Introduction
Atmospheric aerosols may be emitted by both natural and anthropogenic
sources. Given that exposure to particulate matter (PM) is mainly related to
urban environments where anthropogenic activities lead to increased
concentration levels, natural sources are often not considered.
Nevertheless, their contribution may be significant, especially for certain
areas and during specific periods of the year. It has been estimated that
the natural contribution to PM may range from 5 to 50 % in different
European countries (Marelli, 2007). Background annual average PM10 mass
concentration for continental Europe is 7.0 ± 4.1 µg m-3
(Van Dingenen et al., 2004) and is attributed both to natural sources and
anthropogenic long-range transported particles. This background level shows
regional variations, and in some cases (in particular for the southern
European countries) naturally emitted PM may contribute significantly,
causing even exceedances of air quality standards (Pey et al., 2013; Querol
et al., 2009, 1998; Rodriguez et al., 2001). The main natural
sources affecting ambient PM levels are wind-blown soil dust, sea salt,
wildfires, volcanic ash and biogenic aerosol (Viana et al., 2014).
Wind-blown soil dust relates to the transport of mineral dust particles from
agricultural and arid or semiarid regions (Ginoux et al., 2012). North
Africa is the main source of desert dust for European countries (Stuut et
al., 2009). Most of these particles are very coarse (diameter ≥ 10 µm) and are thus deposited close to the source region, while a
significant amount of coarse particles (diameter around 1–10 µm) can
be transported over long distances. An estimation of the emission flux of
desert aerosols that is subject to long-range transport is of the order of
1500 Tg yr-1 (Papayannis et al., 2005).
Sea-salt aerosol is emitted from the sea surface, through bubble-bursting
processes resulting in sea-spray particles with sizes ranging from submicrometre to a few micrometres (O'Dowd and de Leeuw, 2007). Sea-salt
aerosols play an important role in atmospheric chemistry, providing the
surface for heterogeneous reactions and acting as a sink for anthropogenic
and natural gaseous pollutants (Tsyro et al., 2011). The presence of
sea-salt aerosols in the atmosphere was shown to significantly alter the
regional distribution of other inorganic aerosols, namely sulfate, nitrate
and ammonium (van den Berg et al., 2000). It may also appear in both the
coarse and fine fraction (Eleftheriadis et al., 2014). Furthermore, sea salt
helps to reduce the acidity of the air by providing base cations (Tsyro et
al., 2011).
Wildfires relate to the burning of forests and other vegetation, mostly
through natural processes. Large-scale forest fires are a major PM source,
while smoke plumes may be transported over thousands of kilometres,
affecting air quality at local, regional and global scale (Faustini et al.,
2015; Diapouli et al., 2014). Volcanic ash can also have a global impact due
to the fact that emissions may be injected into the stratosphere but have
more infrequent occurrence (von Glasow et al., 2009). Biogenic aerosol is
emitted by vegetation and may be of primary or secondary origin (Caseiro et
al., 2007).
Taking into account that natural sources cannot be controlled, while their
contribution varies between the European countries, EU legislation has
allowed for the subtraction of PM concentrations of natural origin when
Member-States assess and report attainment of air quality standards. Apart
from environmental reporting, quantification of natural contributions to PM
levels is important in terms of exposure assessment as well. Many
epidemiological studies have demonstrated the detrimental effects of
particulate matter pollutants to human health (Ostro et al., 2015; Samoli et
al., 2013). The distinct physico-chemical and toxic properties of
anthropogenic and naturally emitted aerosol call for a differentiation of
peak concentration days due to anthropogenic pollution or natural events,
when assessing population exposure and dose–effect relationships. On the
other hand, extreme natural events that lead to very high exposures may
still adversely affect human health, especially in the case of exposures on
markedly different aerosol size fractions (Zwozdziak et al., 2017) or
sensitive population subgroups (Perez et al., 2008).
High background concentration levels are frequently reported in southern
European countries, often due to the enhanced contribution by natural
sources. The Mediterranean climate, characterised by increased solar
radiation and low rainfall rates, promotes aerosol production and reduces
the potential for dispersion and removal of pollutants (Lazaridis et al.,
2005). The vicinity of southern Europe to North Africa on the other hand
results in frequent and intense dust outbreaks, with high loads of dust from
desert regions transported across the Mediterranean, which often leads to
exceedances of air quality limit values (Pey et al., 2013; Nava et al.,
2012; Athanasopoulou et al., 2010; Querol et al., 1998, 2009; Gerasopoulos et al.,
2006; Kallos et al., 2006; Rodriguez et al., 2001).
In the framework of the AIRUSE-LIFE+ project, the contribution of major
natural sources to PM10 and PM2.5 concentration levels was
quantified for five southern European cities (in Portugal, Spain, Italy and
Greece). The project focused on two sources: the long-range transport of
African dust and sea salt. The contribution from wildfires has been also
detected and quantified in one city (Porto). In addition, a sensitivity
analysis on the calculation of African dust contributions was performed,
providing useful insight into the key factors affecting the quantified dust
concentrations.
Experimental methods
Sampling and analysis
Year-long measurement campaigns were performed from January 2013 to February
2014 in five southern European cities: Porto, Barcelona, Milan, Florence and
Athens (Fig. 1). The cities were selected in order to cover southern Europe
from west to east, as well as sites by the sea and inland. 24 h sampling of
PM10 and PM2.5 (00:00–23:59) was performed once every 3 days
for a full year in all cities. Additional sampling was conducted during days
when African dust episodes were forecasted, in order to better characterise
the contribution of this source to PM levels. Comprehensive chemical
characterisation of PM10 and PM2.5 samples was performed for the
determination of organic and elemental carbon, carbonate carbon,
levoglucosan, ion species and major and trace elements. The measurement
periods, monitoring sites and number of valid chemical speciation samples
for each city are presented in Table 1 (Diapouli et al., 2017). Details about sites, sampling and
analytical procedures are provided in detail in Amato et al. (2016).
Quantification of the contribution of natural sources
Contributions of sea salt and African dust to PM10 and PM2.5
concentrations were quantified based on EU guidelines (SEC 2011/208) for all
five AIRUSE cities. Specifically for African dust, potential African dust
transport events in each city were identified through: (i) 5-day backward
air mass trajectories obtained every 3 h and at three heights (500, 1000 and
1500 m a.s.l.) by the Hybrid Single Particle Lagrangian Integrated
Trajectory (HYSPLIT) Model (Draxler and Rolph, 2003); (ii) dust load surface
concentrations provided by the Barcelona Supercomputing Centre (BSC)-DREAM8b
v2.0 Atmospheric Dust Forecast System (Basart et al., 2012); (iii) dust
concentrations at surface levels and at three additional heights (reaching
up to ∼ 950 m a.s.l.) provided by SKIRON/Dust forecast model
(Spyrou et al., 2010); (iv) 7-day backward air mass trajectories obtained
every 6 h and at three heights (500, 1000 and 1500 m a.s.l.) by the Flextra
model (Stohl and Seibert, 1998). Following the identification of days
potentially affected by long-range transport of African dust, net African
dust concentrations were calculated based on continuous 24 h PM data from
background sites representative of the regional background concentrations at
the studied cities. PM10 and PM2.5 data available from the
national monitoring networks operating at the five cities were used for this
analysis. The 30-day moving averages of the previous and next 15 days of the
regional background concentrations were calculated, excluding days with
potential African dust transport. Averages corresponded to 40th
percentiles in the case of Porto, Barcelona and Florence (Escudero et al.,
2007). In Milan and Athens, a more conservative indicator, the 50th
percentile, was selected since it was found to reproduce better PM
background concentrations (SEC 2011/208). Net African dust load was
quantified for each day forecasted as potential dust event by at least one
of the above-mentioned models, as the observed increase in concentration
with respect to the calculated moving average for that day (representative
of background concentration not affected by dust transport).
Map of Europe and AIRUSE cities: Porto (Portugal), Barcelona
(Spain), Milan and Florence (Italy) and Athens (Greece).
Sea salt (ss) was calculated based on major sea-salt components (Cl and Na)
and typical elemental ratios for sea water (Mg/Na, K/Na, Ca/Na and
SO42-/Na) and the Earth's crust (Na/Al) (Calzolai et al., 2015):
Seasalt=ssNa+[Cl]+ssMg+ssK+ssCa+[ssSO42-],
where
[ssNa]=[Na]-[nssNa],
[nssNa]=0.348×[Al],
[ssMg]=0.119×[ssNa],
[ssK]=0.037×[ssNa],
[ssCa]=0.038×[ssNa], and
[ssSO42-]=0.253×[ssNa].
Description of measurement campaigns: measurement sites, periods and
sampling days.
Monitoring site
Site acronym
Measurement period
Number of samples
Porto Urban traffic
POR-TR
01/2013–01/2014
122 (PM10)/125 (PM2.5)
Barcelona Urban background
BCN-UB
01/2013–01/2014
125 (PM10)/109 (PM2.5)
Milan Urban background
MLN-UB
01/2013–01/2014
276 (PM10)/357 (PM2.5)
Florence Urban background
FI-UB
01/2013–01/2014
223 (PM10)/243 (PM2.5)
Athens Suburban
ATH-SUB
02/2013–02/2014
192 (PM10)/212 (PM2.5)
The contribution of wildfires was estimated only for Porto, where several
wildfires were registered during late August and September of 2013. A
biomass burning factor was obtained by receptor modelling (positive matrix
factorisation, PMF), with several peak concentrations during the wildfires'
period; thus, these concentrations were attributed to wildfires and
classified as natural source contributions. Details on the PMF analysis and
results are presented in Amato et al. (2016).
Sensitivity analysis on the estimation of African dust
contribution
A sensitivity analysis was performed in order to assess the main parameters
affecting the quantification of net African dust concentrations.
Specifically, the following parameters were examined with respect to the
calculation of net African dust: (i) the identification of dust transport
episodes by different modelling tools; (ii) the use of PMF analysis for the
identification of a mixed mineral dust source or a separate African dust
source; (iii) the use of alternative input concentration data, such as the
coarse PM fraction (PM2.5-10) and the mineral component of PM10,
calculated either by PMF analysis or reconstructed from elemental
concentrations based on stoichiometry (Nava et al., 2012; Marcazzan et al.,
2001):
Mineraldust=1.15×1.89×Al+2.14×Si+1.67×[Ti]+1.4×soilCa+1.2×soilK+1.4×[soilFe],
where the soil fractions of Ca, K and Fe have been calculated using their
typical crustal ratios with respect to Al (Mason, 1966):
[soilCa] = 0.45 × [Al], [soilK] = 0.32 × [Al],
[soilFe] = 0.62 × [Al].
Net dust concentrations calculated by PM10 regional background data
were also compared to the dust concentrations provided by the SKIRON/Dust
and BSC – DREAM8b v2.0 transport models.
Results and discussion
Contribution of natural sources to PM concentrations and
exceedances
Mean annual contributions of long-range transported African dust, sea salt
and wildfires (estimated only for Porto) to PM10 and PM2.5
concentrations in each city are presented in Tables 2 and 3, along with
their respective uncertainties. African dust and sea-salt concentrations
were calculated based on SEC 2011/208. Only in the case of Florence, where
PMF analysis produced a separate source attributed to the long-range
transport of Saharan dust, do the concentrations of African dust for PM10
and PM2.5 reported in Tables 2 and 3 correspond to the contributions of
the Saharan dust PMF factor. The contribution of wildfires in Porto was also
estimated by PMF analysis. The uncertainties of the African dust and
sea-salt concentrations were calculated based on the uncertainties of the
parameters included in the respective calculation formulas (PM regional
concentrations in the case of African dust and Na, Cl and Al concentrations
for sea salt). The uncertainties associated with PMF analysis (contribution
of African dust in Florence and of wildfires in Porto) were calculated based
on the standard error of the coefficients of a multiple regression between
the measured PM concentration (independent variable) and the source
contributions estimated by PMF analysis (dependent variables).
Mean annual natural source contributions to the PM10
concentrations and corresponding uncertainties, for the five AIRUSE cities.
Porto
Barcelona
Milan
Florence
Athens
Contributions of natural sources (µg m-3)1
PM10 concentration
34.6
22.5
35.8
19.8
19.6
African dust
0.75 ± 0.02
0.49 ± 0.01
0.76 ± 0.02
1.02 ± 0.14
4.19 ± 0.55
Sea salt
4.27 ± 0.41
1.49 ± 0.18
0.46 ± 0.03
0.64 ± 0.07
1.64 ± 0.14
Wildfires
0.50 ± 0.02
NE2
NE2
NE2
NE2
Total natural
5.52 ± 0.45
1.98 ± 0.19
1.22 ± 0.05
1.66 ± 0.21
5.83 ± 0.69
Relative contributions of natural sources (%)
African dust
2.2 ± 0.1
2.2 ± 0.0
2.2 ± 0.0
5.2 ± 0.7
21.4 ± 2.8
Sea salt
12.3 ± 1,2
6.6 ± 0.8
1.3 ± 0.1
3.3 ± 0.3
8.1 ± 0.7
Wildfires
1.4 ± 0.1
NE2
NE2
NE2
NE2
Total natural
15.9 ± 1.4
8.8 ± 0.8
3.5 ± 0.1
8.5 ± 1.0
29.5 ± 3.5
1 Contributions may slightly differ from the values reported in Amato et al. (2016) due to different statistics of the respective
datasets.2 NE: not estimated.
Mean annual natural source contributions to the PM2.5
concentrations and corresponding uncertainties, for the five AIRUSE cities.
Porto
Barcelona
Milan
Florence
Athens
Contributions of natural sources (µg m-3)1
PM2.5 concentration
26.8
15.2
28.7
14.6
11.0
African dust
0.61 ± 0.01
0.38 ± 0.01
0.41 ± 0.02
0.19 ± 0.02
1.49 ± 0.13
Sea salt
1.22 ± 0.15
0.37 ± 0.10
0.42 ± 0.02
0.11 ± 0.01
0.25 ± 0.03
Wildfires
0.50 ± 0.02
NE2
NE2
NE2
NE2
Total natural
2.33 ± 0.18
0.75 ± 0.11
0.83 ± 0.04
0.30 ± 0.03
1.74 ± 0.16
Relative contributions of natural sources (%)
African dust
2.3 ± 0.1
2.4 ± 0.1
1.4 ± 0.1
1.3 ± 0.2
13.7 ± 1.2
Sea salt
4.6 ± 0.6
2.5 ± 0.7
1.5 ± 0.1
0.7 ± 0.1
2.3 ± 0.3
Wildfires
1.9 ± 0.1
NE2
NE2
NE2
NE2
Total natural
8.7 ± 0.8
4.9 ± 0.8
2.9 ± 0.2
2.0 ± 0.3
16.0 ± 1.5
1 Contributions may slightly differ from the values reported in Amato et al. (2016) due to different statistics of the respective
datasets.2 NE: not estimated.
The African dust contribution to PM concentrations was found to be more
pronounced in the eastern Mediterranean (Athens), with peak concentrations
during springtime reaching up to 127 µg m-3 (maximum 24 h mean
dust concentration during a 15-day dust transport event in May 2013).
Previous studies have also reported the high impact of dust transport events
in Athens and Greece in general (Manousakas et al., 2015; Grigoropoulos et
al., 2009; Mitsakou et al., 2008). The mean annual relative contributions of
African dust to the PM10 concentrations decreased from east to west:
21 % in Athens, 5 % in Florence, and ∼ 2 % in Milan,
Barcelona and Porto. The respective contributions to the PM2.5
concentrations were 13.7 % in Athens, 1.3–1.4 % in Florence and Milan
and 2.3–2.4 % in Barcelona and Porto. The large difference between the
net dust loads calculated for Athens and the other cities is due to the
southern location of Athens, and the severity of some Saharan dust episodes
in the eastern part of the Basin (Athanasopoulou et al., 2016). A high
seasonal variability of contributions was observed, with dust transport
events occurring at different periods in the western and eastern sides of
the Mediterranean (Pey et al., 2013; Querol et al., 2009). The African dust
inputs were highest during spring and lowest during summer in Athens and
Florence. Milan presented high contributions during spring and summer, Porto
during winter and Barcelona during the summer season. These results are in
good agreement with Moulin et al. (1998), who reported that the annual cycle
of African dust transport over the Mediterranean region starts during
springtime in the eastern part, while during summer there is maximum
transport in the western part. Porto was the only city deviating from this
behaviour, suggesting that the studied year may not be representative for
this city for assessing seasonal trends, probably due to the low frequency
and intensity of dust events. Querol et al. (2009) have also noted that when
intense dust transport events are recorded in the eastern Mediterranean
(such as the case for 2013), unusually low African dust contributions are
observed in the western Mediterranean. Sea salt was mostly related to the
coarse mode and exhibited significant seasonal variability as well. The
sea-salt concentrations were highest in Porto, with average relative
contributions equal to 12.3 and 4.6 % for PM10 and PM2.5.
The respective contributions for Athens and Barcelona were 7–8 % to
PM10 and 2.3–2.5 % to PM2.5. The lowest contributions were
observed in Florence and Milan (1.3–3.3 % to PM10). The results
reflect the geographical distribution of the AIRUSE sites: lower levels of
sea salt at the inland Italian cities (Florence and Milan) and higher at the
Mediterranean coastal sites, with the highest contribution observed at the
Atlantic site (Porto). Similar observations were reported by Manders et al. (2010), who showed that the sea-salt load in PM10 at the Atlantic side
of Europe is much higher than in the Mediterranean region, especially the
western Mediterranean. They also showed that the sea-salt load in PM10
is reduced very fast as the air masses progress inland.
Annual mean (left) and 90.4th percentile (right) of PM10
concentrations (PM10_tot) and the respective adjusted
concentrations after subtracting the contribution of natural sources
(PM10_adj), for all AIRUSE sites. Red lines denote the annual
(40 µg m-3) and 24 h limit value (50 µg m-3) set by the
EU (Directive 2008/50/EC).
Annual mean of PM2.5 concentrations (PM2.5_tot) and the respective adjusted concentrations after subtracting the
contribution of natural sources (PM2.5_adj), for the AIRUSE
sites. Red lines denote the annual limit value (25 µg m-3) for
PM2.5 set by the EU (Directive 2008/50/EC).
Mean source contributions (%) to PM10 concentrations
during all days (left) and days with exceedance of the 24 h limit value
(right). In the case of Barcelona, no exceedance was observed, so days with
concentrations greater than the 90th percentile were selected as
representative of high-pollution days.
Net dust concentrations calculated from regional PM10
concentration data: the net dust concentrations when scenarios N2, N3 or N4
are followed are shown in blue. The increment in net dust concentration when
a less strict criterion is selected, thus scenarios N1, N2 or N3 are
followed, is shown in red.
Large-scale uncontrolled forest fires were observed only in Porto during the
period of the study. The average contribution to the PM levels was low (1.4 and 1.9 % to PM10 and PM2.5, respectively) due to the few
event days during the year (after 20 August and during
September). Nevertheless, during event days, the contribution to PM was
greatly increased, reaching 20 and 22 % to PM10 and PM2.5,
respectively.
The uncertainties for the calculated contributions of the different natural
sources were estimated on average around or below 10 %. The relative
uncertainties exhibited low variability during the studied period, except
for the case of African dust, where a significant increase was observed for
net dust concentrations below 5 µg m-3. The relative uncertainties
calculated for each city and PM size fraction were on average at 6–15 %,
10–41 % and above 100 % for African dust loads above 5 µg m-3, between 1 and 5 µg m-3 and below 1 µg m-3, respectively.
The subtraction of the contribution of natural sources from the PM10
concentrations measured at the AIRUSE sites, according to EC regulation, led
to a decrease in the mean annual PM10 concentrations in the range of
3.5 (Milan) to 29.5 % (Athens) (Fig. 2). Attainment of the annual limit
value set by the EU through Directive 2008/50/EC was achieved at all sites
during 2013, although the urban background site in Milan and the urban
traffic site in Porto exhibited concentrations close to the air quality
standard. A similar decrease (1.5–21 %) was observed in the 90.4th
percentiles of PM10 concentrations. The 90.4th percentile
corresponds to the maximum permissible number of exceedance days (35 during
the year). The subtraction of the contribution of natural sources led to a
marginal compliance with the 24 h limit value for Porto, while Milan
continued to present more exceedances than the permitted 35 days (84 days
for PM10 and 82 days for the adjusted PM10 after subtraction of
the contribution of natural sources). Regarding PM2.5 concentrations,
the subtraction of the contribution of natural sources led to decreases in
mean annual concentrations in the range of 1.3 (Florence) to 16 %
(Athens) for the AIRUSE sites. Despite the subtraction of the contribution
of natural sources, Milan did not attain the EU annual limit value, while in
the urban traffic site in Porto, marginal attainment was achieved (Fig. 3).
FLORENCE: Deming regression analysis of net dust concentrations
calculated by the PMF Saharan dust source versus net dust calculated by the
EC methodology with input data: PM10 regional background concentrations
(left) or the PM10 mineral component from stoichiometry (right). The
black line corresponds to the linear regression equation, while the red
dotted lines are the upper and lower bounds, at the 95 % confidence
interval.
ATHENS: Deming regression analysis of net dust concentrations
calculated from PM10 regional background concentrations (PM10)
versus net dust calculated from (i) the PMF mineral dust contributions to
PM10 (MIN-PMF) (left) and (ii) the PM10 mineral component
(MIN-STOICH) (right). The black line corresponds to the linear regression
equation, while the red dotted lines are the upper and lower bounds, at the
95 % confidence interval.
Deming regression analysis of net dust concentrations calculated
from regional background PM10 and PMcoarse (PM2.5-10)
concentrations for Athens (left) and Barcelona (right). The black line
corresponds to the linear regression equation, while the red dotted lines
are the upper and lower bounds, at the 95 % confidence interval.
The average contributions of natural sources to the PM10 concentrations
in each city, during all measurement days and only when exceedance days were
considered, are presented in Fig. 4. Wildfires contributed to exceedances in
Porto. The average concentration during exceedance days was low (below 4 µg m-3); nevertheless it was much higher than the corresponding
mean value during the yearly measurement campaign. Sea salt, on the other
hand, is related to clean air conditions, while no African dust event was
recorded during exceedance days. In the Barcelona urban background site, 24
h concentrations did not exceed the respective EU limit. The highest
concentrations were almost entirely attributed to anthropogenic sources.
Again no dust events were recorded during high concentration days. Similar
results were obtained for Florence and Milan as well. The Athens suburban
site on the other hand is a characteristic example of the effect of natural
sources in background urban environments. The exceedances of the PM10
24 h EU limit value were attributed to African dust by 79 % in terms of
mass concentration (53 out of 67 µg m-3), with a total
contribution from natural sources reaching 88 %. The mean annual
contribution of African dust was also significant (21 %).
Sensitivity analysis on the estimation of net African dust
Based on the available tools for dust transport modelling, different
potential dust event days may be identified. Analysis of the AIRUSE data
showed that the results from the various models are not always in perfect
agreement. A sensitivity analysis was performed in order to assess the
effect of model selection, based on the Athens dataset, which included the
largest number of dust events. Net African dust loads were calculated using
SEC 2011/208. In this analysis, days were marked as dust events for the
following scenarios: (N1) when at least one out of four models gave an event
signal; (N2) when at least two models gave an event signal; (N3) when at least
three models gave an event signal; and (N4) when all models gave an event signal.
The results of the calculated dust concentrations for each of the scenarios
(N2)–(N4) (shown in blue) and the respective increments (shown in red)
when a less strict criterion is selected, (N1)–(N3) respectively, are
presented in Fig. 5.
Small increments in relation to peak dust concentrations were observed
between (N1), (N2) and (N3) scenarios, with mean annual dust contribution
calculated equal to 5.1, 4.3 and 3.9 µg m-3 for scenarios
(N1)–(N3) respectively. Nevertheless, on a daily basis, these increments
reached up to 16 µg m-3 for scenario (N1) in relation to (N2) and
25 µg m-3 for scenario (N2) in relation to (N3), which are of the
same magnitude of typical PM10 concentration levels at this site
(Triantafyllou et al., 2016). When full agreement between models was
required, even very intense events were omitted, as is demonstrated by the
comparison of the (N3) and (N4) scenarios. The analysis highlights the need
for employing as many available tools as possible for the identification of
dust transport events, in order to ensure adequate coverage and reduce
uncertainty.
Another parameter examined was the use of alternative input data in the net
dust calculation algorithm. The net dust loads calculated by PM10
regional background concentrations according to the methodology adopted by
EC, net dust (PM10), were used as reference. Net dust loads were also
calculated by using the following input datasets: (i) the coarse fraction of
PM (PM2.5-10) regional concentrations (instead of the PM10
fraction), net dust (PMcoarse), and the mineral component of PM10, (ii)
either reconstructed through stoichiometry, net dust (MIN-STOICH), or (iii)
obtained by PMF, net dust (PMF).
PMF analysis performed on the datasets of all five studied cities, reported
in Amato et al. (2016), has shown that a distinct African dust factor is not
easily obtained. Only in the case of Florence was a separate PMF factor profile
for African dust identified, providing a potential reference value for
this city and insight into the chemical profile of transported African dust.
The African dust concentrations estimated by PMF in Florence are in very
good agreement with the net dust (MIN-STOICH), while the method based on
PM10 concentrations at the regional site seem to overestimate African
dust loads (Fig. 6). This last observation may be related to the difficulty
of finding a suitable regional background site representative for the city
of Florence in connection to African dust transport, due to the orography of
the region.
In all other AIRUSE cities, a mixed mineral dust factor was obtained,
including both local soil and long-range transported dust. Comparisons of
the net dust loads calculated based on the mineral component of PM10
(quantified stoichiometrically or by PMF) with the reference net dust
(PM10), for the city of Athens, are shown in Fig. 7. For Porto,
Barcelona and Milan no regression between net dust (MIN-STOICH) or net dust
(PMF) with net dust (PM10) was attempted, due to the much lower number
of African dust event days and corresponding chemical speciation data. For
the ATH-SUB dataset, the use of the mineral dust contributions estimated by
PMF provided results in good agreement with net dust (PM10)
concentrations, with the uncertainty increasing in dust concentrations below
10 µg m-3. The net dust calculated from the PM10
stoichiometric mineral component (MIN-STOICH) exhibited very good
correlation with net dust (PM10). Net dust (MIN-STOICH) displayed lower
concentrations by a factor of 1.6 on average, while for net dust loads
< 10 µg m-3 this difference was higher (Fig. 7). Similar
behaviour, with an even higher correlation coefficient, was observed when
PMcoarse concentrations were used in the calculation algorithm (net dust
(PMcoarse)) (Fig. 8). Barcelona exhibited comparable results with Athens
(Fig. 8), while weaker correlations were observed for Porto and Milan (Fig. 9). Florence was not included in this analysis because no PM2.5 or
PMcoarse data were available from the regional background site of the
national monitoring network. The results indicate that African dust is also
found in sizes below 2.5 µm.
Regression analysis of the calculated net dust (PM10) and net dust
(MIN-STOICH) versus PM2.5 / PM10 concentration ratios was used in
order to further examine the calculated dust loads with respect to particle
size (Fig. 10). In the case of net dust (MIN-STOICH), all intense dust
events (with net dust loads greater than 10 µg m-3) were related
with the coarse fraction (low PM2.5 / PM10 ratios). In contrast,
for net dust (PM10) several events with net dust loads from 10 to 20 µg m-3 or higher were related to fine particles
(PM2.5 / PM10 ratios greater than 0.6). This suggests that net dust
(PM10) may include non-mineral fine particles.
The chemical profiles of mineral dust obtained by PMF (Amato et al., 2016)
may provide further information on the discrepancies observed between the
alternative methods. Comparison of the Athens mineral dust profile and the
two mineral dust profiles obtained for Florence (for local and African
dust), showed that the African dust profile differed with respect to the
other two mineral dust profiles in the absence of organic carbon, Zn and Pb,
while a much lower NO3- contribution was also observed. The
presence of these species may reflect the enrichment of local dust with
anthropogenic chemical components. On the other hand, the inclusion of
non-mineral components in the African dust profile (Fig. 11) may explain the
underestimation in Athens of net dust loads when the PM10 mineral
component, net dust (MIN-STOICH), is used (Rodriguez et al., 2001).
Deming regression analysis of net dust concentrations calculated
from regional background PM10 and PMcoarse (PM2.5-10)
concentrations for Porto (left) and Milan (right). The black line
corresponds to the linear regression equation, while the red dotted lines
are the upper and lower bounds, at the 95 % confidence interval.
ATHENS: net dust versus PM2.5 / PM10 concentration
ratios, when dust calculation is based on: (i) PM10 concentrations
(left) and (ii) the PM10 mineral component (right).
Deming regression analysis of dust loads predicted by
transport models versus the net dust concentration calculated: (i) through
regional PM10 concentration data for Athens and (ii) by PMF analysis
for Florence. The lower and upper bounds at the 95 % confidence interval
for the calculated slopes and intercepts are presented in parentheses.
Height
Pearson's coefficient∗
Slope
Intercept
ATHENS
SKIRON model
Surface
0.83
1.2 (0.9; 1.5)
-1.1 (-2.0; -0.1)
450 m a.s.l.
0.86
1.9 (1.6; 2.2)
-1.8 (-2.9; -0.8)
600 m a.s.l.
0.87
2.4 (2.1; 2.7)
-2.4 (-3.5; -1.2)
750 m a.s.l.
0.87
3.0 (2.6; 3.3)
-2.5 (-3.9; -1.2)
DREAM8b v2.0 model
Surface
0.77
2.4 (2.0; 2.8)
-1.3 (-2.6; 0.0)
FLORENCE
SKIRON model
Surface
0.64
1.9 (1.1; 2.7)
0.0 (-0.3; 0.3)
590 m a.s.l.
0.65
2.1 (1.3; 3.0)
0.0 (-0.4; 0.3)
760 m a.s.l.
0.66
2.5 (1.7; 3.4)
-0.1 (-0.5; 0.3)
940 m a.s.l.
0.67
3.1 (2.1; 4.0)
-0.1 (-0.6; 0.3)
DREAM8b v2.0 model
Surface
0.61
4.9 (3.0; 6.8)
-0.6 (-1.7; 0.5)
* All correlations were significant at p=0.05.
Source chemical profiles obtained by the application of the PMF
model (Amato et al., 2016): two mineral dust sources were identified in
Florence (African dust_FI and Local dust_FI)
while a mixed mineral dust profile was found in Athens (Mineral
dust_ATH).
Deming regression analysis between net dust calculated through
PM10 regional background data and dust concentrations modelled at
surface level by (a) the SKIRON/Dust and (b) the BSC DREAM8b v2.0 model, for
the city of Athens. The black line corresponds to the linear regression
equation, while the red dotted lines are the upper and lower bounds, at the
95 % confidence interval.
Net dust concentrations calculated by PM10 regional background data
were also compared to the dust concentrations provided by the SKIRON/Dust
model (at surface level and at three different heights) and the BSC-DREAM8b
v2.0 model (at surface level). Very good correlation was obtained with the
Athens dataset for both models. For the SKIRON/Dust model, the calculated
and modelled dust loads at surface levels were comparable (Table 4).
Nevertheless, no correlation was observed for dust concentrations below 10 µg m-3, suggesting increased uncertainty at these dust levels
(Fig. 12) possibly due to the applied dust cycle parametrisation constraints
and limitations. In the case of Porto, Barcelona and Milan, almost all
modelled dust concentrations were below or equal to 10 µg m-3,
thus producing weak to no correlations with calculated dust loads. Florence
presented similar results as Athens, with somewhat lower Pearson's
coefficients between modelled and calculated data, which may be attributed
to fewer data with concentrations above 10 µg m-3. In addition,
the corresponding slopes of the regression lines were higher than 1.0 in all
cases (Table 4). The differences observed in the slopes and intercepts
calculated for SKIRON/Dust and BSC-DREAM8b v2.0 models are related to the
parametrisations used by each model for simulating the desert dust cycle,
and more specifically with respect to the dust uptake scheme and the soil
characterisation.
Conclusions
The LIFE-AIRUSE project employed a large dataset of PM10 and PM2.5
concentrations and chemical speciation from five southern European cities
(Porto, Barcelona, Milan, Florence and Athens), in order to examine the
contribution of two major natural sources: long-range transport of African
dust and sea salt. The results clearly show that the natural source
contribution may be significant during specific periods, leading to events
of PM limit value exceedances. The African dust contribution to the PM
concentrations was more pronounced in the eastern Mediterranean (Athens),
with peak 24 h concentrations in springtime reaching up to 127 µg m-3 during a 15-day long African dust event in May 2013. The mean
annual relative contributions of African dust to the PM10
concentrations decreased from east to west: 21 % in Athens, 5 % in
Florence, and ∼ 2 % in Milan, Barcelona and Porto. High
seasonal variability of contributions was observed, with dust transport
events occurring at different periods in the western and eastern sides of
the Mediterranean. Sea salt was mostly related to the coarse mode and
exhibited significant seasonal variability. The sea-salt concentrations were
highest in Porto, with average relative contributions equal to 12.3 % for
PM10. The respective contributions for Athens and Barcelona were 7–8 %, while the lowest contributions were observed in Florence and Milan
(1.3–3.3 %). The results reflect the geographical distribution of the
AIRUSE sites: lower levels of sea salt at the inland Italian cities
(Florence and Milan) and higher at the Mediterranean coastal sites, with the
highest contribution observed at the Atlantic site (Porto). Uncontrolled
forest fires were observed to affect PM concentrations only in Porto during
the studied period. The mean annual contribution to the PM levels was low
(1.4 and 1.9 % to PM10 and PM2.5, respectively) due to the
few event days during the year (after 20 August and during
September). Nevertheless, during event days, the contribution to PM was
greatly increased, reaching 20 and 22 % of 24 h PM10 and PM2.5,
respectively.
A sensitivity analysis for the quantification of African dust contribution
was performed, in order to assess the major factors affecting the calculated
net dust concentrations. The analysis indicated that a key parameter to be
considered is the selection of an appropriate regional background site. In
addition, the use of as many available tools as possible for the
identification of dust transport events is recommended, in order to ensure
adequate coverage and reduce uncertainty. The results also indicated that
the calculation of net African dust through the use of regional background
data of PM10 (or PM2.5) mass concentrations provides higher dust
concentration estimates in comparison to the use of the same methodology
with as input data the mineral component of PM, derived stoichiometrically.
The analysis of mineral dust source profiles obtained by PMF provides
further evidence that additional species to the crustal matter, usually
secondary aerosol, are the source of this discrepancy, arriving together or
associated with the crustal component during long-range transport.
The present study has demonstrated that natural sources are often expressed
with high-intensity events, leading to very high daily contributions and
exceedances of the EU air quality standards. Since these sources cannot be
controlled, relevant mitigation measures can only be focused on minimising
the effects of this type of pollution. Namely, measures are recommended to
target reducing the potential of particles deposited on the streets and
other surfaces to resuspend, while emergency action plans, especially for
sensitive population subgroups, may come into force during days when extreme
dust events are forecasted.