Applications atmospheric aerosols

Poet, S. E., H. E. Moore and E. A. Martell, Lead-210, Bi-210, and Polonium-210 in the Atmosphere Accurate Ratio and Application to Aerosol Residence Time Determination, J. Geophys. Res.,... 

MALDI, which is LDI utilizing a particular sample preparation). Although the performance of MALDI is superior to LDI in the analysis of many groups of compounds, LDI is still the perferred choice in some important applications, including cmde oil analysis [155], fullerene detection in rocks [156], atmospheric aerosol analysis [157], semiconductors, and surface analysis [158]. Reference 21 is a comprehensive review of the use of LDI (and several other ion sources) in analysis of inorganics. 

This review of the chemistry and physics of microparticles and their characterization is by no means comprehensive, for the very large range of masses that can be studied with the electrodynamic balance makes it possible to explore the spectroscopy of atomic ions. This field is a large one, and Nobel laureates Hans Dehmelt and Wolfgang Paul have labored long in that fruitful scientific garden. The application of particle levitation to atmospheric aerosols, to studies of Knudsen aerosol phenomena, and to heat and mass transfer in the free-molecule regime would require as much space as this survey. 

Ideally, one would like to describe various size distributions by some relatively simple mathematical function. Because there is no single theoretical basis for a particular function to describe atmospheric aerosols, various empirical matches have been carried out to the experimentally observed size distributions some of these are discussed in detail elsewhere (e.g., see Hinds, 1982). Out of the various mathematical distribution functions for fitting aerosol data, the log-normal distribution (Aitchison and Brown, 1957 Patel et al., 1976) has emerged as the mathematical function that most frequently provides a sufficiently good fit, and hence we briefly discuss its application to the size distribution of atmospheric aerosols. 

An example of practical importance in atmospheric physics is the inference of effective optical constants for atmospheric aerosols composed of various kinds of particles and the subsequent use of these optical constants in other ways. One might infer effective n and k from measurements—made either in the laboratory or remotely by, for example, using bistatic lidar—of angular scattering fitting the experimental data with Mie theory would give effective optical constants. But how effectual would they be Would they have more than a limited applicability Would they be more than merely consistent with an experiment of limited scope It is by no means certain that they would lead to correct calculations of extinction or backscattering or absorption. We shall return to these questions in Section 14.2. 

Atmospheric aerosols usually means the solid and liquid particles in the earth s atmosphere, excluding the solid and liquid water particles in clouds, fog, and rain. Although very tenuous and highly variable, they act as condensation nuclei for cloud droplets, alter the optical properties of clouds, and possibly play a role in the formation of smog and acid rain. And an understanding of their optical properties is needed for many applications ... 

Since atmospheric aerosols comprise particles with a wide range of sizes, it is often convenient to use mathematical models to describe the atmospheric aerosol distribution (Seinfeld and Pandis, 1998). A series of mathematical models have been proposed, of which the lognormal distribution has been the most used in atmospheric applications (Seinfeld and Pandis, 1998 Horvath, 2000). Useful discussions of the various aerosol size distribution models are provided by Seinfeld and Pandis (1998) and Jaenicke (1998). In general, atmospheric aerosols size distributions are shown graphically in terms of the volume (or mass) distributions, surface area distributions, or number distributions as a function of particle size (Jaenicke, 1998). 

Duarte, R. M. B. O., and Duarte, A. C. (2005). Application of non-ionic solid sorbents (XAD resins) for the isolation and fractionation of water-soluble organic compounds from atmospheric aerosols. J. Atmos. Chem. 51,79-93. 

In this section, basic concepts from aerosol dynamics and the general dynamic equation (GDE) are employed to explain important features of atmospheric size di.stribiition functions. The goal is to provide physical insight into these features. For the application of numerical methods to modeling atmospheric aerosol dynamics, the reader is referred to Wexler et al. (1994) and Jacobson (1997). 

In the second edition, I have sharpened the focus on aerosol dynamics. The field has grown rapidly since its original applications to the atmospheric aerosol for which the assumption of panicle sphericity is u.sually adequate, especially for the accumulation mode. Major advances in the eighties and nineties came about when we learned how to deal with (I) the formation of solid primary panicles, the smallest individual panicles that compose agglomerates and (ii) the formation of agglomerate structures by collisions. These phenomena, which have important industrial applications, are covered in two new chapters. One chapter describes the extension of classical coagulation theory for coalescing... 

Coupling between chemical kinetics and aerosol dynamics is important for the atmospheric aerosol, the commercial production of fine particles and aerosol emis.sions from combustion processes. In many cases, the link between the aerosol dynamics and chemical processes can be established in a general way as shown in flte Text. However the chemical processes must often be treated simultaneously for the specific applications this is beyond the scope of this book. 

A measured aerosol size distribution can be reported as a table of the distribution values for dozens of diameters. For many applications carrying around hundreds or thousands of aerosol distribution values is awkward. In these cases it is often convenient to use a relatively simple mathematical function to describe the atmospheric aerosol distribution. These functions are semiempirical in nature and have been chosen because they match well observed shapes of ambient distributions. Of the various mathematical functions that have been proposed, the lognormal distribution (Aitchison and Brown 1957) often provides a good fit and is regularly used in atmospheric applications. A series of other distributions are discussed in the next section. 

An aerosol distribution can be described by the number concentrations of particles of various sizes as a function of time. Let us define Nk(t) as the number concentration (cm-3) of particles containing k monomers, where a monomer can be considered as a single molecule of the species representing the particle. Physically, the discrete distribution is appealing since it is based on the fundamental nature of the particles. However, a particle of size 1 pm contains on the order of 1010 monomers, and description of the submicrometer aerosol distribution requires a vector (N2, N-j,..., N10io) containing 1010 numbers. This makes the use of the discrete distribution impractical for most atmospheric aerosol applications. We will use it in the subsequent sections for instructional purposes and as an intermediate step toward development of the continuous general dynamic equation. 

Surface area distributions are also of great interest in applications to possible adverse health effects of ultraflne atmospheric aerosols and to the monitoring of the products of nanoparticle aerosol reactors. In the past, such calculations have been... 

The multielement analysis of airborne particulate material (atmospheric aerosols) has been a very popular and highly successful application of PIXE since the early days of the technique. Considering that atmospheric aerosols are often collected as a thin sample layer on some thin filter or substrate film, that... 

Pratt, K.A., Prather, K.A. (2012) Mass Spectrometry of Atmospheric Aerosols -Recent Developments and Applications. Part II On-line Mass Spectrometry Techniques. Mass Spectrom. Rev. 31 17-48. 

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