Chapter 6
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Applying Multivariate Curve Resolution to Source Apportionment of the Atmospheric Aerosol Philip K. Hopke* Institute for a Sustainable Environment and Department of Chemical and Biomolecular Engineering, Clarkson University, Potsdam, New York 13699 *E-mail:
[email protected] A major application of chemometrics is the mixture resolution problem. Ideally in a chemical analysis, all of the constituents in a complex mixture are fully separated from one another so that their identification and quantification are relatively simple to achieve. However, in spite of advances in separation science, such resolution of complex mixtures is often not possible. Thus, it becomes necessary to separate overlapping components using mathematical methods. A similar problem exists in atmospheric science where it is useful to identify the sources giving rise to the observed concentrations of chemical constituents in the ambient aerosol and to quantitatively apportion the measured particulate mass concentrations to those identified sources. This process has come to be called Receptor Modeling and various methods have been developed and applied over the past 40 years to provide source apportionments. This chapter will outline these methods and their application to ambient particle composition data.
© 2015 American Chemical Society In 40 Years of Chemometrics – From Bruce Kowalski to the Future; Lavine, et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2015.
Downloaded by MONASH UNIV on October 10, 2015 | http://pubs.acs.org Publication Date (Web): October 7, 2015 | doi: 10.1021/bk-2015-1199.ch006
Introduction One of the major applications of chemometrics is the mixture resolution problem. Ideally in an analysis, all of the various constituents in a mixture are fully separated from one another so that their identification and quantification are relatively simple to achieve. However, in spite of significant advances in separation science, such resolution of complex mixtures is often not possible. Thus, it becomes necessary to separate overlapping components using mathematical methods. The approach used to perform these analysis is called Self-Modeling Curve Resolution (SMCR) (1, 2) and has been in use since around 1960. This approach has been widely applied to a variety of spectrochemical data including vibrational spectroscopy (3) and a variety of complex physical chemistry problems (4). A similar problem exists in atmospheric science where it is useful to identify the sources giving rise to the observed concentrations of chemical constituents in the ambient aerosol and to quantitatively apportion the measured particulate mass concentrations to those identified sources. This process has come to be called Receptor Modeling and various methods have been developed and applied over the past 40 years to provide source apportionments (5–10). This chapter will outline these methods and their application to ambient particle composition data. Airborne particulate matter is a complex mixture of materials from a variety of sources including natural and anthropogenic. Some particles are emitted directly into the atmosphere (primary) while others are formed through atmospheric oxidative processes (secondary). Thus, sources need to be considered in the context of a 2x2 matrix of human and natural versus primary and secondary. Figure 1 provides a useful framework for considering particle origins (11). Most of the coarse mode particles are primary in origin whereas most of the fine mode particles are secondary. Particles larger than about 1 µm in aerodynamic diameter are produced through mechanical processes such as tires rolling over wet pavement or waves breaking on the shore and in the open ocean and throwing droplets of water into the air. When the water evaporates, the material dissolved in the water produces a particle. Fine particles (
