Aerosol composition

Zinsmeister, A. R. and Redman, T. C. (1980). A time series analysis of aerosol composition measurements, Atmos. Environ. 14,201-215. 

Lannefors H, Hansson HC, Granat L. 1983. Background aerosol composition in southern Sweden — Fourteen micro and macro constituents measured in seven particle size intervals at one site during one year. Atmos Environ 17 87-101. 

Lundgren, D. A. Atmospheric aerosol composition and concentration as a function of particle size and of time. J. Air Pollut. Control Assoc. 20 603-608, 1970. 

PLS (partial least squares) multiple regression technique is used to estimate contributions of various polluting sources in ambient aerosol composition. The characteristics and performance of the PLS method are compared to those of chemical mass balance regression model (CMB) and target transformation factor analysis model (TTFA). Results on the Quail Roost Data, a synthetic data set generated as a basis to compare various receptor models, is reported. PLS proves to be especially useful when the elemental compositions of both the polluting sources and the aerosol samples are measured with noise and there is a high correlation in both blocks. 

One of the current problems in environmental chemistry is how to model the ambient aerosol composition, to reveal polluting sources, to determine their contribution to the overall aerosol composition. 

Among the multivariate statistical techniques that have been used as source-receptor models, factor analysis is the most widely employed. The basic objective of factor analysis is to allow the variation within a set of data to determine the number of independent causalities, i.e. sources of particles. It also permits the combination of the measured variables into new axes for the system that can be related to specific particle sources. The principles of factor analysis are reviewed and the principal components method is illustrated by the reanalysis of aerosol composition results from Charleston, West Virginia. An alternative approach to factor analysis. Target Transformation Factor Analysis, is introduced and its application to a subset of particle composition data from the Regional Air Pollution Study (RAPS) of St. Louis, Missouri is presented. 

Among these techniques are various forms of a statistical method called factor analysis. Several forms of factor analysis have been applied to the problem of aerosol source resolution. These different forms provide several different frameworks in which to examine aerosol composition data and Interpret it in terms of source contributions. 

Figure 1. Artificial aerosol composition data plotted for automobile and steel...

Figure 2. A rtificial aerosol composition data after axes rotation...

Hopke, et al. (4) and Gaarenstroom, Perone, and Moyers (7) used the common factor analysis approach in their analyses of the Boston and Tucson area aerosol composition, respectively. In the Boston data, for 90 samples at a variety of sites, six common factors were identified that were interpreted as soil, sea salt, oil-fired power plants, motor vehicles, refuse incineration and an unknown manganese-selenium source. The six factors accounted for about 78 of the system variance. There was also a high unique factor for bromine that was interpreted to be fresh automobile exhaust. Large unique factors for antimony and selenium were found. These factors may possibly represent emission of volatile species whose concentrations do not oovary with other elements emitted by the same source. 

Gatz (8) applied a principal components analysis to aerosol composition data for St. Louis, Mo taken as part of project METROMEX (13-1 t). Nearly 400 filters collected at 12 sites were analyzed for up... 

Sievering and coworkers ( ) have made extensive use of factor analysis in their interpretation of midlake aerosol composition and deposition data for Lake Michigan. 

The second difference is that the correlations between samples are calculated rather than the correlations between elements. In the terminology of Rozett and Peterson ( ), the correlation between elements would be an R analysis while the correlation between samples would be a Q analysis. Thus, the applications of factor analysis discussed above are R analyses. Imbrle and Van Andel ( 6) and Miesch (J 7) have found Q-mode analysis more useful for interpreting geological data. Rozett and Peterson (J ) compared the two methods for mass spectrometric data and concluded that the Q-mode analysis provided more significant informtlon. Thus, a Q-mode analysis on the correlation about the origin matrix for correlations between samples has been made (18,19) for aerosol composition data from Boston and St. Louis. 

It is clear that several forms of factor analysis can be very useful in the interpretation of aerosol composition data. The traditional forms of factor analysis that are widely available permit the identification of sources, the screening of data for noisy results, and the identification of interferences or analytical procedure problems. 

Zinmeister, A.R. and Redman. T.C. (198Q). "A Time Series Analysis of Aerosol Composition Measurements," Atmospheric Environment, 14, 201. 

The aerosol composition distribution. Figure 3, shows pronounced variation with particle size. The distribution has a distinct break in the 0.1 to 0.3 um size range. The larger particles account for the vast majority of the aerosol mass. 

Aerosol Composition in Relation to Air Mass Movements in North China... 

Approximate times of polluted (P), continental (C), and marine (M) air masses are indicated based on synoptic weather maps and consistent with aerosol composition measurements. Times of impactor samples, taken in duplicate concurrently with the streaker, are indicated at the top as midpoints of alternating 10- and 12-h sampling periods. 

The week of observations at Xlnglong reported here, 16-21 March 1980, was one in which the effect of air pollution on aerosol composition was lessened by the end on 15 March, by official policy, of much residential coal burning for space heating in Beijing. A brief report of results at Xlnglong for the week of 9-16 March is published elsewhere W. After 15 March the larger scale aerosol composition characteristics and their relation to air mass movements can be more readily discerned, as has been described above. 

It is of interest to compare the aerosol composition of polluted air on 16-17 March with results of the preceding week. This comparison is given in Table I. Element ratios are presented relative to excess fine particle potassium, Kjj, i.e. the fine mode which has been resolved from total K as shown in Figure 4. The fine modes Fe and Mn j were obtained in a similar way. 

Xinglong Polluted Aerosol Composition, Comparison with Preceding Periods... 

Winchester, J.W. Wang Mingxing Ren Lixin Lii Weixiu Hansson, H.C. Lannefors, H. Leslie, A.C.D. Nonurban aerosol composition near Beijing, China Nucl. Instr. Meth. 1980, in press. 

Wang Mingxing Winchester, J.W. Lii Welxlu Ren Lixin. Aerosol composition in a nonurban area near the Great Wall (in Chinese) Sclentla Atmospherica Slnlca (Daql Kexue) 1980, to be published. 

This is supported by studies of the aerosol composition in forested areas. For example, Kavouras et al. (1998) identified cis- and tram-pinonic acids as well as pinonaldehyde and nopinone in particles in a forest in Portugal. The diurnal variations of the pinonic acids and formic acid were similar, peaking in the afternoon as expected if they were formed by the reaction of O, with a-pinene. On the other hand, the concentrations of pinonaldehyde, expected from the oxidation of a-pinene by OH, O, and NO, and nopinone, from the oxidation of j3-pinene, were the smallest in the after-... 

Artaxo, P., F. Gerab, M. A. Yamasoe, and J. V. Martins, Fine Mode Aerosol Composition at Three Long-Term Atmospheric Monitoring Sites in the Amazon Basin, J. Geophys. Res., 99, 22857-22868... 

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