Sampling and single particle analysis for the chemical characterisation of fine atmospheric particulates: A review

Journal of Environmental Management 202 (2017) 137e150

Contents lists available at ScienceDirect

Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman

Review

Sampling and single particle analysis for the chemical characterisation of fine atmospheric particulates: A review Michele Elmes a, Massimo Gasparon a, b, * a b

School of Earth and Environmental Sciences, University of Queensland, Australia National Institute of Science and Technology on Mineral Resources, Water and Biodiversity (INCT-Acqua), Brazil

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 December 2016 Received in revised form 30 April 2017 Accepted 29 June 2017

To better understand the potential environmental and human health impacts of fine airborne particulate matter (APM), detailed physical and chemical characterisation is required. The only means to accurately distinguish between the multiple compositions in APM is by single particle analysis. A variety of methods and instruments are available, which range from filter-based sample collection for off-line laboratory analysis to on-line instruments that detect the airborne particles and generate size distribution and chemical data in real time. There are many reasons for sampling particulates in the ambient atmosphere and as a consequence, different measurement strategies and sampling devices are used depending on the scientific objectives and subsequent analytical techniques. This review is designed as a guide to some of the techniques available for the sampling and subsequent chemical analysis of individual inorganic particles. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Airborne particulate matter Air quality guidelines Sampling methods Single particle analysis

Contents 1. 2.

3.

4. 5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Air sampling techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 2.1. Active samplers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 2.2. Passive samplers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 2.3. Sampling substrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Off-line analytical techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 3.1. Electron microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 3.2. Atomic spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 On-line analytical techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

1. Introduction Compared to trace gases, airborne particulate matter (APM) is a complex mixture of solid and liquid particles of organic, inorganic

* Corresponding author. School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Qld. 4072, Australia. E-mail address: [email protected] (M. Gasparon). http://dx.doi.org/10.1016/j.jenvman.2017.06.067 0301-4797/© 2017 Elsevier Ltd. All rights reserved.

and biological substances. Some particulates occur naturally but human activities, such as traffic and industrial emissions, also contribute significant amounts of particulates. The assessment of the potential environmental and human health risks associated with fine APM (>1 mm) requires detailed physical and chemical characterisation. In the past, analysis has concentrated on determination of size, mass concentration and bulk chemistry. Individual particles however, vary in properties such as toxicity, light

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attenuation and hygroscopic behaviour which are functions of their three-dimensional chemical composition. The impact and toxicity of APM is related not only to total elemental composition and size distribution but also to their chemical heterogeneity. Information regarding chemical heterogeneity at the individual particle level (the mixing state) is essential for understanding and predicting the reactivity and environmental and human health impacts of APM. The only means to accurately distinguish between the multiple compositions in APM is by single particle analysis. Once in the atmosphere, and under favourable weather conditions, particulates can be transported over long distances by prevailing winds and can act as a vector for pollution. As pollutants are commonly taken up on the particle surface, they are typically present in loosely bound forms that are highly mobile and potentially bio-available. The health effects of exposure to APM are well documented (Møller, 2008; Pope et al., 2004; Zanobetti and Schwartz, 2009) and as a result several guidelines have been adopted (Table 1). These guidelines, though not legally binding in most countries, provide a basis for setting standards and limiting airborne particulate pollution. The most frequently used reference guidelines for ambient particulate concentration are the World Health Organisation Air Quality Guidelines (WHO, 2000), the European Union Limit Values for Air Quality (European Union, 2008) and the United States Environmental Protection Agency National Ambient Air Quality Standard (Environmental Protection Agency (EPA), 1997). Most guidelines are measured in mg/m3 and averaged over a 24 h time period, however the United Kingdom and European Union standards are averaged over a year. These

Table 1 International Air Quality Standards for total suspended particulates (TSP), PM10 and PM2.5 (mg/m3, 24 h mean unless otherwise stated). (a) (Defra, 2012); (b) (National Environment Protection Council, 1998); (c) (National Environmental Standards for Air Quality, 2004); (d) (SANS, 1929, 2011); (e) (Clean Air Initiative for Asian Cities, 2010); (f) (Clean Air Institute, 2012). WHO AQG - World Health Organisation Air Quality Guidelines; NAAQS e National Ambient Air Quality Standard; EU LVAQ e European Union Limit Values for Air Quality.

WHO AQG NAAQS (United States) EU LVAQ (Europe) United Kingdom (a) Australia (b) New Zealand (c) South Africa (d) China (e) Hong Kong (e) India (e) Japan (e) Bangladesh (e) Bhutan (e) Indonesia (e) Malaysia (e) South Korea (e) Mongolia (e) Nepal (e) Singapore (e) Pakistan (e) Philippines (e) Sri Lanka (e) Thailand (e) Vietnam (e) Bolivia (f) Brazil (f) Colombia (f) Chile (f) Ecuador (f) Mexico(f) Peru (f) Puerto Rico(f)

TSP

PM10

PM2.5

e e e e e e e 300 e e e e 200 230 260 e 150 230 e 500 230 e 330 200 e e e e e e e e

50 50 50 50 50 50 120 150 100 100 100 150 100 150 150 100 150 120 150 150 150 150 120 150 150 150 100 150 150 120 150 150

25 35 25 (annual mean) 25 (annual mean) 25 e 65 e 75 60 35 65 e e e 50 50 e 35 35 e 50 e e e e 50 50 65 65 50 35

guidelines are based on clinical, toxicological, and epidemiological evidence and were established by determining the concentrations with the lowest observed adverse effect, however, to date there is no evidence to support a threshold level below which no adverse health effects occur (Kim et al., 2015). Standards have also been implement for other air toxins such as lead, cadmium, arsenic and mercury, however these are outside the scope of this review. Atmospheric residence time, deposition rates, and inhalation processes are predominantly influenced by the size of the particles. Even though many particles are not spherical, they are typically classified to size by their aerodynamic diameter which is defined as the diameter of a spherical particle of density 1 g/cm3 having a settling velocity equal to that of the particle in question (John, 2011). The aerodynamic diameter is useful for particles larger than 0.5 mm and is considered to be the most appropriate measure to describe particle motion in the atmosphere (Sullivan and Prather, 2005) and the ability of the particle to penetrate and deposit at different sites within the respiratory tract (Pinkerton, 2000). The aerodynamic properties of particles also depends on density and shape. The health risks associated with APM arises from the deposition of particles in the human respiratory system (Fig. 1). After inhalation, particles in the 2.5e10 mm size fraction (thoracic particles) are primarily deposited in the tracheal and bronchial region, from where they are transported by mucociliary processes and typically swallowed, thus reaching the gastrointestinal tract. Finer particles can travel deeper into the alveolar region (respirable particles) where they interact with lung fluids (Asgharian et al., 2001). Ultrafine particles (<0.1 mm) can not only deposit in the respiratory tract, they can traverse the alveolar epithelium to be absorbed directly into the bloodstream. Their large specific surface area, with its increased surface reactivity, has the potential to result in greater €rster et al., 2005). These ultrafine particles not only toxicity (Oberdo have an enhanced inflammatory potential, they also have a higher deposition efficiency within the pulmonary system. There has been an increasing awareness of the impacts of these ultrafine particles, however methods for characterizing these particles is outside the scope of this review. The site of particle deposition within the respiratory system strongly influences the health effects of exposure to these particles and as a result, regulation and monitoring of APM has evolved over time from total concentrations (total suspended particulates, TSP) to a focus on smaller inhalable particles that can be deposited into the respiratory system, namely fine (PM2.5) and coarse (PM10) particles, which are defined as particles with an aerodynamic diameter of less than 2.5 mm and 10 mm respectively. While methods for measuring particle concentrations and size distribution are well established, the compositional analysis of single particles remains problematic. A variety of methods and instruments are available, which range from filter-based sample collection for off-line laboratory analysis to on-line instruments that detect the airborne particles and generate size distribution and chemical data in real time, however, a single practical technique does not exist for obtaining all the required information, specifically the size, morphology, composition and molecular structure of fine particulate matter (Pratt and Prather, 2012a). The ultimate goal of analytical techniques developed for APM is to quantitatively identify all species within each individual particle but as single particles are complex mixtures containing in the order of ~102-1015 molecules per particle, which translates to masses in the order of ~10 20 to 10 6 g/particle, measurement can be challenging (Pratt and Prather, 2012a). Off-line techniques generally allow for greater molecular and structural speciation than on-line techniques however, on-line techniques are able to examine the chemical changes in APM on

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139

Fig. 1. Deposition of APM in the respiratory system (background image from msnucleus.org).

short time scales (Pratt and Prather, 2012b). In most APM measurement applications, the challenges of selecting the instruments and the measurement strategy that will provide the desired information often dominate the measurement approach. This review is designed as a guide to some of the techniques available for the sampling and subsequent chemical analysis of individual inorganic particles. 2. Air sampling techniques There are many reasons for sampling particles in the ambient atmosphere including compliance with air quality standards, data for epidemiological studies and assessment of pollution sources. As a consequence, different measurement strategies and sampling devices are used depending on the scientific objectives. 2.1. Active samplers Traditionally, most atmospheric monitoring programs have relied on the use of active air samplers to assess the levels and spatial and temporal variability of atmospheric pollutants. The most common approach is by actively aspirating air through a filtering media using a pump. The rationale of the process is to accumulate a sufficient particle mass over the filtering media (the particle collector) for a statistically robust determination of total particle mass, while at the same time being able to accurately measure the amount of air pumped through the filter so total particle concentrations can be measured and reported as particle mass per cubic meter of air. Typically, active samplers are deployed for short time periods (<24 h) but in high frequency (Hayward et al., 2010). Active samplers provide reliable quantitative concentration data, and high temporal resolution, however, they are expensive to operate and require a stable power source and frequent maintenance which makes them inappropriate for unattended operation and/or in remote areas.

The original reference method for TSP involved a high-volume (hi-vol) active air sampler accompanied by gravimetric analysis to determine the bulk weight of particulate matter in the atmosphere (U.S EPA, 1982). The typical flow rate of hi-vol samplers ranges between 100 and 1000 L min 1 and as a result, these samplers require a relatively porous filter media with low flow resistance. The most commonly used filters for hi-vol sampling are either glass fibre or cellulose however the inherent design of these filters, which generates their high-loading capacity, is typically unsuited for many single particle analytical techniques (see section on sampling substrates). Hi-vol sampling can also be unsuitable for individual particle analysis due to over-accumulation resulting in overlap of particles. As the international guidelines have evolved from measuring TSP to PM10 and PM2.5 so has the range of instrumentation, although most involve either cyclones or impactors for particle-size separation. Conventional cascade impactors operate at atmospheric pressure and as the air passes through a sequence of stages, particles larger than the cut-off size are collected, with smaller particles following the gas flow to be collected in the next stages (Fig. 2). In conventional impactors, the substrates are placed with the collection surface parallel to the air flow at the exit of the particle acceleration nozzle. A constant flow rate results in an increase in velocity at each stage, resulting in deposition of size-fractionated particles. This method of collecting size-fractionated samples can suffer from the effects of particle bounce and re-entrainment which frequently result in substantial errors in the measured mass concentrations (Markowski, 1984). Conventional samplers also require regular cleaning to prevent the build-up of particles which has the potential to affect the cut-off characteristics. The cut-off point can also be affected by flow-rate, acceleration nozzle diameter and particle density, composition and shape. The most common cascade samplers are the Andersen cascade impactor and the low pressure impactor. Another group of cascade impactors are based on the Micro-

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Fig. 2. Cascade impactor (TISCH Environmental, 2015).

Orifice Uniform Deposit Impactor, which uses micro-orifice nozzles for jet acceleration. The MOUDI has features not normally found in other cascade impactors (Marple et al., 1991). The sampler covers a broad range of particle sizes and is equipped with various combinations of impacting stages, capable of collecting particles smaller than 0.056 mm. By rotating the impaction plate relative to the nozzles, particles are deposited uniformly over the entire plate (Maenhaut et al., 1993). The uniform deposit prevents heavy particle build-up under the nozzles thus reducing particle bounce. The type of collection substrate is determined by the type of particulates to be collected and the subsequent analytical method, with thin foils or membrane filters typically used. The virtual impactor, or dichotomous sampler (Fig. 3a), achieves inertial size-segregation under laminar flow conditions as the airstream impacts against a mass of relatively slow moving air rather than against a plate thus avoiding particle bounce and reentrainment from the impaction surface (Solomon et al., 1983).

Air is drawn through a size-selective inlet restricted to particles <10 mm. Particles are subsequently fractionated into a fine (<2.5 mm) and coarse (2.5e10 mm) fraction which are collected on separate Teflon filters. The inlets are designed so that 50% of particles of the critical size are rejected, however as the cut-off characteristics depend on the speed of the air passing through the inlet, flow control and calibration are essential for accurate size fractionation. Typically, the dichotomous sampler operates at a low sampling rate (<20 L min 1), though medium-volume (50 L min 1) and high-volume (>50 L min 1) dichotomous samplers are also commercially available. A range of real-time instruments have been developed to provide the virtually continuous monitoring of TSP, PM10, PM2.5 and PM1. Several papers have provided comprehensive reviews of the capabilities and limitations of these instruments (Chung et al., 2001; Schwab et al., 2006). Although some of these instruments allow the collection of samples for further single particle analysis,

Fig. 3. a) Dichotomous impactor (McFarland et al., 1978); b) Cyclone impactor (Bergmans et al., 2014).

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the physical and chemical nature of the substrate used for sample collection is typically not suitable for optical/SEM analysis and instrumental design makes it difficult to obtain undisturbed and reliable samples. A commonly used real-time instrument is the tapered element oscillating microbalance analyser (TEOM). In this device APM is measured by passing air through a hollowed tapered element where the particles are collected on a filter. The tapered element oscillates at a frequency that is inversely proportional to the weight of particles collected on the filter (Chung et al., 2001). Typically the TEOM operates 3 L min 1 with a filter temperature of 50  C to avoid condensation and to maintain a constant temperature for the tapered element to minimize thermal expansion. This heating however results in losses of semi-volatiles which has been shown to potentially lead to lower estimations of APM concentrations (Allen et al., 1997). To overcome this limitation, a Filter Dynamics Measurement System (FDMS) was developed which operates at 30  C, enabling the measurement of volatile particles. Although TEOM filters are typically composed of glass fibre, several studies have used them with scanning electron microscope with energy dispersive X-ray (SEM-EDX) for elemental and morphological studies (Baquero et al., 2015; Chung et al., 2012; Srivastava and Jain, 2007; Williamson, 2013). Another real-time sampler is the electric low pressure impactor (ELPI). The ELPI is comprised of three main components; a cascade impactor, a unipolar diode charger and a multichannel electrom€ki et al., 2000). Particles are first charged to a welleter (Marjama defined charge level before the particles are introduced into the impactor. The electrometer is used to simultaneously measure the charges carried by the collected particles from each stage which is then converted to number concentration. Typically the ELPI operates at 10 L/minute and can collect particles down to 3 nm in size €ki et al., 2000). (Marjama In contrast with the static instruments described above, which are typically bulky, require a dedicated installation site and are left at the same location for the duration of the monitoring program, personal exposure samplers and monitors have been developed to assess the level of exposure of particular individuals to APM as they perform their daily routine. These devices typically consist of a small battery-operated pump which is attached to the belt or carried in a backpack, tubing to connect the pump with the sampling device and the sampling device itself, which is normally placed near the person's face and consists of a protective casing and substrate for particle collection. The pump is usually configured to low flow-rates of approximately 2 L min 1 to simulate the typical air intake of an adult. The design of these devices are typically based on either cyclone or impactor samplers which allows for the collection of either respirable or inhalable size fraction. Inertial separation in cyclone impactors is achieved by the creation of a vortex-like flow in which large particles that cannot follow the streamlines exit the flow and are deposited on the walls of the cyclone (Fig. 3b). As this method of particle segregation mimics the removal of large particles in the upper respiratory tract, cyclones have been used to measure the respirable fraction of APM in occupational and personal air sampling since the early 1960's (Fig. 4a). With the increasing awareness of the health impacts of respirable particles, personal samplers designed to measure the total inhalable fraction have been developed. An example of a widelyused device is the Institute of Occupational Medicine (IOM) personal sampler (Fig. 4b) (Kenny et al., 2001; Mark and Vincent, 1986). This sampler is designed to sample particles with an aerodynamic diameter of up to 100 mm however the sampling efficiency has been shown to deviate with low (<0.5 ms 1) and high wind (>4 ms 1) (Kenny et al., 1997).

141

2.2. Passive samplers Passive sampling has been suggested as a low-tech and costeffective monitoring tool which can avoid most of the disadvantages of active sampling techniques (Brown et al., 1996). It is especially useful for multipoint sampling over large and remote areas where a reliable power source cannot be guaranteed. Passive samplers, however, have a lower sampling efficiency compared to active samplers and as a result, a longer sampling time (>24 h) is required to obtain sufficient particle mass. Passive samplers are usually based on a combination of gravity, inertia, electrostatic attraction and convective diffusion. Capture by gravitational forces, however, is highly biased towards larger particles and on its own unsuitable for measuring the fine and ultrafine fractions of APM. Also, samplers do not collect all sizes with equal probability, depending significantly on the speed and direction of the wind. As a result, the accuracy and precision of these devices may not meet sampler performance standards prescribed for regulatory measurements (Butler et al., 2013). A common passive sampler is the Sigma-2. With an almost wind-free interior, impaction of particles is achieved via sedimentation, with the virtual elimination of deposition by turbulent diffusion. Coarse particles (>2.5 mm) are sampled onto a transparent collection plate coated with a weather-resistant adhesive, with subsequent single-particle analysis by optical microscopy to obtain size distributions and particle type based on morphology guen et al., 2012). By and optical features in transmitted light (Gue assuming spherical shape and unit density for all particles, a size fractionated mass deposition rate can also be calculated (Norra et al., 2016). The instrument and analysis method have been calibrated to provide an output in size-fractionated bulk particle mass per cubic meter of air, however the results of Sigma-2 analyses cannot be directly compared with those of active samplers although a broad correlation exists between TSP data obtained using hi-vol samplers and Sigma-2 APM data (Anonymous, 2013). Since the 1980's, the Sigma-2 sampler has been used for the assessment of air quality in German spas by the German Meteorological Service and in world-wide research projects. In the passive sampler developed by Wagner and Leith (2001) APM concentrations are estimated from the surface loading of collected particles and a knowledge of the flux, or rate of transfer, of these particles to the sampler. Surface loading is determined by microscopy, by counting and sizing deposited particles while the flux is estimated from a semi-empirical model that is a function of the particle aerodynamic diameter. The sampler consists of a standard aluminium or carbon scanning electron microscope (SEM) stub, a collection substrate and protective mesh cap. During sampling particles are transported by gravity, diffusion and inertia through the holes in the mesh cap and deposited on the foil substrate mounted on the stub. 2.3. Sampling substrates Selecting a substrate is a key issue which needs to take into consideration the proposed analytical technique(s) as there is no filter amenable to the full complement of analyses for individual particle characterisation. Filter selection therefore becomes a compromise between various factors, namely cost, collection efficiency, the requirements of the analytical procedures and the ability of the filter to retain its filtering properties and physical integrity under sampling conditions. The applicable method must also allow particles to be presented as a homogeneous uniform deposit without a significant effect on particle characteristics (Maynard, 2000). A desirable filter should have high collection efficiency and the capacity to retain large sample masses and as all

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Fig. 4. Personal exposure samplers. a) Cyclone; b) IOM (Zefon, 2016).

filters contain various elements as major, minor and trace components, the optimum filter should have little or no background level for the elements being analysed. Commonly used filter substrates include glass fibre, quartz, Teflon and polycarbonate (Table 2). Glass fibre filters are depth filters, consisting of compressed borosilicate glass fibres which form an irregular three-dimensional mesh with interspaces of variable sizes. As a result, particles are

both retained on the surface and trapped within the filter structure. They have a high collection capacity and tolerate high flow rates, however the high and variable filter blanks can affect the analysis of metals in APM, particularly zinc and iron and to a lesser extent aluminium, calcium and barium (Schroeder et al., 1987). The thermal stability of these filters makes them useful for gravimetric analysis though due to the complex structure they are unsuitable

Table 2 Physical and chemical characteristics of common air filters and compatible single particle analytical techniques. (a) (SKC, 2015); (b) (Sartorius, 2015); (c) (Millipore, 2015); (d) (Agar Scientific, 2015). Filter Type

Physical Characteristics

Chemical Characteristics

Glass Fiber (a)

Borosilicate glass High loading capacity Heat resistant (max ~600  C) Diffuses transmitted light Low flow resistance Quartz Humidity resistant Heat resistant (max ~ 1000  C) Diffuses transmitted light Soft/friable edges can flake in filter holders High particle collection efficiencies Moderate flow resistance Mechanically strong Microscopically smooth Precise pore sizes Moderate flow resistance Retains static charge Temperature resistant to 260  C Hydrophobic

XPS (Song and Peng, 2009) Inert Resistant to all but strongly alkaline bases or acids High blank levels of Zn and Fe Low hygroscopicity Inert SP-ICP-MS (Lynam et al., 2013) Large and variable quantities of Al and Si XANES (Higashi and Takahashi, 2009)

Quartz Fiber (a)

Polycarbonate (a)

Teflon (PTFE) (a,b)

Silver membrane (c)

Coated TEM Grid (d)

High flow resistance Resistant to thermal stress (max 550  C) Reusable

Compatible Single Particle Techniques

Inert Chemically resistant Low blank levels Low hygroscopicity

SEM/TEM(Saitoh et al., 2008) XPS(Klejnowski et al., 2012) RMS (Nelson et al., 2001) PIXE (Berghmans et al., 1994)

Inert Low tare mass Low blank levels Resistant to acids, bases and solvents

XPS (Cheng et al., 2013) TOF-SIMS (Zhu et al., 2001) PIXE (Lucarelli et al., 2011) XAFS (Furukawa, 2011) XANES (Higashi and Takahashi, 2009) SEM-EDS (Saitoh et al., 2008) FTIR (Anıl et al., 2014) EPMA (Spolnik et al., 2004) Low-Z EPMA (Ro et al., 2000) RMS (Worobiec et al., 2010) SIMS (Rogowski and Bem, 2006) RMS (Potgieter-Vermaak and Van Grieken, 2006) LMMS (Dierck et al., 1992) EPMA (Dierck et al., 1992) EELS (Berghmans et al., 1994) TEM (Ogura et al., 2014)

Low background Resistant to chemicals Low hygroscopicity High blank weight Porous carbon membrane on Cu, Ni or Au grid No background inteference 15e20 nm thickness Precise and well-defined hole shape, size and arrangement

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for direct analysis using electron microscopy. The chemistry of the fibres can also interfere with X-ray analysis due to the background signals contributed by the elements in the filter matrix. Quartz fibre filters are also depth filters. Their low and relatively constant blank values allows for the analysis of metal components, however analysis of aluminium and silica can be problematic. Similar to the glass fibre filters, they are unsuitable for electron microscopic analysis. They are also relatively friable, with the potential for filters to flake off in the filter holder, making them unsuitable for gravimetric analyses. Membrane and pore filters, such as Teflon and polycarbonate,

143

are surface filters, with particulates collected solely on the top of the membrane. Although their loading capacity is lower than depth filters, their blank values are near zero which makes them ideal for the analysis of metallic components. Teflon filters are made from polytetrafluoroethylene (PTFE) and are hydrophobic, resistant to acids, alkalis and solvents, and have high thermal stability. A low tare rate makes these filters ideal for gravimetric analysis. Polycarbonate filters are pore filters with uniformly sized pores. They have a low, uniform weight, are non-hygroscopic, chemically inert and can be used for gravimetric analyses. They are mechanically strong and their smooth surface allows for good resolution making

Table 3 Advantages and disadvantages of different single particle analytical techniques. Technique

Advantages

Disadvantages

Size Range

Reference

Off-line Scanning electron microscopy with energy dispersive X-ray (SEM-EDX)

Morphological and elemental analysis Fast sampling times

Unable to generate information on mass Loss of volatiles under vacuum Cannot determine low-Z elements Not suitable for ultrafine and nanoparticle

>0.1 mm

(Laskin and Cowin, 2001)

>1 mm

(Sobanska et al., 2014)

Particles need to be transparent to the electron beams Loss of volatiles under vacuum

>5 nm

€ et al., 2002) (M€ akela

Charging can be a problem Need a complete database of reference spectra

>50 nm

(Cheng et al., 2013)

Destructive Quantifying data can be difficult

>50 nm >100 nm

(Li et al., 2016)

Requires synchrotron X-ray source

>20 nm

(Davidson et al., 2015)

Cannot measure elemental concentrations Requires synchrotron X-ray source Difficult to interpret data Problems with shape irregularity

>15 nm

(Fittschen, 2014)

>100 nm

(Marris et)

Cannot quantify low-Z elements

>100 nm

(Lu et al., 2014)

Destructive Sampled ambient particles not compatible with the standard sample introduction system Slow (<1 particle/minute) Destructive

>90 nm

(Suzuki et al., 2010)

Raman Microscopy (RMS)

High-resolution TEM (HRTEM)

X-ray photoelectron spectroscopy (XPS)

Nano-scale secondary ion mass spectrometry (NanoSIMS) Time-of-flight SIMS (TOF-SIMS) X-ray absorption fine structure spectroscopy (XAFS)

X-ray absorption near edge structure (XANES) Electron energy loss spectrometry (EELS) Proton induced X-ray emission (PIXE)

Single particle inductively coupled mass spectrometry (SP-ICPMS)

Laser microprobe mass spectrometry (LMMS) On-line Aerosol time-of-flight mass spectrometer (ATOFMS)

Laser-induced breakdown spectroscopy (LIBS) Aerosol mass spectrometer (AMS)

Enables analysis of composition, phase and crystal orientation Can determine different oxidation states Can generate information on microstructure, composition, morphology and elemental oxidation states Generates information on elemental composition, surface chemistry and chemical states No sample preparation Non-destructive Determination of heterogeneities associated with depth High detection sensitivity Non destructive High detection sensitivity No spectral interference from coexisting elements Non destructive High detection sensitivity Determine oxidation states Higher count rate than EDX Information on bonding and oxidation states Fast Non destructive No sample preparation Low minimum detection limits Information about the elemental chemical composition, number concentration, size and size distribution Low detection limits Can distinguish surface enriched species from bulk composition Measures size and chemical composition in real time Fast (up to 19 particles/second) Information on the distribution of chemical species in an individual particle No sample preparation simultaneous measurement of particle mass and composition Fast Measures mass concentrations

unknown

Destructive Not quantitative Variation in ion signal intensities Instrument sensitivity affected by the size and composition of the particle

>100 nm

(Cahill et al., 2012)

Large interference effects

>300 nm

(Kwak et al., 2012a)

Can only detect non-refractive particles Difficulty detecting some refractory particles such as sea salt and mineral dust Doesn't generate complete mass spectra of particles

>50 nm

(Durant et al., 2010)

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them ideal for electron microscopy, however analysis of micron and submicron size particles can be problematic, with the X-ray signals difficult to detect against the substrate spectra and the broad continuum background (Nelson et al., 2001). Other commonly used membrane filters are silver foil and carbon coated TEM grids. The TEM grid is a metal mesh screen, typically 2e3 mm in diameter and come in a variety of mesh shapes and sizes, ranging generally from 50 to 400 holes per inch. Grids are usually made of copper, though beryllium, gold, molybdenum, steel, titanium and nickel grids are also used. 3. Off-line analytical techniques Off-line techniques are characterized by distinct collection and transportation procedures prior to analysis. These techniques are valuable, especially for detailed, specific investigations and for sampling in areas where on-line instruments cannot be used because of size or operating conditions. These techniques, however, suffer from both positive and negative artefacts: evaporative losses can occur during sampling, storage and transport of samples which can cause significant errors; gases can be adsorbed onto filters and react with surfaces; particulate filter loading can result in some of the sample being bounced or blown off the filter and samples may also further react with other compounds within the sample or the collection substrate during storage (Sipin et al., 2003). For this review, off-line techniques have been divided in electron microscopybased and atomic spectrometry-based (Table 3). 3.1. Electron microscopy Over the years, micro-beam analytical techniques have been increasingly used to provide information on the physical, morphological and chemical properties of single atmospheric particles, including scanning electron microscope (SEM), computer controlled SEM (CCSEM) and transmission electron microscopy (TEM). Among them, Electron Probe X-ray Microanalysis (EPMA) based on SEM equipped with an Energy-Dispersive X-Ray (EDX) detector has shown itself to be an effective technique to investigate the size and elemental composition of single solid dry particles (Sobanska et al., 2003). SEM-EDX has been extensively used to characterize the mineralogical phase of individual particles. It enables the morphological and elemental analysis of single particles down to a nominal diameter of 0.1 mm with a detection limit of 1e10 wt%, depending on the element analysed, and can provide quantitative data on elemental composition (Laskin and Cowin, 2001). Deboudt et al. (2008) noted that the technique, which requires only small sample sizes (1000 particles) and with sampling times as short as a few minutes, was able to describe temporal variations in the chemical composition of the particles. As the 2D geometrical information from the SEM images cannot reveal the full particle shape and volume, a coupling of this technique with atomic force microscopy (AFM) was suggested to determine both morphological and particle mass estimation as the 3D technique of AFM can provide a more accurate morphological measurement (Barkay et al., 2005). AFM, however, provides no elemental data and despite providing a good estimate on volume, it still requires knowledge of the composition to derive the particle density. Automated systems for mineralogical characterisation using SEM-EDX are of increasing importance because of their ability to rapidly analyse individual particles (~10 000 particles/hour) and provide mineral/phase identification alongside size and shape characterisation for different size categories. Originally designed to examine fine grained mineral mixtures in order to improve the

efficiency of mineral processing plants, QEMSCAM (Quantitative Evaluation of Minerals by Scanning Electron Microscopy) has recently been applied to the characterisation of urban PM10 in London (Williamson et al., 2013) and to quantify respirable crystalline silica in ambient air around open-cut coal mines to determine the potential health-risks to the surrounding population (Morrison et al., 2011). Although SEM-EDX measures the elemental composition, it is unable to provide information on volume and Williamson et al. (2013) noted that mass measurements were problematic due to mineral categories containing a number of different mineral and chemical species. Another limitation in EPMA analysis lies in the loss of semi-volatile components under the vacuum and the determination of low-Z elements such as carbon, nitrogen and oxygen is hindered by the absorption of characteristic X-rays by the beryllium windows of the EDX detectors (Stefaniak et al., 2006). To overcome this, a low-Z particle EPMA, using an EDX with an ultrathin polymer window, was developed by Ro et al. (2000), which allows for the quantitative determination of low-Z elements. Current knowledge of the internal structure of atmospheric particles collected under ambient conditions presents a significant analytical challenge due to particle size as well as the physical and chemical complexity (Sobanska et al., 2014). Most molecular characterizations of size-segregated particles to date have used confocal Raman microscopy (RMS) (Batonneau et al., 2006; Iordanidis et al., 2014; Potgieter-Vermaak and Van Grieken, 2006) or Fourier transform infrared (FTIR) spectroscopy (Ryu and Ro, 2009; Song et al., 2010). As each molecular species exhibits a characteristic “fingerprint” Raman spectrum, Raman mapping can be used to determine how different chemical components are distributed spatially within a single particle. The sensitivity of Raman spectrometry, which enables the analysis of composition, phase and crystal orientation, makes it an ideal tool for characterizing particles (Sobanska et al., 2014). The information obtained by this technique includes molecular characteristics, identification of different mineral phases and the determination of different oxidation states. A limitation of this techniques is that due to the refraction limit particles need to be greater than 1 mm. A method combining low-Z particle EPMA and RMS has also been used to characterize the internal structure and physiochemical properties, including speciation and mixing state of APM. This emerging technique, called the Raman SCA (structural and chemical analyser), has been used to speciate iron in underground subway particulates (Eom et al., 2013) and identify the internal structure and physiochemical properties of Asian dust (Sobanska et al., 2012). Detailed knowledge about internal particle structure can also be obtained using a high-resolution transmission electron microscope (HRTEM). HRTEM has been demonstrated to be a powerful tool for the analysis and identification of individual submicron airborne particles by revealing details of the microstructure, composition, morphology, bonding structure and elemental oxidation states  sfai, 2013; Semeniuk et al., 2014). Through the within particles (Po use of high-resolution imaging and selected-area electron diffraction, HRTEM has been used to identify the phase and crystalline structure of particles (Li et al., 2003). It has also been used in combination with electron-beam based spectroscopy to provide element-specific chemical imaging along with structural and sfai and Buseck, 2010). While this elemental characterisation (Po technique provides sensitive chemical analysis, it is limited to the analysis of submicron size particles that are sufficiently transparent to the electron beam and to analyse larger, non-transparent particles using these techniques, the milling of particles down to a thickness accessible by the instrument is required.

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3.2. Atomic spectroscopy As the chemical reactions that occur during atmospheric processing occur primarily on particle surfaces, the determination of the surface composition of APM is important to understand transformation and growth processes (Guascito et al., 2015). Particle surfaces can also adsorb potentially toxic contaminants, which come into direct contact with body fluids after inhalation (Klejnowski et al., 2012). X-ray photoelectron spectroscopy (XPS) is a surface sensitive technique with an analysis depth of less than 10 nm. It has the ability to determine and quantify the average metal speciation of particle surfaces. A number of studies have used this technique to obtain information about elemental composition, surface chemistry and chemical states of particulates collected on filters (Cheng et al., 2013; Vander Wal et al., 2011). Samples can be directly examined by XPS without any sample preparation and the concentration of elements can be determined with an accuracy of up to 0.1% (Cheng et al., 2013). Although this technique offers a contribution in determining average metal speciation of the particle surface, the requirement of charge compensation, spectral decomposition and a complete database of reference spectra was considered by Batonneau et al. (2004) to be a limiting factor. Secondary ion mass spectrometry (SIMS) is another wellestablished surface characterisation technique that has been used for spatially resolved chemical imaging in inorganic particles. The inherent depth profiling capabilities, along with its high sensitivity and full elemental coverage, its capability of measuring isotope ratios and its ion imaging potential of specific constituents makes it a valuable technique (Berghmans et al., 1994). It has the ability to generate multiple mass spectra of both polarities from a single particle, allowing for the determination of heterogeneities associated with depth in a particle (Suess and Prather, 1999). A limiting factor with this technique, however, is due to the size range of atmospheric particulates. To characterize the broad range of particles requires a highly focused ion beam that is not standard in conventional SIMS instruments. Also, most SIMS applications are performed in the dynamic mode that uses high ion doses for elemental analysis as a function of depth. Under these conditions, most of the molecular information is lost (Sipin et al., 2003). The irregular topography of particles can also degrade the depth resolution to a considerable extent (Berghmans et al., 1994). The development of a nano-scale SIMS (NanoSIMS) has aided in overcoming some of these issues. The high resolution (<100 nm) and greater sensitivity (>10 times higher than conventional SIMS) can measure up to five different secondary ions simultaneously, enabling analysis of small particles with a high degree of precision (Harris et al., 2012). Although most of the studies using this technique are related to biological materials and soil aggregates several studies have applied it to the chemical characterisation of APM (Ghosal et al., 2014; Winterholler et al., 2008). With its high spatial resolution (~100 nm) and surface sensitivity, time-of-flight SIMS (TOF-SIMS) has also proven valuable in studying the surface composition of individual particles and a combination of static and dynamic operation of the TOF-SIMS allows the analysis of chemical speciation as a function of depth within an individual particle (Prather et al., 1994). The high detection sensitivity also enables the detection of trace elements, however as the quantification of TOF-SIMS data is done using standards with known chemical compositions and concentrations, quantifying the complex chemical composition of APM can be problematic (Zhu et al., 2001). A technique with significant potential for elemental speciation of fine APM is the synchrotron-based technique of extended X-ray absorption fine structure spectroscopy (EXAFS). This technique is non-destructive, sensitive to ppm concentrations and can be used

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directly either with bulk or filter samples (Huggins et al., 2000). As each element has a characteristic and unique X-ray absorption edge, there is virtually no spectral interference from co-existing elements (Sakata et al., 2014). This method has been successfully used to identify the chemical species of Fe (Takahashi et al., 2011) and Pb (Sakata et al., 2014). X-Ray absorption near edge structure (XANES), which can detect metal chemical forms on the basis of electron states around the target element, has been successfully used to measure the speciation of Pb (Barrett et al., 2010), S(IV) (Higashi and Takahashi, 2009), Cu and Zn (Os an et al., 2010) and As (Kim et al., 2013). This technique is capable of non-destructive speciation of metals in solid samples even when the element occurs in low concentrations. In this technique, the XANES spectra is used as a fingerprint and compared with the spectra of reference materials. Although it can determine elemental oxidation states and main chemical components, it is only a qualitative method and cannot measure elemental concentrations (Hiranuma et al., 2013). Another analytical tool available is electron energy-loss spectrometry (EELS). This technique allows samples to be characterized from energy losses experienced by electrons as they interact with the sample. The primary importance of EELS to single particle analysis is its ability to detect and quantify most elements, including those with low atomic number. Maynard (1995) noted that this technique gave higher count rates than EDX and was able to detect elements not easily accessible by EDX. Chen et al. (2006) also observed that compared to the EDX spectra, which only gave information on chemical composition, EELS provided additional information about bonding and oxidation states. Transmission electron microscopy coupled with EELS (TEM-EELS) was used by Marris et al. (2013) to determine the oxidation states of metals emitted from an Fe-Mn alloy manufacturing plant in the north of France. Despite observing the difficulties that particle heterogeneity, shape-irregularity and sample dimensions introduced to this technique, combining TEM-EELS with SEM-EDX overcame some of these limitations. This combination has also been used to characterize the mixing state of African dust (Deboudt et al., 2010) and identify ultrafine titanium particles retained in the lung tissue of rats (Kapp et al., 2004). Proton induced X-ray emission (PIXE) has been applied to the elemental analysis of APM for many years. It is a fast, nondestructive technique, requiring no sample preparation, with multi-elemental analysis capabilities and low minimum detection limits (Lucarelli et al., 2011). The use of an external beam reduces the risk of the loss of more volatile elements, such as chlorine and bromine, however due to X-ray attenuation inside the target particle PIXE cannot successfully quantify the low-Z elements (Calzolai et al., 2010). The analysis of the lightest measureable elements (Na, Mg, Al, Si and P) can also be difficult to accurately quantify due to difficulties in evaluating the absorption of their X-rays in the sample. Recently, a device for the simultaneous use of PIXE and XRF was built for the analysis of atmospheric particulates, allowing for the detection of heavier elements (Rb, Sr, Zr) which cannot be obtained through PIXE analysis alone (Reyes-Herrera et al., 2015). To analyse individual particle size and composition, Denoyer et al. (1982) pioneered the off-line technique of laser microprobe mass spectrometry (LMMS). This technique involves placing the particles on a surface before selectively desorbing the particles and ionizing the molecules by a single laser pulse. The ions are separated and analysed using a time-of-flight mass spectrometer. LMMS has been used to detect trace levels of metals in individual particles at the parts-per-million (Bruynseels et al., 1988) and distinguish surface species from those contained within the particle (Wouters et al., 1988). Carson et al. (1997) discovered that at high laser irradiance, the mass spectra reflected the bulk composition whereas at low

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irradiance the signal intensities of surface-enriched species were enhanced. This allowed qualitative differences between surface and core compositions to be inferred. A major limiting factor with this analysis technique, however, lies in its destructive nature. It also suffers from the same condensation, evaporation and chemical reaction problems as bulk methods since the sampling process is similar. Another limitation is that it is a relatively slow method (<1 particle per minute) (Noble and Prather, 1996). Single particle ICPMS (SP-ICPMS) is an emerging analytical method with the potential to measure chemical composition, particle size, size distribution and number concentrations at environmentally relevant concentrations (Kawaguchi et al., 1986). Because its dynamic range can extend to the micrometre regions, polydispersed systems as well as aggregation/agglomeration processes may be studied. In traditional ICPMS, multiple intensity readings are integrated over long dwell times (0.3e1s) and averaged to produce an overall concentration for the sample. In contrast, SP-ICPMS intensity readings are integrated over a shorter dwell time (<10 ms) and plotted individually as a function of time. This technique can be used to detect and characterize nanoparticles from low number concentrations (103 cm 3 to 105 cm 3) (Pace et al., 2011). Single particle ICPMS measurements require a series of unique considerations. The number of particles entering the plasma must be limited to prevent the overlapping of signals from two particles (Laborda et al., 2011; Pace et al., 2012). The processes of particle vaporization, ion production and diffusion and the area of the sampling orifice within which ions can be detected can also affect the relationship between particle mass and the measured signal (Olesik and Gray, 2012). Another limitation is that sampled ambient particles are typically not compatible with the sample introduction system that is standard for these instruments and need to be modified prior to analysis (Bustos and Winchester, 2016). 4. On-line analytical techniques Most analytical measurements of APM require collection times of at least 6e12 h, with most more than 24 h, to obtain sufficient material. Such long sampling times, however, do not capture variations in elemental composition and concentrations due to changes in meteorological conditions. To overcome some of these fluctuations, which include emission strength, temperature, relative humidity, wind direction and speed and mixing heights, methods capable of providing continuous or near-continuous measurements of 1 h or less are desirable. Ideally, the technique should be fast enough to track changes in concentration and/or composition as they occur, sensitive enough to detect the species of interest and free of interference from other species present (Sipin et al., 2003). Other considerations may include weight and portability of the developed instrumentation (Table 3). The majority of the work developed for on-line particle analysis has relied on mass spectrometry or atomic emission spectrometry. On-line mass spectrometry provides the ability to determine chemical changes in APM over short timescales and can either measure the chemistry of particulate assemblages (bulk analysis) or individual particles. A number of single-particle mass spectrometers (SPMS) instruments have been developed over the years including the aerosol time-of-flight mass spectrometer (ATOFMS), laser-induced breakdown spectroscopy (LIBS), laser mass analyser for particles in the airborne state (LAMPAS), particle analysis by laser mass spectrometry (PALMS) and rapid single-particle mass spectrometer (RSMS). The major differences between these instruments include the laser wavelength used to perform the desorption and ionization and the method used to determine particle size (Sullivan and Prather, 2005). Most SPMS instruments

transport particles from the air into the vacuum using specially designed inlets. Once inside the vacuum, the particles are detected by optical light scattering and sized either by measuring velocities or the intensity of scattered light. Since the intensity of light scattered by particles decreases considerably with decreasing particle size, the optical detection efficiencies for particles <~150 nm are low (Zelenyuk and Imre, 2005). Prather et al. (1994) developed an aerosol time-of-flight mass spectrometer (ATOFMS) which is capable of measuring the size and chemical composition of individual poly-disperse particles in realtime. Advantages of this technique include a fast analysis time (up to 19 particles per second) and the ability to collect the entire mass spectrum of a single particle over a theoretically unlimited mass range. ATOFMS has been shown to be useful for determining size and chemical correlation information of individual particles (Noble and Prather, 1996); for tracking particles with distinct chemical signatures (Liu et al., 1997) and for observing meteorological effects on particulate pollution (Noble and Prather, 1996). ATOFMS can also provide information on the distribution of chemical species within individual particles (Sullivan et al., 2007). Using a lower laser power Cahill et al. (2015) selectively segregated the surface molecules to create a depth-profile of the major chemical components. A limitation of this technique is that the chemical composition measurements are not quantitative. Ion signal intensities produced by laser ablation/ionization vary greatly from shot to shot for virtually identical particles, primarily because of inhomogeneities in the laser (Bhave et al., 2002). Instrument sensitivities can also be affected by the size and composition of the sampled particle. By comparing ion signals from ATOFMS with the signal intensities of particles collected with an impactor Bhave et al. (2002) noted that the instrument sensitivities to nitrate and ammonium decrease with increasing aerodynamic range of 0.32e1.8 mm. It also can't distinguish the oxidation state and chemical species (Wang et al., 2016). The laser mass analyser for particles in the airborne state (LAMPAS) was used by Hinz et al. (1994) for the on-line analysis of single atmospheric particles and was noted for its ability to detect single particles from ambient air with a high mass resolution constant over the entire mass range. This technique has since been used to perform size-resolved determination of the chemical composition of particle populations (Trimborn et al., 2000) and to investigate environmental exchange processes (Gelhausen et al., 2011). Developed by Murphy and Thomson (1995) the particle analysis by laser mass spectrometry (PALMS) has been used for numerous atmospheric measurements. Murphy et al. (2006) used this technique to examine mercury-containing APM. It has also been used to determine the chemical composition of mineral dust (Gallavardin et al., 2008). The rapid single-particle mass spectrometer (RSMS) was developed by McKeown et al. (1991) and has been used in several field campaigns. As it uses an aerodynamic lens to focus the particles, the divergence of the particle beam is reduced and it can efficiently detect particles <300 nm (Sullivan and Prather, 2005). Murphy et al. (2007) used this technique to measure lead in single atmospheric particles. It has also been used to determine sources of particulate pollution (Bein et al., 2006; Reinard et al., 2007). Laser-induced breakdown spectroscopy (LIBS) is another important laser technique in fine APM analysis. It is based on plasma formation on the surface of analysed samples by means of focused laser pulses. This plasma samples the substrate and excites its atoms. The light emitted from this plasma is then spectrally analysed and compared to well-known atomic emission lines. Quantification of the elemental species concentration is via quantification of the intensity of the emission lines. It can also generate quantitative mass

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measurements of constituent species, which enables a direct simultaneous measurement of particle mass and composition (Hahn and Lunden, 2000). The main advantages of this technique is the ability to provide direct chemical analysis without laborious preparation as only optical access to the sample is required (Evans et al., 2014). LIBS has been applied to the quantification of emissions from incinerators (Dutouquet et al., 2014; Hahn et al., 1997; Neuhauser et al., 1999), to measure hourly concentrations of metals in urban air (Kwak et al., 2012b) and for ambient pollution monitoring (Gaudiuso et al., 2010). A second class of on-line mass spectrometry instruments uses thermal vaporization of particles followed by various ionization techniques, most commonly electron impact. The most widely used instrument is the aerosol mass spectrometer (AMS). Particles are transmitted into the AMS detection region using an aerodynamic focusing lens where they impact on a tungsten vaporiser typically at temperatures of 400e950  C, vaporising the volatile and semivolatile components (Allan et al., 2003). The instrument alternates between two modes during operation, mass spectrum (20s) and time of flight (40s) with the data averaged over 5e30 min. Ambient mass concentrations (mg/m3) and information on the composition can be derived from the mass spectrum while the data obtained in the time of flight mode can be used to calculate mass distributions for a particular component as a function of aerodynamic diameter (Allan et al., 2003). A limitation of using thermal vaporising is that it can only detect non-refractive particles. It is also not designed to refractory such as soot, fly ash, metal oxides and sea salt (Canagaratna et al., 2007). There are three versions of the AMS, however the only difference being the type of mass spectrometric detector; quadrupole (Q-AMS), time-of-flight (TOF-AMS) and high resolution TOF (HF-TOF-AMS). A limitation of this technique is that as only one type of ion can be studied at a time so complete mass spectra of individual particles cannot be obtained (Allan et al., 2003). 5. Conclusion Recently, the negative effects of atmospheric particulate matter on the environment and human health, particularly fine particles, has gained increasing attention. To obtain a true assessment of the risks associated with airborne particles a detailed physical and chemical characterisation is required. Traditionally, the chemical characterisation of APM has been achieved by bulk composition. As the impact and toxicity of APM is related not only to total elemental composition but also to chemical heterogeneity at the individual particle level, single particle analysis has increasingly gained importance. A variety of methods and instruments are available and continue to be developed for single particle analysis, however, a single practical technique does not currently exist for obtaining all the required information, specifically the size, morphology, composition, mixing state and molecular structure of APM. Existing sampling and analytical techniques are also time-intensive and expensive and frequently do not allow for the collection of the large number of particles required to generate a statistically valid data set. As the choice of techniques used to characterize fine atmospheric particulates is strongly dependent on the scientific objectives, careful consideration is needed, even at the sampling stage. This review is designed as a guide to the array of sampling and single particle analysis techniques available for the chemical characterisation of fine airborne particulate matter. Acknowledgements The authors are grateful to the Brazilian government agencies

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CNPq (Conselho Nacional de Desenvolvimento Científico e Tec gico) and Fapemig (Fundaç~ nolo ao de Amparo a Pesquisa do Estado de Minas Gerais) for financial support, including a PVE fellowship from the Science Without Borders program to M. Gasparon. Three anonymous colleagues are thanked for their constructive comments and review of an earlier version of this manuscript. Abbreviations APM Airborne particulate matter AFM Atomic force microscopy AMS Aerosol mass spectrometer ATOFMS Aerosol time-of-flight mass spectrometer EELS Electron energy-loss spectrometry ELPI Electric low pressure impactor EPMA Electron probe X-ray microanalysis FTIR Fourier transform infrared spectroscopy HRTEM High-resolution transmission electron microscopy LAMPAS Laser mass analyser for particles in the airborne state LIBS Laser-induced breakdown spectroscopy LMMS Laser microprobe mass spectrometry LPI Low pressure impactor PALMS Particle analysis by laser mass spectrometry PIXE Proton induced X-ray emission QEMSCAN Quantitative evaluation of minerals by scanning electron microscopy RMS Raman microscopy RSMS Rapid single-particle mass spectrometer SEM-EDXScanning electron microscopy with energy dispersive Xray SIMS Secondary ion mass spectrometry SP-ICPMS Single particle inductively coupled plasma mass spectrometry SPMS Single particle mass spectrometry TEM Transmission electron microscopy TEOM Tapered element oscillating microbalance TOF-SIMS Time-of-flight secondary ion mass spectrometry XAFS X-ray absorption fine structure spectroscopy XANES X-ray absorption near edge structure XRF X-ray fluorescence XPS X-ray photoelectron spectroscopy References Agar Scientific, 2015, Volume 2015. Allan, J.D., Jimenez, J.L., Williams, P.I., Alfarra, M.R., Bower, K.N., Jayne, J.T., Coe, H., Worsnop, D.R., 2003. Quantitative sampling using an Aerodyne aerosol mass spectrometer 1. Techniques of data interpretation and error analysis. J. Geophys. Res. Atmos. 108 (D3) n/a-n/a. Allen, G., Sioutas, C., Koutrakis, P., Reiss, R., Lurmann, F.W., Roberts, P.T., 1997. Evaluation of the TEOM® method for measurement of ambient particulate mass in urban areas. J. Air Waste Manag. Assoc. 47 (6), 682e689. Anıl, I., Golcuk, K., Karaca, F., 2014. Atr-FTIR spectroscopic study of functional groups in aerosols: the contribution of a Saharan dust transport to urban atmosphere in Istanbul, Turkey. Water, Air, & Soil Pollut. 225 (3), 1e14. Anonymous, 2013. Technische Regel VDI. Ambient Air Measurements - Sampling of Atmospheric Particles > 2.5 mm on an Acceptor Surface Using the Sigma-2 Passive Sampler - Characterisation by Optical Microscopy and Calculation of Number Settling Rate and Mass Concentration, vol. 2119. Beuth Verlag, Berlin, 2013e06. Asgharian, B., Hofmann, W., Bergmann, R., 2001. Particle deposition in a multiplepath model of the human lung. Aerosol Sci. Technol. 34 (4), 332e339. Baquero, T., Shukrallah, S., Karolia, R., Osammor, O., Inkson, B.J., 2015. Quantification of airborne road-side pollution carbon nanoparticles. J. Phys. Conf. Ser. 644 (1), 012023. Barkay, Z., Teller, A., Ganor, E., Levin, Z., Shapira, Y., 2005. Atomic force and scanning electron microscopy of atmospheric particles. Microsc. Res. Tech. 68 (2), 107e114. Barrett, J.E.S., Taylor, K.G., Hudson-Edwards, K.A., Charnock, J.M., 2010. Solid-phase speciation of Pb in urban road dust sediment: a XANES and EXAFS study. Environ. Sci. Technol. 44 (8), 2940e2946.

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