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A simplified approach to the indirect evaluation of the chemical composition of atmospheric aerosols from PM mass concentrations
Atmospheric Environment 44 (2010) 5112e5121
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A simplified approach to the indirect evaluation of the chemical composition of atmospheric aerosols from PM mass concentrations Jorge Pey*, Andrés Alastuey, Xavier Querol, Noemí Pérez, Michael Cusack Institute of Environmental Assessment and Water Research, IDÆA-CSIC, C/Lluis Solé i Sabaris, s/n, 08028, Barcelona, Spain
a r t i c l e i n f o
a b s t r a c t
Article history: Received 26 March 2010 Received in revised form 30 August 2010 Accepted 2 September 2010
The present work shows a straightforward procedure to indirectly estimate the chemical composition at a given site only from the determination of the PM concentrations, and the classification of the days according to the main meteorological scenarios. A previous study based on the mean chemical composition associated to the main meteorological scenarios is required. This experiment has been carried out with data from two monitoring sites in the North-Western Mediterranean, one regional and one suburban background. At both sites, one-year datasets on chemical PM10 composition were obtained. Based on these datasets, the mean PM10 compositions according to the most relevant meteorological situations were calculated for both locations. After that, the reconstruction of the chemical composition for all the days with available PM10 concentrations was completed. Subsequently, the estimated PM10 composition was compared with that determined experimentally. The comparison between the rebuilt and the experimental results was very satisfactory in the case of the regional background site, and relatively replaced in the other case. Furthermore, the validation of the method at the regional background site has been conducted from the reconstruction of a 4-year data base, and subsequent comparison with the experimental chemical composition. Our results show that it is possible to attain a good approach to the chemical composition at regional background sites, where local emission sources are negligible. Conversely, when the local sources rise in importance, i.e., at a suburban background site, the approach is suitable only for those components with a more regional origin and/or those from long range transport of air masses. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Western Mediterranean Modelling Regional background Mineral matter PM10
1. Introduction Atmospheric particulate matter comprises of many diverse substances, with different chemical compositions, a large variety of shapes, a wide range of sizes, and diverse physical properties. Atmospheric aerosols are of special scientific interest because of their negative health effects (Dockery and Stone, 2007); their role on the radiative budget (IPCC, 2007); and their effects on ecosystems (Niyogi et al., 2004; Bytnerowicz et al., 2007). Air Quality Directives focus on the measure of PM levels, but little attention is paid to other important parameters such as the particle size distribution, the number concentration, or even the chemical composition. Recently, the new 2008/50/EC European Air Quality Directive recommended some compositional determinations to be carried out in PM2.5 from regional background areas þ þ 2þ þ (SO2 4 , NO3 , NH4 , Na , Ca , K , Cl , organic and elemental carbon).
* Corresponding author. Tel.: þ34 934095410; fax: þ34 934110012. E-mail address: [email protected] (J. Pey). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.09.009
The chemical composition of the atmospheric aerosols is a key parameter for climatic, health and ecosystem studies. However, the chemical characterization of the atmospheric aerosols involves different analytical procedures, incurring important economic costs, for the quantification of the main chemical species in a given sample. Furthermore, with the exception of using relatively recent and sophisticated instruments such as Aerosol Mass Spectrometers, monitors for organic and elemental carbon in real time, or specific monitors (sulphate, nitrate, ammonium, chloride, etc.), the chemical characterization of atmospheric aerosols collected on a filter requires significant time for completion. The chemical composition of the atmospheric aerosols is dependent on different factors. The contribution of specific sources may have a clear impact on different components. The second factor to take into account is the origin of air masses that can significantly affect not only the PM levels, but also the chemical composition. For example, air masses arriving from desert regions to the Mediterranean Basin clearly change the composition of the atmospheric aerosols, which are typically of a mineral nature (Querol et al., 2009). The third important factor to bear in mind is
J. Pey et al. / Atmospheric Environment 44 (2010) 5112e5121
the occurrence of atmospheric processes, which may depend on the emissions (natural and anthropogenic, gaseous and particulate) and the meteorological variables such as insolation, temperature, wind velocity and humidity, among the most relevant. With this in mind, the regional background (RB) location appears to be an ideal site to study the chemical composition of the atmospheric particles according to the origin of the air masses owing to the relatively far distance to anthropogenic sources. The modelling of the PM concentrations, NOx and O3, and the chemical components of the atmospheric aerosols is currently under intensive research and improvement. The large number of variables influencing the air pollutant concentrations and its variability in the atmosphere make their modelling a complex issue. Some atmospheric pollutants are generally well reproduced by models owing to their stability in the atmosphere and their wellknown emission sources. Other components however, are usually underestimated with current models. That is the case for secondary organic aerosols (Kanakidou et al., 2005; Hallquist et al., 2009; Hodzic et al., 2009). This study seeks to reproduce the chemical composition of PM10 at a given site. In order to test the viability of this method in reproducing PM10 composition from different types of monitoring stations, two sites in the NW Mediterranean Basin where chemical compositional PM10 data are available (Pey et al., 2009a,b) were studied: one RB and one suburban background (SU). For this purpose, a previous study of the mean PM10 composition according to the main meteorological scenarios, in our case based on experimental PM10 composition datasets, is needed. After that, a rigorous meteorological study has been done in order to classify each day according to the origin of the air masses. Once the estimated chemical composition was obtained, the feasibility of this procedure was tested for the two sites by comparing the estimated data with one-year of experimental chemical compositional data. The method proposed in this manuscript neither aims to substitute the modelling of the chemical composition of atmospheric particulate matter in a given region, nor to substitute PM10 chemical analysis. It may, however, be considered a complementary tool easily used by anyone to make an estimation of the chemical composition for a given day and at a given site only by using previous data available. 2. Methodology 2.1. Study area: monitoring sites and meteorological features in 2004 The region selected for this study involves the NE of the Iberian Peninsula and the Balearic Islands, both located in the NW of the Mediterranean Basin. In a regional context, the Mediterranean Basin registers high levels of atmospheric pollutants, mainly as a result of the intense anthropogenic activities that not only increase PM levels at a regional scale but also the ozone concentrations (Millán et al., 1992, 1997). Natural contributions of atmospheric aerosols (African dust) are also frequent and important in this region, estimated to be around 2 mg m3 in the NE of the Iberian Peninsula (Pey et al., 2009a), and around 4 mg m3 in the Balearic Islands (Pey et al., 2009b). Furthermore, the regional meteorology plays an important role in the poor dispersion of the atmospheric pollutants (Millán et al., 1992, 1997; Gangoiti et al., 2001; Rodríguez et al., 2001, 2003). In this region, two monitoring sites were selected: 1) The Montseny site is located 50 km to the NNE of Barcelona, in a densely forested natural park, at 720 m a.s.l. This site represents the RB conditions of the NE of the Iberian Peninsula according to Pérez et al. (2008). The Montseny site belongs to
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the Air Quality network of the Autonomous Government of Catalonia, and is one of the 20 monitoring stations for atmospheric research constituting the EUSAAR network (European Supersites for Atmospheric Aerosol Research). 2) The Castillo de Bellver site is located 3 km to the SW of the Palma de Mallorca city, in the Mallorca Isle, at 114 m.a.s.l.. The monitoring site is located in a forested park (with typical Mediterranean tree species although pines prevail). This site is classified as a SU monitoring station (Pey et al., 2009b). The monitoring site of Castillo de Bellver belongs to the Air Quality network from the Balearic Islands. Special meteorological conditions were registered in 2004 affecting the Western Mediterranean Basin (WMB). These peculiarities were: 1) a dry and low-windy 2003e2004 winter that favoured stagnating conditions in the WMB and consequently intense atmospheric pollution episodes; 2) a wet period from mid February to end April, more than usual; 3) very intense African dust episodes in February, July and SeptembereOctober; 4) recurrent regional recirculation of air masses in summer over the WMB; 5) displacement of the typical SeptembereOctober wet period towards December. Despite these particularities, mean annual temperature and precipitation rates fall in the usual values. 2.2. Instruments Montseny: levels of PM10, PM2.5 and PM1 were continuously measured since March 2002 with optical counters (GRIMM 1107), that subsequently convert the obtained number into mass concentrations applying specific algorithms. Simultaneously, 24-hour PM10 and PM2.5 samples (twice per week) were collected on quartz micro-fibre filters (Schleicher and Schuell, QF20 150 mm) by using high volume samplers (30 m3/h) and DIGITEL cut-off inlets. PM mass concentrations were determined by standard gravimetric procedures and used to correct the PM10, PM2.5 and PM1 measurements obtained with the optical counters (PM2.5 correction was applied to PM1 data owing to the absence of PM1 gravimetric measurements). In all cases the R2 correlation coefficients were >0.8 (Pérez et al., 2008). Castillo de Bellver: PM10 levels were continuously measured by a Beta Met One BAM1020 monitor (BETA attenuation). PM10 data were corrected by the factors obtained by the comparison of gravimetric and real time measurements, in all cases with correlation coefficients R2 > 0.8 (Pey et al., 2009b). Two periods were considered for applying the correlations: JanuaryeMay 2004 (slope ¼ 0.87; R2 ¼ 0.83), and June 2004eFebruary 2005 (slope ¼ 1.12; R2 ¼ 0.86), determined by the calibration of the instrument at the end of May 2004. Two PM10 24-hours samples were collected on the same type of quartz fibre filters as at Montseny every week on alternative days from 08/01/2004 to 29/02/ 2005. The instruments used for this purpose were high volume samplers MCV-CAV (30 m3 h1) with MCV inlets for PM10. 2.3. Chemical characterization Quartz fibre filters were first pre-treated at 200 C during 4 h. The filters were conditioned inside an insulated chamber at 20e25 C and 25e30% RH before and after sampling for at least 24 h. They were then weighed at least three times to obtain constant values. PM concentrations were determined by weight difference. Thereafter, filters were analyzed by different techniques in order to determine the concentrations of about 60 elements and components, following the methodology described in Pey et al. (2009b). That was basically a bulk sample acidic (HF:HClO4:HNO3)
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concentrations were measured by using an Elemental Carbon Analyser (LECO). Levels of organic and elemental carbon (OC and EC) were determined in a representative selection of samples by means of a SUNSET instrument. Indirect determinations from analytical data were obtained for: a) CO2 3 , calculated from Ca content, assuming that this element is mainly present as calcite (CaCO3; CO2 3 ¼ 1.5*Ca); b) SiO2, determined from the Al content on the basis of prior experimental equations (SiO2 ¼ 3*Al2O3, see Querol et al., 2001). Following these procedures, it is possible to obtain the concentration of major species (TC, SiO2, CO2 3 , Al, Ca, Na, Mg, K, Fe, þ SO2 4 , NO3 , Cl and NH4 ) and trace elements (Li, P, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Cd, Sn, Sb, Ba, La, Ce, Hf, Pb, Bi, Th, U, among others). Relative errors of the abovementioned analysis of major species and trace elements have been estimated as less than 10% in all the cases (Querol et al., 2008).
2.4. Daily meteorological analysis Fig. 1. Sectors distinguished for the interpretation of the origin of air masses (AN: North Atlantic advection, ANW: North-Western Atlantic advection, AW: Western Atlantic advection, ASW: South-Western Atlantic advection, NAF: African dust outbreaks, MED: Mediterranean advection, EU: European air masses transport; REG: summer regional recirculation, WAE: winter anticyclonic episodes) in the WMB.
digestion of ½ of each filter and subsequent analysis of major and trace elements by means of Inductively Coupled Plasma Atomic Emission Spectrometry, ICP-AES, (IRIS Advantage TJA Solutions, THERMO), and Inductively Coupled Plasma Mass Spectrometry, ICPMS, (X Series II, THERMO), respectively. The concentrations of soluble anions were determined in water leachates from ¼ filter 2 sections. The content of NHþ 4 , Cl , SO4 and NO3 was obtained by means of Ionic Chromatography HPLC (High Performance Liquid Chromatography) using a WATERS IC-pakTM anion column and WATERS 432 conductivity detector. Finally, total carbon (TC)
120
PM10 PM 10
AT
A number of meteorological tools were used to interpret the daily atmospheric scenarios influencing the PM levels and composition at both study areas. These tools include: 1) the calculation of back-trajectories of air masses at receptor points located at 750, 1500 and 2500 m.a.s.l. (HYSPLIT4, Draxler and Rolph, 2003); 2) the interpretation of geopotential height maps at 1000, 900 and 850 mb (NOAA/ESRL Physical Sciences Division, Boulder Colorado from their Web site at http://www.cdc.noaa.gov/); 3) the identification of exotic outbreaks of African dust by interpreting aerosol dust concentration maps (BSC/DREAM, NAAPS, and SKIRON) and satellite imagery (NASA-SeaWiFS Project). With all of these tools we are able to classify each day into the main meteorological scenarios that affect the Western Mediterranean: Atlantic advection (AN: Atlantic North; ANW: Atlantic North-West; AW: Atlantic West; ASW: Atlantic South-West); African dust outbreaks (NAF); Mediterranean advection (MED); European air masses
NAF
MED
EU
REG
WAE
Montseny 2004
PM10 (µg/m3)
100 80 60 40 20 0
120
Castillo de Bellver 2004
PM10 (µg/m3)
100 80 60 40 20 0 jan-04
feb-04
mar-04
apr-04
may-04
jun-04
jul-04
aug-04
sep-04
oct-04
nov-04
dec-04
Fig. 2. Daily variability of PM10 at Montseny (top) and Castillo de Bellver (bottom) in 2004. Different markers show the main meteorological scenarios (AT: Atlantic advection; NAF: African dust outbreaks; MED: Mediterranean advection; EU: European air masses transport; REG: summer regional recirculation of air masses; WAE: winter anticyclonic stagnation scenarios). Note the very similar pattern at both sites located more than 200 km apart.
J. Pey et al. / Atmospheric Environment 44 (2010) 5112e5121
Montseny
Castillo de Belver 3.0 µg/m3 10%
3
2.8 µg/m 14%
3.4 µg/m3 17%
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3.8 µg/m3 13%
SO42NO3-
2.3 µg/m3 8%
1.8 µg/m3 9% 3
1.1 µg/m 5%
6.6 µg/m3 23%
NH4+
1.4 µg/m3 5%
Mineral Sea spray
6.1 µg/m3 30%
EC+OM 4.6 µg/m3 23%
0.5 µg/m3 2%
3.0 µg/m3 10%
Unaccounted
3
8.5 µg/m 29%
þ Fig. 3. Mean concentrations (mg m3 and %) of SO2 4 , NO3 , NH4 , mineral matter, sea spray, OM þ EC and unaccounted mass in PM10 obtained at Montseny and Castillo de Bellver experimentally in 2004.
influence (EU); winter anticyclonic stagnation episodes (WAE); and summer regional recirculation of air masses (REG). The regions considered for the classification of the air masses according to their origin have also been identified in previous studies (Rodríguez et al., 2003; Pérez et al., 2008). These sectors are displayed in Fig. 1. 3. Results
variability in a regional context (the distance between the two sites exceeds 200 km). Furthermore, the seasonal evolution described by the PM10 mass concentrations follows the concatenation of different meteorological scenarios: WAE in winter, REG in summer, Atlantic advection in spring, and episodic NAF all over the year. As for the PM10 levels, the PM10 composition for each meteorological scenario is clearly different at both sites (see below).
3.1. Mean PM levels and seasonal evolution
3.2. Mean PM10 composition for each meteorological scenario
Mean levels of PM10, PM2.5 and PM1 at the RB site of Montseny in 2004 were 18, 14 and 12 mg m3, respectively (Pérez et al., 2008). Alternatively, mean annual PM10 and PM2.5 levels in 2004 at the SU site of Castillo de Bellver were 29 and 20 mg m3, respectively (Pey et al., 2009b). Nevertheless, only PM10 data are used in the present study. Although the PM10 levels are higher at Castillo de Bellver when compared with those at Montseny, the seasonal evolution registered at both sites is rather similar (Fig. 2). This fact reflects the decisive role played by the meteorology in controlling the PM
Fig. 3 shows the mean PM10 composition at Montseny (101 samples in 2004) and Castillo de Bellver (95 samples in 2004). Relative proportions at Montseny and Castillo de Bellver of NO 3 þ (8e9%), SO2 4 (13e14%) and NH4 (5%) are very similar at both sites. On the contrary, mineral matter (23% at Montseny and 29% at Castillo de Bellver) and sea spray (2% in contrast to 10%) concentrations prevail at Castillo de Bellver; and the carbonaceous species dominate at Montseny (30% as compared to 23%). In both cases, the PM10 mass accounted by these components represents between 83 and 90% of the PM10 mass.
Table 1a þ Mean, standard deviation (S.dv), maximum (Max) and minimum (Min) concentrations (in %) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC in PM10 during the different meteorological scenarios at Montseny in 2004. The results are based on a specific number of samples indicated in brackets. %
NO 3
SO2 4
NHþ 4
Mineral matter
Sea spray
OMþEC
Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min AN (9) AW-NW (27) ASW (6) NAF (13) MED (5) EU (7) REG (24) WAE (10)
15 14 13 12 21 15 19 16
3 5 2 5 4 4 4 3
19 24 17 23 26 20 30 21
10 7 10 3 17 8 12 10
15 11 11 4 9 14 4 22
8 6 5 3 3 7 3 5
25 22 20 12 13 23 12 29
1 2 7 1 6 6 1 13
8 6 5 4 7 8 6 10
3 2 3 2 2 3 2 2
11 11 11 8 9 11 13 14
3 2 2 1 3 4 4 6
12 18 22 43 16 8 19 15
7 7 9 15 8 5 6 5
21 36 32 74 23 16 32 24
3 6 11 23 5 3 9 9
2 4 4 2 4 3 3 3
1 3 3 1 2 1 2 2
4 14 8 4 5 5 8 6
1 1 1 1 2 1 1 1
32 40 37 25 40 40 34 25
15 15 9 12 9 14 9 7
63 72 44 41 49 70 54 42
13 23 20 5 30 27 22 17
Table 1b þ Mean concentration (in mg m3) and standard deviation (S.dv) in PM10 of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC during the different meteorological scenarios at Montseny in 2004.
mg m3 AN AW-NW ASW NAF MED EU REG WAE
PM10
12 15 19 31 14 22 22 25
NO 3
SO2 4
NHþ 4
OM þ EC
Mineral matter
Sea spray
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
1.8 2.1 1.8 3.7 2.9 3.0 4.2 4.0
0.4 0.8 0.3 1.6 0.6 0.8 0.9 0.8
1.8 1.7 1.5 1.2 1.3 2.8 0.9 5.5
1.0 0.9 0.7 0.9 0.4 1.4 0.7 1.3
1.0 0.9 0.7 1.2 1.0 1.6 1.3 2.5
0.4 0.3 0.4 0.6 0.3 0.6 0.4 0.5
1.4 2.7 3.1 13.3 2.2 1.6 4.2 3.8
0.8 1.1 1.3 4.7 1.1 1.0 1.3 1.3
0.2 0.6 0.6 0.6 0.6 0.6 0.7 0.8
0.1 0.5 0.4 0.3 0.3 0.2 0.4 0.5
3.8 6.0 5.2 7.8 5.6 8.0 7.5 6.3
1.8 2.3 1.3 3.7 1.3 2.8 2.0 1.8
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Table 2a þ Mean standard deviation (S.dv), maximum (Max) and minimum (Min) concentrations (in %) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC in PM10 during the different meteorological scenarios at Castillo de Bellver in 2004. The results are based on a specific number of samples indicated in brackets. %
NO 3
SO2 4
NHþ 4
Mineral matter
OM þ EC
Sea spray
Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min AN (5) AW-NW (27) ASW (4) NAF (17) MED (4) EU (13) REG (14) WAE (11)
15 10 13 15 9 13 18 19
3 3 2 5 4 3 4 6
19 15 15 24 13 17 24 28
10 5 10 6 4 8 13 9
8 7 15 7 7 7 6 10
1 3 5 3 2 2 1 5
9 18 22 16 9 12 8 19
7 4 12 3 5 4 5 6
5 5 4 4 4 5 5 10
1 3 2 2 4 3 2 5
6 14 6 7 9 11 8 19
3 1 1 1 1 2 2 5
37 22 20 46 30 21 29 22
23 14 10 21 13 14 14 13
66 65 29 80 43 42 62 49
16 5 7 23 12 4 21 8
10 15 14 9 21 12 8 7
5 11 3 5 6 10 4 4
14 49 18 21 27 38 15 14
4 3 10 4 15 4 3 2
20 19 16 13 19 21 20 20
10 12 7 9 13 6 7 9
31 53 26 31 34 32 30 38
7 2 9 3 5 11 10 5
Table 2b þ Mean concentration (in mg m3) and standard deviation (S.dv) in PM10 of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC during the different meteorological scenarios at Castillo de Bellver in 2004.
mg m3 AN AW-NW ASW NAF MED EU REG WAE
PM10
22 25 30 39 21 26 26 29
NO 3
SO2 4
NHþ 4
OM þ EC
Mineral matter
Sea spray
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
3.3 2.5 3.6 5.7 1.8 3.4 4.7 5.2
1.0 0.9 0.9 2.2 0.6 1.4 1.0 1.6
1.7 1.9 4.2 2.8 1.5 1.9 1.7 3.2
0.6 1.2 1.4 1.8 0.5 0.9 0.6 2.5
1.2 1.2 1.3 1.5 0.6 1.6 1.1 3.2
0.6 1.0 1.1 0.6 0.4 1.3 0.5 2.5
6.8 5.0 5.9 18.8 6.6 4.6 7.6 5.7
2.1 2.6 2.9 11.7 3.6 2.2 3.5 2.8
2.1 3.2 4.1 3.6 4.3 2.7 2.0 1.7
1.1 2.0 1.6 1.6 2.0 1.5 1.0 1.2
4.9 5.1 4.7 4.6 3.6 5.2 5.1 5.7
3.3 3.8 2.1 2.8 2.3 1.6 1.7 3.4
The 101 and 95 PM10 samples from Montseny and Castillo de Bellver respectively, were classified according to 9 meteorological scenarios (4 sectors in the Atlantic, North Africa, distant Mediterranean, Central and Eastern Europe, winter anticyclonic stagnation scenarios, and summer regional recirculation of air masses). As observed in Tables 1 and 2, in addition to the differences of the PM10 levels associated with the different meteorological scenarios, the composition in relative proportions in some cases differs markedly from one scenario to another. The compositional results (SO2 4 , NO3 , , mineral dust, sea spray and OM þ EC) obtained previously for NHþ 4 each meteorological scenario were used to estimate the chemical composition for each day, as detailed bellow in Section 3.4. 3.3. Variability of the PM composition during the meteorological scenarios The classification of the samples according to the meteorological scenarios distinguished allows identifying: 1) the mean proportions of each PM constituent for each meteorological scenario; 2) the standard deviation of that PM composition when comparing samples collected under similar meteorological conditions. Tables 1 and 2 contain the mean PM composition, the maximum and minimum concentrations, and the mean standard deviation of the different PM components at Montseny and Castillo de Bellver for each meteorological scenario. In the case of Montseny, there is clear evidence that some components are strongly dependent on the origin of the air mass as shown in Table 1a and b. This is the case for mineral matter, clearly dominant during African dust outbreaks (13 mg m3, 43% of the PM10 mass); nitrate (5.5 mg m3, 22%) and ammonium (2.5 mg m3, 10%) that exhibit the maximum predominance under winter anticyclonic stagnating episodes; or sulphate (4.2 mg m3, 19% of the PM10 mass) during regional recirculation episodes. On the other hand, some components such as sea spray and carbonaceous particles do not vary as drastically (with some exceptions) in relation to the origin of the air masses. The evaluation of the standard deviation associated with the concentrations of each PM
component during the meteorological situations distinguished has been conducted. The results show that for most cases the standard deviation is not higher than 30%. These relatively low standard deviations guaranty the use of these “mean coefficients” as inputs for the estimation of the chemical composition from the PM10 data. After that, only from the PM10 daily concentrations and the classification of the days according to the meteorological scenarios previously distinguished, it will be possible to reproduce the chemical composition of PM10. A similar analysis has been performed for the Castillo de Bellver PM10 data (Table 2a and b). As for Montseny, the mineral matter prevails during African dust outbreaks (19 mg m3, 46%). Sea spray in this case is noticeable during Mediterranean advections (4.3 mg m3, 21%). Nitrate prevails during South-Western advections (4.2 mg m3, 15%) and ammonium during winter anticyclonic episodes (3.2 mg m3, 10%). On the contrary, OM þ EC do not present significant variations according to the origin of air masses. The evaluation of the standard deviations points out a higher variability in the case of Castillo de Bellver with respect to the Montseny.
3.4. Application of the procedure In order to make an estimation of the chemical composition for a given day it is necessary to have: Table 3 þ Mean concentrations (mg m3) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC obtained at Montseny and Castillo de Bellver experimentally (Exp.) and by using the simple approach proposed in this manuscript (Mod.). Mean experimental concentrations were obtained from 101 and 73 samples from Montseny and Castillo de Bellver, respectively. SO42 NO3 NH4þ Mineral OM þ EC Sea Sp. Unacc. Montseny
Mod. 3.0 Exp. 2.8
2.0 1.8
1.2 1.1
4.4 4.7
5.9 5.9
0.6 0.6
2.3 2.5
Castillo de Bellver Mod. 3.6 Exp. 3.8
2.0 2.3
1.2 1.4
7.8 8.5
4.5 4.8
2.7 3.0
4.2 5.1
J. Pey et al. / Atmospheric Environment 44 (2010) 5112e5121
a) the daily PM10 concentration for the specific day; b) the classification of the given day according to the origin of the air masses; and c) the mean proportion of all chemical component according to each meteorological scenario.
a
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For a given day the PM10 is made up of different components which may be encountered in the proportions typically found according to the air masses origin. For a particular day (for example a REG day) the PM10 may be made up of the sum of different chemical components:
10 Modelled
Experimental
Modelled
Experimental
Modelled
Experimental
Modelled
Experimental
Modelled
Experimental
Modelled
Experimental
8 6 4 2 0 14 12 10 8 6 4 2 0 6 5 4 3 2 1 0 30
56
25 20 15 10 5 0 3
2
1
0 16
14 12 10 8 6 4 2
24/12/2004
15/12/2004
14/11/2004
25/11/2004
27/10/2004
10/10/2004
22/09/2004
03/10/2004
26/08/2004
03/09/2004
28/07/2004
08/08/2004
23/07/2004
01/07/2004
10/07/2004
17/06/2004
10/06/2004
04/06/2004
24/05/2004
30/04/2004
20/05/2004
25/04/2004
14/04/2004
27/03/2004
05/03/2004
19/03/2004
07/02/2004
21/02/2004
30/01/2004
0
3 þ Fig. 4. (a) Comparison between the modelled concentrations (colour bars) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC (mg m ) and those determined experi3 þ mentally (unfilled circles) at Montseny in 2004. (b) Scatter plots between the modelled concentrations (x-axis) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OMþEC (mg m ) and those determined experimentally (y-axis) at Montseny in 2004.
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b
Fig. 4. (continued).
þ (1) PM10 (mg m3) ¼ [SO2 4 ] þ [NO3 ] þ [NH4 ] þ [Mineral] þ [Sea spray] þ [OM þ EC] þ [unaccounted] In this equation (1), the concentration of each component may be calculated as follows: 2 3 (2) [SO2 4 ] ¼ PM10 (mg m ) * (%SO4 during REG episodes/100)
where the % applied must be that obtained for the corresponding meteorological scenario. Following this procedure it is possible to assess the PM composition of each day only by determining the PM10 levels and classifying carefully each day according to the meteorological scenarios distinguished. This method has been applied to the PM10 data from the RB site of Montseny and from the SU site of Castillo de Bellver for 2004. Subsequently, the experimental chemical composition of PM10 available for both sites has been used to test the feasibility of this procedure. As shown in Table 3, the mean PM composition calculated for the whole period (around 350 days with PM10 daily concentrations) at each site by applying the described procedure is akin to that determined experimentally (in 101 and 95 daily samples at Montseny and Castillo de Bellver, respectively). After applying this method, the daily PM10 chemical contributions reproduced and those obtained experimentally have been compared. These results are presented in Fig. 4a and b for Montseny and in Fig. 5a and b for Castillo de Bellver. As seen in Fig. 4a and b, the reproducibility of the 2004 PM10 composition at Montseny is very satisfactory for most of the PM constituents including sulphate, nitrate, ammonium and mineral matter (equation slopes from 0.82 to 0.88 and R2 coefficients varying from 0.65 to 0.85). The reconstruction of the OM þ EC concentrations is reasonably acceptable (equation slope 0.91 and R2 0.46). The only component that is not very well reproduced is the sea spray (equation slope 0.63 and R2 0.19), sometimes showing large differences between experimental measurements and modelled concentrations. It is important to note, however, that the sea spray concentrations recorded at Montseny are generally very low. The poor modelling of the sea spray at Montseny may be due to different reasons, including: 1) the relatively intense and infrequent sea spray
events, difficult to be reproduced by using this methodology; 2) the sea spray is calculated as the sum of Cl and total Na, the latter mainly from a marine origin; however, when intense African dust events occur, Na may be partially mineral. In these cases, an overestimation of the sea spray contribution may occur. An identical evaluation as for Montseny has been carried out for Castillo de Bellver (Fig. 5a and b). In this case, the rebuilding of the PM composition from the daily PM10 data and the subsequent comparison with the experimental concentrations of the major components has resulted in good agreement for some components such as mineral matter and sulphate, with equation slopes very close to 1 and R2 coefficients from 0.66 to 0.75. Similar to Montseny, the sea spray concentrations have been poorly reproduced (R2 ¼ 0.08) probably due to their local origin (the monitoring site is located less than 1 km from the shore line). Nitrate and ammonium have been partially reproduced (equation slopes very close to 1 and R2 varying from 0.21 to 0.26), with the exception of episodic days with very high experimental concentrations weakly reconstructed. Nevertheless the OM þ EC concentrations have not been properly reproduced in this case. The results from Castillo de Bellver point out the following considerations: 1) those components typically associated with regional and long range transport are well reproduced (sulphate and mineral matter), even at a monitoring site affected by local emission sources. That is the case of the mineral matter and the sulphate, both components depending clearly on the origin of the air masses at this site; 2) other components such as OM þ EC, nitrate and ammonium may be dominantly of a local origin, in the case of Castillo de Bellver coming from road traffic emissions in Palma de Mallorca, and shipping emissions in the harbour area; 3) the sea spray contributions are rather difficult to be reproduced following this method. It is important to note that during some African dust episodes important differences between experimental and modelled concentrations of sulphate are readily apparent. In those cases an underestimation of the mineral matter is coincident with the overestimation of the sulphate load. This phenomenon may be explained as follows: the reaction between African dust and SOx at this site is frequent, resulting in the formation of sulphated mineral species (Alastuey et al., 2005). In some cases however, the NAF
J. Pey et al. / Atmospheric Environment 44 (2010) 5112e5121
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a
3 þ Fig. 5. (a) Comparison between the modelled concentrations (colour bars) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OMþEC (mg m ) and those determined experimentally 3 þ (unfilled circles) at Castillo de Bellver in 2004. (b) Scatter plots between the modelled concentrations (x-axis) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OMþEC (mg m ) and those determined experimentally (y-axis) at Castillo de Bellver in 2004.
episodes occur uncontaminated, i.e., with very low anthropogenic pollution (probably when fast transport of African air masses occurs) and the former reaction is not as significant as expected. Consequently, the overestimation of sulphate gives rise to an underestimation of the mineral matter in these cases.
3.5. Validation of the method In the case of Montseny, where PM10 compositional data are available until 2007, the coefficients obtained for the 2004 study have been applied to the period 2004e2007. Fig. 6 shows the
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b
Fig. 5. (continued).
scatter plots between experimental and reconstructed data in the period 2004e2007. The application of the coefficients found in 2004 to the 4-year period corroborates the feasibility of this procedure in the case of the RB site of Montseny. For mineral matter, sulphate, nitrate and ammonium the slopes are very close to 1 (0.82e0.87), and the correlation coefficients R2 varied from 0.61 to 0.80. The modelling of ammonium and OM þ EC may be considered as satisfactory, with R2 coefficients around 0.39e0.47 and slopes also very close to 1 (0.82e0.91). As before, the exception is found in the sea spray, which is poorly reproduced following this method.
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4. Discussion In this paper we present a simplified methodology to estimate the PM10 composition at a given site from the daily PM10 concentration and the meteorological classification of the days. A previous study of the mean composition of PM according to the main meteorological scenarios is needed. The goal of this work is to provide a method for the estimation of chemical composition of PM10 under different meteorological scenarios. The accuracy of this method has been tested with 2 different monitoring sites in the Western Mediterranean, one regional
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Modelled concentrations (µg/m3) 3 þ Fig. 6. Scatter plots between the modelled concentrations (x-axis) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC (mg m ) and those determined experimentally (yaxis) at Montseny in 2004e2007.
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background and one suburban background. At the RB site of Montseny, all the PM10 components with the exception of the sea spray were well reproduced. At the suburban site of Castillo de Bellver, the modelling of the mineral matter and the sulphate was adequate, but weak or poor for the rest of the components. From these results we may conclude that this methodology may be applied to estimate the PM composition at RB site, at least in the Western Mediterranean region, where the variability of PM levels and their constituents depends mainly on the origin of the air masses rather than local contributions. The anthropogenic contributions of some components to the PM10 at the Castillo de Bellver site give rise to poor modelling of some PM10 constituents with the exception of those with a dominant external and/or regional origin. This methodology may be applied for the reconstruction of PM composition at regional background sites where long-term data series are available and little PM composition data are available (for example, for Monagrega, in NE Spain, where one-year of PM10 composition data is available and a PM10 data series of more than 15 years exists). This kind of information may be useful in conducting climatic and air quality studies. We would like to remark that the feasibility of this method depends on several factors including: 1) the accuracy of the chemical analysis previously performed; 2) the correct/uniform classification of the days according to the main meteorological scenarios, both in the previous phase and subsequently for the reconstruction of the PM composition; and 3) the accuracy of the PM measurements, as they will be the key for estimation of the contribution of the major species for a given day. In addition, this approach does not take into account possible variations in the contributions of the emission sources. Acknowledgements This study was supported by research projects from the Departament de Medi Ambient i Habitatge de la Generalitat de Catalunya, the D.G. de Calidad y Evaluación Ambiental (Spanish Ministry of the Environment) and the Spanish Ministry of Science and Innovation (CARIATI- CGL2008-06294/CLI, CGL2007-62505/CLI, GRACCIECSD2007-00067), and the European Union (6th framework CIRCE IP, 036961, EUSAAR RII3-CT-2006-026140). The authors would like to thank the NOAA Air Resources Laboratory (ARL), the Atmospheric Modelling and Weather Forecasting Group (University of Athens); the Barcelona Supercomputing Center; the Marine Meteorology Division, Naval Research Laboratory (Monterrey, USA), for the provision of the HYSPLIT, SKIRON, BSC/DREAM, and NAAPS, respectively; and the SeaWiFS Project (NASA). The authors would like express their gratitude to the Direcció General de l’Oficina del Canvi Climàtic de les Illes Balears, especially to José Carlos Cerro for the provision of the data from Castillo de Bellver and his technical support. References Alastuey, A., Querol, X., Castillo, S., Escudero, M., Ávila, A., Cuevas, E., Torres, C., Romero, P.M., Expósito, F., García, O., Díaz, J.P., Van Dingenen, R., Putaud, J.P., 2005. Characterisation of TSP and PM2.5 at Izaña and Sta. Cruz de Tenerife (Canary Islands, Spain) during a Saharan Dust Episode (July 2002). Atmospheric Environment 39, 4715e4728.
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Bytnerowicz, A., Omasa, K., Paoletti, E., 2007. Integrated effects of air pollution and climate change on forests: a northern hemisphere perspective. Environmental Pollution 147, 438e445. Dockery, D.W., Stone, P.H., 2007. Cardiovascular risks from fine particulate air pollution. New England of Journal Medicine 356, 511e513. Draxler, R.R., Rolph, G.D., 2003. HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY Website. NOAA Air Resources Laboratory, Silver Spring, MD. http://www.arl.noaa.gov/ready/ hysplit4.html. Gangoiti, G., Millán, M.M., Salvador, R., Mantilla, E., 2001. Long range transport and re-circulation of pollutants in the Western Mediterranean during the RECAPMA Project. Atmospheric Environment 35, 6267e6276. Hallquist, M., Wenger, J.C., Baltensperger, U., Rudich, Y., Simpson, D., Claeys, M., Dommen, J., Donahue, N.M., George, C., Goldstein, A.H., Hamilton, J.F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M., Jimenez, J.L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, Th.F., Monod, A., Prévôt, A.S.H., Seinfeld, J.H., Surratt, J.D., Szmigielski, R., Wildt, J., 2009. The formation, properties and impact of secondary organic aerosol: current and emerging issues. Atmospheric Chemistry and Physics Discussions 9, 3555e3762. http://www.atmoschem-phys-discuss.net/9/3555/2009/. Hodzic, A., Jimenez, J.L., Madronich, S., Aiken, A.C., Bessagnet, B., Curci, G., Fast, J., Lamarque, J.F., Onasch, T.B., Roux, G., Ulbrich, I.M., 2009. Modeling organic aerosols during MILAGRO: application of the CHIMERE model and importance of biogenic secondary organic aerosols. Atmospheric Chemistry and Physics Discussions 9, 12207e12281. www.atmos-chem-phys-discuss.net/9/12207/2009/. IPCC, 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (ISBN 978 0521 88009-1 Hardback; 978 0521 70596-7 Paperback). Kanakidou, M., Seinfeld, J.H., Pandis, S.N., Barnes, I., Dentene, F.J., Facchini, M.C., Van Dingenen, R., Ervens, B., Nenes, A., Nielsen, C.J., Swietlicki, E., Putaud, J.P., Balkanski, Y., Fuzzi, S., Horth, J., Moortgat, G.K., Winterhalter, R., Myhre, C.E.L., Tsigaridis, K., Vignati, E., Stephanou, E.G., Wilson, J., 2005. Organic aerosol and global climate modelling: a review. Atmospheric Chemistry and Physics 5, 1053e1123. Millán, M.M., Artíñano, B., Alonso, L.A., Castro, M., Fernandez-Patier, R., Goberna, J., 1992. Meso-meteorological cycles of air pollution in the Iberian Peninsula (MECAPIP). Air Pollution Research Rep. 44, EUR 14343. Commission of the European Communities, 291 pp. Millán, M., Salvador, R., Mantilla, E., Kallos, G., 1997. Photo-oxidant dynamics in the Mediterranean basin in summer: results from European research projects. Journal of Geophysical Research 102, 8811e8823. Niyogi, D., Chang, H.-I., Saxena, V.K., Holt, T., Alapaty, K., Booker, F., Chen, F., Davis, K.J., Holben, B., Matsui, T., Meyers, T., Oechel, W.C., Pielke Sr., R.A., Wells, R., Wilson, K., Xue, Y., 2004. Direct observations of the effects of aerosol loading on net ecosystem CO2 exchanges over different landscapes. Geophysical Research Letters 31, 1e5. Pérez, N., Pey, J., Castillo, S., Alastuey, A., Querol, X., Viana, M., 2008. Interpretation of the variability of regional background aerosols in the Western Mediterranean. Science of the Total Environment 407, 527e540. Pey, J., Pérez, N., Castillo, S., Viana, M., Moreno, T., Pandolfi, M., López-Sebastián, J.M., Alastuey, A., Querol, X., 2009a. Geochemistry of regional background aerosols in the Western Mediterranean. Atmospheric Research 94, 422e435. Pey, J., Querol, X., Alastuey, A., 2009b. Variations of levels and composition of PM10 and PM2.5 at an insular site in the Western Mediterranean. Atmospheric Research 94, 285e299. Querol, X., Alastuey, A., Rodríguez, S., Plana, F., Mantilla, E., Ruiz, C.R., 2001. Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources. Atmospheric Environment 35, 845e858. Querol, X., Pey, J., Minguillón, M.C., Pérez, N., Alastuey, A., Viana, M., Moreno, T., Bernabé, R.M., Blanco, S., Cárdenas, B., Vega, E., Sosa, G., Escalona, S., Ruiz, H., Artíñano, B., 2008. PM speciation and sources in Mexico during the MILAGRO2006 Campaign. Atmospheric Chemistry and Physics 8, 111e128. Querol, X., Alastuey, A., Pey, J., Cusack, M., Pérez, N., Mihalopoulos, N., Theodosi, C., Gerasopoulos, E., Kubilay, N., Koçak, M., 2009. Variability in regional background aerosols within the Mediterranean. Atmospheric Chemistry and Physics 9, 4575e4591. Rodríguez, S., Querol, X., Alastuey, A., Kallos, G., Kakaliagou, O., 2001. Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain. Atmospheric Environment 35, 2433e2447. Rodríguez, S., Querol, X., Alastuey, A., Viana, M.m., Mantilla, E., 2003. Events Affecting Levels and Seasonal Evolution of Airborne Particulate Matter Concentrations in the Western Mediterranean. Environmental Science and Technology 37, 216e222.
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
A simplified approach to the indirect evaluation of the chemical composition of atmospheric aerosols from PM mass concentrations Jorge Pey*, Andrés Alastuey, Xavier Querol, Noemí Pérez, Michael Cusack Institute of Environmental Assessment and Water Research, IDÆA-CSIC, C/Lluis Solé i Sabaris, s/n, 08028, Barcelona, Spain
a r t i c l e i n f o
a b s t r a c t
Article history: Received 26 March 2010 Received in revised form 30 August 2010 Accepted 2 September 2010
The present work shows a straightforward procedure to indirectly estimate the chemical composition at a given site only from the determination of the PM concentrations, and the classification of the days according to the main meteorological scenarios. A previous study based on the mean chemical composition associated to the main meteorological scenarios is required. This experiment has been carried out with data from two monitoring sites in the North-Western Mediterranean, one regional and one suburban background. At both sites, one-year datasets on chemical PM10 composition were obtained. Based on these datasets, the mean PM10 compositions according to the most relevant meteorological situations were calculated for both locations. After that, the reconstruction of the chemical composition for all the days with available PM10 concentrations was completed. Subsequently, the estimated PM10 composition was compared with that determined experimentally. The comparison between the rebuilt and the experimental results was very satisfactory in the case of the regional background site, and relatively replaced in the other case. Furthermore, the validation of the method at the regional background site has been conducted from the reconstruction of a 4-year data base, and subsequent comparison with the experimental chemical composition. Our results show that it is possible to attain a good approach to the chemical composition at regional background sites, where local emission sources are negligible. Conversely, when the local sources rise in importance, i.e., at a suburban background site, the approach is suitable only for those components with a more regional origin and/or those from long range transport of air masses. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Western Mediterranean Modelling Regional background Mineral matter PM10
1. Introduction Atmospheric particulate matter comprises of many diverse substances, with different chemical compositions, a large variety of shapes, a wide range of sizes, and diverse physical properties. Atmospheric aerosols are of special scientific interest because of their negative health effects (Dockery and Stone, 2007); their role on the radiative budget (IPCC, 2007); and their effects on ecosystems (Niyogi et al., 2004; Bytnerowicz et al., 2007). Air Quality Directives focus on the measure of PM levels, but little attention is paid to other important parameters such as the particle size distribution, the number concentration, or even the chemical composition. Recently, the new 2008/50/EC European Air Quality Directive recommended some compositional determinations to be carried out in PM2.5 from regional background areas þ þ 2þ þ (SO2 4 , NO3 , NH4 , Na , Ca , K , Cl , organic and elemental carbon).
* Corresponding author. Tel.: þ34 934095410; fax: þ34 934110012. E-mail address: [email protected] (J. Pey). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.09.009
The chemical composition of the atmospheric aerosols is a key parameter for climatic, health and ecosystem studies. However, the chemical characterization of the atmospheric aerosols involves different analytical procedures, incurring important economic costs, for the quantification of the main chemical species in a given sample. Furthermore, with the exception of using relatively recent and sophisticated instruments such as Aerosol Mass Spectrometers, monitors for organic and elemental carbon in real time, or specific monitors (sulphate, nitrate, ammonium, chloride, etc.), the chemical characterization of atmospheric aerosols collected on a filter requires significant time for completion. The chemical composition of the atmospheric aerosols is dependent on different factors. The contribution of specific sources may have a clear impact on different components. The second factor to take into account is the origin of air masses that can significantly affect not only the PM levels, but also the chemical composition. For example, air masses arriving from desert regions to the Mediterranean Basin clearly change the composition of the atmospheric aerosols, which are typically of a mineral nature (Querol et al., 2009). The third important factor to bear in mind is
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the occurrence of atmospheric processes, which may depend on the emissions (natural and anthropogenic, gaseous and particulate) and the meteorological variables such as insolation, temperature, wind velocity and humidity, among the most relevant. With this in mind, the regional background (RB) location appears to be an ideal site to study the chemical composition of the atmospheric particles according to the origin of the air masses owing to the relatively far distance to anthropogenic sources. The modelling of the PM concentrations, NOx and O3, and the chemical components of the atmospheric aerosols is currently under intensive research and improvement. The large number of variables influencing the air pollutant concentrations and its variability in the atmosphere make their modelling a complex issue. Some atmospheric pollutants are generally well reproduced by models owing to their stability in the atmosphere and their wellknown emission sources. Other components however, are usually underestimated with current models. That is the case for secondary organic aerosols (Kanakidou et al., 2005; Hallquist et al., 2009; Hodzic et al., 2009). This study seeks to reproduce the chemical composition of PM10 at a given site. In order to test the viability of this method in reproducing PM10 composition from different types of monitoring stations, two sites in the NW Mediterranean Basin where chemical compositional PM10 data are available (Pey et al., 2009a,b) were studied: one RB and one suburban background (SU). For this purpose, a previous study of the mean PM10 composition according to the main meteorological scenarios, in our case based on experimental PM10 composition datasets, is needed. After that, a rigorous meteorological study has been done in order to classify each day according to the origin of the air masses. Once the estimated chemical composition was obtained, the feasibility of this procedure was tested for the two sites by comparing the estimated data with one-year of experimental chemical compositional data. The method proposed in this manuscript neither aims to substitute the modelling of the chemical composition of atmospheric particulate matter in a given region, nor to substitute PM10 chemical analysis. It may, however, be considered a complementary tool easily used by anyone to make an estimation of the chemical composition for a given day and at a given site only by using previous data available. 2. Methodology 2.1. Study area: monitoring sites and meteorological features in 2004 The region selected for this study involves the NE of the Iberian Peninsula and the Balearic Islands, both located in the NW of the Mediterranean Basin. In a regional context, the Mediterranean Basin registers high levels of atmospheric pollutants, mainly as a result of the intense anthropogenic activities that not only increase PM levels at a regional scale but also the ozone concentrations (Millán et al., 1992, 1997). Natural contributions of atmospheric aerosols (African dust) are also frequent and important in this region, estimated to be around 2 mg m3 in the NE of the Iberian Peninsula (Pey et al., 2009a), and around 4 mg m3 in the Balearic Islands (Pey et al., 2009b). Furthermore, the regional meteorology plays an important role in the poor dispersion of the atmospheric pollutants (Millán et al., 1992, 1997; Gangoiti et al., 2001; Rodríguez et al., 2001, 2003). In this region, two monitoring sites were selected: 1) The Montseny site is located 50 km to the NNE of Barcelona, in a densely forested natural park, at 720 m a.s.l. This site represents the RB conditions of the NE of the Iberian Peninsula according to Pérez et al. (2008). The Montseny site belongs to
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the Air Quality network of the Autonomous Government of Catalonia, and is one of the 20 monitoring stations for atmospheric research constituting the EUSAAR network (European Supersites for Atmospheric Aerosol Research). 2) The Castillo de Bellver site is located 3 km to the SW of the Palma de Mallorca city, in the Mallorca Isle, at 114 m.a.s.l.. The monitoring site is located in a forested park (with typical Mediterranean tree species although pines prevail). This site is classified as a SU monitoring station (Pey et al., 2009b). The monitoring site of Castillo de Bellver belongs to the Air Quality network from the Balearic Islands. Special meteorological conditions were registered in 2004 affecting the Western Mediterranean Basin (WMB). These peculiarities were: 1) a dry and low-windy 2003e2004 winter that favoured stagnating conditions in the WMB and consequently intense atmospheric pollution episodes; 2) a wet period from mid February to end April, more than usual; 3) very intense African dust episodes in February, July and SeptembereOctober; 4) recurrent regional recirculation of air masses in summer over the WMB; 5) displacement of the typical SeptembereOctober wet period towards December. Despite these particularities, mean annual temperature and precipitation rates fall in the usual values. 2.2. Instruments Montseny: levels of PM10, PM2.5 and PM1 were continuously measured since March 2002 with optical counters (GRIMM 1107), that subsequently convert the obtained number into mass concentrations applying specific algorithms. Simultaneously, 24-hour PM10 and PM2.5 samples (twice per week) were collected on quartz micro-fibre filters (Schleicher and Schuell, QF20 150 mm) by using high volume samplers (30 m3/h) and DIGITEL cut-off inlets. PM mass concentrations were determined by standard gravimetric procedures and used to correct the PM10, PM2.5 and PM1 measurements obtained with the optical counters (PM2.5 correction was applied to PM1 data owing to the absence of PM1 gravimetric measurements). In all cases the R2 correlation coefficients were >0.8 (Pérez et al., 2008). Castillo de Bellver: PM10 levels were continuously measured by a Beta Met One BAM1020 monitor (BETA attenuation). PM10 data were corrected by the factors obtained by the comparison of gravimetric and real time measurements, in all cases with correlation coefficients R2 > 0.8 (Pey et al., 2009b). Two periods were considered for applying the correlations: JanuaryeMay 2004 (slope ¼ 0.87; R2 ¼ 0.83), and June 2004eFebruary 2005 (slope ¼ 1.12; R2 ¼ 0.86), determined by the calibration of the instrument at the end of May 2004. Two PM10 24-hours samples were collected on the same type of quartz fibre filters as at Montseny every week on alternative days from 08/01/2004 to 29/02/ 2005. The instruments used for this purpose were high volume samplers MCV-CAV (30 m3 h1) with MCV inlets for PM10. 2.3. Chemical characterization Quartz fibre filters were first pre-treated at 200 C during 4 h. The filters were conditioned inside an insulated chamber at 20e25 C and 25e30% RH before and after sampling for at least 24 h. They were then weighed at least three times to obtain constant values. PM concentrations were determined by weight difference. Thereafter, filters were analyzed by different techniques in order to determine the concentrations of about 60 elements and components, following the methodology described in Pey et al. (2009b). That was basically a bulk sample acidic (HF:HClO4:HNO3)
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concentrations were measured by using an Elemental Carbon Analyser (LECO). Levels of organic and elemental carbon (OC and EC) were determined in a representative selection of samples by means of a SUNSET instrument. Indirect determinations from analytical data were obtained for: a) CO2 3 , calculated from Ca content, assuming that this element is mainly present as calcite (CaCO3; CO2 3 ¼ 1.5*Ca); b) SiO2, determined from the Al content on the basis of prior experimental equations (SiO2 ¼ 3*Al2O3, see Querol et al., 2001). Following these procedures, it is possible to obtain the concentration of major species (TC, SiO2, CO2 3 , Al, Ca, Na, Mg, K, Fe, þ SO2 4 , NO3 , Cl and NH4 ) and trace elements (Li, P, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Cd, Sn, Sb, Ba, La, Ce, Hf, Pb, Bi, Th, U, among others). Relative errors of the abovementioned analysis of major species and trace elements have been estimated as less than 10% in all the cases (Querol et al., 2008).
2.4. Daily meteorological analysis Fig. 1. Sectors distinguished for the interpretation of the origin of air masses (AN: North Atlantic advection, ANW: North-Western Atlantic advection, AW: Western Atlantic advection, ASW: South-Western Atlantic advection, NAF: African dust outbreaks, MED: Mediterranean advection, EU: European air masses transport; REG: summer regional recirculation, WAE: winter anticyclonic episodes) in the WMB.
digestion of ½ of each filter and subsequent analysis of major and trace elements by means of Inductively Coupled Plasma Atomic Emission Spectrometry, ICP-AES, (IRIS Advantage TJA Solutions, THERMO), and Inductively Coupled Plasma Mass Spectrometry, ICPMS, (X Series II, THERMO), respectively. The concentrations of soluble anions were determined in water leachates from ¼ filter 2 sections. The content of NHþ 4 , Cl , SO4 and NO3 was obtained by means of Ionic Chromatography HPLC (High Performance Liquid Chromatography) using a WATERS IC-pakTM anion column and WATERS 432 conductivity detector. Finally, total carbon (TC)
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A number of meteorological tools were used to interpret the daily atmospheric scenarios influencing the PM levels and composition at both study areas. These tools include: 1) the calculation of back-trajectories of air masses at receptor points located at 750, 1500 and 2500 m.a.s.l. (HYSPLIT4, Draxler and Rolph, 2003); 2) the interpretation of geopotential height maps at 1000, 900 and 850 mb (NOAA/ESRL Physical Sciences Division, Boulder Colorado from their Web site at http://www.cdc.noaa.gov/); 3) the identification of exotic outbreaks of African dust by interpreting aerosol dust concentration maps (BSC/DREAM, NAAPS, and SKIRON) and satellite imagery (NASA-SeaWiFS Project). With all of these tools we are able to classify each day into the main meteorological scenarios that affect the Western Mediterranean: Atlantic advection (AN: Atlantic North; ANW: Atlantic North-West; AW: Atlantic West; ASW: Atlantic South-West); African dust outbreaks (NAF); Mediterranean advection (MED); European air masses
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Fig. 2. Daily variability of PM10 at Montseny (top) and Castillo de Bellver (bottom) in 2004. Different markers show the main meteorological scenarios (AT: Atlantic advection; NAF: African dust outbreaks; MED: Mediterranean advection; EU: European air masses transport; REG: summer regional recirculation of air masses; WAE: winter anticyclonic stagnation scenarios). Note the very similar pattern at both sites located more than 200 km apart.
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SO42NO3-
2.3 µg/m3 8%
1.8 µg/m3 9% 3
1.1 µg/m 5%
6.6 µg/m3 23%
NH4+
1.4 µg/m3 5%
Mineral Sea spray
6.1 µg/m3 30%
EC+OM 4.6 µg/m3 23%
0.5 µg/m3 2%
3.0 µg/m3 10%
Unaccounted
3
8.5 µg/m 29%
þ Fig. 3. Mean concentrations (mg m3 and %) of SO2 4 , NO3 , NH4 , mineral matter, sea spray, OM þ EC and unaccounted mass in PM10 obtained at Montseny and Castillo de Bellver experimentally in 2004.
influence (EU); winter anticyclonic stagnation episodes (WAE); and summer regional recirculation of air masses (REG). The regions considered for the classification of the air masses according to their origin have also been identified in previous studies (Rodríguez et al., 2003; Pérez et al., 2008). These sectors are displayed in Fig. 1. 3. Results
variability in a regional context (the distance between the two sites exceeds 200 km). Furthermore, the seasonal evolution described by the PM10 mass concentrations follows the concatenation of different meteorological scenarios: WAE in winter, REG in summer, Atlantic advection in spring, and episodic NAF all over the year. As for the PM10 levels, the PM10 composition for each meteorological scenario is clearly different at both sites (see below).
3.1. Mean PM levels and seasonal evolution
3.2. Mean PM10 composition for each meteorological scenario
Mean levels of PM10, PM2.5 and PM1 at the RB site of Montseny in 2004 were 18, 14 and 12 mg m3, respectively (Pérez et al., 2008). Alternatively, mean annual PM10 and PM2.5 levels in 2004 at the SU site of Castillo de Bellver were 29 and 20 mg m3, respectively (Pey et al., 2009b). Nevertheless, only PM10 data are used in the present study. Although the PM10 levels are higher at Castillo de Bellver when compared with those at Montseny, the seasonal evolution registered at both sites is rather similar (Fig. 2). This fact reflects the decisive role played by the meteorology in controlling the PM
Fig. 3 shows the mean PM10 composition at Montseny (101 samples in 2004) and Castillo de Bellver (95 samples in 2004). Relative proportions at Montseny and Castillo de Bellver of NO 3 þ (8e9%), SO2 4 (13e14%) and NH4 (5%) are very similar at both sites. On the contrary, mineral matter (23% at Montseny and 29% at Castillo de Bellver) and sea spray (2% in contrast to 10%) concentrations prevail at Castillo de Bellver; and the carbonaceous species dominate at Montseny (30% as compared to 23%). In both cases, the PM10 mass accounted by these components represents between 83 and 90% of the PM10 mass.
Table 1a þ Mean, standard deviation (S.dv), maximum (Max) and minimum (Min) concentrations (in %) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC in PM10 during the different meteorological scenarios at Montseny in 2004. The results are based on a specific number of samples indicated in brackets. %
NO 3
SO2 4
NHþ 4
Mineral matter
Sea spray
OMþEC
Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min AN (9) AW-NW (27) ASW (6) NAF (13) MED (5) EU (7) REG (24) WAE (10)
15 14 13 12 21 15 19 16
3 5 2 5 4 4 4 3
19 24 17 23 26 20 30 21
10 7 10 3 17 8 12 10
15 11 11 4 9 14 4 22
8 6 5 3 3 7 3 5
25 22 20 12 13 23 12 29
1 2 7 1 6 6 1 13
8 6 5 4 7 8 6 10
3 2 3 2 2 3 2 2
11 11 11 8 9 11 13 14
3 2 2 1 3 4 4 6
12 18 22 43 16 8 19 15
7 7 9 15 8 5 6 5
21 36 32 74 23 16 32 24
3 6 11 23 5 3 9 9
2 4 4 2 4 3 3 3
1 3 3 1 2 1 2 2
4 14 8 4 5 5 8 6
1 1 1 1 2 1 1 1
32 40 37 25 40 40 34 25
15 15 9 12 9 14 9 7
63 72 44 41 49 70 54 42
13 23 20 5 30 27 22 17
Table 1b þ Mean concentration (in mg m3) and standard deviation (S.dv) in PM10 of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC during the different meteorological scenarios at Montseny in 2004.
mg m3 AN AW-NW ASW NAF MED EU REG WAE
PM10
12 15 19 31 14 22 22 25
NO 3
SO2 4
NHþ 4
OM þ EC
Mineral matter
Sea spray
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
1.8 2.1 1.8 3.7 2.9 3.0 4.2 4.0
0.4 0.8 0.3 1.6 0.6 0.8 0.9 0.8
1.8 1.7 1.5 1.2 1.3 2.8 0.9 5.5
1.0 0.9 0.7 0.9 0.4 1.4 0.7 1.3
1.0 0.9 0.7 1.2 1.0 1.6 1.3 2.5
0.4 0.3 0.4 0.6 0.3 0.6 0.4 0.5
1.4 2.7 3.1 13.3 2.2 1.6 4.2 3.8
0.8 1.1 1.3 4.7 1.1 1.0 1.3 1.3
0.2 0.6 0.6 0.6 0.6 0.6 0.7 0.8
0.1 0.5 0.4 0.3 0.3 0.2 0.4 0.5
3.8 6.0 5.2 7.8 5.6 8.0 7.5 6.3
1.8 2.3 1.3 3.7 1.3 2.8 2.0 1.8
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J. Pey et al. / Atmospheric Environment 44 (2010) 5112e5121
Table 2a þ Mean standard deviation (S.dv), maximum (Max) and minimum (Min) concentrations (in %) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC in PM10 during the different meteorological scenarios at Castillo de Bellver in 2004. The results are based on a specific number of samples indicated in brackets. %
NO 3
SO2 4
NHþ 4
Mineral matter
OM þ EC
Sea spray
Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min Mean S.dv Max Min AN (5) AW-NW (27) ASW (4) NAF (17) MED (4) EU (13) REG (14) WAE (11)
15 10 13 15 9 13 18 19
3 3 2 5 4 3 4 6
19 15 15 24 13 17 24 28
10 5 10 6 4 8 13 9
8 7 15 7 7 7 6 10
1 3 5 3 2 2 1 5
9 18 22 16 9 12 8 19
7 4 12 3 5 4 5 6
5 5 4 4 4 5 5 10
1 3 2 2 4 3 2 5
6 14 6 7 9 11 8 19
3 1 1 1 1 2 2 5
37 22 20 46 30 21 29 22
23 14 10 21 13 14 14 13
66 65 29 80 43 42 62 49
16 5 7 23 12 4 21 8
10 15 14 9 21 12 8 7
5 11 3 5 6 10 4 4
14 49 18 21 27 38 15 14
4 3 10 4 15 4 3 2
20 19 16 13 19 21 20 20
10 12 7 9 13 6 7 9
31 53 26 31 34 32 30 38
7 2 9 3 5 11 10 5
Table 2b þ Mean concentration (in mg m3) and standard deviation (S.dv) in PM10 of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC during the different meteorological scenarios at Castillo de Bellver in 2004.
mg m3 AN AW-NW ASW NAF MED EU REG WAE
PM10
22 25 30 39 21 26 26 29
NO 3
SO2 4
NHþ 4
OM þ EC
Mineral matter
Sea spray
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
Mean
S.dv
3.3 2.5 3.6 5.7 1.8 3.4 4.7 5.2
1.0 0.9 0.9 2.2 0.6 1.4 1.0 1.6
1.7 1.9 4.2 2.8 1.5 1.9 1.7 3.2
0.6 1.2 1.4 1.8 0.5 0.9 0.6 2.5
1.2 1.2 1.3 1.5 0.6 1.6 1.1 3.2
0.6 1.0 1.1 0.6 0.4 1.3 0.5 2.5
6.8 5.0 5.9 18.8 6.6 4.6 7.6 5.7
2.1 2.6 2.9 11.7 3.6 2.2 3.5 2.8
2.1 3.2 4.1 3.6 4.3 2.7 2.0 1.7
1.1 2.0 1.6 1.6 2.0 1.5 1.0 1.2
4.9 5.1 4.7 4.6 3.6 5.2 5.1 5.7
3.3 3.8 2.1 2.8 2.3 1.6 1.7 3.4
The 101 and 95 PM10 samples from Montseny and Castillo de Bellver respectively, were classified according to 9 meteorological scenarios (4 sectors in the Atlantic, North Africa, distant Mediterranean, Central and Eastern Europe, winter anticyclonic stagnation scenarios, and summer regional recirculation of air masses). As observed in Tables 1 and 2, in addition to the differences of the PM10 levels associated with the different meteorological scenarios, the composition in relative proportions in some cases differs markedly from one scenario to another. The compositional results (SO2 4 , NO3 , , mineral dust, sea spray and OM þ EC) obtained previously for NHþ 4 each meteorological scenario were used to estimate the chemical composition for each day, as detailed bellow in Section 3.4. 3.3. Variability of the PM composition during the meteorological scenarios The classification of the samples according to the meteorological scenarios distinguished allows identifying: 1) the mean proportions of each PM constituent for each meteorological scenario; 2) the standard deviation of that PM composition when comparing samples collected under similar meteorological conditions. Tables 1 and 2 contain the mean PM composition, the maximum and minimum concentrations, and the mean standard deviation of the different PM components at Montseny and Castillo de Bellver for each meteorological scenario. In the case of Montseny, there is clear evidence that some components are strongly dependent on the origin of the air mass as shown in Table 1a and b. This is the case for mineral matter, clearly dominant during African dust outbreaks (13 mg m3, 43% of the PM10 mass); nitrate (5.5 mg m3, 22%) and ammonium (2.5 mg m3, 10%) that exhibit the maximum predominance under winter anticyclonic stagnating episodes; or sulphate (4.2 mg m3, 19% of the PM10 mass) during regional recirculation episodes. On the other hand, some components such as sea spray and carbonaceous particles do not vary as drastically (with some exceptions) in relation to the origin of the air masses. The evaluation of the standard deviation associated with the concentrations of each PM
component during the meteorological situations distinguished has been conducted. The results show that for most cases the standard deviation is not higher than 30%. These relatively low standard deviations guaranty the use of these “mean coefficients” as inputs for the estimation of the chemical composition from the PM10 data. After that, only from the PM10 daily concentrations and the classification of the days according to the meteorological scenarios previously distinguished, it will be possible to reproduce the chemical composition of PM10. A similar analysis has been performed for the Castillo de Bellver PM10 data (Table 2a and b). As for Montseny, the mineral matter prevails during African dust outbreaks (19 mg m3, 46%). Sea spray in this case is noticeable during Mediterranean advections (4.3 mg m3, 21%). Nitrate prevails during South-Western advections (4.2 mg m3, 15%) and ammonium during winter anticyclonic episodes (3.2 mg m3, 10%). On the contrary, OM þ EC do not present significant variations according to the origin of air masses. The evaluation of the standard deviations points out a higher variability in the case of Castillo de Bellver with respect to the Montseny.
3.4. Application of the procedure In order to make an estimation of the chemical composition for a given day it is necessary to have: Table 3 þ Mean concentrations (mg m3) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC obtained at Montseny and Castillo de Bellver experimentally (Exp.) and by using the simple approach proposed in this manuscript (Mod.). Mean experimental concentrations were obtained from 101 and 73 samples from Montseny and Castillo de Bellver, respectively. SO42 NO3 NH4þ Mineral OM þ EC Sea Sp. Unacc. Montseny
Mod. 3.0 Exp. 2.8
2.0 1.8
1.2 1.1
4.4 4.7
5.9 5.9
0.6 0.6
2.3 2.5
Castillo de Bellver Mod. 3.6 Exp. 3.8
2.0 2.3
1.2 1.4
7.8 8.5
4.5 4.8
2.7 3.0
4.2 5.1
J. Pey et al. / Atmospheric Environment 44 (2010) 5112e5121
a) the daily PM10 concentration for the specific day; b) the classification of the given day according to the origin of the air masses; and c) the mean proportion of all chemical component according to each meteorological scenario.
a
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For a given day the PM10 is made up of different components which may be encountered in the proportions typically found according to the air masses origin. For a particular day (for example a REG day) the PM10 may be made up of the sum of different chemical components:
10 Modelled
Experimental
Modelled
Experimental
Modelled
Experimental
Modelled
Experimental
Modelled
Experimental
Modelled
Experimental
8 6 4 2 0 14 12 10 8 6 4 2 0 6 5 4 3 2 1 0 30
56
25 20 15 10 5 0 3
2
1
0 16
14 12 10 8 6 4 2
24/12/2004
15/12/2004
14/11/2004
25/11/2004
27/10/2004
10/10/2004
22/09/2004
03/10/2004
26/08/2004
03/09/2004
28/07/2004
08/08/2004
23/07/2004
01/07/2004
10/07/2004
17/06/2004
10/06/2004
04/06/2004
24/05/2004
30/04/2004
20/05/2004
25/04/2004
14/04/2004
27/03/2004
05/03/2004
19/03/2004
07/02/2004
21/02/2004
30/01/2004
0
3 þ Fig. 4. (a) Comparison between the modelled concentrations (colour bars) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC (mg m ) and those determined experi3 þ mentally (unfilled circles) at Montseny in 2004. (b) Scatter plots between the modelled concentrations (x-axis) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OMþEC (mg m ) and those determined experimentally (y-axis) at Montseny in 2004.
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b
Fig. 4. (continued).
þ (1) PM10 (mg m3) ¼ [SO2 4 ] þ [NO3 ] þ [NH4 ] þ [Mineral] þ [Sea spray] þ [OM þ EC] þ [unaccounted] In this equation (1), the concentration of each component may be calculated as follows: 2 3 (2) [SO2 4 ] ¼ PM10 (mg m ) * (%SO4 during REG episodes/100)
where the % applied must be that obtained for the corresponding meteorological scenario. Following this procedure it is possible to assess the PM composition of each day only by determining the PM10 levels and classifying carefully each day according to the meteorological scenarios distinguished. This method has been applied to the PM10 data from the RB site of Montseny and from the SU site of Castillo de Bellver for 2004. Subsequently, the experimental chemical composition of PM10 available for both sites has been used to test the feasibility of this procedure. As shown in Table 3, the mean PM composition calculated for the whole period (around 350 days with PM10 daily concentrations) at each site by applying the described procedure is akin to that determined experimentally (in 101 and 95 daily samples at Montseny and Castillo de Bellver, respectively). After applying this method, the daily PM10 chemical contributions reproduced and those obtained experimentally have been compared. These results are presented in Fig. 4a and b for Montseny and in Fig. 5a and b for Castillo de Bellver. As seen in Fig. 4a and b, the reproducibility of the 2004 PM10 composition at Montseny is very satisfactory for most of the PM constituents including sulphate, nitrate, ammonium and mineral matter (equation slopes from 0.82 to 0.88 and R2 coefficients varying from 0.65 to 0.85). The reconstruction of the OM þ EC concentrations is reasonably acceptable (equation slope 0.91 and R2 0.46). The only component that is not very well reproduced is the sea spray (equation slope 0.63 and R2 0.19), sometimes showing large differences between experimental measurements and modelled concentrations. It is important to note, however, that the sea spray concentrations recorded at Montseny are generally very low. The poor modelling of the sea spray at Montseny may be due to different reasons, including: 1) the relatively intense and infrequent sea spray
events, difficult to be reproduced by using this methodology; 2) the sea spray is calculated as the sum of Cl and total Na, the latter mainly from a marine origin; however, when intense African dust events occur, Na may be partially mineral. In these cases, an overestimation of the sea spray contribution may occur. An identical evaluation as for Montseny has been carried out for Castillo de Bellver (Fig. 5a and b). In this case, the rebuilding of the PM composition from the daily PM10 data and the subsequent comparison with the experimental concentrations of the major components has resulted in good agreement for some components such as mineral matter and sulphate, with equation slopes very close to 1 and R2 coefficients from 0.66 to 0.75. Similar to Montseny, the sea spray concentrations have been poorly reproduced (R2 ¼ 0.08) probably due to their local origin (the monitoring site is located less than 1 km from the shore line). Nitrate and ammonium have been partially reproduced (equation slopes very close to 1 and R2 varying from 0.21 to 0.26), with the exception of episodic days with very high experimental concentrations weakly reconstructed. Nevertheless the OM þ EC concentrations have not been properly reproduced in this case. The results from Castillo de Bellver point out the following considerations: 1) those components typically associated with regional and long range transport are well reproduced (sulphate and mineral matter), even at a monitoring site affected by local emission sources. That is the case of the mineral matter and the sulphate, both components depending clearly on the origin of the air masses at this site; 2) other components such as OM þ EC, nitrate and ammonium may be dominantly of a local origin, in the case of Castillo de Bellver coming from road traffic emissions in Palma de Mallorca, and shipping emissions in the harbour area; 3) the sea spray contributions are rather difficult to be reproduced following this method. It is important to note that during some African dust episodes important differences between experimental and modelled concentrations of sulphate are readily apparent. In those cases an underestimation of the mineral matter is coincident with the overestimation of the sulphate load. This phenomenon may be explained as follows: the reaction between African dust and SOx at this site is frequent, resulting in the formation of sulphated mineral species (Alastuey et al., 2005). In some cases however, the NAF
J. Pey et al. / Atmospheric Environment 44 (2010) 5112e5121
5119
a
3 þ Fig. 5. (a) Comparison between the modelled concentrations (colour bars) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OMþEC (mg m ) and those determined experimentally 3 þ (unfilled circles) at Castillo de Bellver in 2004. (b) Scatter plots between the modelled concentrations (x-axis) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OMþEC (mg m ) and those determined experimentally (y-axis) at Castillo de Bellver in 2004.
episodes occur uncontaminated, i.e., with very low anthropogenic pollution (probably when fast transport of African air masses occurs) and the former reaction is not as significant as expected. Consequently, the overestimation of sulphate gives rise to an underestimation of the mineral matter in these cases.
3.5. Validation of the method In the case of Montseny, where PM10 compositional data are available until 2007, the coefficients obtained for the 2004 study have been applied to the period 2004e2007. Fig. 6 shows the
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b
Fig. 5. (continued).
scatter plots between experimental and reconstructed data in the period 2004e2007. The application of the coefficients found in 2004 to the 4-year period corroborates the feasibility of this procedure in the case of the RB site of Montseny. For mineral matter, sulphate, nitrate and ammonium the slopes are very close to 1 (0.82e0.87), and the correlation coefficients R2 varied from 0.61 to 0.80. The modelling of ammonium and OM þ EC may be considered as satisfactory, with R2 coefficients around 0.39e0.47 and slopes also very close to 1 (0.82e0.91). As before, the exception is found in the sea spray, which is poorly reproduced following this method.
10
15
SO42-
8
12
6
9
4
6
2
3
4. Discussion In this paper we present a simplified methodology to estimate the PM10 composition at a given site from the daily PM10 concentration and the meteorological classification of the days. A previous study of the mean composition of PM according to the main meteorological scenarios is needed. The goal of this work is to provide a method for the estimation of chemical composition of PM10 under different meteorological scenarios. The accuracy of this method has been tested with 2 different monitoring sites in the Western Mediterranean, one regional
6
NO3-
NH4+ 5
Experimental concentrations (µg/m3)
4 3 y = 0.82x R2 = 0.39
2 y = 0.88x R2 = 0.61
y = 0.82x R2 = 0.72
1 0
0
0 0
2
4
6
8
0
10
30
3
6
9
12
1
2
3
4
5
6
15
3
Sea spray
Mineral
OM+EC
y = 0.63x R2 = 0.06
25 20
0
15
12
2 9
15 6 10
1 3
5
y = 0.87x R2 = 0.80
0 0
5
10
15
20
25
30
y = 0.91x R2 = 0.47
0
0 0
1
2
3
0
3
6
9
12
15
Modelled concentrations (µg/m3) 3 þ Fig. 6. Scatter plots between the modelled concentrations (x-axis) of SO2 4 , NO3 , NH4 , mineral matter, sea spray and OM þ EC (mg m ) and those determined experimentally (yaxis) at Montseny in 2004e2007.
J. Pey et al. / Atmospheric Environment 44 (2010) 5112e5121
background and one suburban background. At the RB site of Montseny, all the PM10 components with the exception of the sea spray were well reproduced. At the suburban site of Castillo de Bellver, the modelling of the mineral matter and the sulphate was adequate, but weak or poor for the rest of the components. From these results we may conclude that this methodology may be applied to estimate the PM composition at RB site, at least in the Western Mediterranean region, where the variability of PM levels and their constituents depends mainly on the origin of the air masses rather than local contributions. The anthropogenic contributions of some components to the PM10 at the Castillo de Bellver site give rise to poor modelling of some PM10 constituents with the exception of those with a dominant external and/or regional origin. This methodology may be applied for the reconstruction of PM composition at regional background sites where long-term data series are available and little PM composition data are available (for example, for Monagrega, in NE Spain, where one-year of PM10 composition data is available and a PM10 data series of more than 15 years exists). This kind of information may be useful in conducting climatic and air quality studies. We would like to remark that the feasibility of this method depends on several factors including: 1) the accuracy of the chemical analysis previously performed; 2) the correct/uniform classification of the days according to the main meteorological scenarios, both in the previous phase and subsequently for the reconstruction of the PM composition; and 3) the accuracy of the PM measurements, as they will be the key for estimation of the contribution of the major species for a given day. In addition, this approach does not take into account possible variations in the contributions of the emission sources. Acknowledgements This study was supported by research projects from the Departament de Medi Ambient i Habitatge de la Generalitat de Catalunya, the D.G. de Calidad y Evaluación Ambiental (Spanish Ministry of the Environment) and the Spanish Ministry of Science and Innovation (CARIATI- CGL2008-06294/CLI, CGL2007-62505/CLI, GRACCIECSD2007-00067), and the European Union (6th framework CIRCE IP, 036961, EUSAAR RII3-CT-2006-026140). The authors would like to thank the NOAA Air Resources Laboratory (ARL), the Atmospheric Modelling and Weather Forecasting Group (University of Athens); the Barcelona Supercomputing Center; the Marine Meteorology Division, Naval Research Laboratory (Monterrey, USA), for the provision of the HYSPLIT, SKIRON, BSC/DREAM, and NAAPS, respectively; and the SeaWiFS Project (NASA). The authors would like express their gratitude to the Direcció General de l’Oficina del Canvi Climàtic de les Illes Balears, especially to José Carlos Cerro for the provision of the data from Castillo de Bellver and his technical support. References Alastuey, A., Querol, X., Castillo, S., Escudero, M., Ávila, A., Cuevas, E., Torres, C., Romero, P.M., Expósito, F., García, O., Díaz, J.P., Van Dingenen, R., Putaud, J.P., 2005. Characterisation of TSP and PM2.5 at Izaña and Sta. Cruz de Tenerife (Canary Islands, Spain) during a Saharan Dust Episode (July 2002). Atmospheric Environment 39, 4715e4728.
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