Aerosol Science and Technology, 39:377–393, 2005 c American Association for Aerosol Research Copyright ISSN: 0278-6826 print / 1521-7388 online DOI: 10.1080/027868290935696
Instrument Characterization and First Application of the Single Particle Analysis and Sizing System (SPASS) for Atmospheric Aerosols 1 Nicole Erdmann,1 Alessandro Dell’Acqua,1 Paolo Cavalli,1 Carsten Gruning, ¨ Nicol`o Omenetto,2 Jean-Philippe Putaud,1 Frank Raes,1 and Rita Van Dingenen1 1
European Commission Joint Research Center, Institute for Environment and Sustainability, Ispra (VA), Italy 2 Department of Chemistry, University of Florida, Gainesville, Florida
We describe here the instrumental setup and first experiments with the mobile single particle analysis and sizing system (SPASS) for the on-line characterization of single atmospheric aerosol particles. Aerosols are introduced into the SPASS via a differentially pumped particle inlet system using an aerodynamic lens that forms a narrow particle beam. The particles are sized with a two-laser velocimeter and subsequently desorbed and ionized with a highpower pulsed Nd:YAG laser operating at 266 nm. Positive and negative ions formed are simultaneously detected in a bipolar time-offlight mass spectrometer. Thus, the size and chemical composition of single aerosol particles can be characterized simultaneously in real time. The SPASS system has been installed inside a truck, creating a mobile unit. The performance of the SPASS in terms of mass resolution and sizing capabilities of the laser velocimeter has been evaluated. Positive and negative mass spectra from different types of particles have been obtained to identify “typical” peak patterns. The relative detection sensitivity depending on particle size and chemical composition was studied. Significant differences in detection sensitivities for different compounds were observed, demonstrating that the results obtained from ambient single particle measurements are strongly biased and dominated by “easy-to-detect” particles. The instrument performance is illustrated with results from a 24 h measurement period during winter in Milan, Italy. The period encompasses two meteorologically different episodes, a period of stagnant conditions, where regional background pollutants contribute significantly and the aerosol is dominated by ammonium nitrate and sulfate, and a North-Foehn event, where accumulation mode particles are scavenged and the urban aerosol population is dominated by organic matter due to local emissions.
Received 27 November 2003; accepted 21 December 2004. We would like to thank Dr. Giuseppe Petrucci and Dr. Paul Farnsworth for their many contributions to the concept and setup of the instrument; as well as Dr. Bernabe Ballesteros, Matthieu Orgeval, Dr. Luisa Marelli and Daniel Mira-Salama for their help with the experiments. Address correspondence to Nicole Erdmann, Institute of Nuclear Chemistry, Johannes Gutenberg-University Mainz, D-55099 Mainz, Germany. E-mail:
[email protected]
INTRODUCTION The importance of aerosols in chemical and physical processes involved in, for example, air pollution (human health, acid deposition, and global warming), combustion (soot and soot precursors), materials synthesis and processing (nanoparticles and coatings), and clean-room technology has increased dramatically in the past years. Their physical and chemical characterization and the study of their composition and reaction mechanisms represent an area in which basic information is still needed (Andreae and Crutzen 1997). It has become important not only to perform bulk analysis of aerosol particles but also to be able to analyze single aerosol particles, on-line in real time (Peter 1996). This capability will allow us in particular to investigate the mixing state of the particles. In an internally mixed aerosol, all particles have roughly the same chemical composition, which can be a mixture of inorganic and organic components. Externally mixed particles show chemically different subpopulations, e.g., with a fraction of the particles dominated by organic compounds and another fraction dominated by inorganic compounds. The mixing state is of relevance for optical and hygroscopic properties of an ensemble of particles. These properties are crucial to model the climate effects of particles. Further, by collecting characteristic mass spectra for a variety of primary (i.e., directly emitted) aerosol sources, both of anthropogenic and biogenic origin, the technique can be applied for source-apportionment studies (Bhave et al. 2001). In recent years, a number of instruments for on-line singleparticle analysis have been developed (Johnston and Wexler 1995; Weiss et al. 1996; Reents et al. 1995; Hinz et al. 1996; Murphy et al. 1997; Gard et al. 1997; Reilly et al. 1997; Hunt and Petrucci 2002; Thomson et al. 2000). These instruments are similar in their concept, combining an aerosol inlet, a particlesizing technique, a particle-desorption/ionization technique, and subsequent mass spectrometric detection of the ions produced. The instruments differ, however, in the realization of each of these components. A good overview over the various concepts 377
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is given in Johnston (2000). Different types of inlets using capillaries, nozzle/skimmer combinations, and aerodynamic lenses (Liu et al. 1995a, b) are described. Also, different approaches to determine size information of the particle can be found, from size-selective inlets, chopped particle beams, and detecting the scattered light of a single laser, to the determination of the particle velocity in a two-laser velocimeter. A large number of instruments then use a pulsed ultraviolet (UV) laser for vaporization and ionization of the particles; however, their wavelengths and pulse energies vary. Other systems apply thermal evaporation coupled with electron ionization (Jayne et al. 2000). The most common mass spectrometric techniques used are time of flight (TOF)—both as single or bipolar TOFs—and quadrupole mass spectrometry. At our institute, we have developed a single-particle mass spectrometer, the single particle analysis and sizing system (SPASS). It combines an inlet with an aerodynamic lens (Petrucci et al. 2000), a two-laser velocimeter for sizing, and a frequency quadrupled Nd:YAG laser (266 nm, typically 40 mJ per pulse @ 10 Hz) for vaporization and ionization of the particles, followed by mass spectrometric analysis of the resulting ions in a bipolar
TOF mass spectrometer. The SPASS has been installed inside a truck, thus creating a mobile unit to be used in field experiments. The goal of this article is to describe the complete instrumental setup of the SPASS and show first results from lab and field experiments. EXPERIMENTAL SETUP A schematic drawing of the SPASS is shown in Figure 1, and each part of the instrument—particle inlet (1), particle sizing (2), desorption/ionization laser (3), and TOF mass spectrometer (4)—is described in the following sections below. The SPASS is installed inside a truck. For sampling particles from ambient air, a PM10 sampling head (Derenda) is installed on the truck roof on top of a 1.50 m extendable tube. The Derenda pump is typically operated at a flow of 1 m3 /h (16.6 l/min). From this major flow, a secondary sample flow of 0.3 l/min is drawn into the SPASS via an isokinetic probe. Particle Inlet The particles enter the SPASS through a 150 µm diameter critical orifice, which limits the flow into the instrument to about
FIG. 1. Schematic drawing of the SPASS setup. Particles are (1) introduced into the differentially pumped particle inlet with an aerodynamic lens, (2) sized by the laser velocimeter, and (3) desorbed and ionized by a Nd:YAG laser @ 266 nm. The ions are detected in a bipolar TOF mass spectrometer (4).
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0.3 l/min. An aerodynamic lens consisting of 5 apertures, each separated by a distance of 4 cm, is placed 166 mm downstream this critical orifice, and it concentrates the particles along the axis of the flow. We refer to the system of critical orifice and aerodynamic lens as particle inlet. A detailed description can be found in Petrucci et al. (2000). The pressures in front of and behind the lens are regulated by two electronically controlled butterfly valves (Model 61.1, VAT Vakuumventile AG, Swizerland), one connected behind the critical orifice (stage p1 ) and one behind the last aperture of the lens (stage p2 ), both attached to the same mechanical pump (Edwards, model E2M28). The two valves regulate the individual pump cross section; a fixed pressure can be set via two adaptive pressure controls (VAT, model PL-3). For a given lens configuration (aperture diameters of present setup: 5 mm, 4 mm, 3 mm, 3 mm, and 3 mm) and pressure (p1 = 3.4 Torr, p2 = 0.68 Torr), a range of particle diameters (typically covering about a decade in diameters, for the given parameters ca. 300 nm to 3–4 µm) is confined into a well-collimated beam, which propagates with low divergence for about 35 cm. Particles with a diameter outside the optimum range diverge from the beam and are therefore detected with much lower efficiencies. Behind the lens the particle beam enters the next pumping section of the sizing region via a 750 µm diameter skimmer aperture. Here, the pressure is further reduced to ≈3×10−4 mbar by a turbomolecular pump (Pfeiffer Vacuum, model TMU 260) before the particles enter the high vacuum section of the mass spectrometer through another aperture of 1 mm. Here, typically a pressure lower than 10−6 mbar is achieved by two turbomolecular pumps, one below the ionization region of the instrument (Pfeiffer Vacuum, model TMU 520) and a second one attached to the reflectron arm (Pfeiffer Vacuum, model TMU 260). All turbomolecular pumps use diaphragm pumps (Vacuumbrand, models MZ-2T and MD-4T) as backing pumps. For exact alignment of the particle beam with respect to the laser beams, the particle inlet is connected to the sizing section of the instrument via an x–y positioning unit and a bellows system that allows slight angular adjustment.
Particle “Sizing”/“Free Running” Mode The instrument can be operated in two different modes: “sizing” mode, which allows the determination of the corresponding vacuum aerodynamic diameter (Jimenez et al. 2003) of the particle (described below), or the so-called “free running” mode, where the desorption/ionization laser is fired at a fixed frequency (typically between 1 and 10 Hz). In this mode the laser velocimeter (part 2 in Figure 1) is not operative, and the probability of the desorption laser beam hitting a particle with a given size is roughly proportional to the number concentration of particles with that size. As most ambient aerosol particles are in the 50–200 nm diameter size range, the size range of detectable particles is extended towards those smaller diameters (however, without information on the actual particle size), which are at present not detected with the particle “sizing” mode.
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In the “sizing” mode, particles are sized after exiting the skimmer aperture of the inlet system. Our approach is based on the technique of laser velocimetry. The continuous wave laser beam of a 300 mW Ar-ion laser (LaserPhysics, Reliant 300M) is split by a dichroic mirror. The two beams are guided via optical fibers (OZ Optics, Canada, ca. 130 mW @ 514 nm and ca. 100 mW @ 488 nm are obtained behind the fiber) and sent into the particle beam, separated by 4 cm. Each fiber exit is fitted with a lens that focuses the laser light to a 30 µm waist (FWHM) at a distance of 2 cm. This configuration enables us to send the laser light into the apparatus through a window placed at ca. 1 cm distance from the particle beam without the necessity to introduce the fibers into the vacuum. Light scattered by the particle is collected and detected by two photomultipliers similarly placed outside the vacuum system, one for each laser beam and at 90◦ angle with respect to particle beam and laser. This geometry has been chosen for practical convenience. The time difference between the photomultiplier output signals is measured to obtain the particle velocity, which varies according to particle size. After calibration with particles of known size (see results section), the vacuum aerodynamic diameter of ambient particles can thus be calculated. Ionization and Mass Spectra Detection Once sized, the particles enter the ionization region of a TOF mass spectrometer, where they are desorbed and ionized by the short pulse (≈8 ns) of a Nd:YAG laser (Quanta System Ltd., Milan, Italy, based on model P.I.L.S., custom modified with third and fourth harmonic generator), operated at its fourth harmonic (266 nm, 40 mJ per pulse, typical pulse repetition rate 10 Hz, maximum 25 Hz possible). Two TOF spectrometers (Stefan Kaesdorf, Germany) are used in a bipolar mode so that both the positive and negative mass spectra produced from a single particle are simultaneously collected. The instrument is equipped with one linear TOF mass spectrometer (m/mfwhm ≈ 150 determined for aerosol particles) and one reflectron TOF that allows higher mass resolution (m/mfwhm ≈ 1000 determined for aerosol particles). The operating parameters of the two TOFs are summarized in Table 1. The high voltage that extracts the ions into the two mass spectrometers is applied in a short pulse, several tens of nanoseconds after the desorption/ionizing laser is fired (= delayed pulsed extraction, DPE). This is used to further improve the mass resolution of both TOFs. In normal operation mode, positive ions are detected in the reflectron, negative in the linear TOF. Depending on the type of study, however, the polarities of the mass spectrometers can be inverted. Data Acquisition and Evaluation For measurement control and data acquisition a software package (SYREA s.r.l., Milan, Italy) was specifically developed and installed on a PC. It registers a number of relevant parameters (high voltage settings, pressures, settings of delays for laser
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TABLE 1 Operating parameters of the SPASS TOF mass spectrometers Linear TOF Free drift length Distance ionization zone—repeller Distance repeller—2nd extraction aperture Entrance aperture (repeller) Second aperture Reflector geometry
384 mm 7.5 mm 10 mm Circle Ø 3 mm + slit 20 × 2 mm Circle Ø 6 mm + slit 25 × 3 mm —
Detector
Multi channel plate, Ø 4 cm (Galileo Chevron, type S3040-10-D, detector quality) Repeller: 1.2 kV Liner: 2.0 kV Einzellens: 2.0 kV MCP: 1.7 kV
Operating voltages
triggering and start of TOF acquisition, etc.) and then acquires the transit time between the two laser spots of the velocimeter for each particle. The software uses this information to trigger the Nd:YAG laser, and finally it acquires the two TOF mass spectra (directly, without preamplifier) in binary form from a LeCroy LSA1000 Signalyst unit (−500 to +500 mV range, 8 bit). The user can choose to digitize the signal from each MCP-detector with a sampling rate of 500 MS/s for up to 64 µs. For the reflectron TOF, this corresponds to a mass range from 1 to approximately 600 mass units. For the shorter, linear TOF the same time frame extends to much higher masses; however, for the particles measured so far beyond m/z ≈ 300 no peaks were observed. The linear and reflectron TOF together generate a total of 64 kByte of mass spectral data per laser shot. The data acquisition system can handle a maximum of about 640 kByte of data per second, which happens to match the repetition rate of the Nd:YAG laser of 10 Hz. As a first step of the actual data processing, these spectra are filtered to reduce them to the number of spectra that actually contain peaks. An automated data preprocessing routine, which includes options for mass calibration and particle size calibration, has been developed (SYREA s.r.l., Milan, Italy). It automatically determines the peak information (peak height, FWHM, peak area) from the mass spectra and stores all this information in a Microsoft structured query language (MS SQL) database, which then can be queried. For further interpretation of the data, tables containing peak information can be exported as MS Excel spreadsheets and used in combination with other programs, e.g., MS Access, for queries. Statistical evaluation is performed un-
Reflectron TOF 1206 mm 7.5 mm 10 mm Circle Ø 3 mm + slit 20 × 2 mm Circle Ø 6 mm + slit 25 × 3 mm Distance first grid—center grid ion mirror: 38.12 mm Distance center grid—last grid ion mirror: 63.87 mm Angle between incoming and outgoing beam: 5◦ Multi channel plate, Ø 4 cm (Galileo Chevron, type S3040-10-D, detector quality) Repeller: 1.2 kV Liner: 2.5 kV Einzellens: 7.6 kV MCP: 1.7 kV Reflector central grid: 1.223 kV Reflector second grid: 0.48 kV
der MATLAB. As a first approach to classification of particles, a k-means clustering algorithm was used, which generates a specific number (k) of disjoint, flat (nonhierarchical) clusters. The k-means method (Hartigan and Wong 1979) is numerical, unsupervised, nondeterministic, and iterative. The MATLAB selforganizing maps (SOM) toolbox (Vesanto et al. 1999, 2000), which is available as a free download (http://www.cis.hut.fi/ projects/somtoolbox/), offers an algorithm (“kmeans clusters”) that makes a k-means to a given data set, varying the values of k (starting from 2 classes, up to a number of classes that can be defined by the user; typically, we tested for up to 10 or even 20 classes). The k-means is run multiple times (this number is user selectable) for each k, and the best of these is selected based on sum of squared errors. Finally, the Davies-Bouldin index (Davies and Bouldin 1979) is calculated for each clustering, which is used as a criterion to find out the optimum cluster number, a smaller index representing a better value for k. Cluster centers, calculated as the average overall spectra assigned to a certain class, are stored for each k and can be viewed. These clusters are interpreted as particle classes (or families) with similar spectral features (e.g., sulfate mixed with mineral dust, carbon dominated, etc.).
RESULTS AND DISCUSSION The SPASS performance was studied intensively. Each single component—the particle inlet, the sizing system, the mass spectrometer and the ionization capabilities for different particle types—was carefully evaluated. The system then participated
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in several measurement campaigns. The evaluation of these big data sets is still ongoing. Results from a selected time period during a campaign performed in Milan (Italy), 2003, to characterize urban particles during a winter smog event, are discussed. Characterization of the SPASS Components Particle Inlet A detailed description of the particle inlet characterization can be found in Petrucci et al. (2000). For the present configuration, particles between 300 nm and several (3–4) µm in diameter are confined into a beam that is a few hundred micrometers wide, 4 cm behind the exit of the aerodynamic lens (= at the position of the first sizing laser) and about 1 mm in the ionization region (35 cm behind the exit of the lens). The overall inlet transmission is composed of the transmission curve for the aerodynamic lens and size-dependent biases in the volume behind the critical orifice, as smaller particles are more likely to follow the gas trajectories and are dragged into the first pump. Experiments where the critical orifice was placed closer to the first aperture of the aerodynamic lens (distance orifice to first lens apertureis 40 mm) confirm this; a more detailed investigation of the effect is still ongoing. Rather than determine the transmission curve of the aerodynamic lens separately, we characterized the overall efficiency of the complete instrument in “free running” mode as described in the section
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“Detection Sensitivity as a Function of Particle Size and Composition” below and from this derived particle inlet transmission efficiencies. Laser Velocimeter Sizing System To calibrate the laser velocimeter sizing system, i.e., to relate the particle transit time between the two laser spots to the vacuum aerodynamic particle diameter, monodisperse particles of known diameters (Bangs Laboratories, Inc., Fishers, IN, USA, “certified uniform microspheres”), made of polystyrene and silica were nebulized with different techniques (ultrasonic and pneumatic) and introduced into the SPASS. The scattered signals detected by the two photomultipliers were pulse shaped and sent to an oscilloscope. For each particle size, a clear temporal correlation between the two pulses was observed, allowing the time interval to be accurately measured. The particle velocities for different vacuum aerodynamic diameters can therefore be calculated. The particle transit times obtained from a mixture of monodisperse polystyrene particles of 490, 700, 1070, 2020, and 2800 nm are shown in Figure 2. This information is used to generate a calibration curve of particle transit time versus vacuum aerodynamic diameter, as shown in the insert, which is then used for the calculation of the vacuum aerodynamic diameter of measured ambient particles. For practical convenience, a second-order polynomial is fitted that describes the data in the observed size range reasonably well.
FIG. 2. Particle transit times in the laser velocimeter, for a mixture of monodisperse polystyrene particles of five different diameters; transit time as a function of particle vacuum aerodynamic diameter is shown in the insert.
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Mass Spectrometer By nebulization of various multielement salt solutions, aerosols containing different elements were generated, and they are regularly used as an internal standard to confirm that the mass calibrations used are still valid. From elements that have several isotopes—for example, lead and cadmium, as shown in Figure 3—the mass resolution of the reflectron TOF was determined to be m/m ≈ 1000. The nominal isotopic compositions (shown in the inserts) were reproduced well enough to facilitate largely the interpretation of the data. Positive and negative mass spectra for a number of different compounds—various nebulized salts, mineral dust, calcium carbonate, soot, etc.—were obtained from repeated measurements of particles from the pure compounds, and “typical”
peak combinations were identified for the respective compounds. For example, mineral dust particles typically contained Mg+ (m/z = +24), Al+ (m/z = +27), Ca+ (m/z = +40), Ti+ (m/z = +48; often the whole isotope pattern with additional peaks at m/z = 46; 47; 49; 50 was visible), and Fe+ (m/z = +56 and 54). Strong C-clusters, almost exclusively in the negative spectrum (at m/z = −24; −36; −48; −60 etc.) were found to be typical for soot particles, whereas calcium carbonate particles showed Ca+ , mainly m/z = +40, in the positive and a strong peak at m/z = −24, C− 2 , in the negative spectrum. The peak lists for different compounds are used as a help for particle source identification of aerosol spectra obtained during measurement campaigns. The collection of typical spectral
FIG. 3. Part of the positive mass spectra for single Cd- and Pb-containing particles, demonstrating the mass resolution of the reflectron TOF mass spectrometer. The nominal isotopic compositions are shown in the inserts. They are reproduced well enough to facilitate the interpretation of the spectra.
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patterns is constantly improved and updated, from lab as well as field measurements. Detection Sensitivity as a Function of Particle Size and Composition The overall detection sensitivity of the SPASS, εtot , was studied as a function of particle diameter and particle composition in “free running” mode. εtot is the product of the particle inlet transmission efficiency, εin , the geometrical and the temporal overlaps between the aerosol beam and the pulsed ionizing laser, εgeom and εtemp , and the ionization efficiency, εion : εtot = εin ∗ εgeom ∗ εtemp ∗ εion . εgeom , the geometrical overlap, can be estimated using the diameter of the aerosol beam in the ionization region of the mass spectrometer (≈1 mm) and the laser beam diameter, which has a waist of 700–800 µm in the center of the ionization region. As in our case, the ionizing laser is sent into the SPASS counterpropagating to the aerosol beam, and εgeom is relatively high, as ions can be created over the whole length (2 cm) of the acceptance volume of the mass spectrometer, i.e., even outside the laser focus. As a first estimate, εgeom is similar to the particle hit rate in “sizing” mode, i.e., the fraction of sized particles that are also hit by the ionizing laser leading to spectra, which is ca. 80% when aerosol and laser beams are well aligned. εtemp describes the probability of a particle being present in the ionization region during a laser shot. It is given by the transit time of the particle through the ionization region, ttransit,ionization , and the pulse repetition rate of the laser, νlaser : εtemp = ttransit,ionization ∗ νlaser . The length of the ionization region is determined by the acceptance volume of the mass spectrometer, which as a first estimate is given by a 2 cm long slit in the extraction electrode. ttransit,ionization can be estimated with the help of Figure 2, ttransit,ionization = 12 ttransit,velocimeter , taking into account that the distance between the lasers spots in the velocimeter is 4 cm. As can be seen from Figure 2, for smaller particles the difference in particle transit times becomes smaller and smaller; on average, for particle sizes below 600 nm it is around ttransit,velocimeter ≈270 µs. Together with a laser pulse repetition rate of 2 Hz, this leads to εtemp ≈ 2.7 ∗ 10−4 , which is clearly the limiting factor for the overall detection efficiency in the “free running” mode. In case the SPASS is operated in the “sizing” mode, εtemp is replaced by εsiz , the “sizing” efficiency. Here, the investigations were performed in the “free running” mode of the instrument to avoid any additional size- and composition-dependent influence of the sizing system. The particle inlet transmission is composed of the transmission curve for the aerodynamic lens and size-dependent biases in the volume behind the critical orifice. The laser ionization efficiency depends mainly on the particle chemical composition.
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In our experiments we measured the overall detection efficiency of the instrument, εtot,exp , i.e., the fraction of particles hit by the ionizing laser beam and hence giving spectra per particle entering the SPASS. Thus with knowledge of εgeom and εtemp , as described above, we can make conclusions about the dependence of εin on particle size, and the dependence of εion on particle chemical composition. To create monodisperse aerosols of different compositions, salt solutions (NaCl, (NH4 )2 SO4 , Na2 SO4 , K2 SO4 , KNO3 , and FeCl3 ) were nebulized by an atomizer (TSI, model 3076) and the generated droplets were dried using two 50-cm long drying tubes and neutralized afterwards. A single-particle diameter was then selected with the help of a differential mobility analyzer (DMA). The study has been restricted to particles 300 nm and also the number of SPASS spectra strongly increases and decreases again in the morning hours, in agreement with appearance and disappearance of the Foehn conditions. Figure 7 shows the average particle number size distributions for two time periods, one during the Foehn event with high ambient temperatures and the other during nighttime where ambient temperatures were lower and the total number of particles higher. The data was obtained with a Vienna-type, medium-length differential mobility analyzer (DMA) operated in the second mobile lab. The difference in particle number concentrations for the two time frames shows the same trend that
FIG. 5. Milan winter smog campaign 2003: Temporal variation of the temperature during the 24 h time interval selected for detailed data analysis (28.01.2003, 15:00 to 29.01.2003, 15:00), together with the average diurnal variation for the whole month, illustrating the appearance of Foehn conditions.
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FIG. 6. (a) Temporal variation of the total number of particles per hour giving spectra, together with the hourly averaged particle number concentration (cm−3 ) detected by an OPC and temporal variation of the abundance of certain ion signals, fpeak , relevant for the most prominent substances, (b) ammonium nitrate/sulfate, and (c) carbonaceous material.
was observed in the OPC and SPASS data: fewer particles were present during the Foehn event, in particular in the accumulation mode. A first inspection of the individual spectra shows that the majority of particles contained peaks of nitrate (as NO− 2 and − NO− 3 ) and sulfate (as HSO4 ) in the negative spectrum. From the bulk measurements (Figure 8) it was seen that in the presence of nitrate ammonium was also detected. In order to achieve electric charge compensation one would expect that spectra containing − 2− + NO− 2 / NO3 and/or SO4 have a corresponding NH4 peak in the
positive spectrum. Such a correspondence is indeed observed, but only in a fraction of the spectra consistent with a relatively low detection sensitivity for ammonium, which was also seen in the lab experiments. For organic material, the laser intensity used in the experiments was so high that most organic compounds were fragmented, resulting in carbon clusters present in the mass spectra, sometimes with hydrogen groups attached. Generally, the most prominent carbon-cluster peaks for odd-numbered clusters were observed in the positive spectrum (m/z = +12, +36 etc.,
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FIG. 7. Average DMA size distributions measured in Milan, Italy for two time frames during the selected 24 h period of the 2003 winter campaign. For 28.01, 17:00–23:00 the absence of accumulation mode particles is due to Foehn conditions.
corresponding to C+ , C+ 3 , etc.), whereas even-numbered carbon clusters were seen with higher intensity in the negative spectrum − (m/z = −24, −48, etc. corresponding to C− 2 , C4 , etc.). Due to the strong fragmentation, it is difficult to distinguish subgroups,
FIG. 8.
such as black carbon and particulate organic matter (POM); however, lab experiments have indicated that soot particles show a more prominent pattern of carbon clusters (without hydrogen groups) in the negative spectrum.
Bulk (PM10) chemical composition for the selected time periods during the Milan 2003 winter campaign (OM, organic matter; BC, black carbon).
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Typical tracers for road dust and particulate emissions from brake pad wear—Fe, Al, Ca, Ti (Rogge et al. 1993)—were also observed, as well as Zn, which is a typical indicator for tire wear (Rogge et al. 1993), and Mn, which is used (in the form of methylcyclopentadienyl manganese tricarbonyl, or MMT) as a fuel additive. One of the strengths of single-particle measurements is their capability to deliver information about the mixing state of the particles. In the Milan spectra, particles that clearly represent mixtures of different compounds were identified. An example for such a single-particle spectrum is shown in Figure 9, with the main elements and molecular compounds assigned to the peaks. Here, the traffic-related tracers Mn (fuel additive), Zn (tire wear), and Fe can be found, together with salt (presence of Na+ and Cl− ), which most likely stems from the road because the measurements were performed during wintertime, and ammonium nitrate/sulfate. As a next step, the temporal variation of the abundance of certain ion signals, fpeak , relevant for the most prominent substances, ammonium nitrate/sulfate and carbonaceous material, during the 24 h interval was evaluated. The number of spectra containing peaks at m/z = +18 (ammonium, NH+ 4 ), −46 and
− − −62 (nitrate, NO− 2 and NO3 ), −97 (sulfate, HSO4 ), and m/z = − + +12 (C ) and −24 (C2 ) per hour was counted relative to the total number of spectra:
fpeak =
number of spectra containing peak per hour . total number of spectra per hour
In Figures 6b and c, fpeak is plotted versus time. One clearly observes an anticorrelation between particles containing ammonium nitrate and those containing carbonaceous material. The ammonium nitrate/sulfate particles follow the trend for the total number of particles, i.e., they are dominant when there are many particles present, whereas carbonaceous material is dominating during periods of low particle concentration (Foehn conditions). These results are consistent with bulk-chemistry data shown in Figure 8: during the Foehn episode, organic matter is largely dominating the chemical composition. During the subsequent episode, ammonium sulfate and ammonium nitrate make up 50% of the PM10 mass. Although an in-depth study of aerosol composition related to air mass properties is not envisaged here, these results already indicate that during Foehn
FIG. 9. Single-particle mass spectrum of a mixed aerosol particle during the Milan 2003 winter campaign, showing traffic-related signatures (Zn is attributed to tire wear, Mn is used as a fuel additive) together with salt from the road and ammonium nitrate/sulfate.
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conditions, where the accumulation mode is completely scavenged, the urban aerosol population is mainly governed by local emissions, dominated by traffic, industry, and domestic heating, with organic matter as the major component. During stagnant conditions, the regional aerosol background contributes significantly with ammonium salts, which are a result of secondary
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aerosol formation and are found only in relatively aged air masses. SPASS spectra were then further classified using the k-means clustering algorithm for both time frames within the 24 h period. At first, all spectra during the 24 h were clustered, then the two time frames were selected, one covering the period of
FIG. 10. Average class centers for k-means clustering of the 24 h SPASS dataset during Milan winter campaign. Classes 1, 3, 4, and 5 are dominated by ammonium nitrate and sulfate.
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low particle concentration and high relative carbonaceous content, 28.01.2003, from 17:00 to 23:00 (frame 1) and the second, 29.01.2003, from 01:00 to 07:00 (frame 2), where particle concentration and ammonium nitrate/sulfate content were high. It is expected that the classification for all spectra and that for only frame 2 should lead to very similar class centers, as frame 2
clearly represents the majority of particles during the whole 24 h period, whereas for the small subgroup of particles in frame 1 additional particle classes should be revealed. This is indeed the case. For both the whole 24 h period and frame 2, an optimum number of five cluster centers was determined, using the Davies-Bouldin criterion as described earlier,
FIG. 11. Average class centers for k-means clustering of the subset of SPASS data during Foehn conditions. Ammonium nitrate and sulfate are nearly absent, and mostly carbonaceous material is present.
INSTRUMENT CHARACTERIZATION AND FIRST APPLICATION OF THE SPASS
with identical patterns for the class centers (= average over all particles assigned to a certain class) for the 24 h period and frame 2. These class centers are shown in Figure 10. The first class represents a mixture of several components. Ammonium − nitrate and sulfate are clearly present (NH+ 4 , m/z = +18, NO2 , − − m/z = −46 and NO3 , m/z = −62, HSO4 , m/z = −97), together with salt (strong peaks of Na+ , m/z = +23 and Cl− , m/z = −35 ; −37), which most likely stems from the roads, and Fe (m/z = +56) and Zn (m/z = +64; +66; +68), which are indicators for − car break and tire wear. Some carbonaceous peaks (C+ , C+ 2 , C2 ) are also present, although lower in intensity. The second class consists of nearly pure carbonaceous particles; carbon clusters (Cn , m/z = n∗ 12) up to C5 and C6 are present in the negative spectrum, carbon–hydrogen groups in the positive, and only very small peaks of nitrate and sulfate. Classes 3–5 are all dominated by ammonium nitrate and sulfate, but different ion intensities are observed; classes 3 and 5 show a very strong NH+ 4 signal, whereas the most prominent peak in the positive spectrum of class 4 is K+ (m/z = +39; +41). The highest intensity of HSO− 4 is found in class 3, and here also NO+ (m/z = +30) is prominent. In Class 5 additional peaks appear at m/z = 58 (Ni) and 59 (Co), and m/z = −124 (probably (NO3 )− 2 ). It is interesting to note that
FIG. 12.
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strong O− (m/z = −16) and OH− (m/z = −17) peaks, which are interpreted as presence of water, are only observed in the class of mixed particles with high NaCl content. This was also observed for other data sets. For the subgroup of particles in frame 1 a set of four classes was found to be best suited to describe the data. The respective class centers are presented in Figure 11, named class B1–B4 for distinction from the previous classification. Class B1 consists of ammonium nitrate/sulfate particles with a strong signal of K+ , showing a similarity to class 4. Classes B2–B4 consist of nearly pure carbonaceous particles, revealing a substructure that was not seen in the 24 h classification, which assigned all carbonaceous particles to one group and averaged. Classes B2 and B3 show carbon clusters in the negative spectrum, similar to what was observed for soot particles in the lab, they are therefore called “sootlike carbonaceous.” Na (m/z = 23) is clearly present in the positive spectrum of class B2, whereas K is dominating in that of class B3. In class B4 carbon–hydrogen groups are prominent in the positive spectrum. Unfortunately the fragmentation due to the laser is so strong that a clearer source assignment is difficult at this point. However, the data will be compared to other datasets obtained from a variety of locations where
Temporal variation of the particles’ class abundance distribution during the 24 h period. The classes are the same as those shown in Figure 10.
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characteristic spectra of freshly emitted anthropogenic and biogenic aerosols will be collected (from test benches to road tunnels and remote forests). The bulk analysis data further reveals a significant contribution of dust in both time frames that does not appear as a separate class in the SPASS data. Some typical tracers for dust—Mg, Ca, and Al—can be identified in the class of mixed particles but with low intensity. Further analysis on the basis of the respective single-particle spectra has not been performed yet. The SPASS classification criterion is the number of particles showing certain similarities that are not their mass, whereas in the bulk analysis aerosol mass is measured. As dust particles are found typically in the bigger particle fraction (>1 µm), they can contribute substantially to the overall aerosol mass even if their actual number is small. This contribution would be even more pronounced during the Foehn episode, where fewer particles were present (i.e., the overall mass was much lower) and the majority of particles were also much smaller. The fact that the k-means algorithm stores the information of to which class each particle was assigned can be used to create a histogram plot for the temporal variation of particles’ class abundance distribution, as shown in Figure 12. This plot shows a similar trend as Figures 6b and c; during time frame 1 nearly all particles present belong to the class of carbonaceous particles. The information available from Figure 12, however, is much more refined. It not only describes the behavior of a single component but the compositional variation of the overall particle ensemble with time, including qualitative information about the mixing state of the particles, which can be deduced from the class centers. In particular it shows that during the Foehn event pure carbonaceous particles prevail in the absence of a regional background aerosol, whereas ammonium salts, partly internally mixed with carbonaceous material, partly with other tracers, are the dominating contributors in more aged air masses.
CONCLUSION AND OUTLOOK The SPASS was set up, the single components tested, and the instrument installed on a truck to create a mobile unit. Mass spectra from different types of particles were collected. A study of particle detection sensitivities for different particle diameters and chemical compositions has revealed significant differences for different compounds. This clearly demonstrates that the results obtained from ambient single-particle measurements are strongly biased and dominated by “easy-to-detect” particles, a fact that needs to be considered during data evaluation. A data preprocessing routine has been developed, which stores important information in a MS SQL database, which then can be queried. Peak lists can be exported and used for statistical interpretation. So far, we have used a k-means clustering algorithm to identify particle classes. Different methods can be found in the literature, from grouping the data according to predefined typical classes by using specific queries (Guazzotti et al. 2001), to clustering techniques (hierarchical—Murphy et al.
2003; Fuzzy—Hinz et al. 1999) or neural network approaches (Song et al. 1999), some of which we are planning to test and compare in the future. The SPASS has successfully participated in field exercises. In Milan, during three weeks of experiments (winter smog campaign 2003), more than 200,000 single-particle spectra were obtained, showing both internally and externally mixed particles. A 24 h subset of the campaign was analyzed in more detail, covering a period of of stagnant conditions, where regional background pollutants contributed significantly and the aerosol was dominated by ammonium nitrate and sulfate, and a North-Foehn event, where accumulation mode particles were scavenged and the urban aerosol population was dominated by organic matter due to local emissions from traffic, domestic heating, and industry. During the selected period, the SPASS data showed a very good agreement with OPC, DMA data, and also qualitatively with bulk-chemistry data. The analysis of the full data set is still ongoing; we are planning to compare the single-particle classes to the bulk experiments to obtain relative sensitivity factors of real ambient particles. The analysis of the field data demonstrates the potential of single-particle mass spectroscopy. Data evaluation was started at a rather coarse level by classifying a relatively large set of data. We then checked for variations within the ensemble (in the given example, variation with time; a grouping according to particle diameters is another possibility) and concentrated on a subgroup of particles, like zooming into the data, to reveal more information by a new classification of the selected subset. This procedure, however, requires careful adaptations of the data evaluation routine to each specific dataset. It makes the application of an automated single-particle analysis routine without simultaneous loss of important information almost impossible. Also, the clusters obtained by applying a purely statistical algorithm require further interpretation and comparison to bulk data in order to obtain meaningful particle classes. The strength of single-particle measurements further lies within their capacity to distinguish between internally and externally mixed particles and the capacity to recognize specific tracers that can be related to sources or processes of particulate matter. In future experiments, the mobile SPASS lab will be deployed at a variety of locations where characteristic spectra of freshly emitted anthropogenic and biogenic aerosols will be collected (from test benches to road tunnels and remote forests) in order to establish a spectra library for source apportionment. Apart from in-field studies, the SPASS will also be used for laboratory studies of gas–particle interactions. Such studies are not only important from the mechanistic point of view, but they also allow us to collect reference spectra and to study the performance of the SPASS in more detail in a simulated but wellcontrolled atmosphere where realistic gas-to-particle conversion and other aerosol transformation processes take place. An example is a smog chamber study of suspended Sahara dust that is gradually coated with sulfate or organics.
INSTRUMENT CHARACTERIZATION AND FIRST APPLICATION OF THE SPASS
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