, Mo, Cr and Ti) determination is in accordance with UNE-EN 14902:2006
“Standard method for the measurements of Pb, Cd, As and Ni in the PM10 fraction of
suspended particulate matter”. The analytical methodology is based on thedigestion
in a microwaveovenwith an oxidant mixture (HNO3þH2O2); after the digestion the
samples are measured by an inductively coupled quadrupole mass spectroscopy
(ICP-MS).
Sampling of PM10 and PM2.5 was carried out at SANT urban site during the
period 2008e2009; three samples of PM10 and PM2.5 were collected per week.
A minimum of 14% of the annual sampling period was selected for chemical analysis
in order to fulfil the requirements of the air quality directive for indicative
measurements (Directive 2004/107/EC). The metal levels in PM10 were determined
in 53 and 54 samples during 2008 and 2009, respectively. The number of PM2.5
analyzed samples is 33 and 54, 2008 and 2009, respectively; the number of 2008
samples is lower due to the sampling period started after the winter.
Quality control of the chemical analytical procedure was performed by evalu-
ating the recoveries and the potential sample contamination with the evaluation of
the field blanks; the field blanks were periodically analyzed during the sampling
period. In order to check the analytical recoveries, the procedure was tested with
Standard Reference Material (SRM 1649a “urban dust”) and doped filters; the
recoveries of the regulated metals were in the range recommended by EN-UNE
14902-2006. The values of the detection limits (d.l) are calculated taking into
account the blank values, EN-UNE 14902-2006 methodology; since the field blanks
were not negligible, the metal concentrations in the samples were corrected
Fig. 1. Map showing the location of the sampling sites.
A. Arruti et al. / Environmental Pollution 159 (2011) 1129e1135
1130

Page 3

subtracting the blank values for each metal. Further details about the analytical
methodology, accuracy and precision and detection limits can be found in Arruti
et al. (2010).
2.3. Statistical analysis by positive matrix factorization (PMF)
Positive matrix factorization (PMF) is a useful factorization methodology that
can determine the source profile and contribution (Paatero, 1997). In the present
study, PMF was applied using PMF 3.0 software (available at EPA website). The input
data required by the PMFare: trace metal concentration values and uncertainty data
(Viana et al., 2008). The concentration data below the detection limit are replaced
with 1/2 of the detectionlimit and the corresponding uncertainty is calculated as 5/6
of the detection limit. For data above the detection limit, uncertainties (si) were
calculated according to the widely used eqn (1) (Pandolfi et al., 2008; Polissar et al.,
1998).
si¼ D:L=3 þ cxi
where i is the trace metal index, xiis the concentration, D.L the detection limit and c
is the constant; c values are 0.1 or 0.2 for xivalues higher or lower than 3 times the
detection limit (Polissar et al., 1998).
(1)
The number of factors in PMF is determined by looking at the plot of the Q, eqn
(2), as a function of the number of factors; the solution to the system is the point
where the slope of the curve shows a marked change (Viana et al., 2008).
Q ¼
X
n
i¼1
X
m
j¼1
?
e2
ij=s2
ij
?
(2)
where i is the time index and j is the variable index, eijare the residuals and sijthe
error estimates of the data values (Paatero et al., 2002).
3. Results and discussion
3.1. Economic situation: Cantabria Region
In the course of theyear 2008 the Gross Domestic Product (GDP)
growth for Spain was 0.9%; this declined considerably to -3.6% in
2009 (INE, 2010). Comparing the Spanish values with those
obtained in the Cantabria Region, it could be concluded that the
evolution and the percentages are similar, 1.1% and ?3.5% during
235000
240000
245000
250000
255000
260000
265000
270000
2005
Cantabria
20062007 2008 2009
4600
4650
4700
4750
4800
4850
4900
Spain
Spain
Cantabria trendSpain trend
Cantabria
-30
-25
-20
-15
-10
-5
0
5
10
15
90- ene80- ene70- ene60 - ene50 - ene
-80
-60
-40
-20
0
20
40
06846342210
-15
-10
-5
0
5
10
15
06846342210
Electric demand (%).
Industrial confidence (%).
Industrial production (%).
Annual evolution.
Monthly evolution.
-30
-20
-10
0
10
20
30
40
2005 2006
2007 2008 2009
2010
Industrial production.
Metallurgy industries (%).
Spain
Cantabria
2005 2006
20072008 2009
20052006
20072008 2009
20052006
20072008 2009
Electric demand (GWh).
a
b
c
d
e
Fig. 2. Temporal trend of some economic indicators in the Cantabria Region. (a) Annual electric demand. Monthly inter-annual variation of (b) electric demand, (c) industrial
confidence, (d) industrial production and (e) industrial production: metallurgy industries. (Data from ICANE webpage: http://www.icane.es).
A. Arruti et al. / Environmental Pollution 159 (2011) 1129e1135
1131

Page 4

2008 and 2009, respectively (ICANE, 2010). The economic crisis
causes a reduced industrial output of several European countries;
some economic indicators such as the electric demand, the indus-
trial confidence or the industrial production could show the effects
of the crisis. Fig. 2(a) shows a strong decrease in the annual electric
demand of 2009 with respect to 2008 in Spain and the Cantabria
Region; a similar trend of the annual electric demand is observed in
Fig. 2(a) for Spain and the Cantabria Region in the period
2005e2009. Fig. 2(b) also shows the monthly inter-annual varia-
tion of the electric demand in Spain; there is a minimum value in
April 2009, which is associated to the strongest effect of the
economic crisis.
Fig. 2(c) shows the inter-annual variation of the industrial
confidence in the Cantabria Region; this industrial indicator takes
into account the orders, the production trend and the stocks. The
minimum values of this indicator are found during the first
semester of 2009; a similar trend is found in the euro zone (Banco
de España, 2009). Thus, the industrial production was down 17.5%
in the euro zone (United Nations, 2009); in the region, the
production was also down 28.7% as shown in Fig. 2(d) (ICANE,
2010). The lower levels of the industrial confidence indicator at
Cantabria Region take place at the same time as the industrial
production indicator and electric demand, Fig. 2.
Metallurgy is one of the main industrial sectors in the Santander
agglomeration area, therefore, the industrial production in the
metallurgy industries is also studied. The crisis has reached the
steel industry, so the steel producers have reduced their output as
a consequence of the decreasing of demand, especially for auto-
motive and constructions industries (Ilie, 2009). Fig. 2(e) shows
that the industrial production in metallurgyat the Cantabria Region
agrees well with the trend of the other studied economic
indicators.
By analysing the national and regional indicators, the crisis
started in the last trimester of 2008 and the worst economic situ-
ation took place during the first semester of 2009. During the last
semester of 2009 the economic indicators started to grow up again.
3.2. PM10/PM2.5 and trace metal levels
Mean annual (2008e2009) PM10 levels at the different
sampling sites in Santander are: 29e28 mg/m3, 30e29 mg/m3, and
27e29 mg/m3at SANT, Tetúan and Santander Centro, respectively.
Mean annual (2008e2009) PM2.5 levels at SANT sampling site are
12e14 mg/m3. The PM10 levels at the three sites are very similar;
hence, the three sites represent well the urban background PM10.
With respect to the European Commission (EC) regulation, the
PM10 and PM2.5 levels are clearly lower than the annual limit,
which is set at 40 mg/m3and 25 mg/m3, respectively. The EC direc-
tives about air quality also regulate the maximum number of
exceedances of the PM10 daily limit value (50 mg/m3); at all the
studied sites, the number of exceedances was also lower than that
allowed by the EC directives, which is set at 35 days per year. Taking
into account the WHO recommendations, the PM levels at
Santander could be improved; thus, the PM10 and PM2.5 annual
values were higher than WHO annual recommendation, which is
set at 20 mg/m3and 10 mg/m3(WHO, 2006). Furthermore, there
were exceedances of the 24 h recommended values during the
sampling period; the recommended daily values for PM10 and
PM2.5 are 50 mg/m3and 25 mg/m3respectively (WHO, 2006).
Table 1 shows the concentration values of the studied trace
metals at SANT site during 2008 and 2009. The EC directives about
air quality propose annual limit/target values for the concentration
of some trace metal in PM10: 6, 20 and 5 ng/m3for As, Ni and Cd,
respectively (Directive 2004/107/EC) and 500 ng/m3for Pb (Direc-
tive 1999/30/EC). The concentration values of the regulated metals
in Santander are lower than the limit/target values; hence, it is not
expected to encounter major problems in the near future in
meeting requirements from EU air quality Directives concerning
levels of metals in ambient air. All the metal concentration values
(except Mn) are in the typical range for urban areas, even the levels
of some metals are below this range, Table 1.
Table 1 also reveals the high levels of Mn, (49.1 ng/m3, 2008)
with regard to other studies in Spanish urban areas, from 23 ng/m3
to 4 ng/m3(Querol et al., 2007); the concentration of Mn in
Santander urban area is a little bit lower than the concentration in
areas clearly affected by steel industries, 87 ng/m3(Querol et al.,
2007). Although the mean annual concentration of Mn at SANT
site was below the WHO recommended annual concentration
value, 150 ng/m3(WHO, 2000), there were daily concentration
peaks higher than the WHO proposed annual value (4 exceedances
during 2008 and 2 exceedances in the second semester of 2009).
Moreno et al. (2011) report data about the Mn levels around the
ferromanganese manufacturing plant located 10 km SW from
Santander; the average Mn level during 2006 was 781 ng/m3,
showing that Mn is a good local industrial tracer in the urban
studied area.
3.3. Source apportionment by positive matrix factorization
The studied trace metals could be grouped in factors repre-
senting different origins or emission sources by using some
statistical tools such as Positive Matrix Factorization (PMF); the use
of these tools allows to study the possible relationship between the
trace metal sources and the economic crisis impact. The PMF study
is carried out with the 2008e2009 dataset.
Based on PMF source apportionment, the sources are classified
into four categories (Fig. 3). The first factor represents a mixof urban
contributions,soitiscalledurbanbackgroundproviding the greatest
contribution to PM10, 55%; the factor 1 is characterized primarily by
Ti, Ni, As and V. The main tracers of the factor 1 suggest a mixture
between anthropogenic emissions (city-scale) and natural sources.
The natural sources are represented by Ti, which is a typical crustral
tracer (Cozzi et al., 2008; Smichowoski et al., 2005). The presence of
Ni and V suggests the influence of fossil fuel combustion conse-
quently their sources could be attributed to ship traffic at Santander
bay or residential combustion (Stortini et al., 2009; Almeida et al.,
2005). In factor 1, there is a low contribution of Cu in PM10; this
contribution could be linked to the resuspension of road dust due to
thefactthatCucouldbeatraffictracer(Dongarráetal.,2007;Monaci
Table 1
Trace metals mean concentration values (ng/m3) in SANT sampling site.
SANT site. 2008SANT site. 2009 Spanish urban background rangea
PM10 PM2.5PM10 PM2.5PM10PM2.5
MaxMin MaxMin
As
Ni
Cd
Pb
Ti
V
Cr
Mn
Cu
Mo
Rh
Hg
0.5
0.9
0.3
6.2
2.4
1.2
<0.5
0.5
0.1
3.6
1.0
0.7
<2.3
27.1
0.9
0.5
<0.007
<0.9
0.3
1.5
0.2
6.9
2.6
1.2
8.9
31.6
6.0
0.3
<0.007
1.0
<0.5
0.7
0.1
3.5
1.5
0.8
3.5
11.7
2.7
0.08
<0.007
<0.9
1.6
7
0.7
57
83
15
8
23
88
5
0.3
2
0.1
7
18
2
2
4
7
2
0.7
4
0.4
20
18
9
5
10
44
2
0.3
1
0.2
6
6
1
1
2
3
1
<2.3
49.1
3.7
0.5
0.01
1.4
Concentration mean values are calculated considering values under detection limit
(d.l) as ½ d.l.
aValues obtained from Querol et al. (2007).
A. Arruti et al. / Environmental Pollution 159 (2011) 1129e1135
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Page 5

etal.,2000).The secondfactorextracted fromPMF study isrelatedto
industrial emission sources; Pb and Cu are typical tracers of the steel
production (Querol et al., 2007; Ellison et al.,1976).The relative mass
contribution to PM10 of Pb and Cu is lower than urban background,
22%. Factor 3 is mostly related to Mn; Mn is also a good tracer of
ferromanganese alloys manufacturing plants emissions (Boudissa
et al., 2006; Moreno et al., 2011). The contribution of factor 3 to
PM10 massislowerthan factor 2,8%. Factor4 isrelated to Mo, which
could be a tracer of steel production or traffic (Querol et al., 2007;
Dongarrá et al., 2007); since Mo is not linked to factors 2 and 3
(industry), it is suggested that an important contribution of Mo in
Santander is related to traffic emissions. The contribution of factor 4
to PM10 mass is lower than the urban and industrial (Pb/Cu and Mn)
contributions,15%.
Fig. 4 shows the pollutant roses of the main emission sources in
Santander city. SANT sampling site is located NEeNeNW from
the urban and industrial areas, as shown in Fig. 1; accordingly, the
pollution roses show the major peaks pointing to these areas. The
highest values of urban background factor are related to south
direction; the most populated area in Santander city is found in this
direction. The industry influence is divided into two factors (Pb/Cu
and Mn); on account of Mn levels and the pollutant roses results, it
could be concluded that Santander is under the influence of steel
production and ferroalloys of manganese manufacturing plants
located at suburbs, SW direction. The Mo-related factor pollutant
rose shows the highest peaks pointing to W, SW, S and SE direc-
tions. These peaks are in the same directions as the nearby urban
roads with higher traffic density.
Fig. 3. Main factors obtained from PMF analysis at SANT site.
Urban background Industry: Pb/Cu.
0
3
6
9
0º
30º
60º
90º
120º
150º
180º
210º
240º
270º
300º
330º
0
5
10
15
20
0º
30º
60º
90º
120º
150º
180º
210º
240º
270º
300º
330º
0
1
2
3
4
0º
30º
60º
90º
120º
150º
180º
210º
240º
270º
300º
330º
Industry: Mn.
0
2
4
6
0º
30º
60º
90º
120º
150º
180º
210º
240º
270º
300º
330º
Mo-related factor.
Fig. 4. Pollutant roses of sources contributions to PM10 calculated by PMF (expressed
in mg/m3).
A. Arruti et al. / Environmental Pollution 159 (2011) 1129e1135
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Page 6

3.4. Impact of the global economic crisis on the trace metal levels
Fig. 5 shows the evolution of the PM10 levels during the studied
period; this figure also shows the temporal trend of the studied
trace metal levels clustered according to the PMF study; the trace
metals are divided into two groups, industrial (Pb/Cu and Mn
factors) and urban background. In order to study the crisis effects
on the air pollutants levels the temporal trend of an economic
indicator is also plotted; the grey zone in Fig. 5 represents the
period of the maximum impact of the economic crisis, which took
place during the first semester of 2009. The selected economic
indicator is the industrial production at the Cantabria Region; Fig. 2
showed that the trend for other economic indicators such as the
industrial confidence was very similar.
The evolution of the PM10 levels at SANTsiteis notrelated tothe
industrial production, Fig. 5a; then, the crisis impact on PM10 levels
at SANT site is negligible. The temporal trend of the urban back-
ground tracers is not related either to economic indicators; hence,
the crisis impact on urban background tracers is also negligible.
Fig. 5b shows that the levels of the urban background tracers are
almost constant during the sampling period. Fig. 5c shows that the
temporal trend of the trace metals linked to industrial sources, Mn,
Cu and Pb, are well agree with the economic indicators trend; the
minimum value for the industrial tracer concentrations is found
during the first semester of 2009, at the same time as the minimum
value of the studied economic indicators at the Cantabria Region.
Although Table 1 shows higher mean values for some industrial
trace metals (Cu and Pb) in 2009, this is due to some outliers; the
median values are lower in 2009 (Pb: 3.6 ng/m3and 3.0 ng/m3,
2008 and 2009 respectively). Hence, at SANT site the economic
crisis only affects the PM10 composition, especially the trace metal
levels related to industrial activities.
The developed PMF analysis demonstrates that Mn is an
important industrial tracer in Santander city. As it is said above, the
levels of Mn in Santander are clearly higher than the typical values
found at other Spanish cities; even, sometimes the daily values are
higher than the WHO annual proposed value. Considering that Mn
is an important local industrial tracer in Santander city, an addi-
tional study of this trace metals is carried out.
The inter-annual variation of Mn levels in PM10 calculated
for 2008e2009 agrees well with the inter-annual variation of
production in the metallurgy sector at the Cantabria Region, Fig. 6a.
PM10
10
20
30
40
WinterSpring SummerFallWinter Spring SummerFall
-25
-15
-5
5
90028002
As
Ni
Ti
V
0.1
1.0
10.0
Winter Spring SummerFall Winter Spring SummerFall
Pb
Mn
Cu
1
10
100
Winter Spring
SummerFall Winter SpringSummer Fall
PM10.
Industrial
production
Trace metals related to urban background sources.
Expressed in ng/m3.
Trace metals related to industrial sources.
Expressed in ng/m3.
C
PM10 ( g/m
µ
3)
Industrial production (%)
A
B
Fig. 5. Temporal trend (2008e2009) of the industrial production, PM10 levels and the
studied trace metals levels: (a) PM10 levels and industrial production, (b) urban
background trace metals and (c) industrial trace metals.
1
10
Units in ng/m
Mn_PM2.5
Mn_PM10
100
20082009
WinterSpring SummerFallWinterSpring Summer Fall
3
B
Interannual variation of production in the
metalurgy sector
Interannual variation of Mn levels in PM10
A
WinterSpringSummerFall
-80
-60
-40
-20
0
20
40
60
%
Fig. 6. Economic crisis impact on Mn levels in Santander city: (a) comparison between
inter-annual variation (2008e2009) of the production in the metallurgy sector and Mn
levels and (b) evolution of Mn levels in PM10 and PM2.5 (expressed in ng/m3).
A. Arruti et al. / Environmental Pollution 159 (2011) 1129e1135
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Page 7

The correlation between the metallurgy production and the Mn
levels is high, R2¼0.86. Thus, Mn is a good tracer to explain the
industrial influence on the urban air quality of Santander city; so, it
is obvious that Mn levels must be influenced by the economical
situation. Fig. 6b shows the evolution of Mn levels in PM10 and
PM2.5; the evolution is quite similar in both fractions, there is
a minimum value in the first semester of 2009. Furthermore, the
temporal evolution of PM2.5 levels is quite similar to that of PM10,
which differs from that of the economic indicators.
4. Conclusions
An evaluation of the economic crisis effects on PM levels and
composition at an urban area in the Cantabria Region (Northern
Spain) is presented. The PM10, PM2.5 and the regulated metal (Pb,
As, Ni and Cd) levels did not exceed the European Comission
proposed limit/target values during the 2008 and 2009 years.
The studied trace metals were grouped according to a source
apportionment technique, PMF, in order to study the temporal
trend of each group of metals. Four main sources are identified;
urban background, industrial (Pb and Cu), industrial (Mn) and Mo-
related factor. The highest contributions to PM10 are originated by
the urban background factor; Ti, Ni, As and V were linked to this
factor. The industrial factors were dominated by Cu, Pb and Mn,
which are trace metals of steel and ferro-manganese alloy
production plants emissions.
The studied economic indicators demonstrate that at Cantabria
Region the crisis started in the last trimester of 2008 and the worst
economic situation took place during the first semester of 2009.
The impact of the crisis is higher on the PM composition than on
the PM levels; the temporal trend of PM levels is not related to that
of the economic indicators.
The effects of the crisis on the trace metal associated to the
urban background are negligible. Nevertheless, the industrial trace
metals (Mn, Pb and Cu) levels are well affected by the economic
situation; the temporal trend of the concentration of these metals
during 2008 and 2009 is quite similar to the economic indicators
evolution. Additionally, a study of Mn is also carried out showing
that the effects of the crisis are very similar in PM2.5 and PM10,
thus indicating that Mn linked to PM10 and PM2.5 comes from
industrial sources in the studied area.
Acknowledgements
The authors would like to thank the Spanish Ministry of
Education and Science (CTM2006-00317) and the Government of
Cantabria (“Actions for improving the air quality and its diagnosis
in Cantabria”) for their funding.
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