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Correlations between absorption Angström exponent (AAE) of wintertime ambient urban aerosol and its physical and chemical properties
Atmospheric Environment 91 (2014) 52e59
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Correlations between absorption Angström exponent (AAE) of wintertime ambient urban aerosol and its physical and chemical properties N. Utry a, T. Ajtai b, *, Á. Filep b, M. Pintér a, Zs. Török c, Z. Bozóki a, b, G. Szabó a a b c
University of Szeged, Department of Optics and Quantum Electronics, Dóm tér 9, 6720 Szeged, Hungary Hungarian Academy of Sciences e University of Szeged (MTA-SZTE) Research Group on Photoacoustic Spectroscopy, Dóm tér 9, 6720 Szeged, Hungary Hungarian Academy of Sciences, Institute of Nuclear Research, P.O.B. 51, H-4001 Debrecen, Hungary
h i g h l i g h t s We We We We We
measured photoacoustically the AAE data under urban conditions in wintertime. also investigate the size distribution and chemical composition of aerosol. determine the daily fluctuation of the measured quantities. quantify the correlation between the AAE and the other chemo-physical properties. apply the single wavelength and wavelength segregated AAE approach.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 October 2013 Received in revised form 20 March 2014 Accepted 24 March 2014 Available online 24 March 2014
Based on a two-week measurement campaign in an environment heavily polluted both by transit traffic and household heating in the inner city of Szeged (Hungary), correlations between the absorption Angström exponent (AAE) fitted to the optical absorption coefficients measured with a four wavelength (1064, 532, 355 and 266 nm) photoacoustic aerosol measuring system (4l-PAS) and various aerosol parameters were identified. AAE was found to depend linearly on OCwb/EC and on NGM100/NGMD20, i.e. on the ratio of mass concentrations of elemental carbon (EC) to the fraction of organic carbon associated with wood burning (OCwb), and on the ratio of aerosol number concentrations in the 20 nm (NGMD20) to 100 nm (NGMD100) modes, with a regression coefficient of R ¼ 0.95 and R ¼ 0.86, respectively. In the daily fluctuation of AAE two minima were identified, which coincide with the morning and afternoon rush hours, during which NGMD20 exhibits maximum values. During the campaign the shape of the aerosol volume size distribution (dV/dlogD) was found to be largely invariant, supporting the assumption that the primary driver for the AAE variation was aerosol chemical composition rather than particle size. Furthermore, when wavelength segregated AAE values were calculated, AAE for the shorter wavelengths (AAE355-266) was also found to depend linearly on the above mentioned ratios with similar regression coefficients but with a much steeper correlation line, while the AAE for the longer wavelengths (AAE1064-532) exhibits only a considerably weaker correlation. These results prove the unique advantages of real time multi-wavelength photoacoustic measurement of optical absorption in case the wavelength range includes the ultra-violet too. Ó 2014 Published by Elsevier Ltd.
Keywords: Aerosol Absorption Photoacoustic spectroscopy Angström exponent Wood burning Levoglucosan
1. Introduction Airborne particles are of central importance for atmospheric chemistry and physics, since they strongly affect human health, the
* Corresponding author. E-mail address: [email protected] (T. Ajtai). http://dx.doi.org/10.1016/j.atmosenv.2014.03.047 1352-2310/Ó 2014 Published by Elsevier Ltd.
hydrological cycle, cloud lifetime, as well as the global radiative balance (Finlayson-Pitts and Pitts, 2000; Lohmann and Feichter, 2005). Aerosol can interact with solar radiation via scattering and absorption. Back-scattering the incoming solar irradiation has a cooling effect, while forward scattering redistributes electromagnetic energy into the atmosphere (Seinfeld and Pandis, 1998). Photon energy is transformed into thermal energy via light absorption, therefore the absorption process heats absorbing particles
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and also their surroundings. According to current estimates, the global effect of atmospheric aerosol on the energy balance of the Earth is cooling. However, the extent of this effect depends strongly on the contribution of absorption, which is poorly known (Solomon et al., 2007), partly due to the lack of reliable instruments for aerosol absorption measurements (Moosmüller et al., 2009; Schnaiter et al., 2005), and partly due to our limited knowledge on atmospheric light absorbing aerosols especially on the recently discovered Brown Carbon (BrC) (Andreae and Merlet, 2001; Andreae and Gelencsér, 2006). In optical classification, the light absorbing carbon fraction of atmospheric carbonaceous aerosol can be apportioned further into the black carbon (BC) and the brown carbon (BrC) fractions, which e according to the thermo-chemical classification e correspond to the elemental carbon (EC) and the absorptive fraction of the organic carbon (OC), respectively (Andreae and Gelencsér, 2006; Pöschl, 2003). Furthermore, BrC itself is a heterogeneous and complex mixture of various sub-components, each of which may have different optical absorption properties, which differ substantially from that of BC too. BC absorbs strongly in the entire climate relevant wavelength region of the solar spectrum (i.e. NIR-VIS-UV) with only moderate increment towards the shorter wavelengths, while BrC absorption is often negligible in the visible, but increases steeply towards the shorter (UV) wavelengths. The wavelength dependency of aerosol absorption is usually quantified by the Absorption Ångström exponent (AAE), which is defined as:
AOACðl1 Þ ¼ AOACðl2 Þ
l1 l2
AAE (1)
where AOAC is the Aerosol Optical Absorption Coefficient and l is the wavelength at which AOAC is measured. In case of a multiwavelength measurement either an overall AAE value can be fitted to all of the AOAC values, or, in order to take into account the wavelength dependency of AAE, a wavelength segregated AAE value can be calculated for each neighbouring wavelength pair. Many papers have demonstrated that in the climate relevant wavelength region of the solar spectrum the AAE of BC is around one, and can be considered to be wavelength independent, whereas the AAE of BrC is reported to vary from 1.5 to 7 (Hoffer et al., 2006) and furthermore it is often found to be wavelength dependent (Moosmüller et al., 2011; Favez et al., 2009; Sandradewi et al., 2008b; references therein; Schnaiter et al., 2005). Therefore it is not surprising that when the atmospheric aerosol is a mixture of different components (i.e. BC and BrC aerosol), the measured AAE becomes wavelength dependent as well (Ajtai et al., 2011b; Favez et al., 2009). Recent studies demonstrate that under urban conditions AAE is primarily affected by the chemical properties of the carbonaceous aerosol (Favez et al., 2009, 2010; Lewis et al., 2008; Chakrabatry et al., 2010; Flowers et al., 2010; Ajtai et al., 2011b). This raises the possibility of using the measured AAE as an indicator for the aerosol’s chemical composition e.g. in real time source apportionment models, as it was suggested in several recent publications in which multi-wavelength absorption measurement techniques were used (Moosmüller et al., 2011; Sandradewi et al., 2008a, b; Favez et al., 2010; Ajtai et al., 2011b). However, in addition to chemical composition, AAE was also found to be sensitive to the microphysical properties of aerosol such as size and morphology (Moosmüller et al., 2009). Therefore, whenever the identification of the aerosol’s chemical composition via its optical absorptive properties is attempted, the effect of microphysical properties on the measured optical absorption has to be investigated as well. Unfortunately, no matter how much valuable information the absorptive properties of the atmospheric aerosol have, the most
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commonly applied measurement techniques for ambient aerosol absorption measurement suffer several analytical and methodology artifacts, therefore the reliability of the measurement data and their interpretation is limited in many cases (Andreae and Gelencsér, 2006; Moosmüller et al., 2009; Schnaiter et al., 2005). Most of the on-line methods are based on the measurement of light attenuation on filter accumulated aerosols. The accuracy of these measurements is strongly limited by sampling artifacts, such as shadowing and multiple scattering effects (Collaud Coen et al., 2010). Several correction schemes have been introduced and implemented into data evaluation algorithms to increase the accuracy of this method, and many measurement campaigns have been organized to improve these correction schemes (Gerber, 1982; Saathoff et al., 2003; Sheridan et al., 2005; Park et al., 2006; Slowik et al., 2007; Müller et al., 2011). One of the disadvantages of these correction schemes is that they are executed posteriorly after the measurement, which hinders the possibility of real time data provision. Furthermore, since the applied correction factors depend also on chemical composition, the reliability of the measured absorption values remains questionable even after their posterior correction unless the corrections are based on on-site calibration measurements. In any case, in-situ (i.e. filter-free) methods for aerosol absorption measurements are rare especially in the UV wavelength range (Schnaiter et al., 2005; Andreae and Gelencsér, 2006). So far photoacoustic (PA) spectroscopy is the only method that can measure light absorption by aerosol in-situ (i.e. without filter sampling) with high sensitivity and temporal resolution, nevertheless it is not widely applied yet (Andreae and Merlet, 2001; Lack et al., 2006). The basic principle of the PA method for aerosol measurement is described in numerous papers (Arnott et al., 1999; Krämer et al., 2001). Briefly, the absorption of periodically modulated light by particles is followed by non-radiative relaxation resulting in periodic temperature and thereby pressure changes in the surrounding gas sample. The periodic changes of pressure can be detected with a microphone and can be converted into an electric signal proportional to the absorption of the measured component. When absorption takes place in a properly designed PA cell, and the light modulation frequency is tuned to an acoustic resonance frequency of the cell, resonant amplification of the generated signal is achieved, which improves the signal to noise ratio of the measurement considerably. In the last decades several PA instruments developed for the insitu investigation of ambient aerosols have been reported in literature. Most of them operate at one wavelength in the visible wavelength range (Arnott et al., 1999; Lack et al., 2006; Kramer et al., 2001) which makes them a promising candidate for BC measurements. The recently published multi-wavelength photoacoustic instruments opened up a novel perspective on in-situ characterization of carbonaceous aerosol (Lewis et al., 2008; Flowers et al., 2010; Ajtai et al., 2010a, b). In contrast with dualwavelength PA instruments (Lewis et al., 2008), three or four wavelength PA instruments are capable of determining not only the AAE but also its wavelength dependency either under laboratory or field conditions. The first results based on these instrumentations demonstrated experimentally that not only AAE, but also its wavelength dependency could be used as a chemically selective parameter of carbonaceous particulate (Flowers et al., 2010; Ajtai et al., 2010b, 2011). The goal of the work presented here was to investigate the correlations between the photoacoustically measured multiwavelength absorptive optical and other physico-chemical properties, such as the chemical composition and size of atmospheric carbonaceous aerosols in an urban environment under winter conditions.
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2. Measurement site and instrumentation The measurements reported in the following section were carried out in the centre of Szeged (46.26 N, 20.14 E), the third biggest city of Hungary with 170,000 inhabitants, between 12 and 26 January 2011, next to the automatic monitoring station of the Hungarian Air Quality Network (HAQN). During the time of the measurement the city suffered from extremely heavy traffic emission due to the closeness of the Romanian and Serbian border and the lack of a highway bypassing the city, which meant that each day around 3000 vehicles passed in front of the HAQN station. Furthermore, due to the lack of significant industrial activity in and around Szeged traffic and household heating could be considered as the dominant aerosol source throughout the reported measurement campaign. The HAQN station measures the main meteorological parameters. During the measurement campaign the average temperature, relative humidity, wind speed, PM2.5, NOx and CO concentration were 0.9 1.99 C, 85.8 2.5%, 1.75 1 m/s, 17.5 9.7 mg/m3, 43.7 13 ppbV and 634 60 ppbV, respectively (fluctuations are given as 1 sigma values). There was no considerable precipitation. The HAQN station also measures the temporal variation of the concentrations of air polluting gas components by using standard methods, namely chemiluminescence in case of nitrogen oxides (Environment S.A. AC32M), and the non-dispersive infrared technique with gas filter correlation in case of carbon monoxide (Environment S.A. CO12M). The results of these measurements are presented and discussed in the Supplementary Material. Aerosol characterization measurements were performed by instruments operated in the temperature and humidity controlled mobile station of Hilase Ltd. parked as close as possible to the HAQN station. All instruments were connected to the PM2.5 impactors placed on the roof of the van about 5 m high above the ground. AOACs were measured by our recently developed fourwavelength (266, 355, 532 and 1064 nm) PA system (4l-PAS). Construction, operation and performance of the 4l-PAS are described in detail elsewhere (Ajtai et al., 2010a, b). Before the start of the measurement campaign the 4l-PAS was calibrated (i.e. a factor which can be used to calculate the AOAC from the measured PA signal was determined) by using the method described elsewhere in detail (Ajtai et al., 2010a, b). Throughout the campaign the 4l-PAS measured the AOAC within the continuously sampled gas volume at all four operational wavelengths quasi-simultaneously with a measurement cycle completed approximately in 1 min. The hourly averaged data of the measured AOAC values were used for data analysis. The number concentration and size distribution of the atmospheric aerosol were measured from 5 nm to 32 mm with a Scanning Mobility Particle Sizer (SMPS, GRIMM system Aerosol Technik, Germany, type SMPS þ C) and an Optical Particle Counter (OPC, GRIMM, Aerosol Technik, Germany, type 1.108), respectively. SMPS, which included a Condensation Nucleus Counter (CPC Model #5.400) and a Classifier “Vienna” Type Differential Mobility Analyzer (DMA, Model #5.500) measured the fine fraction of ambient particles from 5 nm to 350 nm. The DMA separates particles according to their mobility by balancing their drag and electrical force on the equally charged aerosol stream. The sized particles are then sent to CPC for size-segregated number density measurement. Coincident correction of the measured data was made in order to minimize the shielding effect that occurs when two or more particles arrive in the detection chamber simultaneously. The OPC deduced the size distribution from the measured intensity of light scattering by the particles. The OPC measures the particle number size distribution in the diameter range of 0.3e 20 mm.
The 12-h long filter sampling for the chemical analysis was scheduled to start at 6:00 and 18:00 every day. In this way one of the sampling periods covered the morning and evening rush hours, while the other coincided with reduced traffic activity outside the rush hours. From the sampled filters the total carbon (TC) concentration was measured with a Zellweger Analytics Astro 2100 TOC Analyzer by the EGA method. The carbon content of the sample was converted catalytically into CO2 at 680 C and it was quantitated by an NDIR detector. Three replicates were measured from each sample. The levoglucosan (LG) concentration was determined as follows. First the filters were spiked with the internal standard methyl-beta-Larabinopyranoside. The filters were dried and extracted in two steps. During the first extraction step dichloromethane was used, whereas in the second step the extractant was a 80:20 dichloromethane:methanol mixture. The extracts were filtered through a 0.45 mm syringe filter (Millipore Millex-HV). After this the extracts were dried until dryness under a gentle stream of nitrogen. For derivatization a 50% BSTFA (N,O-bis(trimethylsilyl)trifluoroacetamide) containing 1% of TMCS (trimethylchlorosilane) and 50% pyridine mixture was added to the samples and placed in an oven at 70 C for 3 h. The samples were measured with an Agilent 6890N gas chromatograph coupled to an Agilent 5973N mass spectrometer. The separation was performed on an Agilent DB-5ms Ultra Inert capillary column (30 m 0.25 mm 0.25 um). From the measured LG and TC data OCwb (i.e. the fraction of OC associated with wood burning) and EC concentrations were estimated as follows. First of all LG is known to be a marker for wood burning, therefore, it is a plausible assumption that by applying a proper conversion factor (marked with c in the following) OCwb can be estimated from the LG concentration (Fine et al., 2002; Puxbaum et al., 2007). The method for the determination of this conversion factor is described in detail below. Furthermore, when wood burning is the dominant source of OC (see Note 1), the EC concentration can be deduced by subtracting the estimated OCwb concentration from the measured TC concentration. So the ratio of EC to OCwb can be calculated as follows:
OCwb c$LG ¼ ðTC c$LGÞ EC
(2)
In addition to the measurements listed above K, Ca, Fe and Si concentrations were measured by using a 2-stage sequential streaker as described in detail in the Supplementary Material together with the results of these measurements and their discussion. Note 1: Based on the survey of the Hungarian Central Statistical Office (KSH 2007) the ratio of individual rooms to district heating is the highest in Szeged compared to other Hungarian big cities, and although there is no information on the frequency of wood burning compared to other individual heating technologies in Szeged, it can be assumed that it is the most commonly used method because of its cost-efficiency compared to other alternatives. 3. Results 3.1. Aerosol properties Fig. 1 shows the temporal variation of the AAE1064-266 calculated by fitting numerically the AOAC values measured on all four wavelengths of the 4l-PAS. (Note that the measured AOAC curves are shown in the Supplementary Material.) The temporal variation of the measured AOAC and the wavelength segregated AAE values, which are calculated from the measured AOAC values at
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Fig. 1. aec. Temporal variation of the 1 h averaged AAE1064-266 values (a), the number concentrations in the GMD20 (b) and GMD100 (c) modes during the measurement campaign. The solid and dashed horizontal lines represent midnight and noon, respectively. The underlined dates denote weekends.
55
Fig. 3. aeb. The temporal variation of the measured levoglucosan and total carbon (a) as well as the fraction of organic carbon associated with wood burning and elemental carbon concentrations (b). The underlined dates denote weekend days.
3.2. The correlations neighbouring wavelength pairs (i.e. AAE1064-532, AAE532-355 and AAE355-266), are also shown in the Supplementary Material. The number concentration of the measured particle size distribution averaged for each day of the measurement campaign over two representative 1-h periods i.e. 7:00e8:00 and 18:00e19:00 are shown in Fig. 2. In the number size distributions two characteristic modes were identified by using a multi-peak lognormal fitting algorithm (Heintzenberg, 1994). One is dominated by particles having a geometric mean diameter (NGMD) between 15 and 25 nm (NGMD20). The other peak corresponded to particles with NGMD z 100 nm (NGMD100). The temporal variations of the number concentrations in these modes are shown in Fig. 1b and c. Particles having a mobility diameter above 350 nm were found to be a negligible contribution (less than 8%) to the total aerosol number, and they did not exhibit any characteristic mode. The temporal variation of the measured LG and TC concentrations are shown in Fig. 3a.
Fig. 2. Particle number size distributions. The grey and black lines represent distributions averaged for each measurement day over a 1-h period of 7:00e8:00 and 18:00e19:00, respectively.
The results of the numerical line fitting to the AAE1064-266 values as a function of the corresponding NGMD100/NGMD20 and LG/TC ratios can be seen in Fig. 4a and b, respectively. In Table 1 the
Fig. 4. aec. Lines fitted to AAE1064-266 and GMD100/GMD20 (a), LG/TC (b) and OCwb/EC (c) data points.
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N. Utry et al. / Atmospheric Environment 91 (2014) 52e59
Table 1 Correlation parameters for the line fittings on AAE values and various ratios of the measured parameters (see text for details). AAE1064-266
NGMD100/NGMD20 LG/TC OCwb/EC
AAE1064-532
AAE532-355
AAE355-266
R
R2
m
R
R2
M
R
R2
m
R
R2
m
0.86 0.89 0.95
0.7 0.8 0.9
3.71 13.03 0.70
0.72 0.74 0.81
0.5 0.6 0.7
2.01 7.10 0.40
0.9 0.9 0.9
0.8 0.8 0.7
3.6 13.1 0.80
0.93 0.95 0.96
0.9 0.9 0.9
7.05 25.2 1.29
parameters of numerical line fitting are given, where R is the coefficient of regression, R2 is the coefficient of determination and m is the slope of the fitted line. Furthermore, Table 1 contains the results of the line fittings for the wavelength segregated AAE values too. Based on the measured LG and TC concentrations, the OCwb/EC ratio was calculated with the help of Equation (2) in the following way: factor c (i.e. the conversion factor for calculating the OCwb concentration from the LG concentration) was varied, and at each c value the correlation between AAE1064-266 and the corresponding OCwb/EC ratio was analyzed. The c value that yielded the highest coefficient of regression (R) was then selected and used here in this work. It was found to be c ¼ 7.0. This value is comparable to the OC to levoglucosan ratio of 7.35, which was identified by Fine et al. (2002) and used by Puxbaum et al. (2007) to estimate the contribution of biomass burning to OC measured at background sites in Europe. The temporal variations of the calculated OCwb and EC concentrations during the campaign are shown in Fig. 3b. The result of the numerical line fitting to the corresponding OCwb/EC and AAE1064-266 values can be seen in Fig. 4c, and the fitting parameters are also listed in Table 1. 4. Discussion There are two possible explanations for the high degree of correlation found between NGMD100/NGMD20 and AAE1064-266. Either the aerosol size has a strong influence on AAE or these two quantities are interconnected via the aerosol’s chemical composition. The effect of the aerosol size on the optical absorption properties is known to be determined by the circumference (i.e. the ratio of the particle diameter to the exciting wavelength). Depending on whether the circumference is smaller or larger than one, the aerosol light absorption is driven by the volume or the surface of the particle, respectively (Moosmüller et al., 2009). In the present case, for particles in either of the characteristic modes (GMD20 or GMD100) the circumference is larger than one even for the shortest excitation wavelength (i.e. 266 nm), consequently in the current case it is the aerosol volume which influences the optical absorption. However, even for those largely different number size distributions shown in Fig. 2 the shape of the volume size distributions (dV/dlogD), which were calculated by assuming spherical particles, is very much the same as it can be seen in Fig. 5. It should be noted that this type of invariance of the volume size distribution in case of traffic and wood combustion aerosol was already demonstrated earlier (Schneider et al., 2005; Hedberg et al., 2002). Consequently it can be concluded that in agreement with the results of other field measurements made under wintertime urban conditions (Sandradewi et al., 2008a, 2008b), it is not the particle size but rather the chemical composition which has a primary effect on AAE, resulting in a strong correlation between AAE and OCwb/EC. Yet the correlation between AAE and NGMD100/NGMD20 is also easily understandable as it is well known that under such circumstances the NGMD20 and NGMD100 aerosol modes are characteristic for traffic and wood combustion aerosol, respectively (Schneider et al., 2005; Bond et al., 2002; Wehner and Wiedensohler, 2003; Rissler
et al., 2006), and the latter aerosol type has a considerably higher characteristic AAE value than the former one (Lewis et al., 2008; Moosmüller et al., 2011). Figs. 6 and 7 show the average daily variation of the characteristic modes in number size distributions and the AAE values, respectively. They were averaged for each measurement day and over the same 1-h period with the weekend data being excluded because of the very different dynamics in traffic (i.e. the lack of rush hours). A similar but opposite variation in AAE1064-266 and NGMD20 can be observed: the former one decreases, while the latter one increases during rush hours, most probably due to the appearance of freshly emitted traffic aerosol typically in the NGMD20 mode having a low AAE value (e.g. around 1). Accordingly, the wavelength dependence of the AOAC is largely different for the rush hours and for the rest of the days as it is shown in the Supplementary Material. As far as the wavelength segregated AAE values are concerned, the strength of the observed correlations depends on the wavelengths used for AAE calculation, as it can be seen in Table 1. For AAE1064-532 the correlations are rather weak compared to the correlations found for AAE fitted to all the four measurement wavelengths (i.e. AAE1064-266). On the other hand, AAE355-266 has comparable or even stronger correlations, and the slopes of the correlation lines (m in Table 1) are much higher than for AAE1064266. These variations in the correlations indicate that the UV wavelengths can be especially useful when selective and sensitive chemical characterization of carbonaceous aerosol is targeted. As it is described in the Supplementary Material, it was also attempted to correlate AAE with other ratios: X/Y where X and Y might be the measured concentration of trace elements or a gas, and X and Y are presumed to be representative for household heating and traffic, respectively. However, only relatively weak correlations were found between AAE and any of these ratios. In the Supplementary Material these results are also discussed. Finally correlation parameters for the line fittings were calculated on AAE values against the individual mass concentration of EC
Fig. 5. Particle volume size distributions. The grey and black lines represent distributions averaged for each measurement day over a 1-h period of 7:00e8:00 and 18:00e19:00, respectively calculated from size distributions shown in Fig. 2.
N. Utry et al. / Atmospheric Environment 91 (2014) 52e59
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Fig. 6. Average daily variation of different AAE values. Lines are drawn to guide the eye only. The error bars represent uncertainty in the day-to-day variation of the AAE data.
components. This further confirms that AAE under typical wintertime urban conditions is influenced basically by the relative emission strength of EC and OCwb. 5. Summary and conclusions
Fig. 7. Average daily variation of the number concentration in the NGMD20 and NGMD100 mode labelled grey and black symbols, respectively. Lines are drawn to guide the eye only. The error bars represent uncertainty in the day-to-day variation of the number concentrations.
and OCwb, as well as the individual number concentration in the NGMD20 and NGMD100 modes. The results of these calculations are summarized in Table 2. All these correlations have actually been found to be much weaker than for the ratios listed in Table 1, which again proves that AAE is much more strongly affected by the OCwb/ EC ratio than by the changes in the amount of the individual
The optical absorption of ambient aerosol was investigated insitu and in real time by using our recently developed multiwavelength photoacoustic instrument under wintertime urban conditions when the major sources of ambient carbonaceous matter are traffic and residential heating, and the latter one is most probably dominated by wood burning. In addition to optical absorption coefficients other quantities such as particle size, chemical properties, elemental composition, and concentrations of some gaseous pollutants were also measured. From the measured AOAC values various AAE values (characteristic of the wavelength dependency of optical absorption) were deduced in the UVeVis-NearIR spectral regions. The correlation of AAE and the other measured quantities was investigated and quantified using both single wavelength and wavelength segregated AAE fitting algorithms. It was also demonstrated experimentally that while AOAC values at different wavelengths are not characteristic to the chemical composition of the measured aerosol (see the Supplementary Material for further details), AAE is a reliable indicator of the relative strength of traffic and wood burning aerosol due to its strong correlation with the ratio of OCwb/EC. Moreover, it was also demonstrated that the degree of correlation between AAE and other aerosol variables depends on the applied wavelength ranges.
Table 2 Correlation parameters for the line fittings on the AAE values and various measured parameters (see text for details). AAE1064-266
NGMD20 NGMD100 OC EC
AAE1064-532
AAE532-355
AAE355-266
R
R2
M
R
R2
M
R
R2
m
R
R2
m
0.7 0.4 0.3 0.2
0.5 0.2 0.1 0.1
3 104 4 104 0.01 0.01
0.6 0.3 0.3 0.2
0.4 0.1 0.1 0.1
2 104 3 104 0.01 0.01
0.72 0.45 0.35 0.25
0.5 0.2 0.1 0.1
3.5 104 4.2 104 0.015 0.02
0.8 0.5 0.4 0.28
0.6 0.3 0.2 0.1
4 104 5 104 0.02 0.02
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Although many real-time measurements of the optical absorption and size distribution of ambient particles have already been reported in literature (Fialho et al., 2005; Sandradewi et al., 2008a, 2008b; Favez et al., 2009), few of them quantified the correlation between these two microphysical parameters (Filep et al., 2013). The statistically strong correlation between AAE and NGMD20/ NGMD100 indicates that both AAE and the NGMD20/NGMD100 ratio are governed by the same changes in the atmosphere, while the strong correlation with OCwb/EC verified that AAE is mainly driven by the changes in the relative emission strength of traffic and residential heating activity. In order to size up the extent of experimental circumstances in which AAE and especially its wavelength dependency could be used as an independent real time indicator of the carbonaceous aerosol composition, further investigations are clearly needed. Finally, multi-wavelength photoacoustic measurements including UV excitations have proved to open up novel perspectives for the in-situ investigation of carbonaceous ambient, and make it possible to investigate the detailed absorption responses of particles without any sampling artifact. Acknowledgements Financial support by the Hungarian Scientific Research Foundation (OTKA, project no. K101905) is gratefully acknowledged. The European Union and the European Social Fund have provided financial support to the project under the project no. TÁMOP4.2.2.A-11/1/KONV-2012-0047 and TÁMOP 4.2.2.A-11/1/KONV2012-0060. The authors are grateful to András Hoffer (Air Chemistry Group of the Hungarian Academy of Sciences) for their skilful assistance on levoglucosan and TC measurements. The research of Noémi Utry was supported by the European Union and the State of Hungary, co-financed by the European Social Fund in the Framework of TÁMOP-4.2.4.A/2-11/1-2012-0001 ‘National Excellence Program’. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2014.03.047. References Ajtai, T., Filep, Á., Schnaiter, M., Linke, C., Vragel, C., Bozóki, Z., Szabó, G., Leisner, T., 2010a. A novel multi-wavelength photoacoustic spectrometer for the measurement of the UV-vis_NIR spectral absorption coefficient of atmospheric aerosols. Journal of Aerosol Science 41, 1020e1029. Ajtai, T., Filep, Á., Kecskeméti, G., Hopp, B., Bozóki, Z., Szabó, G., 2010b. Wavelength dependent mass-specific optical absorption coefficients of laser generated coal aerosols determined from multi-wavelength photoacoustic measurements. Applied Physics A 103 (4), 1165e1172. Ajtai, T., Filep, Á., Utry, N., Schnaiter, M., Linke, C., Bozóki, Z., Szabó, G., Leisner, T., 2011. Inter-comparison of optical absorption coefficients of atmospheric aerosols determined by a multi-wavelength photoacoustic spectrometer and an aethalometer under sub-urban wintry conditions. Journal of Aerosol Science 42, 859e866. Andreae, M.O., Gelencsér, A., 2006. Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols. Atmospheric Chemistry and Physics 6 (10), 3131e3148. Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles 15 (4), 955e966. Bond, T.C., Covert, D.S., Kramlich, J.C., Larson, T.V., Charlson, R.J., 2002. Primary particle emissions from residential coal burning: optical properties and size distributions. Journal of Geophysical Research: Atmospheres (1984e2012) 107 (D21). ICC 9-1eICC 9-14. Chakrabarty, R.K., Moosmüller, H., Chen, L.-W.A., Lewis, K., Arnott, W.P., Mazzoleni, C., Dubey, M.K., Wold, C.E., Hao, W.M., Kreidenweis, S.M., 2010. Brown carbon in tar balls from smoldering biomass combustion. Atmospheric Chemistry and Physics 10, 6363e6370.
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Aerosol light absorption and its measurement: a review. Journal of Quantitative Spectroscopy & Radiative Transfer 110, 844e878. Moosmüller, H., Chakrabarty, R.K., Ehlers, K.M., Arnott, W.P., 2011. Absorption Ångström coefficient, brown carbon, and aerosols: basic concepts, bulk matter, and spherical particles. Atmospheric Chemistry and Physics 11, 1217e1225. Müller, T., Henzing, J.S., de Leeuw, G., Wiedensohler, A., Alastuey, A., Angelov, H., Bizjak, M., Collaud Coen, M., Engström, J.E., Gruening, C., Hillamo, R., Hoffer, A., Imre, K., Ivanow, P., Jennings, G., Sun, J.Y., Kalivitis, N., Karlsson, H., Komppula, M., Laj, P., Li, S.-M., Lunder, C., Marinoni, A., Martins dos Santos, S., Moerman, M., Nowak, A., Ogren, J.A., Petzold, A., Pichon, J.M., Rodriquez, S., Sharma, S., Sheridan, P.J., Teinilä, K., Tuch, T., Viana, M., Virkkula, A., Weingartner, E., Wilhelm, R., Wang, Y.Q., 2011. 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Levoglucosan levels at background sites in Europe for assessing the impact of biomass combustion on the European aerosol background. Journal of Geophysical Research 112, D23S05.
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Correlations between absorption Angström exponent (AAE) of wintertime ambient urban aerosol and its physical and chemical properties N. Utry a, T. Ajtai b, *, Á. Filep b, M. Pintér a, Zs. Török c, Z. Bozóki a, b, G. Szabó a a b c
University of Szeged, Department of Optics and Quantum Electronics, Dóm tér 9, 6720 Szeged, Hungary Hungarian Academy of Sciences e University of Szeged (MTA-SZTE) Research Group on Photoacoustic Spectroscopy, Dóm tér 9, 6720 Szeged, Hungary Hungarian Academy of Sciences, Institute of Nuclear Research, P.O.B. 51, H-4001 Debrecen, Hungary
h i g h l i g h t s We We We We We
measured photoacoustically the AAE data under urban conditions in wintertime. also investigate the size distribution and chemical composition of aerosol. determine the daily fluctuation of the measured quantities. quantify the correlation between the AAE and the other chemo-physical properties. apply the single wavelength and wavelength segregated AAE approach.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 October 2013 Received in revised form 20 March 2014 Accepted 24 March 2014 Available online 24 March 2014
Based on a two-week measurement campaign in an environment heavily polluted both by transit traffic and household heating in the inner city of Szeged (Hungary), correlations between the absorption Angström exponent (AAE) fitted to the optical absorption coefficients measured with a four wavelength (1064, 532, 355 and 266 nm) photoacoustic aerosol measuring system (4l-PAS) and various aerosol parameters were identified. AAE was found to depend linearly on OCwb/EC and on NGM100/NGMD20, i.e. on the ratio of mass concentrations of elemental carbon (EC) to the fraction of organic carbon associated with wood burning (OCwb), and on the ratio of aerosol number concentrations in the 20 nm (NGMD20) to 100 nm (NGMD100) modes, with a regression coefficient of R ¼ 0.95 and R ¼ 0.86, respectively. In the daily fluctuation of AAE two minima were identified, which coincide with the morning and afternoon rush hours, during which NGMD20 exhibits maximum values. During the campaign the shape of the aerosol volume size distribution (dV/dlogD) was found to be largely invariant, supporting the assumption that the primary driver for the AAE variation was aerosol chemical composition rather than particle size. Furthermore, when wavelength segregated AAE values were calculated, AAE for the shorter wavelengths (AAE355-266) was also found to depend linearly on the above mentioned ratios with similar regression coefficients but with a much steeper correlation line, while the AAE for the longer wavelengths (AAE1064-532) exhibits only a considerably weaker correlation. These results prove the unique advantages of real time multi-wavelength photoacoustic measurement of optical absorption in case the wavelength range includes the ultra-violet too. Ó 2014 Published by Elsevier Ltd.
Keywords: Aerosol Absorption Photoacoustic spectroscopy Angström exponent Wood burning Levoglucosan
1. Introduction Airborne particles are of central importance for atmospheric chemistry and physics, since they strongly affect human health, the
* Corresponding author. E-mail address: [email protected] (T. Ajtai). http://dx.doi.org/10.1016/j.atmosenv.2014.03.047 1352-2310/Ó 2014 Published by Elsevier Ltd.
hydrological cycle, cloud lifetime, as well as the global radiative balance (Finlayson-Pitts and Pitts, 2000; Lohmann and Feichter, 2005). Aerosol can interact with solar radiation via scattering and absorption. Back-scattering the incoming solar irradiation has a cooling effect, while forward scattering redistributes electromagnetic energy into the atmosphere (Seinfeld and Pandis, 1998). Photon energy is transformed into thermal energy via light absorption, therefore the absorption process heats absorbing particles
N. Utry et al. / Atmospheric Environment 91 (2014) 52e59
and also their surroundings. According to current estimates, the global effect of atmospheric aerosol on the energy balance of the Earth is cooling. However, the extent of this effect depends strongly on the contribution of absorption, which is poorly known (Solomon et al., 2007), partly due to the lack of reliable instruments for aerosol absorption measurements (Moosmüller et al., 2009; Schnaiter et al., 2005), and partly due to our limited knowledge on atmospheric light absorbing aerosols especially on the recently discovered Brown Carbon (BrC) (Andreae and Merlet, 2001; Andreae and Gelencsér, 2006). In optical classification, the light absorbing carbon fraction of atmospheric carbonaceous aerosol can be apportioned further into the black carbon (BC) and the brown carbon (BrC) fractions, which e according to the thermo-chemical classification e correspond to the elemental carbon (EC) and the absorptive fraction of the organic carbon (OC), respectively (Andreae and Gelencsér, 2006; Pöschl, 2003). Furthermore, BrC itself is a heterogeneous and complex mixture of various sub-components, each of which may have different optical absorption properties, which differ substantially from that of BC too. BC absorbs strongly in the entire climate relevant wavelength region of the solar spectrum (i.e. NIR-VIS-UV) with only moderate increment towards the shorter wavelengths, while BrC absorption is often negligible in the visible, but increases steeply towards the shorter (UV) wavelengths. The wavelength dependency of aerosol absorption is usually quantified by the Absorption Ångström exponent (AAE), which is defined as:
AOACðl1 Þ ¼ AOACðl2 Þ
l1 l2
AAE (1)
where AOAC is the Aerosol Optical Absorption Coefficient and l is the wavelength at which AOAC is measured. In case of a multiwavelength measurement either an overall AAE value can be fitted to all of the AOAC values, or, in order to take into account the wavelength dependency of AAE, a wavelength segregated AAE value can be calculated for each neighbouring wavelength pair. Many papers have demonstrated that in the climate relevant wavelength region of the solar spectrum the AAE of BC is around one, and can be considered to be wavelength independent, whereas the AAE of BrC is reported to vary from 1.5 to 7 (Hoffer et al., 2006) and furthermore it is often found to be wavelength dependent (Moosmüller et al., 2011; Favez et al., 2009; Sandradewi et al., 2008b; references therein; Schnaiter et al., 2005). Therefore it is not surprising that when the atmospheric aerosol is a mixture of different components (i.e. BC and BrC aerosol), the measured AAE becomes wavelength dependent as well (Ajtai et al., 2011b; Favez et al., 2009). Recent studies demonstrate that under urban conditions AAE is primarily affected by the chemical properties of the carbonaceous aerosol (Favez et al., 2009, 2010; Lewis et al., 2008; Chakrabatry et al., 2010; Flowers et al., 2010; Ajtai et al., 2011b). This raises the possibility of using the measured AAE as an indicator for the aerosol’s chemical composition e.g. in real time source apportionment models, as it was suggested in several recent publications in which multi-wavelength absorption measurement techniques were used (Moosmüller et al., 2011; Sandradewi et al., 2008a, b; Favez et al., 2010; Ajtai et al., 2011b). However, in addition to chemical composition, AAE was also found to be sensitive to the microphysical properties of aerosol such as size and morphology (Moosmüller et al., 2009). Therefore, whenever the identification of the aerosol’s chemical composition via its optical absorptive properties is attempted, the effect of microphysical properties on the measured optical absorption has to be investigated as well. Unfortunately, no matter how much valuable information the absorptive properties of the atmospheric aerosol have, the most
53
commonly applied measurement techniques for ambient aerosol absorption measurement suffer several analytical and methodology artifacts, therefore the reliability of the measurement data and their interpretation is limited in many cases (Andreae and Gelencsér, 2006; Moosmüller et al., 2009; Schnaiter et al., 2005). Most of the on-line methods are based on the measurement of light attenuation on filter accumulated aerosols. The accuracy of these measurements is strongly limited by sampling artifacts, such as shadowing and multiple scattering effects (Collaud Coen et al., 2010). Several correction schemes have been introduced and implemented into data evaluation algorithms to increase the accuracy of this method, and many measurement campaigns have been organized to improve these correction schemes (Gerber, 1982; Saathoff et al., 2003; Sheridan et al., 2005; Park et al., 2006; Slowik et al., 2007; Müller et al., 2011). One of the disadvantages of these correction schemes is that they are executed posteriorly after the measurement, which hinders the possibility of real time data provision. Furthermore, since the applied correction factors depend also on chemical composition, the reliability of the measured absorption values remains questionable even after their posterior correction unless the corrections are based on on-site calibration measurements. In any case, in-situ (i.e. filter-free) methods for aerosol absorption measurements are rare especially in the UV wavelength range (Schnaiter et al., 2005; Andreae and Gelencsér, 2006). So far photoacoustic (PA) spectroscopy is the only method that can measure light absorption by aerosol in-situ (i.e. without filter sampling) with high sensitivity and temporal resolution, nevertheless it is not widely applied yet (Andreae and Merlet, 2001; Lack et al., 2006). The basic principle of the PA method for aerosol measurement is described in numerous papers (Arnott et al., 1999; Krämer et al., 2001). Briefly, the absorption of periodically modulated light by particles is followed by non-radiative relaxation resulting in periodic temperature and thereby pressure changes in the surrounding gas sample. The periodic changes of pressure can be detected with a microphone and can be converted into an electric signal proportional to the absorption of the measured component. When absorption takes place in a properly designed PA cell, and the light modulation frequency is tuned to an acoustic resonance frequency of the cell, resonant amplification of the generated signal is achieved, which improves the signal to noise ratio of the measurement considerably. In the last decades several PA instruments developed for the insitu investigation of ambient aerosols have been reported in literature. Most of them operate at one wavelength in the visible wavelength range (Arnott et al., 1999; Lack et al., 2006; Kramer et al., 2001) which makes them a promising candidate for BC measurements. The recently published multi-wavelength photoacoustic instruments opened up a novel perspective on in-situ characterization of carbonaceous aerosol (Lewis et al., 2008; Flowers et al., 2010; Ajtai et al., 2010a, b). In contrast with dualwavelength PA instruments (Lewis et al., 2008), three or four wavelength PA instruments are capable of determining not only the AAE but also its wavelength dependency either under laboratory or field conditions. The first results based on these instrumentations demonstrated experimentally that not only AAE, but also its wavelength dependency could be used as a chemically selective parameter of carbonaceous particulate (Flowers et al., 2010; Ajtai et al., 2010b, 2011). The goal of the work presented here was to investigate the correlations between the photoacoustically measured multiwavelength absorptive optical and other physico-chemical properties, such as the chemical composition and size of atmospheric carbonaceous aerosols in an urban environment under winter conditions.
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2. Measurement site and instrumentation The measurements reported in the following section were carried out in the centre of Szeged (46.26 N, 20.14 E), the third biggest city of Hungary with 170,000 inhabitants, between 12 and 26 January 2011, next to the automatic monitoring station of the Hungarian Air Quality Network (HAQN). During the time of the measurement the city suffered from extremely heavy traffic emission due to the closeness of the Romanian and Serbian border and the lack of a highway bypassing the city, which meant that each day around 3000 vehicles passed in front of the HAQN station. Furthermore, due to the lack of significant industrial activity in and around Szeged traffic and household heating could be considered as the dominant aerosol source throughout the reported measurement campaign. The HAQN station measures the main meteorological parameters. During the measurement campaign the average temperature, relative humidity, wind speed, PM2.5, NOx and CO concentration were 0.9 1.99 C, 85.8 2.5%, 1.75 1 m/s, 17.5 9.7 mg/m3, 43.7 13 ppbV and 634 60 ppbV, respectively (fluctuations are given as 1 sigma values). There was no considerable precipitation. The HAQN station also measures the temporal variation of the concentrations of air polluting gas components by using standard methods, namely chemiluminescence in case of nitrogen oxides (Environment S.A. AC32M), and the non-dispersive infrared technique with gas filter correlation in case of carbon monoxide (Environment S.A. CO12M). The results of these measurements are presented and discussed in the Supplementary Material. Aerosol characterization measurements were performed by instruments operated in the temperature and humidity controlled mobile station of Hilase Ltd. parked as close as possible to the HAQN station. All instruments were connected to the PM2.5 impactors placed on the roof of the van about 5 m high above the ground. AOACs were measured by our recently developed fourwavelength (266, 355, 532 and 1064 nm) PA system (4l-PAS). Construction, operation and performance of the 4l-PAS are described in detail elsewhere (Ajtai et al., 2010a, b). Before the start of the measurement campaign the 4l-PAS was calibrated (i.e. a factor which can be used to calculate the AOAC from the measured PA signal was determined) by using the method described elsewhere in detail (Ajtai et al., 2010a, b). Throughout the campaign the 4l-PAS measured the AOAC within the continuously sampled gas volume at all four operational wavelengths quasi-simultaneously with a measurement cycle completed approximately in 1 min. The hourly averaged data of the measured AOAC values were used for data analysis. The number concentration and size distribution of the atmospheric aerosol were measured from 5 nm to 32 mm with a Scanning Mobility Particle Sizer (SMPS, GRIMM system Aerosol Technik, Germany, type SMPS þ C) and an Optical Particle Counter (OPC, GRIMM, Aerosol Technik, Germany, type 1.108), respectively. SMPS, which included a Condensation Nucleus Counter (CPC Model #5.400) and a Classifier “Vienna” Type Differential Mobility Analyzer (DMA, Model #5.500) measured the fine fraction of ambient particles from 5 nm to 350 nm. The DMA separates particles according to their mobility by balancing their drag and electrical force on the equally charged aerosol stream. The sized particles are then sent to CPC for size-segregated number density measurement. Coincident correction of the measured data was made in order to minimize the shielding effect that occurs when two or more particles arrive in the detection chamber simultaneously. The OPC deduced the size distribution from the measured intensity of light scattering by the particles. The OPC measures the particle number size distribution in the diameter range of 0.3e 20 mm.
The 12-h long filter sampling for the chemical analysis was scheduled to start at 6:00 and 18:00 every day. In this way one of the sampling periods covered the morning and evening rush hours, while the other coincided with reduced traffic activity outside the rush hours. From the sampled filters the total carbon (TC) concentration was measured with a Zellweger Analytics Astro 2100 TOC Analyzer by the EGA method. The carbon content of the sample was converted catalytically into CO2 at 680 C and it was quantitated by an NDIR detector. Three replicates were measured from each sample. The levoglucosan (LG) concentration was determined as follows. First the filters were spiked with the internal standard methyl-beta-Larabinopyranoside. The filters were dried and extracted in two steps. During the first extraction step dichloromethane was used, whereas in the second step the extractant was a 80:20 dichloromethane:methanol mixture. The extracts were filtered through a 0.45 mm syringe filter (Millipore Millex-HV). After this the extracts were dried until dryness under a gentle stream of nitrogen. For derivatization a 50% BSTFA (N,O-bis(trimethylsilyl)trifluoroacetamide) containing 1% of TMCS (trimethylchlorosilane) and 50% pyridine mixture was added to the samples and placed in an oven at 70 C for 3 h. The samples were measured with an Agilent 6890N gas chromatograph coupled to an Agilent 5973N mass spectrometer. The separation was performed on an Agilent DB-5ms Ultra Inert capillary column (30 m 0.25 mm 0.25 um). From the measured LG and TC data OCwb (i.e. the fraction of OC associated with wood burning) and EC concentrations were estimated as follows. First of all LG is known to be a marker for wood burning, therefore, it is a plausible assumption that by applying a proper conversion factor (marked with c in the following) OCwb can be estimated from the LG concentration (Fine et al., 2002; Puxbaum et al., 2007). The method for the determination of this conversion factor is described in detail below. Furthermore, when wood burning is the dominant source of OC (see Note 1), the EC concentration can be deduced by subtracting the estimated OCwb concentration from the measured TC concentration. So the ratio of EC to OCwb can be calculated as follows:
OCwb c$LG ¼ ðTC c$LGÞ EC
(2)
In addition to the measurements listed above K, Ca, Fe and Si concentrations were measured by using a 2-stage sequential streaker as described in detail in the Supplementary Material together with the results of these measurements and their discussion. Note 1: Based on the survey of the Hungarian Central Statistical Office (KSH 2007) the ratio of individual rooms to district heating is the highest in Szeged compared to other Hungarian big cities, and although there is no information on the frequency of wood burning compared to other individual heating technologies in Szeged, it can be assumed that it is the most commonly used method because of its cost-efficiency compared to other alternatives. 3. Results 3.1. Aerosol properties Fig. 1 shows the temporal variation of the AAE1064-266 calculated by fitting numerically the AOAC values measured on all four wavelengths of the 4l-PAS. (Note that the measured AOAC curves are shown in the Supplementary Material.) The temporal variation of the measured AOAC and the wavelength segregated AAE values, which are calculated from the measured AOAC values at
N. Utry et al. / Atmospheric Environment 91 (2014) 52e59
Fig. 1. aec. Temporal variation of the 1 h averaged AAE1064-266 values (a), the number concentrations in the GMD20 (b) and GMD100 (c) modes during the measurement campaign. The solid and dashed horizontal lines represent midnight and noon, respectively. The underlined dates denote weekends.
55
Fig. 3. aeb. The temporal variation of the measured levoglucosan and total carbon (a) as well as the fraction of organic carbon associated with wood burning and elemental carbon concentrations (b). The underlined dates denote weekend days.
3.2. The correlations neighbouring wavelength pairs (i.e. AAE1064-532, AAE532-355 and AAE355-266), are also shown in the Supplementary Material. The number concentration of the measured particle size distribution averaged for each day of the measurement campaign over two representative 1-h periods i.e. 7:00e8:00 and 18:00e19:00 are shown in Fig. 2. In the number size distributions two characteristic modes were identified by using a multi-peak lognormal fitting algorithm (Heintzenberg, 1994). One is dominated by particles having a geometric mean diameter (NGMD) between 15 and 25 nm (NGMD20). The other peak corresponded to particles with NGMD z 100 nm (NGMD100). The temporal variations of the number concentrations in these modes are shown in Fig. 1b and c. Particles having a mobility diameter above 350 nm were found to be a negligible contribution (less than 8%) to the total aerosol number, and they did not exhibit any characteristic mode. The temporal variation of the measured LG and TC concentrations are shown in Fig. 3a.
Fig. 2. Particle number size distributions. The grey and black lines represent distributions averaged for each measurement day over a 1-h period of 7:00e8:00 and 18:00e19:00, respectively.
The results of the numerical line fitting to the AAE1064-266 values as a function of the corresponding NGMD100/NGMD20 and LG/TC ratios can be seen in Fig. 4a and b, respectively. In Table 1 the
Fig. 4. aec. Lines fitted to AAE1064-266 and GMD100/GMD20 (a), LG/TC (b) and OCwb/EC (c) data points.
56
N. Utry et al. / Atmospheric Environment 91 (2014) 52e59
Table 1 Correlation parameters for the line fittings on AAE values and various ratios of the measured parameters (see text for details). AAE1064-266
NGMD100/NGMD20 LG/TC OCwb/EC
AAE1064-532
AAE532-355
AAE355-266
R
R2
m
R
R2
M
R
R2
m
R
R2
m
0.86 0.89 0.95
0.7 0.8 0.9
3.71 13.03 0.70
0.72 0.74 0.81
0.5 0.6 0.7
2.01 7.10 0.40
0.9 0.9 0.9
0.8 0.8 0.7
3.6 13.1 0.80
0.93 0.95 0.96
0.9 0.9 0.9
7.05 25.2 1.29
parameters of numerical line fitting are given, where R is the coefficient of regression, R2 is the coefficient of determination and m is the slope of the fitted line. Furthermore, Table 1 contains the results of the line fittings for the wavelength segregated AAE values too. Based on the measured LG and TC concentrations, the OCwb/EC ratio was calculated with the help of Equation (2) in the following way: factor c (i.e. the conversion factor for calculating the OCwb concentration from the LG concentration) was varied, and at each c value the correlation between AAE1064-266 and the corresponding OCwb/EC ratio was analyzed. The c value that yielded the highest coefficient of regression (R) was then selected and used here in this work. It was found to be c ¼ 7.0. This value is comparable to the OC to levoglucosan ratio of 7.35, which was identified by Fine et al. (2002) and used by Puxbaum et al. (2007) to estimate the contribution of biomass burning to OC measured at background sites in Europe. The temporal variations of the calculated OCwb and EC concentrations during the campaign are shown in Fig. 3b. The result of the numerical line fitting to the corresponding OCwb/EC and AAE1064-266 values can be seen in Fig. 4c, and the fitting parameters are also listed in Table 1. 4. Discussion There are two possible explanations for the high degree of correlation found between NGMD100/NGMD20 and AAE1064-266. Either the aerosol size has a strong influence on AAE or these two quantities are interconnected via the aerosol’s chemical composition. The effect of the aerosol size on the optical absorption properties is known to be determined by the circumference (i.e. the ratio of the particle diameter to the exciting wavelength). Depending on whether the circumference is smaller or larger than one, the aerosol light absorption is driven by the volume or the surface of the particle, respectively (Moosmüller et al., 2009). In the present case, for particles in either of the characteristic modes (GMD20 or GMD100) the circumference is larger than one even for the shortest excitation wavelength (i.e. 266 nm), consequently in the current case it is the aerosol volume which influences the optical absorption. However, even for those largely different number size distributions shown in Fig. 2 the shape of the volume size distributions (dV/dlogD), which were calculated by assuming spherical particles, is very much the same as it can be seen in Fig. 5. It should be noted that this type of invariance of the volume size distribution in case of traffic and wood combustion aerosol was already demonstrated earlier (Schneider et al., 2005; Hedberg et al., 2002). Consequently it can be concluded that in agreement with the results of other field measurements made under wintertime urban conditions (Sandradewi et al., 2008a, 2008b), it is not the particle size but rather the chemical composition which has a primary effect on AAE, resulting in a strong correlation between AAE and OCwb/EC. Yet the correlation between AAE and NGMD100/NGMD20 is also easily understandable as it is well known that under such circumstances the NGMD20 and NGMD100 aerosol modes are characteristic for traffic and wood combustion aerosol, respectively (Schneider et al., 2005; Bond et al., 2002; Wehner and Wiedensohler, 2003; Rissler
et al., 2006), and the latter aerosol type has a considerably higher characteristic AAE value than the former one (Lewis et al., 2008; Moosmüller et al., 2011). Figs. 6 and 7 show the average daily variation of the characteristic modes in number size distributions and the AAE values, respectively. They were averaged for each measurement day and over the same 1-h period with the weekend data being excluded because of the very different dynamics in traffic (i.e. the lack of rush hours). A similar but opposite variation in AAE1064-266 and NGMD20 can be observed: the former one decreases, while the latter one increases during rush hours, most probably due to the appearance of freshly emitted traffic aerosol typically in the NGMD20 mode having a low AAE value (e.g. around 1). Accordingly, the wavelength dependence of the AOAC is largely different for the rush hours and for the rest of the days as it is shown in the Supplementary Material. As far as the wavelength segregated AAE values are concerned, the strength of the observed correlations depends on the wavelengths used for AAE calculation, as it can be seen in Table 1. For AAE1064-532 the correlations are rather weak compared to the correlations found for AAE fitted to all the four measurement wavelengths (i.e. AAE1064-266). On the other hand, AAE355-266 has comparable or even stronger correlations, and the slopes of the correlation lines (m in Table 1) are much higher than for AAE1064266. These variations in the correlations indicate that the UV wavelengths can be especially useful when selective and sensitive chemical characterization of carbonaceous aerosol is targeted. As it is described in the Supplementary Material, it was also attempted to correlate AAE with other ratios: X/Y where X and Y might be the measured concentration of trace elements or a gas, and X and Y are presumed to be representative for household heating and traffic, respectively. However, only relatively weak correlations were found between AAE and any of these ratios. In the Supplementary Material these results are also discussed. Finally correlation parameters for the line fittings were calculated on AAE values against the individual mass concentration of EC
Fig. 5. Particle volume size distributions. The grey and black lines represent distributions averaged for each measurement day over a 1-h period of 7:00e8:00 and 18:00e19:00, respectively calculated from size distributions shown in Fig. 2.
N. Utry et al. / Atmospheric Environment 91 (2014) 52e59
57
Fig. 6. Average daily variation of different AAE values. Lines are drawn to guide the eye only. The error bars represent uncertainty in the day-to-day variation of the AAE data.
components. This further confirms that AAE under typical wintertime urban conditions is influenced basically by the relative emission strength of EC and OCwb. 5. Summary and conclusions
Fig. 7. Average daily variation of the number concentration in the NGMD20 and NGMD100 mode labelled grey and black symbols, respectively. Lines are drawn to guide the eye only. The error bars represent uncertainty in the day-to-day variation of the number concentrations.
and OCwb, as well as the individual number concentration in the NGMD20 and NGMD100 modes. The results of these calculations are summarized in Table 2. All these correlations have actually been found to be much weaker than for the ratios listed in Table 1, which again proves that AAE is much more strongly affected by the OCwb/ EC ratio than by the changes in the amount of the individual
The optical absorption of ambient aerosol was investigated insitu and in real time by using our recently developed multiwavelength photoacoustic instrument under wintertime urban conditions when the major sources of ambient carbonaceous matter are traffic and residential heating, and the latter one is most probably dominated by wood burning. In addition to optical absorption coefficients other quantities such as particle size, chemical properties, elemental composition, and concentrations of some gaseous pollutants were also measured. From the measured AOAC values various AAE values (characteristic of the wavelength dependency of optical absorption) were deduced in the UVeVis-NearIR spectral regions. The correlation of AAE and the other measured quantities was investigated and quantified using both single wavelength and wavelength segregated AAE fitting algorithms. It was also demonstrated experimentally that while AOAC values at different wavelengths are not characteristic to the chemical composition of the measured aerosol (see the Supplementary Material for further details), AAE is a reliable indicator of the relative strength of traffic and wood burning aerosol due to its strong correlation with the ratio of OCwb/EC. Moreover, it was also demonstrated that the degree of correlation between AAE and other aerosol variables depends on the applied wavelength ranges.
Table 2 Correlation parameters for the line fittings on the AAE values and various measured parameters (see text for details). AAE1064-266
NGMD20 NGMD100 OC EC
AAE1064-532
AAE532-355
AAE355-266
R
R2
M
R
R2
M
R
R2
m
R
R2
m
0.7 0.4 0.3 0.2
0.5 0.2 0.1 0.1
3 104 4 104 0.01 0.01
0.6 0.3 0.3 0.2
0.4 0.1 0.1 0.1
2 104 3 104 0.01 0.01
0.72 0.45 0.35 0.25
0.5 0.2 0.1 0.1
3.5 104 4.2 104 0.015 0.02
0.8 0.5 0.4 0.28
0.6 0.3 0.2 0.1
4 104 5 104 0.02 0.02
58
N. Utry et al. / Atmospheric Environment 91 (2014) 52e59
Although many real-time measurements of the optical absorption and size distribution of ambient particles have already been reported in literature (Fialho et al., 2005; Sandradewi et al., 2008a, 2008b; Favez et al., 2009), few of them quantified the correlation between these two microphysical parameters (Filep et al., 2013). The statistically strong correlation between AAE and NGMD20/ NGMD100 indicates that both AAE and the NGMD20/NGMD100 ratio are governed by the same changes in the atmosphere, while the strong correlation with OCwb/EC verified that AAE is mainly driven by the changes in the relative emission strength of traffic and residential heating activity. In order to size up the extent of experimental circumstances in which AAE and especially its wavelength dependency could be used as an independent real time indicator of the carbonaceous aerosol composition, further investigations are clearly needed. Finally, multi-wavelength photoacoustic measurements including UV excitations have proved to open up novel perspectives for the in-situ investigation of carbonaceous ambient, and make it possible to investigate the detailed absorption responses of particles without any sampling artifact. Acknowledgements Financial support by the Hungarian Scientific Research Foundation (OTKA, project no. K101905) is gratefully acknowledged. The European Union and the European Social Fund have provided financial support to the project under the project no. TÁMOP4.2.2.A-11/1/KONV-2012-0047 and TÁMOP 4.2.2.A-11/1/KONV2012-0060. The authors are grateful to András Hoffer (Air Chemistry Group of the Hungarian Academy of Sciences) for their skilful assistance on levoglucosan and TC measurements. The research of Noémi Utry was supported by the European Union and the State of Hungary, co-financed by the European Social Fund in the Framework of TÁMOP-4.2.4.A/2-11/1-2012-0001 ‘National Excellence Program’. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2014.03.047. References Ajtai, T., Filep, Á., Schnaiter, M., Linke, C., Vragel, C., Bozóki, Z., Szabó, G., Leisner, T., 2010a. A novel multi-wavelength photoacoustic spectrometer for the measurement of the UV-vis_NIR spectral absorption coefficient of atmospheric aerosols. Journal of Aerosol Science 41, 1020e1029. Ajtai, T., Filep, Á., Kecskeméti, G., Hopp, B., Bozóki, Z., Szabó, G., 2010b. Wavelength dependent mass-specific optical absorption coefficients of laser generated coal aerosols determined from multi-wavelength photoacoustic measurements. Applied Physics A 103 (4), 1165e1172. Ajtai, T., Filep, Á., Utry, N., Schnaiter, M., Linke, C., Bozóki, Z., Szabó, G., Leisner, T., 2011. Inter-comparison of optical absorption coefficients of atmospheric aerosols determined by a multi-wavelength photoacoustic spectrometer and an aethalometer under sub-urban wintry conditions. Journal of Aerosol Science 42, 859e866. Andreae, M.O., Gelencsér, A., 2006. Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols. Atmospheric Chemistry and Physics 6 (10), 3131e3148. Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles 15 (4), 955e966. Bond, T.C., Covert, D.S., Kramlich, J.C., Larson, T.V., Charlson, R.J., 2002. Primary particle emissions from residential coal burning: optical properties and size distributions. Journal of Geophysical Research: Atmospheres (1984e2012) 107 (D21). ICC 9-1eICC 9-14. Chakrabarty, R.K., Moosmüller, H., Chen, L.-W.A., Lewis, K., Arnott, W.P., Mazzoleni, C., Dubey, M.K., Wold, C.E., Hao, W.M., Kreidenweis, S.M., 2010. Brown carbon in tar balls from smoldering biomass combustion. Atmospheric Chemistry and Physics 10, 6363e6370.
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