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Article Volume 10, Number 8 27 August 2009 Q08Z06, doi:10.1029/2009GC002563

AN ELECTRONIC JOURNAL OF THE EARTH SCIENCES Published by AGU and the Geochemical Society

ISSN: 1525-2027

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Compositional, morphological, and hysteresis characterization of magnetic airborne particulate matter in Rome, Italy Leonardo Sagnotti, Jacopo Taddeucci, Aldo Winkler, and Andrea Cavallo Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, I-00143 Rome, Italy ([email protected])

[1] The magnetic properties of tree leaves may be used to delineate the abundance and dispersal of anthropogenic airborne particulate matter (PM) in urban environments. In the city of Rome, Italy, circulating vehicles are the main source of magnetic PM, already characterized as prevalently lowcoercivity, magnetite-like particles. To further constrain the nature and origin of such magnetic particles, we carried out coupled field emission scanning electron microscopy and a variety of rock magnetic analyses on PM specimens from Quercus ilex leaves and from potential PM sources in circulating motor vehicles in Rome. Fe-rich particles are mostly 0.1–5 mm in size, with irregular shapes and moss-like surface. Particles from disk brakes and diesel and gasoline exhaust pipes show distinct compositional and magnetic hysteresis signatures, suggesting that the magnetic PM collected on tree leaves consists of a mixture of particle populations deriving mostly from the abrasion of disk brakes and, to a lesser extent, from fuel combustion residuals emitted by diesel and gasoline exhausts. The contribution of fine superparamagnetic particles to the overall magnetic assemblage has been evaluated with specific rock magnetic analyses. The combined magnetic and microtextural-compositional analyses provide an effective and original tool to characterize urban PM air pollution. Components: 7931 words, 9 figures, 1 table. Keywords: environmental magnetism; particulate matter; pollution. Index Terms: 1512 Geomagnetism and Paleomagnetism: Environmental magnetism; 0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801, 4906); 0345 Atmospheric Composition and Structure: Pollution: urban and regional (0305, 0478, 4251). Received 15 April 2009; Revised 3 July 2009; Accepted 20 July 2009; Published 27 August 2009. Sagnotti, L., J. Taddeucci, A. Winkler, and A. Cavallo (2009), Compositional, morphological, and hysteresis characterization of magnetic airborne particulate matter in Rome, Italy, Geochem. Geophys. Geosyst., 10, Q08Z06, doi:10.1029/2009GC002563. ————————————

Theme: Magnetism From Atomic to Planetary Scales: Physical Principles and Interdisciplinary Applications in Geoscience Guest Editors: J. Feinberg, F. Florindo, B. Moskowitz, and A. P. Roberts

Copyright 2009 by the American Geophysical Union

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1. Introduction

gional agency for environmental protection [Sagnotti et al., 2006].

[2] Pollution by airborne particulate matter (PM) is presently a source of particular concern in urban and industrial environments, because of the public awareness that its fine fraction (i.e., that with aerodynamic dimensions less than 10 mm, known as PM10) poses severe hazards for the health of citizens. The social interest in the effects of atmospheric pollution drives a demand to develop innovative techniques to better characterize the sources, spreading mechanisms, and dispersal patterns of PM10 into the environment.

[4] Although rock magnetic studies pointed out the substantial homogeneity of the urban magnetic PM populations, with prevalence of low-coercivity, magnetite-like particles, their correct composition, morphology and size, as well as their exact source in circulating vehicles, have not yet been identified. Moreover, the rock magnetic data also show that the assumption that stoichiometric magnetite is the only magnetic phase on anthropogenic airborne magnetic PM in Rome is overly simplistic and that the chemical/mineralogical composition and structure of anthropogenic ferrimagnetic particles may be different from those of natural particles. The net result implies that the magnetic properties of anthropogenic magnetic particles may not be easily compared with those of natural mineral systems. With regards to the possible differences of PM produced by different sources, for instance, it is well known that diesel-powered vehicles produce up to 100 times more PM than gasoline-driven ones [e.g., Hildemann et al., 1991; Maricq et al., 1999; Wang et al., 2003; Shah et al., 2004], but it is not known if the magnetic particles derived from the combustion of the two type of fuels form different populations with distinct compositional, microtextural and magnetic properties. Furthermore, no specific study has been carried out so far on the magnetic particles generated from other potential sources in circulating vehicles, such as disk brakes.

[3] Recent research demonstrated that the study of the magnetic properties of tree leaves reliably delineates the distribution and dispersal patterns of anthropogenic PM in urban environments [e.g., Matzka and Maher, 1999; Moreno et al., 2003; Urbat et al., 2004; Szo¨nyi et al., 2008]. These studies pointed out that the environmental biomagnetic monitoring is a powerful tool to delineate in the details the widespread distribution of magnetic (anthropogenic) PM in towns and may support the planning and the search for a proper and representative allocation of the air quality monitoring stations. The detailed magnetic characterization of PM may also provide proxies for the identification and tracing of the time and space changes in variable mixtures of PM populations originating from distinct natural and anthropogenic sources [e.g., Sagnotti et al., 2006]. By studying the magnetic properties of birch leaves in Norwich (UK) Maher et al. [2008] suggested that vehicle fuel combustion is the main source of Pb, Fe and fine ferromagnetic (in the broad sense) particles in the local airborne PM. This finding concurs with the results of Moreno et al. [2003] and Szo¨nyi et al. [2008], showing that circulating vehicles are the main source of magnetic PM in the city of Rome, Italy, and that the highest concentration of magnetic PM collected on tree leaves is found along high-traffic roads. The biomonitoring magnetic studies carried out on tree leaves, both in Rome and elsewhere, also show that the concentration of magnetic PM decreases significantly within a few tens of meters from the source (i.e., from hightraffic roads) [e.g., Hoffmann et al., 1999; Maher et al., 2008]. The magnetic properties of the PM in Rome were characterized in detail by means of a variety of rock magnetism methods applied to leaves collected from the most common tree species within the Rome metropolitan area [Moreno et al., 2003; Szo¨nyi et al., 2007, 2008] or to filters from air monitoring stations operated by the re-

[5] Here we aim to identify the composition, morphology and range of grain size of the magnetic airborne PM in Rome and to characterize the different populations contributing to the overall magnetic PM assemblage. For this purpose, we carried out a coupled microtextural-compositional and magnetic characterization on selected samples containing PM from various sources.

2. Methods 2.1. Sample Selection [6] A first set of specimens consists of tree leaves collected along a high-traffic square (Piazza Re di Roma) and a large green park (Appian Way natural park) within the Rome metropolitan area. We collected leaves from Quercus ilex (hereafter Q. ilex), an evergreen Mediterranean oak already used for rock magnetism investigations because of its widespread occurrence in Rome and its proved capability to retain magnetic PM [Moreno et al., 2 of 17



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2003; Szo¨nyi et al., 2007, 2008]. Other specimens were selected from the filters of an automatic air monitoring station at Via Magnagrecia, a hightraffic road just outside the ancient Aurelian Walls and characterized by a high concentration of magnetic PM10 [Sagnotti et al., 2006]. As possible pollution sources from motor vehicles, we collected dust samples from the exhaust pipes of gasoline and diesel engines, from wheel rims around disk brakes, and from engine hoods of a number of different circulating motor vehicles of variable age. Dust from the inside of exhaust pipes was collected, on clean paper, at a distance of about 5–10 cm from the pipe exit, while dust from wheel rims and the inside of engine hoods was collected with clean paper directly from the exposed surfaces. With regards to the wheel rims, we collected the powder present on the rim of the front wheels, close to disk brakes. Such powder was not present on the rims of the rear wheels of the same sampled vehicles, equipped with drum brakes, indicating that this powder derives from disk brakes only.

2.2. Morphological and Chemical Characterization [7] We characterized individual particles by using a JEOL JSM 6500F Field Emission (Schottkytype) Scanning Electron Microscope (FE-SEM, resolution 1.5 nm at 15 kV operating voltage), equipped with backscattered electron detector and Energy Dispersion System (EDS, JEOL HYPERNINE, 133 eV resolution) microanalysis. Samples, carbon-coated to allow EDS identification of metals, were observed at high magnification (up to 100,000 X) using both secondary (SE) and backscattered (BSE) electrons, the latter effectively showing higher atomic number, metal-rich particles versus relatively lighter atomic number, mostly rock and organic particles, and also uncovering particles incorporated inside the leaf. The chemical composition of PM was characterized by acquiring EDS X-ray spectra at 10 and 15 kV acceleration voltages and converting them into standardless chemical analyses with errors less than ± 10% relative to the analytical value, as inferred from repeated analyses of single particles and certified rock standards. EDS spectra were first acquired from some tens of randomly chosen particles per sample to broadly characterize the PM, and subsequently from Fe-rich particles only (at least 40 particles per sample), to investigate in detail the composition of the magnetic PM.

2.3. Magnetic Characterization [8] Hysteresis properties, including coercive force (BC), saturation remanent magnetization (MRS) and saturation magnetization (MS) were measured using a MicroMag magnetometer produced by the Princeton Measurement Corporation equipped with an alternating gradient magnetometer probe (AGM, model 2900), with a maximum applied field of 1 T. Stepwise acquisition of an isothermal remanent magnetization (IRM) and subsequent DC backfield remagnetization (both in a succession of fields up to 1 T) were also measured on the same specimens with the MicroMag AGM, and the remanent coercive force (BCR) was computed from the backfield remagnetization curves. We analyzed firstorder reversal curves (FORC) on one specimen for each category of samples. FORCs are a series of partial hysteresis loops made after the sample magnetization is saturated in a large positive applied field. The field is then decreased to a reversal field value BA, and the FORC is defined by the measurement of the magnetization of the sample as a function of an increased field BB, until positive saturation is reached again [Pike et al., 1999; Roberts et al., 2000]. In this study 121 FORCs have been measured for each specimen, in steps of 2.8 mT and an averaging time of 300–600 ms (depending on the magnetic intensity of the specimen), using a 0.5 T saturating field. The FORC data make it possible to define the detailed coercivity distribution of the magnetic particles and their interaction field strengths. To better visualize the produced set of partial hysteresis loops, the data are transformed into contour plots, usually referred to as FORC diagrams, by calculating the second derivative of the measured magnetization plotted as a function of BA and BB in the field space [Pike et al., 1999; Roberts et al., 2000]. Normally, a new set of coordinates is defined, with BC = (BA BB)/2 and BU = (BA + BB)/2, which serves to rotate the FORC distribution counterclockwise by 45°. The final FORC diagram is a contour plot of r(BA, BB), drawn using BC and BU as the horizontal and vertical axes, respectively. To produce FORC diagrams we used the FORCinel software developed by Harrison and Feinberg [2008]. This software employs an improved algorithm based on locally weighted regression smoothing and avoids the introduction of unwanted artifacts during the processing. [9] Since superparamagnetic (SP) particles may play an important role in determining the overall magnetic properties of magnetic PM, the contribu3 of 17



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tion of SP grains has been further evaluated in this study by using specific time-dependent, frequencydependent and temperature-dependent magnetic measurements. [10] The frequency dependence of the magnetic susceptibility has been evaluated by measuring the low-field (applied field of 200 A/m) magnetic susceptibility at low (976 Hz) and high (15616 Hz) frequencies on a MFKA1-FA kappabridge manufactured by AGICO. [11] The time dependency of the coercivity of remanence has been evaluated by measuring the variation of the coercivity of remanence as a function of the effective measuring time, according to the method indicated by Tarduno [1995] and by Smirnov and Tarduno [2001]. We carried out measurements on the dependency of BCR upon the measurement time on the MicroMag AGM. The effective measurement time is defined as the sum of the time comprised between the switching off of the backfield and the starting of the measurement (the ‘‘pause time’’) and of the measurement duration (the ‘‘averaging time’’). In our experiment the averaging time has been kept at the constant minimum value allowed by the instrument, that is 100 ms, whereas the pause time at zero field has been varied between 0 and 1 s. [12] The temperature dependency of the low-field magnetic susceptibility has been investigated by means of the MFKA1-FA kappabridge coupled with the CS-L low-temperature cryostat apparatus and the CS-3 high-temperature furnace apparatus. The CS-L was used for continuous measurements of the magnetic susceptibility in the temperature range from 192°C to ambient temperature and operated in air, whereas the CS-3 was employed for continuous measurements from room temperature to 700°C and was operated under a constant flow of argon, in order to prevent oxidation of the specimen during heating. The Curie point of the magnetic minerals present in the specimens was determined from the thermomagnetic curves as the temperature, or range of temperatures, at which paramagnetic behavior starts to dominate, following the approach outlined by Petrovsky and Kapicka [2006].

3. Results 3.1. General Features of the PM [13] An overview of the morphological and chemical signature of the PM in the analyzed samples

shows that anthropogenic, Fe-rich particles on the leaves occur together with natural fly ashes, mostly composed of silicate minerals (including pyroxene, plagioclase, leucite, quartz, and minor amounts of mainly volcanic minerals) and carbonate minerals. In specimens from diesel and gasoline exhaust pipes, Fe-rich particles are present as rare occurrences in a matrix consisting of carbon soot (i.e., elemental and organic carbon), most abundant in diesel engine exhaust pipes. Brake dust specimens collected around wheel rims are instead almost exclusively composed of Fe-rich particles. Abundance and features of airborne PM from leaves and air filters are remarkably alike, and also close to those of powders from engine hood, which do not appear particularly enriched in Fe-rich particles. On these grounds, we focused systematic compositional analyses on the following four sample categories: (1) Q. ilex leaves (hereafter ‘‘leaves’’), (2) dust from abrasion of disk brakes collected from wheel rims (‘‘brake’’), (3) diesel engine exhaust pipes (‘‘diesel’’), and (4) gasoline engine exhaust pipes (‘‘gasoline’’). [14] A ternary plot showing the EDS-determined relative abundance of FeO, SO3, and SiO2 (Figure 1) on randomly chosen particles of the different samples confirms the dual nature of particles on ‘‘leaves,’’ which forms two distinct clusters around the 100% SiO2 and 100% FeO corners of the diagram. These clusters indicate that the analyzed particles on leaves consist of a mixture of natural, SiO2-dominated, and anthropogenic, FeO-dominated particle end-members. In fact, the 100% FeO corner also hosts most of the ‘‘brake,’’ ‘‘diesel’’ and ‘‘gasoline’’ particles, as expected. The only other noticeable cluster is that of ‘‘diesel’’ particles around the FeO 60%, SO3 40% and SiO2 0% composition, which may represent sulfate species adsorbed onto the PM in diesel emissions [Walker, 2004].

3.2. Chemical Composition of Fe-Rich PM [15] In order to characterize the chemical characteristics of magnetic PM, for each sample category we identified between 50 and 150 high-atomic number particles using BSE FE-SEM images. The EDS spectra collected from these particles and the resulting standardless chemical analyses were used to isolate and further analyze Fe-rich particles, i.e., particles with FeO > 50 wt % (Figure 2). [16] Chemical analyses show that, although a few nonoxidized Fe alloys particles are found on the ‘‘leaves,’’ the vast majority of the Fe-rich particles 4 of 17



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Figure 1. Relative abundance, as measured by standardless EDS analyses, of SO3, FeO, and SiO2 in randomly chosen particles from Q. ilex leaves, brake dust (‘‘Brakes’’), and diesel and gasoline exhaust pipes (‘‘Diesel’’ and ‘‘Gasoline,’’ respectively). Particles on Q. ilex leaves cluster around the SiO2 and FeO corners, likely representative of the natural, silica-dominated, and anthropogenic, iron-rich, particle end-members, respectively. Also note the cluster of diesel-derived particles around the FeO 60%, SO3 40%, and SiO2 0% composition.

from all samples contain about 27 wt % of O, compatible with that expected for magnetite (Fe3O4). Table 1 shows EDS standardless chemical analyses, expressed as oxides, of more than 40 Ferich particles for each of the four sample categories. Beside FeO, which always accounts for more than 70 wt %, SiO2, SO3, CuO and ZnO are the other most abundant oxides (see also Figure 2). Although the chemical variability within each sample is quite high, as outlined by the large standard deviation of the identified oxides (Table 1), some differences between the various samples are evident. On the one hand, ‘‘leaf’’ particles are characterized by larger abundances of crust oxides (such as MgO, Al2O3, SiO2, CaO, K2O, and TiO2). Except for the reduced content of crust oxides, ‘‘brake’’ composition is quite similar to that of ‘‘leaves.’’ On the other hand, both ‘‘diesel’’ and ‘‘gasoline’’ are characterized by a higher content of P2O5, and ‘‘diesel’’ by higher Cr2O3 and NiO contents (Table 1). Moreover, an expansion of the 100% FeO corner of the FeO-SO3-SiO2 ternary plot (Figure 3) shows that Fe-rich particles from ‘‘diesel’’ and ‘‘gasoline’’ tend to be SO 3 -richer and SiO 2 -poorer than

‘‘brake’’ and ‘‘leaves’’ particles, these last two populations largely overlapping.

3.3. Morphology of Fe-Rich PM [17] FE-SEM observations reveal that, except for rare metallic fragments measuring tenths of mm, most Fe-rich particles are in the 0.1–5 mm size range, 1–2 mm being the most common size. In all samples, Fe-rich particles are of variable shapes, ranging from rounded to irregular to aggregated or flaky. All particles show a distinctive rough, mosslike surface (Figure 4) composed of adjoined or aggregated, subrounded particles or ‘‘knobs’’ typically about 50–60 nm in size. Interestingly, Ferich particles in the Q. ilex leaves were found both adhering on the hairs covering the lower surface and protecting the stomata cavities (Figure 4a) and incorporated within the wax coating on the upper surface of the leaf (Figure 4b). This observation explains the results of Szo¨nyi et al. [2008] who showed that cleaning the leaves by hand or by ultrasonic bath reduces the magnetic susceptibility of the Q. ilex leaves by only a 30–50%. 5 of 17



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Figure 2. BSE images of analyzed Fe-rich PM (red dots) and corresponding EDS spectra (labels mark the Ka peaks of major elements, and reported values refer to the FeO wt % range of the particles). (a) Lower surface of a Q. ilex leaf, (b) brake dust, (c) diesel exhaust pipe, and (d) gasoline exhaust pipe. 6 of 17



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Table 1. Standardless Analyses of Fe-Rich Particles From the Four Analyzed Sample Typesa Oxide

Leaf

Brake

Diesel

Gasoline

FeO SiO2 SO3 CuO Al2O3 ZnO CaO P2O5 MnO MgO TiO2 Cr2O3 Cl K2O CoO NiO V2O5

76.0 (9.8) 7.5 (4.7) 2.6 (1.5) 2.7 (1.8) 2.0 (9.1) 1.0 (1.2) 2.2 (2.0) 0.7 (0.8) 0.9 (0.4) 1.0 (0.8) 0.6 (1.6) 0.3 (0.8) 0.4 (0.7) 0.7 (0.6) 0.3 (0.3) 0.1 (0.3) 0.1 (0.2)

82.7 (4.8) 4.6 (1.1) 3.1 (1.5) 3.9 (1.8) 0.7 (9.9) 1.5 (1.6) 0.3 (0.4) 0.4 (0.3) 1.1 (0.3) 0.5 (0.5) 0.3 (0.2) 0.2 (0.2) 0.3 (0.3) 0.1 (0.1) 0.1 (0.2) 0.2 (0.3) 0.0 (0.1)

79.8 (9.8) 3.6 (2.2) 3.4 (2.2) 3.0 (2.3) 0.5 (0.6) 0.9 (1.2) 0.4 (0.6) 1.4 (1.4) 0.9 (0.4) 0.3 (0.5) 0.2 (0.2) 2.8 (6.1) 0.1 (0.1) 0.1 (0.2) 0.3 (0.4) 1.3 (4.0) 0.1 (0.2)

78.7 (9.5) 2.9 (2.7) 5.4 (3.4) 1.9 (2.2) 1.6 (1.6) 2.4 (2.5) 1.4 (1.5) 2.6 (2.8) 0.8 (0.5) 0.6 (0.8) 0.3 (0.5) 0.2 (0.5) 0.1 (0.1) 0.1 (0.2) 0.3 (0.6) 0.2 (0.3) 0.1 (0.2)

a FeO > 50 wt %. For each sample type the mean value (wt %) and the variability (one standard deviation, in parentheses) of the main identified elements is reported, as determined from analyses carried out over more than 40 particles for each group.

3.4. Hysteresis Properties of PM [18] With regard to the hysteresis properties, we notice that the ‘‘brake’’ specimens are characterized by very narrow hysteresis curves and very low coercivities (BC between 4.5 and 6.5 mT) and the gasoline exhaust specimens by distinctly higher coercivities (BC between 14 and 16 mT) and wider hysteresis curves (Figure 5). Both the ‘‘brake’’ and the ‘‘gasoline’’ specimens generally show a highfield weak diamagnetic susceptibility (Figure 5). Such high-field diamagnetic susceptibility is much more evident in most of the specimens from diesel exhaust (Figure 5), which show a marked negative correlation between the magnetization and the magnetic field after that the ferromagnetic fraction is saturated. We link the high-field diamagnetic susceptibility to the various organic carbon compounds that represent that vast majority of the diesel engines PM emissions. The coercivity range of the ‘‘diesel’’ specimens is intermediate between that of the ‘‘brake’’ and the ‘‘gasoline’’ (BC varies between 8 and 11 mT). [19] In a classical ‘‘Day plot’’ of MRS/MS versus BCR/BC [Day et al., 1977; Dunlop, 2002] the data from ‘‘diesel’’ and ‘‘gasoline’’ tend to follow the theoretical trends for mixtures of single domain (SD) and multidomain (MD) magnetites and are clearly distinct from the trend defined both by the filters of the air monitoring stations in Rome [Sagnotti et al., 2006] (Figure 6a) and by the Q.

ilex leaves [Szo¨nyi et al., 2007] (Figure 6b). The latter define a cluster in a region of the plot falling in between the theoretical trends for SD-MD and SD-SP magnetites. The ‘‘brake’’ samples fall instead at the lower right end of the cluster defined by the particles accumulated on leaves and filters (Figure 6). [20] Also the FORC diagrams indicate that the coercivity distribution of the magnetic particles in the ‘‘leaves’’ and ‘‘brake’’ specimens are very much alike (Figure 7). As a general feature, the FORC diagrams for all the categories of samples indicate a mixed contribution of particles with a variety of grain size and domain state, but also clearly suggest a remarkable difference between the coercivity distributions among the different samples. The main features observed in the FORC diagrams have been interpreted mostly following the conceptual and theoretical models outlined in Roberts et al. [2000]. A large peak close to the origin of the FORC distribution, most easily seen in profiles along the BC axis (Figure 7), characterizes both the ‘‘leaf,’’ ‘‘brake’’ and the ‘‘diesel’’ samples and indicates a significant reversible component of magnetization due to superparamagnetic (SP) particles in these samples. In fact, theoretically SP particles would not be expected to produce any manifestation in a FORC diagram since the magnetization of SP particles is entirely reversible. However, particles at or just below the SP-SD threshold volume at room temperature will behave in a quasi reversible manner, with a small but detectable relaxation time that results in a contribution located about the origin of the plot in a FORC diagram and a large reversible bridge observable at BU = 0 in profiles drawn through FORC distribution along the BC axis [e.g., Roberts et al., 2000; Rowan and Roberts, 2006]. The ‘‘leaf’’ and the ‘‘brake’’ samples show also a tail extending to higher coercivities (BC) which suggests the additional presence of SD-pseudo single domain (PSD) grains. This tail extends well beyond the typical coercivity range for stoichiometric magnetite. The presence of open contours that diverge toward the Bb axis is characteristic for MD material, and is evident in all sample types. The FORC diagram from the ‘‘gasoline’’ specimen appears quite distinct from the above features, with contours closed around a coercivity (BC) peak value of 15–20 mT, which is consistent with the result of the simple hysteresis loop (Figure 5). Such FORC distribution indicates the prevalence of SD and PSD grains in this specimen. The narrowness 7 of 17



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Figure 3. Relative abundance of SO3, FeO, and SiO2 in Fe-rich (FeO > 50 wt %) particles. Point density classes show that Fe-rich particles from diesel and gasoline exhaust pipes tend to be SO3-rich and SiO2-poor relatively to particles from brake dust and Q. ilex leaves.

of all these FORC distribution suggests that these grains are not interacting with one another.

3.5. Dependence of the Magnetic Properties on the Frequency and the Measuring Time [21] The frequency dependence of the low-field magnetic susceptibility has been evaluated by the kfd % parameter, computed as ((klowfreq khighfreq)/ klowfreq) *100, which provides a measure of the amount of SP grains. The difference between magnetic susceptibility measurements obtained at two different frequencies is a function of the concentration of grains that have a relaxation frequency that lie between the two measuring frequencies. In fact, by increasing the frequency of the measuring field, the relaxation time of SP grains may exceed the time needed to measure k

and a grain which is SP at low frequency behaves as a stable SD grain at high frequency. This effect is highest for spherical SP ferrimagnetic grains in the grain size range of 10–25 nm, with an empirical upper limit of kfd % = 15% [Dearing et al., 1996] in a decade of frequency change, that is at the 0.465 kHz and 4.65 kHz frequencies used by the Bartington MS2 bridge. Higher kfd % values are theoretically possible [Eyre, 1997; Worm, 1998], and a value of kfd % > 20% has been reported for a volcanic tuff [Eick and Schlinger, 1990]. The data on our samples were obtained at frequencies of 0.976 and 15.616 kHz, using the AGICO MFKA1-FA kappabridge, and indicate a kfd % value of 9–11% for the specimens from brakes, of 7–8% for specimens from diesel exhaust pipes and Q. ilex leaves and of 1.5–2% for specimens from the gasoline exhaust pipes. 8 of 17



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Figure 4. FE-SEM images of the particulate matter (PM). (a) BSE image of the lower surface of a Q. ilex leaf, showing Fe-rich PM (lighter gray tones, e.g., the particles in the center) and natural fly ashes (darker tones, e.g., the two larger particles on the left). (b) Fe-rich PM embedded in the leaf wax on the upper surface of a leaf. (c – e) SE images of Fe-rich PM from leaves. (f) Composite SE-BSE image of a complex Fe-rich particle found on a leaf. Composite SE-BSE images showing Fe-rich particles (lighter gray) included in carbonate soot (darker gray) from (g) gasoline and (h) diesel exhausts. (i) SE image of brake dust entirely composed of Fe-rich particles.

[22] The results of the experiments carried out to investigate the time dependence of BCR indicate an evident effect for the brake and the gasoline specimens, with a decrease of 5–7% for the measured values, whereas such time dependence is barely appreciable for the leaves and the diesel exhaust specimens, being the decrease of BCR of the order of 2% (Figure 8), that is approximately the uncertainty implicit in the measurements. We note that such decreases are considerably smaller than those observed in samples for which the SP contribution overwhelms that of the magnetic particles in other domain states (e.g., a BCR dependency on measuring time of the order of 30–70% has been reported by Tarduno [1995] for some pelagic sediments from the western equatorial Pacific Ocean).

3.6. Thermomagnetic Curves [23] For the low-temperature experiments a distinct increase is observed during heating in the thermomagnetic curves of all samples, with a broad maximum centered at ca. 60°C for the brake specimen and at ca. 30–50°C for the leaf, the diesel and the gasoline specimens (Figure 9). This behavior may be due to the presence of grain sizes in the range close to the threshold between superparamagnetic (SP) and single domain (SD) particles. Particles that are just below to the SP-SD threshold and behave as SP at room temperature become magnetically ordered stable SD grains when cooled at low temperatures, which leads to a decrease of the total susceptibility. During heating back to room temperature such grains turn to 9 of 17



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Figure 5. Representative hysteresis loops for dust specimens collected from the rim around disk brakes, the exhaust of a gasoline engine car, and the exhaust of a diesel sport utility vehicle (SUV). (left) Uncorrected hysteresis data and (right) hysteresis loops after correction for the paramagnetic or diamagnetic high-field susceptibility and magnified close to the origin.

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Figure 6. Plot of hysteresis ratios (BCR/BC versus MRS/MS [after Day et al., 1977]) for the various measured specimens (large circles and purple squares), compared to (a) the data from the filters of the air monitoring station of Via Magnagrecia (MG) and Villa Ada (VA) [Sagnotti et al., 2006] and (b) the data from a wide collection of Q. ilex leaf specimens (small triangles) [Szo¨nyi et al., 2007]. The data referring to the powders from the gasoline and diesel exhausts fall along the theoretical curves, indicated by the solid lines, calculated for mixtures of single domain (SD) and multidomain (MD) magnetite grains [Dunlop, 2002]. The data from the disk brakes fall at the lower right corner of the data distribution of hysteresis properties measured on Q. ilex leaves and the air station filters, in between the above mentioned mixing lines and that calculated for mixture of SD and superparamagnetic (SP) magnetite grains [Dunlop, 2002]. Fields for SD, pseudo single domain (PSD), and MD magnetites are also shown after Dunlop [2002].

the SP state, resulting in an increase of the magnetic susceptibility and of its frequency dependence upon heating, producing a broad peak in the heating curves at the temperature that presumably corresponds to the range of mean blocking temperature of the SP particles. [ 24 ] In the high-temperature thermomagnetic curves a distinct steep decay of the magnetic susceptibility was observed between 530°C and 600°C for all samples (Figure 9). The Curie temperature estimated by linear fit to inverse susceptibility in the heating curve [Petrovsky and Kapicka, 2006] is 580°C for all the analyzed specimens, indicating that the main magnetic mineral is magnetite. A further inflection at ca. 250– 350°C in the heating curves may indicate the additional presence of maghemite (g-Fe2O3) and represents its thermally induced conversion to hematite (a-Fe2O3) during the thermal treatment. The results are similar to those formerly obtained for other Q. ilex leaf specimens in Rome [Szo¨nyi et al., 2008]. The sharp increase in the magnetic

susceptibility values observed for temperatures higher than 450°C in the diesel specimen indicates an alteration of the magnetic mineralogy with production of new magnetic minerals. For all specimens the cooling curve is well above the heating curve, with a distinct steep increase between 600°C and 500°C, indicating that new magnetite grains are also produced by mineralogical transformation during heating in all the specimens. The irreversibility of the thermomagnetic curves has been seen in other investigations on airborne PM [e.g., Gautam et al., 2004; Muxworthy et al., 2002; Szo¨nyi et al., 2008].

4. Discussion and Conclusions 4.1. Composition and Origin of the Magnetic PM [25] The magnetic PM that we investigated in the city of Rome is composed of Fe-rich particles. The Curie temperature of ca. 580°C observed in the high-temperature magnetic susceptibility thermo11 of 17



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Figure 7. FORC distribution and coercivity profiles along the BC axis for specimens from the four main categories considered in this study. The FORC diagrams have been computed, using the FORCinel software developed by Harrison and Feinberg [2008], with smoothing factors of 3, 4, 3, and 5 for the ‘‘leaf,’’ ‘‘brake,’’ ‘‘diesel,’’ and ‘‘gasoline’’ specimens, respectively. The FORC distributions for the two ‘‘leaf’’ and ‘‘brake’’ specimens are remarkably similar, with a maximum peak near the origin of the plot and an extended tail to high BC values. Contours diverging away from the origin toward the BU axis, indicating the contribution of MD grains, are present in all diagrams and particularly developed for the ‘‘brake’’ sample. The FORC distribution for the diesel exhausts shows a maximum peak at low coercivity (BC of 15 –20 mT), which indicates a prevalent contribution of SD and PSD grains in this sample. All data are compatible with a broad distribution of grain size and magnetic states (see section 3.4). 12 of 17



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Figure 8. Normalized coercivity of remanence (BCR) values measured as a function of the pause time for the four categories of specimens. The BCR dependence on the measurement time is larger for the brake specimen, with a decrease of about 7% obtained by reducing the pause time to zero, which implies an effective measurement time equal to the minimum instrumental averaging time of 100 ms (see section 3.5).

Figure 9. Thermomagnetic susceptibility curves for the four categories of specimens, showing the changes in volume magnetic susceptibility during warming back to room temperature after cooling to liquid nitrogen temperature (orange) and during a heating (red)-cooling (blue) cycle from room temperature to 700°C. The high-temperature cycle was measured in an argon atmosphere (see section 3.6 for discussion). 13 of 17



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magnetic curves for all samples indicate that the main magnetic mineral is magnetite (Figure 9). The inflection at ca. 350°C in such curves may indicate the additional presence of maghemite (or of a maghemite shell surrounding a magnetite core). Moreover, the coercivity spectrum of the magnetic mineral population extends beyond the range typical for stoichiometric magnetite, as indicated by the FORC analyses carried out in the present study (Figure 7) and by the measurement of the IRM acquisition curves and BCR determination at 77 K in Q. ilex leaves from Rome [Szo¨nyi et al., 2008]. [26] The EDS X-ray spectra indicate that Fe-rich particles have a composition very close to that of magnetite, with small amounts of Si, Al, S, Cu, Zn and other elements (see Table 1). We point out that the spacing between the analyzed particles (several mm to tens of mm in most cases, see Figure 2) is generally much larger than the interaction volume of the electron beam (less than 1 mm at 15 kV in a Fe-rich matrix), ruling out the possibly of a systematic contamination of the analyses by natural fly ashes. Conversely, diesel vehicle engines are known to emit significant concentrations of crustal elements [Wang et al., 2003] that may contribute as well. [27] Overall, the chemical composition of the Ferich particles indicate a remarkable unevenness in the concentration of the other, accessory, elements, which may reflect a variability in their origin. Such elements may be either incorporated into the lattice structure of the ferromagnetic grains or be adsorbed onto their surface (e.g., the volatile element sulphur enters the vapor phase during combustion and may be present as a coating on the surface of PM particles). Actually, we did not observe the Verwey transition in the magnetic susceptibility thermomagnetic curves measured at low temperature. The Verwey transition in magnetite may be suppressed by superparamagnetism in particles smaller than the SP-SD threshold (ca. 30 nm) and by small deviations from stoichiometry ¨ zdemir et al., 1993]. Therefore, the low-temper[O ature thermomagnetic curves of our samples are consistent with the hypothesis that the magnetic particles do not consist of stoichiometric magnetite grains, but they rather consist of nonstoichiometric Fe3O4 particles (e.g., with an oxidized surface or shell and a number of substituting ions in the lattice). In any case, the extent of cation substitution is not large enough to significantly lower the Curie temperature from the pure magnetite value of 580°C.

[28] The inclusion of various elements other than Fe in these magnetic particles makes them different from natural stoichiometric magnetite grains, both from the compositional and the magnetic point of view. Morphology is not a distinctive feature of particles from the different sets of specimens considered in this study, thus confirming motor vehicles as the prime source of the PM accumulated on the tree leaves. Under FE-SEM, Fe-rich particles appear mostly in the 0.1–5 mm size range, which is consistent with the broad spectrum of magnetic states indicated by the magnetic analyses, and show typically irregular shapes and moss-like surfaces. These morphologies are remarkably different from the typical spherical shapes of industrial fly ashes originated by combustion of black and brown coal [e.g., Sarbak et al., 2004; Veneva et al., 2004; Jordanova et al., 2004, 2006], and confirm that they derive from a completely different source.

4.2. Role of Superparamagnetic Particles [29] It has been formerly suggested that a large percentage of urban PM is