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 13.
Exploratory analysis of the role of PM components on mortality and morbidity
Summary: Report on the methods and results of the short-term effects of particulate matter constituents on
daily mortality and hospitalizations in five Mediterranean cities: results from the MED-PARTICLES
project.
Introduction
Dozens of studies link increases in daily particulate matter (PM) levels to increases in mortality
and hospital admissions (Brook et al. 2010; Brunekreef and Holgate 2002; Ruckerl et al. 2011; Samoli
et al. 2013; Stafoggia et al. 2013). However, particulate matter is a location- and season-dependent
complex mixture of different elements with potentially different toxicity. Since the different chemical
composition of PM may be one of the reasons explaining the heterogeneity of the effects of air pollution
in different studies, several recent investigations have examined the relationship between concentrations
of individual chemical species and mortality or hospital admissions (Bell et al. 2009; Burnett et al. 2000;
Franklin et al. 2008; Ito et al. 2011; Lall et al. 2011; Lippmann et al. 2006; Mar et al. 2000; Ostro et al.
2007; Ostro et al. 2010; Ostro et al. 2011; Peng et al. 2009; Sarnat et al. 2008; Son et al. 2012; Zanobetti
et al. 2009; Zhou et al. 2011). Most of the studies were conducted in North America and studies in
Europe are scarce (Andersen et al. 2007; Atkinson et al. 2010; Ostro et al. 2011). Elemental carbon
(EC), nickel, and silicon are among the components most commonly associated to adverse health events
in previous epidemiological studies. However, the main conclusion derived from these studies is that
although many components contribute to health effects of PM, there is no sufficient evidence to
differentiate the constituents more closely related to health outcomes (Environmental Protection Agency
2009; Rohr and Wyzga 2012; World Health Organization 2013).
In this study, we examine the relationship between PM constituents and mortality and hospital
admissions in five cities in the Mediterranean area, a region characterized by high vehicle and
population densities, a high proportion of diesel cars, high sea traffic and intense solar radiation.
Methods
Study population
The study included five cities, three in Spain (Barcelona, Madrid and Huelva) and two in Italy
(Rome and Bologna). The overall period of study was from January 2003 to April 2013, although the
different cities contributed to different periods according to air pollution data availability.
Mortality data
Daily mortality counts for all non-external causes [International Classification of Diseases, 9th
Revision (ICD-9) codes 001–799; 10th revision (ICD-10) codes A00–R99] (ref: WHO 1999),
cardiovascular causes (ICD-9 codes 390–459, ICD-10 codes I00–I99) and respiratory causes (ICD-9
codes 460–519, ICD10 codes J00–J99) were collected for all cities.
Hospital admissions data
Daily counts of emergency hospital admissions were collected from national or regional health
information systems. Hospitalizations only of residents ≥ 15 years of age were considered. Two study
outcomes were defined on the basis of the primary discharge diagnosis: cardiovascular hospitalizations
[International Classification of Diseases, 9th Revision (ICD-9) codes 390–459; and 10th Revision (ICD10) codes I00–I99] (WHO 1999), and respiratory hospitalizations (ICD-9 codes 460–519; ICD-10 codes
J00–J99). All data were extracted and collected according to a common protocol. Because data were
anonymous and collected as daily counts, no informed consent was needed. And because the analysis on
daily counts was conducted by a public health institute, there was no need for approval by an
institutional review board.
Exposure data
PM10 and PM2.5 data for speciation analysis were available from a single station in each city.
Madrid’ station was urban while all other were urban background stations. More details about the
stations are provided in the Supplemental Material. PM2.5 data were not available for Rome while PM10
data were not available for Bologna. The period and frequency of sampling was (Table 1): Barcelona,
2003-2010, approximately every third day; Madrid, 2007-2008, two measurements per week; Huelva,
2003-2010, one measurement per week; Rome, 2005-2008, daily data; Bologna, 2012-2013, daily data.
PM data collected during intense African dust outbreaks, identified using the methodology of Pey et al.
(2013), were excluded from the analysis.
In the three Spanish cities, daily samples of PM2.5 and PM10 were collected by high-volume
samplers (MCV, S.A., Barcelona, Spain) with a flow rate of 30 m3/hr using quartz filters. PM10 and
PM2.5 concentrations were determined gravimetrically. Subsequently, the quartz filters were analyzed
for water-soluble ions (NH4+, Cl-, SO42-, NO3-), major elements (Al, Ca, K, Mg, Fe, Na) and 46 trace
elements by ion chromatography, inductively coupled plasma atomic emission, ICP-AES, and
inductively coupled plasma mass spectrometry, ICP-MS, respectively (Querol et al. 2001). In Rome,
PM10 sampling was conducted by a beta attenuation monitor SM200 (Opsis AB Furulund-S) operating
at flow rate of 1 m3 h-1 and teflon filters used as a substrate while a MICRODUST sequential sampler
(AQUARIA, Lacchiarella, MI-I) operating at a flow rate of 2 l min-1 was used for PM sampling on
quartz filters. The Teflon filters were subjected to energy-dispersion X-ray fluorescence analysis (XLab2000, 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 analyzed in the quartz filters by
thermo-optical analysis (OCEC Sunset Analyser, Sunset Laboratory) using the NIOSH temperature
protocol. In Bologna, two Dual Channel Monitors (SWAM, Fai Instrument) were used to sample PM on
quartz filters. 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++. Major and trace metals were
determined.
Only 17 pre-specified PM constituents were included in the mortality analyses: silicon (Si)
(calculated as 3 times the aluminum concentration in the Spanish cities), calcium (Ca), aluminum (Al),
iron (Fe), potassium (K), magnesium (Mg), zinc (Zn), copper (Cu), titanium (Ti), manganese (Mn),
vanadium (V), nickel (Ni), nitrate (NO3–), sulfate (SO42-), elemental carbon (EC), organic carbon (OC)
and total carbon (TC), calculated as EC+OC. The list of 17 species was based on their detectability in
the included cities and on a review of the previous literature linking PM constituents and health. If one
of the above constituents had more than 20% of values missing or below the limit of detection or
quantification in a certain city, the city was excluded from the analysis of that particular constituent.
Otherwise, non-detectable values were replaced by half the limit of detection.
Daily average temperature and relative humidity were collected from each city, and they were
combined to calculate apparent temperature (O'Neill et al. 2003). Information on influenza epidemics
was obtained from hospital admissions records. Information on bank holidays was also collected for
each city.
Study design and data analysis
We used a time-stratified case-crossover study design (Levy et al. 2001). In cities with daily
data, all days from the same year, month and day of the week were grouped in the same stratum. In
cities without daily data, strata were composed of days from the same year and month. Since the casecrossover analysis conducts comparisons within strata, seasonal and long-term effects are eliminated
and they do not confound the results. Analyses were performed using conditional Poisson regression
(Whitaker et al. 2006) and were further adjusted for holidays, influenza epidemics, day of the week (in
cities without daily data), and apparent temperature. To account for non-linearity and for the different
lag structure of cold and hot temperatures, we included two separate spline terms for apparent
temperature, with 3 degrees of freedom each, one for the cold period (temperatures below the annual
median) using average temperature for lags 0-5, and the other for the warm period using average
temperature for lags 0-1.
All analyses were conducted separately for each city, and results were combined using randomeffects meta-analysis. Lags 0, 1 and 2 of the effect of each pollutant were examined in different models,
as models accounting for multiple lags could not be built in cities without daily data. Results are
reported for and interquartile range (IQR) increase in pollutant concentrations.
In order to account for potential confounding for total PM levels, while accounting for the fact
that PM constituents are components of PM, we applied the recently proposed constituent residual
method (Mostofsky et al. 2012). First, a linear regression model for the constituent as a function of total
PM levels is fitted. Then, the residuals for this model, which represent the variation in constituent levels
independent of PM, are included as an explanatory variable in the case-crossover model, along with
total PM levels. The coefficient for the residuals represent the effect of the constituent while holding PM
constant, i.e. the independent contribution of the constituent; and the PM coefficient represents the
independent effect of total PM.
Results
Study populations
Table 1 summarizes the daily number of deaths by natural, cardiovascular and respiratory causes
and of cardiovascular and respiratory hospital admissions in the five participating cities.
Madrid, Rome and Barcelona are large cities with an average of at least 40 daily deaths by
natural causes, while Bologna and Huelva had only 12 and 3 daily deaths on average, respectively.
Cardiovascular causes accounted for between 30% and 40% of all deaths, while respiratory causes
accounted for between 5% and 15% of them. We observe the same for hospital admissions, where
Madrid, Rome and Barcelona show more hospitalizations while Rome is the city that contributes the
most in days in the analyses for PM10 and Barcelona for PM2.5.
Air pollution
The concentrations of PM10 and PM2.5 and of their selected constituents in the five cities are
summarized in Table 2. The highest PM10 levels were observed for Barcelona and Madrid, with levels
above 40 g/m3, while Huelva and Rome showed levels between 30 and 35 g/m3. In terms of PM2.5,
the highest levels were found in Barcelona and Bologna, with values above 25 g/m3, while Madrid and
Huelva had values around 20 g/m3. The selected species represent between 37% and 62% of the total
PM levels depending on the city and fraction. Total carbon, sulfates, nitrates, silicon and calcium were
among the species with highest contribution in terms of mass, while the other species had much smaller
contributions that did not exceed 4% of the mass in any city.
Correlations between species and total PM10 levels were all in the range 0.4-0.8, with a few
exceptions: correlations exceeded 0.8 only in Madrid for total carbon, calcium, iron, potassium and
manganese; correlations were lower than 0.4 in some cities for zinc, copper, titanium, manganese,
sulfates and magnesium (Tables 2). Correlations between species and total PM2.5 levels were generally
smaller than those found for PM10, only showing high values for carbon and nitrates (Supplemental
material, Tables S1-S5). The correlations between the same constituent in PM10 and PM2.5 were high
for all constituents in Barcelona, while some low correlations were found for some constituents in
Madrid or Huelva (e.g. for nickel, iron, magnesium, manganese, calcium, zinc and copper) (Tables 2).
This comparison was not possible in the Italian cities as they only contributed with one PM fraction.
The correlations between PM10 species were only high (around 0.8 or higher) for the group composed
by silicon, calcium, aluminum, iron and titanium, which in some cities were also highly correlated with
potassium, magnesium, manganese and vanadium. The correlations between PM2.5 species were only
consistently high in the different cities for aluminum and silicon (correlations greater than 0.9).
Mortality results
The meta-analysis results linking mortality and PM10 constituents are shown in Figure 1a.
Results for PM2.5 constituents are shown in Figure 2a. As shown in the leftmost panels of Figures 1a and
2a, there was no statistically significant elevation in mortality risk by total PM10 or PM2.5 levels for any
of the lags or mortality causes. Several PM10 and PM2.5 constituents showed positive and significant
associations with mortality, but there was little consistency across lags and mortality causes. Results for
PM10 and PM2.5 are not entirely comparable, as they were based on different cities.
Total carbon was associated with total mortality at lag 1 and only for PM10, while elemental
carbon was associated with increased total, cardiovascular and respiratory mortality risk at lags 1 or 2,
again for PM10 but not for PM2.5. For organic carbon, only a significant protective effect for
cardiovascular mortality at lag 0 for PM10 was found. Sulfates were only associated with respiratory
mortality, with increased risks for both PM10 and PM2.5 at different lags. Significant protective effects at
lag 0 were found for nitrates in PM10. Both silicon and aluminum showed a positive association with
total mortality at lag 2 for PM2.5. Iron showed a significant positive association with total mortality at
lag 1 for PM10 and with cardiovascular mortality at lag 2 for PM2.5. Potassium was associated with total
mortality at lag 1 for PM10, and magnesium was only associated with cardiovascular mortality at lag 0
for PM2.5. Titanium was associated with total mortality at lags 1 and 2, and with cardiovascular
mortality at lag 2, and these relationships were only present for PM10. Manganese showed positive and
significant associations with total mortality at lag 1 for PM01, while for PM 2.5 associations were
significant for cardiovascular mortality at lag 0 and for respiratory mortality at lags 0 and 2. Finally,
nickel was associated with respiratory mortality for PM10 at lag 2.
In terms of the size of the associations, most of the significant results represented around a 1%
increase in mortality for an IQR increase in the PM constituent (Figures 1a and 2a). Notable exceptions
to this pattern were iron, with percent changes of around 2%, magnesium, with percent changes around
3%, and all the significant results found for respiratory mortality, which reached values of 4% and 5%
for elemental carbon and sulfates, and were higher than 3% for manganese and nickel. Figure 3a
illustrates the between-city variation in effects and the weight of each city in the final analysis for the
particular case of iron and manganese (in PM10 at lag 1). Barcelona and Rome, and also Madrid for iron,
had a similar weight in the analysis, while Huelva had a small contribution. Results were quite
homogeneous between cities.
Table 4 presents the results of the case-crossover models with and without adjustment for total
PM levels using the constituent residual method for the specific case of the effects of lag 1 on total
mortality. Note that the models that only included total PM levels resulted in non-significant results,
with percent changes of 1.2 (95% confidence interval (CI): -0.2, 2.7) for PM10 and of 0.4 (95% CI: -1.3,
2.1) for PM2.5. The adjusted effect represents the effect of higher levels of the constituent when holding
total PM constant. For PM10, models for constituents not adjusted for PM levels resulted in significant
mortality increases associated to total carbon, iron, potassium, titanium and manganese. When adjusting
for PM levels, all effects were diluted and no longer statistically significant, except for iron, which
showed stronger results with 3.2% increase in mortality associated to an IQR change in the pollutant.
Interestingly, in the models for total carbon and nitrates, the effect of PM reached statistical
significance. For PM2.5, no significant effects were found in models not adjusted for PM levels. When
total PM was also included in the model, significant associations were found for calcium, iron, and
manganese.
Hospitalization results
Figures 1b and 2b show the associations between total PM and each component and respiratory
and cardiovascular admissions. We observed a significant increase of cardiovascular admissions
associated with an increase of a one IQR of total PM2.5 (lag 0 and 2) and its sulfates (lag 0), EC (lag 0)
and Mn (lag 0) components. For PM10, we found a positive and significant association for total PM (lag
0), NO3- (lag 0), Ca (lag 0), Fe (lag 0), Fe (lag 0), Cu (lag 1), Ti (lag 1), Mn (lag 1). For respiratory
admissions, a positive and significant association was observed with total PM (lag1), Ca (lag2), Fe (lag
1), V (lag 0 and 1) and Ni (lag 0 and 1) from PM2.5 and with sulfates (lag 0), silicon (lag 2), V (lag 0)
and Ni (lag 0) from PM10. While most of the effects on cardiovascular hospitalizations are observed at
lag 0, the pattern is less clear for respiratory admissions. Results from PM10 and PM2.5 are not directly
comparable, as they do not include the same cities.
Most of the significant positive associations were around 1% increase for one IQR, reaching 2%
for Fe and Mn from PM10 for cardiovascular admissions and for total PM and EC from PM2.5 for both
cardiovascular and respiratory admissions, and for Ni from PM2.5 for respiratory admissions.
Table 4 presents the results of the case-crossover models with and without adjustment for total
PM levels using the constituent residual method for the specific case of the effects of lag 0 on
cardiovascular admissions. We observed that the total PM10 effect on cardiovascular admissions
persisted regardless of the specie added in the model. For total PM2.5, the PM effect in some models lost
its significance but the estimate was always positive and in the same range (around 2% increase). The
effect of Fe from PM10 decreased and became not significant when adjusting for total PM. The percent
increase went from 2.1 (95%CI: 0.5-3.8) to 0.6 (-3.5, 5.0). The same thing was observed for Ti, but the
Mn effect persisted, the percent increase was 2.0 (1.0-3.1) without adjusting for total PM and 1.9 (0.53.4) adjusting for total PM. For PM2.5 species, adjusting or not for total PM dids not have an impact on
the Mn effect, but it decreased the sulfate and EC effects that in addition became not significant.
Figure 3b shows the between-city variation in effects and the weight of each city in the final
analysis for the particular case of iron and manganese (in PM10 at lag 0) on cardiovascular admissions.
We observe that Rome and Barcelona were the cities that contribute the most to the total effect. In
addition, except for Fe in Huelva, all the effects were positive and significant for Barcelona and Rome,
while in Madrid the confidence intervals are very large.
Conclusions: in the report we have examined the effects of PM constituents on mortality and hospital
admissions in five Mediterranean cities. Our study suggests that several PM constituents are associated to
increases in daily mortality, including elemental carbon, sulfate, silicon, aluminum, iron, potassium,
magnesium, titanium, manganese and nickel. Several of the metals are highly correlated and therefore it is
difficult to attribute effects to a particular constituent. Studies with larger datasets and daily data are needed
to confirm these results in the Mediterranean area.
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Table 1: Data availability and summary of daily deaths by city.
Daily deaths (Mean  Standard deviation)
Date beginning – Date end
Number of days
City
Daily hospitalizations (Mean 
Standard deviation)
Total
Cardiovascular Respiratory
Cardiovascular
Respiratory
Barcelona
2003/01/01 – 2010/12/31
736
41.5  8.6
13  4.1
4.4  2.6
55.8  10.8
55.8  19.4
Madrid
2007/01/01 – 2008/02/29
104
72.1  11
21.7  5
12.2  4.7
129.6  27.4
142.3  42.4
Huelva
2003/01/01 – 2010/12/31
406
2.9  1.7
11
0.3  0.6
1.8  2.3
0.9  1.2
Roma
2005/01/01 – 2007/12/31
1059
57  10.5
23.5  6.3
3.5  2.2
81.9  18.7
35.5  11.3
Bologna
2011/11/15 – 2013/02/27
472
12.2  3.7
-
-
16.8  4.6
9.7  3.7
Table 2: Description (mean  standard deviation) of PM species levels (µg/m3) by city.
Barcelona
Madrid
Huelva
Rome
Bologna
Common
(2003-2010)
(2007-2008)
(2003-2010)
(2005-2007)
(2011-2013)
IQR
PM10
PM2.5
PM10
PM2.5
PM10
PM2.5
PM10
PM2.5
PM10
PM2.5
40.2  17.4
26.3  12.2
41.5  19.5
20.7  9.2
35.2  15.2
20.8  10.7
30.1  12.6
27.6  19.5
19.6
16.0
Total Carbon
6.9  3.7
4.9  2.6
10.4  5.6
7.6  3.9
5.4  3.4
3.9  2.3
10.6  5.2
6.7  4.7
5.2
3.8
OC
Total PM
3.3  1.8
2.9  1.8
na
na
na
na
8.5  4.2
5.1  3.9
3.4
3.0
2-
3.8  2.6
3.4  2.3
3.2  1.4
2.4  1.1
3.8  3.2
3.3  2.7
3.4  2.0
2.4  1.6
2.7
2.3
NO3
-
4.3  3.5
2.4  3.2
2.5  2.1
1.5  1.7
2.7  1.8
1.1  1.2
2.1  1.4
5.1  7.9
2.2
3.0
SiO2
3.8  3.6
1.4  1.8
nd
1.0  0.8
4.9  4.4
1.4  2.0
1.0  0.9
2.7
1.1
EC
1.8  1.2
1.5  1.0
na
na
na
na
2.2  1.3
1.6  1.0
1.4
1.1
Ca
2.13  1.64
0.60  0.62
2.10  1.35
0.26  0.16
1.16  0.94
0.33  0.52
1.29  0.68
0.10  0.06
1.32
0.27
Al2O3
1.28  1.21
0.47  0.60
0.72  0.59
0.36  0.29
1.62  1.46
0.48  0.70
0.27  0.23
0.81
0.39
Fe
0.86  0.54
0.28  0.21
1.67  0.87
0.20  0.15
0.66  0.52
0.18  0.20
0.46  0.26
0.11  0.07
0.63
0.18
K
0.38  0.29
0.20  0.23
0.33  0.22
0.20  0.41
0.41  0.30
0.21  0.20
0.39  0.32
0.18  0.30
0.26
0.15
Mg
0.279  0.165
0.082  0.072
0.225  0.150
0.083  0.084
0.295  0.193
0.084  0.093
0.095  0.098
nd
0.153
0.069
Zn
0.085  0.080
0.059  0.068
nd
0.050  0.026
0.049  0.074
0.045  0.084
0.037  0.035
nd
0.047
0.039
Cu
0.056  0.051
0.027  0.036
0.113  0.069
0.019  0.010
0.047  0.078
0.029  0.033
0.040  0.020
nd
0.051
0.021
Ti
0.040  0.033
0.014  0.013
0.040  0.031
0.008  0.014
0.056  0.084
0.020  0.039
0.021  0.023
nd
0.030
0.010
Mn
0.018  0.011
0.009  0.006
0.020  0.011
0.005  0.003
0.013  0.024
0.004  0.004
0.012  0.008
nd
0.011
0.004
V
0.010  0.007
0.008  0.006
0.002  0.001
0.001  0.001
0.005  0.004
0.004  0.003
0.006  0.004
nd
0.005
0.004
Ni
0.005  0.003
0.004  0.003
0.002  0.001
0.004  0.003
0.003  0.003
0.002  0.003
0.005  0.003
nd
0.003
0.004
SO4
na: not available
nd: non-detectable.
Table 3. Pearson correlation coefficients between PM and PM constituents The lower triangle includes the correlations between PM10 constituents. The
upper triangle includes the correlations between PM2.5 constituents. The main diagonal includes the correlation between the same species in PM10 and
PM2.5.
Table 3a Barcelona
PM
TC
OC
SO42-
NO3-
SiO2
EC
Ca
Al2O3
Fe
K
Mg
Zn
Cu
Ti
Mn
V
Ni
PM
0.85
0.73
0.77
0.57
0.67
0.36
0.61
0.5
0.36
0.63
0.34
0.39
0.27
0.32
0.51
0.6
0.5
0.49
TC
0.71
0.85
0.94
0.31
0.52
0.21
0.74
0.43
0.21
0.61
0.26
0.24
0.27
0.32
0.38
0.58
0.38
0.4
OC
0.71
0.92
0.9
0.34
0.66
0.16
0.72
0.4
0.16
0.53
0.28
0.25
0.16
0.31
0.43
0.52
0.34
0.31
SO42-
0.59
0.35
0.31
0.91
0.29
0.2
0.14
0.19
0.2
0.29
0.23
0.29
0.14
0.19
0.29
0.24
0.55
0.43
NO3-
0.64
0.53
0.61
0.41
0.89
-0.02
0.37
0.1
-0.02
0.23
0.16
-0.05
0.22
0.15
0.06
0.36
0.21
0.24
SiO2
0.68
0.39
0.4
0.4
0.21
0.78
0.21
0.61
1
0.61
0.31
0.64
0.07
0.23
0.66
0.41
0.19
0.17
EC
0.62
0.86
0.72
0.2
0.42
0.39
0.85
0.47
0.21
0.62
0.25
0.29
0.19
0.44
0.44
0.57
0.26
0.24
Ca
0.74
0.61
0.59
0.34
0.32
0.71
0.6
0.75
0.61
0.85
0.3
0.73
0.18
0.34
0.85
0.63
0.22
0.21
Al2O3
0.68
0.39
0.4
0.4
0.21
1
0.39
0.71
0.78
0.61
0.31
0.64
0.07
0.23
0.66
0.41
0.19
0.17
Fe
0.79
0.79
0.72
0.37
0.42
0.71
0.71
0.81
0.71
0.81
0.32
0.69
0.24
0.38
0.82
0.73
0.33
0.31
K
0.55
0.36
0.42
0.31
0.25
0.58
0.45
0.55
0.58
0.51
0.86
0.53
0.14
0.18
0.33
0.32
0.15
0.12
Mg
0.69
0.32
0.35
0.45
0.2
0.77
0.34
0.68
0.77
0.63
0.61
0.67
0.1
0.27
0.78
0.44
0.24
0.18
Zn
0.3
0.33
0.24
0.13
0.25
0.19
0.26
0.32
0.19
0.33
0.18
0.11
0.92
0.13
0.13
0.46
0.09
0.16
Cu
0.41
0.53
0.46
0.15
0.26
0.25
0.54
0.36
0.25
0.48
0.23
0.19
0.21
0.76
0.28
0.27
0.17
0.18
Ti
0.76
0.49
0.5
0.47
0.28
0.88
0.5
0.79
0.88
0.81
0.62
0.81
0.21
0.28
0.67
0.58
0.31
0.26
Mn
0.71
0.67
0.63
0.31
0.41
0.64
0.62
0.75
0.64
0.84
0.54
0.52
0.5
0.38
0.75
0.87
0.24
0.25
V
0.61
0.5
0.48
0.53
0.43
0.4
0.41
0.42
0.4
0.49
0.34
0.42
0.2
0.26
0.48
0.42
0.92
0.82
Ni
0.52
0.45
0.42
0.38
0.36
0.33
0.38
0.37
0.33
0.45
0.26
0.29
0.19
0.23
0.4
0.38
0.76
0.84
Table 3b. Madrid
PM
TC
SO42-
NO3-
SiO2
PM
0.89
0.88
0.44
0.86
0.08
TC
0.9
0.96
0.23
0.68
0.04
SO42-
0.59
0.41
0.96
0.28
0.29
NO3-
0.78
0.73
0.54
0.96
-0.1
SiO2
Ca
Al2O3
Fe
K
Mg
Zn
Cu
Ti
Mn
V
Ni
0.3
0.1
0.38
0.21
0.27
0.04
0.29
0.15
0.12
0.4
0.72
0.17
0.74
0.24
-0.21
0.08
0.43
0.81
0.07
0.74
0.17
-0.15
0.28
0.3
0.17
0.08
-0.07
0.26
0.15
0.23
0.22
0.1
0.22
0.38
0.6
0.07
0.04
-0.06
0.35
0.54
-0.04
0.6
0.07
-0.21
-
-
-
-
-
0.64
0.96
0.26
0
0.32
0.27
0
0.29
0.41
0.48
0.22
Ca
0.87
0.71
0.43
0.49
-
0.53
0.74
0.44
0.02
0.22
0.36
0.18
0.25
0.52
0.46
0.25
Al2O3
0.72
0.47
0.44
0.28
-
0.82
0.65
0.37
-0.01
0.28
0.32
0
0.31
0.44
0.5
0.31
Fe
0.93
0.92
0.37
0.64
-
0.84
0.66
0.32
-0.09
-0.22
0.09
0.29
0.22
0.33
0.2
-0.01
K
0.87
0.66
0.49
0.49
-
0.91
0.93
0.81
0.7
0.78
-0.02
0.26
0.89
0.1
0.11
-0.09
Mg
0.75
0.5
0.42
0.35
-
0.83
0.93
0.7
0.89
0.35
0.21
0.17
0.73
0.16
0.28
0.15
Zn
-
-
-
-
-
-
-
-
-
-
-
0.48
-0.07
0.64
0.3
0.58
Cu
0.64
0.73
0.17
0.5
-
0.53
0.26
0.74
0.43
0.3
-
0.78
0.14
0.68
0.13
-0.14
Ti
0.75
0.51
0.43
0.35
-
0.78
0.97
0.7
0.9
0.92
-
0.34
0.86
0.13
0.24
-0.03
Mn
0.94
0.84
0.45
0.68
-
0.82
0.74
0.92
0.85
0.77
-
0.66
0.8
0.77
0.42
0.03
V
0.69
0.42
0.62
0.37
-
0.74
0.87
0.6
0.85
0.86
-
0.25
0.86
0.69
0.83
0.23
Ni
0.79
0.77
0.42
0.55
-
0.73
0.6
0.79
0.68
0.59
-
0.57
0.63
0.8
0.56
-0.25
Table 3c. Huelva
PM
TC
SO42-
NO3-
SiO2
Ca
Al2O3
Fe
K
Mg
Zn
Cu
Ti
Mn
V
Ni
PM
0.53
0.5
0.5
0.4
0.5
0.55
0.47
0.58
0.58
0.54
0.49
0.29
0.33
0.62
0.42
0.48
TC
0.44
0.53
0.06
0.42
0.33
0.24
0.31
0.46
0.51
0.21
0.13
0.12
0.22
0.41
0.04
0.1
SO42-
0.57
0.22
0.91
0.2
0.1
0.21
0.08
0.17
0.09
0.18
0.27
0.24
0.14
0.25
0.79
0.47
NO3-
0.61
0.32
0.58
0.64
-0.01
0.13
-0.02
0.08
0.37
0.03
0.19
0.14
0.12
0.17
0.09
0.17
SiO2
0.73
0.34
0.23
0.21
0.73
0.51
0.95
0.89
0.53
0.74
0.22
0.14
0.44
0.75
0.17
0.2
Ca
0.77
0.41
0.31
0.35
0.82
0.3
0.53
0.62
0.61
0.8
0.76
0.47
0.29
0.75
0.17
0.69
Al2O3
0.73
0.34
0.23
0.21
1
0.82
0.74
0.85
0.54
0.79
0.22
0.17
0.42
0.69
0.18
0.19
Fe
0.79
0.46
0.26
0.32
0.94
0.87
0.94
0.72
0.65
0.77
0.29
0.21
0.48
0.85
0.22
0.31
K
0.75
0.5
0.23
0.36
0.86
0.78
0.86
0.89
0.64
0.61
0.42
0.27
0.28
0.67
0.12
0.41
Mg
0.66
0.14
0.28
0.19
0.8
0.66
0.8
0.74
0.65
0.65
0.53
0.36
0.38
0.78
0.21
0.48
Zn
0.25
0.08
0.29
0.16
0.16
0.23
0.16
0.16
0.11
0.33
0.38
0.46
0.14
0.5
0.23
0.69
Cu
0.35
0.13
0.27
0.21
0.19
0.39
0.19
0.27
0.17
0.19
0.23
0.28
0.13
0.3
0.27
0.43
Ti
0.31
0.3
0.21
0.23
0.34
0.3
0.34
0.4
0.35
0.23
0.09
0.04
0.55
0.46
0.19
0.19
Mn
0.24
0.32
0.1
0.14
0.29
0.26
0.29
0.36
0.34
0.17
0.02
0.01
0.79
0.18
0.25
0.51
V
0.67
0.24
0.79
0.51
0.43
0.5
0.43
0.47
0.42
0.45
0.38
0.31
0.29
0.18
0.87
0.5
Ni
0.44
0.25
0.47
0.38
0.2
0.26
0.2
0.23
0.25
0.2
0.3
0.18
0.12
0.03
0.5
0.39
Table 3d. Rome (only PM10)
PM
TC
OC
SO42-
NO3-
SiO2
EC
Ca
Al2O3
Fe
K
Mg
Zn
Cu
Ti
Mn
V
PM
1
TC
0.74
1
OC
0.73
0.98
1
SO42-
0.37
0
-0.01
NO3-
0.56
0.32
0.37
0.1
1
SiO2
0.48
0.12
0.09
0.47
0.23
1
EC
0.61
0.82
0.7
-0.02
0.2
0.1
1
Ca
0.54
0.24
0.21
0.36
0.3
0.82
0.22
1
Al2O3
0.48
0.11
0.09
0.46
0.23
0.99
0.09
0.81
1
Fe
0.71
0.57
0.51
0.26
0.37
0.69
0.54
0.76
0.68
1
K
0.67
0.53
0.54
0.17
0.53
0.31
0.4
0.37
0.36
0.52
1
Mg
-0.02
-0.25
-0.24
0.03
0.34
0.07
-0.21
-0.01
0.09
-0.17
0.07
1
Zn
0.49
0.57
0.54
-0.01
0.26
0.08
0.53
0.24
0.07
0.53
0.43
-0.25
1
Cu
0.58
0.61
0.58
0.01
0.28
0.18
0.58
0.38
0.17
0.68
0.53
-0.23
0.56
1
Ti
0.5
0.17
0.14
0.42
0.28
0.92
0.16
0.76
0.92
0.69
0.48
0.13
0.12
0.25
1
Mn
0.55
0.37
0.35
0.2
0.25
0.51
0.34
0.55
0.5
0.65
0.42
-0.12
0.38
0.49
0.62
1
V
0.43
0.04
0.01
0.73
0.21
0.65
0.07
0.55
0.65
0.45
0.36
0.15
0.05
0.16
0.67
0.44
1
Ni
0.52
0.44
0.42
0.28
0.25
0.28
0.39
0.34
0.25
0.53
0.35
-0.16
0.53
0.48
0.3
0.54
0.39
Ni
1
1
Table 3e. Bologna (only PM2.5).
PM
TC
OC
SO42NO3EC
Ca
Fe
K
PM
TC
OC
SO42-
NO3-
EC
Ca
Fe
K
1
0.83
0.84
0.46
0.94
0.59
-0.18
0.52
0.49
1
0.99
0.25
0.81
0.8
0.23
0.54
0.76
1
0.3
0.8
0.69
0.23
0.48
0.74
1
0.32
0.01
-0.03
0.19
0.18
1
0.65
-0.2
0.44
0.4
1
0.19
0.65
0.64
1
0.31
-0.14
1
0.28
1
Table 4a. Association (percent change and 95% confidence interval for an IQR change) between total mortality and PM constituents at lag 1. Single pollutant
models included only one PM constituent at a time. Models adjusted for PM levels were obtained using the constituent residual method: first, a linear
regression model was fitted for each constituent with PM levels as the only predictor; then both the residuals from this model and PM levels are introduced as
predictors in the health model to obtain the percent changes. Results were obtained from a meta-analysis combining the city-specific results of the cities with
available data on each constituent.
Species
Total Carbon
OC
SO42NO3SiO2
EC
Ca
Al2O3
Fe
K
Mg
Zn
Cu
Ti
Mn
V
Ni
Single pollutant
PM10
PM2.5
Species effect
Species effect
1.2 (0.2, 2.2)*
-1.6 (-7.0, 4.1)
-0.0 (-1.3, 1.3)
-0.2 (-1.1, 0.7)
0.9 (-0.3, 2.1)
0.5 (-0.6, 1.6)
1.3 (-0.7, 3.3)
0.8 (-0.3, 1.9)
2.1 (0.3, 3.9)*
1.0 (0.3, 1.7)*
0.2 (-0.9, 1.3)
0.4 (-0.7, 1.5)
0.6 (-1.4, 2.6)
1.0 (0.0, 2.0)*
1.2 (0.0, 2.4)*
0.0 (-1.2, 1.3)
-0.1 (-1.0, 0.8)
1.1 (-0.7, 2.9)
-0.7 (-4.8, 3.7)
-0.6 (-2.2, 1.0)
-0.2 (-1.9, 1.6)
0.2 (-0.7, 1.2)
-0.2 (-2.6, 2.2)
0.6 (-0.1, 1.2)
0.2 (-0.8, 1.2)
1.0 (-0.3, 2.3)
0.3 (-0.4, 1.0)
0.7 (-0.7, 2.1)
-0.5 (-4.2, 3.4)
-0.0 (-1.0, 1.0)
0.7 (-0.2, 1.6)
0.9 (-0.2, 2.1)
-0.6 (-1.5, 0.4)
-0.1 (-2.1, 1.9)
Adjusting for total PM
PM10
Species effect
PM effecta
1.1 (-1.3, 3.6)
0.0 (-1.4, 1.4)
-0.9 (-2.3, 0.6)
-1.4 (-2.6, -0.2)*
0.9 (-0.6, 2.4)
0.1 (-1.7, 1.8)
0.9 (-1.9, 3.7)
0.8 (-0.5, 2.2)
3.2 (1.0, 5.5)*
0.8 (-0.1, 1.7)
0.1 (-1.2, 1.3)
0.1 (-0.7, 0.9)
-0.0 (-1.6, 1.5)
0.8 (-0.5, 2.0)
0.7 (-0.8, 2.3)
-0.6 (-1.6, 0.4)
-0.8 (-1.8, 0.2)
* p<0.05
a
The PM10 effect in single pollutant model was 1.2 (-0.2, 2.7).
b
The PM2.5 effect in single pollutant model was -0.5 (-2.2, 1.2).
1.3 (0.1, 2.6)*
0.2 (-3.7, 4.2)
1.2 (-0.3, 2.7)
1.4 (0.1, 2.8)*
1.1 (-0.9, 3.2)
0.2 (-3.4, 3.9)
1.2 (-0.5, 2.9)
1.3 (-0.1, 2.7)
1.4 (-0.1, 2.9)
1.3 (-0.2, 2.8)
1.3 (-0.1, 2.7)
1.1 (-1.4, 3.7)
1.2 (-0.8, 3.2)
1.3 (-0.2, 2.8)
1.3 (-0.5, 3.1)
1.2 (-0.3, 2.7)
1.3 (-0.5, 3.1)
PM2.5
Species effect
2.8 (-0.0, 5.7)
2.0 (-1.9, 6.1)
-0.3 (-2.2, 1.6)
1.9 (-2.7, 6.8)
0.4 (-0.6, 1.4)
1.3 (-1.3, 4.0)
0.7 (0.0, 1.5)*
0.4 (-0.7, 1.4)
1.7 (0.2, 3.2)*
0.4 (-0.3, 1.1)
1.0 (-0.5, 2.4)
-0.8 (-5.5, 4.2)
0.0 (-1.0, 1.1)
0.9 (-0.0, 1.9)
1.4 (0.2, 2.8)*
-0.5 (-1.5, 0.6)
0.3 (-1.9, 2.5)
PM effectb
-0.4 (-2.4, 1.5)
-2.0 (-7.3, 3.6)
-0.7 (-2.6, 1.2)
-0.7 (-2.6, 1.2)
-0.7 (-2.8, 1.4)
-1.7 (-7.0, 3.8)
-0.5 (-2.4, 1.4)
-0.7 (-2.8, 1.4)
-0.7 (-2.5, 1.2)
-0.8 (-2.6, 1.1)
-0.6 (-2.7, 1.5)
-0.9 (-3.0, 1.2)
-0.7 (-2.8, 1.4)
-0.5 (-2.6, 1.6)
-0.6 (-2.7, 1.5)
-0.7 (-2.8, 1.4)
-0.8 (-2.9, 1.3)
Table 4b. Association (percent change and 95% confidence interval for an IQR change) between cardiovascular admissions and PM constituents at lag 0.
Single pollutant models included only one PM constituent at a time. Models adjusted for PM levels were obtained using the constituent residual method: first,
a linear regression model was fitted for each constituent with PM levels as the only predictor; then both the residuals from this model and PM levels are
introduced as predictors in the health model to obtain the percent changes. Results were obtained from a meta-analysis combining the city-specific results of
the cities with available data on each constituent.
Single pollutant
Specie
Total Carbon
OC
SO42NO3SiO2
EC
Ca
Al2O3
Fe
K
Mg
Zn
Cu
Ti
Mn
V
Ni
Adjusting for PM mass
PM10
PM2.5
Specie effect
Specie effect
Specie effect
PM10
PM effect
Specie effect
PM2.5
PM effect
0.961 (-0.791, 2.745)
0.719 (-1.001, 2.469)
0.915 (-0.2, 2.043)
0.775 (0.01, 1.547)*
0.732 (-0.289, 1.765)
1.436 (-0.662, 3.579)
1.183 (0.209, 2.166)
0.636 (-0.285, 1.567)
2.117 (0.458, 3.803)*
-0.18 (-0.873, 0.516)
-0.13 (-1.586, 1.347)
1.115 (-0.109, 2.355)
0.535 (-1.997, 3.133)
1.28 (0.341, 2.227)*
2.042 (0.995, 3.1)*
0.594 (-0.14, 1.333)
0.936 (-0.01, 1.893)
1.502 (-0.251, 3.288)
1.851 (-0.157, 3.9)
1.535 (0.251, 2.835)*
0.723 (-0.421, 1.881)
-0.083 (-0.893, 0.732)
2.432 (0.521, 4.379)*
0.004 (-0.556, 0.569)
-0.01 (-0.815, 0.801)
1.885 (-0.961, 4.814)
-0.034 (-0.965, 0.905)
-0.22 (-1.442, 1.015)
0.374 (-0.244, 0.997)
-0.017 (-0.859, 0.832)
0.151 (-0.721, 1.031)
1.323 (0.376, 2.279)*
0.274 (-0.466, 1.021)
0.432 (-3.291, 4.301)
0.033 (-1.534, 1.625)
0.163 (-1.177, 1.523)
0.051 (-1.249, 1.369)
0.031 (-1.223, 1.302)
-0.116 (-1.418, 1.202)
0.715 (-0.81, 2.265)
-1.118 (-4.469, 2.35)
-0.202 (-1.377, 0.987)
0.639 (-3.53, 4.989)
-1.285 (-2.168, -0.394)
-1.047 (-2.227, 0.147)
1.239 (-0.896, 3.421)
-0.411 (-3.617, 2.9)
0.609 (-0.617, 1.85)
1.922 (0.471, 3.394)*
0.017 (-0.854, 0.897)
0.581 (-0.398, 1.57)
1.625 (0.525, 2.736)*
1.854 (0.573, 3.152)*
1.671 (0.576, 2.778)*
1.649 (0.559, 2.751)*
1.733 (0.582, 2.897)*
1.805 (0.513, 3.113)*
1.653 (0.569, 2.748)*
1.703 (0.61, 2.807)*
1.698 (0.613, 2.794)*
1.6 (0.51, 2.701)*
1.706 (0.616, 2.807)*
1.693 (0.47, 2.93)*
1.528 (0.381, 2.689)*
1.726 (0.636, 2.829)*
1.636 (0.49, 2.796)*
1.711 (0.62, 2.813)*
1.628 (0.482, 2.788)*
-0.93 (-4.867, 3.168)
1.282 (-1.737, 4.395)
1.111 (-0.459, 2.707)
-0.686 (-2.221, 0.872)
-0.276 (-1.117, 0.57)
2.058 (-0.082, 4.245)
-0.169 (-0.768, 0.434)
-0.199 (-1.044, 0.653)
0.574 (-0.685, 1.851)
-0.29 (-1.154, 0.58)
-0.515 (-1.783, 0.769)
0.269 (-0.366, 0.91)
-0.23 (-1.107, 0.654)
-0.095 (-1.026, 0.845)
1.143 (0.066, 2.232)*
-0.026 (-0.886, 0.84)
0.267 (-2.952, 3.594)
1.922 (0.305, 3.566)*
1.869 (-0.291, 4.077)
1.689 (0.146, 3.257)*
1.857 (0.329, 3.409)*
1.654 (-0.101, 3.441)
2.56 (0.322, 4.848)*
1.821 (0.286, 3.379)*
1.686 (-0.068, 3.473)
2.009 (0.491, 3.551)*
1.871 (0.344, 3.421)*
1.617 (-0.14, 3.405)
1.673 (-0.082, 3.46)
1.657 (-0.101, 3.447)
1.653 (-0.107, 3.445)
1.864 (0.109, 3.649)
1.72 (-0.032, 3.503)
1.635 (-0.128, 3.431)
Figure 1a. Association between total, cardiovascular and respiratory mortality and PM10 constituents for lags 0, 1 and 2. Associations are
expressed as the percent change in mortality for an interquartile range increase in PM10 constituent levels with 95% confidence intervals. Results
were obtained from a meta-analysis combining the city-specific results of the cities with available data on each constituent. The cities
contributing to the results for a particular constituent are indicated on top of each graph (Bc: Barcelona, M: Madrid, H: Huelva, R: Rome). A
single star (*) indicates a p-value<0.10, while two stars (**) indicate a p-value<0.05.
Figure 1b. Association between cardiovascular and respiratory hospital admissions and PM10 constituents for lags 0, 1 and 2. Associations are
expressed as the percent change in hospital admissions for an interquartile range increase in PM10 constituent levels with 95% confidence
intervals. Results were obtained from a meta-analysis combining the city-specific results of the cities with available data on each constituent. The
cities contributing to the results for a particular constituent are indicated on top of each graph (Bc: Barcelona, M: Madrid, H: Huelva, R: Rome).
A single star (*) indicates a p-value<0.10, while two stars (**) indicate a p-value<0.05.
Figure 2a. Association between cardiovascular and respiratory mortality and PM2.5 constituents for lags 0, 1 and 2. Associations are expressed as
the percent change in mortality for an interquartile range increase in PM2.5 constituent levels with 95% confidence intervals. Results were
obtained from a meta-analysis combining the city-specific results of the cities with available data on each constituent. The cities contributing to
the results for a particular constituent are indicated on top of each graph (Bc: Barcelona, M: Madrid, H: Huelva, Bg: Bologna). A single star (*)
indicates a p-value<0.10, while two stars (**) indicate a p-value<0.05.
Figure 2b. Association between cardiovascular and respiratory admissions and PM2.5 constituents for lags 0, 1 and 2. Associations are expressed
as the percent change in hospital admissions for an interquartile range increase in PM2.5 constituent levels with 95% confidence intervals. Results
were obtained from a meta-analysis combining the city-specific results of the cities with available data on each constituent. The cities
contributing to the results for a particular constituent are indicated on top of each graph (Bc: Barcelona, M: Madrid, H: Huelva, Bg: Bologna). A
single star (*) indicates a p-value<0.10, while two stars (**) indicate a p-value<0.05.
Figure 3a. Forest plots of the association (percent change and 95% confidence interval for an IQR
change in PM10 constituent) between total mortality and iron or manganese at lag 1.
Figure 3b. Forest plots of the association (percent change and 95% confidence interval for an IQR
change in PM10 constituent) between cardiovascular admissions and iron or manganese at lag 0.