Aerosol receptor models, composition

Composition of Source Components Needed for Aerosol Receptor Models... 

Because of the uncertainties In the use of source-emissions Inventories to estimate contributions from various sources to ambient levels of suspended particles, many workers have been developing and testing aerosol receptor models (1 ). The basic Idea of receptor models Is that chemical compositions of particles from various types of sources are sufficiently different that one can determine contributions from the sources by making detailed measurements of the compositions of ambient aerosols and of particles from the sources. Several computational methods have been used... 

Gordon, G.E. Zoller, W.H. Kowalczyk, G.S. Rheingrover, S.W. "Composition of Source Components Needed for Aerosol Receptor Models." (This symposium). 

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. 

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. 

As noted above, we had hoped that one could perform measurements on several sources of a particular class and observe trends that would allow one reliably to predict compositions of particles from other members of that class to avoid the need to study each Individual major source In an area before performing receptor-model Interpretations of ambient aerosols of that area. 

When sources are studied, several things should be done to provide data needed for receptor-model applications. First, particles should be collected In at least two different size fractions corresponding to the division at about 2.5-ym dlam now used In many studies of ambient aerosols. In some cases. It may be desirable to have more size cuts. As noted above, compositions of particles from coal combustion change dramatically below about 0.5-pm dlam (44, 46). Above we Identified a minimum of about twenty elements that should be measured. Also, In order to develop adequate markers for sources that emit carbonaceous particles, measurements of organic compounds and other properties related to carbonaceous particles should be made. 

There are two general types of aerosol source apportionment methods dispersion models and receptor models. Receptor models are divided into microscopic methods and chemical methods. Chemical mass balance, principal component factor analysis, target transformation factor analysis, etc. are all based on the same mathematical model and simply represent different approaches to solution of the fundamental receptor model equation. All require conservation of mass, as well as source composition information for qualitative analysis and a mass balance for a quantitative analysis. Each interpretive approach to the receptor model yields unique information useful in establishing the credibility of a study s final results. Source apportionment sutdies using the receptor model should include interpretation of the chemical data set by both multivariate methods. 

While the source-oriented model begins with measurements at the source (i.e., emission rates for the period under study), and estimates ambient concentrations, the receptor-oriented model begins with the actual ambient measurements and estimates the source contributions to them. The receptor model relies on properties of the aerosol which are common to source and receptor and that are unique to specific source types. These properties are composition, size and variability. 

A receptor model analysis in western Germany separated nitrate-rich from sulphate-rich secondary aerosols, with the latter being accompanied with vanadium and nickel [7]. Such factor composition pinpoints to heavy oil combustion sources which can be found, e.g. in oil refineries, off-shore platforms and overseas ships. In addition, trans-boundary pollution from eastern European countries is a significant source. 

Certainly it is possible to apply also other display methods for the visualization of such complex environmental data, as particulate emissions. TREIGER et al. [1993 1994] describe the study of different aerosol samples by nonlinear mapping of electron probe microanalysis data. Different interpretable groups of chemical elements which determine the composition of aerosol samples can be obtained. More recent work by WIENKE and HOPKE [1994] and WIENKE et al. [1994] discuss the combination of different chemometric techniques for better graphical representation of aerosol particle data. The authors use receptor modeling with a minimal spanning tree combined with a neural network. 

Trace element compositions of airborne particles are important for determining sources and behavior of regional aerosol, as emissions from major sources are characterized by their elemental composition patterns. We have investigated airborne trace elements in a complex regional environment through application of receptor models. A subset (200) of fine fraction samples collected by Shaw and Paur (1,2) in the Ohio River Valley (ORV) and analyzed by x-ray fluorescence (XRF) were re-analyzed by instrumental neutron activation analysis (INAA). The combined data set, XRF plus INAA, was subjected to receptor-model interpretations, including chemical mass balances (CMBs) and factor analysis (FA). Back trajectories of air masses were calculated for each sampling period and used with XRF data to select samples to be analyzed by INAA. 

In this study we have employed the simultaneous collection of atmospheric particles and gases followed by multielement analysis as an approach for the determination of source-receptor relationships. A number of particulate tracer elements have previously been linked to sources (e.g., V to identify oil-fired power plant emissions, Na for marine aerosols, and Pb for motor vehicle contribution). Receptor methods commonly used to assess the interregional impact of such emissions include chemical mass balances (CMBs) and factor analysis (FA), the latter often including wind trajectories. With CMBs, source-strengths are determined (1) from the relative concentrations of marker elements measured at emission sources. When enough sample analyses are available, correlation calculations from FA and knowledge of source-emission compositions may identify groups of species from a common source type and identify potential marker elements. The source composition patterns are not necessary as the elemental concentrations in each sample are normalized to the mean value of the element. Recently a hybrid receptor model was proposed by Lewis and Stevens (2) in which the dispersion, deposition, and conversion characteristics of sulfur species in power-plant emissions... 

The two most widely used receptor models in industrialized regions are the CMB and various forms of factor analysis (Hopke 1986). The CMB model requires information about the composition of the contributing aerosol sources in the model region. A great achievement of the CMB method was the identification of road dust as a major contributor to the urban aerosol mass (Cooper 1980) as well as the identification of wood combustion as an important aerosol source (Core etal. 1981). Major drawbacks of the CMB method arise when... 

Most aerosol materials will vary in their refractive index depending on the wavelength of light used, their chemical composition, and, in some cases, their orientation with respect to the light source and receptor. Since complex indices of refraction are not well established for most materials (Deirmendjian, 1969), optical models of aerosols may contain errors because of the uncertainty of these values. 

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